Concrete mixer truck and method for adjusting mixture in tank of concrete mixer truck

Abstract

The invention belongs to the technical field of concrete construction, and particularly relates to a concrete mixer truck and a method for adjusting a mixture in a tank of the concrete mixer truck, the adjusting method monitors the workability and the real-time temperature condition of the concrete mixture in the tank of the concrete mixer truck in real time, and additive addition and temperature control are controlled through a machine learning model output strategy. The target in-mold temperature of construction is balanced through a weight distribution algorithm, a machine learning model is trained through historical construction data and receives real-time workability and temperature feedback input, and an adjustment strategy is dynamically generated, so that the reliability of the adjustment strategy is further improved; in addition, the method achieves control of the workability and the target mold entering temperature of the concrete mixture at the same time, avoids mutual influence and conflict of strategies of the workability and the target mold entering temperature, and achieves efficient and automatic real-time control. According to the method, the influence of various adverse factors on the construction process can be reduced, countermeasures can be rapidly and accurately provided, the system carries out data processing, and personal errors are avoided.

Description

Technical Field

[0001] The invention belongs to the technical field of concrete construction, and in particular relates to a concrete mixer truck and a method for adjusting a mixture in the tank thereof. Background Art

[0002] It takes a certain amount of time for concrete mixture to be transported from the mixing plant to the construction site. During this process, the loss of workability of the concrete mixture over time is difficult to be effectively controlled, which usually results in the concrete workability failing to meet the on-site construction requirements. Therefore, in order to meet the construction needs of concrete, construction workers are often required to conduct further on-site mixing at the feed port of the tank truck to achieve working performance that meets the construction requirements. This mixing method is overly dependent on manual labor, the quality is difficult to guarantee, and the efficiency is low.

[0003] Cement releases a large amount of heat during the hydration process, which can easily cause the structure to generate excessive hydration heat and cause temperature-induced cracks in the structure. These cracks will affect the safety and durability of the structure. The higher the temperature of the concrete entering the mold, the faster the hydration heat release, the higher the peak temperature, and it is easy to cause excessive internal and external temperature differences and temperature-induced cracks. On the contrary, if the temperature of the concrete entering the mold is too low (such as below 10°C), the cement hydration is slow, affecting the early age strength growth. It generally takes a certain amount of time to transport concrete from the mixing plant to the site. During this time, cooling / heating measures can be applied in the tank truck to control the temperature entering the mold to be too high / too low.

[0004] However, current concrete mixer trucks do not have control facilities, which may easily lead to concrete failing to 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 defect in the prior art that the workability and the temperature of the concrete mixture entering the mold are difficult to effectively control in the concrete mixer truck when the concrete mixture is transported from the mixing station to the construction site, thereby affecting the construction quality, and to provide a concrete mixer truck and a method for adjusting the mixture in the tank thereof.

[0006] A method for adjusting a mixture in a tank of a concrete mixer truck comprises the following steps: Obtain the inspection and workability of the mixture in the concrete mixer tank; Obtain the detected temperature of the mixture in the tank of the concrete mixer truck; Inputting historical construction data, the detected workability and the detected temperature into a machine learning model, and outputting a workability adjustment strategy; the workability adjustment strategy includes an admixture addition strategy and a workability temperature control strategy; The workability temperature control strategy is balanced with the target mold entry temperature of the construction through a weight distribution algorithm, and a temperature adjustment strategy is output; The in-tank mix is ​​adjusted according to the workability adjustment strategy and the temperature adjustment strategy.

[0007] Specifically, the training method of the machine learning model includes the following steps: Collecting historical construction data, wherein the historical construction data includes concrete mix proportions and workability data during concrete mixing; Data preprocessing, including outlier removal, standardization and time series alignment; time series alignment forms time series data; Feature extraction, extracting key features from the time series data, wherein the key features include temperature change gradient and slump decay gradient; Feature interaction, obtaining associated variables in the time series data to form interactive features, wherein the interactive features include temperature×humidity and transportation time×slump; Model construction: 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 interactive features and predict to form a preliminary workability adjustment strategy; the long short-term memory network is used to process time series data and key features, and dynamically correct to form the workability adjustment strategy. Model training: dividing the historical construction data into a training set, a validation set, and a test set, and training to form the machine learning model.

[0008] Furthermore, in the data preprocessing, the elimination of outliers includes eliminating abnormal data in the historical construction data; the standardization processing includes normalizing the continuous variables in the historical construction data, and the continuous variables include temperature and humidity; the standardization processing includes unique-hot encoding the categorical variables, and the categorical variables include aggregate type; the time series alignment includes aligning the discrete variables in the historical construction data according to timestamps to construct time series data.

