CN112396159A - Method for forecasting spiral conveying amount of concrete distribution - Google Patents

Method for forecasting spiral conveying amount of concrete distribution Download PDF

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CN112396159A
CN112396159A CN202011140558.1A CN202011140558A CN112396159A CN 112396159 A CN112396159 A CN 112396159A CN 202011140558 A CN202011140558 A CN 202011140558A CN 112396159 A CN112396159 A CN 112396159A
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spiral conveying
neural network
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deviation
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张珂
李冬
周鹏
郭菁菁
张成龙
吴玉厚
于文达
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Shenyang Jianzhu University
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Abstract

The invention discloses a method for forecasting the spiral conveying capacity of concrete distribution, which comprises the following steps of obtaining a calculated value of a spiral conveying capacity mechanism model according to the axial conveying speed of concrete in a screw of a distributing machine; obtaining a deviation model of the actual value of the spiral conveying capacity and the calculated value of the mechanism model according to the neural network structure of the spiral conveying capacity deviation, the neural network training and the spiral conveying capacity deviation of the neural network; and correcting the calculated value of the spiral conveying amount mechanism model through a deviation model of the actual value of the spiral conveying amount and the calculated value of the mechanism model, so that the predicted value of the final spiral conveying amount can be obtained. The method can improve the forecast calculation precision of the spiral conveying quantity, and the calculated value can be used as a target forecast value of the automatic cloth weight control system and is beneficial to the stable operation of the automatic weight control system.

Description

Method for forecasting spiral conveying amount of concrete distribution
Technical Field
The invention belongs to the technical field of automatic control of concrete distributing machines, and particularly relates to a method for forecasting the spiral conveying amount of concrete distribution.
Background
Concrete pouring is a key link in the production process of the concrete prefabricated part, and the pouring effect directly influences the product quality of the prefabricated part. In the quality of prefabricated part products, the weight precision is one of the important quality indexes in the casting production link, and the concrete distribution production mode implemented by manual operation is difficult to ensure the precision and stability of the indexes, and in addition, more control objects are provided for a multi-spiral concrete distributor, so that the difficulty in implementing concrete distribution in a refined manner is increased. The effective method for solving the problems is to introduce a digital intelligent pouring method so as to improve the cloth production level, and the concrete cloth weight automatic control method is an effective means for improving the weight index precision of the prefabricated part as a core.
The function of automatic control of the concrete distribution weight is based on the premise that a target value for the effective weight is obtained, which can be obtained by accumulating the screw conveying amount. However, the screw conveying capacity mechanism model of the traditional distributing machine adopts an empirical interval parameter method to approximately describe the state of the screw conveying process, so that the calculation accuracy of the screw conveying capacity is low, the deviation is increased after the weight target value is formed by accumulation, and the requirement of the automatic control and the accurate operation of the concrete distributing weight on the weight forecast target value cannot be met.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a method for forecasting the spiral conveying amount of concrete distribution.
In a first aspect, a method for forecasting a spiral conveying amount of concrete distribution is provided, which includes:
obtaining a calculation value of a spiral conveying capacity mechanism model according to the axial conveying speed of concrete in a screw of a distributing machine;
obtaining a deviation model of the actual value of the spiral conveying capacity and the calculated value of the mechanism model according to the neural network structure of the spiral conveying capacity deviation, the neural network training and the spiral conveying capacity deviation of the neural network;
and correcting the calculated value of the spiral conveying amount mechanism model through a deviation model of the actual value of the spiral conveying amount and the calculated value of the mechanism model, so that the predicted value of the final spiral conveying amount can be obtained.
Further, when obtaining the calculation value of the spiral conveying capacity mechanism model according to the axial conveying speed of the concrete in the screw of the distributing machine, the method comprises the following steps:
calculating the axial conveying speed of the concrete particles under the friction condition according to the axial conveying speed, the relation between the traction speed and the absolute speed of the concrete particles in the spiral conveying process, the relation between the traction speed and the spiral rotation number of the concrete particles and the relation between the friction coefficient and the angle;
and obtaining a screw conveying quantity mechanism forecast calculation value under the friction condition according to the axial conveying speed of the concrete particles under the friction condition.
