A concrete mixer and a method of conditioning the mix in the drum thereof

Abstract

This invention belongs to the field of concrete construction technology, specifically relating to a method for adjusting a concrete mixer truck and the mixture inside its tank. The method monitors the workability and real-time temperature of the concrete mixture inside the mixer truck in real time. It uses a machine learning model to output strategies for controlling the addition of admixtures and temperature control, and balances the target pouring temperature through a weighted allocation algorithm. The machine learning model is trained on historical construction data and receives real-time workability and temperature feedback to dynamically generate adjustment strategies, further improving the reliability of the adjustment strategies. Furthermore, this method simultaneously achieves workability and target pouring temperature control of the concrete mixture, avoiding conflicts between these strategies and achieving efficient, automated real-time control. This invention can reduce the impact of various adverse factors on the construction process, providing rapid and accurate countermeasures. The system processes data to avoid human error.

Description

Technical Field

[0001] This invention belongs to the field of concrete construction technology, specifically relating to a concrete mixer truck and a method for adjusting the mixture inside the truck. Background Technology

[0002] The transportation of concrete mix from the batching plant to the construction site takes time. During this process, the time-dependent loss of workability of the concrete mix is ​​difficult to control effectively, often resulting in workability that fails to meet on-site construction requirements. Therefore, to meet the construction needs of the concrete, construction personnel often need to perform further on-site mixing at the mixer truck inlet to achieve the required workability. This mixing method relies excessively on manual labor, making it difficult to guarantee quality and resulting in low efficiency.

[0003] Cement releases a large amount of heat during hydration, which can easily lead to excessive heat of hydration in the structure and cause temperature-induced cracks. These cracks will affect the safety and durability of the structure. The higher the concrete's pouring temperature, the faster the heat release during hydration, and the higher the peak temperature, which can easily cause excessive internal and external temperature differences and temperature-induced cracks. Conversely, if the concrete's pouring temperature is too low (e.g., below 10℃), cement hydration is slow, affecting early-age strength development. Concrete generally requires a certain transportation time from the mixing plant to the site. During this time, cooling / heating measures can be applied inside the concrete mixer truck to control whether the pouring temperature is too high or too low.

[0004] Current concrete mixer trucks lack control facilities, which can easily lead to concrete that does not meet construction requirements, affecting construction efficiency and costs. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defects in the prior art where the workability and temperature of concrete mixtures are difficult to control effectively when transported from the batching plant to the construction site, thus affecting the construction quality. The present invention provides a method for adjusting the mixture in the concrete mixer truck and its tank.

[0006] A method for adjusting the mixture inside a concrete mixer truck includes the following steps:

[0007] To obtain the detection and workability of the mixture inside the concrete mixer truck;

[0008] To obtain the detection temperature of the mixture inside the concrete mixer truck;

[0009] Historical construction data, along with the detected workability and temperature, are input into a machine learning model to output a workability adjustment strategy; the workability adjustment strategy includes an admixture addition strategy and a workability temperature control strategy.

[0010] The workability temperature control strategy is balanced with the target mold entry temperature during construction using a weight allocation algorithm, and a temperature adjustment strategy is output.

[0011] The mixture in the tank is adjusted according to the workability adjustment strategy and the temperature adjustment strategy.

[0012] Specifically, the training method for the machine learning model includes the following steps:

[0013] Collect historical construction data, including concrete mix proportions and workability data during the concrete mixing process;

[0014] Data preprocessing includes outlier removal, standardization, and time series alignment; time series alignment forms time-series data.

[0015] Feature extraction: extracting key features from the time-series data, including temperature change gradient and slump decay gradient;

[0016] Feature interaction: Obtain the associated variables in the time series data to form interactive features, including temperature × humidity and transportation time × slump.

[0017] The model is constructed using a hybrid model of random forest and long short-term memory network. The random forest is used to process static data and interaction features and predict and form an initial ease-adjustment strategy. The long short-term memory network is used to process time-series data and key features and dynamically correct and form the ease-adjustment strategy.

[0018] Model training involves dividing the historical construction data into a training set, a validation set, and a test set, and then training the machine learning model.

