CN113008440A - Flexible liquid injection sensor detection method based on genetic algorithm optimization neural network - Google Patents
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Abstract
The invention discloses a flexible liquid injection sensor detection method based on a genetic algorithm optimized neural network, which relates to the technical field of intelligent flexible pressure sensing equipment.
Description
Technical Field
The invention relates to the technical field of intelligent flexible pressure sensing equipment, in particular to a flexible liquid injection sensor detection method based on a genetic algorithm optimized neural network.
Background
The dynamic change of the stress of the surrounding rock is an important reason for causing various geological disasters. In recent years, with the development of economy in China and the continuous increase of coal mining depth and strength, the surrounding rock stress distribution change rule is more and more complex, and the probability of geological disasters caused by the change is gradually increased. At present, the monitoring equipment for surrounding rock stress change is mainly rigid equipment such as a borehole stress meter, the change of surrounding rock stress cannot be sensitively sensed, and the surrounding rock stress change in a fixed direction can only be monitored, so that the surrounding rock rule is not accurately mastered, and hidden dangers are left for geological disasters. Therefore, the relation between the stress change of the surrounding rock, the deformation of the flexible sensor and the output of the pressure gauge is found, and the rule of the change of the supporting pressure is found, so that the method has important significance for the safety production of the coal mine.
Disclosure of Invention
In order to solve the technical problems, the invention provides the flexible liquid injection sensor detection method based on the genetic algorithm optimization neural network, which can improve the detection efficiency, the automation degree and the intelligence of the flexible liquid injection sensor in a reply manner and has stronger practicability.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a flexible liquid injection sensor detection method based on genetic algorithm optimization neural network comprises the steps of setting a plurality of flexible liquid injection sensors, constructing and training a neural network prediction model, optimizing the neural network prediction model by genetic algorithm and setting a control system, arranging a plurality of drill holes on surrounding rock, mounting the flexible liquid injection sensors in each drill hole, detecting the stress at different positions on the surrounding rock, dividing each flexible liquid injection sensor into 4 sections, applying different pressures respectively, performing simulation acquisition on the stress of the surrounding rock, taking the stress value of the surrounding rock measured at each section 1 as the input value of the neural network prediction model, taking the reading of a pressure gauge as the output value of the neural network prediction model, obtaining the high-precision approximate nonlinear function relation between the stress change of the surrounding rock and the deformation of the pressure gauge, and performing parameter optimization on the neural network prediction model by the genetic algorithm, the control system adopts a high-performance chip as a control center controller, and a plurality of flexible liquid injection sensors are in communication connection with a data bus of a field bus network to realize communication between an output pressure gauge and the control center.
The neural network prediction model construction and training method is realized by the following steps:
the first stage is as follows: the method comprises the steps of building a neural network prediction model and collecting data, wherein the neural network prediction model is composed of an input layer, a hidden layer and an output layer, the neuron of the input layer is set to have four sections of diameters d1, d2, d3 and d4 corresponding to 4 sections of equal distribution of a flexible liquid injection sensor, the neuron of the output layer is set to be a pressure reading p output by a pressure gauge, the number of the neuron of the hidden layer is generally determined by an empirical formula, different surrounding rock stresses are simulated and applied through experimental equipment, required input and output data are collected, n groups of data are recorded, and corresponding sample data are obtained;
and a second stage: and training the neural network prediction model, wherein the training process mainly comprises two processes of forward propagation of signals and backward propagation of errors, when the error between actual output and target output does not meet the preset precision requirement, the neural network can continuously adjust the weight, update the network until the error is smaller than the preset precision, and the training is finished.
The number of layers of the hidden layer is set to be 1, and the number of neurons of the hidden layer is set to be 9.
The genetic algorithm is designed and realized by the following steps:
s1, selecting binary codes;
s2, initializing a group;
s3, selecting an error performance index function to define a fitness function, evaluating the fitness value and storing the optimal chromosome;
s4, selecting operation by roulette, single-point crossing operation and variation operation with variation rate of 0.000-0.1;
s5, generating a new population, and recalculating the adaptive value of each chromosome;
and S6, judging whether the termination condition is met, when the algorithm reaches the termination condition, terminating the algorithm, outputting a result, and if the termination condition is not met, returning to S3.
The flexible liquid injection sensor comprises at least one flexible pressure sensing device, a liquid injection device, a conveying device and a pressure measuring device, wherein the liquid injection device is connected with the flexible pressure sensing device through the conveying device and used for injecting emulsion into the flexible pressure sensing device, and the pressure measuring device is communicated with the conveying device and the flexible pressure sensing device respectively and used for displaying the pressure of the emulsion through a pressure gauge.
