A concrete mixing control method suitable for high temperature and high humidity environment

Abstract

The present invention relates to the field of concrete mixing, and discloses a concrete mixing control method suitable for a high temperature and high humidity environment, which is used to solve the problem that the mixing time cannot be automatically controlled according to the actual situation when the concrete is mixed, including obtaining the situation data of the mixing equipment, evaluating the equipment influence coefficient according to the situation data of the mixing equipment, obtaining the raw material data, evaluating the material influence coefficient according to the raw material data, obtaining the temperature and humidity of the mixing workshop through a temperature and humidity sensor, evaluating the environmental influence coefficient according to the temperature and humidity of the mixing workshop, comprehensively evaluating the equipment influence coefficient, the material influence coefficient and the environmental influence coefficient to obtain a mixing time adjustment index, obtaining the actual mixing time according to the mixing time adjustment index, and controlling the concrete mixing time according to the actual mixing time. The present invention effectively realizes the automatic adjustment of the mixing time and improves the mixing efficiency.

Description

Technical Field

[0001] The invention relates to the field of concrete mixing, and more specifically to a concrete mixing control method suitable for high temperature and high humidity environments. Background Art

[0002] Concrete mixing is the process of uniformly mixing cement, sand, gravel, water and necessary admixtures or additives according to a certain mix ratio to produce a concrete mixture with good workability and meeting construction requirements. This process is a key link in concrete production, directly affecting the performance of concrete and project quality, and requires strict control of parameters such as raw material quality, mix ratio and mixing time.

[0003] Concrete mixing is the core process of concrete production, which directly affects its workability, uniformity, and ultimate strength and durability. Traditional concrete mixing methods usually set a fixed mixing time based on empirical formulas or industry specifications under standard environmental conditions. However, in high temperature and high humidity environments, many dynamic factors will significantly affect the quality and temperature rise of concrete mixing. Existing concrete mixing methods lack a mixing time control mechanism based on dynamic adjustment of multiple factors, and cannot avoid over-mixing or uneven problems caused by fixed mixing time. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a concrete mixing control method suitable for high temperature and high humidity environments to meet the mixing control of concrete in high temperature and high humidity environments.

[0005] The present invention solves the technical problem by the following technical solutions:

[0006] A concrete mixing control method suitable for high temperature and high humidity environment comprises the following steps:

[0007] Step 1: Obtain the condition data of the mixing equipment, including the degree of wear of the mixing blades, the speed of the mixing plant, and the internal temperature of the mixing plant. The equipment influence coefficient is obtained by evaluating the condition data of the mixing equipment;

[0008] Step 2: Obtain raw material data, including aggregate moisture content, cement temperature, and aggregate size distribution, and evaluate the material influence coefficient based on the raw material data;

[0009] Step 3: Obtain the temperature and humidity of the mixing workshop through the temperature and humidity sensor, and obtain the environmental impact coefficient based on the temperature and humidity of the mixing workshop;

[0010] Step 4: The mixing time adjustment index is obtained by comprehensive evaluation of the equipment influence coefficient, material influence coefficient and environmental influence coefficient. , where Expressed as the mixing time adjustment index, Expressed as the equipment influence coefficient, Expressed as the material influence coefficient, Expressed as the environmental impact coefficient, , , It is expressed as the weight coefficient of equipment influence coefficient, material influence coefficient and environmental influence coefficient;

[0011] Step 5: Obtain the actual mixing time according to the mixing time adjustment index, and control the concrete mixing time according to the actual mixing time.

[0012] Preferably, the step of evaluating the equipment influence coefficient based on the situation data of the mixing equipment is:

[0013] Install vibration sensors at key locations of the mixing plant;

[0014] The vibration signal generated during the operation of the mixing plant is recorded by a vibration sensor, the sampling frequency and sampling time are set, and the collected signal is a time series;

[0015] A bandpass filter is used to remove high-frequency noise and low-frequency interference, and only the signal related to the vibration of the stirring blade is retained, which is recorded as the stirring blade vibration signal;

[0016] Obtaining vibration signal data when the stirring blade is initially running, recorded as an initial vibration signal, and obtaining a vibration difference at each moment according to the difference between the vibration signal of the stirring blade and the initial vibration signal at each moment;

