CN111169942A - Belt conveyor control device and method based on multi-target particle swarm algorithm - Google Patents

Belt conveyor control device and method based on multi-target particle swarm algorithm Download PDF

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CN111169942A
CN111169942A CN202010062636.4A CN202010062636A CN111169942A CN 111169942 A CN111169942 A CN 111169942A CN 202010062636 A CN202010062636 A CN 202010062636A CN 111169942 A CN111169942 A CN 111169942A
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conveyor
belt
sampling period
speed
current sampling
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CN111169942B (en
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曾飞
刘雅婷
黄书伟
章生
宋杰杰
严诚
刘欣
贾媛媛
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Wuhan University of Science and Engineering WUSE
Wuhan University of Science and Technology WHUST
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
    • B65G43/00—Control devices, e.g. for safety, warning or fault-correcting
    • B65G43/08—Control devices operated by article or material being fed, conveyed or discharged
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
    • B65G43/00—Control devices, e.g. for safety, warning or fault-correcting
    • B65G43/02—Control devices, e.g. for safety, warning or fault-correcting detecting dangerous physical condition of load carriers, e.g. for interrupting the drive in the event of overheating
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
    • B65G2203/00—Indexing code relating to control or detection of the articles or the load carriers during conveying
    • B65G2203/02—Control or detection
    • B65G2203/0266—Control or detection relating to the load carrier(s)
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
    • B65G2203/00—Indexing code relating to control or detection of the articles or the load carriers during conveying
    • B65G2203/02—Control or detection
    • B65G2203/0266—Control or detection relating to the load carrier(s)
    • B65G2203/0291—Speed of the load carrier

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  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
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  • Control Of Conveyors (AREA)

Abstract

The invention discloses a belt conveyor control system of a multi-target particle swarm algorithm, which comprises a material flow laser acquisition device, a photoelectric encoder, a signal acquisition processing module and a conveyor coordination control device, wherein the conveyor coordination control device is used for establishing the optimal belt speed V of the next sampling periodn+1And an optimum capacity Qn+1According to the target planning model and the constraint conditions of the target planning model, the optimal belt speed V is obtained by calculationn+1And an optimum capacity Qn+1. The invention also discloses a belt conveyor control system adopting the multi-target particle swarm algorithm, which can scientifically realize the self-adaptive speed regulation and quantity control emission reduction of the belt conveyor, speed regulation and energy consumption saving, quantity control avoiding of large flow, and stability enhancement,and the running efficiency of the belt conveyor can be kept to be optimal, and the service life is prolonged.

Description

Belt conveyor control device and method based on multi-target particle swarm algorithm
Technical Field
The invention belongs to the technical field of sensing, measurement and control, and particularly relates to a belt conveyor control device based on a multi-target particle swarm algorithm, and a belt conveyor control method based on the multi-target particle swarm algorithm.
Background
The long-distance belt conveyor can realize large-volume transportation, so that the long-distance belt conveyor is widely used for bulk material or whole finished product transportation in the fields of ports, coal mines, transportation and the like. However, with the increase of productivity and the increasing of industrial scale, the electric energy consumed by the belt conveyor in the using process is greatly increased, and the problem of carbon emission pollution caused by the electric energy is more serious, which is obviously inconsistent with the low-carbon technical call put forward by the state. Meanwhile, the existing port belt conveyor is designed according to the maximum transportation volume, and the constant speed operation mode of the existing port belt conveyor causes that when the transportation volume is reduced, the bandwidth and the belt strength have larger margin, so that the transportation cost is wasted. Therefore, it is necessary to reasonably match the belt speed according to the transportation volume so as to improve the loading rate and achieve the purpose of reducing the energy consumption. At present, the existing energy-saving control methods (Chinese patent CN201710495972.6 and Chinese patent CN201910163976.3) of belt conveyors predict the optimal belt speed based on the information of the current materials to achieve the aim of energy saving, but the existing energy-saving control methods are not related to carbon emission and cannot predict the optimal transport capacity. The invention aims to realize an energy-saving control method for optimizing belt speed and transportation capacity by simultaneously considering multiple targets of energy consumption and carbon emission of a belt conveyor through a multi-target particle swarm optimization method for energy conservation and emission reduction of the belt conveyor, so that the system global property is optimal, and the method has important significance in the aspects of enhancing the stability and energy conservation and emission reduction of the belt conveyor.