Further, the determination of the neural network structure of the spiral delivery amount deviation includes:
taking the diameter of a spiral blade, the pitch, the spiral rotation number, the type of concrete, the liquid level height of the concrete in a hopper and the calculated value of a spiral conveying capacity mechanism model which influence the conveying capacity as 6 input quantities of a BP neural network conveying capacity deviation model;
obtaining 6 neurons needed by the input layers according to the neurons of each input layer corresponding to the 6 input quantities;
empirical calculation formula based on hidden layer neurons
Figure BDA0002738138310000021
Calculating the neuron quantity range of the hidden layer to be 4-13, 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;
and taking the number of hidden layer neurons corresponding to the BP neural network in the training data as the final number of hidden layer neurons, and finishing the finally determined neural network structure.
Further, the determination of the neural network training of the spiral delivery amount deviation comprises:
and (3) sequentially carrying out normalization calculation, input layer calculation, hidden layer calculation and output layer calculation on the input values of the 6 spiral conveying amount calculations to finish the information forward propagation calculation of the neural network.
Further, when the input value of the spiral conveying amount calculation is subjected to the input layer calculation, the calculation formula is as follows:
Figure BDA0002738138310000031
when the hidden layer calculation is carried out on the input value of the spiral conveying amount calculation, the calculation formula is as follows:
Figure BDA0002738138310000032
when the output layer calculation is carried out on the input value of the spiral conveying amount calculation, the calculation formula is as follows:
Figure BDA0002738138310000033
wherein, IiInputting the ith neuron of the input layer;
Figure BDA0002738138310000034
is the hidden layer neuron output; v. ofijThe 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 layerjA threshold value for hidden layer neurons; o is the actual output of the neuron in the output layer; w is ajThe 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, training the neural network of the spiral delivery amount deviation further includes:
using error function calculation
Figure BDA0002738138310000035
Comparing the actual output of the neural network of the spiral conveying amount deviation with the expected output to obtain a sample error;
using calculation formulas
Figure BDA0002738138310000036
Calculating a global error of the neural network training of the spiral conveying amount deviation;
correcting the output layer of the neural network training of the spiral conveying amount deviation to a hidden layer by layer to form back propagation, and performing back propagation correction on the neural network training of the spiral conveying amount deviation for multiple times until the output error of the BP neural network is reduced to a specified target precision range, namely outputting a final prediction result;
wherein, tpIs the desired output of the output layer neurons; p is the training sample serial number; n is the number of training samples.
Further, before the output layer trained by the neural network of the spiral delivery deviation is modified layer by layer to the hidden 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 amount deviation by adopting an error gradient descent method, wherein the calculation formula is as follows:
Figure BDA0002738138310000041
Figure BDA0002738138310000042
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.
Further, the determining of the neural network spiral delivery capacity deviation comprises:
and obtaining a screw delivery deviation forecast value of experimental data according to the neural network structure of the screw delivery deviation and the neural network weight and threshold determined by the neural network training.
In a second aspect, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the method of forecasting an auger delivery amount for a concrete distribution according to any one of the above.
In a third aspect, a forecasting device for the spiral conveying amount of the concrete distribution is provided, which includes a storage medium, a processor and a computer program stored on the storage medium and operable on the processor, and when the processor executes the program, the processor implements any one of the methods for forecasting the spiral conveying amount of the concrete distribution.
