CN109188981A - A kind of gas station monitoring system and its working method - Google Patents
A kind of gas station monitoring system and its working method Download PDFInfo
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- 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
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Abstract
The present invention relates to a kind of gas station monitoring system and its working method, the gas station monitoring system includes: Cloud Server, monitor terminal and the monitoring device positioned at gas station；Wherein the monitoring device includes: control module, data acquisition module, alarm module and the communication module being connected with the control module；The data acquisition module is suitable for acquiring the monitoring data of gas station, and is sent to Cloud Server by communication module；The Cloud Server is stored with the corresponding data threshold of every monitoring data, and when any monitoring data exceeds corresponding data threshold, the alarm module issues alarm；And monitoring data and warning message are sent to monitor terminal by the Cloud Server；Gas station monitoring system of the present invention can be realized the data of long-range monitoring gas station, and when monitoring data is abnormal, alert enables staff reacting at the first time, it is ensured that safety.
Description
Technical field
The invention belongs to monitoring technology field more particularly to a kind of gas station monitoring systems and its working method.
Background technique
Nowadays coal gas is widely used in people's daily life, however due to equipment and artificial factor, coal gas
Leakage accident occurs repeatedly, and brings massive losses to the life and property of people.Currently, what vast rural resident used mostly
It is the gas stove of gas tank gas supply, the valve of gas cooker switch and gas tank needs hand-reset.For various reasons, forget to close
Gas cooker switch and coal gas tank valve are closed, can be caused a hidden trouble to life and property.In order to more relievedly use coal gas, coal gas is studied
Stove switch and coal gas tank valve, which can be realized to be automatically closed in the case where not being shut off promptly, just seems especially significant.
Currently, the combustion gas in city is mainly based on piped gas, although bringing convenience for people's daily life,
Due to the combustibility of coal gas and containing toxic gas carbon monoxide, there is some potential safety problemss.People are using sometimes
Forget to close the valve that gas piping exports after coal gas, this may lead fire or gas poisoning or even coal gas blast accident.
Summary of the invention
The purpose of the present invention is to provide a kind of gas station monitoring system and its working method, gas station is remotely supervised in realization
Control.
In order to solve the above-mentioned technical problems, the present invention provides a kind of gas station monitoring systems, comprising: Cloud Server, prison
Control terminal and monitoring device positioned at gas station；Wherein the monitoring device includes: control module, is connected with the control module
Data acquisition module, alarm module and communication module；The data acquisition module is suitable for acquiring the monitoring data of gas station, and leads to
It crosses communication module and is sent to Cloud Server；The Cloud Server is stored with the corresponding data threshold of every monitoring data, when any
When monitoring data exceeds corresponding data threshold, the alarm module issues alarm；And the Cloud Server is by monitoring data
Monitor terminal is sent to warning message.
Further, the data acquisition module includes: gas sensor and temperature sensor.
Further, the monitor terminal includes smart machine；The smart machine includes front-end processing unit, for defeated
The voice signal entered is pre-processed；Voice signal output circuit；Double-core CPU, for the voice signal progress to input and output
Processing；ARM microprocessor works for manipulating smart machine according to user instruction；And intelligent power, with double-core CPU
It is connected, and powers to multiple functional modules.
