CN114348535B - Conveyor belt system optimization method based on Internet of things - Google Patents
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Abstract
The invention discloses a conveyor belt system optimization method based on the Internet of things, which comprises the following steps: laying down various sensors and controllers in the conveying system; integrating sensor data through a PLC machine and synchronizing the sensor data to an offline database; when a real-time production data stream is input, the cargo condition on the whole production line is updated through a real-time production simulation system so as to realize real-time monitoring; the energy consumption of the whole transmission system is optimized through an algorithm model by utilizing the historical data deposited in the production process, and after the algorithm is deployed, a speed regulation signal and safety early warning are output according to a real-time production data stream and a safety strategy; verifying the speed regulation effect through an algorithm crossing strategy; the invention provides a data driving method for improving the energy consumption problem of a transmission system by increasing the capacity of the Internet of things.
Description
Technical Field
The invention relates to the technical field of Internet of things, in particular to a conveyor belt system optimization method based on the Internet of things.
Background
The Internet of things (IoT) is a connection of physical objects equipped with sensors and actuators to a network through data communication technology, which can enable new interactions between enterprises, customers and smart things. With the increasing number of IoT connections, the world of people awareness is expanding. In recent years, the issue of how to fully exploit the potential of IoT information streams in production operations management has raised academic interest. Gerts and O' spare explored IoT-based innovations from the perspective of logistics and supply chain management. Hashme et al discussed the vision of big data analysis to support smart cities, and presented a future business model of smart city big data. Qi and Shen further transition the movement of the smart city from a technology guide stage to a decision guide stage, make plans and operation decisions within the city range, reflect multidimensional requirements, and adapt to mass data and innovation. Wunderlich et al studied the adoption and dissemination of home IoT and focused on the application of home smart metering technology to power consumption. Anokhin et al relies on industrial 4.0 technology to provide a mobile power restoration technique that takes advantage of its mobility and space-time flexibility to address effective response and restoration during power distribution system disasters. Another important issue is sustainable manufacturing, including: productivity, flexibility and resource efficiency are improved; waste, energy consumption and excess production are reduced; the quality of a working environment is improved, and conventional work is reduced; participation/collaboration of service and stakeholders, etc. Kamble et al conducted research analysis and modeling institutional equations for 115 manufacturing companies in India, and discussed the impact of industrial 4.0 technology on lean production practices and sustainable organizational performance. Andrew considers intelligent manufacturing to be a collection of network physical system concepts such as internet of things, cloud computing, service-oriented computing, artificial intelligence and data science, discusses six major pillars of intelligent manufacturing (manufacturing technologies and procedures, materials, data, predictive engineering, sustainability, networking and resource sharing), and proposes ten guesses to capture future trends.
Conveyor systems are part of industrial production operations, which are the basis for the flow of internal goods in production operations, and most research has focused on conveyor systems as a function of assembly, sorting and transport. For example, boysen et al have investigated the scientific literature of various conveyor belt-based fully automated sorting systems from an operational research perspective, describing a wide range of applications and their different sorting systems; frey et al optimize the workload of the airline baggage handling system, provide a time-indexed mathematical programming formula for outbound baggage planning, and provide an innovative decomposition process in conjunction with a column generation scheme. The solution reduces the maximum workload by more than 60% in real applications at major airports in europe; mosadegh et al developed open-loop and closed-loop models based on control theory to study the dynamic behavior of the hybrid model assembly line to optimize overall work overload and idleness.
The research targets of the transmission system in the literature related to operation management are usually load balancing, idle period reduction, transmission distance reduction and the like, and the literature directly researching the energy consumption of the transmission system is few, wherein one important reason is that the energy consumption is difficult to describe explicitly because the method relates to some physical concepts and not only simple production variables. Existing energy consumption studies on transport systems are mostly based on physical engineering. For example, hiltermann et al, by calculating the motion resistance, performs power consumption analysis, derives the friction coefficient and the electrical drive power, and selects appropriate belt speeds for different cargo flows; he et al examined the potential risks in the transient operation of the conveyor belt and the dynamic performance of the conveyor, and proposed an Initiation-calibration-Optimization three-step method as a speed regulation scheme. As shown in fig. 1, a coal excavation operation generally includes an excavation system, a conveying system, and a transportation system. The excavated coal directly falls on a conveyor belt, is transported to the ground surface from the ground through a multi-stage conveyor belt, and is transported by a transport tool such as a truck. When the capacity of the internet of things is lacked, the transmission system often runs at full speed for safety because information such as production and the state of a transmission belt cannot be acquired, which causes huge energy waste. In actual production, the transmission system is usually the part with the highest energy consumption, and the research on the energy consumption of the transmission system from the perspective of the internet of things and production operation is still blank.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to provide a conveyor belt system optimization method based on the Internet of things.
In order to realize the purpose, the invention adopts the technical scheme that: a conveyor belt system optimization method based on the Internet of things comprises the following steps:
step 2, integrating sensor data through a PLC machine and synchronizing the sensor data to an offline database;
step 3, when the real-time production data stream is input, updating the goods condition on the whole production line through a real-time production simulation system so as to realize real-time monitoring;
step 4, optimizing the energy consumption of the whole transmission system by using the historical data deposited in the production process through an algorithm model, and outputting a speed regulation signal and a safety early warning according to a real-time production data stream and a safety strategy after the algorithm is deployed;
and 5, verifying the speed regulation effect through an algorithm crossing strategy.