[0017] Cluster the vibration difference at each moment, and calculate the wear degree of the mixing blade based on the clustering results;

[0018] Real-time acquisition of the current speed of the mixing plant, the current internal temperature of the mixing plant, the standard speed of the mixing plant, the ideal operating temperature of the equipment, and the maximum internal temperature allowed by the equipment;

[0019] The equipment influence coefficient is obtained by evaluating the wear degree of the mixing blades, the speed of the mixing plant and the internal temperature of the mixing plant. The specific method of obtaining it is as follows:

[0020] ;

[0021] In the formula, Expressed as the equipment influence coefficient, Indicates the degree of wear of the mixing blades. It is expressed as the standard speed of the mixing plant. is the current speed of the mixing plant, It is represented by the current internal temperature of the mixing plant. Indicates the ideal operating temperature of the device. Indicates the maximum internal temperature allowed for the device.

[0022] Preferably, the step of clustering the vibration difference at each moment is:

[0023] Step 1.1: Use the vibration difference as a clustering feature, and use the vibration difference at all times within the detection time as a data set. Each vibration difference in the data set is a data point.

[0024] Step 1.2: Use the elbow method to determine the optimal number of clusters K for the data set;

[0025] Step 1.3: Randomly select K data points in the data set as the initial cluster centers. For each data point, calculate its Euclidean distance to each initial cluster center. For each data point, traverse the K initial cluster centers and assign it to the cluster corresponding to the nearest initial cluster center.

[0026] Step 1.4: After traversing all data points, the initial clusters are obtained. For each initial cluster, the mean of the data points in it is calculated to obtain a new cluster center.

[0027] Step 1.5: Repeat steps 1.3 and 1.4 until the cluster center no longer changes, and obtain the final cluster and the final cluster center.

[0028] Preferably, the step of calculating the degree of wear of the stirring blade according to the clustering result is:

[0029] The weight of each final cluster is calculated by calculating the ratio of the number of data points in each final cluster to the total number of data points in the data set;

[0030] The weight of each final cluster is weighted and summed with the final cluster center to get the degree of wear of the mixing blade, which is calculated as follows: ,in Indicates the degree of wear of the mixing blades. Represented as the weight of the jth final cluster, It is represented as the jth final cluster center, and K is the optimal number of clusters of the data set.

[0031] Preferably, the step of evaluating and obtaining the material influence coefficient according to the raw material data is:

[0032] Install moisture sensors in the aggregate conveyor belt and use them to monitor the moisture content of aggregates in real time;

[0033] Use infrared thermometer to monitor cement temperature in real time;

[0034] Obtain aggregate diameter data through screening experiments, and evaluate the aggregate size distribution based on the aggregate diameter data;

[0035] The material influence coefficient is obtained by evaluating the aggregate moisture content, cement temperature and aggregate size distribution. The specific method of obtaining it is as follows:

[0036] ;

[0037] In the formula, Expressed as the material influence coefficient, is the aggregate size distribution, is the moisture content of aggregate, is the cement temperature.

[0038] Preferably, the step of obtaining the diameter data of aggregate through screening experiment is:

[0039] Select a certain amount of aggregate samples from the aggregates and dry the aggregate samples;

[0040] Stack the sieves in descending order according to the mesh size, place a receiving tray at the bottom, and pour the aggregate sample into the sieve with the largest mesh size;

[0041] Start the screen vibrator to screen the aggregate sample. After the screening is completed, weigh the mass of the remaining aggregate on each layer of the screen starting from the top screen, calculate the ratio of the mass of the remaining aggregate on each layer of the screen to the total mass of the aggregate sample, and obtain the mass proportion corresponding to each layer of the screen.

[0042] Preferably, the step of evaluating the aggregate size distribution according to the aggregate diameter data is:

[0043] Calculate the average particle size of aggregate and the dispersion of particle size distribution;

[0044] The particle size uniformity coefficient is calculated by obtaining the particle size with a cumulative passing rate of 60% and the particle size with a cumulative passing rate of 10% from the sieving curve;

[0045] The particle size distribution index is obtained by evaluating the average particle size of the aggregate and the degree of dispersion of the particle size distribution;

[0046] The aggregate size distribution is evaluated based on the discrete degree of particle size distribution, particle size uniformity coefficient and particle size distribution index. The specific method of obtaining it is as follows:

[0047] ;

[0048] In the formula, Expressed as the aggregate size distribution, It is expressed as the degree of dispersion of particle size distribution. Expressed as the particle size uniformity coefficient, Expressed as particle size distribution index.