Disclosure of Invention
The invention aims to provide a belt conveyor control device based on a multi-target particle swarm algorithm and a belt conveyor control method based on the multi-target particle swarm algorithm aiming at the problems in the prior art.
In order to achieve the purpose, the invention adopts the following technical measures:
a belt conveyor control system of multi-target particle swarm algorithm comprises a material flow laser acquisition device, a photoelectric encoder, a signal acquisition processing module and a conveyor coordination control device,
a signal acquisition processing module for acquiring the instantaneous sectional area of the material obtained by the material flow laser acquisition device in the current sampling period, acquiring the instantaneous belt speed of the conveying belt obtained by the photoelectric encoder in the current sampling period, and calculating the average sectional area S of the material in the current sampling period according to the instantaneous sectional area of the material and the instantaneous belt speed of the conveying beltnAnd the average tape speed v in the current sampling periodnAnd according to the average material sectional area S in the current sampling periodnAnd the average tape speed v in the current sampling periodnCalculating the volume flow q of the material in the current sampling periodnAnd the average material sectional area S of the current sampling period is calculatednAverage belt speed vnVolume flow q of materialnThe sampling data is transmitted to a conveyor coordination control device through a wireless transmission module, wherein n is a serial number of a sampling period;
a conveyor coordination control device for establishing the optimal belt speed V of the next sampling periodn+1And an optimum capacity Qn+1According to the target planning model and the constraint conditions of the target planning model, the optimal belt speed V is obtained by calculationn+1And an optimum capacity Qn+1。
A belt conveyor control system of a multi-target particle swarm algorithm, which also comprises a conveyor execution module, wherein the conveyor execution module comprises a material flow limiting device and a frequency converter execution module,
the material flow limiting device comprises an adjusting gate and a material guide chute arranged at the top of the adjusting gate, and the adjusting gate is arranged atAbove the conveyor belt, the damper drive means operates in accordance with the optimum delivery Q of the next sampling period obtained from the conveyor coordinate control meansn+1The height of the adjusting gate is controlled,
the frequency converter execution module obtains the optimal belt speed V of the next sampling period from the conveyor coordination control devicen+1And controlling a conveyer belt driving motor.
A belt conveyor control method based on a multi-target particle swarm algorithm comprises the following steps:
step 1, obtaining the instantaneous sectional area of each frame of material in the nth sampling period, and obtaining the instantaneous belt speed of each frame of conveying belt in the nth sampling period;
step 2, arranging a conveyor execution module for adjusting the belt speed and the conveying capacity of the conveying belt;
step 3, calculating the average material sectional area S in the current sampling periodn(ii) a Calculating the average tape speed v in the current sampling periodn；
Step 4, calculating the volume flow q of the material in the current sampling periodn；
Step 5, calculating the mass q of the material in unit length in the current sampling periodm；
Step 6, establishing the optimal tape speed V of the next sampling periodn+1And an optimum capacity Qn+1According to the target planning model and the constraint conditions of the target planning model, the optimal belt speed V of the next sampling period is obtained through calculationn+1And an optimum capacity Qn+1；
Step 7, the conveyor execution module adjusts the belt speed of the next sampling period of the conveyor belt to be the optimal belt speed Vn+1The conveyor execution module is based on the optimal belt speed Vn+1And an optimum traffic Qn+1And adjusting the distance between the conveying belt and the adjusting brake above the conveying belt.
