CN113077628B - Algorithm of composite geomagnetic vehicle detector - Google Patents
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/042—Detecting movement of traffic to be counted or controlled using inductive or magnetic detectors
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/048—Detecting movement of traffic to be counted or controlled with provision for compensation of environmental or other condition, e.g. snow, vehicle stopped at detector
Abstract
The invention discloses an algorithm of a composite geomagnetic vehicle detector, and relates to the technical field of traffic vehicle detection; the invention is provided with a fault evaluation module which judges faults and acquires fault labels according to sensor data; the fault evaluation module judges the faults of the ultrasonic sensor, the infrared sensor and the microwave sensor by combining the sensor data and a polynomial fitting method, can ensure the working efficiency of the invention, and simultaneously provides data support for the detection execution module; the invention is provided with a geomagnetic compensation module, and the geomagnetic compensation module acquires magnetic field compensation data according to environmental data; the geomagnetic compensation module realizes prediction of magnetic field change through combination of the environmental data and the magnetic field compensation model, acquires magnetic field compensation data, makes full use of the advantages of the artificial intelligence model, improves acquisition precision of the magnetic field compensation data, and can realize detection efficiency and detection precision of the geomagnetic vehicle detector.
Description
Technical Field
The invention belongs to the field of traffic vehicle detection, relates to a geomagnetic detection technology, and particularly relates to an algorithm of a composite geomagnetic vehicle detector.
Background
The earth surface magnetic field is kept relatively stable in a certain range, when ferromagnets such as vehicles enter a specific detection range, the micro-change of the local magnetic field intensity can be influenced, when a three-axis magnetoresistive sensor is used for measurement, the XYZ three-axis magnetoresistive data can be subjected to micro-change, and if a non-vehicle background magnetic field and a vehicle background magnetic field are used as threshold values for division, whether the vehicles leave can be detected.
The invention patent with publication number CN108053655A provides a composite geomagnetic vehicle detector and a detection method, which select a high-performance 8-bit singlechip, utilize an 11C interface, a high-precision AD interface, a digital 10-day interface, a serial interface and a high-capacity Flash, mainly use a triaxial geomagnetic sensor, combine sensors such as vibration, infrared, ultrasonic wave and microwave to perform auxiliary detection, acquire sensor data, optimize an algorithm and avoid the defects of the auxiliary sensor in different application occasions.
The scheme can realize the initial automatic calibration and the magnetic field background change tracking calibration of the geomagnetic vehicle detector, and improve the vehicle detection precision by combining with an axial sensor; however, the initial calibration precision of the geomagnetic vehicle detector in the above scheme is not high, and the working state judgment of each component in the geomagnetic vehicle detector is not accurate enough; therefore, the above solution still needs further improvement.
Disclosure of Invention
In order to solve the problems of the above-described schemes, the present invention provides an algorithm for a composite geomagnetic vehicle detector.
The purpose of the invention can be realized by the following technical scheme: an algorithm of a composite geomagnetic vehicle detector, specifically comprising the steps of:
the method comprises the following steps: the method comprises the steps that sensor data, environment data and magnetic field data of a geomagnetic vehicle detector are obtained through a control system, the sensor data are respectively sent to a data storage module, a geomagnetic compensation module and a fault assessment module, the environment data are respectively sent to the geomagnetic compensation module and the data storage module, and the magnetic field data are respectively sent to a data storage module and a detection execution module;
step two: after the fault evaluation module receives the sensor data, fault analysis is carried out on the ultrasonic sensor, the infrared sensor and the microwave sensor by combining a polynomial fitting method, a fault label is generated, and the fault label is respectively sent to the data storage module and the detection execution module through the processor;
step three: when the local magnetic compensation module receives the environmental data, integrating and marking the temperature, the humidity, the season and the date in the environmental data as input data; acquiring a magnetic field compensation model in a data storage module; inputting input data into a magnetic field compensation model to obtain an output result, and marking the output result as a magnetic field compensation value; respectively sending the magnetic field compensation value to a detection execution module and a data storage module through a processor;
step four: when the detection execution module receives the fault label and the fault label is 0, compensating the magnetic field data through the magnetic field compensation value, and marking the compensated magnetic field data as standard magnetic field data; and determining the existence condition of the vehicle according to the magnetic field intensity of the standard magnetic field.