As a further improvement of the present invention, in step 1, the sensor and the controller specifically include: the system comprises a measuring ammeter, a cargo quantity measuring instrument, a speed controller, a motor internal sensor and a sick laser scanner.
As a further improvement of the present invention, in step 2, the data of the sick laser scanner in the integrated sensor are specifically as follows:
the sick laser scanner is arranged at the head of the conveyor belt, emits hundreds of laser points to the conveyor belt, reflects after being touched with an object on the conveyor belt, and has distance information s when each laser point returns to the sick laser scanner i When the sine laser scanner is fixed, the distance l from the bottom of the conveyor belt and the scanning angle alpha are fixed, so that the angle alpha between the scanning point and the vertical direction is deduced from the number of scanning points i The coordinate position (x) of each scanning point is obtained by the following formula i ,y i )：
x i ＝s i *sinα i
y i ＝l-s i *conα i
In order to filter noise data of wall bodies, passing workers, sundries on the ground and abnormal laser points, a filtering area is set, the coordinate positions of all scanning points can be acquired at any time, the thought of multiple integral is used for reference, the cross section area at the time is acquired firstly, then the volume of goods passing through a sick laser scanner in delta t time is acquired, the delta t is the time interval of two times of scanning, and according to the thought of calculus, when the delta t and the delta x are sufficiently small, the volume V of the goods is acquired T The following were used:
V T ＝∫∫y i dxdt≈∑∑y i ΔxΔt＝∑∑y i (x i -x i-1 )T。
as a further improvement of the present invention, in step 3, the step of updating the cargo condition on the whole production line by the real-time production simulation system specifically comprises the following steps:
a. let the conveying system be a double-conveyor system, and the speeds of the front and rear conveyor belts in the double-conveyor system at time T are respectively v 1 ，v 2 Dividing the conveyor belt into total lengths/l of the sameEach interval has a corresponding cargo volume;
b. setting a time step T, when the next time step T +1 arrives, the output interval length of the rear conveyor belt is int (v) 2 * T) while the amount of goods is received from the front conveyor, assuming that the amount of goods received is at length int (v) 2 * T) are uniformly distributed;
c. the newly introduced quantity of goods is known from the scale on the front conveyor, assuming that it is distributed uniformly over a length int (v) 1 * T), while setting the interval length to int (v) 1 * T) the amount of goods is conveyed to the rear conveyor belt;
d. and updating the distribution condition of the goods on the conveyor belt at the T +1 moment according to the distribution of the goods on the conveyor belt at the T moment and the speed of the conveyor belt.
As a further improvement of the present invention, in step 4, the energy consumption of the whole transmission system is optimized by the algorithm model specifically as follows:
assuming that the power of the conveyor belt is related to the transport speed and the carrying capacity, a function P = f (m, v) of the power is obtained through a data fitting mode, and the optimization target is an optimization problem of the sum of the work of the conveyor belt in n × T time, and the mathematical form of the optimization problem is shown as the following formula:
s.t.m 2,i ,v 2,i ≥0
m 2,t ＝g(V t ,m 1,t-1 ,m 2,t-1 ,v 2,t ,v 1,t )
wherein v is 1 And v 2 Representing the speed of the conveyor belt, m 1 And m 2 Indicating the volume distribution, i.e. the amount of load, V, of goods on the front and rear conveyors t Indicating volume flow meter reading due to V t Exogenous gene given, m 1 And m 2 Indirectly adjusting the quantity of goods on the conveyor belt by adjusting the speed of the conveyor belt, depending on other variables, the true independent variable, i.e. the decision variable being v only 1 And v 2 。
As a further improvement of the invention, when the desired value of the total load on the conveyor belt is constant and the desired values of the amounts of goods input and output at any time are equal, the conveyor system reaches a steady state, and the densities of the front and rear conveyor belts are respectively ρ 1 And ρ 2 The expected values of the input and output quantities of the goods are equal, meaning that:
v 1 *T*ρ 1 ＝v 2 *T*ρ 2
v 1 *ρ 1 ＝v 2 *ρ 2
then the optimization problem is transformed:
s.t.v 1 *ρ 1 ＝v 2 *ρ 2
the solution of the problem can be completed by adopting a conventional optimization method to obtain the optimal speed.
As a further improvement of the invention, when the production of goods is unstable, the distribution of the quantity of goods fed into the transfer system will change, when the transfer system will undergo a steady-state transition from one of the steady states to the other, when the optimization problem becomes:
s.t.m 2,i ,v 2,i ≥0
where n denotes that steady state conversion is complete after n time periods T, i.e.:
when the transmission system is in the steady-state conversion process, the optimization process is as follows:
determining a state parameter from one steady state to the other, i.e. s 1 (v 11 ,ρ 11 ,v 12 ,ρ 12 ) And s n (v 21 ,ρ 21 ,v 22 ,ρ 21 )；
From s 1 (v 11 ,ρ 11 ,v 12 ,ρ 12 ) Conversion to s n (v 21 ,ρ 21 ,v 22 ,ρ 21 ) In the process of (2), a path with the minimum work, namely the shortest path, is solved; in the steady-state conversion process, the state of each step depends on the speed of the conveying belt and the distribution sequence of the carrying capacity on the conveying belt, the distribution sequence of the carrying capacity generates a great number of intermediate states, and the solving time of the shortest path becomes unacceptable, so that the speed of the conveying belt is directly set to be the value of another steady state, and the successful conversion of the steady state can be ensured.
