CN110577091A - method, system and medium for stabilizing quality of blended ore based on artificial intelligence - Google Patents

Abstract

The invention provides a method, a system and a medium for stabilizing quality of a uniformly mixed ore based on artificial intelligence, which comprises the following steps: a target rate obtaining step: acquiring a current blending stacking plan, calculating and acquiring a total target speed, a target component content of the material and a discharging speed of a discharging groove according to the acquired blending stacking plan, and calculating the current component content of the material according to the discharging speed of the discharging groove; material content judging step: judging whether the current component content of the material meets the target component content of the material or not according to the obtained target component content of the material and the current component content of the material: if yes, returning to the target rate obtaining step to continue execution; otherwise, entering a slot rate optimization step to continue execution. According to the method for adjusting the cutting rate of each groove on line through the neural network, the stability of the cut material content is intelligently controlled, the cost of frequent trial and error of manpower is reduced, and the system is more intelligent.

Description

Method, system and medium for stabilizing quality of blended ore based on artificial intelligence

Technical Field

the invention relates to the technical field of steel manufacturing, in particular to a method, a system and a medium for stabilizing quality of uniformly mixed ore based on artificial intelligence.

background

In the field of steel manufacturing industry, the blending technology of a raw material factory is always a core technology, qualified finished product blended ore is required to be blended, the material is mainly prepared in a mode of manually combining computer-aided calculation, the cutting rate of a disc feeder is adjusted, the contents of the output finished product blended ore and TFe reach qualified indexes by a multi-trial method, although the method is implemented for many years, actual needs can be basically met, a great amount of labor is consumed to try to adjust the rate, and the qualified finished product blended ore can be blended more quickly by depending on years of experience. Because most raw material plants are open-air, the contents of finished product blending ore, TFe and the like are deviated due to the influence of external factors such as climate, temperature or misoperation of operators, so that operators are required to readjust the speed to maintain the quality of the finished product blending ore, and the quality deviation is large and even the loss of the finished product blending ore is caused sometimes.

The qualified finished product blended ore is mixed in a raw material factory, the material proportioning and the cutting rate of a disc feeder are mainly carried out by manually combining a computer-aided calculation mode, the contents of the output finished product blended ore and TFe reach the qualified indexes by a multi-trial method, although the actual requirements can be basically met after the implementation for many years, a great deal of manual effort is consumed to try to adjust the rate, and the qualified finished product blended ore can be mixed more quickly by depending on the experience of many years.

An intelligent mixing system is developed, and the cutting rate of the disc feeder is adjusted on line in real time by using an artificial intelligence method to stabilize the material quality, so that the cost is reduced, and the mixing effect and efficiency are improved

patent document CN104649036B (application number: 201410811600.6) discloses a stacking method for improving the stability of blending material, which makes a stacking plan according to the relationship between the total number M of blending varieties and the number N of blending bins of blending ore, and the blending capacity V of the blending bins: the method comprises the steps of setting m BLOCKs of grouped ingredients according to total stockpile amount A and participated ingredients N, when a certain BLOCK has more ingredients and less ingredient bins, selecting the ingredients according to a near-Si principle to realize that two or more ingredients are fed in the same bin in a single BLOCK, namely the SiO2 contents of the two or more selected ingredients are similar, dividing the single BLOCK into a plurality of sections, sequentially feeding the various ingredients into the bins according to the planned amount of pre-proportioning, and sequentially cutting the ingredients to realize that two or more ingredients are fed in the same bin in the single BLOCK to ensure the ingredient components of the uniformly mixed ore are stable.

Patent document CN1299051A (application No. 00100426.3) discloses an intelligent stacking control method for a blended ore, which includes the steps of making a rough plan, a dynamic batching tank and fault processing, wherein the rough plan is made according to a stacking plan table and a pre-thought component table, calculating the optimal value of each raw material variety and quantity according to experience and the aim of closest approach to a target stacking component by using a fuzzy comprehensive judgment principle to obtain the rough plan table, then dynamically batching the cut quantity of each batching tank, i.e. calculating the content of specific components such as SiO2 and TFe in the stacked blended ore according to the components and quantity of raw materials output from a quantitative feeder, and adjusting the cut-out speed of the quantitative feeder according to the value

patent document CN104561411A (application No. 201510034618.4) discloses a blending method capable of effectively increasing the amount of blended minerals, which is characterized by comprising a raw material preparation step of pre-blending and stacking small ore species and peripheral blending ores; the number of the prepared material mixing bins is equal to the number of material stacking layers/stacking layers + X, wherein X is more than or equal to 0; according to the priority of the component difference: SiO2 > TFe > moisture > granularity, the raw material loading step of selecting the batching ore species with big component difference to load in two adjacent bins; under the condition of meeting production requirements, the stacking and feeding amount approaches to a feeding amount lower limit value, wherein: and (4) stacking the materials with the lower limit value of the feeding amount being equal to the lower limit value of the measuring range of the proportioning bins multiplied by the number of the proportioning bins.

the above patents are all groove and ingredient methods, and the invention relates to an artificial intelligence speed regulation method.

disclosure of Invention

aiming at the defects in the prior art, the invention aims to provide a method, a system and a medium for stabilizing the quality of a uniformly mixed ore based on artificial intelligence.

the method for stabilizing the quality of the blended ore based on artificial intelligence provided by the invention comprises the following steps:

a target rate obtaining step: acquiring a current blending stacking plan, calculating and acquiring a total target speed, a target component content of the material and a discharging speed of a discharging groove according to the acquired blending stacking plan, and calculating the current component content of the material according to the discharging speed of the discharging groove;

Material content judging step: judging whether the current component content of the material meets the target component content of the material or not according to the obtained target component content of the material and the current component content of the material: if yes, returning to the target rate acquisition step for continuous execution; otherwise, entering a groove rate optimization step to continue execution;

a groove speed optimizing step: calculating the optimized discharging rate of each discharging groove by using a neural network algorithm according to the obtained target component content of the material and the current component content of the material;

a groove speed adjusting step: and adjusting the discharging speed of each discharging groove according to the obtained optimized discharging speed and the total target speed of each discharging groove.

preferably, the target rate acquiring step:

the current blending windrow plan comprises: the method comprises the following steps of (1) stacking planning time table, raw material distribution usage table, raw material composition table, dry weight, water content percentage, total blending stacking time, residual discharging time of a discharging groove, sum of residual moisture contents of all kinds of materials in the discharging groove at each moment and component content of each kind of material;

according to the current blending and stacking plan, obtaining the dry weight and the water content percentage of each variety of material, and calculating to obtain the moisture content of each variety of material, wherein the calculation formula is as follows:

Obtaining total blending and stacking time according to the current blending and stacking plan, and calculating to obtain a total target speed according to the obtained moisture content of each variety of materials, wherein the calculation formula is as follows:

obtaining the target component content of each variety of materials according to the current blending and stacking plan, and calculating to obtain the target component content of the materials according to the obtained moisture content of each variety of materials, wherein the calculation formula is as follows:

Moisture content of variety material

according to the current blending and stacking plan, obtaining the residual moisture content of all kinds of materials in the discharge chute and the residual time of the discharge chute, and calculating the discharge rate of the discharge chute, wherein the calculation formula is as follows:

calculating to obtain the current component content of the material according to the obtained discharge rate of the discharge chute, wherein the calculation formula is as follows:

current ingredient content of the material

the ingredient content of the variety of material being cut out in the discharge chute 1 is multiplied by the discharge rate of the discharge chute 1

+ the contents of the ingredients of the variety material being cut out by the discharge chute 2 x the discharge rate of the discharge chute 2 + …

+ the component content of the material of the variety being cut by the discharge chute n x the discharge rate of the discharge chute n

Wherein,

n represents the number of discharge chutes.

preferably, the tank rate optimizing step:

acquiring a speed change value of the discharge chute: and calculating the optimized discharging rate of each discharging groove by taking the obtained target component content of the material, the current component content of the material and the deviation of the preset component content as the input of a neural network algorithm, wherein the calculation process is as follows:

The input of the jth neuron of the hidden layer of the artificial neural network is as follows:

Wherein,

m is the number of neurons of the input layer;

representing the jth neuron of the input layer of the pth training sample, and respectively inputting the obtained target component content of the material, the current component content of the material and the deviation of the preset component content into the neuron

Representing the input of the ith neuron of the hidden layer under the action of the p training sample;

ω_jiRepresenting the weight between the input layer and the hidden layer;

Representing the output of the jth neuron of the hidden layer under the action of the pth training sample;

representing the output of the ith neuron of the hidden layer under the action of the p training sample;

θ_ia threshold representing hidden layer neuron i;

g (x) is a non-linear mapping function for hidden layer neurons, comprising: a Sigmoid function;

the input of the kth neuron of the artificial neural network output layer is as follows:

Wherein,

representing the input of the kth neuron of the output layer under the action of the pth training sample;

ω_kirepresenting a weight coefficient between the output layer and the hidden layer;

θ_kA threshold value representing output layer neuron k;

q is the number of neurons in the hidden layer;

the output of the kth neuron of the artificial neural network output layer is as follows:

wherein,

representing the output of the kth neuron of the output layer, namely the speed change value of the discharge chute;

Output layer activation functionthe derivative function of (d) is:

the quadratic error function of the input pattern pair for the p-th training sample, i.e. the performance index, is:

wherein,

J_pRepresenting a performance index;

representing a preset target output value;

determination of Performance index J_pwhether the standard meets the preset standard or not: if yes, entering a groove speed adjusting step to continue execution; otherwise, entering the performance index adjusting step to continue execution.

preferably, the performance index adjusting step:

press error function J_pand reducing the fastest direction adjustment weighting coefficient, and utilizing a gradient descent method until a satisfactory weighting coefficient set is obtained, wherein the adjustment process is as follows:

wherein,

η represents the learning rate;

Δω_kirepresents an adjustment increment of the weight coefficient;

expressing a performance index derivation;

weight coefficients representing the hidden layer and the output layer;

Representing the output derivative to the output layer;

therefore, the weighting coefficient modification formula of any neuron k of the output layer is:

wherein,

An intermediate variable representing an output layer;

The weight variable of the available hidden layer is adjusted as follows:

Δω_jirepresenting the weight coefficient increment between the adjusted input layer and the hidden layer;

an intermediate variable representing an input layer;

the weighting coefficient improvement formula of any neuron k of the output layer when the p training sample acts is as follows:

Δω_kiRepresenting the weight coefficient increment between the adjusted output layer and the hidden layer;

k represents a neuron number of the output layer;

the weighting coefficient improvement formula of any neuron k of the hidden layer when the p training sample acts is as follows:

Δω_jiRepresenting the weight coefficient increment between the adjusted input layer and the hidden layer;

The learning process adjusts the weighting coefficient according to the direction which enables the error function J to reduce the fastest, and the weighting coefficient increment formula when all samples of any neurons k and i of the output layer and the hidden layer act can be obtained:

according to the obtained weighting coefficient increment, the weighting coefficient omega is subjected to_kiand ω_jithe modification is made and the return slot rate optimization step continues.

preferably, the tank rate adjusting step:

according to the obtained total target rate and the output of the k-th neuron of the output layeradjusting the speed of each discharge chute, adding the adjusted speeds of the discharge chutes to obtain the sum of the speeds of the discharge chutes after the neural network optimization, and calculating to obtain the final target speed of the discharge chute k, wherein the calculation mode is as follows:

the final target speed of the discharge chute k is equal to the target speed of the chute k after the optimization of the Muxneural network, and k is equal to 1, 2, … …, n;

and adjusting the speed of each discharge chute according to the obtained final target speed of the discharge chute k.

the system for stabilizing the quality of the blended ore based on artificial intelligence can be realized through the steps and the flow of the method for stabilizing the quality of the blended ore based on artificial intelligence. The method for stabilizing the quality of the blended ore based on artificial intelligence can be understood as a preferred example of the system for stabilizing the quality of the blended ore based on artificial intelligence by those skilled in the art.

The system for stabilizing the quality of the blended ore based on artificial intelligence provided by the invention comprises:

A target rate acquisition module: acquiring a current blending stacking plan, calculating and acquiring a total target speed, a target component content of the material and a discharging speed of a discharging groove according to the acquired blending stacking plan, and calculating the current component content of the material according to the discharging speed of the discharging groove;

material content judging module: judging whether the current component content of the material meets the target component content of the material or not according to the obtained target component content of the material and the current component content of the material: if yes, calling a target rate acquisition module; otherwise, calling a slot rate optimization module;

a tank rate optimization module: calculating the optimized discharging rate of each discharging groove by using a neural network algorithm according to the obtained target component content of the material and the current component content of the material;

a tank rate adjustment module: and adjusting the discharging speed of each discharging groove according to the obtained optimized discharging speed and the total target speed of each discharging groove.

preferably, the target rate obtaining module:

the current blending windrow plan comprises: the method comprises the following steps of (1) stacking planning time table, raw material distribution usage table, raw material composition table, dry weight, water content percentage, total blending stacking time, residual discharging time of a discharging groove, sum of residual moisture contents of all kinds of materials in the discharging groove at each moment and component content of each kind of material;