Further, the control system comprises a processor, a fault evaluation module, a data acquisition module, a geomagnetic compensation module, a detection execution module, a global monitoring module and a data storage module;
the data acquisition module is electrically connected with at least one geomagnetic vehicle detector; the data acquisition module acquires sensor data, environment data and magnetic field data of the geomagnetic vehicle detector, respectively sends the sensor data to the data storage module, the geomagnetic compensation module and the fault evaluation module, respectively sends the environment data to the geomagnetic compensation module and the data storage module, and respectively sends the magnetic field data to the data storage module and the detection execution module;
the earth magnetism compensation module obtains magnetic field compensation data according to environmental data, includes:
when the local magnetic compensation module receives the environmental data, integrating and marking the temperature, the humidity, the season and the date in the environmental data as input data;
acquiring a magnetic field compensation model in a data storage module;
inputting input data into a magnetic field compensation model to obtain an output result, and marking the output result as a magnetic field compensation value; the output result is a pre-difference value of the three-axis magnetic field sensor measurement data and the magnetic field standard data under the condition of predicted input data;
respectively sending the magnetic field compensation value to a detection execution module and a data storage module through a processor;
the detection execution module compensates the magnetic field data according to the fault tag and the magnetic field compensation value, and the method comprises the following steps:
when the detection execution module receives the fault label and the fault label is 0, compensating the magnetic field data through the magnetic field compensation value, and marking the compensated magnetic field data as standard magnetic field data;
and determining the existence condition of the vehicle according to the magnetic field intensity of the standard magnetic field.
Further, the global monitoring module displays the fault label in real time by combining the geographic coordinate; the global monitoring module is also used for dispatching workers to carry out troubleshooting and fault maintenance on fault positions, and the fault positions are geographic coordinates corresponding to the fault labels.
Further, the generation of the magnetic field compensation model specifically comprises the following steps:
acquiring historical analysis data of the magnetic field through a data storage module; the historical analysis data comprises environmental historical data and corresponding magnetic field difference data, wherein the environmental historical data comprises temperature, humidity, season and date, and the magnetic field difference data is the difference between the measured data of the triaxial magnetic field sensor and the standard magnetic field value;
constructing an artificial intelligence model; the artificial intelligence model at least comprises one of an error reverse propagation neural network model, an RBF neural network model and a deep convolution neural network model;
dividing the magnetic field historical analysis data into a training set, a test set and a check set according to a set proportion; the set ratio comprises 2:1:1 and 3:1: 1;
training, testing and verifying the artificial intelligent model through a training set, a testing set and a verifying set; marking the trained artificial intelligence model as a magnetic field compensation model;
and sending the magnetic field compensation model to a data storage module for storage through the processor.