Further, the intelligent power includes: input voltage+VC~-VC, and left inductance group and right inductance group all have centre
Transformer T1, transformer T2, the transformer T3 of magnetic core, switch element S1, switch element S2, diode D1, diode D2, two poles
Pipe D3, diode D4, diode D5, diode D6, diode D7, diode D8, compensation diode Ds1, compensation diode
Ds2, diode Ds3, capacitor C1, capacitor C2, compensating electric capacity Cs1, compensating electric capacity Cs2, feedback capacitor CB are compensated；Wherein transformer
T1 is the right inductance group Lsc1 that left side is equipped with intermediate magnetic core；Transformer T2 is the right inductance group Lsc2 that left side is equipped with intermediate magnetic core；
Transformer T3 is that left side is equipped with the right inductance group Lsc3 of intermediate magnetic core and with right inductance group Lsc3 at the upper left inductance group of image, a left side
Lower inductance group；The collector of the end input voltage+VC connection switch element S1；The collector of its end-VC connection switch element S2；It is defeated
Enter the end voltage+VC and is also respectively connected with the cathode of diode D1, the cathode of compensating electric capacity Cs1, the anode of diode D2, diode
The cathode of D8, diode D6；The end input voltage-VC is also respectively connected with feedback resistors R1, the anode of diode D2, compensating electric capacity
The anode of Cs2, capacitor C1, capacitor C2, diode D7；Compensate diode Ds1 cathode, compensate diode Ds3 cathode with
The anode of diode D1 is connected；The anode of compensation diode Ds1 is connected with the anode of right inductance group Lsc3；Compensate diode
The anode of Ds3 is connected with the anode of right inductance group Lsc1；The anode of diode D6 is connected with the anode of right inductance group Lsc2；
The opposite end of the collector of switch element S1 is connected with the secondary end of upper left inductance group；The opposite end of the collector of switch element S2
It is connected with the anode of lower-left inductance group；One end phase of the tie point of upper left inductance group and lower-left inductance group and feedback capacitor CB
Even；The other end of feedback capacitor CB is connected with feedback resistors R1；The opposite end of the collector of switch element S2 also passes through connection and mends
The anode for repaying diode Ds2 is connected with the secondary end of right inductance group Lsc3 and compensating electric capacity Cs2 respectively；The cathode of diode D5 with
The secondary end of right inductance group Lsc1 is connected；And the cathode of diode D2 also pass through connection diode D3 anode respectively with right electricity
The secondary end of sense group Lsc2 is connected with capacitor C2.
Further, the front-end processing unit include: sample circuit, signal amplification circuit, shaping circuit, filter circuit and
A/D converter；Wherein a microphone successively turns through over-sampling circuit, signal amplification circuit, shaping circuit, filter circuit and A/D
Parallel operation connects with the input terminal of double-core CPU；The input terminal of the double-core CPU is also connected to respectively for by the micro- place double-core CPU and ARM
Manage RS232 serial communication interface, memory and the intelligent power for stored voice message that device carries out two-way communication；
The voice signal output circuit includes: voice playing circuit module and power amplification circuit module；The output of the double-core CPU
End passes sequentially through voice playing circuit module and power amplification circuit module connects with a loudspeaker；And the ARM micro process
The output end of device is connected by drive module with execution unit.
Further, the intelligent power further includes voltage correction module and current correction module；Voltage correction module and electricity
Stream correction module is all made of eight quadrant interpolation methods and is corrected；The smart machine further include one be connected with double-core CPU it is wireless
Control module；Wireless control module send information to the smart phone of distal end by WIFI module, receives smart phone feedback
Operation signal simultaneously sends back wireless control module, is further processed by double-core CPU according to the operation signal；And it is described
Smart phone is the first priority.
Further, following step is used to eight quadrant interpolation methods of current data correction:
Step S1 selects any one current data I from the data acquired in certain period of time, is vertical with its amplitude
Axis, time are horizontal axis, angularly divide eight quadrants, are successively searched in each quadrant apart from nearest several of current data I
Data point, search radius be initially first threshold, if the current data number that can be found in certain quadrant less than 3, radius by
Secondary to increase to second threshold, maximum is no more than the 5th threshold value, forms a data set DS (st, qua, stx, dit), wherein st
It is current data I, qua is quadrant number, and stx is consecutive number strong point number, and dit is the distance of stx distance st；
Each current data parameters are calculated in DS from the measurement time in past half a minute, in 1 minute, 1 point
Variation difference Df (st, stx, elem, t, dt) in clock half, in 2 minutes, wherein dt refers to above-mentioned time interval；
Using quadrant as grouping unit, using interpolation algorithm calculate each variation difference Df (st, stx, elem, t, dt) away from
Interpolation PI (st, qua, stx, elem, t, dt) from current data I；For confidence level Ar belong to suspicious (50 Ar≤90 <) or
The element value of mistake (Ar≤50), is not involved in interpolation calculation；
The eight quadrant interpolation algorithm formula that this step uses are as follows:
In above formula, m is the quantity of current data in a certain quadrant qua in data set DS；Dit is a certain electric current number in qua
According to the distance between I ' and current data I；Ag_dit be in qua all current datas to current data I distance arithmetic it is flat
Mean value；Ag_dit' is that (with current data I ' for the center of circle, first threshold is radius, searches for the range on the basis of current data I '
Interior all current datas, if the current data quantity searched is less than 3, radius gradually increases to second threshold, maximum
No more than the 5th threshold value), within the scope of this all current datas to current data I ' distance algorithm average value；
Step S2 is on the basis of current data I ', to each current data in its search range with step S1, Df '
Df carry out interpolation calculation obtain as a result, the interpolation formula are as follows:
In above formula, n is the quantity of current data in search range on the basis of current data I '；Dit' is this range
The distance between interior a certain current data and current data I '；Df ' ' is the Df and electric current of a certain current data itself within the scope of this
The difference of the Df of data I '；The meaning of Ag_dit ' is same as above；And
Step S3, correcting current data are W=PI+Df'.