[0009] Furthermore, the historical construction data includes concrete mix ratio, transportation time, slump, setting time, water seepage rate, admixture type, admixture dosage, temperature, humidity, and pressure value.

[0010] Furthermore, the static data includes concrete mix ratio, initial temperature, initial humidity, admixture type, and admixture dosage; the time series data includes remaining transportation time, slump, setting time, water seepage rate, temperature, humidity, pressure value, and viscosity value.

[0011] Furthermore, during model training, model parameters are determined by grid search, and the model parameters include the tree depth of the random forest and the number of hidden layer units of the long short-term memory network.

[0012] Furthermore, the temperature adjustment strategy is executed by a PID controller, and the output of the PID controller adjusts the power of the vehicle refrigeration system and the vehicle heating system. The output of the PID controller is expressed as: ; in, e ( t ) represents the current temperature error, that is, the difference between the target temperature and the detected temperature. Kp represents the proportionality coefficient; Ki represents the integral coefficient, and Ki = Kp / Ti ,in, Ti Indicates the integration time; Kd represents the differential coefficient, and Kd = Kp ⋅ Td ,in, Td Indicates the derivative time.

[0013] A concrete mixer truck, which adjusts the mixture in the tank by the above method, comprises a workability adjustment device and a temperature adjustment device; 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 ejector, a torque sensor, a rotation speed sensor, a vibration sensor and a humidity sensor; the auxiliary box is used to store admixtures; the porous conduit is located in the tank body of the concrete mixer truck and is connected to the auxiliary box; the pressure ejector is used to add the admixture in the auxiliary box to the tank body through the porous conduit; the control panel is used to control the pressure ejector; The temperature adjustment device is used to execute the temperature adjustment strategy, and includes a PID controller, a temperature sensor, a vehicle-mounted refrigeration system and a vehicle-mounted heating system. The PID controller obtains the detection temperature of the temperature sensor and controls the vehicle-mounted refrigeration system and the vehicle-mounted heating system.

[0014] Furthermore, 32 temperature sensors are provided; 12 of the temperature sensors are provided on the inner side of the tank body for detecting the air temperature inside the tank body; 12 of the temperature sensors are provided on the stirring blades inside the tank body for detecting the temperature of the mixture; 2 of the temperature sensors are provided on the discharge port for detecting the discharge temperature; 2 of the temperature sensors are provided on the air inlet for detecting the intake temperature; 2 of the temperature sensors are provided on the air outlet for detecting the outlet temperature; 2 of the temperature sensors are provided on the outside of the tank body for detecting the ambient temperature.

[0015] Furthermore, the temperature adjustment device also includes a phase change material, and the phase change material is arranged in the interlayer of the tank body.

[0016] Beneficial effects: The present invention discloses a method for regulating a concrete mixer truck and a mixture in the tank thereof, monitors the workability and real-time temperature of the concrete mixture in the concrete mixer truck tank in real time, controls the addition of admixtures and temperature control through a machine learning model output strategy, and balances the target mold entry temperature of the construction through a weight distribution algorithm. The machine learning model is trained through historical construction data and receives real-time workability and temperature feedback input, dynamically generates an adjustment strategy, and further improves the reliability of the adjustment strategy. In addition, the method simultaneously realizes the workability of the concrete mixture and the target mold entry temperature control, avoids the mutual influence and conflict of the strategies of workability and target mold entry temperature, and realizes efficient and automated real-time control. The present invention can reduce the impact of various adverse factors on the construction process, quickly and accurately provide countermeasures, and the system processes data to avoid human errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 The figure is a schematic block diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0020] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0021] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0022] In this application, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0023] Embodiment 1: Reference Figure 1 As shown, this embodiment discloses a method for adjusting a mixture in a tank of a concrete mixer truck, comprising the following steps: Obtain the inspection and workability of the mixture in the concrete mixer tank; Obtain the detected temperature of the mixture in the tank of the concrete mixer truck; The historical construction data, the detected workability and the detected temperature are input into the 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 admixture addition strategy includes an admixture type and an admixture addition amount, and the workability temperature control strategy includes a workability control temperature; the admixture includes a water reducer (such as a polycarboxylic acid series), a viscosity enhancer (such as a cellulose ether), and a thickener (such as a bentonite).

[0024] The workability temperature control strategy is balanced with the target mold entry temperature of the construction through a weight distribution algorithm, and a temperature adjustment strategy is output; The in-tank mix is ​​adjusted according to the workability adjustment strategy and the temperature adjustment strategy.