The optimum belt speed V as described aboven+1And an optimum capacity Qn+1The object planning model includes f1(X) and f2(X)：
Figure BDA0002374985070000031
Figure BDA0002374985070000032
The constraint conditions are as follows:
Figure BDA0002374985070000033
wherein:
f1(X) -a minimum power objective planning function consumed to transport a unit of mass per unit of time;
f2(X) -a conveyor minimum total carbon emission target planning function;
rho is the bulk density of the conveyed material;
Ptm-the power consumed to transport a unit of mass per unit of time;
Qc-total conveyor carbon emissions;
Pn-conveyor sampling cycle transmission drum shaft power;
Figure BDA0002374985070000034
c is resistance coefficient at the positions of a conveying belt, a bearing and the like;
f, carrying roller resistance coefficient;
l is the horizontal projection length of the conveyor;
h, lifting height in a conveying material sampling period;
Gmthe weight of rotating parts such as a conveyor belt, a carrier roller, a bend pulley and the like;
qm-mass of material per unit length in current sampling period;
Sn-average material cross-sectional area over the current sampling period;
Qsum-total amount of material required to be transported;
k is the reduction coefficient of the area of the inclined conveyor;
vmin-the minimum speed allowed for the normal operation of the belt conveyor;
vmax-maximum speed allowed for normal operation of the belt conveyor;
Smin-minimum cross-sectional area of material during normal operation of the belt conveyor;
Smax-maximum cross-sectional area of material during normal operation of the belt conveyor;
Qmax-maximum capacity allowed for normal operation of the belt conveyor.
The distance h1 between the conveyer belt and the regulating brake above the conveyer belt in step 7 is as follows:
Figure BDA0002374985070000041
α -width of the damper, m;
a-adjustment factor, generally 0.15 to 0.5.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention has high automation degree, can save manpower and material resources to a certain degree and can improve the transportation efficiency.
2. The self-adaptive speed regulation and quantity control emission reduction of the belt conveyor can be scientifically realized, the energy consumption is saved through speed regulation, the quantity control is prevented from being large, the stability is enhanced, the optimal running efficiency of the belt conveyor can be kept, and the service life is prolonged.
Detailed Description
The present invention will be described in further detail with reference to examples for the purpose of facilitating understanding and practice of the invention by those of ordinary skill in the art, and it is to be understood that the present invention has been described in the illustrative embodiments and is not to be construed as limited thereto.
A belt conveyor control system of multi-target particle swarm algorithm comprises: the device comprises a material flow laser acquisition device, a photoelectric encoder, a signal acquisition and processing module, a conveyor coordination control device and a conveyor execution module.
The material flow laser collecting device is used for monitoring the instantaneous sectional area of the material on the conveying belt. The material flow laser collecting device is vertically arranged above the conveying belt, is vertically aligned with the material flow downwards, ensures that the material flow direction is vertical to a laser scanning sector, and detects the material flow on the conveying belt of the current belt conveyor through the material flow laser collecting device.
And the photoelectric encoder is used for monitoring the instantaneous belt speed of the conveying belt. The device is coaxially connected with a driving roller of the belt conveyor, converts mechanical geometric displacement of a driving roller shaft into pulse quantity through photoelectric conversion, and detects the rotating speed of the roller of the belt conveyor.
A signal acquisition processing module for acquiring the instantaneous sectional area of the material obtained by the material flow laser acquisition device in the current sampling period, acquiring the instantaneous belt speed of the conveying belt obtained by the photoelectric encoder in the current sampling period, and calculating the average sectional area S of the material in the current sampling period according to the instantaneous sectional area of the material and the instantaneous belt speed of the conveying beltnAnd the average tape speed v in the current sampling periodnAnd according to the average material sectional area S in the current sampling periodnAnd the average tape speed v in the current sampling periodnCalculating the volume flow q of the material in the current sampling periodnAnd the average material sectional area S of the current sampling period is calculatednAverage belt speed vnVolume flow q of materialnAnd transmitting the data to the conveyor coordination control device and the mobile terminal through the wireless transmission module.