Further, the smart machine further include: electricity consumption statistic device；The electricity consumption statistic device includes:
Return processing module and energy consumption section module；Wherein the processing module that returns is born what is read out in electric quantity monitor database
It carries energy consumption data and creation data is converted to the training data of regression model, and utilize the regression function f in regression model
(x) training data is pre-processed；And energy consumption section module is used to supervise electricity according to Estimating Confidence Interval method
Historical energy consumption data in control device database is analyzed, and confidence level 1- α is given, and obtains the normal interval of energy consumption prediction.
Further, the pretreatment is will to load energy consumption data and creation data is converted to the training number of regression model
According to energy consumption data { f (x will be loaded that is, according to the time of acquisition1), f (x2) ..., f (xn) and corresponding creation data { x1,
x2..., xnIt is used as one group of data < f (xi), xi>, i=1,2 ..., n, for training regression function f (x)=wx+b, w
It is respectively the hyperplane parameter for being fitted training data with b, training process is by way of solving equation, with multi-group data <
f(xi), xi>, i=1,2 ..., n calculate the process of hyperplane parameter w and b；
X1, X2 ... Xn obeys sample distribution (μ, σ2),And S2Respectively indicate the sample average and sample of prediction power consumption
Variance, then stochastic variable
For given confidence level 1- α,Wherein P indicates probability, then in advance
The confidence interval of mean μ for surveying power consumption is
Another aspect, the present invention also provides a kind of working methods of gas station monitoring system, comprising: Cloud Server, prison
Control terminal and monitoring device positioned at gas station；Wherein the monitoring device includes: control module, is connected with the control module
Data acquisition module, alarm module and communication module；The data acquisition module is suitable for acquiring the monitoring data of gas station, and leads to
It crosses communication module and is sent to Cloud Server；The Cloud Server is stored with the corresponding data threshold of every monitoring data, when any
When monitoring data exceeds corresponding data threshold, the alarm module issues alarm；And the Cloud Server is by monitoring data
Monitor terminal is sent to warning message.
The invention has the benefit that gas station monitoring system of the present invention can be realized the data of long-range monitoring gas station,
And when monitoring data is abnormal, alert enables staff reacting at the first time, it is ensured that safety.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples.
Fig. 1 is the functional block diagram of gas station monitoring system of the present invention；
Fig. 2 is the functional block diagram of smart machine in gas station monitoring system of the present invention；
Fig. 3 is the circuit diagram of intelligent power in gas station monitoring system of the present invention.
Specific embodiment
In conjunction with the accompanying drawings, the present invention is further explained in detail.These attached drawings are simplified schematic diagram, only with
Illustration illustrates basic structure of the invention, therefore it only shows the composition relevant to the invention.
Embodiment 1
As shown in Figure 1, the present embodiment 1 provides a kind of gas station monitoring system, comprising: Cloud Server, monitor terminal and
Positioned at the monitoring device of gas station；Wherein the monitoring device includes: control module, the data acquisition being connected with the control module
Module, alarm module and communication module；The data acquisition module is suitable for acquiring the monitoring data of gas station, and passes through communication mould
Block is sent to Cloud Server；The Cloud Server is stored with the corresponding data threshold of every monitoring data, when any monitoring data
When beyond corresponding data threshold, the alarm module issues alarm；And the Cloud Server is by monitoring data and alarm signal
Breath is sent to monitor terminal.
Specifically, the controller such as, but not limited to uses PLC controller；The communication module uses Ethernet interface
Module, WiFi module etc..