[0049] Preferably, the step of evaluating the environmental impact coefficient according to the temperature and humidity of the mixing workshop is:

[0050] Obtain real-time temperature data of the mixing workshop through the temperature sensor, and obtain the maximum allowable workshop temperature;

[0051] Obtain real-time humidity data of the mixing workshop through humidity sensors, and obtain the maximum allowable workshop humidity;

[0052] The environmental impact coefficient is obtained by evaluating the real-time temperature data and the real-time humidity data. The specific method of obtaining it is as follows:

[0053] ;

[0054] In the formula, Expressed as the environmental impact coefficient, Represented as real-time temperature data, Expressed as the maximum allowable workshop temperature, Represented as real-time humidity data, Expressed as the maximum allowable workshop humidity.

[0055] Preferably, the step of obtaining the actual mixing time according to the mixing time adjustment index is:

[0056] Set the initial mixing time and the mixing time threshold, and when the mixing time adjustment index is equal to the mixing time threshold, the mixing time is the initial mixing time;

[0057] The time adjustment coefficient is obtained by calculating the ratio of the mixing time adjustment index to the mixing time threshold;

[0058] The actual mixing time is calculated by multiplying the time adjustment coefficient by the initial mixing time. The actual mixing time is obtained as follows:

[0059] ;

[0060] In the formula, It is expressed as the actual mixing time. It is expressed as the initial mixing time, Expressed as a time adjustment factor.

[0061] The beneficial effects of the present invention are:

[0062] 1. The concrete mixing control method suitable for high temperature and high humidity environment of the present invention obtains the situation data of the mixing equipment, obtains the equipment influence coefficient according to the situation data of the mixing equipment, obtains the raw material data, obtains the material influence coefficient according to the raw material data, obtains the temperature and humidity of the mixing workshop through the temperature and humidity sensor, obtains the environmental influence coefficient according to the temperature and humidity of the mixing workshop, obtains the mixing time adjustment index according to the comprehensive evaluation of the equipment influence coefficient, the material influence coefficient and the environmental influence coefficient, obtains the actual mixing time according to the mixing time adjustment index, controls the concrete mixing time according to the actual mixing time, effectively realizes the automatic adjustment of the mixing time, and improves the mixing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A flow chart of a concrete mixing control method suitable for a high temperature and high humidity environment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0064] The present invention is further described in detail below through specific examples. The following examples are only illustrative and not restrictive, and the protection scope of the present invention cannot be limited thereto.

[0065] A concrete mixing control method suitable for high temperature and high humidity environment comprises the following steps:

[0066] Step 1: Obtain the condition data of the mixing equipment, including the degree of wear of the mixing blades, the speed of the mixing plant, and the internal temperature of the mixing plant. The equipment influence coefficient is obtained by evaluating the condition data of the mixing equipment;

[0067] In this embodiment, it should be specifically explained that the steps of evaluating the equipment influence coefficient according to the situation data of the mixing equipment are as follows:

[0068] Install vibration sensors at key locations of the mixing plant (such as the mixing shaft or equipment housing) to ensure that the sensors can collect the main vibration signals during the operation of the mixing plant;

[0069] The vibration signal generated during the operation of the mixing plant is recorded by a vibration sensor, the sampling frequency and sampling time are set, and the collected signal is a time series;

[0070] Use a bandpass filter to remove high-frequency noise and low-frequency interference, and only retain the signal related to the vibration of the stirring blade, which is recorded as the stirring blade vibration signal. The bandpass filter is a signal processing tool used to allow signals to pass within a specified frequency range while suppressing signals below the lower limit frequency and above the upper limit frequency. Its function is to retain the target frequency component (such as the characteristic frequency of the stirring blade vibration) while removing high-frequency noise and low-frequency interference;

[0071] Obtaining vibration signal data when the stirring blade is initially running, recorded as an initial vibration signal, and obtaining a vibration difference at each moment according to the difference between the vibration signal of the stirring blade and the initial vibration signal at each moment;