A conveyor coordination control device for establishing the optimal belt speed V of the next sampling periodn+1And an optimum capacity Qn+1According to the target planning model and the constraint conditions of the target planning model, the optimal belt speed V is obtained by calculationn+1And an optimum capacity Qn+1And will optimize the tape speed Vn+1And an optimum capacity Qn+1And outputting the data to a conveyor execution module.
And the conveyor execution module comprises a material flow limiting device for changing the transport capacity in real time and a frequency converter execution module for changing the belt speed of the conveying belt.
The material flow limiting device comprises an adjusting gate and a guide chute arranged at the top of the adjusting gate, the adjusting gate is arranged above the conveying belt, the height of the adjusting gate is controlled by an adjusting gate driving device, and the conveying capacity on the conveying belt is further controlled. The baffle box both sides are provided with the material flow pipe that leads to the bottom surface, and when adjusting the floodgate and reducing to blockking the material, the material that is blockked can get into the baffle box buffer memory, and when the material in the baffle box spills over, can form the leftover bits to the bottom surface through the material flow pipe flow of baffle box both sides, and when adjusting the floodgate rising and not blockking the material, the material of buffer memory in the baffle box can fall into the conveyer belt.
A belt conveyor control method based on a multi-target particle swarm algorithm comprises the following steps:
step 1, knowing that the total amount of conveyed materials is QsumThere is no specific requirement for the belt speed V and the hourly transport Q of the conveyor during this time. Under the condition of conveying materials, arranging a material flow laser collecting device for collecting instantaneous sectional areas of the materials to obtain the instantaneous sectional area of each frame of the materials in an nth sampling period, arranging and matching auxiliary mounting supports on the instantaneous sectional areas of the materials (in the embodiment, the length of the sampling period is 1s, continuous sampling is carried out for multiple times in the sampling period to obtain the instantaneous sectional area of each frame of the materials), and fixedly suspending the instantaneous sectional areas of the materials above a conveying belt; arranging a photoelectric encoder for acquiring the instantaneous speed of the conveying belt to obtain the instantaneous speed of the conveying belt of each frame in the nth sampling period of the conveying belt;
step 2, arranging a conveyor execution module for adjusting the transportation volume and the belt speed at any time;
step 3, calculating the average material sectional area S in the current sampling periodn(ii) a Average tape speed v over the current sampling periodn；
The signal acquisition processing module calculates the average material sectional area S in the current sampling period according to the instantaneous sectional area S (t) of the material at the time t in the current sampling period and the instantaneous belt speed v (t) of the conveying belt in the current sampling periodnAnd the average tape speed v in the current sampling periodnWherein n is the serial number of the current sampling period;
step 4, calculating current acquisition by the signal acquisition processing moduleVolume flow q of material in sample periodnUnit is m3/s；
qn＝Sn×vn(formula 1)
Step 5, the signal acquisition processing module acquires the volume flow q of the material in the current sampling periodnConversion to material mass per unit length q within the current sampling periodmMass of material in kg/m and unit length qmObtained by the following formula:
Figure BDA0002374985070000061
wherein rho is the bulk density of conveyed materials, kg/m3。
Step 6, the data acquisition and processing module acquires the average sectional area S of the materialnAverage belt speed vnMass of material per unit length qmThe conveyor coordination control device establishes the optimal belt speed V of the next sampling periodn+1And an optimum capacity Qn+1According to the target planning model and the constraint conditions of the target planning model, the optimal belt speed V of the next sampling period is obtained by calculationn+1And an optimum capacity Qn+1The method comprises the following specific steps:
step 6.1, establishing a target planning model, wherein the target planning model comprises f1(X) and f2(X)，
Figure BDA0002374985070000062
Figure BDA0002374985070000063
Figure BDA0002374985070000064
S.T. is a constraint condition of the target planning model; optimum belt speed Vn+1And an optimum capacity Qn+1In satisfying the constraint stripIn the case of pieces, let the object planning model f1(X)、f2(X) are all the smallest.