According to the method for forecasting the spiral conveying capacity of the concrete distribution, the calculated value of a spiral conveying capacity mechanism model is obtained according to the axial conveying speed of the concrete in the screw of the distributing machine; obtaining a deviation model of the actual value of the spiral conveying capacity and the calculated value of the mechanism model according to the neural network structure of the spiral conveying capacity deviation, the neural network training and the spiral conveying capacity deviation of the neural network; and correcting the calculated value of the spiral conveying amount mechanism model through a deviation model of the actual value of the spiral conveying amount and the calculated value of the mechanism model, so that the predicted value of the final spiral conveying amount can be obtained. After the spiral conveying amount is intelligently calculated, the prediction value of the conveying amount mechanism model is adopted as a main calculation value, and the prediction deviation of the intelligent conveying amount model is used as an auxiliary value, so that the overall prediction calculation precision of the conveying amount model is improved, and meanwhile, the stability of the prediction calculation result is facilitated. The method can improve the forecast calculation precision of the spiral conveying quantity, and the calculated value can be used as a target forecast value of the automatic cloth weight control system and is 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 flowchart illustrating a method for forecasting a screw delivery amount of concrete distribution according to an exemplary embodiment of the present invention;
fig. 3 is a flowchart illustrating a forecasting method for a screw delivery amount of concrete distribution according to another exemplary embodiment of the present invention;
FIG. 4 is an exploded view of the concrete particle velocity;
FIG. 5 is a flow chart illustrating another method for forecasting the spiral delivery amount of concrete distribution according to an exemplary embodiment of the present invention;
fig. 6 is a flowchart illustrating a method for forecasting a screw feeding amount of concrete distribution according to another exemplary embodiment of the present invention;
fig. 7 is a schematic structural diagram of a screw delivery amount model according to an 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 industryZCalculating the spiral conveying amount, wherein: q is the spiral conveying 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；VzIs 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:
Figure BDA0002738138310000061
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 delivery model with the traditional mechanism is adopted to forecast the delivery, the calculation formula is
Figure BDA0002738138310000062
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 as
Figure BDA0002738138310000063
Expressed, 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
Figure BDA0002738138310000064
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.
Aiming at the problems, the invention establishes an intelligent spiral conveying capacity forecasting model. Firstly, providing a spiral conveying amount mechanism forecasting method considering a friction coefficient based on a movement mechanism of spiral conveying concrete particles; secondly, designing an intelligent forecasting method for the deviation of the spiral conveying capacity by adopting a BP neural network; and then, taking the mechanism model prediction value as a main value, taking the BP neural network prediction value as a deviation compensation value, and forming a final spiral conveying capacity prediction calculation method by adopting an addition form.
The method for forecasting the spiral conveying amount of the concrete distribution, which is provided by the invention, is shown in fig. 2 and comprises the following steps:
s100, obtaining a calculation value of a spiral conveying capacity mechanism model according to the axial conveying speed of concrete in a screw of the distributing machine.
When step S100 is executed, as shown in fig. 3, the specific implementation steps further include:
s101, according to the axial conveying speed V of the concrete particles in the spiral conveying processzThe bulk velocity VoRelation with absolute velocity V, and bulk velocity V of concrete particlesoCalculating the axial conveying speed V of the concrete particles under the condition of friction according to the relationship between the axial conveying speed V and the spiral rotation number n and the relationship between the friction coefficient mu and the angle phiz。
S102, according to the axial conveying speed V of concrete particles under the friction conditionzAnd obtaining a screw conveying quantity mechanism forecast calculation value under the friction condition.
In particular, the concrete particle velocity profile is shown in FIG. 4, since the axial transport velocity V of the concrete in the screw iszThe calculation formula of the absolute speed V of the concrete particles is as follows:
Figure BDA0002738138310000071
Figure BDA0002738138310000072
therefore, the velocity V is consideredoRelation to number of helical turns n
Figure BDA0002738138310000073
And the relation mu between the friction coefficient mu and the angle phi is tan phi, and the axial conveying speed of the concrete particles under the friction condition is calculated and considered, wherein the calculation formula is as follows:
Figure BDA0002738138310000074
wherein, VrThe radial movement speed of the concrete particles; v is the absolute speed of the concrete particles; vNAbsolute speed without friction; voThe drawing speed is; r is the distance of the O point from the spiral axis; beta is a helix angle; alpha is the internal friction angle of the concrete; phi is the angle at which the absolute velocity of the concrete particles deviates from normal due to friction, i.e. the angle of friction between the particles and the helix.
Then will be
Figure BDA0002738138310000075
Calculation formula for traditional screw conveying amount
Figure BDA0002738138310000076
In the method, the forecasting calculation of the spiral conveying amount mechanism of friction can be realized, and the calculation formula is as follows:
Qc＝0.1644n(D3-Dd2)ψcρ·sinβ(cosβ-μsinβ)。
because the spiral conveying capacity mechanism model increases the description of friction on the basis of the traditional conveying capacity mechanism model, the precision of the forecast calculation value of the traditional conveying capacity mechanism model is improved, meanwhile, the improved spiral conveying capacity mechanism model can reflect most conditions of the spiral conveying process, the forecast calculation value is relatively reliable and stable, and huge oscillation cannot be generated along with the change of material or equipment parameters.