The data acquisition module includes: gas sensor and temperature sensor.
Specifically, monitoring the temperature of gas station by temperature sensor；An oxygen of gas station is monitored by gas sensor
Change concentration of carbon.
Specifically, the gas station monitoring system of the present embodiment can be realized the data of long-range monitoring gas station, and monitoring
When data are abnormal, alert enables staff reacting at the first time, it is ensured that safety.
Further, in order to ensure the effective monitoring of this gas station monitoring system, the monitor terminal using smart machine into
Row monitoring, the smart machine high reliablity, long service life.
Specifically, as shown in Fig. 2, the smart machine includes for carrying out pretreated front end to the voice signal of input
Processing unit, voice signal output circuit, the double-core CPU for being handled the voice signal of input and output and for manipulating
The ARM microprocessor that smart machine works according to user instruction, and connect double-core CPU and power to multiple functional modules
Intelligent power.
As shown in figure 3, the intelligent power includes input voltage+VC~-VC, during left inductance group and right inductance group all have
Between magnetic core transformer T1, transformer T2, transformer T3, switch element S1, switch element S2, diode D1, diode D2, two
Pole pipe D3, diode D4, diode D5, diode D6, diode D7, diode D8, compensation diode Ds1, compensation diode
Ds2, diode Ds3, capacitor C1, capacitor C2, compensating electric capacity Cs1, compensating electric capacity Cs2, feedback capacitor CB are compensated；Wherein transformer
T1 is the right inductance group Lsc1 that left side is equipped with intermediate magnetic core, and transformer T2 is the right inductance group Lsc2 that left side is equipped with intermediate magnetic core,
Transformer T3 is that left side is equipped with the right inductance group Lsc3 of intermediate magnetic core and with right inductance group Lsc3 at the upper left inductance group of image, a left side
Lower inductance group；The collector of the end voltage+VC connection switch element S1, the collector of the end voltage-VC connection switch element S2；Voltage
The end+VC connects the cathode of diode D1, the cathode of compensating electric capacity Cs1, the anode of diode D2, diode D8, diode D6
Cathode；The end voltage-VC connects feedback resistors R1, diode D2 anode, compensating electric capacity Cs2, capacitor C1, capacitor C2, diode
The anode of D7；The cathode for compensating diode Ds1, the cathode for compensating diode Ds3 are connected with the anode of diode D1；Compensation two
The anode of pole pipe Ds1 is connected with the anode of right inductance group Lsc3, compensates the anode and right inductance group Lsc1 of diode Ds3
Anode be connected；The anode of diode D6 is connected with the anode of right inductance group Lsc2；The collector of switch element S1
Opposite end is connected with the secondary end of upper left inductance group, the opposite end of the collector of switch element S2 and the anode phase of lower-left inductance group
The tie point of connection, upper left inductance group and lower-left inductance group is connected with feedback capacitor CB, the feedback capacitor CB other end and feedback electricity
Hinder R1It is connected；The opposite end of the collector of switch element S2 also pass through connection compensation diode Ds2 anode, then with right inductance
The secondary end of group Lsc3, compensating electric capacity Cs2 are connected；The cathode of diode D5 is connected with the secondary end of right inductance group Lsc1；Two poles
The cathode of pipe D2 also passes through the anode of connection diode D3, is then connected with the secondary end of right inductance group Lsc2, capacitor C2.
Two switch elements S1, S2 are when connecting, because electric current crosses negative effect, no longer generate switching losses；It is multiple simultaneously
Low, power density height is lost so that not generating extra power capacity in the specific cooperation of inductance group and corresponding electric appliance element,
Improve the working life and reliability of smart machine and its power supply.
The front-end processing unit includes sample circuit, signal amplification circuit, shaping circuit, filter circuit and A/D conversion
Device, microphone is successively through over-sampling circuit, signal amplification circuit, shaping circuit, filter circuit and A/D converter and double-core CPU
Input terminal connect, the input terminal of the double-core CPU is further connected with for double-core CPU and ARM microprocessor to be carried out two-way communication
RS232 serial communication interface, the intelligence for the 64G memory of stored voice message and for powering for each unit module
Power supply, the voice signal output circuit include voice playing circuit module and power amplification circuit module, the output of double-core CPU
End passes sequentially through voice playing circuit module and power amplification circuit module connects with loudspeaker, the ARM microprocessor it is defeated
Connected out by drive module with execution unit, the intelligent power can be automatically regulated to be suspend mode when not in use.
