CN110716012A - Oil gas concentration intelligent monitoring system based on field bus network - Google Patents
Oil gas concentration intelligent monitoring system based on field bus network Download PDFInfo
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital
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- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
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- G—PHYSICS
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- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital
- G01N2033/0068—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital using a computer specifically programmed
Abstract
The invention discloses an intelligent oil and gas concentration monitoring system based on a field bus network, which consists of a fuel tank area environment parameter acquisition platform of a fuel station based on a CAN field bus network and a multi-point oil and gas concentration leakage classification subsystem of the fuel tank area environment of the fuel station, and realizes intelligent detection of the oil and gas leakage concentration of the fuel tank area environment of the fuel station and classification of the oil and gas leakage concentration grade; the invention not only effectively solves the problems of the traditional oil gas concentration detection device in the oil tank area of the gas station caused by unreasonable design, backward equipment, imperfect detection system and the like, but also effectively solves the problems of the existing oil tank area environment monitoring system of the gas station in detecting and classifying the concentration of the environment of the oil tank area of the gas station, thereby greatly improving the accuracy and robustness of the oil gas concentration detection of the environment of the oil tank area of the gas station.
Description
Technical Field
The invention relates to the technical field of automatic monitoring of oil and gas environments of gas stations, in particular to an intelligent oil and gas concentration monitoring system based on a field bus network.
Background
The oil tank area environment of a gas station mainly stores an oil storage container or pipeline, if leakage occurs, flammable liquid steam can be generated, when the steam pressure is high, the danger of combustion and explosion can be generated, the flammable liquid has flowing performance, when the flammable liquid meets a fire source at normal temperature, the flammable liquid can be ignited and combusted, if the storage container leaks, the flammable steam can be continuously evaporated in the flowing process, and even the slightest sparks can be caused to combust once the storage container contacts the fire source. Therefore, the fire safety management is strengthened, the oil gas leakage concentration causing the fire disaster is detected in real time, the potential safety hazard is eliminated, and the fire accident is avoided, so that the most important work of the environmental fire safety management of the oil tank area of the gas station is realized. When the storage jar takes place to leak, meet the mars, cause the conflagration, if put out a fire and take measures untimely, will arouse a series of chain reaction, cause bigger loss, produce the continuity explosion, produce shock wave strength and can destroy equipment and factory building in the twinkling of an eye greatly, the destructive power is extremely strong. The oil tank area environment of a gas station is an area where oil products are the most, the oil products all belong to flammable liquid, the probability of fire and explosion accidents is high, and once the accidents happen, the consequences are quite serious. The fire accident in the tank area of the gasoline station not only can harm people and surrounding equipment and facilities, but also can cause explosion accident when the concentration of steam rises, such as reaching the limit of gasoline explosion concentration. Thus, the economic loss is more serious, and the social influence is stronger. Therefore, a great deal of work is needed in the aspects of safety management and emergency management of the oil tank field environment of the gas station. From the analysis of accident types of the environment of the oil tank area of the gas station, the leakage and fire explosion accidents are the key points for the safety protection of the environment of the oil tank area of the gas station. The invention discloses an intelligent oil-gas concentration monitoring system based on a field bus network, which consists of a filling station tank area environment parameter acquisition platform based on a CAN field bus network and a multi-point oil-gas concentration leakage classification subsystem of the filling station tank area environment, realizes the detection, prediction and classification of leakage concentration of the filling station tank area environment, and has higher accuracy and robustness in the detection, prediction and classification of the filling station tank area oil-gas leakage concentration.
Disclosure of Invention
The invention provides an intelligent oil-gas concentration monitoring system based on a field bus network, which not only effectively solves the problems of the traditional oil-gas concentration detection device in the oil tank area of a gas station in the detection of the oil-gas concentration in the oil tank area of the gas station caused by unreasonable design of the oil-gas concentration detection equipment, backward equipment, imperfect detection system and the like, but also effectively detects and classifies the concentration of the environment in the oil tank area of the gas station according to the characteristics of nonlinearity and large lag of the change of the oil-gas concentration, large change of the oil-gas concentration in the environment area of the oil tank area of the gas station and the like, thereby greatly improving the accuracy and robustness of the detection of the oil-gas concentration in the environment of the oil tank area of the gas station.
The invention is realized by the following technical scheme:
the system realizes intelligent detection, prediction and classification of oil gas leakage concentration in the environment of the oil tank area of the gas station.
The invention further adopts the technical improvement scheme that:
the environment parameter acquisition platform of the oil tank district of the gas station based on the CAN field bus network consists of a detection node and a field monitoring terminal, and the detection node and the field monitoring terminal constitute the parameter acquisition platform of the oil tank district of the gas station through the CAN field bus. The detection nodes respectively consist of a sensor group module, a single chip microcomputer and a communication module, wherein the sensor group module is responsible for detecting the environmental parameters of the oil tank area of the oil filling station, such as temperature, oil gas concentration, wind speed, smoke and the like of the oil tank area of the oil filling station, and the single chip microcomputer controls sampling intervals and sends the sampling intervals to an on-site monitoring end through the communication module; the field monitoring terminal consists of an industrial control computer and an RS232/CAN communication module, and realizes management of the detection node for detecting the parameters of the oil tank area of the gas station and fusion, prediction and classification of the concentration of multi-point oil gas in the oil tank area of the gas station. A platform for acquiring environmental parameters of a fuel tank field of a gas station based on a CAN field bus is shown in figure 1.
The invention further adopts the technical improvement scheme that:
the classification subsystem for the oil gas leakage of the oil tank area environment at the oil filling station consists of 5 parts including a plurality of detection point oil gas concentration sensors, a plurality of time series triangular fuzzy number neural networks, a plurality of time series triangular fuzzy number fusion models, a triangular fuzzy number prediction module and a wavelet neural network oil gas leakage concentration classifier, wherein the oil gas concentration sensors at the plurality of detection points sense the oil gas leakage concentration of the detected points, the output of each detection point oil gas concentration sensor is used as the input of each corresponding time series triangular fuzzy number neural network, the output of the plurality of time series triangular fuzzy number neural networks is used as the input of the oil tank area environment multi-point oil gas concentration fusion model at the oil filling station, the output of the oil gas concentration fusion model at the oil tank area environment at the oil filling station is used as the input of the triangular fuzzy number prediction module, and the output of the triangular fuzzy number prediction module is used as the input of the, the wavelet neural network oil gas leakage concentration classifier divides the detected oil gas leakage concentration of the gas station into different grades, the multi-point oil gas concentration leakage classification subsystem of the oil tank area environment of the gas station realizes the classification processes of detection, fuzzy quantization, multi-point fusion, prediction and oil gas leakage concentration grade of the oil gas leakage concentration of the gas station, and the multi-point oil gas concentration leakage classification subsystem of the oil tank area environment of the gas station is shown in figure 2.
