CN116667524B - Intelligent internet of things path optimization safety inspection equipment and system - Google Patents

Intelligent internet of things path optimization safety inspection equipment and system Download PDF

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CN116667524B
CN116667524B CN202310429747.8A CN202310429747A CN116667524B CN 116667524 B CN116667524 B CN 116667524B CN 202310429747 A CN202310429747 A CN 202310429747A CN 116667524 B CN116667524 B CN 116667524B
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seagull
motor
target area
motor controller
module
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CN116667524A (en
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周孟雄
郭仁威
苏姣月
汤健康
纪捷
曾淼
王夫诚
秦泾鑫
黄佳惠
林张楠
马梦宇
温文潮
纪润东
张佳钰
孙娜
黄慧
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Huaiyin Institute of Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H04L67/125—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/02—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems using reflection of acoustic waves
    • G01S15/06—Systems determining the position data of a target
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/86—Combinations of sonar systems with lidar systems; Combinations of sonar systems with systems not using wave reflection
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
    • G08B25/002—Generating a prealarm to the central station
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power
    • H02J50/005—Mechanical details of housing or structure aiming to accommodate the power transfer means, e.g. mechanical integration of coils, antennas or transducers into emitting or receiving devices
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power
    • H02J50/80—Circuit arrangements or systems for wireless supply or distribution of electric power involving the exchange of data, concerning supply or distribution of electric power, between transmitting devices and receiving devices
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

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  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Power Engineering (AREA)
  • Emergency Management (AREA)
  • Business, Economics & Management (AREA)
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  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Automation & Control Theory (AREA)
  • Acoustics & Sound (AREA)
  • Computing Systems (AREA)
  • Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The invention discloses intelligent internet of things path optimization safety inspection equipment which comprises a wireless charging device, a zinc ion supercapacitor, a motor, an electricity meter, an Arduino UNO, a path planning module and a motor controller, wherein the wireless charging device is connected with the zinc ion supercapacitor; the wireless charging device charges in a contact mode, and the received electric energy charges the zinc ion super capacitor through the energy converter; when the electric quantity of the zinc ion super capacitor is lower than a preset value set by the chip, the chip sends an instruction to the motor controller, and the motor controller controls the motor to drive the equipment to a designated charging area point; the motor controller controls the motor to run, so as to control the equipment to move; the motor controller receives an operation control strategy instruction obtained by the chip from the path planning module, and controls the motor to operate according to the operation control strategy. The invention adopts a wireless charging mode to meet the complex charging requirements under different scenes, and the improved ISOA algorithm can comprehensively consider sudden situations such as roadblocks and the like in the running process to obtain the optimal path planning.

Description

Intelligent internet of things path optimization safety inspection equipment and system
Technical Field
The invention relates to the technical field of wireless charging and path planning, in particular to intelligent internet of things safety inspection equipment and system.
Background
In recent years, the infrastructure construction of China is steadily developing, the automation degree is high, and unattended operation is gradually a new way of safety inspection. The traditional inspection has the problems of high labor intensity, low inspection efficiency, environmental restriction and the like, cannot meet the current new requirement of safe inspection, and the intelligent safe inspection equipment is introduced, so that the safety and the efficiency are met, and meanwhile, the problems and the defects of the traditional manual inspection are overcome.
The path planning problem is one of the kernels of the safety inspection equipment, and the purpose of the path planning is to select a collision-free path with the shortest process and high efficiency for the equipment under the specified constraint condition, and the most main path planning algorithm at present mostly adopts an ant colony algorithm, a bee colony algorithm and a genetic algorithm to solve the path planning problem, but has the problems of low convergence precision and easiness in falling into local optimum;
On the other hand, the current inspection equipment generally adopts contact type to charge, plug charges, track type charges, and this can lead to when the power supply is not enough, inspection equipment can not carry out the work of safety inspection, can cause a series of problems easily, and naked electrode can cause oxidation and fouling easily in addition, leads to the trouble of charging, can produce the electric spark when heavy current charges, produces electric leakage, and the charging process is unreliable.
Therefore, an intelligent internet of things safety inspection device and system are needed, which not only can optimize the path planning problem, but also can solve a series of safety problems encountered by charging, and ensure efficient and full operation of the inspection device.
Disclosure of Invention
The invention aims to: aiming at the problems set forth in the background art, the invention provides intelligent internet of things safety inspection equipment and a system, provides a charging mode suitable for the inspection equipment, can also establish a target area static grid matrix according to target area requirements, an ultrasonic position sensor detects the position of an obstacle to establish a target area dynamic grid matrix, establishes the target area static grid matrix, optimizes by using an ISOA algorithm through a path planning module to obtain an optimal path strategy, and sends a control instruction to a motor controller to control the motor to operate.