When the smart machine realizes human-computer interaction, activation system, user is instructed by microphone input voice signal, voice
Signal is sent into double-core CPU after front-end processing, and double-core CPU passes through RS232 after being analyzed and processed to the received voice signal of institute
Serial communication interface gives information to ARM microprocessor, and ARM microprocessor passes through drive module according to the received command information of institute
Control execution unit executes corresponding operation order, while microprocessor passes through RS232 serial communication interface for corresponding voice
Output signal is transmitted to double-core CPU, controls loudspeaker by voice playing circuit module and power amplification circuit module by double-core CPU
Export voice signal.
When multiple functional modules of the smart machine constantly start, intelligent power its electric current, voltage instability in conversion,
It is not easy to determine to be therefore to need to be arranged corresponding correction module in order to judge and further in normal condition or abnormality
Ground uses.The intelligent power further includes voltage correction module and current correction module, voltage correction module and current correction mould
Block is all made of eight quadrant interpolation methods and is corrected.By taking electric current as an example, it is corrected using eight quadrant interpolation methods.
Following step is used to eight quadrant interpolation methods of current data correction: from the data of the acquisition in certain period of time
Any one current data (current data I might as well be defined as) is selected, using its amplitude as the longitudinal axis, the time is horizontal axis, is angularly drawn
Point eight quadrants successively search several data points nearest apart from current data I in each quadrant, search radius and are initially the
One threshold value, if the current data number that can be found in certain quadrant is less than 3, radius gradually increases to second threshold, most very much not
More than the 5th threshold value, a data set DS (st, qua, stx, dit) is formed, wherein qua is quadrant number, and stx is consecutive number
Strong point number, dit is the distance of stx distance st (i.e. current data I)；Each current data parameters in DS are calculated to survey certainly
Measure from the time in past half a minute, in 1 minute, variation difference Df in 1 minute half, in 2 minutes (st, stx, elem, t,
Dt), wherein dt refers to above-mentioned time interval.
Using quadrant as grouping unit, using interpolation algorithm calculate each variation difference Df (st, stx, elem, t, dt) away from
Interpolation PI (st, qua, stx, elem, t, dt) from current data I；For confidence level Ar belong to suspicious (50 Ar≤90 <) or
The element value of mistake (Ar≤50), is not involved in interpolation calculation；The interpolation algorithm formula that this step uses are as follows:
In above formula, m is the quantity of current data in a certain quadrant qua in data set DS；Dit is a certain electric current number in qua
According to the distance between (I ' might as well be defined as) and current data I；Ag_dit be in qua all current datas to current data I
The arithmetic mean of instantaneous value of distance；Ag_dit' is that (with current data I ' for the center of circle, first threshold is half on the basis of current data I '
Diameter searches for current data all within the scope of this, if the current data quantity searched is less than 3, radius gradually increases to
Second threshold, maximum be no more than the 5th threshold value), within the scope of this all current datas to current data I ' distance algorithm be averaged
Value.
Same method, Df' are to carry out interpolation meter to the Df of each current data in its search range on the basis of current data I '
It is obtaining as a result, the interpolation formula are as follows:
In above formula, n is the quantity of current data in search range on the basis of current data I '；Dit' is this range
The distance between interior a certain current data and current data I '；Df " is the Df and electric current of a certain current data itself within the scope of this
The difference of the Df of data I '；The meaning of Ag_dit ' is same as above.
Correcting current data are W=PI+Df'.
The voltage data that above-mentioned method obtains correction also can be used.Essence is obtained using unique eight quadrants interpolation method
Really, whether stable electric current and voltage data are in normal according to above-mentioned accurate data judging smart machine and its power supply
Use state or abnormality are convenient for subsequent processing.
In order to improve the operation convenience of smart machine, in addition to manual control switch, can also be arranged a wireless control module with
Double-core CPU is connected with each other, and wireless control module send information to the smart phone of distal end by WIFI module, receives smart phone
The operation signal of feedback simultaneously sends back wireless control module, is further processed by double-core CPU according to the operation signal.It can
Selectively, smart phone is the first priority, i.e. the manual operation of wireless remote is preferred operations.
