阅读说明：本技术 一种预制构件混凝土布料的螺旋输送量的分析方法 (Analysis method for spiral conveying amount of prefabricated member concrete cloth ) 是由 李冬 周鹏 郭菁菁 张成龙 张珂 吴玉厚 于文达 于 2020-10-22 设计创作，主要内容包括：本发明公开了一种预制构件混凝土布料的螺旋输送量的分析方法,基于神经网络可以高精度的逼近任意非线性函数,而无需知道非线性函数的具体数学描述的特点,通过确定螺旋输送量的神经网络的计算模型,对无法采用数学表达式精确描述螺旋输送过程的输送量计算问题进行智能化高精度预报计算,进而提高螺旋输送量的计算精度,且其计算值既可以作为布料重量自动控制系统的目标预报值,又有助于重量自动控制系统的稳定运行。(The invention discloses an analysis method for spiral conveying capacity of prefabricated concrete distribution, which can approach any nonlinear function with high precision based on a neural network without knowing the specific mathematical description characteristics of the nonlinear function, intelligently and precisely forecast and calculate the conveying capacity calculation problem which cannot accurately describe the spiral conveying process by adopting a mathematical expression through determining a calculation model of the neural network of the spiral conveying capacity, thereby improving the calculation precision of the spiral conveying capacity, and the calculated value can be used as a target forecast value of a distribution weight automatic control system and is beneficial to stable operation of the weight automatic control system.)

1. A method for analyzing the spiral conveying amount of a precast concrete cloth is characterized by comprising the following steps:

determining the number of input and output neurons of a neural network of the spiral conveying capacity of the concrete distribution material, and determining a neural network structure of the spiral conveying capacity;

forming a sample data set by the input value of the neural network and the corresponding output value of the neural network, and sequentially carrying out forward and backward propagation calculation on training data in the sample data set so as to determine parameters for training the neural network;

obtaining a neural network calculation model of the spiral conveying capacity according to the neural network structure of the spiral conveying capacity and parameters of a training neural network;

and (4) obtaining a final prediction value of the spiral conveying amount through a neural network calculation model of the spiral conveying amount.

2. The method for analyzing the spiral conveying amount of the concrete cloth material of the prefabricated part according to claim 1, wherein the method for determining the number of input and output neurons of the neural network of the spiral conveying amount of the concrete cloth material and determining the neural network structure of the spiral conveying amount comprises the following steps:

acquiring the forecast calculation spiral conveying capacity of a 3-layer BP neural network through an input layer, a hidden layer and an output layer;

the properties, the rotating speed and the screw pitch of the conveyed materials which influence the spiral conveying amount are used as 3 input layer neurons of the BP neural network;

by setting the number of neurons in the output layer to 1, the target predicted value of the spiral delivery amount corresponding to the output layer can be obtained.

3. The method for analyzing the spiral conveying amount of the precast concrete cloth according to claim 2, wherein the method comprises the following steps of, when constructing a sample data set from input values and corresponding output values of a neural network:

respectively carrying out normalization processing on input neurons respectively corresponding to the property, the rotating speed and the screw pitch of the conveyed material influencing the spiral conveying amount; wherein, the used normalization processing calculation formula is as follows:in the formula: x is data before normalization; x is the number of_minAnd x_maxRespectively the minimum value and the maximum value of all input values; y is normalized data; y is_minAnd y_maxLower and upper limits of the input value planning range, y, respectively_min＝-1，y_max＝1。

4. The method for analyzing spiral conveying amount of precast concrete cloth according to claim 1, when performing forward propagation calculation on training data in the sample data set, comprising:

and determining the output condition of each layer of neurons of the neural network, the weight value and the initial value of the threshold value of each layer of the neural network and the number of the neurons of the hidden layer.