As a further improvement of the present invention, in order to solve the problem that the conveyor belt is pressed to be dead under overload operation due to excessive load carried on the conveyor belt as a whole, the safety strategy in step 4 specifically comprises the following steps:
monitoring the total cargo quantity, current, voltage and power safety indexes in real time, and judging whether any one index exceeds a given safety threshold value;
step (2), if any index exceeds a safety threshold, immediately accelerating, and continuously operating at a high speed for a certain time;
step (3), judging whether each safety index is reduced to 85% of a safety threshold value, if so, switching back to the optimal speed, and returning to the step (1); if not, continuing to operate at high speed;
in order to avoid frequent speed regulation of the conveyor belt, in the step (2) and the step (3), after the conveyor belt is accelerated, the conveyor belt needs to return to a low-speed state after a certain period of time.
As a further improvement of the present invention, in order to solve the problem that the rear conveyor belt receives a large amount of goods from the front conveyor belt in a short time due to an excessive amount of instant goods, and the rear conveyor belt receives a large amount of goods in a short time, and the space is insufficient to cause goods scattering, the safety strategy in step 4 specifically comprises the following steps:
step i, monitoring the volume of the goods at a certain distance from the tail of the front conveyor belt in real time, and judging whether the volume of the goods exceeding a safety threshold exists at any position;
step ii, if the cargo volume exceeding the safety threshold exists, the speed is increased immediately, and the high-speed operation lasts for 200/v 1 +10 seconds, v 1 Is the front conveyor running speed;
step iii, judging whether the goods volume exceeding the safety threshold exists at the integral position of the front conveyor belt, and if so, continuing to run at a high speed; if not, switching to the optimal speed, and returning to the step i.
The invention has the beneficial effects that:
the invention utilizes the capability of the Internet of things to find the opportunity of energy conservation in production operation activities and obtain the profit effect in actual production. Taking a transmission system in the industry as an example, a set of internet of things technology framework and data flow comprising a bottom layer, a middle layer and an application layer are provided, and the internet of things technology framework and the data flow show high stability and reliability in actual production. Meanwhile, based on the energization of the Internet of things, the optimization problem of the transmission system is analyzed and modeled in a data-driven mode, and a steady-unsteady speed regulation scheme is provided. The results of numerical experiments and production experiments show that the method is superior to the existing speed regulation control scheme and other methods, and the energy consumption saving of 10.85 percent and the estimated profit of about 600 ten thousand yuan are realized.
Drawings
FIG. 1 is a schematic view of the overall production environment in coal excavation;
FIG. 2 is an overall technical framework and data flow diagram of an embodiment of the present invention;
FIG. 3 is a schematic view of a sick laser scanner in an embodiment of the invention;
FIG. 4 is a schematic diagram of a sick laser scanner acquiring a coal flow in an embodiment of the invention;
FIG. 5 is a schematic diagram illustrating the updating of the coal flow of the real-time production simulation system according to an embodiment of the present invention;
FIG. 6 is a schematic illustration of a coal delivery system according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a real-time production simulation system according to an embodiment of the present invention;
FIG. 8 is a numerical experiment-power equation image in an embodiment of the present invention;
FIG. 9 is a power equation image in an embodiment of the invention;
FIG. 10 is a graph of a production run versus power function for an embodiment of the present invention;
FIG. 11 is a schematic diagram illustrating throughput timing dependence in an embodiment of the present invention;
FIG. 12 is a diagram illustrating a power saving experiment result according to an embodiment of the present invention;
FIG. 13 is a schematic diagram illustrating power saving effect estimation according to an embodiment of the invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Examples
As shown in fig. 2, a conveyor belt system optimization method based on the internet of things, which is described in this embodiment with coal as a specific cargo, first arranges various sensors and controllers in a conveyor system, and the sensors and controllers include: various measuring electric meters, coal measuring instruments, speed controllers and the like; then, integrating sensor data through a PLC machine and synchronizing the sensor data to an offline database; in the embodiment, a real-time production simulation system is developed, and when a real-time production data stream is input, the coal quantity condition on the whole production line can be updated so as to realize real-time monitoring; an algorithm model is designed to optimize the energy consumption of the whole transmission system by utilizing the historical data deposited in the production process, and a speed regulation signal and a safety early warning are output according to a real-time data stream and a safety strategy after the algorithm is deployed; and finally, designing a reliable algorithm crossing strategy to verify the speed regulation effect.
The present embodiment is further described below:
1. enabling and real-time production simulation system of internet of things:
the key of the capability endowing of the Internet of things is extraction, pretreatment and integration of sensor data, and the sensors comprise various electric meters, sensors inside motors and sick laser scanners. Of which the most important is for the sine shockAnd (4) data processing of the optical scanner. As shown in FIG. 3, the sick is mounted at the head of the conveyor belt and emits hundreds of laser spots onto the conveyor belt, which reflect off objects on the conveyor belt and each laser spot returns to the sick with distance information s i . When the Sick is fixed, the distance l from the bottom of the conveyor belt and the scanning angle alpha are fixed, so that alpha can be deduced from the number of scanning points i Finally, the coordinate position of each scanning point can be obtained from equations (1) and (2). In order to filter noise data such as wall bodies, workers passing by, sundries on the ground, abnormal laser points and the like, a filtering area is provided.
x i ＝s i *sinα i (1)
y i ＝l-s i *cosα i (2)
As shown in fig. 4, at any time, the coordinate positions of all the scanning points can be obtained, and by using the idea of multiple integration, the cross-sectional area at this time can be obtained first, and then the volume of the coal passing through the mock within the time Δ t, where Δ t is the time interval between two scans. According to the calculus idea, when Δ t and Δ x are sufficiently small, the volume of the coal flow can be perfectly obtained.