[0019] Furthermore, in the data preprocessing, the outlier removal includes removing abnormal data from the historical construction data; the standardization process includes normalizing continuous variables in the historical construction data, including temperature and humidity; the standardization process includes one-heat encoding of categorical variables, including aggregate type; and the time series alignment includes aligning discrete variables in the historical construction data according to timestamps to construct time series data.

[0020] Furthermore, the historical construction data includes concrete mix proportions, transportation time, slump, setting time, bleeding rate, admixture type, admixture dosage, temperature, humidity, and pressure value.

[0021] Furthermore, the static data includes concrete mix proportions, initial temperature, initial humidity, admixture type, and admixture dosage, while the time-series data includes remaining transport time, slump, setting time, bleeding rate, temperature, humidity, pressure value, and viscosity value.

[0022] Furthermore, during model training, model parameters are determined through grid search, including the tree depth of the random forest and the number of hidden layer units of the long short-term memory network.

[0023] Furthermore, the temperature adjustment strategy is executed by a PID controller, which outputs a power adjustment for the vehicle's cooling and heating systems. The output of the PID controller is expressed as follows:

[0024] ;

[0025] in, e ( t The ) indicates the current temperature error, which is the difference between the target temperature and the detected temperature. Kp Indicates the proportionality coefficient;

[0026] Ki Denotes the integral coefficient, and Ki = Kp / Ti ,in, Ti Indicates the integration time;

[0027] Kd Denotes the differential coefficients, and Kd = Kp ⋅ Td ,in, Td This represents the differential time.

[0028] A concrete mixer truck that adjusts the mixture inside the tank using the above method includes a workability adjustment device and a temperature adjustment device.

[0029] The workability adjustment device is used to implement the admixture addition strategy and includes a porous conduit, an auxiliary box, a control panel, a pressure injector, a torque sensor, a speed sensor, a vibration sensor, and a humidity sensor. The auxiliary box is used to store the admixture. The porous conduit is located inside the tank of the concrete mixer truck and connected to the auxiliary box. The pressure injector is used to add the admixture from the auxiliary box to the tank through the porous conduit. The control panel is used to control the pressure injector.

[0030] The temperature adjustment device is used to execute a temperature adjustment strategy and includes a PID controller, a temperature sensor, an on-board cooling system, and an on-board heating system. The PID controller acquires the detected temperature from the temperature sensor and controls the on-board cooling system and the on-board heating system.

[0031] Furthermore, 32 temperature sensors are provided; 12 temperature sensors are located inside the tank to detect the air temperature inside the tank; 12 temperature sensors are located on the stirring blades inside the tank to detect the temperature of the mixture; 2 temperature sensors are located at the discharge port to detect the discharge temperature; 2 temperature sensors are located at the air inlet to detect the air inlet temperature; 2 temperature sensors are located at the air outlet to detect the air outlet temperature; and 2 temperature sensors are located outside the tank to detect the ambient temperature.

[0032] Furthermore, the temperature adjustment device also includes a phase change material, which is disposed within the tank body interlayer.

[0033] Beneficial Effects: This invention discloses a method for adjusting a concrete mixer truck and the mixture inside its tank. It monitors the workability and real-time temperature of the concrete mixture inside the mixer truck tank in real time. A machine learning model outputs strategies to control the addition of admixtures and temperature control. A weighted allocation algorithm balances the target pouring temperature during construction. The machine learning model is trained on historical construction data and receives real-time workability and temperature feedback to dynamically generate adjustment strategies, further improving the reliability of the adjustment strategies. Furthermore, this method simultaneously controls both the workability of the concrete mixture and the target pouring temperature, avoiding conflicts between these strategies and achieving efficient, automated real-time control. This invention can reduce the impact of various adverse factors on the construction process, providing rapid and accurate countermeasures. The system processes data, avoiding human error. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of this application 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a schematic flowchart of the method of the present invention. Detailed Implementation

[0036] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0037] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0038] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0039] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0040] Example 1:

[0041] Reference Figure 1 As shown in the figure, this embodiment discloses a method for adjusting the mixture inside a concrete mixer truck, including the following steps:

[0042] To obtain the detection and workability of the mixture inside the concrete mixer truck;

[0043] To obtain the detection temperature of the mixture inside the concrete mixer truck;

[0044] Historical construction data, along with the detected workability and temperature, are input into a machine learning model to output a workability adjustment strategy. This strategy includes an admixture addition strategy and a workability temperature control strategy. The admixture addition strategy includes the type and amount of admixture added, while the workability temperature control strategy includes a workability control temperature. The admixtures include water-reducing agents (such as polycarboxylate-based admixtures), thickeners (such as cellulose ethers), and thickeners (such as bentonite).

