Large-volume low-slump pre-cooled concrete warehousing cooperative control method

Abstract

The invention discloses a large-volume low-slump pre-cooled concrete warehousing cooperative control method, and particularly relates to the technical field of concrete temperature control. Concrete raw materials are pre-cooled through a preset temperature control system, the factory temperature is reduced to a target range, and environment, equipment and concrete temperature data are collected in real time in the transportation process; generating a conveying operation data set; pre-cooling effect loss analysis is carried out based on the data set, a transportation environment influence index and a transportation equipment influence index are generated after hidden dangers are recognized and input into a convolutional neural network classification model, and high-quality or low-quality transportation operation is judged; for low-quality conveying operation, the optimization type is judged through fuzzy logic, and a corresponding strategy is executed; the temperature control state in the conveying process can be monitored in real time, low-quality operation can be intelligently recognized and optimized, dependence on manual intervention is reduced, temperature fluctuation in the conveying process can be effectively reduced through targeted regulation and optimization, and it is ensured that the temperature control effect is more reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete temperature control, and more specifically to a method for coordinated control of large-volume low-slump precooled concrete entering a warehouse. Background Art

[0002] In large-volume concrete construction, concrete will release a large amount of hydration heat during the hardening process, which is caused by the chemical reaction between cement and water. If the hydration heat cannot be released in time or evenly distributed, the temperature inside the concrete will rise significantly, forming a large temperature gradient. This temperature difference can easily cause temperature stress inside the concrete, and the accumulation of temperature stress may cause temperature cracks in the concrete, thus affecting the construction quality and even posing a hidden danger to the safety of the entire structure.

[0003] Especially for low slump concrete, the slump is an important indicator to measure the fluidity of concrete. Low slump usually means poor fluidity of concrete, which requires greater vibration force during construction, which increases the difficulty of construction. At the same time, due to its lower water-cement ratio and higher cohesion, low slump concrete is more susceptible to temperature control problems during transportation and warehousing, resulting in loss of pre-cooling effect.

[0004] In the prior art, precooling concrete is a common method to reduce the accumulation of hydration heat by lowering the temperature of concrete before it leaves the factory. Precooling technology usually includes cooling aggregates (such as sand and gravel), using cold water instead of normal temperature water to mix concrete, and even directly injecting liquid nitrogen during the mixing process to quickly cool the concrete. However, during the transportation of concrete from leaving the factory to entering the warehouse, the temperature of the concrete may rise again due to the influence of the external environment temperature (such as high temperature, strong sunlight) and the performance of the transportation equipment (such as insulation layer, sealing performance), so the precooling effect is weakened or even ineffective.

[0005] The transportation process of concrete generally requires a long time or long distance, and is affected by a combination of factors, including external environmental conditions (such as temperature fluctuations, sunlight intensity) and the performance of transportation equipment (such as insulation capacity, heat conduction performance). Most existing technologies lack real-time monitoring methods for the temperature control effect during transportation, and cannot effectively identify the loss of pre-cooling effect. Even if there are abnormalities, traditional methods make it difficult to take targeted adjustment measures in a timely manner. Summary of the invention

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The method for collaboratively controlling the entry of large-volume low-slump precooled concrete into a warehouse comprises the following steps:

[0008] The concrete raw materials are pre-cooled through the preset temperature control system to reduce the concrete factory temperature to the target temperature and then transported. During the transportation process, the environmental data, transportation equipment operation data, and concrete temperature data are collected and then summarized to obtain the transportation operation data set;

[0009] Perform pre-cooling effect loss analysis based on the transportation operation data set to identify whether there are hidden dangers that the pre-cooling effect cannot be maintained during transportation;

[0010] If there is a hidden danger that the pre-cooling effect cannot be maintained, further environmental analysis and transportation equipment analysis are performed to generate a transportation environmental impact index for measuring the impact of ambient temperature on concrete temperature rise and a transportation equipment impact index for measuring the impact of transportation equipment on pre-cooling effect;

[0011] The transport environment impact index and transport equipment impact index are input into the pre-trained classification model to generate 1 or 0, where 1 corresponds to high-quality transport operations and 0 corresponds to low-quality transport operations;

[0012] For low-quality conveying operations, fuzzy logic is used to determine the optimization type of the conveying operation and execute the corresponding optimization strategy.

[0013] In a preferred embodiment, the preset temperature control system includes a raw material precooling system and a transportation process control system. The raw material precooling system is used to precool the concrete raw materials and reduce the concrete factory temperature to the target temperature. The transportation process control system is used to execute the optimization strategy corresponding to the optimization type of the transportation operation determined by fuzzy logic for low-quality transportation operations.