The technical proposal is as follows:
the utility model provides a safe equipment of patrolling and examining of intelligent thing networking route optimization, includes wireless charging device, zinc ion supercapacitor, motor, fuel gauge, arduino UNO, ultrasonic wave position sensor, route planning module, motor controller, APP terminal;
The wireless charging device comprises a power supply, a transmitting end, a receiving end, an energy converter and an APP terminal; the transmitting end is connected with a power supply and is arranged in a designated area of a target position, the power supply sends signals to a transmitting coil of the transmitting end through a circuit, the transmitting coil sends electric energy to a receiving coil of the receiving end, the receiving coil is arranged at the bottom of the wireless charging device and is charged in a contact mode, and the received electric energy charges a zinc ion super capacitor through an energy converter;
The zinc ion supercapacitor is particularly high in charging and discharging speed, and the wireless charging template is low in charging speed, so that the wireless charging device can be well matched with the zinc ion supercapacitor for use.
The electric quantity meter detects the electric quantity of the zinc ion super capacitor in real time, the Arduino UNO chip receives the electric quantity of the zinc ion super capacitor in real time, and when the electric quantity of the zinc ion super capacitor is lower than a preset value set by the chip, the Arduino UNO chip sends an instruction to the motor controller, and the motor controller controls the motor to drive the equipment to a specified charging area point;
the motor controller controls the motor to run, so as to control the equipment to move; and the motor controller receives an operation control strategy instruction obtained by the Arduino UNO chip from the path planning module, and controls the motor operation according to the operation control strategy.
The ultrasonic position sensor is used for detecting the position between the equipment and the obstacle, is arranged at the left side and the right side of the equipment, and feeds back the position information to the path planning module in real time.
Further, the device further comprises an infrared sensing camera, the infrared sensing camera can detect whether a person passes through, when the person passes through, the infrared sensing camera collects images and sends out signals, the Arduino UNO chip receives the signals of the infrared sensing camera and receives the real-time position of the ultrasonic position sensor, and the position signals and the images collected by the infrared sensing camera are sent to the APP terminal.
Further, the device further comprises a cooperative alarm module, and the cooperative alarm module sends an alarm signal after receiving a signal sent by the Arduino UNO chip and passed by a person.
Further, a static grid matrix of the target area is established according to the requirement of the target area, and the ultrasonic position sensor detects the position of the obstacle to establish a dynamic grid matrix of the target area; and the target area global grid matrix is established, the optimal path strategy is obtained by optimizing the target area global grid matrix through the path planning module by utilizing an ISOA algorithm, and a control instruction is sent to the Arduino UNO to control the motor to run.
Further, the path planning module optimizes by using an ISOA algorithm, specifically:
step one: initializing parameters;
step two: establishing a static grid matrix of a target area;
step three: acquiring ultrasonic position sensor data;
Step four: establishing a dynamic grid matrix of the target area according to the data of the ultrasonic position sensor, so as to obtain a global grid matrix of the target area;
step five: calculating the path from the starting point to the end point of each seagull, wherein the expression is as follows:
(x 0,y0)、(xg,yg) coordinates representing the start point and the end point, respectively
Step six: global search, expressed by the following formula:
The updated new position of the seagull; /(I) Is a new position which does not collide with other seagulls; /(I)The direction of the optimal position of the seagull;
in order to avoid collision in the global searching process, a variable A is added to calculate the new position of the seagull, and the formula of the individual collision avoiding process of the seagull is as follows:
Representing the current position of the seagull; a is a linear perturbation whose value decreases from 2 to 0, since the value of linear a is not fully applicable to the nonlinear search process of SOA; by carrying out nonlinear processing on A, the A is improved, and the improvement formula is as follows:
Wherein, an initial value A final of A start represents that the termination value T max is the maximum iteration number delta is a convergence adjustment factor, betarand is a random generator of matlab, and can conform to beta distribution;
step seven: local search, utilize sine and cosine factor to improve the iterative mode of ISOA, improve the iterative mode as follows:
Wherein P bs represents the optimal individual position of the seagull, P s t+1 represents the final attack position of the seagull, r 1 represents the balance development coefficient, The method can improve the global and local searching capability of the algorithm, balance the development and exploration capability of the algorithm, r 2 is a distance coefficient, r 2 epsilon [0,2 pi ] defines the distance between the current position of the seagull and the worse position of the seagull, r 3 is a sine and cosine conversion coefficient, r 3 epsilon [0,1], and when r 3 is more than 0.5, the method is switched to a sine operator, and when r 3 is less than 0.5, the method is switched to a cosine operator;
Step eight: judging whether an obstacle exists in front, if so, returning to the step four, and if not, turning to the step nine;
Step nine: updating the optimal position;
Step ten: judging whether the target end point is reached, if so, turning to a step eleven, and if not, returning to a step six;
Step eleven: outputting the optimal path.