Further, the fault evaluation module performs fault judgment according to the sensor data and acquires a fault tag, including:
after the fault evaluation module receives the sensor data, extracting sound wave frequency, infrared data and microwave data, and respectively marking the sound wave frequency, the infrared data and the microwave data as SP, HS and WS;
establishing a sound wave frequency change curve by a polynomial fitting method by taking time as an independent variable and sound wave frequency SP as a dependent variable; acquiring a first derivative value of a sound wave frequency change curve, optionally selecting one independent variable to acquire a corresponding first derivative value and a corresponding absolute value of the sound wave frequency, and respectively marking the first derivative value and the absolute value as a change rate and a verification frequency;
when the change rate is smaller than the change rate threshold value and the verification frequency is less than or equal to 40KHz, judging that the ultrasonic sensor is abnormal, and generating and sending an ultrasonic sensing abnormal signal to the global monitoring module; otherwise, judging that the ultrasonic sensor is normal, generating and sending an ultrasonic sensing normal signal to the global monitoring module; wherein the change rate threshold is obtained by big data simulation;
acquiring an infrared change curve by a polynomial fitting method by taking time as an independent variable and infrared data HS as a dependent variable; acquiring a first derivative value of the infrared change curve, acquiring a mean square error of the first derivative, judging that the infrared sensor is normal when the mean square error is smaller than a mean square error threshold value, and generating and sending an infrared sensing normal signal to the global monitoring module; otherwise, judging that the infrared sensor is abnormal, generating and sending an infrared sensing abnormal signal to the global monitoring module; wherein the mean square error threshold is obtained through big data simulation;
under the condition that vibration exists, when the microwave data are smaller than the microwave difference threshold value, judging that the microwave sensor is abnormal, and generating and sending a microwave sensing abnormal signal to the global monitoring module; otherwise, judging that the microwave sensor is normal, generating and sending a microwave sensing normal signal to the global monitoring module;
generating a fault label; the value of the fault label is 0 and 1, when the value of the fault label is 0, the fault label indicates that the ultrasonic sensor, the infrared sensor and the microwave sensor are normal, and when the value of the fault label is 1, the fault label indicates that at least one of the ultrasonic sensor, the infrared sensor and the microwave sensor is abnormal;
and respectively sending the fault label to the data storage module and the detection execution module through the processor.
Further, the sensor data comprises ultrasonic data, infrared data and microwave data, and the sensor data at least comprises measurement data of more than 15 days; the ultrasonic data comprises the frequency and the numerical value of an ultrasonic signal received by the piezoelectric ultrasonic receiver, and the frequency and the numerical value are respectively marked as acoustic frequency and acoustic data; the infrared data are signal values received by an infrared receiver; the microwave data is the microwave difference value measured by the microwave sensor under the vibration condition and the non-vibration condition; the environmental data includes temperature, humidity, season, and date; the magnetic field data are the measurement data of the triaxial geomagnetic sensor.
Further, the geomagnetic vehicle detector comprises an ultrasonic modulation circuit, a piezoelectric ultrasonic transmitter, a piezoelectric ultrasonic receiver, an ultrasonic filtering and shaping circuit, an infrared transmitter, an infrared transmitting power driving circuit, an infrared receiver, an infrared receiving and filtering circuit, a microwave sensor, a microwave frequency meter, a high-performance single chip microcomputer, a stabilized voltage power supply, a battery pack, a triaxial accelerometer with a vibration awakening function and a triaxial geomagnetic sensor; the high-performance single chip microcomputer comprises an analog-to-digital conversion interface, an IIC interface and a multi-path high-speed IO port, and is internally provided with a state working mode, wherein the state working mode comprises a watchdog mode and a sleep working mode.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention is provided with a fault evaluation module which judges faults and acquires fault labels according to sensor data; the fault evaluation module judges the faults of the ultrasonic sensor, the infrared sensor and the microwave sensor by combining the sensor data and a polynomial fitting method, can ensure the working efficiency of the invention, and simultaneously provides data support for the detection execution module;
2. the invention is provided with a geomagnetic compensation module, and the geomagnetic compensation module acquires magnetic field compensation data according to environmental data; the geomagnetic compensation module realizes prediction of magnetic field change through combination of the environmental data and the magnetic field compensation model, acquires magnetic field compensation data, makes full use of the advantages of the artificial intelligence model, improves acquisition precision of the magnetic field compensation data, and can realize detection efficiency and detection precision of the geomagnetic vehicle detector.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic representation of the steps of the present invention;
fig. 2 is a schematic diagram of the control system of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, an algorithm of a composite geomagnetic vehicle detector specifically includes the following steps:
the method comprises the following steps: the method comprises the steps that sensor data, environment data and magnetic field data of a geomagnetic vehicle detector are obtained through a control system, the sensor data are respectively sent to a data storage module, a geomagnetic compensation module and a fault assessment module, the environment data are respectively sent to the geomagnetic compensation module and the data storage module, and the magnetic field data are respectively sent to a data storage module and a detection execution module;
step two: after the fault evaluation module receives the sensor data, fault analysis is carried out on the ultrasonic sensor, the infrared sensor and the microwave sensor by combining a polynomial fitting method, a fault label is generated, and the fault label is respectively sent to the data storage module and the detection execution module through the processor;
step three: when the local magnetic compensation module receives the environmental data, integrating and marking the temperature, the humidity, the season and the date in the environmental data as input data; acquiring a magnetic field compensation model in a data storage module; inputting input data into a magnetic field compensation model to obtain an output result, and marking the output result as a magnetic field compensation value; respectively sending the magnetic field compensation value to a detection execution module and a data storage module through a processor;
step four: when the detection execution module receives the fault label and the fault label is 0, compensating the magnetic field data through the magnetic field compensation value, and marking the compensated magnetic field data as standard magnetic field data; and determining the existence condition of the vehicle according to the magnetic field intensity of the standard magnetic field.