[0045] The workability temperature control strategy is balanced with the target mold entry temperature during construction using a weight allocation algorithm, and a temperature adjustment strategy is output.

[0046] The mixture in the tank is adjusted according to the workability adjustment strategy and the temperature adjustment strategy.

[0047] Specifically, the training method for the machine learning model includes the following steps:

[0048] Historical construction data was collected, including concrete mix proportions and workability data during the concrete mixing process. This historical construction data comprised over 5000 sets of project data, covering an ambient temperature range of 5–40°C, an ambient humidity range of 30–95%RH, a concrete mix proportion range of C20–C60, and a transportation time range of 0.5–3 hours. The construction scenarios included buildings, bridges, and tunnels. The historical construction data included concrete mix proportions, aggregate type, transportation time, slump, setting time, bleeding rate, admixture type, admixture dosage, temperature, humidity, pressure, and viscosity. The concrete mix proportions included water-cement ratio, sand ratio, and aggregate gradation.

[0049] Data preprocessing includes outlier removal, standardization, and time series alignment. Time series alignment forms time-series data. In the data preprocessing, outlier removal includes removing abnormal data from the historical construction data. In this embodiment, abnormal data includes extreme temperature, humidity, slump, and viscosity values ​​caused by sensors; values ​​exceeding preset threshold ranges are considered abnormal. Standardization includes normalizing continuous variables in the historical construction data, including temperature and humidity. Standardization also includes uniquely thermally encoding categorical variables, including aggregate type. Time series alignment involves aligning discrete variables in the historical construction data by timestamps to construct time-series data. The discrete data includes slump, setting time, bleeding rate, temperature, humidity, pressure, and viscosity.

[0050] Feature extraction: extracting key features from the time-series data, including temperature change gradient and slump decay gradient;

[0051] In this embodiment,

[0052] Temperature gradient T 当前 T represents the current temperature. 初始 The initial temperature is represented by t, and the transportation time is represented by t.

[0053] Slump decay gradient S 当前S represents the current collapse degree. 初始 t represents the initial slump, and t represents the transport time.

[0054] Feature interaction involves acquiring correlated variables from the time-series data to form interactive features, including temperature × humidity and transport time × slump. Temperature × humidity reflects the overall temperature influence, while transport time × slump can be used to assess the loss of workability over time. Interactive features can strengthen the interaction between corresponding features during model training, thereby improving the prediction accuracy of the machine learning model.

[0055] The model is constructed using a hybrid model of random forest and long short-term memory network. The random forest is used to process static data and interactive features, predicting and forming an initial ease-adjustment strategy. The long short-term memory network is used to process temporal data and key features, dynamically refining the ease-adjustment strategy. This hybrid architecture of random forest and long short-term memory network retains the strong interpretability of tree models for structured data while leveraging deep learning to capture complex temporal dynamics. Under the premise of meeting hard engineering constraints, it achieves multi-objective optimization of prediction accuracy, security, and real-time performance.

[0056] Model training involves dividing the historical construction data into training, validation, and test sets to train the machine learning model. In this embodiment, the historical construction data is divided into training, validation, and test sets in a ratio of 7:2:1. Cross-validation is used to optimize the hyperparameters of the random forest, including tree depth and minimum number of samples per leaf node. A sliding time window method is used on the LSTM network, with a window length of 10 minutes, to predict the ease of use in the next 5 minutes. When validating the machine learning model, the following requirements are met: 92% ease of use classification accuracy on the test set; mean absolute error (MAE) of admixture dosage prediction ≤3%; and single inference time <5 seconds, thus meeting the computing power requirements of the vehicle-mounted embedded system.