According to the current blending and stacking plan, obtaining the dry weight and the water content percentage of each variety of material, and calculating to obtain the moisture content of each variety of material, wherein the calculation formula is as follows:

obtaining total blending and stacking time according to the current blending and stacking plan, and calculating to obtain a total target speed according to the obtained moisture content of each variety of materials, wherein the calculation formula is as follows:

obtaining the target component content of each variety of materials according to the current blending and stacking plan, and calculating to obtain the target component content of the materials according to the obtained moisture content of each variety of materials, wherein the calculation formula is as follows:

moisture content of variety material

according to the current blending and stacking plan, obtaining the residual moisture content of all kinds of materials in the discharge chute and the residual time of the discharge chute, and calculating the discharge rate of the discharge chute, wherein the calculation formula is as follows:

calculating to obtain the current component content of the material according to the obtained discharge rate of the discharge chute, wherein the calculation formula is as follows:

current ingredient content of the material

the ingredient content of the variety of material being cut out in the discharge chute 1 is multiplied by the discharge rate of the discharge chute 1

+ the contents of the ingredients of the variety material being cut out by the discharge chute 2 x the discharge rate of the discharge chute 2 + …

+ the component content of the material of the variety being cut by the discharge chute n x the discharge rate of the discharge chute n

wherein,

n represents the number of discharge chutes.

preferably, the slot rate optimization module:

the speed change value of the discharge chute obtains the module: and calculating the optimized discharging rate of each discharging groove by taking the obtained target component content of the material, the current component content of the material and the deviation of the preset component content as the input of a neural network algorithm, wherein the calculation process is as follows:

the input of the jth neuron of the hidden layer of the artificial neural network is as follows:

wherein,

m is the number of neurons of the input layer;

Representing the jth neuron of the input layer of the pth training sample, and respectively inputting the obtained target component content of the material, the current component content of the material and the deviation of the preset component content into the neuron

representing the input of the ith neuron of the hidden layer under the action of the p training sample;

ω_jirepresenting the weight between the input layer and the hidden layer;

representing the output of the jth neuron of the hidden layer under the action of the pth training sample;

representing the output of the ith neuron of the hidden layer under the action of the p training sample;

θ_iA threshold representing hidden layer neuron i;

g (x) is a non-linear mapping function for hidden layer neurons, comprising: a Sigmoid function;

the input of the kth neuron of the artificial neural network output layer is as follows:

Wherein,

representing the input of the kth neuron of the output layer under the action of the pth training sample;

ω_kirepresenting a weight coefficient between the output layer and the hidden layer;

θ_ka threshold value representing output layer neuron k;

q is the number of neurons in the hidden layer;

The output of the kth neuron of the artificial neural network output layer is as follows:

Wherein,

representing the output of the kth neuron of the output layer, namely the speed change value of the discharge chute;

Output layer activation functionthe derivative function of (d) is:

the quadratic error function of the input pattern pair for the p-th training sample, i.e. the performance index, is:

wherein,

J_pRepresenting a performance index;

representing a preset target output value;

determination of Performance index J_pWhether the standard meets the preset standard or not: if yes, calling a slot rate adjusting module; otherwise, the performance index adjusting module is called.

Preferably, the performance index adjustment module:

Press error function J_pAnd reducing the fastest direction adjustment weighting coefficient, and utilizing a gradient descent method until a satisfactory weighting coefficient set is obtained, wherein the adjustment process is as follows:

wherein,

η represents the learning rate;

Δω_kirepresents an adjustment increment of the weight coefficient;

Expressing a performance index derivation;

Weight coefficients representing the hidden layer and the output layer;

representing the output derivative to the output layer;

therefore, the weighting coefficient modification formula of any neuron k of the output layer is:

wherein,

An intermediate variable representing an output layer;

the weight variable of the available hidden layer is adjusted as follows:

Δω_jiRepresenting the weight coefficient increment between the adjusted input layer and the hidden layer;

an intermediate variable representing an input layer;

the weighting coefficient improvement formula of any neuron k of the output layer when the p training sample acts is as follows:

Δω_kirepresenting the weight coefficient increment between the adjusted output layer and the hidden layer;

k represents a neuron number of the output layer;

The weighting coefficient improvement formula of any neuron k of the hidden layer when the p training sample acts is as follows:

Δω_jirepresenting the weight coefficient increment between the adjusted input layer and the hidden layer;

the learning process adjusts the weighting coefficient according to the direction which enables the error function J to reduce the fastest, and the weighting coefficient increment formula when all samples of any neurons k and i of the output layer and the hidden layer act can be obtained:

according to the obtained weighting coefficient increment, the weighting coefficient omega is subjected to_kiand ω_jiModifying, and calling a slot rate optimization module;

the slot rate adjustment module:

According to the obtained total target rate and the output of the k-th neuron of the output layeradjusting the speed of each discharge chute, adding the adjusted speeds of the discharge chutes to obtain the sum of the speeds of the discharge chutes after the neural network optimization, and calculating to obtain the final target speed of the discharge chute k, wherein the calculation mode is as follows:

the final target speed of the discharge chute k is equal to the target speed of the chute k after the optimization of the Muxneural network, and k is equal to 1, 2, … …, n;

and adjusting the speed of each discharge chute according to the obtained final target speed of the discharge chute k.

according to the invention, the computer-readable storage medium is stored with a computer program, and the computer program is characterized in that when being executed by a processor, the computer program realizes the steps of any one of the above-mentioned methods for stabilizing the quality of the blended ore based on artificial intelligence.

compared with the prior art, the invention has the following beneficial effects:

According to the method for adjusting the cutting rate of each groove on line through the neural network, the stability of the cut material content is intelligently controlled, the cost of frequent trial and error of manpower is reduced, and the system is more intelligent. In the field operation process, the method for intelligently adjusting the cutting-out rate of the disk feeder is effectively utilized, the cut-out material quality is stable through one-year monitoring, the waste rate is reduced, the effect of intelligently adjusting the cutting-out rate of the disk feeder is achieved, and the cost is effectively saved for a raw material factory.

drawings

Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of the non-limiting embodiments with reference to the following drawings:

Fig. 1 is a schematic diagram of a neural network model according to preferred embodiment 1 of the present invention.

fig. 2 is a schematic flow chart of the intelligent blending system according to preferred embodiment 1 of the present invention.

Detailed Description

the present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.