Further, the control system comprises a processor, a fault evaluation module, a data acquisition module, a geomagnetic compensation module, a detection execution module, a global monitoring module and a data storage module;
the data acquisition module is electrically connected with at least one geomagnetic vehicle detector; the data acquisition module acquires sensor data, environment data and magnetic field data of the geomagnetic vehicle detector, respectively sends the sensor data to the data storage module, the geomagnetic compensation module and the fault evaluation module, respectively sends the environment data to the geomagnetic compensation module and the data storage module, and respectively sends the magnetic field data to the data storage module and the detection execution module;
the earth magnetism compensation module obtains magnetic field compensation data according to environmental data, includes:
when the local magnetic compensation module receives the environmental data, integrating and marking the temperature, the humidity, the season and the date in the environmental data as input data;
acquiring a magnetic field compensation model in a data storage module;
inputting input data into a magnetic field compensation model to obtain an output result, and marking the output result as a magnetic field compensation value; the output result is a pre-difference value of the three-axis magnetic field sensor measurement data and the magnetic field standard data under the condition of predicted input data;
respectively sending the magnetic field compensation value to a detection execution module and a data storage module through a processor;
the detection execution module compensates the magnetic field data according to the fault tag and the magnetic field compensation value, and the method comprises the following steps:
when the detection execution module receives the fault label and the fault label is 0, compensating the magnetic field data through the magnetic field compensation value, and marking the compensated magnetic field data as standard magnetic field data;
and determining the existence condition of the vehicle according to the magnetic field intensity of the standard magnetic field.
Further, the global monitoring module displays the fault label in real time by combining the geographic coordinate; the global monitoring module is also used for dispatching workers to carry out troubleshooting and fault maintenance on fault positions, and the fault positions are geographic coordinates corresponding to the fault labels.
Further, the generation of the magnetic field compensation model specifically comprises the following steps:
acquiring historical analysis data of the magnetic field through a data storage module; the historical analysis data comprises environmental historical data and corresponding magnetic field difference data, wherein the environmental historical data comprises temperature, humidity, season and date, and the magnetic field difference data is the difference between the measured data of the triaxial magnetic field sensor and the standard magnetic field value;
constructing an artificial intelligence model; the artificial intelligence model at least comprises one of an error reverse propagation neural network model, an RBF neural network model and a deep convolution neural network model;
dividing the magnetic field historical analysis data into a training set, a test set and a check set according to a set proportion; the set ratio comprises 2:1:1 and 3:1: 1;
training, testing and verifying the artificial intelligent model through a training set, a testing set and a verifying set; marking the trained artificial intelligence model as a magnetic field compensation model;
and sending the magnetic field compensation model to a data storage module for storage through the processor.