[0014] In a preferred embodiment, performing precooling effect loss analysis refers to:

[0015] The concrete temperature is collected in real time by sensors arranged during transportation to obtain a time series of concrete temperature changes. Then, the corresponding data of two adjacent time points in the time series are obtained and marked as , , t represents the sequence number in the time series, and then the following calculation is performed:

[0016] ; Indicates the time point corresponding to sequence number t, Represents the data variation value, and then calculates the mean and standard deviation of all data variation values.

[0017] In a preferred embodiment, identifying whether there is a hidden danger that the pre-cooling effect cannot be maintained during transportation refers to:

[0018] The average value of all data change amplitude values ​​is compared with the preset numerical change interval one, and the standard deviation of all data change amplitude values ​​is compared with the preset numerical change interval two. If the average value of all data change amplitude values ​​falls within the preset numerical change interval one and the standard deviation of all data change amplitude values ​​falls within the preset numerical change interval two, a normal signal is generated. If the average value of all data change amplitude values ​​does not fall within the preset numerical change interval one and the standard deviation of all data change amplitude values ​​falls within the preset numerical change interval two, an abnormal signal is generated. When a normal signal is generated, it indicates that there is no hidden danger of pre-cooling effect not being maintained during transportation. When an abnormal signal is generated, it indicates that there is a hidden danger of pre-cooling effect not being maintained during transportation.

[0019] In a preferred embodiment, the logic for obtaining the transport environmental impact index is:

[0020] First, make a preliminary assessment of the environmental impact through temperature differences:

[0021] ; is the average temperature difference between the ambient temperature and the initial temperature during transportation, represents the ambient temperature at the i-th time node, It represents the initial temperature of concrete, i.e. the temperature of concrete leaving the factory, and n is the number of time nodes in the transportation process;

[0022] Then calculate the environmental fluctuation impact factor:

[0023] ; Indicates the environmental temperature fluctuation impact factor, which is used to reflect the degree of environmental temperature fluctuation. is the average value of the ambient temperature;

[0024] Next, calculate the transport time weighting factor: ; represents the transport time weighting factor, Indicates that the ambient temperature during transportation is higher than The total duration of Indicates the total transportation time;

[0025] Calculate the ambient heat exchange coefficient: ; Indicates the environmental heat exchange coefficient, which is used to reflect the heat exchange intensity between the ambient temperature and concrete. represents the heat transfer coefficient of the transport equipment, Indicates the heat exchange area on the outer surface of the transport equipment;

[0026] The calculation formula of the transport environmental impact index is: ; Represents the transport environmental impact index.

[0027] In a preferred embodiment, the logic for obtaining the transport equipment impact index is:

[0028] Calculating the thermal insulation performance factor for transport tank trucks , evaluate the effect of ambient temperature difference on concrete temperature rise: ; ; Indicates the minimum value of the ambient temperature. Indicates the change in ambient temperature during transportation;

[0029] ; represents the maximum concrete temperature, Indicates the variation of concrete temperature;

[0030] Calculating the sealing performance factor for transportation equipment , evaluate the heat loss ratio inside the transport equipment: ; Indicates the heat loss caused by evaporation of concrete in the transport equipment. Indicates the total heat loss of the transport equipment;

[0031] Calculating the Thermal Performance Factor for Transportation Equipment , evaluate the ability of the inner surface of the transportation equipment to transfer heat to the concrete: ; Indicates the thermal conductivity of the material used in transportation equipment, Represents the surface area of ​​the inner surface of the transport equipment, Refers to the difference between the average temperature of concrete and the temperature of the inner wall of the transportation equipment. is a non-negative constant;

[0032] Calculating the Vibration Performance Factor of a Transport Tank Truck , evaluate the vibration uniformity during tank truck transportation: ; represents the vibration frequency at the ith time point, Indicates the average value of vibration frequency;

[0033] The calculation formula of the transport equipment impact index is:

[0034] ; Represents the transport equipment impact index.

[0035] In a preferred embodiment, the classification model is completed by convolutional neural network training.

[0036] In a preferred embodiment, for low-quality conveying operations, using fuzzy logic to determine the optimal type of conveying operations refers to:

[0037] The transportation environment impact index and transportation equipment impact index of low-quality transportation operations are taken as input variables, and the optimization type of transportation operations is taken as output variables. The input variables are fuzzified and the values ​​of the input variables are converted into fuzzy sets. The output variables are fuzzified and the output variables are converted into fuzzy sets. Fuzzy rules are formulated to describe the optimization requirements of transportation operations under different combinations of data types. The fuzzified input variables are inferred through fuzzy rules to obtain the optimization type of transportation operations. The optimization type is to improve the temperature control capability of the transportation environment, improve the internal temperature control capability of the transportation equipment, or synergistically improve the temperature control capability of the transportation equipment and the transportation environment.