[0072] Cluster the vibration difference at each moment, and calculate the wear degree of the mixing blade based on the clustering results;

[0073] Real-time acquisition of the current speed of the mixing plant, the current internal temperature of the mixing plant, the standard speed of the mixing plant, the ideal operating temperature of the equipment, and the maximum internal temperature allowed by the equipment;

[0074] The equipment influence coefficient is obtained by evaluating the wear degree of the mixing blades, the speed of the mixing plant and the internal temperature of the mixing plant. The specific method of obtaining it is as follows:

[0075] ;

[0076] In the formula, Expressed as the equipment influence coefficient, Indicates the degree of wear of the mixing blades. It is expressed as the standard speed of the mixing plant. is the current speed of the mixing plant, It is represented by the current internal temperature of the mixing plant. Indicates the ideal operating temperature of the device. Indicates the maximum internal temperature allowed for the device.

[0077] When the speed decreases, the cutting speed and mixing force of the mixing blades weaken, and the distribution of aggregate and paste in the concrete is uneven. The lower speed will cause the mixing time to be extended to compensate for the decrease in efficiency, thereby increasing the burden on the equipment. When the current speed of the mixing plant is less than the standard speed of the mixing plant, the relative movement between the concrete paste and aggregate is weakened and cannot be fully mixed. The blades cannot cover all areas of the mixing barrel per unit time, resulting in some material retention or insufficient mixing.

[0078] When the current temperature inside the mixing plant is equal to the ideal operating temperature of the equipment, the impact of temperature on the equipment is minimal; when the current temperature inside the mixing plant is greater than the maximum allowable internal temperature of the equipment, the temperature has a great impact on the equipment.

[0079] In this embodiment, it should be specifically explained that the steps of clustering the vibration difference at each moment are:

[0080] Step 1.1: Use the vibration difference as a clustering feature, and use the vibration difference at all times within the detection time as a data set. Each vibration difference in the data set is a data point.

[0081] Step 1.2: Use the elbow method to determine the optimal number of clusters K for the data set;

[0082] The elbow method is an algorithm used to determine the optimal number of clusters for a data set. It calculates the total error sum of squares under different numbers of clusters, that is, the sum of the squares of the distances from each point to its cluster center, and plots the relationship curve between the number of clusters and the total error sum of squares. When the number of clusters increases, the total error sum of squares gradually decreases, but begins to slow down at a certain point, forming an elbow-like inflection point. The number of clusters at this inflection point is the optimal number of clusters, because increasing the number of clusters after this point will reduce the error less, indicating that the model has better described the data structure;

[0083] Step 1.3: Randomly select K data points in the data set as the initial cluster centers. For each data point, calculate the Euclidean distance from each initial cluster center. The specific method is as follows:

[0084] ;

[0085] In the formula, It is expressed as the Euclidean distance from the data point to the cluster center, where Represented as data points, Represented as the initial cluster center, for each data point, traverse the K initial cluster centers and assign it to the cluster corresponding to the nearest initial cluster center;

[0086] Euclidean distance is a geometric distance calculation method used to measure the straight-line distance between two points. It is defined as the square root of the sum of the squares of the differences in the corresponding dimensions of the coordinates of the two points in multidimensional space. For two-dimensional space, it is the straight-line distance formula between two points on a plane; in three-dimensional or higher-dimensional space, it is expanded to the shortest distance between two points in multidimensional space.

[0087] Step 1.4: After traversing all data points, the initial clusters are obtained. For each initial cluster, the mean of the data points in it is calculated to obtain a new cluster center.

[0088] Step 1.5: Repeat steps 1.3 and 1.4 until the cluster center no longer changes, and obtain the final cluster and the final cluster center.

[0089] In this embodiment, it should be specifically explained that the steps of calculating the wear degree of the stirring blade according to the clustering results are:

[0090] The weight of each final cluster is calculated by calculating the ratio of the number of data points in each final cluster to the total number of data points in the data set;

[0091] The weight of each final cluster is weighted and summed with the final cluster center to get the degree of wear of the mixing blade, which is calculated as follows: ,in Indicates the degree of wear of the mixing blades. Represented as the weight of the jth final cluster, It is represented as the jth final cluster center, and K is the optimal number of clusters of the data set.