V T ＝∫∫y i dxdt≈∑∑y i ΔxΔt＝∑∑y i (x i -x i-1 )T (3)
Obtains the coal flow V in any time T Later, real-time production simulation systems were developed. Taking a typical dual-conveyor system as an example, the inputs are: the belt speed v1, v2 at the time T, the volume distribution of coal on the belt, the reading of a volume meter and the output: the volume distribution of the coal on the belt at the time T +1 (the input coal quantity and the output coal quantity can be obtained correspondingly at the time T-T + 1). FIG. 5 is an example of a production update, following an update flow:
1. the minimum unit length l is set, the belt is divided into the same sections of the total length/l, and each section is provided with the corresponding volume of coal.
2. A time step T is set, and when the next time step T +1 comes, the belt 2 outputs an amount of coal having a section length of int (v 2 × T), and receives the amount of coal from the belt 1, assuming that the received amount of coal is uniformly distributed over the length int (v 2 × T).
3. The weighing scale on the conveyor belt 1 knows the amount of coal which is newly introduced, assuming that it is uniformly distributed in the interval of length int (V1 × T), while the amount of coal of length int (V1 × T) is fed to the belt 2.
4. And updating the coal distribution condition on the belt at the T +1 moment according to the coal distribution on the belt at the T moment and the speed of the belt.
2. Data-driven energy consumption optimization modeling:
(1) modeling problem:
the phenomenon of mismatch between the transport speed and the capacity is common in industrial conveying systems, and the mismatch causes waste of energy consumption. Fig. 5 is a typical dual conveyor system, with front and rear conveyors rotating at speeds v1 and v2 to transport cargo, and a volume meter on the front conveyor to measure the volume of cargo passing through, given specific data such as current, power, etc. meter readings. Since the production of goods is not completely stable, nor is the amount of goods delivered to the conveyor belt completely stable, there may be inefficient work if the conveyor belt moves in a uniform manner. Whether or not power consumption can be reduced by optimizing the transportation speed and the carrying capacity is discussed.
Assuming that the power of the conveyor belt is related to the transport speed and the capacity, a function P = f (m, v) of the power can be obtained by means of data fitting. Generally, it is desirable to minimize the total work done by the conveyor belt over a period of time, not just the power at a certain moment. Therefore, the optimization objective should be the sum of the belt work in nxt time, which is mathematically expressed by the formula (4):
s.t.m 2,i ,v 2,i ≥0
m 2,t ＝g(V t ,m 1,t-1 ,m 2,t-1 ,v 2,t ,v 1,t ) (5)
wherein v is 1 And v 2 Denotes the speed of the conveyor, m 1 And m 2 Indicating the volume distribution (capacity) of the goods on both conveyor belts, V t Indicating a volumetric flow meter reading. Due to V t Exogenous gene given m 1 And m 2 Depending on other variables (indirectly adjusting the amount of goods on the conveyor by adjusting the speed of the conveyor), the true independent variable (decision variable) is only v 1 And v 2 。
There are two difficulties in solving the optimization problem directly.
First, the distribution of the capacity is difficult to express by an exact functional analytic formula, and although only the total capacity/average capacity needs to be used when calculating the power of the conveyor at any time, the distribution sequence information of the goods on the conveyor is needed when dynamically updating the conveyor capacity. For example, the high capacity at the head of the conveyor belt and the high capacity at the tail of the conveyor belt have a different effect on capacity renewal.
Secondly, the optimization aims to be performed by the conveyor belt in a period of time n multiplied by T in total, however, n at the moment lacks a proper definition, and when the value of n is small, the n deviates from the global optimal solution, because the change of the total carrying capacity is small at the moment, the power can be rapidly reduced by changing the transportation speed, and the work is reduced. However, this optimization does not take into account the impact on the total capacity for a longer period of time in the future. For example, reducing the speed for a short period of time may reduce the total work during that period of time, but may increase the total capacity over time, and low speed high capacity may not be an optimal result (as will be demonstrated by later experiments). One reasonable way to define n is to let n → ∞, transform the optimization objective into work averaged over infinite time, as shown in equation (6), but due to the first difficulty it is very difficult to find this limit.
(2) The problem is solved:
in order to solve the problems, the original optimization problem in the step (1) is converted, and the conveying system is divided into a stable state and a stable conversion state according to whether the goods production (input) is stable or not.
The definition of the steady state and the reach conditions are first given.
Defining: the conveyor system reaching a steady state means that the desired value of the total capacity on the conveyor belt is constant and the desired values of the amounts of goods input and output at any time are equal.
Theorem one: given that the amount of goods entering the conveyor follows a certain known profile and conveyor speed, the conveyor system reaches a steady state.