[0025] Specifically, the training method of the machine learning model includes the following steps: Collect historical construction data, including concrete mix ratio, including workability data during concrete mixing; the historical construction data includes more than 5,000 sets of engineering data, the ambient temperature range is 5~40°C, the ambient temperature range is 30~95%RH, the concrete mix ratio range is C20~C60, the transportation time range is 0.5~3 hours, and the construction scenes include buildings, bridges and tunnels. The historical construction data includes concrete mix ratio, aggregate type, transportation time, slump, setting time, water bleeding rate, admixture type, admixture dosage, temperature, humidity, pressure value, viscosity value. The concrete mix ratio includes water-cement ratio, sand ratio and aggregate grading.

[0026] Data preprocessing includes outlier removal, standardization and time series alignment; time series alignment forms time series data; in the data preprocessing, the outlier removal includes removing abnormal data in the historical construction data. In this embodiment, the abnormal data includes extreme temperature, humidity, slump and viscosity values ​​caused by sensors. Values ​​exceeding the preset threshold range are abnormal; the standardization includes normalizing continuous variables in the historical construction data, and the continuous variables include temperature and humidity; the standardization includes unique hot encoding of categorical variables, and the categorical variables include aggregate types; the time series alignment includes aligning discrete variables in the historical construction data according to timestamps to construct time series data. The discrete data includes slump, setting time, water seepage rate, temperature, humidity, pressure value, and viscosity value.

[0027] Feature extraction, extracting key features from the time series data, wherein the key features include temperature change gradient and slump decay gradient; In this embodiment, Temperature gradient , where T 当前 Indicates the current temperature, T 初始 represents the initial temperature, and t represents the transportation time; Slump decay gradient , where S 当前 Indicates the current slump, S 初始 represents the initial slump, and t represents the transportation time.

[0028] Feature interaction, obtaining the associated variables in the time series data to form interactive features, the interactive features include temperature×humidity and transportation time×slump; temperature×humidity can reflect the comprehensive temperature impact, transportation time×slump can be used to evaluate the time loss of workability, and interactive features can strengthen the interaction between corresponding features during model training, thereby improving the prediction accuracy of the machine learning model.

[0029] Model construction: 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 interactive features and predict the formation of preliminary and tradeability adjustment strategies; the long short-term memory network is used to process time series data and key features, and dynamically correct the formation of the tradeability adjustment strategy. Through the hybrid architecture of random forest and long short-term memory network, the strong interpretability of the tree model for structured data is retained, and deep learning is used to capture complex time series dynamics. Under the premise of meeting engineering hard constraints, multi-objective optimization of prediction accuracy, security and real-time performance is achieved.

[0030] Model training, dividing the historical construction data into training set, validation set and test set, and training to form the machine learning model. In this embodiment, the historical construction data is divided into training set, validation set and test set in a ratio of 7:2:1, and cross validation is used to optimize the hyperparameters of the random forest, including tree depth and minimum number of samples of leaf nodes; the time window sliding method is used for the LSTM network, and the window length is set to 10 minutes to predict the workability status in the next 5 minutes. When verifying the machine learning model, it is required to achieve a workability classification accuracy of 92% on the test set; the mean absolute error (MAE) of the admixture dosage prediction is ≤3%; the single inference time is <5 seconds, so as to meet the computing power requirements of the vehicle embedded system.

[0031] During the training process of the machine learning model, the objective function is: ; Among them, the weight coefficient α =0.6 ensures performance priority, β =0.3 to achieve cost control, γ =0.1 to ensure construction period.

[0032] The constraints are: the total amount of admixture ≤ safety threshold, and the temperature adjustment rate ≤ 2°C / min, thus preventing thermal stress cracks.

[0033] Specifically, in the machine learning model, static features are processed by random forests, and dynamic time series features are modeled by long short-term memory networks. The prediction results of the two are fused through weights, which not only retains the interpretability of structured data but also captures time dependencies.

[0034] In this embodiment, the static data includes concrete mix ratio, initial temperature, initial humidity, admixture type, and admixture dosage; the time series data includes remaining transportation time, slump, setting time, water seepage rate, temperature, humidity, pressure value, and viscosity value.

[0035] As a further improvement of this embodiment, during model training, model parameters are determined by grid search, and the model parameters include the tree depth of the random forest and the number of hidden layer units of the long short-term memory network. In this embodiment, the tree depth of the random forest determined is 15, and the number of hidden layer units of the long short-term memory network is 64.