The method for stabilizing the quality of the blended ore based on artificial intelligence provided by the invention comprises the following steps:

a target rate obtaining step: acquiring a current blending stacking plan, calculating and acquiring a total target speed, a target component content of the material and a discharging speed of a discharging groove according to the acquired blending stacking plan, and calculating the current component content of the material according to the discharging speed of the discharging groove;

material content judging step: judging whether the current component content of the material meets the target component content of the material or not according to the obtained target component content of the material and the current component content of the material: if yes, returning to the target rate acquisition step for continuous execution; otherwise, entering a groove rate optimization step to continue execution;

a groove speed optimizing step: calculating the optimized discharging rate of each discharging groove by using a neural network algorithm according to the obtained target component content of the material and the current component content of the material;

a groove speed adjusting step: and adjusting the discharging speed of each discharging groove according to the obtained optimized discharging speed and the total target speed of each discharging groove.

specifically, the target rate obtaining step:

the current blending windrow plan comprises: the method comprises the following steps of (1) stacking planning time table, raw material distribution usage table, raw material composition table, dry weight, water content percentage, total blending stacking time, residual discharging time of a discharging groove, sum of residual moisture contents of all kinds of materials in the discharging groove at each moment and component content of each kind of material;

according to the current blending and stacking plan, obtaining the dry weight and the water content percentage of each variety of material, and calculating to obtain the moisture content of each variety of material, wherein the calculation formula is as follows:

Obtaining total blending and stacking time according to the current blending and stacking plan, and calculating to obtain a total target speed according to the obtained moisture content of each variety of materials, wherein the calculation formula is as follows:

obtaining the target component content of each variety of materials according to the current blending and stacking plan, and calculating to obtain the target component content of the materials according to the obtained moisture content of each variety of materials, wherein the calculation formula is as follows:

moisture content of variety material

according to the current blending and stacking plan, obtaining the residual moisture content of all kinds of materials in the discharge chute and the residual time of the discharge chute, and calculating the discharge rate of the discharge chute, wherein the calculation formula is as follows:

calculating to obtain the current component content of the material according to the obtained discharge rate of the discharge chute, wherein the calculation formula is as follows:

Current ingredient content of the material

the ingredient content of the variety of material being cut out in the discharge chute 1 is multiplied by the discharge rate of the discharge chute 1

+ the contents of the ingredients of the variety material being cut out by the discharge chute 2 x the discharge rate of the discharge chute 2 + …

+ the component content of the material of the variety being cut by the discharge chute n x the discharge rate of the discharge chute n

wherein,

n represents the number of discharge chutes.

Specifically, the tank rate optimization step:

Acquiring a speed change value of the discharge chute: and calculating the optimized discharging rate of each discharging groove by taking the obtained target component content of the material, the current component content of the material and the deviation of the preset component content as the input of a neural network algorithm, wherein the calculation process is as follows:

the input of the jth neuron of the hidden layer of the artificial neural network is as follows:

wherein,

m is the number of neurons of the input layer;

Representing the jth neuron of the input layer of the pth training sample, and respectively inputting the obtained target component content of the material, the current component content of the material and the deviation of the preset component content into the neuron

Representing the input of the ith neuron of the hidden layer under the action of the p training sample;

ω_jirepresenting the weight between the input layer and the hidden layer;

representing the output of the jth neuron of the hidden layer under the action of the pth training sample;

Representing the output of the ith neuron of the hidden layer under the action of the p training sample;

θ_ia threshold representing hidden layer neuron i;

g (x) is a non-linear mapping function for hidden layer neurons, comprising: a Sigmoid function;

the input of the kth neuron of the artificial neural network output layer is as follows:

Wherein,

representing the input of the kth neuron of the output layer under the action of the pth training sample;

ω_kirepresenting a weight coefficient between the output layer and the hidden layer;

θ_kA threshold value representing output layer neuron k;

q is the number of neurons in the hidden layer;

the output of the kth neuron of the artificial neural network output layer is as follows:

wherein,

representing the output of the kth neuron of the output layer, namely the speed change value of the discharge chute;

output layer activation functionthe derivative function of (d) is:

the quadratic error function of the input pattern pair for the p-th training sample, i.e. the performance index, is:

wherein,

J_prepresenting a performance index;

Representing a preset target output value;

determination of Performance index J_pwhether the standard meets the preset standard or not: if yes, entering a groove speed adjusting step to continue execution; otherwise, entering the performance index adjusting step to continue execution.

specifically, the performance index adjusting step:

press error function J_pand reducing the fastest direction adjustment weighting coefficient, and utilizing a gradient descent method until a satisfactory weighting coefficient set is obtained, wherein the adjustment process is as follows:

Wherein,

η represents the learning rate;

Δω_kirepresents an adjustment increment of the weight coefficient;

Expressing a performance index derivation;

Weight coefficients representing the hidden layer and the output layer;

representing the output derivative to the output layer;

Therefore, the weighting coefficient modification formula of any neuron k of the output layer is:

wherein,

an intermediate variable representing an output layer;

the weight variable of the available hidden layer is adjusted as follows:

Δω_jirepresenting the weight coefficient increment between the adjusted input layer and the hidden layer;

an intermediate variable representing an input layer;

The weighting coefficient improvement formula of any neuron k of the output layer when the p training sample acts is as follows:

Δω_kirepresenting the weight coefficient increment between the adjusted output layer and the hidden layer;

k represents a neuron number of the output layer;

the weighting coefficient improvement formula of any neuron k of the hidden layer when the p training sample acts is as follows:

Δω_jirepresenting the weight coefficient increment between the adjusted input layer and the hidden layer;

the learning process adjusts the weighting coefficient according to the direction which enables the error function J to reduce the fastest, and the weighting coefficient increment formula when all samples of any neurons k and i of the output layer and the hidden layer act can be obtained:

According to the obtained weighting coefficient increment, the weighting coefficient omega is subjected to_kiand ω_jithe modification is made and the return slot rate optimization step continues.

specifically, the tank rate adjustment step:

according to the obtained total target rate and the output of the k-th neuron of the output layeradjusting the speed of each discharge chute, adding the adjusted speeds of the discharge chutes to obtain the sum of the speeds of the discharge chutes after the neural network optimization, and calculating to obtain the final target speed of the discharge chute k, wherein the calculation mode is as follows:

the final target speed of the discharge chute k is equal to the target speed of the chute k after the optimization of the Muxneural network, and k is equal to 1, 2, … …, n;

and adjusting the speed of each discharge chute according to the obtained final target speed of the discharge chute k.