And (3) proving that:
assuming that the quantity of the goods input into the conveyor belt is subjected to the distribution with the mean value mu and the speed of the conveyor belt is v, the quantity of the goods input into the conveyor belt at any moment is X t ＝m t Distributed over the length of the belt head at a density of v x T
Meanwhile, the conveyer belt transports the goods in the length of the tail part vXT out with the carrying capacity of
Y t ＝ρ t-n v(T-s)+ρ t-n-1 vs (7)
Where n denotes that the cargo at that location was input n times T ago and s denotes that there is a case where the inputs and outputs are not perfectly aligned. If desired, the following results are obtained for (7):
let total load on the belt be M at time t t Then, there are:
M t ＝M t-1 +X t -Y t (9)
two sides get the expectation
E(M t )＝E(M t-1 )+E(X t )-E(Y t )＝E(M t-1 ) (10)
(3) The transmission system reaches a steady state:
according to theorem one, only the input conveyer belt is neededThe quantity of the goods is subjected to parameter estimation to judge whether the production is stable or not, and whether the conveying system reaches a steady state or not can be judged. Since the length of the conveyor belt is fixed, equation (9) means that the density of the carrier belt is expected to be constant when the conveyor system reaches a steady state, and in a double conveyor system, the densities of the conveyor belt 1 and the conveyor belt 2 are respectively ρ 1 And ρ 2 The expected values of the input and output quantities of the goods are equal, meaning that:
v 1 *T*ρ 1 ＝v 2 *T*ρ 2
v 1 *ρ 1 ＝v 2 *ρ 2 (11)
up to this point, the original optimization problem can be transformed:
s.t.v 1 *ρ 1 ＝v 2 *ρ 2
the solution of the problem can be completed by adopting a conventional optimization method to obtain the optimal speed.
(4) The transmission system is in steady state conversion:
when the production of goods is unstable, the distribution of the quantity of goods fed to the conveyor system will change, and the conveyor system will go through the process from steady state 1 to steady state 2, and the optimization problem in this process becomes:
s.t.m 2,i ,v 2,i ≥0
wherein n represents the completion of steady state conversion after n time periods T, i.e.
Compared with the original optimization problem, n is clearly defined and does not need to be limited, although the distribution of the carrying capacity is still difficult to express by using an exact function analytic expression, the distribution of the carrying capacity of the conveyor belt can be updated by a simulation method, and at the moment, the solution can be completed by some search algorithms. Therefore, when the transmission system is in a steady-state conversion process, the optimization process is as follows:
(1) determining the State parameters of Steady 1 and Steady 2, i.e. s 1 (v 11 ，ρ 11 ，v 12 ，ρ 12 ) And s n (v 21 ，ρ 21 ，v 22 ，ρ 21 )
(2) From s 1 (v 11 ，ρ 11 ，v 12 ，ρ 12 ) Conversion to s n (v 21 ，ρ 21 ，v 22 ，ρ 21 ) In the process of (2), a path with minimum work (shortest path) is solved
During steady state conversion, the state of each step depends on the conveyor belt speed and the sequence of the load distribution on the conveyor belt. In practice, the sequence of the distribution of the capacity will produce a considerable number of intermediate states, resulting in the solution time for the shortest path becoming unacceptable. Therefore, the speed of the conveying belt can also be directly set to be the value of the steady state 2, so that the successful conversion of the steady state can be ensured, and the speed of the conveying belt is directly set to be the value of a new steady state in subsequent numerical experiments.
3. And (4) security policy:
there are two major potential safety hazards in coal delivery systems. Firstly, the belt conveyor is pressed to be dead under the condition of overload operation when the coal amount carried on the whole belt conveyor is excessive; secondly, the instantaneous coal quantity is too large, the downstream belt conveyor bears a large amount of coal of the upstream belt conveyor in a short time, and the coal scattering is caused by insufficient space. When the internet of things capability is not supported, the first potential safety hazard is avoided by operating the belt conveyor at high power, but a large amount of energy is wasted; a second safety risk is to stabilize the input of the coal flow by providing a surge bin (as shown in fig. 6) at the belt conveyor junction, but due to the often limited capacity of the surge bin, coal spillage is still easily created in the case of large coal volumes. For this reason, the two potential safety hazards are caused by the fact that the real-time coal carrying condition cannot be known, and corresponding adjustment cannot be made.
With the support of the real-time production simulation system, the coal carrying situation at any position at any time can be known, as shown in fig. 7. The first row in fig. 7 represents the amount of coal volume scanned by the current puck, the second represents the total amount of coal on the downstream belt machine, the third represents the current optimum speed, and the second and third rows represent the coal metering at any location on the upstream and downstream belt machines.
In order to solve the first type of potential safety hazards, the algorithm pays attention to the total coal quantity on the belt conveyor in real time, and judges whether the motor has the first type of safety risks or not by combining indexes such as current, voltage and power of the motor, and the specific steps and strategies are as follows:
(1) monitoring safety indexes such as total coal quantity, current, voltage, power and the like in real time, and judging whether any one of the indexes exceeds a given safety threshold;
(2) if any index exceeds the safety threshold, the speed is increased immediately, and the high-speed operation lasts for 2 minutes;
(3) judging whether each safety index is reduced to 85% of the safety threshold value or not, if so, switching back to the optimal speed, and returning to the step (1); if not, continuing to operate at high speed;
in order to avoid frequent speed regulation of the belt conveyor, the design in (2) and (3) is that after the belt conveyor is accelerated, the belt conveyor needs to return to the low-speed state after a certain period of time.
In order to solve the second type of potential safety hazard, the algorithm focuses on the coal amount distribution of each position on the belt conveyor in real time, focuses on the coal carrying capacity to enter the downstream belt conveyor, and judges whether the second type of safety risk exists or not, and the specific steps and strategies are as follows:
(1) monitoring the coal volume amount of 200 meters at the tail part of the upstream belt conveyor in real time, and judging whether the coal volume amount exceeding a safety threshold exists at any position;
(2) if the coal volume amount exceeds the safety threshold value, the speed is increased immediately, and the high-speed operation lasts for 200/v1+10 seconds (v 1 is the operation speed of the upstream belt conveyor);
(3) judging whether the coal volume quantity exceeding a safety threshold value exists on the whole position of the upstream belt conveyor, and if so, continuing to operate at a high speed; if not, switching to the optimal speed, and returning to the step (1).
