CN108732972B - Intelligent data acquisition system for multiple robots - Google Patents

Intelligent data acquisition system for multiple robots Download PDF

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CN108732972B
CN108732972B CN201810590342.1A CN201810590342A CN108732972B CN 108732972 B CN108732972 B CN 108732972B CN 201810590342 A CN201810590342 A CN 201810590342A CN 108732972 B CN108732972 B CN 108732972B
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field working
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CN108732972A (en
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邱林新
周宇
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Shandong Lushang Innovation Development Co ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G05B19/0428—Safety, monitoring
    • G—PHYSICS
    • G08—SIGNALLING
    • G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
    • G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
    • G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2612—Data acquisition interface

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Abstract

The invention provides an intelligent data acquisition system for multiple robots, which comprises a wireless sensor network monitoring module and a data processing and analyzing platform, wherein the wireless sensor network monitoring module is used for acquiring field working parameters of the robots and transmitting the field working parameters to the data processing and analyzing platform; the data analysis processing platform is used for monitoring the received field working parameters of the robot in real time, analyzing and processing the field working parameters and outputting corresponding alarm signals when the field working parameters exceed a set threshold range.

Description

Intelligent data acquisition system for multiple robots
Technical Field
The invention relates to the field of multi-robot communication, in particular to an intelligent data acquisition system for multiple robots.
Background
In current industrial production, in order to save labor cost and improve production quality, an automatic production line is generally equipped with a plurality of robots to complete production processes through cooperative work. In order to ensure stable production, the field working parameters of the multiple robots are required to be collected and monitored in real time, so that an observer can reasonably arrange the operation conditions of the multiple robots in time, and the occurrence of an accident situation is avoided.
Disclosure of Invention
In view of the above problems, the present invention provides an intelligent data acquisition system for multiple robots.
The purpose of the invention is realized by adopting the following technical scheme:
the system comprises a wireless sensor network monitoring module and a data processing and analyzing platform, wherein the wireless sensor network monitoring module is used for acquiring the field working parameters of the robot and transmitting the field working parameters to the data processing and analyzing platform; the data analysis processing platform is used for monitoring the received field working parameters of the robot in real time, analyzing and processing the field working parameters and outputting corresponding alarm signals when the field working parameters exceed a set threshold range; the wireless sensor network monitoring module comprises a base station and a plurality of sensor nodes deployed in a set monitoring area, wherein the base station and the sensor nodes jointly form a wireless sensor network, the plurality of sensor nodes cooperatively acquire, process and transmit field working parameters to the base station, and the base station is used for converging the received field working parameters and transmitting the field working parameters to the data processing center; the sensor nodes generate cluster heads through clustering, and the cluster heads are used for receiving field working parameters sent by each sensor node in a cluster every time a set monitoring period passes.
Preferably, the data processing and analyzing platform comprises a data transceiver module, a data anomaly detection module and a data fusion module which are connected in sequence; the data transceiver module is used for receiving and storing the field working parameters sent by the wireless sensor network monitoring module; the field working parameters are sent to a data anomaly detection module; the data anomaly detection module is used for carrying out anomaly detection on the field working parameters sent by the data transceiver module and repairing the detected anomalous data; and the data fusion module is used for carrying out fusion processing on the field working parameters.
Preferably, the sensor node comprises a sensor for acquiring field operating parameters of the monitored robot.
Further, the device also comprises a power supply module used for supplying power to each sensor.
The invention has the beneficial effects that: the wireless sensor network technology is used for acquiring the field working parameters of the multiple robots, analyzing and processing the field working parameters, outputting corresponding alarm signals when the field working parameters exceed the set threshold range, and alarming when the field working parameters exceed the set threshold range, so that an observer can make reasonable arrangement in time according to the operating conditions of the multiple robots, and the occurrence of unexpected situations is avoided.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a block diagram of a schematic configuration of an intelligent data acquisition system for multiple robots in accordance with an exemplary embodiment of the present invention;