[0038] Technical effects and advantages of the present invention:

[0039] The present invention combines the transport environment impact index and the transport equipment impact index to monitor the temperature change of concrete in the transport process in real time, accurately identify the hidden dangers of loss of pre-cooling effect, and dynamically adjust the temperature control state during the transport process. Compared with traditional methods, the present invention can significantly reduce the risk of concrete temperature increase and ensure the stability of the pre-cooling effect throughout the entire transport process.

[0040] The present invention scientifically analyzes the transportation environment and equipment performance, and comprehensively evaluates the impact of multiple factors such as ambient temperature difference, temperature fluctuation, equipment insulation performance and sealing performance on concrete temperature control. Through targeted regulation and optimization, the temperature fluctuation during transportation can be effectively reduced, ensuring a more reliable temperature control effect.

[0041] The present invention introduces a convolutional neural network classification model and fuzzy logic reasoning method, which can make intelligent judgments on low-quality conveying operations and generate targeted optimization strategies, such as improving the temperature control capability of the transportation environment, improving equipment performance, or collaborative optimization. This intelligent control method reduces the reliance on manual intervention and significantly improves the efficiency and stability of the conveying process.

[0042] By collecting real-time temperature data of concrete during transportation through temperature sensors, the present invention can timely analyze the loss of pre-cooling effect and issue early warning signals according to temperature change trends, providing decision support for subsequent optimization strategies and avoiding potential problems of temperature control failure.

[0043] The present invention reduces heat loss during transportation by optimizing the thermal insulation and sealing performance of transportation equipment and scientifically regulating transportation time and environmental adaptability, thereby reducing energy consumption. In addition, the optimization strategy avoids unnecessary cooling requirements caused by temperature control failure and improves overall energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0045] Figure 1 It is a schematic diagram of the coordinated control method for large-volume low-slump pre-cooled concrete entering the warehouse in the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Reference Figure 1 The following embodiments are obtained:

[0048] Embodiment 1: A method for collaboratively controlling the entry of large-volume low-slump precooled concrete into a warehouse, comprising the following steps:

[0049] The concrete raw materials are pre-cooled through the preset temperature control system to reduce the factory temperature of the concrete to the target temperature and then transported. During the transportation process, the environmental data, transportation equipment operation data, and concrete temperature data are collected respectively, and then summarized to obtain the transportation operation data set; the pre-cooling treatment reduces the initial temperature of the concrete, effectively reduces the accumulation of hydration heat inside the concrete, and avoids cracks caused by excessive temperature differences. Ensure that the concrete maintains a stable temperature state during large-volume construction to meet the requirements of structural strength and durability. Through the comprehensive collection of environmental, equipment and concrete temperature data, a complete transportation operation data set is formed to provide a data basis for subsequent analysis, realize real-time monitoring of the transportation process, ensure timely capture of abnormal temperature control trends, and provide reliable input for subsequent environmental and equipment impact analysis, as well as classification models and optimization strategies.

[0050] Based on the transportation operation data set, the pre-cooling effect loss analysis is carried out to identify whether there are hidden dangers of pre-cooling effect not being maintained during transportation; by analyzing the changing trend of concrete temperature, it is identified whether the pre-cooling effect has failed, providing a basis for early warning of warehousing quality problems; by calculating the mean and standard deviation of temperature changes, the temperature control effect is quantitatively analyzed to provide accurate judgment for further optimization, to determine whether there are hidden dangers of pre-cooling effect loss, and to decide whether it is necessary to start further analysis and optimization measures of the environment and equipment.

[0051] If there is a hidden danger that the pre-cooling effect cannot be maintained, further environmental analysis and transportation equipment analysis are carried out to generate a transportation environment impact index for measuring the impact of ambient temperature on concrete temperature rise, and a transportation equipment impact index for measuring the impact of transportation equipment on the pre-cooling effect; quantitative analysis of environmental and equipment impacts: Transportation environment impact index: reflects the direct impact of the external environment (such as high temperature, temperature fluctuations) on the increase in concrete temperature. Transportation equipment impact index: evaluates the impact of tank truck insulation, sealing, vibration and other properties on the pre-cooling effect. Clarify whether it is an external environmental problem or a transportation equipment problem that causes the loss of pre-cooling effect, providing a clear basis for the selection of optimization solutions. Through indexed analysis, ensure that the input parameters of the subsequent classification model are accurate and reliable.

[0052] The transport environment impact index and transport equipment impact index are input into the pre-trained classification model to generate 1 or 0, where 1 corresponds to high-quality transport operations and 0 corresponds to low-quality transport operations. The classification model based on the convolutional neural network can realize intelligent classification of the quality of transport operations, quickly distinguish high-quality and low-quality transport operations in large amounts of data, shorten the analysis time, and for low-quality transport operations, the output of the classification model can directly trigger the optimization module to improve the overall efficiency of the temperature control system.