5. The method for analyzing the spiral delivery amount of the precast concrete cloth according to claim 4, wherein the determining of the neuron output conditions of each layer of the neural network comprises:

the formula of the transfer function corresponding to the output condition of the input layer of the neural network is as follows:

the formula of the transfer function corresponding to the output condition of the hidden layer of the neural network is as follows:

the formula of the transfer function corresponding to the output condition of the output layer of the neural network is as follows:

in the formula I_iInputting the ith neuron of the input layer;is the hidden layer neuron output; v. of_ijThe weight value from the ith neuron of the input layer to the jth neuron of the hidden layer is calculated, wherein j is the number of neurons of the hidden layer and q is the number of the neurons of the hidden layer_jThreshold values for hidden layer neurons. O is the actual output of the neuron in the output layer; w is a_jThe connection weight value from the jth neuron of the hidden layer to the neuron of the output layer; θ is the threshold of the output layer neurons.

6. The method for analyzing the spiral delivery amount of the precast concrete cloth according to claim 5, further comprising, when determining neuron output conditions of each layer of the neural network:

determining the threshold values of the hidden layer and the output layer which are randomly generated, and the weights from the input layer to the hidden layer and from the hidden layer to the output layer;

empirical calculation formula based on hidden layer neuronsCalculating the neuron quantity range of the hidden layer to be 3-12, wherein j is the neuron number of the hidden layer, i is the neuron number of an input layer, and u is the neuron number of an output layer; z is a constant between 1 and 10;

and determining the number of the hidden layer neurons by adopting the sample training data to calculate and analyze the number of the hidden layer neurons one by one.

7. The method for analyzing spiral conveying amount of precast member concrete cloth according to claim 1, when performing back propagation calculation on training data in the sample data set, comprising:

using error function calculationComparing the actual output of the neural network of the spiral conveying amount with the expected output to obtain a sample error;

using calculation formulasCalculating the global error of the neural network training of the spiral conveying amount;

correcting the output layer of the neural network training of the spiral conveying amount to a hidden layer by layer to form back propagation, and outputting a final prediction result after the back propagation is corrected for a plurality of times through the neural network training of the spiral conveying amount until the output error of the BP neural network is reduced to a specified target precision range;

wherein, t_pIs the desired output of the output layer neurons; p is the training sample serial number; n is the number of training samples.

8. The method of claim 7, wherein before the step of modifying the output layer to the hidden layer by layer trained by the neural network of the spiral conveying amount to form the back propagation, the method further comprises:

correcting the weight values of the output layer and the hidden layer of the neural network training of the spiral conveying quantity by adopting an error gradient descent method, wherein the calculation formula is as follows:

wherein eta is the learning rate, eta belongs to (0,1), and the corresponding learning rate value with better calculation effect in the training data is selected through a trial and error method.

9. A storage medium on which a computer program is stored, wherein the program, when executed by a processor, implements the method of analyzing the auger delivery amount of a precast member concrete cloth according to any one of claims 1 to 8.

10. A device for calculating the spiral conveying amount of a precast concrete cloth material, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the method for analyzing the spiral conveying amount of a precast concrete cloth material according to any one of claims 1 to 8 when executing the program.

Technical Field

The invention belongs to the technical field of automatic control of concrete distributing machines, and particularly relates to a method for analyzing the spiral conveying capacity of concrete distribution of a prefabricated part.

Background

The spiral conveyor is typical material conveying equipment and is widely applied to the industries of prefabricated part production, agriculture, mines and the like. The spiral concrete spreader is important equipment in the production of concrete prefabricated parts, and the manual control mode is mainly adopted for spreading production at present. In the face of the revolution of the production mode of precast concrete members, the manual control mode of the original concrete spreader can not meet the industrial development requirement, and the manual control mode is urgently required to be changed to the direction of automation, informatization and even intellectualization.

Wherein. The spiral conveying capacity model of the concrete distributing machine is an important model for realizing automatic distribution, and is mainly used for providing a distribution weight control target value, and the calculation precision of the distribution weight control target value directly determines the stability of a distribution weight control system and influences the weight precision of a member produced by final distribution. Many parameters of the traditional concrete distributing machine spiral conveying capacity neural network calculation model are determined by experience, such as material stacking density, filling coefficient and the like, so that the calculation accuracy of the conveying capacity of the existing model is low, the calculation cannot be used for setting a distributing weight control target value, the realization of automatic control of the concrete distributing weight is limited, and further the industry upgrading process is limited.