[0092] Step 2: Obtain raw material data, including aggregate moisture content, cement temperature, and aggregate size distribution, and evaluate the material influence coefficient based on the raw material data;

[0093] In this embodiment, it should be specifically explained that the steps of evaluating and obtaining the material influence coefficient according to the raw material data are as follows:

[0094] Install moisture sensors in the aggregate conveyor belt and use them to monitor the moisture content of aggregates in real time;

[0095] Use an infrared thermometer to monitor cement temperature in real time. The infrared thermometer is a non-contact temperature measurement device that receives infrared radiation from the surface of an object and uses the relationship between the wavelength of infrared light and the temperature of the object to calculate and display the surface temperature of the object. It is fast and accurate, and is suitable for use in high-temperature, dangerous or inconvenient contact environments. Infrared thermometers are often used to monitor the temperature of materials such as cement and aggregates in real time. They can effectively avoid errors and inconveniences caused by contact temperature measurement, and are widely used in the production of building materials and quality control.

[0096] The diameter data of aggregates are obtained through screening experiments. The aggregate size distribution is evaluated based on the aggregate diameter data. Screening experiments are a commonly used particle classification and particle size distribution test method. Aggregate samples are separated according to particle size through a series of standard sieves with different apertures. In the experiment, the aggregates pass through the sieves in turn under vibrating screens or manual operation, and the mass of aggregates intercepted by each layer of sieves is recorded, and the mass proportion and cumulative pass rate of each particle size range are calculated;

[0097] The material influence coefficient is obtained by evaluating the aggregate moisture content, cement temperature and aggregate size distribution. The specific method of obtaining it is as follows:

[0098] ;

[0099] In the formula, Expressed as the material influence coefficient, is the aggregate size distribution, is the moisture content of aggregate, is the cement temperature.

[0100] In this embodiment, it should be specifically explained that the steps for obtaining the diameter data of aggregates through the screening experiment are as follows:

[0101] Select a certain amount of aggregate samples from the aggregates and dry the aggregate samples;

[0102] Stack the sieves in descending order according to the mesh size, place a receiving tray at the bottom, and pour the aggregate sample into the sieve with the largest mesh size;

[0103] Start the screen vibrator to screen the aggregate sample. After the screening is completed, weigh the mass of the remaining aggregate on each layer of the screen starting from the top screen, calculate the ratio of the mass of the remaining aggregate on each layer of the screen to the total mass of the aggregate sample, and obtain the mass proportion corresponding to each layer of the screen.

[0104] In this embodiment, it should be specifically explained that the steps of evaluating the aggregate size distribution according to the aggregate diameter data are as follows:

[0105] Calculate the average particle size of aggregate. The specific method is as follows:

[0106] ;

[0107] In the formula, Expressed as the average particle size of aggregate, is the aperture of the i-th sieve, It is expressed as the mass proportion corresponding to the i-th layer of screen;

[0108] Calculate the discrete degree of particle size distribution. The specific method is as follows:

[0109] ;

[0110] In the formula, It is expressed as the degree of dispersion of particle size distribution. The larger the value, the more dispersed the particle size distribution. Expressed as the average particle size of aggregate;

[0111] From the sieving curve, the particle size with a cumulative passing rate of 60% and the particle size with a cumulative passing rate of 10% are obtained, and the particle size uniformity coefficient is calculated. The specific method of obtaining it is as follows:

[0112] ;

[0113] In the formula, It is expressed as the particle size uniformity coefficient. The smaller the particle size uniformity coefficient, the more uniform the particle size distribution. The larger the particle size uniformity coefficient, the more uneven the particle size distribution. It is expressed as the particle size with a cumulative passing rate of 60%. It is expressed as the particle size at which the cumulative passing rate is 10%;

[0114] The particle size distribution index is obtained by evaluating the average particle size of the aggregate and the degree of dispersion of the particle size distribution. The specific method of obtaining it is as follows:

[0115] ;

[0116] In the formula, It is expressed as the particle size distribution index. The larger the particle size distribution index is, the more dispersed the aggregate particle size distribution is. Expressed as the average particle size of aggregate, Expressed as the degree of dispersion of particle size distribution;