The intelligent internet of things path optimization safety inspection system comprises a wireless charging module, a zinc ion supercapacitor module, an Arduino UNO module, an ultrasonic position sensor module, a path planning module and a motor controller module;
The wireless charging module comprises a power supply, a transmitting end, a receiving end, an energy converter and an APP terminal; the transmitting end is connected with a power supply and is arranged in a designated area of a target position, the power supply sends signals to a transmitting coil of the transmitting end through a circuit, the transmitting coil sends electric energy to a receiving coil of the receiving end, the receiving coil is arranged at the bottom of the wireless charging module and is charged in a contact mode, and the received electric energy charges a zinc ion super capacitor through an energy converter;
The electric quantity meter detects the electric quantity of the zinc ion super capacitor in real time, the Arduino UNO chip receives the electric quantity of the zinc ion super capacitor in real time, and when the electric quantity of the zinc ion super capacitor is lower than a preset value set by the chip, the Arduino UNO chip sends an instruction to the motor controller, and the motor controller controls the motor to drive the equipment to a specified charging area point;
The motor controller module controls the motor to run so as to control the equipment to move; and the motor controller receives an operation control strategy instruction obtained by the Arduino UNO chip from the path planning module, and controls the motor operation according to the operation control strategy.
Further, the system further comprises an infrared sensing camera, the infrared sensing camera can detect whether a person passes through, when the person passes through, the infrared sensing camera collects images and sends out signals, the Arduino UNO chip receives the signals of the infrared sensing camera and receives the real-time position of the ultrasonic position sensor, and the position signals and the images collected by the infrared sensing camera are sent to the APP terminal.
Further, the system also comprises a cooperative alarm module, and the cooperative alarm module sends an alarm signal after receiving a signal sent by the Arduino UNO chip and passed by a person.
Further, a static grid matrix of the target area is established according to the requirement of the target area, and the ultrasonic position sensor detects the position of the obstacle to establish a dynamic grid matrix of the target area; and the target area global grid matrix is established, the optimal path strategy is obtained by optimizing the target area global grid matrix through the path planning module by utilizing an ISOA algorithm, and a control instruction is sent to the Arduino UNO module to control the motor to run.
Further, the path planning module optimizes by using an ISOA algorithm, specifically:
step one: initializing parameters;
step two: establishing a static grid matrix of a target area;
step three: acquiring ultrasonic position sensor data;
Step four: establishing a dynamic grid matrix of the target area according to the data of the ultrasonic position sensor, so as to obtain a global grid matrix of the target area;
step five: calculating the path from the starting point to the end point of each seagull, wherein the expression is as follows:
(x 0,y0)、(xg,yg) coordinates representing the start point and the end point, respectively
Step six: global search, expressed by the following formula:
The updated new position of the seagull; /(I) Is a new position which does not collide with other seagulls; /(I)The direction of the optimal position of the seagull;
in order to avoid collision in the global searching process, a variable A is added to calculate the new position of the seagull, and the formula of the individual collision avoiding process of the seagull is as follows:
Representing the current position of the seagull; a is a linear perturbation whose value decreases from 2 to 0, since the value of linear a is not fully applicable to the nonlinear search process of SOA; by carrying out nonlinear processing on A, the A is improved, and the improvement formula is as follows:
Wherein, an initial value A final of A start represents that the termination value T max is the maximum iteration number delta is a convergence adjustment factor, betarand is a random generator of matlab, and can conform to beta distribution;
step seven: local search, utilize sine and cosine factor to improve the iterative mode of ISOA, improve the iterative mode as follows:
Wherein P bs represents the optimal individual position of the seagull, P s t+1 represents the final attack position of the seagull, r 1 represents the balance development coefficient, The method can improve the global and local searching capability of the algorithm, balance the development and exploration capability of the algorithm, r 2 is a distance coefficient, r 2 epsilon [0,2 pi ] defines the distance between the current position of the seagull and the worse position of the seagull, r 3 is a sine and cosine conversion coefficient, r 3 epsilon [0,1], and when r 3 is more than 0.5, the method is switched to a sine operator, and when r 3 is less than 0.5, the method is switched to a cosine operator;
Step eight: judging whether the front surface has an obstacle, if yes, returning to the step four, and if not, turning to the step nine
Step nine: updating optimal position
Step ten: judging whether the target end point is reached, if yes, turning to step eleven, if not, returning to step six
Step eleven: outputting the optimal path.
The beneficial effects are that: compared with the prior art, the invention has the following beneficial effects:
1. According to the operation characteristic of the safety inspection equipment, the charging mode which is adaptive to the safety inspection equipment is provided, the charging unit is installed inside the safety inspection equipment without exposing an electrode, physical contact and electric shock and electric leakage are not needed, the occupied area is reduced, the working efficiency is improved, and the complex charging requirements under different scenes are met.