S200, obtaining a deviation model of the actual value of the spiral conveying quantity and the calculated value of the mechanism model according to the neural network structure of the spiral conveying quantity deviation, the neural network training and the neural network spiral conveying quantity deviation.
In the present embodiment, a spiral conveying amount forecast calculation value is calculated by using a 3-layer BP neural network, which is an input layer, a hidden layer and an output layer. Firstly, determining the number of input neurons and the output number of neurons of a neural network according to an application object; secondly, the number of middle layer layers and the number of hidden layer neurons are determined. Therefore, when step S200 is executed, as shown in fig. 5, the specific implementation steps further include determining a neural network structure of the spiral conveying amount deviation, which includes:
s201, taking the diameter of a spiral blade, the pitch, the spiral rotation number, the type of concrete, the liquid level height of the concrete in a hopper and the calculated value of a spiral conveying capacity mechanism model which influence the conveying capacity as 6 input quantities of a BP neural network conveying capacity deviation model;
s202, obtaining 6 neurons needed by the input layers according to the neurons of each input layer corresponding to the 6 input quantities;
s203, calculating formula according to hidden layer neuron experience
Figure BDA0002738138310000081
Calculating the neuron quantity range of the hidden layer to be 4-13, 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;
and S204, taking the number of hidden layer neurons corresponding to the BP neural network in the training data as the final number of hidden layer neurons, and finishing the finally determined neural network structure.
When the neural network training of the spiral conveying amount deviation is determined, the neural network training needs to sequentially carry out information forward propagation calculation and error backward propagation correction. Therefore, when step S200 is executed, the specific implementation step further includes performing information forward propagation calculation on the neural network training, which includes:
s205, carrying out normalization calculation, input layer calculation, hidden layer calculation and output layer calculation on the input values of 6 spiral conveying quantity calculations in sequence to finish information forward propagation calculation of the neural network.
Specifically, when the input value of the spiral conveying amount calculation is input layer calculated, the calculation formula is as follows:
Figure BDA0002738138310000082
in the formula: x is data before normalization; x is the number ofminAnd xmaxRespectively, the minimum value and the maximum value in all data; y is normalizedTransforming the data; y isminAnd ymaxLower and upper limits of the range, y, are planned for the input data, respectivelymin＝-1，ymax＝1。
When the input value of the spiral conveying amount calculation is subjected to input layer calculation, the calculation formula is as follows:
Figure BDA0002738138310000091
when the hidden layer calculation is carried out on the input value of the spiral conveying amount calculation, the calculation formula is as follows:
Figure BDA0002738138310000092
when the output layer calculation is carried out on the input value of the spiral conveying amount calculation, the calculation formula is as follows:
Figure BDA0002738138310000093
wherein, IiInputting the ith neuron of the input layer;
Figure BDA0002738138310000094
is the hidden layer neuron output; v. ofijThe 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 layerjA threshold value for hidden layer neurons; o is the actual output of the neuron in the output layer; w is ajThe 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, the weight of each layer is adjusted by adopting an error back propagation mode to the BP neural network, so that the output value of the network gradually approaches to a target value. Therefore, when step S200 is executed, as shown in fig. 6, the specific implementation steps further include performing information back propagation calculation on the neural network training, which includes:
s206, calculating formula by adopting error function
Figure BDA0002738138310000095
Comparing the actual output of the neural network of the spiral conveying amount deviation with the expected output to obtain a sample error;
s207, adopting a calculation formula
Figure BDA0002738138310000096
Calculating a global error of the neural network training of the spiral conveying amount deviation;
s208, correcting the output layer of the neural network training of the spiral conveying amount deviation to a hidden layer by layer to form back propagation, and performing back propagation correction on the neural network training of the spiral conveying amount deviation for multiple times until the output error of the BP neural network is reduced to a specified target precision range, namely outputting a final prediction result;
wherein, tpIs the desired output of the output layer neurons; p is the training sample serial number; n is the number of training samples.
More specifically, before the output layer trained by the neural network of the spiral delivery deviation is modified layer by layer to the hidden layer to form back propagation, the method further includes:
correcting the weight values of the output layer and the hidden layer of the neural network training of the spiral conveying amount deviation by adopting an error gradient descent method, wherein the calculation formula is as follows:
Figure BDA0002738138310000101
Figure BDA0002738138310000102
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.