The system for stabilizing the quality of the blended ore based on artificial intelligence can be realized through the steps and the flow of the method for stabilizing the quality of the blended ore based on artificial intelligence. The method for stabilizing the quality of the blended ore based on artificial intelligence can be understood as a preferred example of the system for stabilizing the quality of the blended ore based on artificial intelligence by those skilled in the art.

the system for stabilizing the quality of the blended ore based on artificial intelligence provided by the invention comprises:

a target rate acquisition module: acquiring a current blending stacking plan, calculating and acquiring a total target speed, a target component content of the material and a discharging speed of a discharging groove according to the acquired blending stacking plan, and calculating the current component content of the material according to the discharging speed of the discharging groove;

Material content judging module: judging whether the current component content of the material meets the target component content of the material or not according to the obtained target component content of the material and the current component content of the material: if yes, calling a target rate acquisition module; otherwise, calling a slot rate optimization module;

a tank rate optimization module: calculating the optimized discharging rate of each discharging groove by using a neural network algorithm according to the obtained target component content of the material and the current component content of the material;

a tank rate adjustment module: and adjusting the discharging speed of each discharging groove according to the obtained optimized discharging speed and the total target speed of each discharging groove.

specifically, the target rate obtaining module:

The current blending windrow plan comprises: the method comprises the following steps of (1) stacking planning time table, raw material distribution usage table, raw material composition table, dry weight, water content percentage, total blending stacking time, residual discharging time of a discharging groove, sum of residual moisture contents of all kinds of materials in the discharging groove at each moment and component content of each kind of material;

According to the current blending and stacking plan, obtaining the dry weight and the water content percentage of each variety of material, and calculating to obtain the moisture content of each variety of material, wherein the calculation formula is as follows:

obtaining total blending and stacking time according to the current blending and stacking plan, and calculating to obtain a total target speed according to the obtained moisture content of each variety of materials, wherein the calculation formula is as follows:

Obtaining the target component content of each variety of materials according to the current blending and stacking plan, and calculating to obtain the target component content of the materials according to the obtained moisture content of each variety of materials, wherein the calculation formula is as follows:

moisture content of variety material

according to the current blending and stacking plan, obtaining the residual moisture content of all kinds of materials in the discharge chute and the residual time of the discharge chute, and calculating the discharge rate of the discharge chute, wherein the calculation formula is as follows:

calculating to obtain the current component content of the material according to the obtained discharge rate of the discharge chute, wherein the calculation formula is as follows:

Current ingredient content of the material

the ingredient content of the variety of material being cut out in the discharge chute 1 is multiplied by the discharge rate of the discharge chute 1

+ the contents of the ingredients of the variety material being cut out by the discharge chute 2 x the discharge rate of the discharge chute 2 + …

+ the component content of the material of the variety being cut by the discharge chute n x the discharge rate of the discharge chute n

Wherein,

n represents the number of discharge chutes.

specifically, the slot rate optimization module:

the speed change value of the discharge chute obtains the module: and calculating the optimized discharging rate of each discharging groove by taking the obtained target component content of the material, the current component content of the material and the deviation of the preset component content as the input of a neural network algorithm, wherein the calculation process is as follows:

the input of the jth neuron of the hidden layer of the artificial neural network is as follows:

wherein,

m is the number of neurons of the input layer;

Representing the jth neuron of the input layer of the pth training sample, and respectively inputting the obtained target component content of the material, the current component content of the material and the deviation of the preset component content into the neuron

Representing the input of the ith neuron of the hidden layer under the action of the p training sample;

ω_jirepresenting the weight between the input layer and the hidden layer;

Representing the output of the jth neuron of the hidden layer under the action of the pth training sample;

representing the output of the ith neuron of the hidden layer under the action of the p training sample;

θ_iA threshold representing hidden layer neuron i;

g (x) is a non-linear mapping function for hidden layer neurons, comprising: a Sigmoid function;

the input of the kth neuron of the artificial neural network output layer is as follows:

wherein,

representing the input of the kth neuron of the output layer under the action of the pth training sample;

ω_kiRepresenting a weight coefficient between the output layer and the hidden layer;

θ_kA threshold value representing output layer neuron k;

q is the number of neurons in the hidden layer;

the output of the kth neuron of the artificial neural network output layer is as follows:

wherein,

Representing the output of the kth neuron of the output layer, namely the speed change value of the discharge chute;

output layer activation functionthe derivative function of (d) is:

The quadratic error function of the input pattern pair for the p-th training sample, i.e. the performance index, is:

wherein,

J_pindicating performance meansmarking;

representing a preset target output value;

Determination of Performance index J_pWhether the standard meets the preset standard or not: if yes, calling a slot rate adjusting module; otherwise, the performance index adjusting module is called.

specifically, the performance index adjustment module:

press error function J_pAnd reducing the fastest direction adjustment weighting coefficient, and utilizing a gradient descent method until a satisfactory weighting coefficient set is obtained, wherein the adjustment process is as follows:

wherein,

η represents the learning rate;

Δω_kirepresents an adjustment increment of the weight coefficient;

Expressing a performance index derivation;

weight coefficients representing the hidden layer and the output layer;

representing the output derivative to the output layer;

Therefore, the weighting coefficient modification formula of any neuron k of the output layer is:

wherein,

An intermediate variable representing an output layer;

The weight variable of the available hidden layer is adjusted as follows:

Δω_jirepresenting the weight coefficient increment between the adjusted input layer and the hidden layer;

an intermediate variable representing an input layer;

the weighting coefficient improvement formula of any neuron k of the output layer when the p training sample acts is as follows:

Δω_kirepresenting the weight coefficient increment between the adjusted output layer and the hidden layer;

k represents a neuron number of the output layer;

The weighting coefficient improvement formula of any neuron k of the hidden layer when the p training sample acts is as follows:

Δω_jirepresenting the weight coefficient increment between the adjusted input layer and the hidden layer;

The learning process adjusts the weighting coefficient according to the direction which enables the error function J to reduce the fastest, and the weighting coefficient increment formula when all samples of any neurons k and i of the output layer and the hidden layer act can be obtained:

according to the obtained weighting coefficient increment, the weighting coefficient omega is subjected to_kiand ω_jiModifying, and calling a slot rate optimization module;

the slot rate adjustment module:

according to the obtained total target rate and the output of the k-th neuron of the output layerAdjusting the speed of each discharge chute, adding the adjusted speeds of the discharge chutes to obtain the sum of the speeds of the discharge chutes after the neural network optimization, and calculating to obtain the final target speed of the discharge chute k, wherein the calculation mode is as follows:

The final target speed of the discharge chute k is equal to the target speed of the chute k after the optimization of the Muxneural network, and k is equal to 1, 2, … …, n;

and adjusting the speed of each discharge chute according to the obtained final target speed of the discharge chute k.

According to the invention, the computer-readable storage medium is stored with a computer program, and the computer program is characterized in that when being executed by a processor, the computer program realizes the steps of any one of the above-mentioned methods for stabilizing the quality of the blended ore based on artificial intelligence.

the present invention will be described more specifically below with reference to preferred examples.

preferred example 1:

the invention mainly solves the technical problem of providing a method for stabilizing the material quality by adjusting the cutting rate of a disc feeder of a blending system based on artificial intelligence, wherein SiO is used in the blending process₂when the content of TFe fluctuates, the method can adjust the disc feeding in real time on linethe cutting speed of the material machine is cut out to ensure the quality of the uniformly mixed materials to be stable, the adopted algorithm is a neural network algorithm, the characteristics of feedforward operation and error back propagation of a BP neural network and the approximation of any nonlinear function are utilized, the cutting speed of each groove reaches reasonable requirements, and SiO₂when the TFe content is within the acceptable range, the cell velocities (y1 to y10) in the following graph are adjusted so that the following formula is finally established:

SiO₂target content is SiO of material being cut out by groove 1₂content X groove 1 Rate (y1)

+ groove 2 cutting out SiO of the material₂content x groove 2 Rate (y2) + …

+ groove 10 cutting out SiO of the material₂Content x groove 10 Rate (y10)

the specific embodiment is as follows:

the mixing system loads a large pile plan including a pile plan time table, a raw material distribution measuring table, a raw material component table and the like, and calculates SiO to be mixed according to the plan table₂Target content of TFe (in SiO)₂for example, the calculation formula of TFe is the same), the total target rate of the large heap, and the like.