[0057] During the training of a machine learning model, the objective function is:

[0058] ;

[0059] Among them, the weighting coefficient α =0.6 ensures performance is prioritized. β =0.3 achieves cost control. γ =0.1 ensures project schedule.

[0060] The constraints are: total amount of admixture ≤ safety threshold, temperature adjustment rate ≤ 2℃ / minute, thereby preventing thermal stress cracks.

[0061] Specifically, in the machine learning model, static features are processed by random forests, while dynamic temporal features are modeled by long short-term memory networks. The prediction results of the two are fused through weights, which preserves the interpretability of structured data and captures temporal dependencies.

[0062] In this embodiment, the static data includes concrete mix proportion, initial temperature, initial humidity, admixture type, and admixture dosage, while the time-series data includes remaining transport time, slump, setting time, bleeding rate, temperature, humidity, pressure value, and viscosity value.

[0063] As a further improvement to this embodiment, during model training, model parameters are determined through grid search. These parameters include the tree depth of the random forest and the number of hidden layer units in the long short-term memory network. In this embodiment, the determined tree depth of the random forest is 15, and the number of hidden layer units in the long short-term memory network is 64.

[0064] In this embodiment, the temperature adjustment strategy is executed by a PID controller. The PID controller outputs power to adjust the vehicle's cooling system and heating system. The output of the PID controller is expressed as follows:

[0065] ;

[0066] in, e ( t The ) indicates the current temperature error, which is the difference between the target temperature and the detected temperature. Kp Indicates the proportionality coefficient;

[0067] Ki Denotes the integral coefficient, and Ki = Kp / Ti ,in, Ti Indicates the integration time;

[0068] Kd Denotes the differential coefficients, and Kd = Kp ⋅ Td ,in, Td This represents the differential time.

[0069] In this embodiment, the proportional coefficient is initially determined using the Ziegler-Nichols tuning method. Kp ), points time ( Ti ), differential time ( Td ): Obtaining the critical gain of the system through step response experiments Ku and oscillation period Tu Calculate the initial parameters: thus obtaining Kp =0.6 Ku , Ti =0.5 Tu ,Td =0.125 Tu.

[0070] As a further improvement to this embodiment, fuzzy logic control is introduced into the PID control, and the PID parameters are dynamically fine-tuned according to the rate of change of ambient temperature and the real-time viscosity of concrete: if the temperature rises rapidly (>1℃ / minute), the weight of the derivative term is increased ( Kd Increase by 20% to suppress overshoot; if the temperature is close to the target value (error < ±1℃), reduce the weight of the proportional term ( Kp Reduce by 30% to avoid fluctuations.

[0071] The concrete mixer truck tanker adjustment method provided in this embodiment monitors the workability and real-time temperature of the concrete mixture in the tanker in real time. It uses a machine learning model to output strategies for controlling admixture addition and temperature control, and a weighted allocation algorithm balances the target pouring temperature. The machine learning model is trained on historical construction data and receives real-time workability and temperature feedback to dynamically generate adjustment strategies, further improving the reliability of the adjustment strategies. This method simultaneously achieves workability and target pouring temperature control of the concrete mixture, avoiding conflicts between these strategies and achieving efficient, automated real-time control. This invention can reduce the impact of various adverse factors on the construction process, providing rapid and accurate countermeasures. The system processes data to avoid human error.

[0072] The machine learning model employs a hybrid approach combining random forest and long short-term memory (LSTM) networks. Random forest statically optimizes the initial mix design, while the LTM network dynamically compensates for slump loss. Furthermore, temperature and humidity interaction features are used in the training process to provide real-time warnings of thermal stress risks. Simultaneously, it enables time-series monitoring of viscosity and pressure, allowing for early intervention against irreversible defects such as bleeding and segregation, thus mitigating quality risks arising from the irreversibility of concrete hardening. Therefore, this method, by integrating the static mix design optimization of random forest with the dynamic process control of LTM through a trained machine learning model, achieves full lifecycle performance assurance of concrete mixtures within the mixer truck.

[0073] Example 2:

[0074] This embodiment provides a concrete mixer truck that adjusts the mixture inside the tank using the method of Embodiment 1, including a workability adjustment device and a temperature adjustment device.