The invention further adopts the technical improvement scheme that:
the time series triangular fuzzy neural network is composed of each time series triangular fuzzy neural network corresponding to each detection point, the time series triangular fuzzy neural network is composed of a section of conventional time series value output by an oil gas concentration sensor of a detected point as the input of the radial basis neural network, the radial basis neural network and the triangular fuzzy value of the oil gas concentration of the detected point as the output of the radial basis neural network, and the triangular fuzzy value output by the radial basis neural network respectively represents the lower limit value, the maximum possible value and the upper limit value of the oil gas concentration of the detected point; the time series triangular fuzzy neural network converts a period of conventional time series value of the oil gas concentration of the detected point into the triangular fuzzy value of the detected oil gas concentration according to the dynamic change characteristic of the oil gas concentration of the detected point to represent, and the conversion is more in line with the dynamic change rule of the oil gas concentration of the detected point.
The invention further adopts the technical improvement scheme that:
the oil gas concentration time series triangular fuzzy number array is formed by 3 parts including an oil gas concentration time series triangular fuzzy number array, the relative closeness of an oil gas concentration triangular fuzzy number predicted value and a positive ideal value and the negative ideal value is calculated, the triangular fuzzy number predicted value of oil gas concentration of a plurality of parameter detection units in a period of time forms the oil gas concentration time series triangular fuzzy number array, the positive ideal value and the negative ideal value of the oil gas concentration time series triangular fuzzy number array are determined, the distance between the oil gas concentration time series triangular fuzzy number predicted value and the positive ideal value and the negative ideal value of the oil gas concentration time series triangular fuzzy number array are respectively calculated, the distance between the negative ideal value of the time series triangular fuzzy number predicted value of each detection unit is divided by the sum of the distance between the negative ideal value of the time series triangular fuzzy number predicted value of each detection unit and the distance between the positive ideal value of the time series triangular fuzzy number predicted value of each detection unit The obtained quotient is the relative closeness of the time-series triangular fuzzy number predicted value of each detection unit, the obtained quotient obtained by dividing the relative closeness of the time-series triangular fuzzy number predicted value of each detection unit by the sum of the relative closeness of the time-series triangular fuzzy number predicted values of all the detection units is the fusion weight of the time-series triangular fuzzy number predicted value of each detection unit, and the sum of the products of the time-series triangular fuzzy number predicted value of each detection unit and the fusion weight of the time-series triangular fuzzy number predicted value of the detection unit is the fusion value of the time-series triangular fuzzy number predicted values of a plurality of detection points.
The invention further adopts the technical improvement scheme that:
the triangular fuzzy number prediction module consists of 3 NARX neural network prediction models and 3 Elman neural network prediction models of a phase space reconstruction technology, the lower limit value, the maximum possible value and the upper limit value of the triangular fuzzy number of the detected environment oil gas concentration output by the multipoint oil gas concentration fusion model in the oil tank area of the gas station are respectively input into the NARX neural network prediction model 1, the NARX neural network prediction model 2 and the NARX neural network prediction model 3, the difference between the lower limit value, the maximum possible value and the upper limit value of the detected environment oil gas concentration triangular fuzzy number output by the multipoint oil gas concentration fusion model in the oil tank area of the gas station and the output of the NARX neural network prediction model 1, the NARX neural network prediction model 2 and the NARX neural network prediction model 3 is respectively input into the Elman neural network prediction model 1 of the phase space reconstruction technology, the Elman neural network prediction model 2 of the phase space reconstruction technology and the Elman neural network prediction model 3 of the phase space reconstruction technology, the outputs of the NARX neural network prediction model 1, the NARX neural network prediction model 2 and the NARX neural network prediction model 3 are respectively added with the outputs of the Elman neural network prediction model 1 of the phase space reconstruction technology, the Elman neural network prediction model 2 of the phase space reconstruction technology and the Elman neural network prediction model 3 of the phase space reconstruction technology to be used as a triangular fuzzy number prediction value of the oil gas concentration of the detected environment, and the triangular fuzzy number prediction value is output as a triangular fuzzy number prediction module.
The invention further adopts the technical improvement scheme that:
the wavelet neural network oil gas leakage concentration classifier establishes a language variable and 5 different triangular fuzzy number corresponding relation tables of 5 concentration levels for evaluating the detected oil gas leakage concentration of the oil tank area environment of the gas station according to the engineering practice of the oil gas leakage concentration of the environment of the oil tank area of the detected gas station and the national 'leakage pollution control standard of the gas station', and divides the detected oil gas leakage concentration of the environment of the oil tank area of the gas station into 5 leakage levels of very high oil gas leakage concentration, relatively high oil gas leakage concentration, normal oil gas leakage concentration and very low oil gas leakage concentration; the wavelet neural network oil gas leakage concentration classifier classifies the oil gas leakage concentration grade of the detected oil gas tank area environment, the output of the wavelet neural network oil gas leakage concentration classifier is a triangular fuzzy numerical value representing the oil gas leakage concentration grade, the similarity between the output of the wavelet neural network oil gas leakage concentration classifier and 5 triangular fuzzy numbers representing the detected oil gas tank area environment 5 oil gas leakage concentration grades is calculated respectively, and the oil gas leakage grade corresponding to the triangular fuzzy number with the maximum similarity is the current oil gas leakage concentration grade of the detected oil gas tank area environment.
Compared with the prior art, the invention has the following obvious advantages:
the invention relates to a method for measuring the environmental parameters of a gas station tank area, which aims at the uncertainty and randomness of the problems of sensor precision error, interference, abnormal measured temperature value and the like in the measuring process of the environmental parameters of the gas station tank area.
The oil gas concentration triangular fuzzy prediction values of the plurality of detection points are dynamically fused by the multipoint oil gas concentration fusion model in the oil tank area environment of the gas station, positive and negative ideal values of the oil gas concentration time series triangular fuzzy prediction values of the plurality of detection points are determined by determining the oil gas concentration time series triangular fuzzy array of the time series triangular fuzzy prediction values of the plurality of detection points, the distance between the oil gas concentration time series triangular fuzzy prediction value of each detection unit and the positive and negative ideal values of the oil gas concentration time series triangular fuzzy array, the relative closeness between each detection unit and the positive and negative ideal values and the fusion weight are respectively calculated, and the accuracy of the oil gas concentration triangular fuzzy prediction value of the detected points is improved.