[0053] For low-quality conveying operations, fuzzy logic is used to determine the optimization type of the conveying operation and implement the corresponding optimization strategy. The relative importance of the transportation environment and equipment issues is comprehensively judged through the fuzzy logic method, and the optimal optimization type (environmental optimization, equipment optimization or collaborative optimization) is selected. Fuzzy logic allows flexible adjustment of optimization strategies under complex and partially uncertain conditions to adapt to the requirements of different warehousing conditions. By implementing corresponding optimization strategies (such as improving the performance of transportation equipment, i.e. tank trucks), the temperature control effect is finally improved to ensure the quality of concrete entering the warehouse.

[0054] The preset temperature control system includes a raw material precooling system and a transportation process control system. The raw material precooling system is used to precool the concrete raw materials and reduce the concrete factory temperature to the target temperature. The transportation process control system is used to execute the optimization strategy corresponding to the optimization type of the transportation operation judged by fuzzy logic for low-quality transportation operations.

[0055] The purpose of the raw material precooling system is to reduce the initial factory temperature of concrete by precooling the main raw materials during the concrete production process, so as to ensure that the concrete has a good temperature control foundation before entering the warehouse. In the prior art, the raw material precooling system usually includes the following methods:

[0056] Cold aggregate technology: Method: Store coarse and fine aggregates (such as sand and gravel) in a cooling device and spray them with cold air or cold water to reduce the temperature of the aggregate. Application scenario: Aggregate is the main component of concrete, and its temperature directly affects the overall temperature of concrete.

[0057] Cooling water technology: Method: Use cold water (such as circulating cooling water or ice water) instead of normal temperature water for concrete mixing to reduce the concrete temperature from the source. Advantages: Cooling water has a significant impact on the temperature of concrete and is one of the most commonly used methods in precooling systems.

[0058] Liquid nitrogen cooling technology: Method: During the concrete mixing stage, liquid nitrogen is directly injected to quickly reduce the temperature of the concrete. Features: High cooling efficiency, which can quickly meet the temperature control requirements in high temperature environments.

[0059] Optimization of raw material storage environment: Method: Store aggregates and water in a low-temperature environment, such as a constant temperature warehouse or refrigerated tank. Advantage: Avoid heat absorption and temperature rise of materials, and reduce the load of pre-cooling equipment.

[0060] Through pre-cooling treatment, the factory temperature of concrete can be reduced to the target temperature (such as below 10°C), providing a good foundation for temperature control during transportation and warehousing, and reducing subsequent temperature control pressure.

[0061] The goal of the transportation process control system is to maintain the temperature control effect of concrete and prevent pre-cooling failure through dynamic monitoring and adjustment during the transportation stage of concrete from production to warehousing. Examples of transportation process control content are:

[0062] Real-time temperature monitoring: Method: Install temperature sensors in the transport tanker to monitor the internal temperature changes of the concrete in real time and obtain the time series of temperature changes. Significance: Provide data support for subsequent pre-cooling effect loss analysis and determine whether there are temperature control risks during transportation.

[0063] Insulation control of transport tank trucks: Methods: Insulate the transport tank trucks, such as adding insulation layers, improving sealing performance, etc. Technical measures: Use multiple layers of insulation materials to reduce heat transfer. Add radiation-proof coatings on the outside of the tank trucks to reduce the impact of external heat sources. Significance: Reduce the impact of external temperature on the temperature rise of concrete and prolong the pre-cooling effect.

[0064] Cooling medium supplement: Method: Inject cooling medium (such as liquid nitrogen) during transportation to cool the concrete for a second time. Application scenario: In the case of long-distance transportation or high ambient temperature, secondary cooling can significantly reduce the temperature rise of concrete.

[0065] Fuzzy logic optimization strategy: Fuzzy logic judgment is performed on low-quality transportation operations to determine the optimization type (such as improving the temperature control capability of the transportation environment, improving the temperature control capability of the transportation equipment, or collaborative optimization). If the ambient temperature has a significant impact, the transportation time and route are optimized. If the equipment performance is insufficient, the tanker insulation measures are adjusted or the cooling medium is increased.

[0066] The pre-cooling effect loss analysis means: using sensors arranged during transportation to collect concrete temperature in real time, obtaining a time series of concrete temperature changes, and then obtaining the corresponding data of two adjacent time points in the time series and marking them as , , t represents the sequence number in the time series, and then the following calculation is performed:

[0067] ; Indicates the time point corresponding to sequence number t, Represents the data change amplitude value, and then calculates the average and standard deviation of all data change amplitude values. Through sensors during transportation, the concrete temperature is recorded in real time to form a temperature change sequence with a time dimension, providing complete temperature control data. The construction of the time series makes it possible to dynamically observe the temperature change trend and capture temperature rise and abnormal fluctuations in a short period of time. Time series data is an important basis for subsequent analysis of the loss of pre-cooling effect; by calculating the temperature difference change rate between adjacent time points, it intuitively reflects the immediate effect of temperature control of concrete during transportation. It not only pays attention to the temperature itself, but also quantifies the change amplitude in the time dimension, which is convenient for identifying time periods with faster or abnormal temperature rise, and takes into account the time interval of data sampling. , ensuring the reliability of calculation results under different sampling frequencies.