2. The ISOA algorithm is not applied in the intelligent inspection field, and meanwhile, the method combines the static grid matrix of the target area and the dynamic grid matrix of the target area, so that a global grid matrix of the target area is formed, and the ISOA algorithm is combined with the ISOA algorithm, so that emergency situations such as roadblocks in the running process can be comprehensively considered, optimal path planning is obtained, and the accuracy of the path planning is improved.
3. The intelligent patrol system has the advantages that the collaborative alarm module is effectively coupled, the infrared camera is utilized to monitor abnormal conditions at any time in the operation process, and when the abnormal conditions are found, the intelligent patrol system can give an alarm in real time and send images and positions to the APP terminal, so that the safety of patrol areas is guaranteed.
4. The algorithm is improved:
(1) The sine and cosine operators are introduced to perfectly fit with the optimizing mechanism of the seagull algorithm, so that the global searching and local development capabilities of the algorithm are further balanced. A part of seagulls are far away from the optimal solution in a new iteration mode, so that the search space is enlarged, the diversity of seagull population is increased, and blind spots existing in the original optimizing mechanism are avoided. And the other part of seagulls approach the optimal solution at a faster speed, so that a better optimizing effect is achieved with fewer iterations, and the convergence speed of the algorithm is improved. The sine and cosine operators are integrated, so that the diversity of seagull populations is enriched, the convergence speed and precision of the algorithm are improved, and the performance of the algorithm is greatly improved.
(2) The parameters introducing linear convergence can be suitable for the nonlinear search process of the SOA, and the random integer pairs conforming to beta distribution are introduced to perform local disturbance, so that the accuracy of the algorithm is improved.
Drawings
FIG. 1 is a schematic view of the structure of the present invention
FIG. 2 shows a target area actual structure Kuang Tu not according to the present invention
FIG. 3 is a schematic diagram of a grid construction according to the present invention
FIG. 4 is a flow chart of the ISOA algorithm of the invention
FIG. 5 is a graph of the convergence curve before and after algorithm improvement
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings.
As shown in FIG. 1, the intelligent Internet of things path-optimized safety inspection device comprises a wireless charging device, a zinc ion supercapacitor, a motor, an electricity meter, an Arduino UNO, an ultrasonic position sensor, a path planning module, a motor controller and an APP terminal;
The wireless charging device comprises a power supply, a transmitting end, a receiving end, an energy converter and an APP terminal; the transmitting end is connected with a power supply and is arranged in a designated area of a target position, the wireless charging device is arranged at the starting point and the finishing point of the designated area, the power supply sends signals to a transmitting coil of the transmitting end through a circuit, the transmitting coil sends electric energy to a receiving coil of the receiving end, the receiving coil is arranged at the bottom of the wireless charging device, and the received electric energy charges a zinc ion super capacitor through an energy converter;
The current equipment of patrolling and examining generally adopts contact charging, plug charging, rail mounted charging, and this can lead to when the power supply is not enough, the equipment of patrolling and examining can't carry out the work of safety inspection, and this device provides wireless charging device, just can charge when being close to the region of charging. In addition, the traditional plug-in charging can easily cause a series of problems, and the exposed electrode can easily cause oxidation and fouling
The electric quantity meter detects the electric quantity of the zinc ion super capacitor in real time, the Arduino UNO chip receives the electric quantity of the zinc ion super capacitor in real time, and when the electric quantity of the zinc ion super capacitor is lower than a preset value set by the chip, the Arduino UNO chip sends an instruction to the motor controller, and the motor controller controls the motor to drive the equipment to a specified charging area point;
the motor controller controls the motor to run, so as to control the equipment to move; and the motor controller receives an operation control strategy instruction obtained by the Arduino UNO chip from the path planning module, and controls the motor operation according to the operation control strategy.
Further, the device further comprises an infrared sensing camera, the infrared sensing camera can detect whether a person passes through, when the person passes through, the infrared sensing camera collects images and sends out signals, the Arduino UNO chip receives the signals of the infrared sensing camera and receives the real-time position of the ultrasonic position sensor, and the position signals and the images collected by the infrared sensing camera are sent to the APP terminal.
Further, the device further comprises a cooperative alarm module, and the cooperative alarm module sends an alarm signal after receiving a signal sent by the Arduino UNO chip and passed by a person.
Further, a static grid matrix of the target area is established according to the requirement of the target area, and the ultrasonic position sensor detects the position of the obstacle to establish a dynamic grid matrix of the target area; and the target area global grid matrix is established, the optimal path strategy is obtained by optimizing the target area global grid matrix through the path planning module by utilizing an ISOA algorithm, and a control instruction is sent to the Arduino UNO to control the motor to run.