The flexible pressure sensing device comprises a pipe body, a sealing layer, a rubber layer and a joint, wherein the sealing layer and the rubber layer are arranged outside the pipe body respectively, one end of the pipe body is sealed, the other end of the pipe body is connected with the joint, a liquid injection hole is formed in the middle of the joint, an exhaust hole is formed in the side face of the joint, and the liquid injection hole and the exhaust hole are communicated with an inner cavity of the pipe body.
The priming device includes pump, flowmeter and notes liquid rifle, conveyor sets up to the rubber tube, pump, flowmeter, notes liquid rifle and rubber tube are connected in order for carry the emulsion.
The pressure measuring device comprises a pressure gauge and a three-way valve, one end of the three-way valve is communicated with the pressure gauge, and the other two ends of the three-way valve are respectively communicated with the flexible pressure sensing device and the liquid injection device.
STM32, DSP, CPLD or FPGA high performance chip is used as controller.
The invention designs a flexible liquid injection sensor which is arranged in a plurality of drill holes on the surrounding rock, detects the stress values of different positions on the surrounding rock and acquires data, utilizes the nonlinear mapping capability of an artificial neural network, trains a neural network prediction model to enable the network to approach a nonlinear function with high precision, obtains the relation between the stress change of the surrounding rock and the deformation of a pressure gauge according to input stress value data and pressure gauge output value data, realizes the prediction of the output data, introduces a genetic algorithm to carry out parameter optimization on a BP neural network prediction model, improves the precision of the network and the training speed, thereby greatly improving the precision and the rapidity of the detected data, solves the problem that the neural network is easy to fall into local optimization to a great extent, greatly improves the detection efficiency of the system and the automation degree of a surrounding rock stress detection system by a field bus communication mode of a plurality of master stations, the practicability is strong, and the method is suitable for large-scale popularization and application.
Drawings
FIG. 1 is a schematic diagram of the principle of neural network prediction model construction and training according to the present invention;
FIG. 2 is a schematic diagram of a flexible fluid injection sensor according to the present invention;
fig. 3 is a schematic structural view of the flexible pressure-sensing device of the present invention.
Detailed Description
The invention is described in detail below with reference to the following figures and specific embodiments:
the flexible liquid injection sensor detection method based on the genetic algorithm optimized neural network comprises the steps of setting a plurality of flexible liquid injection sensors, constructing and training a neural network prediction model, optimally setting the neural network prediction model by the genetic algorithm and controlling the neural network prediction model, arranging a plurality of drill holes on a surrounding rock, installing the flexible liquid injection sensors in each drill hole, detecting the stress at different positions on the surrounding rock, dividing each flexible liquid injection sensor into 4 sections, applying different pressures respectively, performing simulated acquisition on the stress of the surrounding rock, taking the stress value of the surrounding rock measured at each section 1 as the input value of the neural network prediction model, taking the reading value of a pressure gauge as the output value of the neural network prediction model, obtaining the high-precision approximate nonlinear function relation between the stress change of the surrounding rock and the deformation of the pressure gauge, and performing parameter optimization on the neural network prediction model by the genetic algorithm, the control system adopts a high-performance chip as a control center controller, and a plurality of flexible liquid injection sensors are in communication connection with a data bus of a field bus network to realize communication between an output pressure gauge and the control center.
As shown in fig. 1, in this embodiment, as a preferred mode, the method for constructing and training the neural network prediction model is implemented by the following steps:
the first stage is as follows: the method comprises the steps of building a neural network prediction model and collecting data, wherein the neural network prediction model is composed of an input layer, a hidden layer and an output layer. The neuron of the input layer is set to have four diameters of d1, d2, d3 and d4 which correspond to 4 sections of the flexible liquid injection sensor, and the neuron of the output layer is set to have a pressure reading p output by a pressure gauge, so that the number of input nodes is 4, and the number of output nodes is 1. The number of neurons in the hidden layer is generally determined by an empirical formula. Different surrounding rock stresses are applied through simulation of experimental equipment, required input and output data are collected, n groups of data are recorded, and corresponding sample data are obtained.