[0068] Identifying whether there are hidden dangers of pre-cooling effect not being maintained during transportation means:

[0069] The average value of all data change amplitude values ​​is compared with the preset numerical change interval 1, and the standard deviation of all data change amplitude values ​​is compared with the preset numerical change interval 2. If the average value of all data change amplitude values ​​falls within the preset numerical change interval 1 and the standard deviation of all data change amplitude values ​​falls within the preset numerical change interval 2, a normal signal is generated. If the average value of all data change amplitude values ​​does not fall within the preset numerical change interval 1 and the standard deviation of all data change amplitude values ​​does not fall within the preset numerical change interval 2, an abnormal signal is generated. When a normal signal is generated, it means that there is no hidden danger of precooling effect not being maintained during transportation. When an abnormal signal is generated, it means that there is a hidden danger of precooling effect not being maintained during transportation. The average amplitude of temperature change of concrete during the entire transportation process reflects whether the global temperature control effect is stable. By comparing with the preset change interval, it is judged whether the temperature control effect meets the engineering requirements. The standard deviation is used to describe the degree of fluctuation of the temperature change amplitude and capture the stability of temperature control during transportation. A large standard deviation indicates that the temperature change is unstable, and there may be problems of local precooling failure or significant environmental impact. Normal signal: The average value and standard deviation are within the preset range, indicating that the temperature control effect is normal during transportation and the pre-cooling effect is maintained. Abnormal signal: The average value or standard deviation exceeds the preset range, indicating that there may be temperature control risks and the pre-cooling effect may be lost. When an abnormal signal is generated, the temperature control risks can be identified in advance to avoid affecting the subsequent large-volume low-slump pre-cooled concrete storage, providing data support for subsequent optimization strategies and deciding whether environmental or equipment improvement measures are needed.

[0070] The logic for obtaining the transport environmental impact index is as follows:

[0071] First, make a preliminary assessment of the environmental impact through temperature differences:

[0072] ; is the average temperature difference between the ambient temperature and the initial temperature during transportation, represents the ambient temperature at the i-th time node, represents the initial temperature of concrete, i.e. the temperature of concrete leaving the factory, and n is the number of time nodes during transportation. By calculating the difference between the ambient temperature of each sampling point during transportation and the initial temperature of concrete, the overall trend of the effect of ambient temperature on the temperature rise of concrete is obtained. This is the direct quantification of the impact of ambient temperature on the temperature control effect of concrete, which provides a basis for subsequent comprehensive analysis. Moreover, the mean method is used to eliminate the randomness of a single sampling point, reflecting the overall effect of ambient temperature on the temperature rise of concrete.

[0073] Then calculate the environmental fluctuation impact factor:

[0074] ; Indicates the environmental temperature fluctuation impact factor, which is used to reflect the degree of environmental temperature fluctuation. is the average value of the ambient temperature; when the fluctuation range is large, the concrete temperature may be disturbed by frequent rise and fall, affecting the pre-cooling effect. By calculating the variance of the ambient temperature, the influence of temperature volatility on the concrete temperature control effect is fully reflected.

[0075] Next, calculate the transport time weighting factor: ; represents the transport time weighting factor, Indicates that the ambient temperature during transportation is higher than The total duration of Indicates the total transportation time; when When it is high, it means that the concrete is in a high temperature environment for a long time, and the temperature control effect may decrease. The cumulative effect of the environment on the temperature rise of concrete is quantified by the time proportion, providing time dimension information for subsequent analysis.

[0076] Calculate the ambient heat exchange coefficient: ; Indicates the environmental heat exchange coefficient, which is used to reflect the heat exchange intensity between the ambient temperature and concrete. represents the heat transfer coefficient of the transport equipment, It represents the heat exchange area of ​​the outer surface of the transportation equipment; it reflects the influence of heat conduction, convection and radiation on concrete temperature control from a physical level. The environmental heat exchange coefficient represents the intensity of heat exchange between the environment and concrete, and quantifies the heat transfer capacity between the ambient temperature and the outer surface of the equipment.

[0077] The calculation formula of the transport environmental impact index is: ; It represents the transport environment impact index. It comprehensively evaluates the impact of ambient temperature difference, volatility and high temperature exposure time on the temperature control effect of concrete. It is a dimensionless index used to quantitatively evaluate the negative effects of the transportation environment on concrete temperature control. The larger it is, the more significant the impact of the transportation environment on the temperature control of concrete. Specifically: there is a significant temperature difference between concrete and the environment, and the driving effect of ambient temperature on the temperature rise of concrete is enhanced; the instability of ambient temperature increases the complexity of concrete temperature control and increases the possibility of temperature anomalies; concrete is in a high temperature environment for a long time, resulting in its temperature cumulatively rising, further weakening the pre-cooling effect; the thermal insulation or heat insulation capacity of the transportation equipment is weak, and the concrete is easily affected by the temperature of the external environment.