f1(X) -target planning function of minimum power consumed to transport a unit of mass per unit of time, w.kg-1·s-1；
f2(X) -a conveyor minimum carbon emission total objective planning function, kg;
Ptm-the power consumed by transporting a unit of mass per unit of time, w.kg-1·s-1；
Qc-total conveyor carbon emissions in kg; calculating according to 1 kw.h electricity saving and 0.272kg carbon emission reduction;
Pn-the conveyor samples the periodic transmission drum shaft power, kw;
Figure BDA0002374985070000071
c is resistance coefficient of a conveying belt, a bearing and the like, and C is 2.3;
f is the resistance coefficient of the carrier roller, and f is 0.027;
l is the horizontal projected length of the conveyor, m;
h-lifting height in the sampling period of conveyed materials, m;
Gmthe weight of the rotary parts such as the conveyer belt, the carrier roller, the turnabout drum and the like is kg/m;
qm-mass of material per unit length in current sampling period, kg · m-1；
SnAverage material cross-sectional area, m, over the current sampling period2；
Qsum-total amount of material to be transported, t;
k is the reduction coefficient of the area of the inclined conveyor;
vmin-the minimum speed allowed for the normal operation of the belt conveyor, m/s;
vmax-maximum speed allowed for normal operation of the belt conveyor, m/s;
Sminminimum cross-sectional area of material, m, during normal operation of the belt conveyor2；
SmaxMaximum cross-sectional area of material, m, during normal operation of the belt conveyor2；
QmaxThe maximum transport capacity allowed by the normal operation of the belt conveyor, t/h.
Step 6.2, calculating and obtaining the optimal belt speed V according to the target planning model and the constraint conditions of the target planning modeln+1And an optimum capacity Qn+1，
Calculating and obtaining the optimal belt speed V according to the target planning model and the constraint condition of the target planning model by utilizing a multi-target particle swarm algorithmn+1And an optimum capacity Qn+1The method specifically comprises the following steps:
and 6.2.1, initializing the population. Each particle represents a set of optimal belt velocities V to be optimizedn+1And an optimum capacity Qn+1And in the initialization interval, initializing the initial position and the initial speed of the particle swarm and simultaneously checking whether the initial position and the initial speed meet the constraint condition. The initialization parameters are selected as follows: inertia factor ω 0.5+ rand, acceleration constant c1＝c21.5, 50 for the particle swarm size, and setting the iteration number to be 300;
step 6.2.2, according to the fitness function, namely the target planning function f1(X)、f2(X) calculating a fitness value of each particle. Comparing the dominance condition between each particle pairwise, wherein the Pareto solution set is used for storing the obtained non-inferior solutions;
and 6.2.3, evaluating an adaptive value. The objective function value of each particle is calculated. Storing it in vector form;
and 6.2.4, updating the position and the speed of the particle by using an updating formula, wherein the initial historical optimal position pbest and the global optimal position gbest of the particle can be randomly selected from the current Pareto solution set, and if the updated particle does not meet the constraint condition, the particle is deleted and then one particle is randomly generated again.