Further, the path planning module optimizes by using an ISOA algorithm, specifically:
step one: initializing parameters;
step two: establishing a static grid matrix of a target area;
step three: acquiring ultrasonic position sensor data;
Step four: establishing a dynamic grid matrix of the target area according to the data of the ultrasonic position sensor, so as to obtain a global grid matrix of the target area;
step five: calculating the path from the starting point to the end point of each seagull, wherein the expression is as follows:
(x 0,y0)、(xg,yg) coordinates representing the start point and the end point, respectively
Step six: global search, expressed by the following formula:
The updated new position of the seagull; /(I) Is a new position which does not collide with other seagulls; /(I)The direction of the optimal position of the seagull;
in order to avoid collision in the global searching process, a variable A is added to calculate the new position of the seagull, and the formula of the individual collision avoiding process of the seagull is as follows:
Representing the current position of the seagull; a is a linear perturbation whose value decreases from 2 to 0, since the value of linear a is not fully applicable to the nonlinear search process of SOA; by carrying out nonlinear processing on A, the A is improved, and the improvement formula is as follows:
Wherein, an initial value A final of A start represents that the termination value T max is the maximum iteration number delta is a convergence adjustment factor, betarand is a random generator of matlab, and can conform to beta distribution;
step seven: local search, utilize sine and cosine factor to improve the iterative mode of ISOA, improve the iterative mode as follows:
Wherein P bs represents the optimal individual position of the seagull, P s t+1 represents the final attack position of the seagull, r 1 represents the balance development coefficient, The method can improve the global and local searching capability of the algorithm, balance the development and exploration capability of the algorithm, r 2 is a distance coefficient, r 2 epsilon [0,2 pi ] defines the distance between the current position of the seagull and the worse position of the seagull, r 3 is a sine and cosine conversion coefficient, r 3 epsilon [0,1], and when r 3 is more than 0.5, the method is switched to a sine operator, and when r 3 is less than 0.5, the method is switched to a cosine operator;
Step eight: judging whether an obstacle exists in front, if so, returning to the step four, and if not, turning to the step nine;
Step nine: updating the optimal position;
Step ten: judging whether the target end point is reached, if so, turning to a step eleven, and if not, returning to a step six;
Step eleven: outputting the optimal path.
Referring to fig. 1, the invention discloses intelligent internet of things safety inspection equipment and a system, which are characterized by comprising a wireless charging device, an electricity meter, an Arduino UNO, an ultrasonic position sensor, a path planning module, a cooperative alarm module, an infrared induction camera, a motor controller, a motor and a zinc ion super capacitor;
The wireless charging device also comprises a power supply, a transmitting coil, a receiving coil, an energy converter and an APP terminal; the wireless charging device is matched with a zinc ion super capacitor for use; the system is arranged at a starting point and a finishing point, the power supply sends signals to a transmitting coil through a circuit, the transmitting coil sends electric energy, a receiving coil is arranged at the bottom of the equipment, and the received electric energy charges a zinc ion super capacitor through an energy converter;
The electric quantity meter detects the electric quantity of the zinc ion super capacitor in real time, the Arduino UNO chip receives the electric quantity of the zinc ion super capacitor in real time, and when the electric quantity of the zinc ion super capacitor is lower than a preset value set by the chip, the Arduino UNO chip sends an instruction to the motor controller, and the motor controller controls the motor to reach a specified charging area point;
the motor controller is used for controlling the operation of the motor so as to control the operation of the trolley, and receives an operation control strategy instruction of the Arduino UNO chip from the path planning module, and controls the operation of the motor according to the operation control strategy;
The infrared sensing camera is used for detecting whether someone passes through, when people pass through, the infrared sensing camera collects images and acts, the Arduino UNO chip receives signals of the infrared sensing camera, the Arduino UNO chip enables the cooperative alarm module to act to send alarm signals, the infrared sensing camera receives real-time positions of the ultrasonic position sensors, and the position signals and the images collected by the infrared sensing camera are sent to the APP terminal.