[0117] The aggregate size distribution is evaluated based on the discrete degree of particle size distribution, particle size uniformity coefficient and particle size distribution index. The specific method of obtaining it is as follows:

[0118] ;

[0119] In the formula, It is expressed as the degree of aggregate size distribution. The larger the degree of aggregate size distribution, the more uneven the particle size distribution is, and the greater the impact on mixing time. It is expressed as the degree of discreteness of the particle size distribution, reflecting the discreteness of the particle size range. Expressed as the particle size uniformity coefficient, it measures the ratio of large particles to small particles. Expressed as a particle size distribution index, it is used to further correct the effect of distribution on mixing performance.

[0120] Step 3: Obtain the temperature and humidity of the mixing workshop through the temperature and humidity sensor, and obtain the environmental impact coefficient based on the temperature and humidity of the mixing workshop;

[0121] In this embodiment, it should be specifically explained that the steps of evaluating the environmental impact coefficient according to the temperature and humidity of the mixing workshop are as follows:

[0122] Obtain real-time temperature data of the mixing workshop through the temperature sensor, and obtain the maximum allowable workshop temperature;

[0123] Obtain real-time humidity data of the mixing workshop through humidity sensors, and obtain the maximum allowable workshop humidity;

[0124] The environmental impact coefficient is obtained by evaluating the real-time temperature data and the real-time humidity data. The specific method of obtaining it is as follows:

[0125] ;

[0126] In the formula, Expressed as the environmental impact coefficient, Represented as real-time temperature data, Expressed as the maximum allowable workshop temperature, Represented as real-time humidity data, Expressed as the maximum allowable workshop humidity.

[0127] Step 4: The mixing time adjustment index is obtained by comprehensive evaluation based on the equipment influence coefficient, material influence coefficient and environmental influence coefficient. It should be specifically noted that the equipment influence coefficient, material influence coefficient and environmental influence coefficient are all normalized to keep the units consistent. The mixing time adjustment index is obtained as follows:

[0128] ;

[0129] In the formula Expressed as the mixing time adjustment index, Expressed as the equipment impact coefficient, the equipment performance status directly affects the need to adjust the concrete mixing time. When the equipment condition deteriorates, the mixing time needs to be extended to ensure mixing uniformity. When the mixing blades are severely worn, the mixing efficiency of the equipment decreases, affecting the full mixing of materials in the concrete. At this time, the equipment impact coefficient increases, and the mixing time needs to be extended accordingly to compensate for the insufficient equipment performance; conversely, when the equipment is in good condition and the equipment impact coefficient is low, a shorter mixing time can be maintained to avoid concrete temperature rise or performance degradation due to excessive mixing. Expressed as the material influence coefficient, changes in material properties directly affect the adjustment range of concrete mixing time. When the difficulty of concrete mixing caused by material properties increases, the material influence coefficient will also increase. At this time, extending the mixing time can ensure that the cement slurry evenly wraps the aggregate to avoid stratification or segregation; on the contrary, when the material performance is in an ideal state, the influence coefficient is low and the mixing time can be appropriately shortened, thereby saving energy and improving production efficiency. This proportional relationship helps to monitor the material status in real time and dynamically adjust the mixing time to ensure the quality of concrete mixing while reducing unnecessary resource consumption. It is particularly suitable for concrete production control in complex construction environments such as high temperature and high humidity. It is expressed as the environmental impact coefficient. The more the environmental conditions deviate from the standard, the higher the impact coefficient will be. It is necessary to extend the mixing time to compensate for the adverse effects on the mixing effect. In a high temperature environment, the hydration reaction of the concrete is accelerated and the fluidity of the mixture decreases, which may cause the materials to not be fully mixed. Therefore, the environmental impact coefficient increases and the mixing time needs to be increased to ensure uniformity. In a high humidity environment, too much water may adhere to the surface of the aggregate, causing fluctuations in the water-cement ratio. At this time, extending the mixing time can improve the full combination of the slurry and the aggregate. At the same time, in a low temperature or dry environment, the water loss on the surface of the concrete is fast, and the mixing time also needs to be appropriately extended to ensure workability. , , It is expressed as the weight coefficient of equipment influence coefficient, material influence coefficient and environmental influence coefficient, and , , , The specific value is determined by professionals according to the actual situation, for example, , , It can be 0.4, 0.4, 0.2.