Thirdly, the input of the NARX neural network prediction model adopted by the invention comprises a period of time input and output historical feedback of a lower limit value a, a possible value b and an upper limit value c of the triangular fuzzy number of the detected point, the feedback input can be considered to contain a period of time of state historical information of the detected triangular fuzzy number to participate in the prediction of the detected triangular fuzzy number, and the prediction obtains a good effect for a proper feedback time length.
The NARX neural network prediction model adopted by the invention is a dynamic neural network model which can effectively predict the nonlinear and non-stationary time sequence of the lower limit value a, the possible value b and the upper limit value c of the triangular fuzzy number of the detected point parameter of the gas station, and can improve the prediction precision of the time sequence of the triangular fuzzy number of the detected point of the gas station under the condition that the non-stationarity of the time sequence is reduced. Compared with the traditional prediction model method, the method has the advantages of good effect of processing the non-stationary time sequence, high calculation speed and high accuracy. Through the actual comparison of experimental data of oil gas leakage concentration of a non-stable oil tank truck, the method verifies the feasibility of the NARX neural network prediction model for predicting the triangular fuzzy time series of the detected points of the gas station. Meanwhile, the experimental result also proves that the NARX neural network prediction model is more excellent in non-stationary time series prediction compared with the traditional model.
The invention utilizes NARX neural network to establish the triangular fuzzy parameter prediction model of the detected point of the gas station, because of introducing the dynamic recursive network of the delay module and the output feedback establishment model, the invention introduces the input and output vector delay feedback into the network training to form a new input vector, and has good nonlinear mapping capability, the input of the network model not only comprises the original input data, but also comprises the output data after training, the generalization capability of the network is improved, and the network has better prediction precision and self-adaptive capability in the time series prediction of the oil gas leakage concentration of the nonlinear oil tank truck compared with the traditional static neural network.
The Elman neural network prediction model of the phase space reconstruction technology adopted by the invention realizes prediction of residual errors of parameter triangular fuzzy numbers of detected points, the predicted value is used as a compensation value of the triangular fuzzy numbers of the detected points, and the detection accuracy of the triangular fuzzy numbers of the detected points is improved. The transfer function of the hidden layer unit can adopt a linear or non-linear function, and the accepting layer is also called a context layer or a state layer, and is used for memorizing the output value of the hidden layer unit at the previous moment, which can be regarded as a time delay operator. The Elman neural network prediction model is characterized in that the output of the hidden layer is self-connected to the input of the hidden layer through the delay and storage of the supporting layer, the self-connection mode enables the output to have sensitivity to the data of the historical state, and the addition of the internal feedback network increases the capability of the network for processing dynamic information, so that the purpose of dynamic modeling is achieved. The regression neural network of the Elman neural network prediction model is characterized in that the output of a hidden layer is self-connected to the input of the hidden layer through the delay and storage of a structural unit, the self-connection mode enables the prediction model to have sensitivity to data of a historical state, and the addition of an internal feedback network increases the capability of the network for processing dynamic information, thereby being beneficial to modeling of a dynamic process; the model fuses information of a future prediction network and information of a past prediction network by using feedback connection of dynamic neurons of a correlation layer, so that the memory of the network to time series characteristic information is enhanced, and the prediction accuracy of the triangular fuzzy number of a detected point is improved.
Seventhly, the invention combines the phase space reconstruction and the Elman neural network to construct an Elman neural network prediction model of the phase space reconstruction to influence the oil gas concentration, the temperature and the smoke parameters of the fire risk degree for compensation prediction, the prediction model can estimate the evolution information of a one-dimensional time sequence of the prediction parameters by the reconstruction phase space and expand the one-dimensional time sequence into a multi-dimensional sequence containing polymorphic information, so that the result of the predicted parameters is more consistent with the actual value; in addition, the output vector of the phase space reconstruction is used as the input value of the Elman neural network, the randomness of selecting the input parameters of the Elman neural network is avoided, the Elman neural network prediction model of the phase space reconstruction is adopted to improve the accuracy and reliability of predicting the fire parameters influencing the gas station, and the method has important value for accurately predicting the fire risk of the gas station.
The wavelet neural network oil gas leakage concentration classifier of the invention establishes a language variable and 5 different triangular fuzzy number corresponding relation tables for evaluating 5 concentration levels of the detected oil gas leakage concentration of the oil tank region of the gas station according to the engineering practice of the oil gas leakage concentration of the environment of the oil tank region of the detected gas station and the national 'oil gas leakage pollution control standard' of the gas station, and divides the detected oil gas leakage concentration of the environment of the oil tank region of the gas station into 5 leakage levels of very high oil gas leakage concentration, relatively high oil gas leakage concentration, normal oil gas leakage concentration and very low oil gas leakage concentration; the wavelet neural network oil gas leakage concentration classifier classifies the oil gas leakage concentration grade of the environment of a detected oil tank area of a gas station, the output of the wavelet neural network oil gas leakage concentration classifier is a triangular fuzzy numerical value representing the oil gas leakage concentration grade, the similarity between the output of the wavelet neural network oil gas leakage concentration classifier and 5 triangular fuzzy numbers representing the 5 oil gas leakage concentration grades of the environment of the detected oil tank area of the gas station is calculated respectively, the oil gas leakage grade corresponding to the triangular fuzzy number with the maximum similarity is the current oil gas leakage concentration grade of the environment of the detected oil tank area of the gas station, and the dynamic performance and the scientific classification of the fire hazard grade classification of the gas station are achieved.
Drawings
FIG. 1 shows a platform for collecting environmental parameters of a tank farm of a gas station based on a CAN field bus network;
FIG. 2 shows a multi-point oil and gas concentration leakage classification subsystem in the environment of a gas station tank farm;
FIG. 3 is a functional diagram of a detection node;
FIG. 4 is a functional diagram of the site monitoring software;
FIG. 5 is a time series triangular fuzzy number neural network model;
FIG. 6 is a plan view of an environmental parameter acquisition platform of a gas station tank farm.
Detailed Description
The technical scheme of the invention is further described by combining the attached drawings 1-6:
1. design of overall system function
The system is composed of a CAN field bus network-based environment parameter acquisition platform of the oil tank area of the gas station and a multi-point oil and gas concentration leakage classification subsystem of the oil tank area of the gas station. The system consists of a plurality of detection nodes 1 and an on-site monitoring terminal 2 of a gas station oil tank field environment parameter acquisition platform based on a CAN (controller area network), wherein the detection nodes 1 and the on-site monitoring terminal 2 are communicated on site by constructing a measurement and control network in a CAN on-site bus mode; the detection node 1 sends the detected environmental parameters of the oil tank field of the gas station to the field monitoring terminal 2 and carries out primary processing on the sensor data; the field monitoring terminal 2 transmits the control information to the detection node 1. The whole system structure is shown in figure 1.