The ultrasonic sensor is used for detecting the position of the equipment between the obstacles, is arranged at the left side and the right side of the equipment, and feeds back the position information to the path planning module in real time;
Referring to fig. 2 and 3, the intelligent internet of things safety inspection device and system can establish a target area static grid matrix according to target area requirements, an ultrasonic position sensor detects an obstacle position to establish a target area dynamic grid matrix, establish the target area static grid matrix, optimize by using an ISOA algorithm through a path planning module to obtain an optimal path strategy, and send a control instruction to an Arduino UNO to control the motor to run;
Referring to fig. 4, an intelligent internet of things safety inspection device and system, a path planning module thereof optimizes by using an isua algorithm, and the optimization steps are as follows:
Step one: initializing parameters: the method comprises the steps of searching a dimension D, a spiral coefficient u and a control coefficient f c, setting an initial f c = 2,u =1 and v=1, randomly generating a certain number of codes with a certain length as an initial population N, and representing the path from a starting point to an end point of equipment. The device path planning of the ISOA algorithm is to perform optimization operation on the paths, then generate an optimal path group, output an optimal path,
Step two: establishing a static grid matrix for a target area
Assuming that the target area is a regular field with a length L and a width W, dividing the environment into grids with m, n lengths and a width L and W, and marking the whole environment as Q, wherein the expression is as follows:
Q＝∑Cxy|(x∈[1,m],y∈[1,n])|
c xy is the grid information of each state, the value of 0 indicates that no barrier can pass freely, and the value of 1 indicates that barrier can not pass
Step three: acquiring ultrasonic position sensor data
Step four: according to the ultrasonic position sensor data, a dynamic grid matrix of the target area is established, so that a global grid matrix of the target area is obtained
Step five: calculating the path from the starting point to the end point of each seagull, wherein the expression is as follows:
(x 0,y0)、(xg,yg) coordinates representing the start point and the end point, respectively
Step six: migration: global search, expressed by the following formula
The distance between the current seagull and the seagull with the minimum adaptation value; /(I)The position of the search agent is not conflicted with other search agents; /(I)Indicating the direction of the current individual seagull flying to the position of the optimal individual seagull.
In order to prevent collision with other seagulls of the population, a variable A is added to calculate a new search agent position, and the formula of the seagull collision avoidance process is as follows:
representing the current location of the search agent; a represents a movement parameter of a search agent in the process of searching in a target space, and is expressed as follows:
A＝fc-(x×(fc/MAXiteration))
Where f c represents the frequency at which the control variable A linearly decreases from f c to 0, typically set to 2; x represents the current iteration; MAX iteration represents the maximum number of iterations
When seagull is avoiding collision between search agents, the search agents move to the optimal neighbor direction
The optimal individual seagull position is represented, B is a random number, and the balance of global optimization and local optimization of the enhancement algorithm is represented as follows:
B＝2×A2×rd
rd is a random number with a value range of 0,1
Step seven: attack: the behavior of a local search, seagull attack prey, can be expressed in terms of a helical motion in the xyz plane, specifically described as:
r＝uekv
x＝rcosk
y＝rsink
z＝rk
wherein r is the motion radius of spiral flight, k is a random number (i.e. attack angle) uniformly distributed in the interval of [0,2 pi ], u and v are constants defining the spiral shape and are 1, so that the final attack position of the seagull can be obtained
Step eight: judging whether the front surface has an obstacle, if yes, returning to the step four, and if not, turning to the step nine
Step nine: updating the optimal position of seagull
Step ten: judging whether the target end point is reached, if yes, turning to step eleven, if not, returning to step six
Step eleven: outputting the optimal path
Further, an intelligent internet of things safety inspection device and system, the proposed improved seagull algorithm, i.e. ISOA algorithm improvement part 1, is characterized in that: introducing a nonlinear decrementing strategy improvement parameter A to locally disturb the parameter A in the step four, wherein the improvement formula is as follows:
Wherein, A start represents the initial value A final of A, the termination value T max is the maximum iteration number delta is the convergence adjustment factor, 0.1 is taken, and betarand is the random generator of matlab, which can conform to the beta distribution
Further, the proposed improving seagull algorithm, i.e. the ISOA algorithm improving part 2, is characterized in that: and step four, introducing sine and cosine factors to change an iterative mode of ISOA, wherein the improved iterative mode is as follows:
Wherein the method comprises the steps of The method can improve the global and local searching capability of the algorithm, balance the development and exploration capability of the algorithm, and define the current solution or the distance of long dissociation by r 2 E [0,2 pi ], and switch the sine operator and the cosine operator with the same probability by r 3 E [0,1]
The proposed improved seagull algorithm, i.e. ISOA algorithm, has the following improved principle and advantages:
Firstly, a sine and cosine operator is introduced to perfectly fit with the optimizing mechanism of the seagull algorithm, so that the global searching and local development capability of the algorithm is further balanced. A part of seagulls are far away from the optimal solution in a new iteration mode, so that the search space is enlarged, the diversity of seagull population is increased, and blind spots existing in the original optimizing mechanism are avoided. And the other part of seagulls approach the optimal solution at a faster speed, so that a better optimizing effect is achieved with fewer iterations, and the convergence speed of the algorithm is improved. The sine and cosine operators are integrated, so that the diversity of seagull populations is enriched, the convergence speed and precision of the algorithm are improved, and the performance of the algorithm is greatly improved.
