CN114348535B - Optimization Method of Conveyor Belt System Based on Internet of Things - Google Patents

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

Optimization Method of Conveyor Belt System Based on Internet of Things

technical field

The invention relates to the technical field of the Internet of Things, in particular to an optimization method for a conveyor belt system based on the Internet of Things.

Background technique

The Internet of Things (IoT) is the connection of physical objects equipped with sensors and actuators with the network through data communication technology, which can realize new interactions between enterprises, customers and intelligent things. As the number of IoT connections continues to grow, so does the world we can perceive. In recent years, the issue of how to make full use of the potential of IoT information flow in production and operation management has attracted the attention of academic circles. Geerts and O'Leary explore IoT-based innovations from a logistics and supply chain management perspective. Hashem et al. discussed the vision of big data analysis supporting smart cities and proposed a future business model of big data for smart cities. Qi and Shen further transition the smart city movement from a technology-oriented stage to a decision-oriented stage, and make planning and operational decisions on a city-wide scale, reflecting multidimensional needs, and adapting to massive data and innovation. Wunderlich et al. study the adoption and diffusion of IoT in the home, with a focus on the application of home smart metering technology to electricity consumption. Relying on Industry 4.0 technology, Anokhin et al. proposed mobile power recovery technology, using its mobility and space-time flexibility to solve the effective response and recovery during power distribution system disasters. Another important topic is sustainable manufacturing, including: increasing productivity, flexibility and resource efficiency; reducing waste, energy consumption and overproduction; improving the quality of the working environment and reducing routine work; services and stakeholder engagement/collaboration, etc. Kamble et al conducted a survey and analysis of 115 manufacturing companies in India and established an institutional equation model to discuss the impact of Industry 4.0 technologies on lean production practices and sustainable organizational performance. Andrew believes that smart manufacturing is a collection of cyber-physical system concepts such as Internet of Things, cloud computing, service-oriented computing, artificial intelligence and data science, and discusses the six pillars of smart manufacturing (manufacturing technology and process, materials, data, predictive engineering , Sustainability, Networking and Resource Sharing), and put forward ten conjectures to capture future trends.

Conveyor systems are part of industrial production operations, which are the basis for the internal flow of goods in production operations, and most of the research has focused on conveyor systems as functions for assembly, sorting, and delivery. For example, Boysen et al. surveyed the scientific literature on various conveyor belt-based fully automated sorting systems from an operations research perspective, describing a wide range of applications and their different sorting systems; Frey et al. The workload is optimized, the time-indexed mathematical programming formula for outbound baggage planning is given, and an innovative decomposition process combined with column generation scheme is proposed. Its solution reduces the maximum workload by more than 60% in real applications at major European airports; based on control theory, Mosadegh et al. developed an open-loop model and a closed-loop model to study the dynamic behavior of a hybrid model assembly line to optimize the overall workload and idle.

In the literature related to operation management, the research goals of the transmission system are usually to balance the load, reduce the idle time, reduce the transmission distance, etc., and there are few literatures that directly study the energy consumption of the transmission system. One of the important reasons is that it is difficult to explicitly describe the energy consumption. Consumption, as it involves some physical concepts rather than simply production variables. Existing studies on energy consumption of transmission systems are mostly based on physical engineering. For example, Hiltermann et al. analyzed the power consumption by calculating the motion resistance, deduced the friction coefficient and electrical drive power, and selected the appropriate belt speed for different cargo flows; He et al. examined the potential risks in the transient operation of the conveyor belt and For the dynamic performance of the conveyor, the "Initiation-Calculation-Optimization" three-step method is proposed as the speed regulation scheme. As shown in Figure 1, at present, coal excavation operations usually consist of an excavation system, a transmission system, and a transportation system. The excavated coal falls directly on the conveyor belt, and after being transported from the ground to the surface through the multi-stage conveyor belt, it is then transported by trucks and other transportation tools. In the absence of Internet of Things capabilities, the transmission system often runs at full speed for safety because it is impossible to obtain information such as production and conveyor belt status, which will lead to huge waste of energy. 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 operations is still blank.

Contents of the invention

In order to solve the problems existing in the prior art, the object of the present invention is to provide a conveyor belt system optimization method based on the Internet of Things. The present invention proposes a data-driven method to improve the energy consumption of the conveyor system by increasing the Internet of Things capability .