[0075] The workability adjustment device is used to implement the admixture addition strategy and includes a porous conduit, an auxiliary box, a control panel, a pressure injector, a torque sensor, a speed sensor, a vibration sensor, and a humidity sensor. The auxiliary box is used to store the admixture. The porous conduit is located inside the tank of the concrete mixer truck and connected to the auxiliary box. The pressure injector is used to add the admixture from the auxiliary box to the tank through the porous conduit. The control panel is used to control the pressure injector.

[0076] The temperature adjustment device is used to execute a temperature adjustment strategy and includes a PID controller, a temperature sensor, an on-board cooling system, and an on-board heating system. The PID controller acquires the detected temperature from the temperature sensor and controls the on-board cooling system and the on-board heating system.

[0077] In this embodiment, 32 temperature sensors are provided; 12 temperature sensors are located inside the tank to detect the air temperature inside the tank; 12 temperature sensors are located on the stirring blades inside the tank to detect the temperature of the mixture; 2 temperature sensors are located at the discharge port to detect the discharge temperature; 2 temperature sensors are located at the air inlet to detect the air inlet temperature; 2 temperature sensors are located at the air outlet to detect the air outlet temperature; and 2 temperature sensors are located outside the tank to detect the ambient temperature.

[0078] As a further improvement to this embodiment, the temperature adjustment device further includes a phase change material disposed within the tank's interlayer. When the temperature of the concrete mixture exceeds the phase change temperature, the phase change material changes from a solid to a liquid state, absorbing heat from the tank and lowering the temperature; when the temperature of the concrete mixture falls below the phase change temperature, the phase change material releases the stored heat, maintaining a stable temperature. In this embodiment, the phase change temperature of the phase change material is 28°C.

[0079] As a further improvement to this embodiment, the concrete mixer truck is linked in real time with the central control system of the mixing plant via an IoT module, enabling remote monitoring and parameter optimization throughout the transportation process. The remote monitoring system architecture includes: a data acquisition layer, a communication layer, and a decision-making layer, wherein:

[0080] Data acquisition layer: Each sensor collects data in real time; the GPS module tracks the transportation route and estimated arrival time;

[0081] Communication layer: Data is transmitted to the cloud control center via 4G / 5G or LoRa;

[0082] Decision-making level: Deploy machine learning models in the cloud to analyze data and generate admixture adjustment instructions, which can be manually intervened or reviewed by humans through a web interface.

[0083] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0084] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for adjusting the mixture inside a concrete mixer truck, characterized in that, Includes the following steps: To obtain the detection and workability of the mixture inside the concrete mixer truck; To obtain the detection temperature of the mixture inside the concrete mixer truck; Historical construction data, along with the detected workability and temperature, are input into a machine learning model to output a workability adjustment strategy; the workability adjustment strategy includes an admixture addition strategy and a workability temperature control strategy. The machine learning model is a hybrid model of random forest and long short-term memory network. The random forest is used to process static data and interaction features and predict the formation of an initial ease-adjustment strategy. The long short-term memory network is used to process time-series data and key features, dynamically refining the ease-adjustment strategy. The interaction features include temperature × humidity and transport time × slump. The key features include temperature change gradient and slump decay gradient. The temperature change gradient is represented as... T 当前 T represents the current temperature. 初始 The initial temperature is represented by t, and the transportation time is represented by t. The collapse decay gradient is expressed as: S 当前 S represents the current collapse degree. 初始 The initial slump is represented by t, and the transportation time is represented by t. The objective function trained by the machine learning model includes cost and construction time risk. The workability temperature control strategy is balanced with the target mold insertion temperature during construction using a weight allocation algorithm, and a temperature adjustment strategy is output. The mixture in the tank is adjusted according to the workability adjustment strategy and the temperature adjustment strategy; The temperature adjustment strategy is executed by a PID controller, which outputs power to adjust the vehicle's cooling and heating systems. If the temperature rise rate is greater than 1°C / minute, the weight of the derivative term in the PID control is increased; if the temperature is close to the target value with an error of less than ±1°C, the weight of the proportional term in the PID control is decreased.