When step S200 is executed, the implementation steps further include spiral inputting to the neural network
And obtaining a screw delivery deviation forecast value of experimental data according to the neural network structure of the screw delivery deviation and the neural network weight and threshold determined by the neural network training.
S300, correcting the calculated value of the spiral conveying amount mechanism model through a deviation model of the actual value of the spiral conveying amount and the calculated value of the mechanism model, and obtaining the final predicted value of the spiral conveying amount.
Specifically, a spiral conveying capacity intelligent model based on a BP neural network is further introduced on the basis of a traditional spiral conveying capacity mechanism model, so that the spiral conveying capacity can be intelligently forecasted. And forecasting and calculating the actual spiral conveying quantity by adopting a mechanism model, forecasting and calculating the forecasting and calculating deviation caused by the fact that the mechanism model cannot completely and accurately describe the spiral conveying process by adopting the BP neural network spiral conveying quantity, and finally adding the two results to obtain a final intelligent forecasting value of the spiral conveying quantity. The combined model structure is shown in fig. 7, and the mathematical model expression is as follows: qh＝QJ+ΔQJ；
Wherein Q isJThe calculated value is a spiral conveying quantity mechanism model; delta QJEstablishing a deviation model of the actual value of the spiral conveying quantity and the calculated value of the mechanism model by adopting a BP neural network and a relative error theory; qhAnd intelligently predicting the final spiral conveying amount.
According to the method for forecasting the spiral conveying capacity of the concrete distribution, the description of friction is added on the basis of the traditional conveying capacity mechanism model through the spiral conveying capacity mechanism model, the forecasting calculation precision of the traditional conveying capacity mechanism model is improved, meanwhile, the improved spiral conveying capacity mechanism model can reflect most conditions in the spiral conveying process, the forecasting calculation value is relatively reliable and stable, and large-amplitude oscillation cannot be generated along with the change of material or equipment parameters; the neural network spiral conveying amount deviation forecasting model utilizes the characteristic that an artificial neural network method can approach any nonlinear function with high precision, accurately forecasts the part which cannot be described by the mechanism model, and improves the overall forecasting precision of the spiral conveying amount. After the calculation of the spiral conveying amount is intelligentized, the forecasting value of the conveying amount mechanism model is adopted as a main calculating value, and the forecasting deviation of the intelligent conveying amount model is used as an auxiliary value, so that the whole forecasting calculation precision of the conveying amount model is improved, and meanwhile, the stability of a forecasting calculation result is facilitated. The method can improve the forecast calculation precision of the spiral conveying quantity, and the calculated value can be used as a target forecast value of the automatic cloth weight control system and is beneficial to the stable operation of the automatic weight control system.
Based on the method shown in fig. 2 to 7, correspondingly, the embodiment of the invention further provides a storage device, on which a computer program is stored, and the program is executed by a processor to implement the method for forecasting the spiral conveying amount of the concrete cloth shown in fig. 2 to 7.
In order to achieve the above object, an embodiment of the present invention further provides an apparatus for detecting an iron content in an iron ore, where the apparatus 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 method for forecasting the screw delivery amount for concrete distribution as described above with reference to fig. 2 to 7.
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.

Claims (10)

1. A method for forecasting the spiral conveying amount of concrete distribution is characterized by comprising the following steps:
obtaining a calculation value of a spiral conveying capacity mechanism model according to the axial conveying speed of concrete in a screw of a distributing machine;
obtaining a deviation model of the actual value of the spiral conveying capacity and the calculated value of the mechanism model according to the neural network structure of the spiral conveying capacity deviation, the neural network training and the spiral conveying capacity deviation of the neural network;
and correcting the calculated value of the spiral conveying amount mechanism model through a deviation model of the actual value of the spiral conveying amount and the calculated value of the mechanism model, so that the predicted value of the final spiral conveying amount can be obtained.
2. The method for forecasting the screw conveying capacity of the concrete distribution according to claim 1, wherein when the calculation value of the screw conveying capacity mechanism model is obtained according to the axial conveying speed of the concrete in the screw of the distribution machine, the method comprises the following steps:
calculating the axial conveying speed of the concrete particles under the friction condition according to the axial conveying speed, the relation between the traction speed and the absolute speed of the concrete particles in the spiral conveying process, the relation between the traction speed and the spiral rotation number of the concrete particles and the relation between the friction coefficient and the angle;
and obtaining a screw conveying quantity mechanism forecast calculation value under the friction condition according to the axial conveying speed of the concrete particles under the friction condition.