In order to achieve the above object, the technical solution adopted in the present invention is: a method for optimizing the conveyor belt system based on the Internet of Things, comprising the following steps:

Step 1. Lay various sensors and controllers in the transmission system;

Step 2. Integrate sensor data through PLC machine and synchronize to offline database;

Step 3. When the real-time production data flow is input, the goods on the entire production line are updated through the real-time production simulation system to achieve real-time monitoring;

Step 4. Use the historical data deposited in the production process to optimize the energy consumption of the entire transmission system through the algorithm model. After the algorithm is deployed, the speed regulation signal and safety warning will be output according to the real-time production data flow and safety strategy;

Step 5. Verify the speed regulation effect through the algorithmic crossover strategy.

As a further improvement of the present invention, in step 1, the sensors and controllers specifically include: measuring electric meters, cargo volume measuring instruments, speed controllers, internal sensors of motors and sick laser scanners.

As a further improvement of the present invention, in step 2, the data of the sick laser scanner in the integrated sensor is specifically as follows:

The sick laser scanner is installed at the head of the conveyor belt, and it emits hundreds of laser points onto the conveyor belt, which reflect after touching objects on the conveyor belt, and each laser point returns to the sick laser scanner with distance information s _i , when the sick laser scanner is fixed, the distance l from the bottom of the conveyor belt and the scanning angle α are fixed, so the angle α _i between the scanning point and the vertical direction can be deduced from the number of scanning points, and each scanning can be obtained by the following formula The coordinate position of the point (x _i , y _i ):

x _i =s _i *sinα _i

y _i =ls _i *conα _i

In order to filter the noise data of walls, passing workers, debris on the ground, and abnormal laser points, the filtering area is set. At any time, the coordinate positions of all scanning points can be obtained. Using the idea of multiple integration, first obtain this The cross-sectional area at one moment, and then obtain the volume of the cargo that passes the sick laser scanner within Δt. Δt is the time interval between two scans. According to the idea of calculus, when Δt and Δx are small enough, the volume V of the cargo is obtained. _T as follows:

V _T =∫∫y _i dxdt≈∑∑y _i ΔxΔt=∑∑y _i ( _xi −xi ₋₁ )T.

As a further improvement of the present invention, in step 3, the real-time production simulation system updates the goods on the entire production line specifically including the following steps:

a. Assuming that the conveying system is a double conveyor belt system, the speeds of the front and rear conveyor belts in the double conveyor belt system at time T are v ₁ and v ₂ respectively, and the conveyor belt is divided into total length/l identical intervals, each interval has a corresponding the volume of the cargo;

b. Set the time step T. When the next time step T+1 arrives, the rear conveyor belt will output the volume of goods whose interval length is int(v ₂ *T), and at the same time receive the volume of goods from the front conveyor belt. Assume that the volume of goods received is in the length Evenly distributed over int(v ₂ *T);

c. The amount of newly input goods can be known from the scale on the front conveyor belt, assuming that it is evenly distributed in the interval of length int(v ₁ *T), and at the same time, the amount of goods whose interval length is int(v ₁ *T) is conveyed to the rear conveyor belt;

d. Update the distribution of goods on the conveyor belt at time T+1 according to the distribution of goods on the belt at time T and the speed of the conveyor belt.

As a further improvement of the present invention, in step 4, the energy consumption of the entire transmission system is optimized through an algorithm model as follows:

Assuming that the power of the conveyor belt is related to the transportation speed and carrying capacity, the power function P=f(m,v) is obtained by data fitting, and the optimization goal is the optimization problem of the sum of the work done by the conveyor belt within n×T time. Its mathematical form is As shown in the following formula:

stm _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 )

Among them, v ₁ and v ₂ represent the speed of the conveyor belt, m ₁ and m ₂ represent the volume distribution of the goods on the front and rear conveyor belts, that is, the carrying capacity, and V _t represents the reading of the volume flowmeter. Since V _t is given exogenously, m ₁ and m ₂ depend on other variables, and indirectly adjust the quantity of goods on the conveyor belt by adjusting the speed of the conveyor belt. The real independent variables, that is, the decision variables are only v ₁ and v ₂ .