The smart machine further includes electricity consumption statistic device, to count the electricity consumption system of the smart machine different periods
Meter, consequently facilitating it is clear, and then adjust and guarantee the operating time of the smart machine.
Electricity consumption statistic device includes returning processing module and energy consumption section module；By taking electric quantity monitor as an example, wherein
It returns processing module and the load energy consumption data and creation data that read out in electric quantity monitor database is converted into machines for regression
The pretreatment of the training data of type, utilizing is regression function f (x) in regression model；Energy consumption section module is used for basis
Estimating Confidence Interval method analyzes the historical energy consumption data in electric quantity monitor database, gives confidence level 1- α, obtains
The normal interval of energy consumption prediction.
The pretreatment is will to load energy consumption data and creation data is converted to the training data of regression model, i.e., according to
The time of acquisition will load energy consumption data { f (x1), f (x2) ..., f (xn) and corresponding creation data { x1, x2..., xn}
As one group of data < f (xi), xi>, i=1,2 ..., n, for training regression function f (x)=wx+b, w and b to be respectively
It is fitted the hyperplane parameter of training data, training process is by way of solving equation, with multi-group data < f (xi), xi
>, i=1,2 ..., n calculate the process of hyperplane parameter w and b；
X1, X2 ... Xn obeys sample distribution (μ, σ2),And S2Respectively indicate the sample average and sample of prediction power consumption
Variance, then stochastic variableFor given confidence level 1- α,
Wherein P indicates probability, then predicts that the confidence interval of the mean μ of power consumption is
In short, the smart machine of the present embodiment can realize human-computer interaction, power supply selects the intelligent power of specific circuit design,
There is no switching losses, loss is low, and power density is high, improves the working life and reliability of smart machine and its power supply；Using
Unique eight quadrants interpolation method obtains accurate, stable electric current and voltage data, is convenient for subsequent processing.
Embodiment 2
On the basis of the present embodiment 1, the present embodiment 2 provides a kind of gas station monitoring system, comprising: Cloud Server,
Monitor terminal and monitoring device positioned at gas station；Wherein the monitoring device includes: control module, is connected with the control module
Data acquisition module, alarm module and communication module；The data acquisition module is suitable for acquiring the monitoring data of gas station, and
Cloud Server is sent to by communication module；The Cloud Server is stored with the corresponding data threshold of every monitoring data, when appoint
When one monitoring data exceeds corresponding data threshold, the alarm module issues alarm；And the Cloud Server will monitor number
Monitor terminal is sent to according to warning message.
Specifically, the working principle of gas station monitoring system, working method and the course of work described in the present embodiment with
Gas station monitoring system in embodiment 1 is identical, and details are not described herein again.
The above content is a further detailed description of the present invention in conjunction with specific preferred embodiments, and it cannot be said that
Specific implementation of the invention is only limited to these instructions.For those of ordinary skill in the art to which the present invention belongs, exist
Under the premise of not departing from present inventive concept, a number of simple deductions or replacements can also be made, all shall be regarded as belonging to of the invention
Protection scope.
Claims (10)
1. a kind of gas station monitoring system characterized by comprising
Cloud Server, monitor terminal and the monitoring device positioned at gas station；Wherein
The monitoring device includes: control module, data acquisition module, alarm module and the communication mould being connected with the control module
Block；
The data acquisition module is suitable for acquiring the monitoring data of gas station, and is sent to Cloud Server by communication module；
The Cloud Server is stored with the corresponding data threshold of every monitoring data, when any monitoring data exceeds corresponding data
When threshold value, the alarm module issues alarm；And
Monitoring data and warning message are sent to monitor terminal by the Cloud Server.
2. gas station monitoring system according to claim 1, which is characterized in that
The data acquisition module includes: gas sensor and temperature sensor.
3. gas station monitoring system according to claim 2, which is characterized in that
The monitor terminal includes smart machine；
The smart machine includes:
Front-end processing unit, for being pre-processed to the voice signal of input；
Voice signal output circuit；
Double-core CPU, for handling the voice signal of input and output；
ARM microprocessor works for manipulating smart machine according to user instruction；And
Intelligent power is connected with double-core CPU, and powers to multiple functional modules.