Disclosure of Invention

Aiming at the defects in the prior art, the invention provides a method for analyzing the spiral conveying amount of the concrete cloth of the prefabricated part.

The invention provides a method for analyzing the spiral conveying capacity of concrete cloth of a prefabricated part, which comprises the following steps:

determining the number of input and output neurons of a neural network of the spiral conveying capacity of the concrete distribution material, and determining a neural network structure of the spiral conveying capacity;

forming a sample data set by the input value of the neural network and the corresponding output value of the neural network, and sequentially carrying out forward and backward propagation calculation on training data in the sample data set so as to determine parameters for training the neural network;

obtaining a neural network calculation model of the spiral conveying capacity according to the neural network structure of the spiral conveying capacity and parameters of a training neural network;

and (4) obtaining a final prediction value of the spiral conveying amount through a neural network calculation model of the spiral conveying amount.

Further, when determining the number of input and output neurons of the neural network for the spiral conveying amount of the concrete cloth and determining the neural network structure for the spiral conveying amount, the method includes:

acquiring the forecast calculation spiral conveying capacity of a 3-layer BP neural network through an input layer, a hidden layer and an output layer;

the properties, the rotating speed and the screw pitch of the conveyed materials which influence the spiral conveying amount are used as 3 input layer neurons of the BP neural network;

by setting the number of neurons in the output layer to 1, the target predicted value of the spiral delivery amount corresponding to the output layer can be obtained.

Further, when a sample data set is formed by the input values of the neural network and the corresponding output values thereof, the method includes:

respectively carrying out normalization processing on input neurons respectively corresponding to the property, the rotating speed and the screw pitch of the conveyed material influencing the spiral conveying amount; wherein, the used normalization processing calculation formula is as follows:in the formula: x is data before normalization; x is the number of_minAnd x_maxRespectively the minimum value and the maximum value of all input values; y is normalized data; y is_minAnd y_maxLower and upper limits of the input value planning range, y, respectively_min＝-1，y_max＝1。

Further, when performing forward propagation calculation on training data in the sample data set, the method includes:

and determining the output condition of each layer of neurons of the neural network, the weight value and the initial value of the threshold value of each layer of the neural network and the number of the neurons of the hidden layer.

Further, when determining the output condition of each layer of neurons of the neural network, the method comprises the following steps:

the formula of the transfer function corresponding to the output condition of the input layer of the neural network is as follows:

the formula of the transfer function corresponding to the output condition of the hidden layer of the neural network is as follows:

the formula of the transfer function corresponding to the output condition of the output layer of the neural network is as follows:

in the formula I_iInputting the ith neuron of the input layer;is the hidden layer neuron output; v. of_ijThe weight value from the ith neuron of the input layer to the jth neuron of the hidden layer is calculated, wherein j is the number of neurons of the hidden layer and q is the number of the neurons of the hidden layer_jThreshold values for hidden layer neurons. O is the actual output of the neuron in the output layer; w is a_jThe connection weight value from the jth neuron of the hidden layer to the neuron of the output layer; θ is the threshold of the output layer neurons.

Further, when determining the output condition of each layer of neurons of the neural network, the method further comprises the following steps:

determining the threshold values of the hidden layer and the output layer which are randomly generated, and the weights from the input layer to the hidden layer and from the hidden layer to the output layer;

empirical calculation formula based on hidden layer neuronsCalculating the neuron quantity range of the hidden layer to be 3-12, wherein j is the neuron number of the hidden layer, i is the neuron number of an input layer, and u is the neuron number of an output layer; z is a constant between 1 and 10;

and determining the number of the hidden layer neurons by adopting the sample training data to calculate and analyze the number of the hidden layer neurons one by one.

Further, when performing back propagation calculation on the training data in the sample data set, the method includes:

using error function calculationComparing the actual output of the neural network of the spiral conveying amount with the expected output to obtain a sample error;

using calculation formulasCalculating the global error of the neural network training of the spiral conveying amount;

correcting the output layer of the neural network training of the spiral conveying amount to a hidden layer by layer to form back propagation, and outputting a final prediction result after the back propagation is corrected for a plurality of times through the neural network training of the spiral conveying amount until the output error of the BP neural network is reduced to a specified target precision range;

wherein, t_pIs the desired output of the output layer neurons; p is the training sample serial number; n is the number of training samples.