As a further improvement of the present invention, when the expected value of the total carrying capacity on the conveyor belt is constant, and the expected value of the input and output cargo volumes at any time is equal, the transmission system reaches a steady state at this time, and the densities of the front and rear conveyor belts are respectively ρ ₁ and ρ ₂ , Then the expected value of the input and output quantities of goods being equal means that:

v ₁ *T*ρ ₁ ＝v ₂ *T*ρ ₂

v ₁ *ρ ₁ =v ₂ *ρ ₂

Then transform the optimization problem:

stv ₁ *ρ ₁ = v ₂ *ρ ₂

The problem can be solved by conventional optimization methods, and the optimal speed can be obtained.

As a further improvement of the present invention, when the production of goods is unstable, the distribution of the quantity of goods input into the transmission system will change, and the transmission system will experience a steady state conversion process from one of the steady states to another steady state at this time. The optimization problem becomes:

stm _2,i ,v _2,i ≥0

Among them, n means that the steady-state conversion is completed after n time periods T, that is:

Then when the transmission system is in the steady-state conversion process, the optimization process is:

Calculate the state parameters from one steady state to another steady state, namely s ₁ (v ₁₁ ,ρ ₁₁ ,v ₁₂ ,ρ ₁₂ ) and s _n (v ₂₁ ,ρ ₂₁ ,v ₂₂ ,ρ ₂₁ );

During the transition from s ₁₁ (v ₁₁ , ρ ₁₁ , v ₁₂ , ρ ₁₂ ) to s _n (v ₂₁ , ρ ₂₁ , v ₂₂ , ρ ₂₁ ), find a path with the least work, that is, the shortest path; In the process of state transformation, the state of each step depends on the distribution sequence of the speed of the conveyor belt and the carrying capacity on the conveyor belt. The distribution sequence of the carrying capacity will produce quite a few intermediate states, resulting in the shortest path solution time becoming unacceptable. Therefore, setting the speed of the conveyor belt directly to another steady-state value can ensure the successful transformation of the steady state.

As a further improvement of the present invention, in order to solve the situation that the conveyor belt carries too much cargo as a whole and the conveyor belt is crushed to death under overload operation, the safety strategy in step 4 specifically includes the following steps:

Step ①. Real-time monitoring of the total cargo volume, current, voltage, and power safety indicators to determine whether any of the indicators exceed a given safety threshold;

Step ②, if any indicator exceeds the safety threshold, immediately increase the speed, and run at high speed for a certain period of time;

Step ③, judge whether each safety index has been reduced to 85% of the safety threshold, if so, switch back to the optimal speed, and return to step ①; if not, continue to run at high speed;

Among them, in order to avoid frequent speed adjustment of the conveyor belt, after the conveyor belt speeds up in steps ② and ③, it takes a certain period of time before returning to the low-speed state.

As a further improvement of the present invention, in order to solve the situation that the instantaneous cargo volume is too large, the rear conveyor belt has undertaken a large amount of cargo from the front conveyor belt in a short period of time, and the space is insufficient and the cargo is scattered, the security strategy in step 4 specifically includes the following steps:

Step i, real-time monitoring of the volume of goods at a certain distance from the tail of the front conveyor belt, and judging whether there is a volume of goods exceeding the safety threshold at any position;

Step ii. If there is a volume of goods exceeding the safety threshold, speed up immediately, and the high-speed operation lasts for 200/v ₁ +10 seconds, where v ₁ is the running speed of the front conveyor belt;

Step iii. Determine whether there is a volume of goods exceeding the safety threshold on the overall position of the front conveyor belt. If so, continue to run at high speed; otherwise, switch to the optimal speed and return to step i.

The beneficial effects of the present invention are:

The invention utilizes the ability of the Internet of Things to search for energy-saving opportunities in production and operation activities, and achieve profitable effects in actual production. Taking the transmission system in the industry as an example, a set of IoT technology framework and data flow including the bottom layer, middle layer and application layer is proposed, which shows a high degree of stability and reliability in actual production. At the same time, based on the empowerment of the Internet of Things, the optimization problem of the transmission system is analyzed and modeled in a data-driven manner, and a steady-state-unsteady-state speed regulation scheme is proposed. The results of numerical experiments and production experiments show that this method is superior to the existing speed control scheme and other methods, and realizes 10.85% energy saving and estimated profit of about 6 million yuan.