4. gas station monitoring system according to claim 3, which is characterized in that
The intelligent power includes:
Input voltage+VC~-VC, left inductance group and right inductance group all have the transformer T1 of intermediate magnetic core, transformer T2, transformation
Device T3, switch element S1, switch element S2, diode D1, diode D2, diode D3, diode D4, diode D5, two poles
Pipe D6, diode D7, diode D8, compensation diode Ds1, compensation diode Ds2, compensation diode Ds3, capacitor C1, capacitor
C2, compensating electric capacity Cs1, compensating electric capacity Cs2, feedback capacitor CB；Wherein
Transformer T1 is the right inductance group Lsc1 that left side is equipped with intermediate magnetic core；
Transformer T2 is the right inductance group Lsc2 that left side is equipped with intermediate magnetic core；
Transformer T3 is that left side is equipped with the right inductance group Lsc3 of intermediate magnetic core and with right inductance group Lsc3 at the upper left inductance of image
Group, lower-left inductance group；
The collector of the end input voltage+VC connection switch element S1；The collector of its end-VC connection switch element S2；
The end input voltage+VC is also respectively connected with the cathode of diode D1, the cathode of compensating electric capacity Cs1, the anode of diode D2, two
The cathode of pole pipe D8, diode D6；
The end input voltage-VC is also respectively connected with feedback resistors R1, the anode of diode D2, compensating electric capacity Cs2, capacitor C1, capacitor
The anode of C2, diode D7；
The cathode for compensating diode Ds1, the cathode for compensating diode Ds3 are connected with the anode of diode D1；
The anode of compensation diode Ds1 is connected with the anode of right inductance group Lsc3；
The anode of compensation diode Ds3 is connected with the anode of right inductance group Lsc1；
The anode of diode D6 is connected with the anode of right inductance group Lsc2；
The opposite end of the collector of switch element S1 is connected with the secondary end of upper left inductance group；
The opposite end of the collector of switch element S2 is connected with the anode of lower-left inductance group；
The tie point of upper left inductance group and lower-left inductance group is connected with one end of feedback capacitor CB；
The other end of feedback capacitor CB is connected with feedback resistors R1；
The opposite end of the collector of switch element S2 also pass through connection compensation diode Ds2 anode respectively with right inductance group Lsc3
Secondary end be connected with compensating electric capacity Cs2；
The cathode of diode D5 is connected with the secondary end of right inductance group Lsc1；And
The cathode of diode D2 also pass through connection diode D3 anode respectively with the secondary end of right inductance group Lsc2 and capacitor C2 phase
Connection.
5. gas station monitoring system according to claim 3, which is characterized in that
The front-end processing unit includes:
Sample circuit, signal amplification circuit, shaping circuit, filter circuit and A/D converter；Wherein
One microphone is successively through over-sampling circuit, signal amplification circuit, shaping circuit, filter circuit and A/D converter and double-core
The input terminal of CPU connects；
The input terminal of the double-core CPU is also connected to respectively for double-core CPU and ARM microprocessor to be carried out two-way communication
RS232 serial communication interface, memory and the intelligent power for stored voice message；
The voice signal output circuit includes: voice playing circuit module and power amplification circuit module；
The output end of the double-core CPU passes sequentially through voice playing circuit module and power amplification circuit module and a loudspeaker phase
It connects；And
The output end of the ARM microprocessor is connected by drive module with execution unit.
6. according to the described in any item gas station monitoring systems of claim 3~5, which is characterized in that
The intelligent power further includes voltage correction module and current correction module；Wherein
Voltage correction module and current correction module are all made of eight quadrant interpolation methods and are corrected；
The smart machine further includes a wireless control module being connected with double-core CPU；
Wireless control module send information to the smart phone of distal end by WIFI module, receives the operation letter of smart phone feedback
Number and send back wireless control module, be further processed by double-core CPU according to the operation signal；And
The smart phone is the first priority.