The update formula is:
vi(t1+1)＝ω·vi(t1)+c1×rand()×(pbesti(t1)-xi(t1))+c2×rand()×(gbesti(t1)-xi(t1))
(formula 7)
xi(t1+1)＝xi(t1)+vi(t1) (equation 8)
vi(t1+1) -the velocity value of the particle at the next update time;
xi(t1+1) -the position value of the particle at the next update time;
vi(t1) -the velocity value of the particle at the current update time;
xi(t1) -the position value of the particle at the current update time;
omega-inertia weight factors distributed in the interval of [0,1 ];
c1-the acceleration constant, non-negative;
c2-the acceleration constant, non-negative;
rand () -random numbers distributed in the interval [0,1 ];
pbesti(t1) -the current update time individual's own optimal solution position;
gbesti(t1) -optimal solution positions for all individuals at the current update time;
t1 is the current update time;
the particle swarm is an optimization search algorithm, and is searched by a mechanism of the particle swarm, and each particle has the optimal belt speed Vn+1And an optimum capacity Qn+1Given a particle population size of 50, 50 particles move within the given constraint to find the optimal solution, since there must be velocity and position to move within the range, after the initial random initialization there is the first vi(t1)，xi(t1), the velocity and position of the particle, i.e., v at the next update time, are then updated by the optimal solutions of the individuals themselves and all individuals according to equations 7 and 8i(t1+1)，xi(t1+1)。
And step 6.2.5, dynamically updating the Pareto solution set. And checking the dominance condition of the Pareto solution set of each particle of the particle swarm, and if a certain particle is not inferior to the particles in the Pareto solution set, storing the particle in the Pareto solution set as a new Pareto solution set obtained currently.
Step 6.2.6, repeatedly executing the steps 6.2.2-6.2.4 until the given iteration times are reached, ending the circulation to obtain the particles with the optimal desired fitness value, namely obtaining the optimal belt speed Vn+1And an optimum capacity Qn+1。
Step 7, the conveyor execution module adjusts the belt speed of the next sampling period of the conveyor belt to be the optimal belt speed Vn+1The conveyor execution module is based on the optimal belt speed Vn+1And an optimum traffic Qn+1And adjusting the distance between the conveying belt and the adjusting brake above the conveying belt.
The speed of the conveying belt is adjusted by controlling the frequency converter, and the conveying amount is adjusted by the height of an adjusting brake above the conveying belt. The height of the regulating gate is in the following functional relationship with the transport amount and the speed of the conveyor belt (the distance between the conveyor belt and the regulating gate is in direct proportion with the transport amount and in inverse proportion with the speed of the conveyor belt).
Figure BDA0002374985070000091
h 1-distance between conveyor belt and upper damper, m;
α -width of the damper, m;
a-adjustment factor, generally 0.15 to 0.5.
Step 8, the mobile terminal receives the average sectional area S of the material in the current sampling period transmitted by the data acquisition and processing module through the Si4432 wireless network nodenAverage belt speed vnMass of material per unit length qnThe transportation condition of the conveyer belt can be monitored on line in real time. In an emergency situation, the mobile terminal sends an emergency regulation and control instruction to the conveyor coordination control device through the Si4432 wireless network node, and the conveyor coordination control device controls and regulates the material current limiting device and the frequency converter execution module of the conveyor execution module through the emergency regulation and control instruction.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the protection scope of the present invention, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims (5)

1. A belt conveyor control system of multi-target particle swarm algorithm comprises a material flow laser acquisition device and a photoelectric encoder, and is characterized by also comprising a signal acquisition processing module and a conveyor coordination control device,
a signal acquisition processing module for acquiring the instantaneous sectional area of the material obtained by the material flow laser acquisition device in the current sampling period, acquiring the instantaneous belt speed of the conveying belt obtained by the photoelectric encoder in the current sampling period, and calculating the average sectional area S of the material in the current sampling period according to the instantaneous sectional area of the material and the instantaneous belt speed of the conveying beltnAnd the average tape speed v in the current sampling periodnAnd according to the average material sectional area S in the current sampling periodnAnd the average tape speed v in the current sampling periodnCalculating the volume flow q of the material in the current sampling periodnAnd the average material sectional area S of the current sampling period is calculatednAverage belt speed vnVolume flow q of materialnThe sampling data is transmitted to a conveyor coordination control device through a wireless transmission module, wherein n is a serial number of a sampling period;
a conveyor coordination control device for establishing the optimal belt speed V of the next sampling periodn+1And an optimum capacity Qn+1According to the target planning model and the constraint conditions of the target planning model, the optimal belt speed V is obtained by calculationn+1And an optimum capacity Qn+1。
2. The belt conveyor control system of the multi-target particle swarm algorithm according to claim 1, further comprising a conveyor execution module, wherein the conveyor execution module comprises a material flow limiting device and a frequency converter execution module,
the material flow limiting device comprises an adjusting gate and a material guide chute arranged at the top of the adjusting gate, the adjusting gate is arranged above the conveying belt, and the adjusting gate driving device is used for driving the adjusting gate to perform optimal conveying quantity Q according to the next sampling period obtained from the conveyor coordination control devicen+1The height of the adjusting gate is controlled,
the frequency converter execution module obtains the optimal belt speed V of the next sampling period from the conveyor coordination control devicen+1And controlling a conveyer belt driving motor.