Further, the fault evaluation module performs fault judgment according to the sensor data and acquires a fault tag, including:
after the fault evaluation module receives the sensor data, extracting sound wave frequency, infrared data and microwave data, and respectively marking the sound wave frequency, the infrared data and the microwave data as SP, HS and WS;
establishing a sound wave frequency change curve by a polynomial fitting method by taking time as an independent variable and sound wave frequency SP as a dependent variable; acquiring a first derivative value of a sound wave frequency change curve, optionally selecting one independent variable to acquire a corresponding first derivative value and a corresponding absolute value of the sound wave frequency, and respectively marking the first derivative value and the absolute value as a change rate and a verification frequency;
when the change rate is smaller than the change rate threshold value and the verification frequency is less than or equal to 40KHz, judging that the ultrasonic sensor is abnormal, and generating and sending an ultrasonic sensing abnormal signal to the global monitoring module; otherwise, judging that the ultrasonic sensor is normal, generating and sending an ultrasonic sensing normal signal to the global monitoring module; wherein the change rate threshold is obtained by big data simulation;
acquiring an infrared change curve by a polynomial fitting method by taking time as an independent variable and infrared data HS as a dependent variable; acquiring a first derivative value of the infrared change curve, acquiring a mean square error of the first derivative, judging that the infrared sensor is normal when the mean square error is smaller than a mean square error threshold value, and generating and sending an infrared sensing normal signal to the global monitoring module; otherwise, judging that the infrared sensor is abnormal, generating and sending an infrared sensing abnormal signal to the global monitoring module; wherein the mean square error threshold is obtained through big data simulation;
under the condition that vibration exists, when the microwave data are smaller than the microwave difference threshold value, judging that the microwave sensor is abnormal, and generating and sending a microwave sensing abnormal signal to the global monitoring module; otherwise, judging that the microwave sensor is normal, generating and sending a microwave sensing normal signal to the global monitoring module;
generating a fault label; the value of the fault label is 0 and 1, when the value of the fault label is 0, the fault label indicates that the ultrasonic sensor, the infrared sensor and the microwave sensor are normal, and when the value of the fault label is 1, the fault label indicates that at least one of the ultrasonic sensor, the infrared sensor and the microwave sensor is abnormal;
and respectively sending the fault label to the data storage module and the detection execution module through the processor.
Further, the sensor data comprises ultrasonic data, infrared data and microwave data, and the sensor data at least comprises measurement data of more than 15 days; the ultrasonic data comprises the frequency and the numerical value of an ultrasonic signal received by the piezoelectric ultrasonic receiver, and the frequency and the numerical value are respectively marked as acoustic frequency and acoustic data; the infrared data are signal values received by an infrared receiver; the microwave data is the microwave difference value measured by the microwave sensor under the vibration condition and the non-vibration condition; the environmental data includes temperature, humidity, season, and date; the magnetic field data are the measurement data of the triaxial geomagnetic sensor.
Further, the geomagnetic vehicle detector comprises an ultrasonic modulation circuit, a piezoelectric ultrasonic transmitter, a piezoelectric ultrasonic receiver, an ultrasonic filtering and shaping circuit, an infrared transmitter, an infrared transmitting power driving circuit, an infrared receiver, an infrared receiving and filtering circuit, a microwave sensor, a microwave frequency meter, a high-performance single chip microcomputer, a stabilized voltage power supply, a battery pack, a triaxial accelerometer with a vibration awakening function and a triaxial geomagnetic sensor; the high-performance single chip microcomputer comprises an analog-to-digital conversion interface, an IIC interface and a multi-path high-speed IO port, and is internally provided with a state working mode, wherein the state working mode comprises a watchdog mode and a sleep working mode.