SiO₂The current content being the SiO of the material being cut out of the groove 1₂content x current rate of cell 1

+ groove 2 cutting out SiO of the material₂content x slot 2 Current Rate + …

+ groove10 SiO of the material being cut₂content x current rate of cell 10

and then selecting a large pile, namely starting the large pile, starting all disk feeders to run, selecting varieties to be cut for each groove in a non-full-automatic control mode, generally more than half of the grooves, clicking 'optimization', adjusting the cutting rate of each groove on line through a neural network, or selecting a 'full-automatic mode' button in an automatic mode, cutting out the uniform mixing system according to a default sequence, and automatically starting the neural network to optimize the rate of each groove when the deviation between the current SiO2 content and the target SiO2 content is greater than an allowable deviation.

the proposed neural network is realized in a computer by C + +, and the specific method is as follows:

1. SiO2₂Target content, SiO₂Current content, target content of TFe, current content of TFe and SiO₂TFe content deviation as input end of neural network, SiO₂Target content, SiO₂The current content, TFe target content and TFe current content are all calculated by the formula and are calculated according to the hidden layer neuron formulain the course of carrying out numerous sample training, increasing or decreasing the number of neurons in the hidden layer, when 12 neurons in the hidden layer are found, the effect is optimal, and the optimal rate of 10 grooves is output at the output end of the artificial neural network control module, and the model of the neural network is shown in the attached figure 1.

2. through the feedback principle, the feedback value is used as the input of control, which is the core of the automatic control theory, the intelligent blending system uses the principle, and the flow chart is as shown in the attached figure 2:

3. after the model is built, the neural network algorithm is realized:

1) Carrying out normalization processing on training sample data obtained by sampling;

2) initialization weight omega_ijAnd ω_ki，ω_ijIs the weight, omega, between the input layer and the hidden layer of the artificial neural network_kiis a manual spiritpassing through the weight between the network hidden layer and the output layer;

the forward calculation mode is as follows:

The input of the jth neuron of the hidden layer of the artificial neural network is as follows:

wherein,

m is the number of neurons of the input layer;

representing the jth neuron of the input layer of the pth training sample;

representing the input of the ith neuron of the hidden layer under the action of the sample P

ω_jirepresenting weights between input and hidden layers

representing the output of the jth neuron of the hidden layer under the action of the sample P;

θ_ithreshold representing hidden layer neuron i

where g (x) is a non-linear mapping function for hidden layer neurons, such as Sigmoid function:

The input of the kth neuron of the artificial neural network output layer is as follows:

wherein,

Represents the input of the k-th neuron of the output layer under the action of the sample P

ω_ikrepresenting weights between a hidden layer and an output layer

θ_krepresenting threshold values for output layer neurons i

q is the number of neurons in the hidden layer,

the output of the ith neuron of the hidden layer.

the output of the kth neuron of the artificial neural network output layer is as follows:

Namely:

...

In the equation, because y1, y2 … … y10 are rate increment values that may be increasing or decreasing, the output layer neuron activation function takes the form of a symmetric Sigmoid function:

representing the output of the kth neuron of the output layer

Representing the output of the kth neuron of the output layer

output layer activation functionthe derivative function of (d) is:

the quadratic error function of the input pattern pair for each sample p, i.e. the performance index J, is:

J_PIndicating performance index

Representing target output

learning process pressing error function J_pAnd reducing the fastest direction adjustment weighting coefficient, and utilizing a gradient descent method until a satisfactory weighting coefficient set is obtained.

Wherein,

η represents the learning rate;

Δω_kiRepresents an adjustment increment of the weight coefficient;

expressing a performance index derivation;

Weight coefficients representing the hidden layer and the output layer;

Representing the output derivative to the output layer;

ω_ikRepresents: weight coefficients between output layer and hidden layer

Represents: output of hidden layer

θ_krepresents: threshold of output layer

wherein,

Represents: to simplify the calculation, intermediate variables of the output layer are set.

therefore, the weighting coefficient correction formula of any neuron k of the output layer is

Then finally

similarly, the weight variable of the available hidden layer is adjusted as follows:

Δω_ijrepresents: adjusted weight coefficient delta between input layer and hidden layer

Represents: to simplify the calculation, intermediate variables of the input layer are set.

Represents: output of input layer

the weight coefficient improvement formula of any neuron k of the output layer when the sample p acts is as follows:

Δω_ikrepresents: weight coefficient delta between adjusted output layer and hidden layer

k represents: a certain neuron of the output layer

the weight coefficient improvement formula of any neuron k of the hidden layer when the sample p acts is as follows:

Δω_ijrepresents: adjusted weight coefficient delta between input layer and hidden layer

if the learning process adjusts the weighting coefficients in the direction that makes the error function J decrease the fastest, a similar derivation process can be used to obtain the weighting coefficient increment formula when all samples of any neuron k and i of the output layer and the hidden layer act:

4. after the neural network outputs the velocity of each slot each time, the velocity of each slot needs to be added, and the total target velocity is used for amplification or reduction, and the applied formula is as follows:

final target rate of slot k (k 1, 2 … … 10) optimized slot k target rate of muxneural network

5. the neural network algorithm is used for regulating the cutting-out rate of the disc feeder and is compiled by adopting a C + + development language.

preferred example 2:

According to the algorithm of the neural network, a C + + compiling program is adopted, a C # compiling foreground picture is adopted, when the operation is carried out, a large pile is selected on the picture, namely the large pile is started, all disc feeders start to operate, and a selectable full-automatic mode is arranged on the picture. The variety to be cut out is selected for each groove in a non-full-automatic control mode, generally more than half of the groove is selected, optimization is clicked, the cut-out speed of each groove can be adjusted on line through a neural network, or in an automatic mode, namely, a full-automatic mode button is selected, the blending system can cut out the groove according to a default sequence, and when the deviation between the current content and the target content is found to be more than the allowable deviation, the neural network is automatically started to optimize the speed of each groove.

Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and individual modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps into logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.

the foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.