2. Design of detection node
The detection node 1 based on the CAN field bus is used as an environmental parameter sensing terminal of a gas station oil tank area, and the detection node 1 and the field monitoring terminal 2 realize mutual information interaction with the field monitoring terminal 2 in a CAN field bus mode. The detection node 1 comprises a sensor for collecting parameters such as the ambient temperature, the oil-gas concentration, the wind speed and the smoke of a fuel tank area of a gas station, a corresponding signal conditioning circuit and an STC89C52RC microprocessor; the software of the detection node mainly realizes field bus communication and acquisition and pretreatment of environmental parameters of a gas station tank field. The software is designed by adopting a C language program, so that the compatibility degree is high, the working efficiency of software design and development is greatly improved, and the reliability, readability and transportability of program codes are enhanced. The structure of the detection node is shown in fig. 3.
3. Site monitoring terminal software
The field monitoring terminal 2 is an industrial control computer, the field monitoring terminal 2 mainly collects environmental parameters of a gas station oil tank area, fuses, predicts and classifies multi-point oil gas concentration, and realizes information interaction with the detection node 1 and the control node 2, and the field monitoring terminal 2 mainly has the functions of communication parameter setting, data analysis and data management and multi-point temperature fusion of the gas station oil tank area environment. The management software selects Microsoft Visual + +6.0 as a development tool, calls the Mscomm communication control of the system to design a communication program, and the functions of the field monitoring end software are shown in figure 4. The classification subsystem for the oil gas leakage of the oil tank area environment at the oil filling station consists of 5 parts including a plurality of detection point oil gas concentration sensors, a plurality of time series triangular fuzzy number neural networks, a plurality of time series triangular fuzzy number fusion models, a triangular fuzzy number prediction module and a wavelet neural network oil gas leakage concentration classifier, wherein the oil gas concentration sensors at the plurality of detection points sense the oil gas leakage concentration of the detected points, the output of each detection point oil gas concentration sensor is used as the input of each corresponding time series triangular fuzzy number neural network, the output of the plurality of time series triangular fuzzy number neural networks is used as the input of the oil tank area environment multi-point oil gas concentration fusion model at the oil filling station, the output of the oil gas concentration fusion model at the oil tank area environment at the oil filling station is used as the input of the triangular fuzzy number prediction module, and the output of the triangular fuzzy number prediction module is used as the input of the, wavelet neural network oil gas reveals the concentration classifier and is divided into different grades to the oil gas that is detected filling station oil gas that is revealed the concentration, filling station tank field environment multiple spot oil gas concentration reveals categorised subsystem and realizes the detection, fuzzy quantization, multiple spot integration, prediction and the categorised process that the concentration grade was revealed to the oil gas of filling station, filling station tank field environment multiple spot oil gas concentration reveals categorised subsystem and sees figure 2, filling station tank field environment multiple spot oil gas concentration reveals categorised subsystem algorithm as follows:
1. time series triangle fuzzy number neural network model
Setting a time sequence of oil gas concentration values of detected points of a gas station as x (t), x (t-1), …, x (t-d +1) and x (t-d), using a conventional time sequence value of oil gas concentration parameters of the detected points of the gas station as the input of a radial basis function neural network, setting the output of the radial basis function neural network as the triangular fuzzy value of the oil gas concentration parameters of the detected points of the gas station at the moment of t +1 as S, and setting the S triangular fuzzy values as [ a, b, c ] as]Is equal to [ s ]1,s2,s3]a represents the lower limit value of the oil gas concentration of the detected point, b represents the maximum possible value of the oil gas concentration of the detected point, c represents the upper limit value of the oil gas concentration of the detected point, the triangular fuzzy value of the detected parameter at the t +1 moment depends on the conventional time series numerical value of the previous d moments of the detected parameter, d is a time window, according to the characteristic that the S and the parameter time series numerical value of the oil gas concentration value at the detected point at the previous d moments have a functional dependence relationship, the relation between the triangular fuzzy values of the detected point oil gas concentration value parameter at the t +1 moment of the detected point oil gas concentration value parameter predicted by a period of time sequence conventional sequence value of the detected point oil gas concentration value parameter is established through a time sequence triangular fuzzy neural network of the detected point oil gas concentration value parameter, and the structure chart of the time sequence triangular fuzzy neural network model 1 of the detected point oil gas concentration value parameter is shown as 5. The radial basis vector of the neural network is H ═ H1,h2,…；hp]T，hpIs a basis function. Common radial in radial basis function neural networksThe basis function is a gaussian function, whose expression is:
wherein X is the time sequence output of the sensor of the detected parameter, C is the coordinate vector of the central point of the Gaussian basis function of the hidden layer neuron, and deltajThe width of the Gaussian base function of the jth neuron of the hidden layer; the output connection weight vector of the network is wijThe time series triangular fuzzy number neural network model outputs the expression as follows:
the key of the time series triangular fuzzy number neural network model 1 of the detected point oil gas concentration parameter is to fit a mapping relation f according to detected point oil gas concentration value data of d moments of the detected point oil gas concentration parameter and triangular fuzzy data of the detected point oil gas concentration parameter of t +1 moments in the past, and further obtain a triangular fuzzy number S of a detected point oil gas concentration value fitting function through forward propagation of a radial basis function neural network. The mathematical model of the time series triangular fuzzy number neural network of the oil and gas concentration value parameters of the detected point can be expressed as follows:
S＝f(x(t),x(t-1),…,x(t-d+1),x(t-d)) (3)
the design method of the time series triangular fuzzy neural network model 2 corresponding to the detecting point temperature sensor and the time series triangular fuzzy neural network model 3 corresponding to the detecting point smoke sensor is similar to that of the time series triangular fuzzy neural network model 1.