2. The method for adjusting the mixture inside a concrete mixer truck according to claim 1, characterized in that, The training method for the machine learning model includes the following steps: Collect historical construction data, including concrete mix proportions and workability data during the concrete mixing process; Data preprocessing includes outlier removal, standardization, and time series alignment; time series alignment forms time-series data. Feature extraction: extracting key features from the time-series data, including temperature change gradient and slump decay gradient; Feature interaction: Obtain the associated variables in the time series data to form interactive features, including temperature × humidity and transportation time × slump. The model is constructed using a hybrid model of random forest and long short-term memory network. The random forest is used to process static data and interaction features and predict and form an initial ease-adjustment strategy. The long short-term memory network is used to process time-series data and key features and dynamically correct and form the ease-adjustment strategy. Model training involves dividing the historical construction data into a training set, a validation set, and a test set, and then training the machine learning model.

3. The method for adjusting the mixture inside a concrete mixer truck according to claim 2, characterized in that, In the data preprocessing, outlier removal includes removing abnormal data from the historical construction data; the standardization process includes normalizing continuous variables in the historical construction data, including temperature and humidity; the standardization process includes one-heat encoding of categorical variables, including aggregate type; and the time series alignment includes aligning discrete variables in the historical construction data according to timestamps to construct time series data.

4. The method for adjusting the mixture inside a concrete mixer truck according to claim 2, characterized in that, The historical construction data includes concrete mix proportions, transportation time, slump, setting time, bleeding rate, admixture type, admixture dosage, temperature, humidity, and pressure value.

5. The method for adjusting the mixture inside a concrete mixer truck according to claim 2, characterized in that, The static data includes concrete mix proportions, initial temperature, initial wet admixture type, and admixture dosage. The time-series data includes remaining transport time, slump, setting time, bleeding rate, temperature, humidity, pressure value, and viscosity value.

6. The method for adjusting the mixture inside a concrete mixer truck according to claim 2, characterized in that, During model training, model parameters are determined through grid search, including the tree depth of the random forest and the number of hidden layer units of the long short-term memory network.

7. The method for adjusting the mixture inside a concrete mixer truck according to claim 1, characterized in that, The temperature adjustment strategy is executed by a PID controller, which outputs a power adjustment for the vehicle's cooling and heating systems. The output of the PID controller is expressed as follows: ； in, e ( t The ) indicates the current temperature error, which is the difference between the target temperature and the detected temperature. K p Indicates the proportionality coefficient; K i Denotes the integral coefficient, and K i =K p / T i ,in, T i Indicates the integration time; K d Denotes the differential coefficients, and K d =K p T d ,in, T d This represents the differential time.

8. A concrete mixer truck, wherein the mixture in the tank is adjusted by the method described in any one of claims 1 to 7, characterized in that, Includes a workability adjustment device and a temperature adjustment device; The workability adjustment device is used to execute the admixture addition strategy and includes a porous conduit, an auxiliary box, a control panel, a pressure injector, a torque sensor, a speed sensor, a vibration sensor, and a humidity sensor. The auxiliary box is used to store the admixture. The porous conduit is located inside the tank of the concrete mixer truck and connected to the auxiliary box. The pressure injector is used to add the admixture from the auxiliary box to the tank through the porous conduit. The control panel is used to control the pressure injector. The temperature adjustment device is used to execute a temperature adjustment strategy and includes a PID controller, a temperature sensor, an on-board cooling system, and an on-board heating system. The PID controller acquires the detected temperature from the temperature sensor and controls the on-board cooling system and the on-board heating system.

9. A concrete mixer truck according to claim 8, characterized in that, The system includes 32 temperature sensors: 12 sensors are located inside the tank to detect the air temperature inside the tank; 12 sensors are located on the stirring blades inside the tank to detect the temperature of the mixture; 2 sensors are located at the discharge port to detect the discharge temperature; 2 sensors are located at the air inlet to detect the air inlet temperature; 2 sensors are located at the air outlet to detect the air outlet temperature; and 2 sensors are located outside the tank to detect the ambient temperature.

10. A concrete mixer truck according to claim 8, characterized in that, The temperature adjustment device also includes a phase change material, which is disposed within the tank body interlayer.

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