[0036] In this embodiment, the temperature adjustment strategy is executed by a PID controller, and the output of the PID controller adjusts the power of the vehicle refrigeration system and the vehicle heating system. The output of the PID controller is expressed as: ; in, e ( t ) represents the current temperature error, that is, the difference between the target temperature and the detected temperature. Kp represents the proportionality coefficient; Ki represents the integral coefficient, and Ki = Kp / Ti ,in, Ti Indicates the integration time; Kd represents the differential coefficient, and Kd = Kp ⋅ Td ,in, Td Indicates the derivative time.

[0037] In this embodiment, the ZieglerNichols tuning method is used to preliminarily determine the proportional coefficient ( Kp )、Integral time( Ti )、Derivative time( Td ): Obtain the critical gain of the system through step response experiment Ku and the oscillation period Tu ,, calculate the initial parameters: thus obtaining Kp =0.6 Ku , Ti =0.5 Tu , Td =0.125 Tu.

[0038] As a further improvement of this embodiment, fuzzy logic control is introduced into PID control to dynamically fine-tune PID parameters according to the ambient temperature change rate and the real-time viscosity of concrete: if the temperature rises rapidly (>1°C / minute), the differential term weight is enhanced ( 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 shock.

[0039] The method for regulating the mixture in the concrete mixer tank provided by the present embodiment monitors the workability and real-time temperature of the concrete mixture in the concrete mixer tank in real time, controls the addition of admixtures and temperature control through the machine learning model output strategy, and balances the target mold entry temperature of the construction through the weight distribution algorithm. The machine learning model is trained by historical construction data and receives real-time workability and temperature feedback input, dynamically generates an adjustment strategy, and further improves the reliability of the adjustment strategy. In addition, the method simultaneously realizes the workability of the concrete mixture and the target mold entry temperature control, avoids the mutual influence and conflict of the strategies of workability and target mold entry temperature, and realizes efficient and automated real-time control. The present invention can reduce the impact of various adverse factors on the construction process, quickly and accurately provide countermeasures, and the system processes data to avoid human errors.

[0040] The machine learning model uses a hybrid model of random forest and long short-term memory network. Random forest statically optimizes the initial mix ratio, and long short-term memory network dynamically compensates for slump loss. In addition, temperature × humidity interactive feature training is used to warn of thermal stress risks in real time. At the same time, it can realize viscosity and pressure time series monitoring, intervene in irreversible defects such as bleeding and segregation in advance, and avoid quality risks caused by irreversibility in the concrete hardening process. Therefore, this method integrates the static mix ratio optimization of random forest and the dynamic process control of long short-term memory network through the trained machine learning model, and realizes the full life cycle performance guarantee of concrete mixture in the mixer truck.

[0041] Embodiment 2: This embodiment provides a concrete mixer truck, which adjusts the mixture in the tank by the method of embodiment 1, including a workability adjustment device and a temperature adjustment device; 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 ejector, a torque sensor, a rotation speed sensor, a vibration sensor and a humidity sensor; the auxiliary box is used to store admixtures; the porous conduit is located in the tank body of the concrete mixer truck and is connected to the auxiliary box; the pressure ejector is used to add the admixture in the auxiliary box to the tank body through the porous conduit; the control panel is used to control the pressure ejector; The temperature adjustment device is used to execute the temperature adjustment strategy, and includes a PID controller, a temperature sensor, a vehicle-mounted refrigeration system and a vehicle-mounted heating system. The PID controller obtains the detection temperature of the temperature sensor and controls the vehicle-mounted refrigeration system and the vehicle-mounted heating system.

[0042] In this embodiment, 32 temperature sensors are provided; 12 temperature sensors are provided on the inside of the tank body to detect the air temperature inside the tank body; 12 temperature sensors are provided on the stirring blades inside the tank body to detect the temperature of the mixture; 2 temperature sensors are provided on the discharge port to detect the discharge temperature; 2 temperature sensors are provided on the air inlet to detect the intake temperature; 2 temperature sensors are provided on the air outlet to detect the outlet temperature; 2 temperature sensors are provided on the air outlet to detect the outlet temperature; 2 temperature sensors are provided on the outside of the tank body to detect the ambient temperature.

[0043] As a further improvement of this embodiment, the temperature adjustment device further includes a phase change material, which is arranged in the interlayer of the tank body. When the temperature of the concrete mixture exceeds the phase change temperature, the phase change material changes from solid to liquid, absorbs the heat in the tank, and reduces the temperature; when the temperature of the concrete mixture is lower than the phase change temperature, the phase change material releases the stored heat to maintain a stable temperature. In this embodiment, the phase change temperature of the phase change material is 28°C.