2. Multi-point oil gas concentration fusion model for oil tank area environment of gas station
The oil gas concentration time series triangular fuzzy number array is formed by 3 parts of an oil gas concentration time series triangular fuzzy number array, the relative closeness degree of an oil gas concentration triangular fuzzy number predicted value and an ideal value is calculated, the oil gas concentration triangular fuzzy number fusion value is calculated, the oil gas concentration triangular fuzzy number predicted values of a plurality of parameter detection units in a period of time form the oil gas concentration time series triangular fuzzy number array, the distance between the time series triangular fuzzy number predicted value of each detection unit and the positive ideal value of the oil gas concentration time series triangular fuzzy number array and the distance between the time series triangular fuzzy number predicted value of each detection unit and the negative ideal value of the oil gas concentration time series triangular fuzzy number array are calculated respectively, the distance between the negative ideal value of the time series triangular fuzzy number predicted value of each detection unit is divided by the distance between the negative ideal value of the time series triangular fuzzy number predicted value of each detection unit and the time series triangular fuzzy number array of each detection unit The quotient obtained by the sum of the distances of positive ideal values of the angle fuzzy number predicted values is the relative closeness of the time-series triangular fuzzy number of each detection unit, the quotient obtained by dividing the closeness of the time-series triangular fuzzy number of each detection unit by the sum of the closeness of the time-series triangular fuzzy numbers of all the detection units is the fusion weight of the time-series triangular fuzzy number of each detection unit, and the sum of the products of the time-series triangular fuzzy number of each detection unit and the fusion weight of the time-series triangular fuzzy number of the detection unit is used for obtaining the time-series triangular fuzzy fusion values of a plurality of detection points; the algorithm of the multipoint oil gas concentration fusion model in the oil tank area environment of the gas station is as follows:
⑴, constructing oil gas concentration time series triangular fuzzy number array
The triangular fuzzy number prediction value of oil-gas concentration of a plurality of parameter detection units in a period of time forms an oil-gas concentration time series triangular fuzzy number array, the triangular fuzzy number prediction value of the nm parameter detection units with n detection points and m moments forms an oil-gas concentration time series triangular fuzzy number array with n rows and m columns, and the fuzzy triangular number prediction value of the oil-gas concentration of different parameter detection units at different moments is set as Xij(t),Xij(t+1),…,Xij(d) And then the oil gas concentration time series triangular fuzzy number array is as follows:
⑵, calculating the relative closeness of the oil gas concentration triangle fuzzy number predicted value and the ideal value
The average value of the triangular fuzzy number predicted values of the oil gas concentration of all the detection units at the same moment forms a positive ideal value of an oil gas concentration time series triangular fuzzy number array, and the positive ideal value of the time series triangular fuzzy number is as follows:
the triangular fuzzy number predicted value of the oil gas concentration of the detection unit at the same moment and the triangular fuzzy number predicted value with the maximum distance between the positive ideal value form a negative ideal value of an oil gas concentration time series triangular fuzzy number array, and the time series triangular fuzzy number negative ideal value is as follows:
the distance between the time series triangular fuzzy number prediction value of each detection unit and the positive ideal value of the oil gas concentration time series triangular fuzzy number array is as follows:
the distance between the time series triangular fuzzy number prediction value of each detection unit and the negative ideal value of the oil gas concentration time series triangular fuzzy number array is as follows:
the relative closeness of the time-series triangular fuzzy value of each detection unit is obtained by dividing the distance of the negative ideal value of the time-series triangular fuzzy number predicted value of each detection unit by the sum of the distance of the negative ideal value of the time-series triangular fuzzy number predicted value of each detection unit and the distance of the positive ideal value of the time-series triangular fuzzy number predicted value of each detection unit:
⑶, calculating the oil-gas concentration triangular fuzzy number fusion value
The calculation of the formula (9) can know that the greater the relative closeness of the time series triangular fuzzy value of each detection unit and the positive and negative ideal values of the oil-gas concentration time series triangular fuzzy number array, the closer the time series triangular fuzzy value of the detection unit is to the positive ideal value, otherwise, the farther the time series triangular fuzzy value of the detection point is from the positive ideal value, and according to the principle, the fusion weight of the time series triangular fuzzy number of each detection unit is determined as the quotient of the closeness of the time series triangular fuzzy value of each detection unit divided by the closeness of the time series triangular fuzzy values of all detection units and the sum of the closeness of the time series triangular fuzzy values of each detection unit:
the time series triangular fuzzy fusion value of a plurality of detection points obtained according to the sum of the products of the time series triangular fuzzy value of each detection unit and the fusion weight of the time series triangular fuzzy value of the detection unit is as follows:
3. triangular fuzzy number prediction module
The triangular fuzzy number prediction module comprises 3 NARX neural network prediction models and 3 Elman neural network prediction models of a phase space reconstruction technology, wherein the 3 NARX neural network prediction models 1,2 and 3 predict a detected point parameter lower limit value a, a detected point parameter maximum possible value b and a detected point parameter upper limit value c of the S triangular fuzzy number output by the time sequence triangular fuzzy number neural network model 1 respectively; the Elman neural network prediction model 1 of the 3 phase space reconstruction technologies, the Elman neural network prediction model 2 of the phase space reconstruction technologies and the Elman neural network prediction model 3 of the phase space reconstruction technologies respectively predict a lower limit value a of the triangular fuzzy number S of the detected point and a residual error output by the NARX neural network prediction model 1, a maximum possible value b of the triangular fuzzy number S of the detected point and a residual error output by the NARX neural network prediction model 2 and a residual error between an upper limit value c of the triangular fuzzy number S of the detected point and an output by the NARX neural network prediction model 3; the output of the 3 NARX neural network prediction models 1,2 and 3 is respectively added with the output of the Elman neural network prediction model 1 of the 3 phase space reconstruction technology, the Elman neural network prediction model 2 of the phase space reconstruction technology and the output of the Elman neural network prediction model 3 of the phase space reconstruction technology to obtain a predicted value of the lower limit value a of the oil gas concentration of the triangular fuzzy number S of the detected point, a predicted value b of the maximum possible value of the oil gas concentration of the triangular fuzzy number S of the detected point and a predicted value c of the upper limit value c of the oil gas concentration of the detected point, and the predicted values of the triangular fuzzy numbers are formed, namely S ' is [ a ', b ', c ]. The Elman neural network prediction model 1 of the 3 phase space reconstruction technology, the Elman neural network prediction model 2 of the phase space reconstruction technology and the Elman neural network prediction model 3 of the phase space reconstruction technology compensate the S triangular fuzzy numbers a, b and c output by the three-dimensional fuzzy number neural network model 1 of the 3 NARX neural network prediction models 1, NARX neural network prediction model 2 and NARX neural network prediction model 3 respectively for predicting the time series triangular fuzzy numbers for further residual prediction, and accuracy of prediction a, b and c is improved.