[0078] The logic for obtaining the transport equipment impact index is:

[0079] Calculating the thermal insulation performance factor for transport tank trucks , evaluate the effect of ambient temperature difference on concrete temperature rise, and express the effect efficiency of ambient temperature difference on concrete temperature rise:

[0080] ; ; Indicates the minimum value of the ambient temperature. Indicates the change in ambient temperature during transportation;

[0081] ; represents the maximum concrete temperature, Indicates the change in concrete temperature; by calculating the ratio of the ambient temperature difference to the concrete temperature rise, the thermal insulation performance of the equipment is quantified. The lower the value.

[0082] Calculating the sealing performance factor for transportation equipment , evaluate the heat loss ratio inside the transport equipment: ; Indicates the heat loss caused by evaporation of concrete in the transport equipment. Indicates the total heat loss of the transport equipment; The closer it is to 1, the better the sealing performance of the equipment.

[0083] Calculating the Thermal Performance Factor for Transportation Equipment , evaluate the ability of the inner surface of the transportation equipment to transfer heat to the concrete: ; Indicates the thermal conductivity of the material used in transportation equipment, Represents the surface area of ​​the inner surface of the transport equipment, Refers to the difference between the average temperature of concrete and the temperature of the inner wall of the transportation equipment. is a non-negative constant; Quantify the impact of transportation equipment materials and structures on heat transfer, The larger it is, the worse the thermal insulation performance of the equipment.

[0084] Calculating the Vibration Performance Factor of a Transport Tank Truck , evaluate the vibration uniformity during tank truck transportation: ; represents the vibration frequency at the ith time point, Indicates the average value of vibration frequency; Quantify the effect of vibration during transportation on the uniformity of concrete temperature control. The more uniform the vibration ( The smaller the concrete temperature, the better the concrete temperature control effect.

[0085] The calculation formula of the transport equipment impact index is:

[0086] ; Represents the transport equipment impact index. Combining the comprehensive impact of heat conduction and vibration performance on the temperature control effect of the equipment, the thermal insulation performance factor represents the ratio of the ambient temperature difference to the temperature rise of the concrete, and the sealing performance factor represents the inhibitory effect of the sealing of the equipment on heat loss. The index term further reflects the synergistic effect of the thermal insulation performance factor and the vibration performance factor on the performance of the equipment. The transport equipment impact index is an indicator used to comprehensively evaluate the negative impact of transport equipment on the temperature control effect of concrete. The larger the value, the worse the equipment's ability to maintain the pre-cooling effect of concrete. Specifically, it includes: thermal insulation performance factor A large value indicates that the thermal insulation performance of the equipment is poor, the temperature of the concrete changes greatly, the equipment cannot effectively isolate the heat influence of the external environment, the temperature control effect of the concrete is significantly reduced, and temperature rise is prone to occur. Sealing performance factor If it is small, it means that there is a large heat loss in the transportation equipment, and the heat leakage caused by evaporation or convection inside the equipment is large. The concrete temperature rises quickly and the precooling effect is difficult to maintain. A large value indicates that the thermal insulation performance of the transport equipment is poor, heat is quickly transferred to the concrete through the tank truck wall, the thermal conductivity of the equipment material is high or the temperature difference of the inner wall is large, and changes in the external ambient temperature are more likely to affect the concrete temperature. Vibration performance factor A large value indicates that the equipment vibrates greatly and unevenly during transportation, and the equipment is in an unstable operating state, resulting in uneven heat distribution inside the concrete and abnormally high local temperature, which may further damage the precooling effect of the concrete.

[0087] The classification model is trained through convolutional neural networks and is used to intelligently classify the quality of transportation operations according to the transportation environment impact index and transportation equipment impact index. The convolutional neural network can accurately capture the comprehensive impact of environmental and equipment factors on the temperature control effect of concrete through feature extraction and nonlinear mapping of input data, and generate classification results. Specifically, the model outputs "1" for high-quality transportation operations and "0" for low-quality transportation operations based on the feature patterns learned during the training process, providing a scientific basis for subsequent optimization decisions and improving the intelligence and automation level of the system.

[0088] For low-quality conveying operations, the optimization type of conveying operations using fuzzy logic refers to:

[0089] The transportation environment impact index and transportation equipment impact index of low-quality transportation operations are taken as input variables, and the optimization type of transportation operations is taken as output variables. The input variables are fuzzified and the values ​​of the input variables are converted into fuzzy sets. The output variables are fuzzified and the output variables are converted into fuzzy sets. Fuzzy rules are formulated to describe the optimization requirements of transportation operations under different combinations of data types. The fuzzified input variables are inferred through fuzzy rules to obtain the optimization type of transportation operations. The optimization type is to improve the temperature control capability of the transportation environment, improve the internal temperature control capability of the transportation equipment, or synergistically improve the temperature control capability of the transportation equipment and the transportation environment.