3. A belt conveyor control method based on a multi-target particle swarm algorithm is characterized by comprising the following steps:
step 1, obtaining the instantaneous sectional area of each frame of material in the nth sampling period, and obtaining the instantaneous belt speed of each frame of conveying belt in the nth sampling period;
step 2, arranging a conveyor execution module for adjusting the belt speed and the conveying capacity of the conveying belt;
step 3, calculating the average material sectional area S in the current sampling periodn(ii) a Calculating the average tape speed v in the current sampling periodn；
Step 4, calculating the volume flow q of the material in the current sampling periodn；
Step 5, calculating the mass q of the material in unit length in the current sampling periodm；
Step 6, establishing the optimal tape speed V of the next sampling periodn+1And an optimum capacity Qn+1According to the target planning model and the constraint conditions of the target planning model, the optimal belt speed V of the next sampling period is obtained through calculationn+1And an optimum capacity Qn+1；
Step 7, the conveyor execution module adjusts the belt speed of the next sampling period of the conveyor belt to be the optimal belt speed Vn+1According to the optimal belt speed V, the execution module of the conveyorn+1And an optimum traffic Qn+1And adjusting the distance between the conveying belt and the adjusting brake above the conveying belt.
4. The method as claimed in claim 3, wherein the optimal belt speed V isn+1And an optimum capacity Qn+1The object planning model includes f1(X) and f2(X)：
Figure FDA0002374985060000021
Figure FDA0002374985060000022
The constraint conditions are as follows:
Figure FDA0002374985060000023
wherein:
f1(X) -a minimum power objective planning function consumed to transport a unit of mass per unit of time;
f2(X) -a conveyor minimum total carbon emission target planning function;
rho is the bulk density of the conveyed material;
Ptm-the power consumed to transport a unit of mass per unit of time;
Qc-total conveyor carbon emissions;
Pn-conveyor sampling cycle transmission drum shaft power;
Figure FDA0002374985060000024
c is resistance coefficient at the positions of a conveying belt, a bearing and the like;
f, carrying roller resistance coefficient;
l is the horizontal projection length of the conveyor;
h, lifting height in a conveying material sampling period;
Gmconveyer belt, carrier roller and turnabout drumThe weight of the rotating parts;
qm-mass of material per unit length in current sampling period;
Sn-average material cross-sectional area over the current sampling period;
Qsum-total amount of material required to be transported;
k is the reduction coefficient of the area of the inclined conveyor;
vmin-the minimum speed allowed for the normal operation of the belt conveyor;
vmax-maximum speed allowed for normal operation of the belt conveyor;
Smin-minimum cross-sectional area of material during normal operation of the belt conveyor;
Smax-maximum cross-sectional area of material during normal operation of the belt conveyor;
Qmax-maximum capacity allowed for normal operation of the belt conveyor.
5. The method for controlling the belt conveyor with the multi-target particle swarm algorithm according to claim 3, wherein the distance h1 between the conveyor belt and the adjusting brake above the conveyor belt in the step 7 is as follows:
Figure FDA0002374985060000031
α -width of the damper, m;
a-adjustment factor, generally 0.15 to 0.5.
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