4. Numerical experiment and production experiment:
(1) numerical experiments:
i. designing a power fitting equation:
to better simulate a practical conveyor system, the design of the power equation P (M, v) needs to be in accordance with physical intuition: (1) other conditions are certain, the larger the carrying capacity is, the larger the power is; (2) other conditions the greater the speed, the greater the power. The power is supposed to be positively correlated with the carrying capacity and the carrying speed, and a nonlinear relation exists; since the actual contribution of both to power is not known, both are considered to contribute the same to power. The power equation thus written is shown in equation (14) and the image of this function is shown in figure 8.
P(M,v)＝100+10M+10v+5M 2 +5v 2 -Mv (14)
ii. System input design:
in real goods production, there are production busy periods and production idle periods, the mean and variance of the distribution of the produced goods amount may not be the same, and the duration of the busy periods and the idle periods, i.e. the duration of the same distribution, also needs to be considered. Therefore 5 sets of test data were designed, the characteristics of which are shown in table 1.
TABLE 1 data set characterization
iii, comparing and optimizing strategy design:
in order to prove the performance of the proposed steady-state adjustment strategy, four strategies are proposed for comparison, wherein the strategy 1 and the strategy 2 are commonly used speed regulation methods in actual industrial production, and the strategy 2 is a simple version of the strategy 4; strategy 3 is to verify the consequences caused by too small selection of n in the original optimization problem, and is a local optimal solution; strategy 4 is a proposed steady state adjustment strategy, and the specific strategy description is shown in table 2.
TABLE 2 description of the strategies
iv, numerical experiment results:
table 3 shows the results of the numerical experiments, and it can be seen that the proposed steady-state adjustment strategy realizes the best energy consumption saving, strategy 1 has the highest energy consumption, strategy 2 has a certain energy saving effect, and the energy consumption loss of strategy 3 is greater than that of strategy 4, because strategy 1 actually sets n in the original optimization problem to a smaller value, the optimization is not a global optimization.
Table 3 results of numerical experiments
(2) Production experiment:
i. power equation data acquisition and function fitting:
a first problem before the application of the pacing algorithm is to obtain a high quality power function, which enables a relatively good estimation of the power P in the expected range of V-V (velocity-volume) intervals. The required fitting data needs to be distributed in the expected range of the V-V plane, and as shown in the box of fig. 9, the sample data is preferably able to fill the area where the box is located. The block area is determined by the speed V and the coal volume V, and represents the range of the speed and the coal volume under the expected normal working state, and the speed regulation strategy should not be adopted when the range is exceeded, wherein the V is 1.2m/s-4.5m/s; v is 0-0.2.
After the high-quality training data is obtained, the power function P (M, v) can be learned by using a conventional machine learning method and a deep learning method, where a polynomial regression method is used, and MSE is used as a loss function to obtain the power function P (M, v), as shown in fig. 10, which is similar to a power function image in a numerical experiment.
ii. The production experiment inspection method comprises the following steps:
in numerical experiments, identical data was used as input under different strategies. However, this is difficult to do in production. The actual production is not reproducible and it is difficult to obtain exactly the same input for comparison. Even if the same production input can be reproduced by the real-time production simulation system, a power consumption method based on electric meter measurement is still required to be provided for verifying the effectiveness of the algorithm.
Perfect comparison experiments require comparing power consumption generated by different strategies in the same time, carrying capacity and production environment, and the power consumption per ton of coal or the power consumption in unit time is often used as the standard for measuring the energy consumption in actual production. The same capacity is guaranteed only by the ton coal power consumption, and no time factor is considered (the longer the running time, the larger the power consumption); the power consumption per unit time only guarantees the same time without considering the actual capacity (the larger the capacity, the larger the power consumption). The reason for this is due to production uncertainty. There is therefore a need for a method that allows similar production conditions before and after the optimization strategy is used.
Let a minimum time interval Δ T, T- Δ T-T time throughput be X T Production of X at T-T + Δ T time T+Δt When Δ t is small, both can be considered to follow the same distribution; as Δ t gradually increases, both tend to follow different distributions due to production uncertainties. As shown in fig. 11, taking data from 2/month 4 to 2/month 10 in 2021 as an example, autocorrelation coefficients of production amounts at two adjacent stages were calculated at different time periods, and it was found that the correlation of production amounts decreased as the time period increased. When the production cycle exceeded 30 minutes, the generally considered strong correlation (0.6) was lost, which is a visual evidence of production uncertainty. That is, the longer the two production states are separated, the greater the difference in the distribution of their production amounts. Therefore, the optimization strategy is switched once every half hour, and the unit coal production power consumption under the speed regulation strategy and the non-speed regulation strategy is compared for a long time.
iii, production experiment result:
the whole IoT process and algorithm are applied to a conveying system of a tower-drawn trench coal mine, and a front conveying belt runs at a high speed and a uniform speed in the whole process. The production experiment is used for testing the engineering, scientificity and effectiveness of the solution of the internet of things, and mainly has the following three results:
first, the reliability of the real-time production simulation system. Under the matching of a safety strategy, zero adhesive tape deadpressing and zero coal scattering are realized, and a foundation is provided for algorithm optimization.
Secondly, the scientificity of the production experiment inspection method. It can be seen from figure 12 that the throughput for the different pacing strategy intervals is about the same under the experimental approach.