[0090] In the optimization process of low-quality transportation operations, the working steps of fuzzy logic include the following aspects: Fuzzification of input variables: The specific numerical values ​​of the two input variables, transportation environment impact index and transportation equipment impact index, are converted into fuzzy linguistic variables in the fuzzy set, such as "low", "medium", "high", etc., which represent the fuzzy attributes of the variables.

[0091] Fuzzification of output variables: The output variables (i.e., the type of transportation operation optimization) are also converted into fuzzy linguistic variables in the fuzzy set, such as "environmental optimization", "equipment optimization" and "cooperative optimization".

[0092] Formulation of fuzzy rules: According to the different combinations of environmental impact and equipment impact in actual transportation operations, corresponding fuzzy rules are formulated. For example, if the environmental impact is large and the equipment impact is small, select "environmental optimization"; if the equipment impact is large and the environmental impact is small, select "equipment optimization"; if both are large, select "cooperative optimization".

[0093] Reasoning process: The fuzzified input variables are reasoned through fuzzy rules, and the optimization types of different input conditions are matched according to the rules, and finally the fuzzy optimization type results are obtained.

[0094] Clarification processing: Convert the fuzzy optimization type into a specific output decision, such as a specific optimization strategy or improvement measure.

[0095] The meaning of the three types of optimization: Improve the temperature control ability of the transportation environment: refers to taking optimization measures against the influence of ambient temperature during transportation, such as adjusting the transportation time to avoid high temperature periods, optimizing the transportation route to select areas with smaller temperature differences, or adding external shading measures to reduce the direct impact of the external environment on the temperature of concrete.

[0096] Improving the internal temperature control capability of transportation equipment: refers to improving the insulation, sealing and vibration performance of the transportation equipment itself, such as increasing the thickness of the tank truck's insulation layer, improving the sealing performance inside the tank truck, reducing the vibration amplitude of the equipment during transportation, or using additional cooling media (such as liquid nitrogen) to enhance the internal temperature control effect.

[0097] Synergistically improve the temperature control capabilities of transportation equipment and transportation environment: refers to taking measures to optimize the environment and equipment at the same time. For example, while optimizing the transportation time and route, further improve the thermal insulation and sealing performance of the tank truck to achieve dual optimization effects of the environment and equipment, and maximize the maintenance of the concrete pre-cooling effect.

[0098] Fuzzy logic generates optimization types based on the environmental and equipment problems in actual transportation operations by processing the uncertainty and ambiguity of input variables. The three optimization types target different problems. Environmental optimization focuses on improving external conditions, equipment optimization focuses on improving internal performance, and collaborative optimization is a comprehensive solution for comprehensive response to complex problems.

[0099] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0100] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0101] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0102] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0103] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A coordinated control method for large-volume low-slump precooled concrete entering a warehouse, characterized in that: The following steps are involved: The concrete raw materials are pre-cooled through the preset temperature control system to reduce the concrete factory temperature to the target temperature and then transported. During the transportation process, the environmental data, transportation equipment operation data, and concrete temperature data are collected and then summarized to obtain the transportation operation data set; Perform pre-cooling effect loss analysis based on the transportation operation data set to identify whether there are hidden dangers that the pre-cooling effect cannot be maintained during transportation; If there is a hidden danger that the pre-cooling effect cannot be maintained, further environmental analysis and transportation equipment analysis are performed to generate a transportation environmental impact index for measuring the impact of ambient temperature on concrete temperature rise and a transportation equipment impact index for measuring the impact of transportation equipment on pre-cooling effect; The transport environment impact index and the transport equipment impact index are input into the pre-trained classification model to generate 1 or 0, where 1 corresponds to high-quality transport operations and 0 corresponds to low-quality transport operations; For low-quality conveying operations, fuzzy logic is used to determine the optimization type of the conveying operation and execute the corresponding optimization strategy.

2. The method for collaboratively controlling the storage of large-volume low-slump precooled concrete according to claim 1 is characterized in that: The preset temperature control system includes a raw material precooling system and a transportation process control system. The raw material precooling system is used to precool the concrete raw materials and reduce the concrete factory temperature to the target temperature. The transportation process control system is used to execute the optimization strategy corresponding to the optimization type of the transportation operation judged by fuzzy logic for low-quality transportation operations.