⑴ NARX neural network prediction model design
The invention discloses a method for predicting a detected point parameter lower limit value a, a maximum possible value b and a detected point parameter upper limit value c of an S triangular fuzzy number output by a 3 time series triangular fuzzy number neural network model 1 by using 3 NARX neural network prediction models, wherein the NARX neural network (Nonlinear Auto-Regression with external linear neural network) is a dynamic feedforward neural network, the NARX neural network is a Nonlinear autoregressive network with predicted input parameters, has a dynamic characteristic of multistep time delay and inputs a plurality of layers of closed networks of the input parameters through feedback connection, and the NARX neural network is a dynamic neural network which is most widely applied in a Nonlinear dynamic system and has performance generally superior to that of an all-Regression neural network. The NARX neural network prediction model of the present patent is composed of an input layer, a hidden layer, an output layer, and input and output delay time delays, and before application, the delay order and the number of hidden layer neurons of the input and output are generally determined in advance, and the current output of the NARX neural network prediction model depends not only on the past output y (t-n), but also on the current input vector x (t), the delay order of the input vector, and the like. The NARX neural network prediction model structure comprises an input layer, an output layer, a hidden layer and a time extension layer, wherein predicted input parameters are transmitted to the hidden layer through the time delay layer, an input signal is processed by the hidden layer and then transmitted to the output layer, the output layer linearly weights an output signal of the hidden layer to obtain a final neural network prediction output signal, and the time delay layer delays a signal fed back by a network and a signal output by the input layer and then transmits the signal to the hidden layer. The NARX neural network prediction model has the characteristics of nonlinear mapping capability, good robustness, adaptability and the like, and is suitable for predicting input parameters. x (t) represents the external input of the neural network, namely the time-series triangular fuzzy number neural network model 1 outputs the detected point parameter lower limit value a of the S triangular fuzzy number; m represents the delay order of the external input a; y (t) is the output of the neural network, i.e., the predicted value of a; n is the output delay order; s is the number of hidden layer neurons; the output of the jth implicit element can thus be found as:
in the above formula, wjiAs a connection weight between the ith input and the jth implicit neuron, bjIs the bias value of the jth implicit neuron, the output y (t +1) of the NARX neural network prediction model represents the predicted value of a:
y(t+1)＝f[y(t),y(t-1),…,y(t-n),x(t),x(t-1),…,x(t-m+1)；W](13)
the NARX neural network prediction model 2 and the NARX neural network prediction model 3 respectively output the maximum possible value b of the detected point parameter of the S triangular fuzzy number to the time series triangular fuzzy number neural network model 1 and predict the upper limit value c of the detected point parameter, and the design methods of the two are similar to the NARX neural network prediction model 1.
⑵ Elman neural network prediction model design of phase space reconstruction technology
The method comprises the steps that an Elman neural network prediction model 1 of 3 phase space reconstruction technologies, an Elman neural network prediction model 2 of the phase space reconstruction technologies and an Elman neural network prediction model 3 of the phase space reconstruction technologies, which are formed by the phase space reconstruction technologies and the Elman neural network, respectively predict a lower limit value a of a triangular fuzzy number S of a detected point and a residual error output by a NARX neural network prediction model 1, a maximum possible value b of the triangular fuzzy number S of the detected point and a residual error output by the NARX neural network prediction model 2, and predict an upper limit value c of the triangular fuzzy number S of the detected point and a residual error output by the NARX neural network prediction model 3; the Elman neural network prediction model 1 of the 3-phase space reconstruction technology, the Elman neural network prediction model 2 of the phase space reconstruction technology and the Elman neural network prediction model 3 of the phase space reconstruction technology are respectively used for predicting the residual errors of the lower limit value a, the maximum possible value b and the upper limit value c of the triangular fuzzy number S of the detected point, and the Elman neural network prediction model of the 3-phase space reconstruction technology simulates the variation of the residual errors of the lower limit value a, the maximum possible value b and the upper limit value c of the triangular fuzzy number S of the detected point through training of enough training samples, so that the compensation of the prediction of the triangular fuzzy number S of the detected point is realized.
The prediction method of the 3-phase space reconstructed Elman neural network prediction model comprises the following specific steps:
step 1: and 3 series of differences of the lower limit value a, the maximum possible value b and the upper limit value c of the output triangular fuzzy number S of the time series triangular fuzzy number neural network model 1 corresponding to the detected point sensor, and 3 output values of the NARX neural network prediction model 1, the NARX neural network prediction model 2 and the NARX neural network prediction model 3 are collected to respectively form 3 residual time series data corresponding to a, b and c.
Step 2: and conventionally determining the optimal delay constant tau and the embedding dimension m.
And 3, step 3: and constructing an Elman neural network prediction model, wherein the Elman neural network prediction model is a forward neural network with a local memory unit and local feedback connection, the correlation layer receives a feedback signal from the hidden layer, and each hidden layer node is connected with the corresponding correlation layer node. And the association layer takes the hidden layer state at the previous moment and the network input at the current moment as the input of the hidden layer as the state feedback. The transfer function of the hidden layer is generally a Sigmoid function, and the associated layer and the output layer are linear functions. Setting the numbers of an input layer, an output layer and a hidden layer of the Elman neural network prediction model as m, n and r respectively; w is a1，w2，w3And w4Respectively representing the connection weight matrixes from the structural layer unit to the hidden layer, from the input layer to the hidden layer, from the hidden layer to the output layer and from the structural layer to the output layer, and then the output value expressions of the hidden layer, the associated layer and the output layer of the network are respectively:
cp(k)＝xp(k-1) (15)
the input data of the Elman neural network prediction model 1 of the 3 phase space reconstruction technology, the Elman neural network prediction model 2 of the phase space reconstruction technology and the Elman neural network prediction model 3 of the phase space reconstruction technology are respectively residual time sequence data of 3 corresponding a, b and c formed by 3 series of differences of the output values of the triangular fuzzy number S of the triangular fuzzy number neural network model 1, the output values of the NARX neural network prediction model 2 and the output values of the NARX neural network prediction model 3, the Elman neural network prediction model 1 of the 3 phase space reconstruction technology, the Elman neural network prediction model 2 of the phase space reconstruction technology and the Elman neural network prediction model 3 of the phase space reconstruction technology, the output values of a, b and cThe value is obtained. The input dimension of the Elman neural network prediction model is equal to the embedding dimension m, the time difference between each input datum is tau time points, namely residual time sequence data of a, b and c are used as the input of the Elman neural network prediction model; the hidden layer is a single layer, and the number of the hidden layers is determined by a method of 2m + 1; the output layer contains a neuron, and the output of the neuron is the residual prediction value of the time point to be predicted. If one of the residual time series data is X (t)0),X(t1),…,X(ti),…,X(tn) First, phase space reconstruction is performed on the time series by using MatlabR2012a programming, and the output result of the phase space reconstruction is as follows:
and 4, step 4: and (3) carrying out Elman neural network prediction model network training, selecting partial data from the residual time sequence original data of a, b and c, and carrying out network training until the training meets the requirement.