Description of drawings

Figure 1 is a schematic diagram of the overall production environment in coal mining operations;

Fig. 2 is a schematic diagram of the overall technical framework and data flow of the embodiment of the present invention;

Fig. 3 is the schematic diagram of sick laser scanner in the embodiment of the present invention;

Fig. 4 is a schematic diagram of coal flow obtained by a sick laser scanner in an embodiment of the present invention;

Fig. 5 is a schematic diagram of coal flow update in the real-time production simulation system in an embodiment of the present invention;

Fig. 6 is the schematic diagram of coal transmission system in the embodiment of the present invention;

7 is a schematic diagram of a real-time production simulation system in an embodiment of the present invention;

Fig. 8 is numerical experiment-power equation image in the embodiment of the present invention;

Fig. 9 is a power equation image in an embodiment of the present invention;

Fig. 10 is a production experiment-power function image in an embodiment of the present invention;

Fig. 11 is a schematic diagram of the time-series correlation of production volume in the embodiment of the present invention;

Fig. 12 is a schematic diagram of the results of the power saving experiment in the embodiment of the present invention;

FIG. 13 is a schematic diagram of power saving effect estimation in an embodiment of the present invention.

detailed description

Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

Example

As shown in Figure 2, a conveyor belt system optimization method based on the Internet of Things. In this embodiment, coal is used as a specific cargo for illustration. First, various sensors and controllers are placed in the conveyor system, including: various Measure electricity meters, coal measuring instruments, speed controllers, etc.; then, integrate sensor data through PLC machines and synchronize to offline databases; this embodiment develops a real-time production simulation system that can update the entire production line when inputting real-time production data streams The amount of coal on the grid can be monitored in real time; using the historical data deposited in the production process, an algorithm model is designed to optimize the energy consumption of the entire transmission system. After the algorithm is deployed, it will output speed regulation signals and Safety warning; Finally, a reliable algorithmic crossover strategy is designed to verify the speed regulation effect.

Below this embodiment is further described:

1. IoT empowerment and real-time production simulation system:

The core of the Internet of Things capability is the extraction, preprocessing and integration of sensor data. Sensors include various electric meters, internal sensors of motors, and sick laser scanners. Among them, the most important is the data processing of the sick laser scanner. As shown in Figure 3, the sick is installed at the head of the conveyor belt, and it emits hundreds of laser points onto the conveyor belt, which will reflect after touching objects on the conveyor belt, and each laser point will return 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 α are fixed, so that α _i can be deduced from the number of scanning points, and finally the coordinate position of each scanning point can be obtained from formulas (1) and (2). In order to filter noise data such as walls, passing workers, debris on the ground, and abnormal laser points, a filtering area is set.

x _i =s _i *sinα _i (1)

y _i =ls _i *cosα _i (2)

As shown in Figure 4, at any time, the coordinate positions of all scanning points can be obtained. Drawing on the idea of multiple integration, the cross-sectional area at this moment can be obtained first, and then the volume of coal passing through the sick within Δt can be obtained , Δt is the time interval between two scans. According to the idea of calculus, when Δt and Δx are small enough, the volume of coal flow can be obtained perfectly.

V _T ＝∫∫y _i dxdt≈∑∑y _i ΔxΔt＝∑∑y _i ( _xi -xi _-1 )T (3)

After obtaining the coal flow V _T at any time, a real-time production simulation system was developed. Take a typical double conveyor belt system as an example, input: the belt speed v1, v2 at time T, the volume distribution of coal on the belt, and the indication of the volume meter, output: the volume distribution of coal on the belt at time T+1 (The amount of coal input and the amount of coal output at time T～T+1 can be obtained accordingly). Figure 5 is an example of a production update that follows the update flow:

1. Set the minimum unit length l, divide the belt into the same interval of total length/l, and each interval has a corresponding coal volume.

2. Set the time step T. When the next time step T+1 arrives, the belt 2 outputs the coal volume whose interval length is int(v2*T), and at the same time accepts the coal volume from the belt 1. Assume that the accepted coal volume is within the length Uniform distribution over int(v2*T).

3. The amount of newly input coal can be known from the scale on conveyor belt 1, assuming that it is evenly distributed in the interval of length int(V1*T), and at the same time, the amount of coal whose interval length is int(v1*T) is conveyed to the belt 2.