And secondly, the parameters introducing linear convergence can be suitable for the nonlinear search process of the SOA, and the random adjustment number pairs conforming to beta distribution are introduced to perform local disturbance, so that the accuracy of the algorithm is improved.
Referring to fig. 5, in order to compare the convergence curves before and after improvement, it can be known by comparison that under the condition of 100 iterations, the iteration number of the improved algorithm is reduced by 25%, the path length is reduced by 8.01m, the IASO algorithm of the invention has good improvement effect, high convergence speed, high adaptability to intelligent inspection and reduced path planning length.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (6)

1. The intelligent internet of things path optimization safety inspection equipment is characterized by comprising a wireless charging device, a zinc ion supercapacitor, a motor, an electricity meter, an Arduino UNO, an ultrasonic position sensor, a path planning module, a motor controller and an APP terminal;
The wireless charging device comprises a power supply, a transmitting end, a receiving end, an energy converter and an APP terminal; the transmitting end is connected with a power supply and is arranged in a designated area of a target position, the power supply sends signals to a transmitting coil of the transmitting end through a circuit, the transmitting coil sends electric energy to a receiving coil of the receiving end, the receiving coil is arranged at the bottom of the wireless charging device and is charged in a contact mode, and the received electric energy charges a zinc ion super capacitor through an energy converter;
The electric quantity meter detects the electric quantity of the zinc ion super capacitor in real time, the Arduino UNO chip receives the electric quantity of the zinc ion super capacitor in real time, and when the electric quantity of the zinc ion super capacitor is lower than a preset value set by the chip, the Arduino UNO chip sends an instruction to the motor controller, and the motor controller controls the motor to drive the equipment to a specified charging area point;
The motor controller controls the motor to run, so as to control the equipment to move; the motor controller receives an operation control strategy instruction obtained by the Arduino UNO chip from the path planning module, and controls the motor operation according to the operation control strategy;
Establishing a static grid matrix of the target area according to the requirements of the target area, and detecting the position of the obstacle by the ultrasonic position sensor to establish a dynamic grid matrix of the target area; the method comprises the steps of establishing a target area global grid matrix, optimizing by using an ISOA algorithm through a path planning module to obtain an optimal path strategy, and sending a control instruction to a motor controller to control the motor to run;
the path planning module optimizes by using an ISOA algorithm, and specifically comprises the following steps:
step one: initializing parameters;
step two: establishing a static grid matrix of a target area;
step three: acquiring ultrasonic position sensor data;
Step four: establishing a dynamic grid matrix of the target area according to the data of the ultrasonic position sensor, so as to obtain a global grid matrix of the target area;
step five: calculating the path from the starting point to the end point of each seagull, wherein the expression is as follows:
(x 0,y0)、(xg,yg) coordinates representing the start point and the end point, respectively
Step six: global search, expressed by the following formula:
The updated new position of the seagull; /(I) Is a new position which does not collide with other seagulls; /(I)The direction of the optimal position of the seagull;
in order to avoid collision in the global searching process, a variable A is added to calculate the new position of the seagull, and the formula of the individual collision avoiding process of the seagull is as follows:
Representing the current position of the seagull; a is a linear perturbation whose value decreases from 2 to 0, since the value of linear a is not fully applicable to the nonlinear search process of SOA; by carrying out nonlinear processing on A, the A is improved, and the improvement formula is as follows:
Wherein, an initial value A final of A start represents that the termination value T max is the maximum iteration number delta is a convergence adjustment factor, betarand is a random generator of matlab, and can conform to beta distribution;
step seven: local search, utilize sine and cosine factor to improve the iterative mode of ISOA, improve the iterative mode as follows:
Where P bs represents the optimal gull individual position, R 1 is the balance development coefficient for the final attack position of the seagull,R 2 is a distance coefficient, r 2 epsilon [0,2 pi ] defines the distance between the current position of the sea gull and the worse position of the sea gull, r 3 is a sine-cosine transform coefficient, r 3 epsilon [0,1], when r 3 is more than 0.5, switching to a sine operator, and when r 3 is less than 0.5, switching to a cosine operator;
Step eight: judging whether an obstacle exists in front, if so, returning to the step four, and if not, turning to the step nine;
Step nine: updating the optimal position;
Step ten: judging whether the target end point is reached, if so, turning to a step eleven, and if not, returning to a step six;
Step eleven: outputting the optimal path.
2. The intelligent internet of things path-optimized safety inspection device according to claim 1, further comprising an infrared sensing camera, wherein the infrared sensing camera can detect whether a person passes or not, and when the person passes, the infrared sensing camera collects images and sends out signals, the Arduino UNO chip receives the signals of the infrared sensing camera and receives the real-time position of the ultrasonic position sensor, and the position signals and the images collected by the infrared sensing camera are sent to the APP terminal.