Further, before modifying the output layer trained by the neural network of the spiral delivery volume to the hidden layer by layer to form back propagation, the method further comprises the following steps:

correcting the weight values of the output layer and the hidden layer of the neural network training of the spiral conveying quantity by adopting an error gradient descent method, wherein the calculation formula is as follows:

wherein eta is the learning rate, eta belongs to (0,1), and the corresponding learning rate value with better calculation effect in the training data is selected through a trial and error method.

Another aspect of the present invention provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the method of analyzing a screw conveying amount of a precast member concrete cloth described above.

The invention further provides a device for calculating the spiral conveying amount of the precast concrete cloth material, which comprises a storage medium, a processor and a computer program which is stored on the storage medium and can run on the processor, and is characterized in that the processor realizes the analysis method of the spiral conveying amount of the precast concrete cloth material when executing the program.

The invention provides a method for analyzing the spiral conveying capacity of concrete cloth of a prefabricated part, which comprises the steps of determining the quantity of input and output neurons of a neural network of the spiral conveying capacity of the concrete cloth and determining the neural network structure of the spiral conveying capacity; forming a sample data set by the input value of the neural network and the corresponding output value of the neural network, and sequentially carrying out forward and backward propagation calculation on training data in the sample data set so as to determine parameters for training the neural network; obtaining a neural network calculation model of the spiral conveying capacity according to the neural network structure of the spiral conveying capacity and parameters of a training neural network; and (4) obtaining a final prediction value of the spiral conveying amount through a neural network calculation model of the spiral conveying amount. According to the method, according to the characteristic that the neural network can approach any nonlinear function with high precision without knowing the specific mathematical description of the nonlinear function, the calculation model of the neural network of the spiral conveying capacity is determined, the intelligent high-precision prediction calculation is carried out on the conveying capacity calculation problem which cannot be accurately described in the spiral conveying process by adopting a mathematical expression, the calculation precision of the spiral conveying capacity is further improved, and the calculated value can be used as a target prediction value of an automatic cloth weight control system and is also beneficial to the stable operation of the automatic weight control system.

Drawings

FIG. 1 is a schematic view of a spiral concrete spreader production process;

fig. 2 is a schematic flowchart of a method for analyzing a screw conveying amount of a precast member concrete cloth according to an exemplary embodiment of the present invention;

fig. 3 is a schematic flowchart of a method for analyzing a screw conveying amount of a concrete cloth for a prefabricated part according to an exemplary embodiment of the present invention;

fig. 4 is a schematic flowchart of another method for analyzing the spiral conveying amount of the precast member concrete cloth according to the exemplary embodiment of the present invention;

FIG. 5 is a schematic flowchart illustrating a method for analyzing a screw conveying amount of a concrete cloth for a prefabricated part according to another exemplary embodiment of the present invention;

in the figure, 1-a material distributor control cabinet, 2-a material distributor cart, 3-a material distributor trolley, 4-a material distributor hopper, 5-a scattering rod, 6-a spiral driving motor, 7-a spiral, 8-a discharge gate, 9-a bottom die tray, 10-a precast concrete member side die and 11-a walking beam bracket.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In the prior art, a schematic diagram of a pouring production process of a precast concrete member of an adopted spiral distributing machine is shown in fig. 1.

Therefore, the volume of concrete passing through the cross section of the outlet of the distributing opening in unit time of the spiral distributing machine in the prior art is the spiral conveying amount, and the formula Q ═ S rho V is generally adopted in the industry_ZCalculating the spiral conveying amount, wherein: q is the screw delivery capacity of the distributing machine, kg/s; s is the cross-sectional area of the concrete layer in the screw rod, m²(ii) a Rho is the bulk density of the concrete, kg/m³；V_zIs the axial conveying speed of the concrete in the screw rod, m/s.