4. Update the distribution of coal on the belt at time T+1 according to the distribution of coal on the belt at time T and the speed of the belt.

2. Data-driven energy consumption optimization modeling:

①Modeling problem:

In the industrial conveying system, there is a common phenomenon that the transportation speed does not match the carrying capacity, and this mismatch will lead to waste of energy consumption. Figure 5 is a typical double conveyor belt system. The front and rear conveyor belts rotate at speeds v1 and v2 to transport goods. There is a volumetric measuring instrument on the front conveyor belt to measure the volume of the passing goods, and specific data such as current and power are given for the readings of the electric meter. Since the production of goods is not completely stable, the amount of goods delivered to the conveyor belt is also not completely stable. If the conveyor belt moves at a uniform speed, there may be ineffective work. Discuss whether power consumption can be reduced by optimizing transport speed and load capacity.

Assuming that the power of the conveyor belt is related to the transport speed and carrying capacity, the power function P=f(m,v) can be obtained by means of data fitting. Usually, it is desired that the total work done by the conveyor belt be minimized over a period of time, not just the minimum power at a certain moment. Therefore, the optimization target should be the sum of work done by the conveyor belt within n×T time, and its mathematical form is shown in formula (4):

stm _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)

Among them, v ₁ and v ₂ represent the speed of the conveyor belt, m ₁ and m ₂ represent the volume distribution (carrying capacity) of the goods on the two conveyor belts, and V _t represents the reading of the volume flow meter. Since V _t is given exogenously and m ₁ and m ₂ depend on other variables (indirectly adjust the amount of goods on the conveyor belt by adjusting the conveyor belt speed), the real independent variables (decision variables) are only v ₁ and v ₂ .

There are two difficulties in directly solving this optimization problem.

First, it is difficult to express the distribution of the carrying capacity with an exact functional analytical formula. Although only the total carrying capacity/average carrying capacity is needed to calculate the power of the conveyor belt at any time, it is necessary to use the goods on the conveyor belt when dynamically updating the carrying capacity of the conveyor belt distribution sequence information. For example, a high load at the head of a conveyor will affect the update of the load differently than a high load at the end of the conveyor belt.

Second, the goal of optimization is the total of the conveyor belt in a period of time n×T. However, n at this time lacks a suitable definition. When n is small, it will deviate from the global optimal solution, because the total carrying capacity at this time The change is small, and the power can be quickly reduced by changing the transportation speed to reduce the work. However, this optimization does not take into account the impact on the total carrying capacity for a longer period of time in the future. For example, reducing the speed in a short period of time will reduce the total work during this period, but will increase the total carrying capacity under the accumulation of time. Low speed and high carrying capacity may not be an optimal result (later experiments will illustrate this point) . A reasonable way to define n is to make n → ∞, and transform the optimization goal into the average work in infinite time, as shown in formula (6). However, due to the existence of the first difficulty, it is difficult to find this limit.

② Problem Solving:

In order to solve the above problems, the original optimization problem of (1) is transformed, and whether the production (input) of goods is stable or not, the transmission system is divided into two states: steady state and steady state transformation.

Firstly, the definition and arrival condition of steady state are given.

Definition: The steady state of the conveyor system means that the expected value of the total carrying capacity on the conveyor belt is constant, and the expected value of the input and output quantities of goods at any time is equal.

Theorem 1: Given the quantity of goods input to the conveyor belt obeys a known distribution and the velocity of the conveyor belt, the conveyor system reaches a steady state.

prove:

同时，传送带将尾部v×T的长度内的货物运出，运载量为Assuming that the amount of goods input to the conveyor belt obeys the distribution with the mean value μ, and the speed of the conveyor belt is v, then the amount of goods input into the conveyor belt at any time is X _t = m _t , which is distributed within the length of the conveyor belt head v×T, and the density is

At the same time, the conveyor belt transports the goods within the length of v×T at the tail, and the carrying capacity is

Y _t =ρ _tn v(Ts)+ρ _tn-1 vs (7)

Among them, n indicates that the goods at this place were input before n Ts, and s indicates that there is a situation where the input and output are not fully aligned. Finding the expectation of (7) can get:

Assuming the total carrying capacity on the belt at time t is M _t , then:

M _t ＝M _t-1 +X _t -Y _t (9)

get both sides

E(M _t )=E(M _t-1 )+E(X _t )-E(Y _t )=E(M _t-1 ) (10)

③The transmission system reaches a steady state:

According to Theorem 1, it is only necessary to estimate the parameters of the quantity of goods input to the conveyor belt to determine whether the production is stable to determine whether the transmission system has reached a steady state. Since the length of the conveyor belt is fixed, formula (9) means that when the conveyor system reaches a steady state, the expectation of the loading density of the conveyor belt is also constant. In the double conveyor belt system, the densities of conveyor belt 1 and conveyor belt 2 are set to be ρ ₁ and ρ ₂ , then the equal expected value of the input and output quantities means:

v ₁ *T*ρ ₁ ＝v ₂ *T*ρ ₂

v ₁ *ρ ₁ =v ₂ *ρ ₂ (11)

So far, the original optimization problem can be transformed:

stv ₁ *ρ ₁ = v ₂ *ρ ₂

The problem can be solved by conventional optimization methods, and the optimal speed can be obtained.

④The transmission system is in steady state conversion:

When the production of goods is unstable, the distribution of the quantity of goods input into the transmission system will change. At this time, the transmission system will go through the process from steady state 1 to steady state 2. The optimization problem in this process becomes:

stm _2,i ,v _2,i ≥0

Among them, n means that the steady-state transformation is completed after n time periods T, that is,

Compared with the original optimization problem, n here has a clear definition, and there is no need to seek the limit. Although the distribution of the carrying capacity is still difficult to express analytically with an exact function, the update of the carrying capacity distribution of the conveyor belt can be realized through the simulation method. At this point, some search algorithms can be used to complete the solution. Therefore, when the transmission system is in the steady-state conversion process, the optimization process is:

① Calculate the state parameters of steady state 1 and steady state 2, namely s ₁ (v ₁₁ , ρ ₁₁ , v ₁₂ , ρ ₁₂ ) and s _n (v ₂₁ , ρ ₂₁ , v ₂₂ , ρ ₂₁ )

②In the process of converting from s ₁ (v ₁₁ , ρ ₁₁ , v ₁₂ , ρ ₁₂ ) to s _n (v ₂₁ , ρ ₂₁ , v ₂₂ , ρ ₂₁ ), find a path with the least work (shortest path)

In the process of steady-state conversion, the state of each step depends on the conveyor belt speed and the distribution sequence of the carrying capacity on the conveyor belt. In practice, the sequence of distributions of the loads will generate quite a few intermediate states, causing the shortest path solution time to become unacceptable. Therefore, the speed of the conveyor belt can also be directly set to the value of the steady state 2, which can ensure the successful transformation of the steady state. In the subsequent numerical experiments, the speed of the conveyor belt can be directly set to the value of the new steady state.

3. Security policy:

There are two main potential safety hazards in the coal conveying system. First, the overall amount of coal carried on the belt machine is too much, and the belt machine is crushed to death under overload operation; second, the instantaneous coal volume is too large, and the downstream belt machine has undertaken a large amount of coal from the upstream belt machine in a short period of time, and the space is insufficient lead to sprinkling of coal. When there is no support of the Internet of Things capability, the first potential safety hazard is avoided by running the tape machine at high power, but this will waste a lot of energy; As shown in Figure 6), the input of coal flow becomes stable, but because the capacity of the buffer bin is usually very limited, it is still easy to cause coal scattering in the case of a large amount of coal. The reason is that these two potential safety hazards are due to the inability to know the real-time coal loading situation, so corresponding adjustments cannot be made.

With the support of the real-time production simulation system, the coal loading situation at any time and any location can be known, as shown in Figure 7. The first picture in the first line of Figure 7 shows the current coal volume scanned by sick, the second picture shows the total coal volume on the downstream belt conveyor, the third picture shows the current optimal speed, the second line and the third The line diagram represents the coal metering at any position of the upstream belt conveyor and the downstream belt conveyor.