Claims (10)

1. A method for stabilizing quality of a blended ore based on artificial intelligence is characterized by comprising the following steps:

A target rate obtaining step: acquiring a current blending stacking plan, calculating and acquiring a total target speed, a target component content of the material and a discharging speed of a discharging groove according to the acquired blending stacking plan, and calculating the current component content of the material according to the discharging speed of the discharging groove;

material content judging step: judging whether the current component content of the material meets the target component content of the material or not according to the obtained target component content of the material and the current component content of the material: if yes, returning to the target rate obtaining step to continue execution; otherwise, entering a groove rate optimization step to continue execution;

A groove speed optimizing step: calculating the optimized discharging rate of each discharging groove by using a neural network algorithm according to the obtained target component content of the material and the current component content of the material;

a groove speed adjusting step: and adjusting the discharging speed of each discharging groove according to the obtained optimized discharging speed and the total target speed of each discharging groove.

2. the artificial intelligence based method for stabilizing the quality of the blended ore according to claim 1, wherein the target rate obtaining step comprises:

the current blending windrow plan comprises: the method comprises the following steps of (1) stacking planning time table, raw material distribution usage table, raw material composition table, dry weight, water content percentage, total blending stacking time, residual discharging time of a discharging groove, sum of residual moisture contents of all kinds of materials in the discharging groove at each moment and component content of each kind of material;

According to the current blending and stacking plan, obtaining the dry weight and the water content percentage of each variety of material, and calculating to obtain the moisture content of each variety of material, wherein the calculation formula is as follows:

Obtaining total blending and stacking time according to the current blending and stacking plan, and calculating to obtain a total target speed according to the obtained moisture content of each variety of materials, wherein the calculation formula is as follows:

obtaining the target component content of each variety of materials according to the current blending and stacking plan, and then calculating the target component content of the obtained materials according to the obtained moisture content of each variety of materials, wherein the calculation formula is as follows:

moisture content of variety material

according to the current blending and stacking plan, obtaining the residual moisture content of all kinds of materials in the discharge chute and the residual time of the discharge chute, and calculating the discharge rate of the discharge chute, wherein the calculation formula is as follows:

calculating to obtain the current component content of the material according to the obtained discharge rate of the discharge chute, wherein the calculation formula is as follows: the current component content of the material is the component content of the variety material being cut out by the discharging chute 1 × the discharging rate of the discharging chute 1 + the component content of the variety material being cut out by the discharging chute 2 × the discharging rate of the discharging chute 2 + … + the component content of the variety material being cut out by the discharging chute n × the discharging rate of the discharging chute n

wherein,

n represents the number of discharge chutes.

3. the artificial intelligence-based method for stabilizing the quality of the blended ore according to claim 2, wherein the slot velocity optimization step comprises:

acquiring a speed change value of the discharge chute: and calculating the optimized discharging rate of each discharging groove by taking the obtained target component content of the material, the current component content of the material and the deviation of the preset component content as the input of a neural network algorithm, wherein the calculation process is as follows:

the input of the jth neuron of the hidden layer of the artificial neural network is as follows:

wherein,

m is the number of neurons of the input layer;

Representing the jth neuron of the input layer of the pth training sample, and respectively inputting the obtained target component content of the material, the current component content of the material and the deviation of the preset component content into the neuron

representing the input of the ith neuron of the hidden layer under the action of the p training sample;

ω_jiRepresenting the weight between the input layer and the hidden layer;

Representing the output of the jth neuron of the hidden layer under the action of the pth training sample;

representing the output of the ith neuron of the hidden layer under the action of the p training sample;

θ_iA threshold representing hidden layer neuron i;

g (x) is a non-linear mapping function for hidden layer neurons, comprising: a Sigmoid function;

The input of the kth neuron of the artificial neural network output layer is as follows:

Wherein,

representing the input of the kth neuron of the output layer under the action of the pth training sample;

ω_kirepresenting a weight coefficient between the output layer and the hidden layer;

θ_kA threshold value representing output layer neuron k;

q is the number of neurons in the hidden layer;

the output of the kth neuron of the artificial neural network output layer is as follows:

wherein,

Representing the output of the kth neuron of the output layer, namely the speed change value of the discharge chute;

Output layer activation functionthe derivative function of (d) is:

the quadratic error function of the input pattern pair for the p-th training sample, i.e. the performance index, is:

Wherein,

J_prepresenting a performance index;

representing a preset target output value;

determination of Performance index J_pwhether the standard meets the preset standard or not: if yes, entering a groove speed adjusting step to continue execution; otherwise, entering the performance index adjusting step to continue execution.

4. the method for stabilizing the quality of the blended ore based on artificial intelligence according to claim 3, wherein the performance index adjusting step comprises:

press error function J_pand reducing the fastest direction adjustment weighting coefficient, and utilizing a gradient descent method until a satisfactory weighting coefficient set is obtained, wherein the adjustment process is as follows:

wherein,

η represents the learning rate;

Δω_kirepresents an adjustment increment of the weight coefficient;

expressing a performance index derivation;

weight system for representing hidden layer and output layercounting;

representing the output derivative to the output layer;

therefore, the weighting coefficient modification formula of any neuron k of the output layer is:

wherein,

an intermediate variable representing an output layer;

The weight variable of the available hidden layer is adjusted as follows:

Δω_jirepresenting the weight coefficient increment between the adjusted input layer and the hidden layer;

An intermediate variable representing an input layer;

the weighting coefficient improvement formula of any neuron k of the output layer when the p training sample acts is as follows:

Δω_kirepresenting the weight coefficient increment between the adjusted output layer and the hidden layer;

k represents a neuron number of the output layer;

the weighting coefficient improvement formula of any neuron k of the hidden layer when the p training sample acts is as follows:

Δω_jirepresenting the weight coefficient increment between the adjusted input layer and the hidden layer;

the learning process adjusts the weighting coefficient according to the direction which enables the error function J to reduce the fastest, and the weighting coefficient increment formula when all samples of any neuron k and i of the output layer and the hidden layer act can be obtained:

According to the obtained weighting coefficient increment, the weighting coefficient omega is subjected to_kiAnd ω_jithe return slot rate optimization step continues with the modification.

5. the artificial intelligence based method for stabilizing the quality of the blended ore according to claim 4, wherein the tank rate adjusting step comprises:

according to the obtained total target rate and the output of the k-th neuron of the output layerAdjusting the speed of each discharge chute, adding the adjusted speeds of the discharge chutes to obtain the sum of the speeds of the discharge chutes after the neural network optimization, and calculating to obtain the final target speed of the discharge chute k, wherein the calculation mode is as follows:

The final target speed of the discharge chute k is equal to the target speed of the chute k after the optimization of the Muxneural network, and k is equal to 1, 2, … …, n;

and adjusting the speed of each discharge chute according to the obtained final target speed of the discharge chute k.