And 5, step 5: and (4) selecting a test sample from the original data of the residual time sequence of the a, the b and the c, if the requirement is met, entering the 6 th step for prediction, and returning to the 4 th step for retraining or returning to the 3 rd step for redesigning the network structure if the test error is larger.
And 6, step 6: selecting a prediction time point, applying the Elman neural network prediction model established in the prior art to predict,
the predicted value can be obtained from the following equation:
Y＝f(X(t0+mτ),X(t1+mτ)…X(ti+mτ)…X(tn+τ)) (18)
4. design of oil gas leakage concentration classifier of wavelet neural network
The triangular fuzzy number prediction module outputs a triangular fuzzy number prediction value as the environment oil gas concentration of the detected oil tank area of the gas station, the prediction value is used as the input of a wavelet neural network oil gas leakage concentration classifier, and the wavelet neural network oil gas leakage concentration classifier is used for detecting the oil gas concentration of the detected gas station according to the dynamic change condition of the environment oil gas concentration prediction value of the oil tank area of the gas stationThe oil gas leakage concentration of the tank area environment is divided into 5 leakage levels including high oil gas leakage concentration, normal oil gas leakage concentration and low oil gas leakage concentration, and the output of the wavelet neural network oil gas leakage concentration classifier is a triangular fuzzy value representing the oil gas leakage concentration level; according to engineering practice of oil gas leakage concentration of the environment of a detected oil tank area of a gas station and national 'oil gas leakage pollution control standard', the wavelet neural network oil gas leakage concentration classifier classifies the oil gas leakage concentration grade of the environment of the detected oil tank area of the gas station, and similarity of the output of the wavelet neural network oil gas leakage concentration classifier and triangular fuzzy numbers representing 5 oil gas leakage concentration grades of the environment of the detected oil tank area of the gas station is calculated respectively, wherein the oil gas leakage grade corresponding to the triangular fuzzy number with the maximum similarity is the oil gas leakage concentration grade of the environment of the detected oil tank area of the gas station, and the oil gas leakage concentration grade is the current oil gas leakage concentration grade of the environment of the detected oil tank area of the gas station. The wavelet Neural network oil gas leakage concentration classifier is a gas station oil gas leakage concentration classifier constructed based on WNN (wavelet Neural networks) theoretical basis of a wavelet Neural network, and the wavelet Neural network is a feedforward network provided by taking a wavelet function as an excitation function of a neuron and combining an artificial Neural network. The expansion, translation factors and connection weights of wavelets in the wavelet neural network oil gas leakage concentration classifier are adaptively adjusted in the optimization process of the error energy function. Setting the clearness value of the triangular fuzzy number predicted value of the oil-gas leakage concentration of a gas station in a period of continuous time as a one-dimensional vector x input into a wavelet neural network oil-gas leakage concentration classifieri(i 1,2, …, n) and outputting a triangular fuzzy value y which is expressed as the oil and gas leakage concentration level of the gas stationk(k ═ 1,2, …, m), where m equals 3, the formula for the wavelet neural network oil and gas leak concentration classifier output representing the gas station oil and gas leak concentration level is:
in the formula omegaijInputting the connection weight between the i node of the layer and the j node of the hidden layer,
as wavelet basis functions, bjIs a shift factor of the wavelet basis function, ajScale factor, omega, of wavelet basis functionsjkThe connection weight between the node of the hidden layer j and the node of the output layer k. The correction algorithm of the weight and the threshold of the wavelet neural network oil gas leakage concentration classifier in the patent adopts a gradient correction method to update the network weight and the wavelet basis function parameters, so that the output of the wavelet neural network oil gas leakage concentration classifier is continuously close to the expected output. And respectively calculating the similarity of the triangular fuzzy numbers corresponding to the language variables representing the oil gas leakage concentration levels of the environments of the oil tank areas of the 5 gas stations output by the wavelet neural network oil gas leakage concentration classifier, wherein the oil gas leakage concentration level corresponding to the triangular fuzzy number with the maximum similarity is the oil gas leakage level of the environment of the oil tank area of the detected gas station.
According to engineering practice of the oil gas leakage concentration of the environment of the detected oil tank area of the gas station and the national 'oil gas leakage pollution control standard', a corresponding relation table of language variables of 5 concentration levels for evaluating the oil gas leakage concentration of the environment of the detected oil tank area of the gas station and triangular fuzzy numbers is established, and the table is shown in table 1.
| Serial number | Oil gas leakage grade | Triangular |
| 1 | Oil gas leakage concentration is very low | (0.00，0.00，0.25) |
| 2 | Normal oil gas leakage concentration | (0.00，0.25，0.50) |
| 3 | Oil gas leakage concentration ratio | (0.25，0.50，0.75) |
| 4 | High oil gas leakage concentration | (0.50，0.75，1.00) |
| 5 | High oil gas leakage concentration | (0.75，1.00，1.0) |
5. Design example of oil tank field environmental parameter detection system of gas station
According to the situation of the environment of the oil tank area of the gas station, a plane layout installation diagram of a detection node 1 and a field monitoring terminal 2 is arranged in the system, wherein the detection node 1 is arranged in the environment of the oil tank area of the detected gas station in a balanced manner, the plane layout of the whole system is shown in a figure 6, and the collection of the environmental parameters of the oil tank area of the gas station and the intelligent classification of the oil gas concentration of the environment of the oil tank area of the gas station are realized through the system.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and such improvements and modifications are also considered to be within the scope of the present invention.