In order to solve the first type of safety hazard, the algorithm pays attention to the total amount of coal on the belt conveyor in real time, and judges whether the motor has the first type of safety risk based on the current, voltage, power and other indicators of the motor. The specific steps and strategies are as follows:

①Real-time monitoring of safety indicators such as total coal, current, voltage, power, etc., to determine whether any indicator exceeds a given safety threshold;

② If any indicator exceeds the safety threshold, immediately increase the speed and run at high speed for 2 minutes;

③Judge whether each safety index has been reduced to 85% of the safety threshold, if so, switch back to the optimal speed, and return to ①; if not, continue to run at high speed;

In order to avoid frequent speed adjustment of the tape machine, it is designed in ② and ③ that when the tape machine speeds up, it takes a certain period of time before returning to the low-speed state.

In order to solve the second type of safety hazard, the algorithm pays attention to the coal volume distribution at each position on the belt conveyor in real time, focusing on the coal load that is about to enter the downstream belt conveyor, and judges whether there is a second type of safety risk. The specific steps and strategies are as follows:

①Real-time monitoring of the coal volume at the tail of the upstream belt conveyor 200 meters to determine whether there is a coal volume exceeding the safety threshold at any position;

② If there is a coal volume exceeding the safety threshold, immediately increase the speed, and the high-speed operation lasts for 200/v1+10 seconds (v1 is the running speed of the upstream belt conveyor);

③Judge whether there is a coal volume exceeding the safety threshold at the overall position of the upstream belt conveyor, if so, continue to run at high speed; otherwise switch to the optimal speed, return to ①.

4. Numerical experiment and production experiment:

①Numerical experiment:

i. Power fitting equation design:

In order to better simulate the actual conveyor belt system, the design of the power equation P(M,v) needs to conform to physical intuition: ① other conditions are certain, the greater the carrying capacity, the greater the power; ② other conditions are certain, the greater the speed, the greater the power. It is assumed that the power is positively correlated with the carrying capacity and the carrying speed, and there is a nonlinear relationship; since the actual contribution of the two to the power is unknown, it is considered that the two contribute the same to the power. Then the written power equation is shown in formula (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 the actual production of goods, there are busy production periods and idle periods, and the mean and variance of the distribution of the quantity of goods produced may be different. In addition, the duration of the busy period and the idle period, that is, the duration of the same distribution, also needs to be considered. Therefore, five sets of test data sets are designed, and their characteristics are shown in Table 1.

Table 1 Dataset characteristics

iii. Comparative optimization strategy design:

In order to prove the performance of the proposed steady-state adjustment strategy, four strategies are proposed for comparison, among which strategy 1 and strategy 2 are commonly used speed regulation methods in actual industrial production, strategy 2 is essentially a simplified version of strategy 4; strategy 3 is In order to verify the consequences caused by the selection of too small n in the original optimization problem, it 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 Policy description

iv. Numerical experiment results:

Table 3 shows the results of numerical experiments. It can be seen that the proposed steady-state adjustment strategy achieves the best energy 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. , this is because strategy 1 actually sets n in the original optimization problem to a small value, resulting in an optimization that is not a global optimization.

Table 3 Numerical experiment results

②Production experiment:

i. Power equation data acquisition and function fitting:

Before applying the speed regulation algorithm, the first problem is to obtain a high-quality power function, which can better estimate the power P within the expected v-V (velocity-volume) interval. This requires the fitted data to be distributed within the expected range of the v-V plane, roughly as shown in the box in Figure 9, and the sample data should preferably be able to fill the area where the box is located. The box area is determined by the speed v and the coal volume V, which indicates the range of the expected speed and coal volume under normal working conditions. If it exceeds this range, it is considered that the speed regulation strategy should not be adopted. Here, v is taken as 1.2m/s- 4.5m/s; V takes 0-0.2.

After obtaining high-quality training data, you can use traditional machine learning methods and deep learning methods to learn the power function P(M,v). Here, the polynomial regression method is used, and MSE is used as the loss function to obtain the power function P(M,v). As shown in Fig. 10, it is similar to the image of the power function in the numerical experiment.

ii. Production experiment inspection method:

In numerical experiments, the exact same data is used as input under different strategies. However, this is difficult to do in production. Actual production is not repeatable and it is difficult to obtain exactly the same input for comparison. Even if the same production input can be reproduced by a real-time production simulation system, it is still necessary to propose a power consumption method based on metering to verify the effectiveness of the algorithm.