3. The method for forecasting the spiral conveying amount of the concrete distribution material according to claim 1, wherein the determination of the neural network structure of the spiral conveying amount deviation comprises:
taking the diameter of a spiral blade, the pitch, the spiral rotation number, the type of concrete, the liquid level height of the concrete in a hopper and the calculated value of a spiral conveying capacity mechanism model which influence the conveying capacity as 6 input quantities of a BP neural network conveying capacity deviation model;
obtaining 6 neurons needed by the input layers according to the neurons of each input layer corresponding to the 6 input quantities;
empirical calculation formula based on hidden layer neurons
Figure FDA0002738138300000011
Calculating the neuron number of the hidden layer to be in the range of 4-13, wherein j is the neuron number of the hidden layer, i is the neuron number of the input layer, and u is the neuron number of the output layerCounting;
and taking the number of hidden layer neurons corresponding to the BP neural network in the training data as the final number of hidden layer neurons, and finishing the finally determined neural network structure.
4. The method for forecasting the spiral conveying amount of the concrete distribution according to claim 3, wherein the determination of the neural network training of the spiral conveying amount deviation comprises:
and (3) sequentially carrying out normalization calculation, input layer calculation, hidden layer calculation and output layer calculation on the input values of the 6 spiral conveying amount calculations to finish the information forward propagation calculation of the neural network.
5. The method for forecasting the spiral conveying amount of the concrete distribution according to claim 4, wherein when the input value of the calculation of the spiral conveying amount is input into the calculation of the input layer, the calculation formula is as follows:
Figure FDA0002738138300000021
when the hidden layer calculation is carried out on the input value of the spiral conveying amount calculation, the calculation formula is as follows:
Figure FDA0002738138300000022
when the output layer calculation is carried out on the input value of the spiral conveying amount calculation, the calculation formula is as follows:
Figure FDA0002738138300000023
wherein, IiInputting the ith neuron of the input layer;
Figure FDA0002738138300000024
to be hidden(ii) cortical neuronal output; v. ofijThe 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 layerjA threshold value for hidden layer neurons; o is the actual output of the neuron in the output layer; w is ajThe 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 forecasting the spiral conveying amount of the concrete distribution according to claim 4, wherein the training of the neural network of the deviation of the spiral conveying amount further comprises:
using error function calculation
Figure FDA0002738138300000025
Comparing the actual output of the neural network of the spiral conveying amount deviation with the expected output to obtain a sample error;
using calculation formulas
Figure FDA0002738138300000026
Calculating a global error of the neural network training of the spiral conveying amount deviation;
correcting the output layer of the neural network training of the spiral conveying amount deviation to a hidden layer by layer to form back propagation, and performing back propagation correction on the neural network training of the spiral conveying amount deviation for multiple times until the output error of the BP neural network is reduced to a specified target precision range, namely outputting a final prediction result;
wherein, tpIs the desired output of the output layer neurons; p is the training sample serial number; n is the number of training samples.
7. The method as claimed in claim 6, wherein before the step of modifying the output layer to the hidden layer by layer trained by the neural network of the screw delivery deviation 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 amount deviation by adopting an error gradient descent method, wherein the calculation formula is as follows:
Figure FDA0002738138300000031
Figure FDA0002738138300000032
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.
8. The method for forecasting the spiral conveying capacity of the concrete distribution material according to claim 7, wherein the step of determining the deviation of the spiral conveying capacity of the neural network comprises the following steps:
and obtaining a screw delivery deviation forecast value of experimental data according to the neural network structure of the screw delivery deviation and the neural network weight and threshold determined by the neural network training.
9. A storage medium on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out a method for forecasting a screw delivery for concrete distribution according to any one of claims 1 to 8.
10. A forecasting apparatus for a screw conveying amount of concrete distribution, comprising a storage medium, a processor and a computer program stored on the storage medium and operable on the processor, wherein the processor implements the forecasting method for the screw conveying amount of concrete distribution according to any one of claims 1 to 8 when executing the program.
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