fig. 2 is a block diagram schematically illustrating the structure of a data processing analysis module according to an exemplary embodiment of the present invention.
Reference numerals:
the system comprises a wireless sensor network monitoring module 1, a data processing and analyzing module 2, a power supply module 3, a data transceiving unit 10, a data abnormity detection unit 20 and a data fusion unit 30.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1, an embodiment of the present invention provides an intelligent data acquisition system for multiple robots, where the system includes a wireless sensor network monitoring module 1 and a data processing and analyzing module 2, where the wireless sensor network monitoring module 1 is configured to acquire field working parameters of a robot and transmit the field working parameters to the data processing and analyzing module 2; the data analysis processing platform is used for monitoring the received field working parameters of the robot in real time, analyzing and processing the field working parameters and outputting corresponding alarm signals when the field working parameters exceed a set threshold range; the wireless sensor network monitoring module 1 comprises a base station and a plurality of sensor nodes deployed in a set monitoring area, wherein the base station and the sensor nodes jointly form a wireless sensor network, the plurality of sensor nodes cooperatively acquire, process and transmit field working parameters to the base station, and the base station is used for converging the received field working parameters and transmitting the field working parameters to a data processing center; the sensor nodes generate cluster heads through clustering, and the cluster heads are used for receiving field working parameters sent by each sensor node in a cluster every time a set monitoring period passes.
The sensor nodes comprise sensors used for collecting field working parameters of the monitored robot.
The intelligent data acquisition system further comprises a power supply module 3 for supplying power to each sensor.
In a possible implementation manner, as shown in fig. 2, the data processing and analyzing module 2 includes a data transceiver unit 10, a data anomaly detection unit 20, and a data fusion unit 30, which are connected in sequence; the data transceiver unit 10 is used for receiving and storing the field working parameters sent by the wireless sensor network monitoring module 1; and sends the field working parameters to the data anomaly detection unit 20; the data anomaly detection unit 20 is configured to perform anomaly detection on the field working parameters sent by the data transceiver unit 10, and repair the detected anomalous data; the data fusion unit 30 is used for performing fusion processing on the field working parameters.
The embodiment of the invention realizes the acquisition of the field working parameters of the multiple robots by the wireless sensor network technology, outputs the corresponding alarm signal when the field working parameters exceed the set threshold range by analyzing and processing the field working parameters, and can alarm when the field working parameters exceed the set threshold range, so that an observer can make reasonable arrangement in time aiming at the operating conditions of the multiple robots, and the occurrence of unexpected conditions is avoided.
In a possible implementation mode, only when newly acquired field working parameters meet set change conditions, the sensor nodes send the newly acquired field working parameters to corresponding cluster heads, otherwise, the newly acquired field working parameters are not sent; setting newly acquired field working parameters as xm+1The set change conditions are as follows:
Figure BDA0001690467360000031
in the formula, xiRepresenting the collected field working parameters of the ith monitoring period,
Figure BDA0001690467360000032
is the average value of the field working parameters collected in the first m +1 monitoring periods,
Figure BDA0001690467360000033
is the median value of the field working parameters collected in the first m +1 monitoring periods,
Figure BDA0001690467360000034
is the average value of the field working parameters collected in the previous m monitoring periods,
Figure BDA0001690467360000035
the median value of the field working parameters collected for the first m monitoring periods, wherein the field working parameters collected for the first m +1 monitoring periods are { x1,x2,…,xm+1And the field working parameters collected in the first m monitoring periods are { x }1,x2,…,xm}；
Figure BDA0001690467360000036
Is a set field working parameter threshold value.
The embodiment innovatively sets the change conditions, and the satisfaction of the change conditions indicates that the newly acquired field working parameters change to the set degree compared with the previous field working parameters; in the embodiment, the sensor node sends the newly acquired field working parameters to the corresponding cluster head only when the newly acquired field working parameters meet the set change conditions, and unnecessary field working parameters can be prevented from being sent to the cluster head, so that the energy consumption of field working parameter transmission is reduced, the energy of the sensor node can be saved, the network communication traffic is reduced, and the communication cost of the system is saved on the whole.
In one embodiment, a cluster head periodically detects faults of all sensor nodes in a cluster, sends a sleep instruction to the corresponding fault node according to a fault detection result, and the sensor nodes enter a sleep state after receiving the sleep instruction; wherein, the cluster head carries out fault detection to each sensor node in the cluster regularly, specifically includes:
(1) setting a detection unit k, acquiring field working parameters acquired by the sensor node a and the natural neighbor nodes of the sensor node a in the first k monitoring periods, respectively calculating average values, and obtaining a field working parameter average value sequence
Figure BDA0001690467360000037
Wherein the discrete data node set S is constructed by other sensor nodes in the communication range of the sensor node aaUsing S according to the geographical position of the sensor nodeaConstructing a first-order Voronoi diagram of the sensor node a so as to obtain a plurality of natural neighbors of the sensor node aNode b, b 1, …, na，naRepresenting the number of natural neighbor nodes of the sensor node a;
(2) to pair
Figure BDA0001690467360000038
Sequencing the data according to the sequence from small to large to obtain a sequence of the average values of the field working parameters after sequencing
Figure BDA0001690467360000039
Calculating the median of the sorted sequence of the mean values of the field operating parameters
Figure BDA00016904673600000310
Mean value of
Figure BDA0001690467360000041
And standard deviation
Figure BDA0001690467360000042
(3) The average value of the field working parameters collected by the sensor node a in the first k monitoring periods is set as
Figure BDA0001690467360000043
If it is
Figure BDA0001690467360000044
And if the following fault judgment formula is satisfied, judging the sensor node a as a fault node:
Figure BDA0001690467360000045
where ρ is a predetermined probability threshold.
If the field working parameters collected by one sensor node obviously deviate from the field working parameters of other sensor nodes in the communication range of the sensor node, the sensor node can be considered to be in fault. Based on this principle, the present embodiment sets a node failure determination mechanism. In the mechanism, natural neighbor nodes of sensor nodes are obtained by utilizing a Voronoi diagram dividing methodAnd (4) point. Voronoi diagrams, which divide the region in which spatial objects of interest lie into a number of sub-regions depending on their proximity properties, are one of the very important research contents in the field of computational geometry. Each sub-region
Figure BDA0001690467360000046
Representing the distance in a given set p of discrete data points relative to other discrete data points
Figure BDA0001690467360000047
The set of all the spatial points closer. The natural neighbor node acquired by the embodiment is actually a true neighbor of the sensor node. In the embodiment, the fault of the sensor node is judged by comparing the deviation between the field working parameters acquired by the sensor node and the natural neighbor nodes thereof, and compared with the method of comparing the field working parameters with the field working parameters of the neighbor nodes, the misjudgment rate is reduced, and the accuracy of fault node detection is improved; the embodiment further sets a fault determination formula, and the fault determination formula is used for performing fault determination on the sensor node, so that the efficiency of fault node detection is improved.
In one embodiment, the standard deviation is calculated according to the following standard deviation improvement formula
Figure BDA0001690467360000048
Figure BDA0001690467360000049
In the formula (I), the compound is shown in the specification,
Figure BDA00016904673600000410
is a sequence of the average values of the sorted field working parameters
Figure BDA00016904673600000411
The v-th average value of (1).
The standard deviation is calculated as the sum of the squares of the differences between each number in a set of data and the mean of the set of data divided by the number of data. When a sensor node fails, the sensor node deviates from other sensor nodes by a larger value, based on this, the present embodiment improves the existing standard deviation calculation method, and uses the median of the median and the mean to replace the mean, because the median of the median and the mean can more represent the actual center of the mean relative to the mean, the standard deviation is calculated according to the above calculation formula, which can reduce the misjudgment of the failure node and improve the accuracy of the failure node detection.
In the above embodiment, the cluster head sends the sleep instruction to the corresponding fault node according to the fault detection result, and the sensor node enters the sleep state after receiving the sleep instruction, so that the sensor node with the fault can collect the field working parameters by stopping collecting the field working parameters, thereby relatively reducing the energy consumption for collecting and transmitting the field working parameters, and avoiding the fault node from influencing the precision of the field working parameters.
From the above description of embodiments, it is clear for a person skilled in the art that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code or any appropriate combination thereof. For a hardware implementation, the modules may be implemented in one or more of the following units: an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to perform the functions described herein, or a combination thereof. For a software implementation, some or all of the procedures of an embodiment may be performed by a computer program instructing associated hardware. In practice, the program may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. Computer-readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims (5)