[0130] Step 5: Obtain the actual mixing time according to the mixing time adjustment index, and control the concrete mixing time according to the actual mixing time.

[0131] In this embodiment, it should be specifically explained that the steps for obtaining the actual mixing time according to the mixing time adjustment index are:

[0132] Set the initial mixing time and the mixing time threshold, and when the mixing time adjustment index is equal to the mixing time threshold, the mixing time is the initial mixing time;

[0133] The time adjustment coefficient is calculated by comparing the mixing time adjustment index with the mixing time threshold. The time adjustment coefficient is obtained as follows:

[0134] ;

[0135] In the formula, Expressed as the time adjustment factor, Expressed as the mixing time adjustment index, Expressed as mixing time threshold;

[0136] The actual mixing time is calculated by multiplying the time adjustment coefficient by the initial mixing time. The actual mixing time is obtained as follows:

[0137] ;

[0138] In the formula, It is expressed as the actual mixing time. It is expressed as the initial mixing time, Expressed as a time adjustment factor.

[0139] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0140] Although the embodiments and drawings of the present invention are disclosed for illustrative purposes, those skilled in the art will appreciate that various substitutions, changes and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the scope of the present invention is not limited to the contents disclosed in the embodiments and drawings.

Claims

1. A concrete mixing control method suitable for high temperature and high humidity environment, characterized in that: The following steps are involved: Step 1: Obtain the condition data of the mixing equipment, including the degree of wear of the mixing blades, the speed of the mixing plant, and the internal temperature of the mixing plant. The equipment influence coefficient is obtained by evaluating the condition data of the mixing equipment; Step 2: Obtain raw material data, including aggregate moisture content, cement temperature, and aggregate size distribution, and evaluate the material influence coefficient based on the raw material data; Step 3: Obtain the temperature and humidity of the mixing workshop through the temperature and humidity sensor, and obtain the environmental impact coefficient based on the temperature and humidity of the mixing workshop; Step 4: The mixing time adjustment index is obtained by comprehensive evaluation of the equipment influence coefficient, material influence coefficient and environmental influence coefficient. , where Expressed as the mixing time adjustment index, Expressed as the equipment influence coefficient, Expressed as the material influence coefficient, It is expressed as the environmental impact coefficient, , , It is expressed as the weight coefficient of equipment influence coefficient, material influence coefficient and environmental influence coefficient; Step 5: Obtain the actual mixing time according to the mixing time adjustment index, and control the concrete mixing time according to the actual mixing time; The step of evaluating the equipment influence coefficient based on the situation data of the mixing equipment is as follows: Install vibration sensors at key locations of the mixing plant; The vibration signal generated during the operation of the mixing plant is recorded by a vibration sensor, the sampling frequency and sampling time are set, and the collected signal is a time series; A bandpass filter is used to remove high-frequency noise and low-frequency interference, and only the signal related to the vibration of the stirring blade is retained, which is recorded as the stirring blade vibration signal; Obtaining vibration signal data when the stirring blade is initially running, recorded as an initial vibration signal, and obtaining a vibration difference at each moment according to the difference between the vibration signal of the stirring blade and the initial vibration signal at each moment; Cluster the vibration difference at each moment, and calculate the wear degree of the mixing blade based on the clustering results; Real-time acquisition of the current speed of the mixing plant, the current internal temperature of the mixing plant, the standard speed of the mixing plant, the ideal operating temperature of the equipment, and the maximum internal temperature allowed by the equipment; The equipment influence coefficient is obtained by evaluating the wear degree of the mixing blades, the speed of the mixing plant and the internal temperature of the mixing plant. The specific method of obtaining it is as follows: In the formula, Expressed as the equipment influence coefficient, Indicates the degree of wear of the mixing blades. It is expressed as the standard speed of the mixing plant. is the current speed of the mixing plant, It is represented by the current internal temperature of the mixing plant. Indicates the ideal operating temperature of the device. Expressed as the maximum internal temperature allowed for the device; The steps of evaluating the material influence coefficient according to the raw material data are as follows: Install moisture sensors in the aggregate conveyor belt and use them to monitor the moisture content of aggregates in real time; Use infrared thermometer to monitor cement temperature in real time; Obtain aggregate diameter data through screening experiments, and evaluate the aggregate size distribution based on the aggregate diameter data; The material influence coefficient is obtained by evaluating the aggregate moisture content, cement temperature and aggregate size distribution. The specific method of obtaining it is as follows: In the formula, Expressed as the material influence coefficient, is the aggregate size distribution, is the moisture content of aggregate, is the cement temperature; The steps of evaluating the environmental impact coefficient according to the temperature and humidity of the mixing workshop are as follows: Obtain real-time temperature data of the mixing workshop through the temperature sensor, and obtain the maximum allowable workshop temperature; Obtain real-time humidity data of the mixing workshop through humidity sensors, and obtain the maximum allowable workshop humidity; The environmental impact coefficient is obtained by evaluating the real-time temperature data and the real-time humidity data. The specific method of obtaining it is as follows: In the formula, Expressed as the environmental impact coefficient, Represented as real-time temperature data, Expressed as the maximum allowable workshop temperature, Represented as real-time humidity data, Expressed as the maximum allowable workshop humidity.