3. The method for collaboratively controlling the storage of large-volume low-slump precooled concrete according to claim 2 is characterized in that: Precooling effect loss analysis refers to: The concrete temperature is collected in real time by sensors arranged during transportation to obtain a time series of concrete temperature changes. Then, the corresponding data of two adjacent time points in the time series are obtained and marked as , , t represents the sequence number in the time series, and then the following calculation is performed: ; Indicates the time point corresponding to sequence number t, Represents the data variation value, and then calculates the mean and standard deviation of all data variation values.

4. The method for collaboratively controlling the storage of large-volume low-slump precooled concrete according to claim 3 is characterized in that: Identifying whether there are hidden dangers of pre-cooling effect not being maintained during transportation means: The average value of all data change amplitude values ​​is compared with the preset numerical change interval one, and the standard deviation of all data change amplitude values ​​is compared with the preset numerical change interval two. If the average value of all data change amplitude values ​​falls within the preset numerical change interval one and the standard deviation of all data change amplitude values ​​falls within the preset numerical change interval two, a normal signal is generated. If the average value of all data change amplitude values ​​does not fall within the preset numerical change interval one and the standard deviation of all data change amplitude values ​​falls within the preset numerical change interval two, an abnormal signal is generated. When a normal signal is generated, it indicates that there is no hidden danger of pre-cooling effect not being maintained during transportation. When an abnormal signal is generated, it indicates that there is a hidden danger of pre-cooling effect not being maintained during transportation.

5. The method for collaboratively controlling the storage of large-volume low-slump precooled concrete according to claim 4 is characterized in that: The logic for obtaining the transport environmental impact index is as follows: First, make a preliminary assessment of the environmental impact through temperature differences: ; is the average temperature difference between the ambient temperature and the initial temperature during transportation, represents the ambient temperature at the i-th time node, It represents the initial temperature of concrete, i.e. the temperature of concrete leaving the factory, and n is the number of time nodes in the transportation process; Then calculate the environmental fluctuation impact factor: ; Indicates the environmental temperature fluctuation impact factor, which is used to reflect the degree of environmental temperature fluctuation. is the average value of the ambient temperature; Next, calculate the transport time weighting factor: ; represents the transport time weighting factor, Indicates that the ambient temperature during transportation is higher than The total duration of Indicates the total transportation time; Calculate the ambient heat exchange coefficient: ; Indicates the environmental heat exchange coefficient, which is used to reflect the heat exchange intensity between the ambient temperature and concrete. represents the heat transfer coefficient of the transport equipment, Indicates the heat exchange area on the outer surface of the transport equipment; The calculation formula of the transport environmental impact index is: ; Represents the transport environmental impact index.

6. The method for collaboratively controlling the storage of large-volume low-slump precooled concrete according to claim 5, characterized in that: The logic for obtaining the transport equipment impact index is: Calculating the thermal insulation performance factor of a transport tank truck , evaluate the effect of ambient temperature difference on concrete temperature rise: ; ; Indicates the minimum value of the ambient temperature. Indicates the change in ambient temperature during transportation; ; represents the maximum concrete temperature, Indicates the variation of concrete temperature; Calculating the sealing performance factor for transportation equipment , evaluate the heat loss ratio inside the transport equipment: ; Indicates the heat loss caused by evaporation of concrete in the transport equipment. Indicates the total heat loss of the transport equipment; Calculating the Thermal Performance Factor for Transportation Equipment , evaluate the ability of the inner surface of the transportation equipment to transfer heat to the concrete: ; Indicates the thermal conductivity of the material used in transportation equipment, Represents the surface area of ​​the inner surface of the transport equipment, Refers to the difference between the average temperature of concrete and the temperature of the inner wall of the transportation equipment. is a non-negative constant; Calculating the Vibration Performance Factor of a Transport Tank Truck , evaluate the vibration uniformity during tank truck transportation: ; represents the vibration frequency at the ith time point, Indicates the average value of vibration frequency; The calculation formula of the transport equipment impact index is: ; Represents the transport equipment impact index.

7. The method for collaboratively controlling the storage of large-volume low-slump precooled concrete according to claim 6, characterized in that: The classification model is trained through convolutional neural network.

8. The method for collaboratively controlling the storage of large-volume low-slump precooled concrete according to claim 7, characterized in that: For low-quality conveying operations, the optimization type of conveying operations using fuzzy logic refers to: The transportation environment impact index and transportation equipment impact index of low-quality transportation operations are taken as input variables, and the optimization type of transportation operations is taken as output variables. The input variables are fuzzified and the values ​​of the input variables are converted into fuzzy sets. The output variables are fuzzified and the output variables are converted into fuzzy sets. Fuzzy rules are formulated to describe the optimization requirements of transportation operations under different combinations of data types. The fuzzified input variables are inferred through fuzzy rules to obtain the optimization type of transportation operations. The optimization type is to improve the temperature control capability of the transportation environment, improve the internal temperature control capability of the transportation equipment, or synergistically improve the temperature control capability of the transportation equipment and the transportation environment.

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