Because, the area S of the bed of material cross section of spiral cloth machine is:in the formula: d is the diameter of the helical blade m; d is the diameter of the spiral shaft, m; Ψ is a fill factor; c is a tilt correction coefficient. Therefore, when the screw conveying capacity model adopting the traditional mechanism is adopted to forecast the conveying capacity, the used calculation formula is that Q is 0.05236 phi Sn rho (D)²-d²) In the formula, phi is the filling rate, S is the helical pitch, n is the helical number of turns, rho is the density of the concrete, D is the external diameter of the helix, and D is the internal diameter of the helix.

Since Vz can be calculated asExpressed, in the formula: p is the pitch, m; n is the screw revolution, r/min; therefore, the calculation formula of the traditional mechanism model of the spiral conveying capacity of the distributing machine obtained by calculation is as follows

Because the traditional material distributor screw conveying capacity mechanism model adopts an empirical interval parameter method to approximately describe the screw conveying process state, the calculation accuracy of the screw conveying capacity is low, and therefore, the deviation is increased after the weight target value is formed by accumulation, and finally the requirement of the automatic control and the accurate operation of the concrete material distribution weight on the weight forecast target value cannot be met.

In view of the above problems, the present invention aims to improve the prediction accuracy of the screw conveying capacity of the concrete distributor, thereby providing an accurate target prediction value for the concrete distribution weight control system.

In order to achieve the above object, an aspect of the present invention provides a method for analyzing a spiral conveying amount of a concrete cloth of a prefabricated part, referring to fig. 2, including:

s100, determining the number of input and output neurons of the neural network of the spiral conveying amount of the concrete cloth, and determining the neural network structure of the spiral conveying amount.

When step S100 is executed, as shown in fig. 3, the specific implementation steps further include:

s101, acquiring the forecast calculation spiral conveying capacity of a 3-layer BP neural network through an input layer, a hidden layer and an output layer;

s102, using the material conveying property, the rotating speed and the screw pitch which influence the spiral conveying amount as 3 input layer neurons of the BP neural network;

s103, setting the number of neurons in the output layer to be 1, and obtaining the target forecast value of the spiral delivery amount corresponding to the output layer.

S200, forming a sample data set by the input values of the neural network and the corresponding output values of the input values of the neural network, and sequentially carrying out forward and backward propagation calculation on training data in the sample data set so as to determine parameters for training the neural network.

When step S200 is executed, when a sample data set is formed by the input values of the neural network and the corresponding output values thereof, the specific implementation steps further include:

respectively carrying out normalization processing on input neurons respectively corresponding to the property, the rotating speed and the screw pitch of the conveyed material influencing the spiral conveying amount; wherein, the used normalization processing calculation formula is as follows:in the formula: x is data before normalization; x is the number of_minAnd x_maxRespectively the minimum value and the maximum value of all input values; y is normalized data; y is_minAnd y_maxLower and upper limits of the input value planning range, y, respectively_min＝-1，y_max＝1。

When step S200 is executed, and when the forward propagation calculation is performed on the training data in the sample data set, the specific implementation steps further include:

and determining the output condition of each layer of neurons of the neural network, the weight value and the initial value of the threshold value of each layer of the neural network and the number of the neurons of the hidden layer.

Further, when determining the output condition of each layer of neurons of the neural network, the method comprises the following steps:

the formula of the transfer function corresponding to the output condition of the input layer of the neural network is as follows:

the formula of the transfer function corresponding to the output condition of the hidden layer of the neural network is as follows:

the formula of the transfer function corresponding to the output condition of the output layer of the neural network is as follows:

in the formula I_iInputting the ith neuron of the input layer;is the hidden layer neuron output; v. of_ijThe weight value from the ith neuron of the input layer to the jth neuron of the hidden layer is calculated, wherein j is the number of neurons of the hidden layer and q is the number of the neurons of the hidden layer_jThreshold values for hidden layer neurons. O is the actual output of the neuron in the output layer; w is a_jThe connection weight value from the jth neuron of the hidden layer to the neuron of the output layer; θ is the threshold of the output layer neurons.