2. The concrete mixing control method suitable for high temperature and high humidity environment according to claim 1, characterized in that: The step of clustering the vibration difference at each moment is: Step 1.1: Use the vibration difference as a clustering feature, and use the vibration difference at all times within the detection time as a data set. Each vibration difference in the data set is a data point. Step 1.2: Use the elbow method to determine the optimal number of clusters K for the data set; Step 1.3: Randomly select K data points in the data set as the initial cluster centers. For each data point, calculate its Euclidean distance to each initial cluster center. For each data point, traverse the K initial cluster centers and assign it to the cluster corresponding to the nearest initial cluster center. Step 1.4: After traversing all data points, the initial clusters are obtained. For each initial cluster, the mean of the data points in it is calculated to obtain a new cluster center. Step 1.5: Repeat steps 1.3 and 1.4 until the cluster center no longer changes, and obtain the final cluster and the final cluster center.

3. The concrete mixing control method suitable for high temperature and high humidity environment according to claim 1, characterized in that: The step of calculating the wear degree of the stirring blade according to the clustering result is: The weight of each final cluster is calculated by calculating the ratio of the number of data points in each final cluster to the total number of data points in the data set; The weight of each final cluster is weighted and summed with the final cluster center to get the degree of wear of the mixing blade, which is calculated as follows: ,in Indicates the degree of wear of the mixing blades. Represented as the weight of the jth final cluster, It is represented as the jth final cluster center, and K is the optimal number of clusters of the data set.

4. The concrete mixing control method suitable for high temperature and high humidity environment according to claim 1, characterized in that: The steps of obtaining the diameter data of aggregates through screening experiments are as follows: Select a certain amount of aggregate samples from the aggregates and dry the aggregate samples; Stack the sieves in descending order according to the mesh size, place a receiving tray at the bottom, and pour the aggregate sample into the sieve with the largest mesh size; Start the screen vibrator to screen the aggregate sample. After the screening is completed, weigh the mass of the remaining aggregate on each layer of the screen starting from the top screen, calculate the ratio of the mass of the remaining aggregate on each layer of the screen to the total mass of the aggregate sample, and obtain the mass proportion corresponding to each layer of the screen.

5. The concrete mixing control method suitable for high temperature and high humidity environment according to claim 1, characterized in that: The step of evaluating the aggregate size distribution according to the aggregate diameter data is as follows: Calculate the average particle size of aggregate and the dispersion of particle size distribution; The particle size uniformity coefficient is calculated by obtaining the particle size with a cumulative passing rate of 60% and the particle size with a cumulative passing rate of 10% from the sieving curve; The particle size distribution index is obtained by evaluating the average particle size of the aggregate and the degree of dispersion of the particle size distribution; The aggregate size distribution is evaluated based on the discrete degree of particle size distribution, particle size uniformity coefficient and particle size distribution index. The specific method of obtaining it is as follows: ; In the formula, Expressed as the aggregate size distribution, It is expressed as the degree of dispersion of particle size distribution. Expressed as the particle size uniformity coefficient, Expressed as particle size distribution index.

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