Claims (6)
1. The utility model provides an oil gas concentration intelligent monitoring system based on field bus network which characterized in that: the system consists of a gas station tank area environment parameter acquisition platform based on a CAN field bus network and a multi-point oil and gas concentration leakage classification subsystem in the gas station tank area environment, and the system realizes intelligent detection of the oil and gas leakage concentration in the gas station tank area environment and classification of the oil and gas leakage concentration grade; the classification subsystem for the multi-point oil-gas concentration leakage of the oil tank area environment of the gas station consists of five parts, namely a plurality of detection point oil-gas concentration sensors, a plurality of time-series triangular fuzzy number neural networks, a multi-point oil-gas concentration fusion model of the oil tank area environment of the gas station, a triangular fuzzy number prediction module and a wavelet neural network oil-gas leakage concentration classifier, wherein the plurality of detection point oil-gas concentration sensors sense the concentration of the oil-gas leakage of the detected points, the output of each detection point oil-gas concentration sensor is used as the input of each corresponding time-series triangular fuzzy number neural network, the output of the time-series triangular fuzzy number neural network is used as the input of the multi-point oil-gas concentration fusion model of the oil tank area environment of the gas station, the output of the multi-point oil-gas concentration fusion model of the oil tank area environment of the gas station is used, the wavelet neural network oil gas leakage concentration classifier divides the detected oil gas leakage concentration of the gas station into different grades, and the multi-point oil gas concentration leakage classification subsystem of the environment of the oil tank area of the gas station realizes the detection, fuzzy quantization, multi-point fusion and prediction of the oil gas leakage concentration of the gas station and the classification process of the oil gas leakage concentration grade.
2. The intelligent oil and gas concentration monitoring system based on the field bus network as claimed in claim 1, wherein: the time series triangular fuzzy neural network consists of each time series triangular fuzzy neural network corresponding to each detection point, the time series triangular fuzzy neural network consists of a section of conventional time series value output by an oil gas concentration sensor of a detected point as the input of the radial basis neural network, the radial basis neural network and a triangular fuzzy numerical value of the oil gas concentration of the detected point as the output of the radial basis neural network, and the triangular fuzzy numerical value output by the radial basis neural network respectively represents the lower limit value, the maximum possible value and the upper limit value of the oil gas concentration of the detected point; the time series triangular fuzzy neural network converts a period of conventional time series value of the oil and gas concentration of the detected point into a triangular fuzzy value of the detected oil and gas concentration to represent according to the dynamic change characteristic of the oil and gas concentration of the detected point.
3. The intelligent oil and gas concentration monitoring system based on the field bus network as claimed in claim 1, wherein: the oil gas concentration time series triangular fuzzy number array is formed by three parts of an oil gas concentration time series triangular fuzzy number array, the relative closeness of an oil gas concentration triangular fuzzy number predicted value and a positive ideal value and the negative ideal value is calculated, the oil gas concentration triangular fuzzy number fused value is calculated, the oil gas concentration triangular fuzzy number predicted values of a plurality of parameter detection units in a period of time form the oil gas concentration time series triangular fuzzy number array, the positive ideal value and the negative ideal value of the oil gas concentration time series triangular fuzzy number array are determined, the distance between the oil gas concentration time series triangular fuzzy number predicted value of each detection unit and the positive ideal value and the negative ideal value of the oil gas concentration time series triangular fuzzy number array are calculated respectively, the distance between the negative ideal value of the time series triangular fuzzy number predicted value of each detection unit is divided by the distance between the negative ideal value of the time series triangular fuzzy number predicted value of each detection unit and the positive ideal value of the time series triangular fuzzy number predicted value The quotient obtained by the summation is the relative closeness of the time-series triangular fuzzy number predicted value of each detection unit, the quotient obtained by dividing the relative closeness of the time-series triangular fuzzy number predicted value of each detection unit by the sum of the relative closeness of the time-series triangular fuzzy number predicted values of all the detection units is the fusion weight of the time-series triangular fuzzy number predicted value of each detection unit, and the sum of the products of the time-series triangular fuzzy number predicted value of each detection unit and the fusion weight of the time-series triangular fuzzy number predicted value of the detection unit is the fusion value of the time-series triangular fuzzy number predicted values of a plurality of detection points.
4. The intelligent oil and gas concentration monitoring system based on the field bus network as claimed in claim 1, wherein: the triangular fuzzy number prediction module consists of three NARX neural network prediction models and three Elman neural network prediction models of phase space reconstruction technology, the lower limit value, the maximum possible value and the upper limit value of the triangular fuzzy number of the detected environment oil gas concentration output by the multipoint oil gas concentration fusion model in the oil tank area of the gas station are respectively input of the corresponding NARX neural network prediction models, the difference between the lower limit value, the maximum possible value and the upper limit value of the detected environment oil gas concentration triangular fuzzy number output by the multipoint oil gas concentration fusion model in the oil tank area of the gas station and the output of the corresponding NARX neural network prediction model is respectively input of the Elman neural network prediction model of the corresponding phase space reconstruction technology, the output of the NARX neural network prediction model is respectively added with the output of the Elman neural network prediction model of the corresponding phase space reconstruction technology and is used as a triangular fuzzy number prediction value of the detected environment oil gas concentration, the triangular fuzzy number prediction value is output as a triangular fuzzy number prediction module.
5. The intelligent oil and gas concentration monitoring system based on the field bus network as claimed in claim 1, wherein: the wavelet neural network oil gas leakage concentration classifier establishes a language variable and five different triangular fuzzy number corresponding relation tables for evaluating five concentration levels of the detected oil gas leakage concentration of the oil tank area environment of the gas station according to the engineering practice of the oil gas leakage concentration of the oil tank area environment of the detected gas station and the national 'gas station leakage pollution control standard', and divides the detected oil gas leakage concentration of the oil tank area environment of the gas station into five leakage levels of high oil gas leakage concentration, normal oil gas leakage concentration and low oil gas leakage concentration; the wavelet neural network oil gas leakage concentration classifier classifies the oil gas leakage concentration grade of the environment of the detected oil tank area of the gas station, the output of the wavelet neural network oil gas leakage concentration classifier is a triangular fuzzy numerical value representing the oil gas leakage concentration grade, the similarity between the output of the wavelet neural network oil gas leakage concentration classifier and five triangular fuzzy numbers representing the five oil gas leakage concentration grades of the environment of the detected oil tank area of the gas station is calculated respectively, and the oil gas leakage grade corresponding to the triangular fuzzy number with the maximum similarity is the current oil gas leakage concentration grade of the environment of the detected oil tank area of the gas station.
6. The intelligent oil and gas concentration monitoring system based on the field bus network as claimed in claim 1, wherein: the CAN field bus network-based oil tank field environment parameter acquisition platform of the gas station consists of a plurality of parameter detection nodes and a field monitoring end, and information communication between the parameter detection nodes and the field monitoring end is realized through the CAN field bus network; the detection nodes are responsible for detecting the actual values of oil gas concentration, temperature, wind speed and smoke of the environment of the oil tank area of the gas station, and the field monitoring end manages the environment parameters of the oil tank area of the gas station, manages the parameters of multi-point detection of the environment of the oil tank area of the gas station, fuses the oil gas concentration of a plurality of detection points, predicts and classifies the oil gas leakage concentration grade.
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