3. The intelligent internet of things path-optimized security inspection device of claim 2, further comprising a cooperative alarm module, wherein the cooperative alarm module sends an alarm signal after receiving a signal sent by an Arduino UNO chip and passed by a person.
4. The intelligent internet of things path optimization safety inspection system is characterized by comprising a wireless charging module, a zinc ion supercapacitor module, an Arduino UNO module, an ultrasonic position sensor module, a path planning module and a motor controller module;
The wireless charging module comprises a power supply, a transmitting end, a receiving end, an energy converter and an APP terminal; the transmitting end is connected with a power supply and is arranged in a designated area of a target position, the power supply sends signals to a transmitting coil of the transmitting end through a circuit, the transmitting coil sends electric energy to a receiving coil of the receiving end, the receiving coil is arranged at the bottom of the wireless charging device and is charged in a contact mode, and the received electric energy charges a zinc ion super capacitor through an energy converter;
The electric quantity meter detects the electric quantity of the zinc ion super capacitor in real time, the Arduino UNO chip receives the electric quantity of the zinc ion super capacitor in real time, and when the electric quantity of the zinc ion super capacitor is lower than a preset value set by the chip, the Arduino UNO chip sends an instruction to the motor controller, and the motor controller controls the motor to drive the equipment to a specified charging area point;
the motor controller module controls the motor to run so as to control the equipment to move; the motor controller receives an operation control strategy instruction obtained by the Arduino UNO chip from the path planning module, and controls the motor operation according to the operation control strategy;
Establishing a static grid matrix of the target area according to the requirements of the target area, and detecting the position of an obstacle by the ultrasonic position sensor module to establish a dynamic grid matrix of the target area; the method comprises the steps of establishing a target area global grid matrix, optimizing by using an ISOA algorithm through a path planning module to obtain an optimal path strategy, and sending a control instruction to a motor controller to control the motor to run to the Arduino UNO module;
the path planning module optimizes by using an ISOA algorithm, and specifically comprises the following steps:
step one: initializing parameters;
step two: establishing a static grid matrix of a target area;
step three: acquiring ultrasonic position sensor data;
Step four: establishing a dynamic grid matrix of the target area according to the data of the ultrasonic position sensor, so as to obtain a global grid matrix of the target area;
step five: calculating the path from the starting point to the end point of each seagull, wherein the expression is as follows:
(x 0,y0)、(xg,yg) coordinates representing the start point and the end point, respectively
Step six: global search, expressed by the following formula:
The updated new position of the seagull; /(I) Is a new position which does not collide with other seagulls; /(I)The direction of the optimal position of the seagull;
in order to avoid collision in the global searching process, a variable A is added to calculate the new position of the seagull, and the formula of the individual collision avoiding process of the seagull is as follows:
Representing the current position of the seagull; a is a linear perturbation whose value decreases from 2 to 0, since the value of linear a is not fully applicable to the nonlinear search process of SOA; by carrying out nonlinear processing on A, the A is improved, and the improvement formula is as follows:
Wherein, an initial value A final of A start represents that the termination value T max is the maximum iteration number delta is a convergence adjustment factor, betarand is a random generator of matlab, and can conform to beta distribution;
step seven: local search, utilize sine and cosine factor to improve the iterative mode of ISOA, improve the iterative mode as follows:
Where P bs represents the optimal gull individual position, R 1 is the balance development coefficient for the final attack position of the seagull,R 2 is a distance coefficient, r 2 epsilon [0,2 pi ] defines the distance between the current position of the sea gull and the worse position of the sea gull, r 3 is a sine-cosine transform coefficient, r 3 epsilon [0,1], when r 3 is more than 0.5, switching to a sine operator, and when r 3 is less than 0.5, switching to a cosine operator;
Step eight: judging whether an obstacle exists in front, if so, returning to the step four, and if not, turning to the step nine;
Step nine: updating the optimal position;
Step ten: judging whether the target end point is reached, if so, turning to a step eleven, and if not, returning to a step six;
Step eleven: outputting the optimal path.
5. The intelligent internet of things path-optimized safety inspection system according to claim 4, further comprising an infrared sensing camera, wherein the infrared sensing camera can detect whether a person passes or not, and when the person passes, the infrared sensing camera collects images and sends out signals, the Arduino UNO chip receives the signals of the infrared sensing camera and receives the real-time position of the ultrasonic position sensor, and the position signals and the images collected by the infrared sensing camera are sent to the APP terminal.
6. The intelligent internet of things path-optimized security inspection system of claim 4, further comprising a cooperative alarm module, wherein the cooperative alarm module sends an alarm signal after receiving a signal sent by the Arduino UNO chip and passed by a person.
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