And thirdly, the effectiveness of a speed regulation algorithm. Within a one week testing period, the algorithm achieves 10.85% energy savings, with local electricity charges estimated to generate 600 ten thousand dollars of profit per year for the individual mine.
Finally, as shown in fig. 13, the lowest speed of the production experiment is only set to be 75% of the highest speed, and under the condition of setting the lower speed, 6% -8% of power saving space is expected, and the profit is expected to reach 1000 ten thousand yuan.
The research and landing of the internet of things is a difficult task, and only 4.9% of previous researches support industrial application. As is said by microsoft's report of internet of things published in 2020, complexity, technical issues and internal resource allocation remain the first challenges for more applications of internet of things. In large-scale industrial production, a plurality of systems need to be coordinated simultaneously in a complex environment, which puts high requirements on the reliability and stability of the infrastructure construction of the internet of things. In the technical framework, the infrastructure of the internet of things comprises a sensor and a network at the bottom layer, an off-line and real-time data stream at the middle layer, a real-time production simulation system and a safety early warning system at an application layer. The reliable data pipeline of the internet of things helps to accurately sense the production environment, so that theoretical design and engineering realization, and the experimental environment and the production environment can be butted together, which is a remarkable characteristic different from the previous research and practice.
On the industrial site of the Internet of things, new methods are needed, and the existing complex environment and problems cannot be met due to the fact that the existing analysis and optimization methods based on abstract operation problems and physical engineering are too many in the past. A large transformation caused by the energization of the Internet of things is accumulation and precipitation of data, so that a large data analysis method and a data-driven modeling mode are necessarily introduced. Firstly, the problem of energy consumption waste caused by unstable production is discovered through data insight, then the relation between energy consumption and speed and carrying capacity is obtained through a design data obtaining experiment and a machine learning/deep learning mode, and finally a steady-unsteady modeling scheme and a speed regulation strategy are provided. The method breaks through the limitation of the traditional engineering modeling analysis mode, enables the data to directly participate in the solution, and gives full play to the value of the data. The method provides a brand new idea for the subsequent research and application of the Internet of things.
The above embodiments only express specific embodiments of the present invention, and the description is specific and detailed, but not to be understood as limiting the scope of the present invention. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention.
Claims (3)
1. A conveyor belt system optimization method based on the Internet of things is characterized by comprising the following steps:
step 1, arranging various sensors and controllers in a conveying system;
step 2, integrating sensor data through a PLC machine and synchronizing the sensor data to an offline database;
step 3, when a real-time production data stream is input, updating the goods condition on the whole production line through a real-time production simulation system so as to realize real-time monitoring;
in step 3, the step of updating the cargo condition on the whole production line by the real-time production simulation system specifically comprises the following steps:
a. let the conveying system be a double-conveyor system, and the speeds of the front and rear conveyor belts in the double-conveyor system at time T are respectively v 1 ，v 2 Dividing the conveyor belt into sections with the same total length/unit length, wherein each section is provided with a corresponding cargo volume;
b. setting a time step T, when the next time step T +1 arrives, the output interval length of the rear conveyor belt is int (v) 2 * T) while receiving the amount of goods from the front conveyor, assuming that the amount of goods received is at length int (v) 2 * T) are uniformly distributed;
c. the amount of the newly introduced product is known from the scale on the front conveyor, assuming it is distributed uniformly over a length int (v) 1 * T), while setting the interval length to int (v) 1 * The goods amount of T) is conveyed to a rear conveyor belt;
d. updating the distribution condition of the goods on the conveyor belt at the T +1 moment according to the distribution of the goods on the conveyor belt at the T moment and the speed of the conveyor belt;
step 4, optimizing the energy consumption of the whole transmission system by using the historical data deposited in the production process through an algorithm model, and outputting a speed regulation signal and a safety early warning according to a real-time production data stream and a safety strategy after the algorithm is deployed;
in step 4, optimizing the energy consumption of the whole transmission system by the algorithm model specifically comprises the following steps:
assuming that the power of the conveyor belt is related to the transport speed and the carrying capacity, a function P = f (m, v) of the power is obtained by means of data fitting, and the optimization target is an optimization problem of the sum of the work W of the conveyor belt within the time of n × T, and the mathematical form of the optimization problem is shown as the following formula:
s.t.m 2,i ,v 2,i ≥0
m 2,t ＝g(V t ,m 1,t-1 ,m 2,t-1 ,v 2,t ,v 1,t )
wherein v is 1 And v 2 Denotes the speed of the conveyor belt, m 1 And m 2 Indicating the volume distribution, i.e. the amount of load, V, of goods on the front and rear conveyors t Indicating volumetric flow meter reading due to V t Exogenous gene given m 1 And m 2 Indirectly adjusting the quantity of goods on the conveyor belt by adjusting the speed of the conveyor belt, depending on other variables, the true independent variable, i.e. the decision variable being v only 1 And v 2 ；
When the expected value of the total carrying capacity on the conveyor belt is constant and the expected values of the input and output cargo quantities at any time are equal, the conveyor system reaches a steady state, and the densities of the front conveyor belt and the rear conveyor belt are respectively rho 1 And ρ 2 Then the expected values of the input and output cargo amounts being equal means:
v 1 *T*ρ 1 ＝v 2 *T*ρ 2
v 1 *ρ 1 ＝v 2 *ρ 2
then the optimization problem is transformed:
s.t.v 1 *ρ 1 ＝v 2 *ρ 2
the solution of the problem can be completed by adopting a conventional optimization method to obtain the optimal speed;
when the production of goods is unstable, the distribution of the quantity of goods fed into the transfer system will change, at which point the transfer system will undergo a steady state transition from one of the steady states to the other, at which point the optimization problem becomes:
s.t.m 2,i ,v 2,i ≥0
where n represents the completion of steady state conversion over n time periods T, i.e.:
when the transmission system is in the steady-state conversion process, the optimization process is as follows:
determining a state parameter from one steady state to another, i.e. s 1 (v 11 ,ρ 11 ,v 12 ,ρ 12 ) And s n (v 21 ,ρ 21 ,v 22 ,ρ 21 )；
From s 1 (v 11 ,ρ 11 ,v 12 ,ρ 12 ) Conversion to s n (v 21 ,ρ 21 ,v 22 ,ρ 21 ) In the process of (2), a path with the minimum work, namely the shortest path, is solved; in the process of steady-state conversion, the state of each step depends on the speed of a transmission belt and the distribution sequence of the carrying capacity on the transmission belt, the distribution sequence of the carrying capacity generates a plurality of intermediate states, and the solving time of the shortest path becomes unacceptable, so that the speed of the transmission belt is directly set to be the value of another steady state, and the successful conversion of the steady state can be ensured;
in order to solve the problem that the conveyor belt is pressed to be dead under overload operation due to excessive load carried on the conveyor belt integrally, the safety strategy in the step 4 specifically comprises the following steps:
monitoring safety indexes of total cargo quantity, current, voltage and power in real time, and judging whether any one of the indexes exceeds a given safety threshold value;
step (2), if any index exceeds a safety threshold, immediately accelerating, and continuously operating at a high speed for a certain time;
step (3), judging whether each safety index is reduced to 85% of a safety threshold value or not, if so, switching back to the optimal speed, and returning to the step (1); if not, continuing to operate at high speed;
in order to avoid frequent speed regulation of the conveyor belt, in the step (2) and the step (3), after the conveyor belt is accelerated, the conveyor belt needs to return to a low-speed state after a certain period of time;
in order to solve the problem that the instant goods amount is too large, the rear conveyor belt receives a large amount of goods on the front conveyor belt in a short time, and the space is insufficient to cause the goods scattering, the safety strategy in the step 4 specifically comprises the following steps:
step i, monitoring the volume of the goods at a certain distance from the tail of the front conveyor belt in real time, and judging whether the volume of the goods exceeding a safety threshold exists at any position;
step ii, if the cargo volume exceeding the safety threshold exists, the speed is increased immediately, and the high-speed operation lasts for 200/v 1 +10 seconds, v 1 Is the front conveyor running speed;
step iii, judging whether the goods volume exceeding the safety threshold exists at the integral position of the front conveyor belt, and if so, continuing to run at a high speed; if not, switching to the optimal speed, and returning to the step i;
and 5, verifying the speed regulation effect through an algorithm crossing strategy.
2. The method for optimizing a conveyor belt system based on the internet of things as claimed in claim 1, wherein in step 1, the sensor and the controller specifically comprise: the system comprises a measuring ammeter, a cargo quantity measuring instrument, a speed controller, a motor internal sensor and a sick laser scanner.
3. The method for optimizing a conveyor belt system based on the internet of things as claimed in claim 2, wherein in the step 2, data of a sine laser scanner in the integrated sensor are specifically as follows:
the sick laser scanner is arranged at the head of the conveyor belt, emits hundreds of laser points to the conveyor belt, reflects after being touched with an object on the conveyor belt, and has distance information s when each laser point returns to the sick laser scanner i When the sick laser scanner is fixed, its distance l from the bottom of the conveyor belt and the scanning angle α are fixed, whereby the angle α of the scanning point to the vertical is deduced from the number of scanning points i The coordinate position (x) of each scanning point is obtained by the following formula i ,y i )：
x i ＝s i *sinα i
y i ＝l-s i *conα i
For filtering walls, passing workers,Noise data of sundries and abnormal laser points on the ground are set, a filtering area is set, the coordinate positions of all scanning points can be acquired at any time, the thought of multiple integral is used for reference, the cross section area at the time is acquired firstly, then the volume of goods passing through a sick laser scanner in delta t time is acquired, the delta t is the time interval of two times of scanning, and according to the thought of calculus, when the delta t and the delta x are sufficiently small, the volume V of the goods is acquired T The following:
V T ＝∫∫y i dxdt≈∑∑y i ΔxΔt＝∑∑y i (x i -x i-1 )T。
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| CN103612889A (en) * | 2013-11-11 | 2014-03-05 | 陕西合开电气有限公司 | Automatic speed regulating and controlling method of coal mine tape machine conveying system |
| CN105858132A (en) * | 2016-05-12 | 2016-08-17 | 天地（常州）自动化股份有限公司 | Coal flow conveying system with conveyor load distribution detection function and energy-saving control method of coal flow conveying system |
| CN113562429A (en) * | 2021-08-11 | 2021-10-29 | 中煤科工集团上海有限公司 | Belt conveyor speed regulation control system and method based on load distribution |
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| CN103612889A (en) * | 2013-11-11 | 2014-03-05 | 陕西合开电气有限公司 | Automatic speed regulating and controlling method of coal mine tape machine conveying system |
| CN105858132A (en) * | 2016-05-12 | 2016-08-17 | 天地（常州）自动化股份有限公司 | Coal flow conveying system with conveyor load distribution detection function and energy-saving control method of coal flow conveying system |
| CN113562429A (en) * | 2021-08-11 | 2021-10-29 | 中煤科工集团上海有限公司 | Belt conveyor speed regulation control system and method based on load distribution |
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