1. The intelligent data acquisition system for multiple robots is characterized by comprising a wireless sensor network monitoring module and a data processing and analyzing platform, wherein the wireless sensor network monitoring module is used for acquiring field working parameters of the robots and transmitting the field working parameters to the data processing and analyzing platform; the data analysis processing platform is used for monitoring the received field working parameters of the robot in real time, analyzing and processing the field working parameters and outputting corresponding alarm signals when the field working parameters exceed a set threshold range; the wireless sensor network monitoring module comprises a base station and a plurality of sensor nodes deployed in a set monitoring area, wherein the base station and the sensor nodes jointly form a wireless sensor network, the plurality of sensor nodes cooperatively acquire, process and transmit field working parameters to the base station, and the base station is used for converging the received field working parameters and transmitting the field working parameters to the data processing center; the sensor nodes generate cluster heads through clustering, and the cluster heads are used for receiving field working parameters sent by each sensor node in a cluster every time a set monitoring period passes; the cluster head periodically detects faults of all sensor nodes in the cluster, sends a sleep instruction to the corresponding fault node according to the fault detection result, and the sensor nodes enter a sleep state after receiving the sleep instruction; wherein, the cluster head carries out fault detection to each sensor node in the cluster regularly, specifically includes:
(1) setting a detection unit k, and acquiring the first k monitoring weeksRespectively calculating the average values of the field working parameters collected by the sensor node a and the natural neighbor nodes thereof to obtain the average value sequence of the field working parameters
Figure FDA0002581747380000011
Wherein the discrete data node set S is constructed by other sensor nodes in the communication range of the sensor node aaUsing S according to the geographical position of the sensor nodeaConstructing a first-order Voronoi graph of the sensor node a, and obtaining a plurality of natural neighbor nodes b, b-1, …, n of the sensor node aa，naRepresenting the number of natural neighbor nodes of the sensor node a;
(2) to pair
Figure FDA0002581747380000012
Sequencing the data according to the sequence from small to large to obtain a sequence of the average values of the field working parameters after sequencing
Figure FDA0002581747380000013
Calculating the median of the sorted sequence of the mean values of the field operating parameters
Figure FDA0002581747380000014
Mean value of
Figure FDA0002581747380000015
And standard deviation
Figure FDA0002581747380000016
(3) The average value of the field working parameters collected by the sensor node a in the first k monitoring periods is set as
Figure FDA0002581747380000017
If it is
Figure FDA0002581747380000018
If the following fault determination formula is satisfied, the sensor node a is determined to beAnd (3) a fault node:
Figure FDA0002581747380000019
where ρ is a predetermined probability threshold.
2. The intelligent data acquisition system for multiple robots according to claim 1, wherein the data processing and analysis platform comprises a data transceiver module, a data anomaly detection module and a data fusion module which are connected in sequence; the data transceiver module is used for receiving and storing the field working parameters sent by the wireless sensor network monitoring module; the field working parameters are sent to a data anomaly detection module; the data anomaly detection module is used for carrying out anomaly detection on the field working parameters sent by the data transceiver module and repairing the detected anomalous data; and the data fusion module is used for carrying out fusion processing on the field working parameters.
3. The intelligent data acquisition system for multiple robots of claim 1 wherein the sensor nodes comprise sensors for acquiring field operating parameters of the monitored robot.
4. The intelligent data acquisition system for multiple robots according to claim 3 further comprising a power supply module for supplying power to each sensor.
5. The intelligent data acquisition system for multiple robots according to any one of claims 1 to 4, wherein the sensor node transmits the newly acquired field work parameters to the corresponding cluster head only when the newly acquired field work parameters satisfy the set change conditions, and otherwise, does not transmit the newly acquired field work parameters; setting newly acquired field working parameters as xm+1The set change conditions are as follows:
Figure FDA0002581747380000021
in the formula, xiRepresenting the collected field working parameters of the ith monitoring period,
Figure FDA0002581747380000022
is the average value of the field working parameters collected in the first m +1 monitoring periods,
Figure FDA0002581747380000023
is the median value of the field working parameters collected in the first m +1 monitoring periods,
Figure FDA0002581747380000024
is the average value of the field working parameters collected in the previous m monitoring periods,
Figure FDA0002581747380000025
the median value of the field working parameters collected for the first m monitoring periods, wherein the field working parameters collected for the first m +1 monitoring periods are { x1,x2,…,xm+1And the field working parameters collected in the first m monitoring periods are { x }1,x2,…,xm}；
Figure FDA0002581747380000026
Is a set field working parameter threshold value.
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