Further, the processor is respectively in communication connection with the fault evaluation module, the data acquisition module, the geomagnetic compensation module, the detection execution module, the global monitoring module and the data storage module; the data acquisition module is respectively in communication connection with the fault evaluation module and the geomagnetic compensation module, the global monitoring module is respectively in communication connection with the data storage module and the detection execution module, and the detection execution module is in communication connection with the geomagnetic compensation module.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows:
the data acquisition module acquires sensor data, environment data and magnetic field data of the geomagnetic vehicle detector, respectively sends the sensor data to the data storage module, the geomagnetic compensation module and the fault evaluation module, respectively sends the environment data to the geomagnetic compensation module and the data storage module, and respectively sends the magnetic field data to the data storage module and the detection execution module;
after the fault evaluation module receives the sensor data, extracting sound wave frequency, infrared data and microwave data, and respectively marking the sound wave frequency, the infrared data and the microwave data as SP, HS and WS; establishing a sound wave frequency change curve by a polynomial fitting method by taking time as an independent variable and sound wave frequency SP as a dependent variable; acquiring a first derivative value of a sound wave frequency change curve, optionally selecting one independent variable to acquire a corresponding first derivative value and a corresponding absolute value of the sound wave frequency, and respectively marking the first derivative value and the absolute value as a change rate and a verification frequency; when the change rate is smaller than the change rate threshold value and the verification frequency is less than or equal to 40KHz, judging that the ultrasonic sensor is abnormal, and generating and sending an ultrasonic sensing abnormal signal to the global monitoring module; otherwise, judging that the ultrasonic sensor is normal, generating and sending an ultrasonic sensing normal signal to the global monitoring module; acquiring an infrared change curve by a polynomial fitting method by taking time as an independent variable and infrared data HS as a dependent variable; acquiring a first derivative value of the infrared change curve, acquiring a mean square error of the first derivative, judging that the infrared sensor is normal when the mean square error is smaller than a mean square error threshold value, and generating and sending an infrared sensing normal signal to the global monitoring module; otherwise, judging that the infrared sensor is abnormal, generating and sending an infrared sensing abnormal signal to the global monitoring module; under the condition that vibration exists, when the microwave data are smaller than the microwave difference threshold value, judging that the microwave sensor is abnormal, and generating and sending a microwave sensing abnormal signal to the global monitoring module; otherwise, judging that the microwave sensor is normal, generating and sending a microwave sensing normal signal to the global monitoring module; generating a fault label; respectively sending the fault label to a data storage module and a detection execution module through a processor;
when the local magnetic compensation module receives the environmental data, integrating and marking the temperature, the humidity, the season and the date in the environmental data as input data; acquiring a magnetic field compensation model in a data storage module; inputting input data into a magnetic field compensation model to obtain an output result, and marking the output result as a magnetic field compensation value; respectively sending the magnetic field compensation value to a detection execution module and a data storage module through a processor;
when the detection execution module receives the fault label and the fault label is 0, compensating the magnetic field data through the magnetic field compensation value, and marking the compensated magnetic field data as standard magnetic field data; and determining the existence condition of the vehicle according to the magnetic field intensity of the standard magnetic field.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (6)
1. An algorithm of a composite geomagnetic vehicle detector, characterized by specifically comprising the steps of:
the method comprises the following steps: the method comprises the steps that sensor data, environment data and magnetic field data of a geomagnetic vehicle detector are obtained through a control system, the sensor data are respectively sent to a geomagnetic compensation module and a fault evaluation module, the environment data are sent to the geomagnetic compensation module, and the magnetic field data are sent to a detection execution module;
step two: fault analysis is carried out on the ultrasonic sensor, the infrared sensor and the microwave sensor by combining a polynomial fitting method, fault labels are generated, and the fault labels are respectively sent to the data storage module and the detection execution module through the processor;
step three: acquiring input data; acquiring a magnetic field compensation model in a data storage module; inputting input data into a magnetic field compensation model to obtain an output result, and marking the output result as a magnetic field compensation value; respectively sending the magnetic field compensation value to a detection execution module and a data storage module through a processor;
step four: when the detection execution module receives the fault label and the fault label is 0, compensating the magnetic field data through the magnetic field compensation value, and marking the compensated magnetic field data as standard magnetic field data; judging the existence condition of the vehicle according to the magnetic field intensity of the standard magnetic field;
the fault evaluation module carries out fault judgment and acquires a fault label according to the sensor data, and the fault evaluation module comprises:
after the fault evaluation module receives the sensor data, extracting sound wave frequency, infrared data and microwave data, and respectively marking the sound wave frequency, the infrared data and the microwave data as SP, HS and WS;
establishing a sound wave frequency change curve by a polynomial fitting method by taking time as an independent variable and sound wave frequency SP as a dependent variable; acquiring a first derivative value of a sound wave frequency change curve, optionally selecting one independent variable to acquire a corresponding first derivative value and a corresponding absolute value of the sound wave frequency, and respectively marking the first derivative value and the absolute value as a change rate and a verification frequency;
when the change rate is smaller than the change rate threshold value and the verification frequency is less than or equal to 40KHz, judging that the ultrasonic sensor is abnormal, and generating and sending an ultrasonic sensing abnormal signal to the global monitoring module; otherwise, judging that the ultrasonic sensor is normal, generating and sending an ultrasonic sensing normal signal to the global monitoring module; wherein the change rate threshold is obtained by big data simulation;
acquiring an infrared change curve by a polynomial fitting method by taking time as an independent variable and infrared data HS as a dependent variable; acquiring a first derivative value of the infrared change curve, acquiring a mean square error of the first derivative, judging that the infrared sensor is normal when the mean square error is smaller than a mean square error threshold value, and generating and sending an infrared sensing normal signal to the global monitoring module; otherwise, judging that the infrared sensor is abnormal, generating and sending an infrared sensing abnormal signal to the global monitoring module; wherein the mean square error threshold is obtained through big data simulation;
under the condition that vibration exists, when the microwave data are smaller than the microwave difference threshold value, judging that the microwave sensor is abnormal, and generating and sending a microwave sensing abnormal signal to the global monitoring module; otherwise, judging that the microwave sensor is normal, generating and sending a microwave sensing normal signal to the global monitoring module;
generating a fault label; the value of the fault label is 0 and 1, when the value of the fault label is 0, the fault label indicates that the ultrasonic sensor, the infrared sensor and the microwave sensor are normal, and when the value of the fault label is 1, the fault label indicates that at least one of the ultrasonic sensor, the infrared sensor and the microwave sensor is abnormal;
and respectively sending the fault label to the data storage module and the detection execution module through the processor.
2. The algorithm of the composite geomagnetic vehicle detector according to claim 1, wherein the control system comprises a processor, a fault assessment module, a data acquisition module, a geomagnetic compensation module, a detection execution module, a global monitoring module and a data storage module;
the data acquisition module acquires sensor data, environment data and magnetic field data of the geomagnetic vehicle detector, respectively sends the sensor data to the geomagnetic compensation module and the fault evaluation module, sends the environment data to the geomagnetic compensation module, and sends the magnetic field data to the detection execution module; the earth magnetism compensation module obtains magnetic field compensation data according to environmental data, includes:
when the local magnetic compensation module receives the environmental data, integrating and marking the temperature, the humidity, the season and the date in the environmental data as input data; acquiring a magnetic field compensation model in a data storage module;
inputting input data into a magnetic field compensation model to obtain an output result, and marking the output result as a magnetic field compensation value; respectively sending the magnetic field compensation value to a detection execution module and a data storage module through a processor;
the detection execution module compensates the magnetic field data according to the fault tag and the magnetic field compensation value, and the method comprises the following steps:
when the detection execution module receives the fault label and the fault label is 0, compensating the magnetic field data through the magnetic field compensation value, and marking the compensated magnetic field data as standard magnetic field data; and determining the existence condition of the vehicle according to the magnetic field intensity of the standard magnetic field.
3. The algorithm of the composite geomagnetic vehicle detector according to claim 2, wherein the global monitoring module displays a fault tag in real time in combination with geographic coordinates; the global monitoring module is also used for dispatching working personnel to carry out troubleshooting and fault maintenance on fault positions.
4. The algorithm of claim 2, wherein the generation of the magnetic field compensation model specifically comprises the following steps:
acquiring historical analysis data of the magnetic field through a data storage module; constructing an artificial intelligence model; dividing the magnetic field historical analysis data into a training set, a test set and a check set according to a set proportion; training, testing and verifying the artificial intelligent model through a training set, a testing set and a verifying set; marking the trained artificial intelligence model as a magnetic field compensation model; and sending the magnetic field compensation model to a data storage module for storage through the processor.
5. The algorithm of the composite geomagnetic vehicle detector according to claim 2, wherein the fault evaluation module performs fault judgment according to the sensor data and obtains a fault tag, and the fault tag is sent to the data storage module and the detection execution module through the processor.
6. The algorithm of the composite geomagnetic vehicle detector according to claim 2, wherein the sensor data comprises ultrasonic data, infrared data, and microwave data.
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