And a second stage: the training process of the neural network prediction model mainly comprises two processes of forward propagation of signals and backward propagation of errors. And during forward propagation, inputting the input sample data acquired in the first stage into an input layer of the neural network, and transmitting the input sample data into an output layer after the hidden layer processing. If the actual output of the output layer has a larger error value with the target output, the error is transmitted to the back propagation stage. The error back propagation is to reversely transmit the output error to the input layer by layer through the hidden layer and distribute the error to all units of each layer, thereby obtaining the error signal of each layer of units, and the error signal is used as the basis for correcting the weight of each unit. And when the error between the actual output and the target output does not meet the preset precision requirement, the neural network can continuously adjust the weight and update the network until the error is smaller than the preset precision, and the training is finished.
Specifically, in this embodiment, the number of layers of the hidden layer is set to 1, and the number of neurons in the hidden layer is set to 9.
Preferably, the genetic algorithm described in this embodiment is designed and implemented by the following steps:
s1, selecting binary codes;
s2, initializing a group;
s3, selecting an error performance index function to define a fitness function, evaluating the fitness value and storing the optimal chromosome; the fitness function is a measure for representing the quality of an individual object in a problem by considering the adaptation degree of a biological individual to the environment, and is generally determined according to an objective function of an actual problem;
s4, selecting operation by roulette, single-point crossing operation and variation operation with variation rate of 0.000-0.1;
s5, generating a new population, and recalculating the adaptive value of each chromosome;
and S6, judging whether the termination condition is met, and when the algorithm reaches the termination condition, terminating the algorithm and outputting the result. The termination conditions are generally of three types: an optimal or near optimal solution has been obtained; the fitness is saturated; continued iteration does not result in better individuals. The set maximum evolution algebra is reached, and the artificially set evolution algebra is generally 100-500 generations. If the termination condition is not satisfied, the flow returns to S3.
As shown in fig. 2-3, in a preferred embodiment, the flexible liquid injection sensor includes at least one flexible pressure sensing device 1, a liquid injection device 2, a conveying device 3, and a pressure measuring device 4, where the liquid injection device 1 is connected to the flexible pressure sensing device 1 through the conveying device 2 for injecting emulsion into the flexible pressure sensing device 1, and the pressure measuring device 4 is respectively communicated with the conveying device 2 and the flexible pressure sensing device 1, and is used for displaying pressure of the emulsion therein through a pressure gauge. When the stress of the surrounding rock is detected, a plurality of drill holes are generally formed in the surrounding rock, and a flexible pressure sensing device 1 is installed in each drill hole to detect the stress at different positions on the surrounding rock. Flexible pressure sensing device 1 is the cartridge type structure for in the drilling of country rock is packed into, after the emulsion that has predetermined pressure is injected into, flexible pressure sensing device 1 can with the laminating of drilling inner wall, and the stress that any direction was received to the drilling inner wall all can be transmitted to flexible pressure sensing device 1's surface, detects the pressure of emulsion in the flexible pressure sensing device 1 through pressure measuring device 4, just can detect the stress in the drilling of different positions on the country rock.
Specifically, in this embodiment, the flexible pressure sensing device 1 includes a pipe body 11, a sealing layer 12, a rubber layer 13, and a joint 14, the sealing layer 12 and the rubber layer 13 are respectively disposed outside the pipe body 11, one end of the pipe body 11 is closed, the other end of the pipe body is connected to the joint 14, a liquid injection hole 141 is disposed in the middle of the joint 14, an exhaust hole 142 is disposed in the side of the joint, and the liquid injection hole 141 and the exhaust hole 142 are both communicated with an inner cavity of the pipe body 11. The rubber layer 13 is formed by pressing and weaving high polymer material rubber and steel wires.
Specifically, in this embodiment, the liquid injection device 2 includes a pump 21, a flow meter 22 and a liquid injection gun 23, the conveying device 3 is provided as a rubber tube, and the pump 21, the flow meter 22, the liquid injection gun 23 and the rubber tube are connected in sequence for conveying the emulsion.
Specifically, in this embodiment, the pressure measuring device 4 includes a pressure gauge and a three-way valve 41, one end of the three-way valve 41 is communicated with the pressure gauge, and the other two ends are respectively communicated with the flexible pressure sensing device 3 and the liquid injection device 2.
As a preferable mode, in this embodiment, an STM32, a DSP, a CPLD, or an FPGA high-performance chip is used as the controller, which can rapidly and continuously acquire system parameters, perform high-precision data processing and arithmetic operations, and timely and accurately provide control signals.
The field bus communication mode can be wired or wireless. Wired communication modes include optical fiber, shielded twisted pair, coaxial cable, RS485, RS232, Profibus, Profinet, FF, Modbus, CAN bus and the like, and wireless communication modes include WiFi, Zigbee, LoRa, infrared and the like.