[0044] As a further improvement of this embodiment, the concrete mixer truck is linked to the central control system of the mixing station in real time through the Internet of Things module to achieve remote monitoring and parameter optimization of the entire transportation process. The remote monitoring system architecture includes: data acquisition layer, communication layer, and decision-making layer, among which: Data collection layer: each sensor collects data in real time; the GPS module tracks the transportation route and estimated arrival time; Communication layer: transmit data to the cloud control center via 4G / 5G or LoRa; Decision-making layer: Machine learning models are deployed in the cloud to generate admixture adjustment instructions after analyzing the data. Humans can intervene or review the instructions through the web interface.

[0045] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described 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.

[0046] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A method for adjusting a mixture in a tank of a concrete mixer truck, characterized in that: The following steps are involved: Obtain the inspection and workability of the mixture in the concrete mixer tank; Obtain the detected temperature of the mixture in the tank of the concrete mixer truck; Inputting historical construction data, the detected workability and the detected temperature into a machine learning model, and outputting a workability adjustment strategy; the workability adjustment strategy includes an admixture addition strategy and a workability temperature control strategy; The temperature control strategy for workability and the target mold entry temperature for construction are balanced by a weight distribution algorithm, and a temperature adjustment strategy is output; The in-tank mix is ​​adjusted according to the workability adjustment strategy and the temperature adjustment strategy.

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

3. The method for adjusting the mixture in the tank of a concrete mixer truck according to claim 2, characterized in that: In the data preprocessing, the elimination of outliers includes eliminating abnormal data in the historical construction data; the standardization processing includes normalizing the continuous variables in the historical construction data, and the continuous variables include temperature and humidity; the standardization processing includes unique hot encoding of categorical variables, and the categorical variables include aggregate type; the time series alignment includes aligning the discrete variables in the historical construction data according to timestamps to construct time series data.

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

5. The method for adjusting the mixture in the tank of a concrete mixer truck according to claim 2, characterized in that: The static data includes concrete mix ratio, initial temperature, initial wet admixture type, and admixture dosage; the time series data includes remaining transportation time, slump, setting time, water seepage rate, temperature, humidity, pressure value, and viscosity value.

6. The method for adjusting the mixture in the tank of a concrete mixer truck according to claim 2, characterized in that: During model training, model parameters are determined by grid search, and the model parameters include 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 in the tank of a concrete mixer truck according to claim 1, characterized in that: The temperature adjustment strategy is executed by a PID controller, and the output of the PID controller adjusts the power of the vehicle refrigeration system and the vehicle heating system. The output of the PID controller is expressed as: ； in, e ( t ) represents the current temperature error, that is, the difference between the target temperature and the detected temperature. K p represents the proportionality coefficient; K i represents the integral coefficient, and K i =K p / T i ,in, T i Indicates the integration time; K d represents the differential coefficient, and K d =K p ⋅ T d ,in, T d Indicates the derivative time.

8. A concrete mixer truck, which adjusts the mixture in the tank by the method according to any one of claims 1 to 7, characterized in that: Including workability adjustment device and temperature adjustment device; 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 rotation speed sensor, a vibration sensor and a humidity sensor; the auxiliary box is used to store admixtures; the porous conduit is located in the tank body of the concrete mixer truck and is connected to the auxiliary box; the pressure injector is used to add the admixture in the auxiliary box to the tank body through the porous conduit; the control panel is used to control the pressure injector; The temperature adjustment device is used to execute the temperature adjustment strategy, and includes a PID controller, a temperature sensor, a vehicle-mounted refrigeration system and a vehicle-mounted heating system. The PID controller obtains the detection temperature of the temperature sensor and controls the vehicle-mounted refrigeration system and the vehicle-mounted heating system.

9. A concrete mixer truck according to claim 8, characterized in that: There are 32 temperature sensors; 12 of the temperature sensors are arranged on the inside of the tank body to detect the air temperature inside the tank body; 12 of the temperature sensors are arranged on the stirring blades inside the tank body to detect the temperature of the mixture; 2 of the temperature sensors are arranged on the discharge port to detect the discharge temperature; 2 of the temperature sensors are arranged on the air inlet to detect the intake temperature; 2 of the temperature sensors are arranged on the air outlet to detect the outlet temperature; 2 of the temperature sensors are arranged on the outside of the tank body to detect the ambient temperature.

10. A concrete mixer truck according to claim 8, characterized in that: The temperature adjustment device further comprises a phase change material, and the phase change material is arranged in the interlayer of the tank body.

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