6. the utility model provides a system for stabilize blending ore quality based on artificial intelligence which characterized in that includes:

A target rate acquisition module: acquiring a current blending stacking plan, calculating and acquiring a total target speed, a target component content of the material and a discharging speed of a discharging groove according to the acquired blending stacking plan, and calculating the current component content of the material according to the discharging speed of the discharging groove;

material content judging module: judging whether the current component content of the material meets the target component content of the material or not according to the obtained target component content of the material and the current component content of the material: if yes, calling a target rate acquisition module; otherwise, calling a slot rate optimization module;

a tank rate optimization module: calculating the optimized discharging rate of each discharging groove by using a neural network algorithm according to the obtained target component content of the material and the current component content of the material;

A tank rate adjustment module: and adjusting the discharging speed of each discharging groove according to the obtained optimized discharging speed and the total target speed of each discharging groove.

7. the system for stabilizing quality of blended ore based on artificial intelligence of claim 6, wherein the target rate obtaining module:

the current blending windrow plan comprises: the method comprises the following steps of (1) stacking planning time table, raw material distribution usage table, raw material composition table, dry weight, water content percentage, total blending stacking time, residual discharging time of a discharging groove, sum of residual moisture contents of all kinds of materials in the discharging groove at each moment and component content of each kind of material;

according to the current blending and stacking plan, obtaining the dry weight and the water content percentage of each variety of material, and calculating to obtain the moisture content of each variety of material, wherein the calculation formula is as follows:

obtaining total blending and stacking time according to the current blending and stacking plan, and calculating to obtain a total target speed according to the obtained moisture content of each variety of materials, wherein the calculation formula is as follows:

obtaining the target component content of each variety of materials according to the current blending and stacking plan, and then calculating the target component content of the obtained materials according to the obtained moisture content of each variety of materials, wherein the calculation formula is as follows:

moisture content of variety material

according to the current blending and stacking plan, obtaining the residual moisture content of all kinds of materials in the discharge chute and the residual time of the discharge chute, and calculating the discharge rate of the discharge chute, wherein the calculation formula is as follows:

Calculating to obtain the current component content of the material according to the obtained discharge rate of the discharge chute, wherein the calculation formula is as follows: the current component content of the material is the component content of the variety material being cut out by the discharging chute 1 × the discharging rate of the discharging chute 1 + the component content of the variety material being cut out by the discharging chute 2 × the discharging rate of the discharging chute 2 + … + the component content of the variety material being cut out by the discharging chute n × the discharging rate of the discharging chute n

Wherein,

n represents the number of discharge chutes.

8. the system for stabilizing blending ore quality based on artificial intelligence of claim 7, wherein the tank rate optimization module:

the speed change value of the discharge chute obtains the module: and calculating the optimized discharging rate of each discharging groove by taking the obtained target component content of the material, the current component content of the material and the deviation of the preset component content as the input of a neural network algorithm, wherein the calculation process is as follows:

the input of the jth neuron of the hidden layer of the artificial neural network is as follows:

Wherein,

m is the number of neurons of the input layer;

Representing the jth neuron of the input layer of the pth training sample, and respectively inputting the obtained target component content of the material, the current component content of the material and the deviation of the preset component content into the neuron

representing the input of the ith neuron of the hidden layer under the action of the p training sample;

ω_jiRepresenting the weight between the input layer and the hidden layer;

representing the output of the jth neuron of the hidden layer under the action of the pth training sample;

Representing the output of the ith neuron of the hidden layer under the action of the p training sample;

θ_iA threshold representing hidden layer neuron i;

g (x) is a non-linear mapping function for hidden layer neurons, comprising: a Sigmoid function;

the input of the kth neuron of the artificial neural network output layer is as follows:

wherein,

representing the input of the kth neuron of the output layer under the action of the pth training sample;

ω_kiRepresenting a weight coefficient between the output layer and the hidden layer;

θ_kA threshold value representing output layer neuron k;

q is the number of neurons in the hidden layer;

the output of the kth neuron of the artificial neural network output layer is as follows:

wherein,

representing the output of the kth neuron of the output layer, namely the speed change value of the discharge chute;

output layer activation functionThe derivative function of (d) is:

the quadratic error function of the input pattern pair for the p-th training sample, i.e. the performance index, is:

wherein,

J_prepresenting a performance index;

representing a preset target output value;

determination of Performance index J_pwhether the standard meets the preset standard or not: if yes, calling a slot rate adjusting module; otherwise, the performance index adjusting module is called.

9. The system for stabilizing quality of blended ore based on artificial intelligence of claim 8, wherein the performance index adjustment module:

press error function J_pand reducing the fastest direction adjustment weighting coefficient, and utilizing a gradient descent method until a satisfactory weighting coefficient set is obtained, wherein the adjustment process is as follows:

Wherein,

η represents the learning rate;

Δω_kirepresents an adjustment increment of the weight coefficient;

expressing a performance index derivation;

weight coefficients representing the hidden layer and the output layer;

representing the output derivative to the output layer;

therefore, the weighting coefficient modification formula of any neuron k of the output layer is:

wherein,

An intermediate variable representing an output layer;

The weight variable of the available hidden layer is adjusted as follows:

Δω_jirepresenting the weight coefficient increment between the adjusted input layer and the hidden layer;

an intermediate variable representing an input layer;

the weighting coefficient improvement formula of any neuron k of the output layer when the p training sample acts is as follows:

Δω_kiRepresenting the weight coefficient increment between the adjusted output layer and the hidden layer;

k represents a neuron number of the output layer;

the weighting coefficient improvement formula of any neuron k of the hidden layer when the p training sample acts is as follows:

Δω_jirepresenting the weight coefficient increment between the adjusted input layer and the hidden layer;

the learning process adjusts the weighting coefficient according to the direction which enables the error function J to reduce the fastest, and the weighting coefficient increment formula when all samples of any neuron k and i of the output layer and the hidden layer act can be obtained:

according to the obtained weighting coefficient increment, the weighting coefficient omega is subjected to_kiand ω_jimodifying, and calling a slot rate optimization module;

the slot rate adjustment module:

according to the obtained total target rate and the output of the k-th neuron of the output layeradjusting the speed of each discharge chute, adding the adjusted speeds of the discharge chutes to obtain the sum of the speeds of the discharge chutes after the neural network optimization, and calculating to obtain the final target speed of the discharge chute k, wherein the calculation mode is as follows:

the final target speed of the discharge chute k is equal to the target speed of the chute k after the optimization of the Muxneural network, and k is equal to 1, 2, … …, n;

and adjusting the speed of each discharge chute according to the obtained final target speed of the discharge chute k.

10. A computer-readable storage medium storing a computer program which, when executed by a processor, performs the steps of the method for artificial intelligence based stabilization of blending ore quality of any of claims 1 to 5.