When determining the output condition of each layer of neurons of the neural network, referring to fig. 4, the method further includes:

s201, determining a threshold value for randomly generating a hidden layer and an output layer and weights from an input layer to the hidden layer and weights from the hidden layer to the output layer;

s202, calculating formula according to hidden layer neuron experienceCalculating the neuron quantity range of the hidden layer to be 3-12, wherein j is the neuron number of the hidden layer, i is the neuron number of an input layer, and u is the neuron number of an output layer; z is a constant between 1 and 10;

s203, determining the number of the hidden layer neurons by adopting the sample training data to calculate and analyze the number of the hidden layer neurons one by one.

When the number of the hidden layer neurons is determined, the number of the hidden layer neurons is calculated one by adopting sample training data, and then the number of the corresponding hidden layer neurons when the calculation effect is relatively good is selected as the final number of the hidden layer neurons.

In executing step S200, when performing back propagation calculation on training data in the sample data set, referring to fig. 5, the implementation steps further include:

s204, adopting an error function calculation formulaComparing the actual output of the neural network of the spiral conveying amount with the expected output to obtain a sample error;

s205, adopting calculation formulaCalculating the global error of the neural network training of the spiral conveying amount;

s206, correcting the output layer of the neural network training of the spiral conveying capacity to the hidden layer by layer to form back propagation, and outputting a final prediction result after the back propagation is corrected for the neural network training of the spiral conveying capacity for multiple times until the output error of the BP neural network is reduced to be within a specified target precision range;

wherein, t_pIs the desired output of the output layer neurons; p is the training sample serial number; n is the number of training samples.

Further, before modifying the output layer trained by the neural network of the spiral conveying capacity to the hidden layer by layer to form back propagation, the method further comprises the following steps:

correcting the weight values of the output layer and the hidden layer of the neural network training of the spiral conveying quantity by adopting an error gradient descent method, wherein the calculation formula is as follows:

wherein eta is the learning rate, eta belongs to (0,1), and the corresponding learning rate value with better calculation effect in the training data is selected through a trial and error method.

S300, obtaining a neural network calculation model of the spiral conveying amount according to the neural network structure of the spiral conveying amount and parameters of the training neural network.

The neural network parameters comprise weights and thresholds of an input layer, a hidden layer and an output layer, and the neural network calculation model for determining the spiral transmission output is determined through forward propagation training of the BP neural network according to the neural network parameters.

S400, obtaining a final prediction value of the spiral conveying amount through a neural network calculation model of the spiral conveying amount.

When step S400 is executed, the specific implementation steps further include:

based on step S200, normalizing the sample experimental data or production data, and based on step S300, determining the trained BP neural network structure and parameters, and then adopting the information forward propagation calculation formula of the neural networkAndand carrying out intelligent prediction calculation on the screw conveying quantity on the normalized sample experimental data or production data.

According to the analysis method for the spiral conveying capacity of the prefabricated concrete distribution, provided by the invention, according to the characteristic that a neural network can approach any nonlinear function with high precision without knowing the specific mathematical description of the nonlinear function, the intelligent high-precision forecasting calculation is carried out on the conveying capacity calculation problem which cannot accurately describe the spiral conveying process by adopting a mathematical expression through determining the calculation model of the neural network of the spiral conveying capacity, so that the calculation precision of the spiral conveying capacity is improved, and the calculated value can be used as a target forecasting value of a distribution weight automatic control system and is beneficial to the stable operation of the weight automatic control system.

Based on the method shown in fig. 2 to 5, correspondingly, the embodiment of the invention also provides a storage device, wherein a computer program is stored on the storage device, and when the computer program is executed by a processor, the method for analyzing the spiral conveying amount of the precast member concrete cloth shown in fig. 2 to 5 is realized.

In order to achieve the above object, an embodiment of the present invention further provides a device for calculating a spiral conveying amount of a precast concrete cloth, where the device includes a storage device and a processor; a storage device for storing a computer program; a processor for executing a computer program to implement the above-described method for analyzing the spiral conveying amount of the precast member concrete cloth as shown in fig. 2 to 5.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention and not to limit it; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art will understand that: modifications to the specific embodiments of the invention or equivalent substitutions for parts of the technical features may be made; without departing from the spirit of the present invention, it is intended to cover all aspects of the invention as defined by the appended claims.