7. gas station monitoring system according to claim 6, which is characterized in that
Following step is used to eight quadrant interpolation methods of current data correction:
Step S1 selects any one current data I from the data acquired in certain period of time, using its amplitude as the longitudinal axis, when
Between be horizontal axis, angularly divide eight quadrants, successively search nearest apart from current data I several data in each quadrant
Point searches radius and is initially first threshold, if the current data number that can be found in certain quadrant is less than 3, radius gradually increases
It is added to second threshold, maximum is no more than the 5th threshold value, forms a data set DS (st, qua, stx, dit), wherein st is electricity
Flow data I, qua are quadrant numbers, and stx is consecutive number strong point number, and dit is the distance of stx distance st；
Each current data parameters are calculated in DS from the measurement time in past half a minute, in 1 minute, 1 minute half
Variation difference Df (st, stx, elem, t, dt) interior, in 2 minutes, wherein dt refers to above-mentioned time interval；
Using quadrant as grouping unit, each variation difference Df (st, stx, elem, t, dt) distance electricity is calculated using interpolation algorithm
The interpolation PI (st, qua, stx, elem, t, dt) of flow data I；
The element value for belonging to suspicious (50 Ar≤90 <) or wrong (Ar≤50) for confidence level Ar, is not involved in interpolation calculation；
The eight quadrant interpolation algorithm formula that this step uses are as follows:
In above formula, m is the quantity of current data in a certain quadrant qua in data set DS；Dit is a certain current data I ' in qua
The distance between current data I；Ag_dit be in qua all current datas to current data I distance arithmetic mean of instantaneous value；
Ag_dit' is that (with current data I ' for the center of circle, first threshold is radius, searches for institute within the scope of this on the basis of current data I '
Some current datas, if the current data quantity searched is less than 3, radius gradually increases to second threshold, most very much not surpasses
Cross the 5th threshold value), within the scope of this all current datas to current data I ' distance algorithm average value；
Step S2 is to be carried out on the basis of current data I ' to the Df of each current data in its search range with step S1, Df
It is that interpolation calculation obtains as a result, the interpolation formula are as follows:
In above formula, n is the quantity of current data in search range on the basis of current data I '；Dit ' is certain within the scope of this
The distance between one current data and current data I '；Df ' is the Df and current data of a certain current data itself within the scope of this
The difference of the Df of I '；The meaning of Ag_dit ' is same as above；And
Step S3, correcting current data are W=PI+Df '.
8. gas station monitoring system according to claim 6, which is characterized in that
The smart machine further include: electricity consumption statistic device；
The electricity consumption statistic device includes: to return processing module and energy consumption section module；Wherein
The load energy consumption data and creation data that the recurrence processing module will be read out in electric quantity monitor database are converted to
The training data of regression model, and training data is pre-processed using the regression function f (x) in regression model；With
And
Energy consumption section module is used for according to Estimating Confidence Interval method to the history energy consumption number in electric quantity monitor database
According to being analyzed, confidence level 1- α is given, obtains the normal interval of energy consumption prediction.
9. gas station monitoring system according to claim 8, which is characterized in that
The pretreatment is will to load energy consumption data and creation data is converted to the training data of regression model, i.e., according to acquisition
Time, will load energy consumption data { f (x1), f (x2) ..., f (x0) and corresponding creation data { x1, x2..., xnConduct
One group of data < f (xi), xi>, i=1,2 ..., n are fitting instructions for training regression function f (x)=wx+b, w and b respectively
Practice the hyperplane parameter of data, training process is by way of solving equation, with multi-group data < f (xi), xi>, i=1,
The process of 2 ..., n calculating hyperplane parameter w and b；
X1, X2 ... Xn obeys sample distribution (μ, σ2),And S2Respectively indicate sample average and the sample side of prediction power consumption
Difference, then stochastic variable
For given confidence level 1- α,Wherein P indicates probability, then predicts to consume
The confidence interval of the mean μ of electricity is
10. a kind of working method of gas station monitoring system characterized by comprising
Cloud Server, monitor terminal and the monitoring device positioned at gas station；Wherein
The monitoring device includes: control module, data acquisition module, alarm module and the communication mould being connected with the control module
Block；
The data acquisition module is suitable for acquiring the monitoring data of gas station, and is sent to Cloud Server by communication module；
The Cloud Server is stored with the corresponding data threshold of every monitoring data, when any monitoring data exceeds corresponding data
When threshold value, the alarm module issues alarm；And
Monitoring data and warning message are sent to monitor terminal by the Cloud Server.
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