A perfect comparative experiment requires comparing the power consumption of different strategies under the same time, load, and production environment. In actual production, the power consumption per ton of coal or per unit time is often used as the standard for measuring energy consumption. The power consumption per ton of coal only guarantees the same carrying capacity without considering the time factor (the longer the running time, the greater the power consumption); the power consumption per unit time only ensures the same time without considering the actual carrying capacity (the greater the carrying capacity , the greater the power consumption). The reason is the uncertainty of production. Therefore, a method is needed to make the production conditions similar before and after adopting the optimization strategy.

Assuming a minimum time interval Δt, the production volume at T-Δt-T time is X _T , and the production volume at T-T+Δt time is X _T+Δt , when Δt is very small, it can be considered that the two obey the same distribution; when Δt When increasing gradually, the two tend to obey different distributions due to the uncertainty of production. As shown in Figure 11, taking the data from February 4th to February 10th, 2021 as an example, the autocorrelation coefficients of production volumes in two adjacent periods were calculated with different time periods, and it was found that with the increase of time periods, the Correlation is decreasing. When the production cycle exceeds 30 minutes, the strong correlation (0.6) in the general sense is lost, which is the intuitive evidence of production uncertainty. In other words, the longer the time between two production states, the greater the difference in the distribution of their production volume. Therefore, consider switching the optimization strategy every half an hour, and compare the power consumption per unit of coal production under the speed regulation and non-speed regulation strategies for a long time.

iii. Production experiment results:

The entire set of IoT processes and algorithms are applied to the transmission system of Talahao Coal Mine. Previously, the transmission belt ran at a high speed and uniform speed throughout the entire process. This production experiment is a test of the engineering, scientific and effectiveness of the IoT solution, with the following three main results:

First, the reliability of the real-time production simulation system. With the support of security policies, zero tape crushing and zero coal sprinkling have been realized, which provides a basis for algorithm optimization.

Second, the scientificity of the production experiment test method. It can be seen from Figure 12 that the production volumes of different speed regulation strategy intervals are roughly the same under the experimental method.

Third, the effectiveness of the speed regulation algorithm. During the one-week test period, the algorithm achieved a 10.85% energy saving, and it is estimated that a single mine will generate a profit of 6 million yuan per year based on local electricity costs.

Finally, it is worth mentioning that, as shown in Figure 13, this production experiment only set the minimum speed to 75% of the maximum speed. In the case of setting a lower speed, it is estimated that there is still 6%-8% power saving space , the profit is expected to reach 10 million yuan.

The research and implementation of the Internet of Things is a difficult task, and only 4.9% of the previous research was supported by industrial applications. As Microsoft said in its IoT report released in 2020, complexity, technical issues, and internal resource allocation are still the top challenges for more IoT applications. In large-scale industrial production, multiple systems need to be coordinated simultaneously in a complex environment, which places high demands on the reliability and stability of IoT infrastructure construction. In the technical framework of the Internet of Things, the infrastructure of the Internet of Things includes the underlying sensors and networks, the offline and real-time data streams in the middle layer, the real-time production simulation system and the security early warning system in the application layer. Reliable IoT data pipelines help accurately perceive the production environment, making it possible to connect theoretical design and engineering implementation, experimental environment and production environment, which is a distinctive feature that is different from previous research and practice.

In the industrial implementation of the Internet of Things, some new methodologies are needed. The previous analysis and optimization methods based on abstract operational research problems and physical engineering have too many restrictive conditions, which can no longer meet the existing complex environments and problems. A major change brought about by the empowerment of the Internet of Things is the accumulation and precipitation of data. Therefore, it is necessary to introduce big data analysis methods and data-driven modeling methods. Firstly, through data insights, the problem of energy consumption waste caused by unstable production was discovered, and then the relationship between energy consumption, speed, and carrying capacity was obtained by designing data acquisition experiments and using machine learning/deep learning, and finally proposed a steady-state-unsteady state The modeling scheme and speed regulation strategy. This idea breaks through the limitations of traditional engineering modeling and analysis methods, and allows data to directly participate in the solution, so that its value can be fully utilized. This provides a new idea for the subsequent research and application of the Internet of Things.

The above-mentioned embodiments only express the specific implementation manner of the present invention, and the description thereof is relatively specific and detailed, but should not be construed as limiting the patent scope of the present invention. It should be pointed out that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these all belong to the protection scope of the present 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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