The operation of the detection method of the present invention is briefly described as follows:
s1, evenly dividing the flexible liquid injection sensor into 4 sections according to the length;
s2, applying different pressures to each section respectively to simulate the change of the stress of the surrounding rock and observe the output condition of the pressure dial plate;
s3, respectively recording the pressure value of 4 segments during each pressure application and the display value of the corresponding pressure gauge to obtain sample data of the neural network;
s4, dividing the sample data into two groups, namely a training set and a test set, training the neural network model by using the training set, and testing the trained model by using the test set;
and S5, combining the trained neural network model with the genetic algorithm optimization parameters for predicting the output value of the pressure gauge.
The specific detection method comprises the following steps:
1) the structural design of the neural network prediction model comprises an input layer, a hidden layer and an output layer, wherein neurons of the input layer are 4 sections of diameters of the flexible liquid injection sensor, neurons of the output layer are pressure readings output by a pressure gauge, nodes of the input layer are 4, and nodes of the output layer are 1. The number of hidden layers is generally selected to be a single hidden layer. It has been theoretically demonstrated that a three-layer feedforward neural network can approximate any continuous function with any accuracy. Thus implying a number of layers of 1. In the process of network design, the determination of the number of the neurons in the hidden layer is very important. The number of hidden layer neurons is too large, so that the network calculation amount is increased, and the overfitting problem is easy to generate; if the number of the neurons is too small, the network performance is affected and the expected effect cannot be achieved. The number of hidden layer neurons in the network has a direct link to the complexity of the real problem, the number of neurons in the input and output layers, and the setting of the expected error. At present, there is no clear formula for determining the number of neurons in the hidden layer, and only some empirical formulas are used, and the final determination of the number of neurons still needs to be determined according to experience and multiple experiments. The invention refers to the following empirical formula on the problem of selecting the number of hidden layer neurons:
in the formula: j is the number of hidden layer neurons, i and k are the number of input layer and output layer neurons, respectively, and a is generally a constant between [1,10 ]. In the invention, the number of the neurons in the hidden layer is taken as 9.
2) The training process of the BP neural network prediction model mainly comprises two processes of forward propagation of signals and backward propagation of errors. And during forward propagation, inputting the input sample data acquired in the first stage into an input layer of the neural network, and transmitting the input sample data into an output layer after the hidden layer processing. If the actual output of the output layer has a larger error value with the expected output, the error is transmitted to the back propagation stage. The error back propagation is to reversely transmit the output error to the input layer by layer through the hidden layer and distribute the error to all units of each layer, thereby obtaining the error signal of each layer of units, and the error signal is used as the basis for correcting the weight of each unit. And when the error between the actual output and the target output does not meet the preset precision requirement, the neural network can continuously adjust the weight and update the network until the error is smaller than the preset precision, and the training is finished.
And collecting sample data, measuring the pressure of 4 sections of the flexible liquid injection sensor, and recording the pressure value of 4 sections and the corresponding display value of the pressure gauge when the pressure is applied each time to obtain the sample data of the neural network. The sample is divided into a training set and a test set. Input sample data is recorded as: d (m) ═ d1(m),d2(m),K dn(m)]Where n is 1,2, … 200, and m is the number of training studies.
Initializing data, initializing weight V between input layer and hidden layerijAnd the weight W between the hidden layer and the output layerjkAnd a threshold a for the hidden layer and a threshold b for the output layer, given the learning rate and activation function.
Hidden layer input:
hidden layer output calculation: from the input samples, the output of the hidden layer is computed forward. The hidden layer output is:
and (3) inputting by an output layer:
and (4) output calculation of the output layer, and further calculating the output of the output layer according to the output of the hidden layer. The output of the output layer is:
and an error signal obtained by comparing the output of the output layer with a desired value.
The error signals are: e.g. of the typek＝Tk-Pk。
Error performance index:
where T is the desired output and P is the actual output.
Partial derivative of error function to output layer input:
the output layer inputs the partial derivative of the connection weight between the hidden layer and the output layer:
partial derivative of the error function to the connection weight between the hidden layer and the output layer:
partial derivative of error function to hidden layer input:
the partial derivative of the hidden layer input to the connection weight between the input layer and the hidden layer is as follows:
partial derivative of the error function to the connection weight between the input layer and the hidden layer:
the network error is a function of the weights of the layers, so adjusting the weights can change the error E. The principle of adjusting the weight is to make the error decrease continuously.
Updating the weight between the hidden layer and the output layer:
Wjk(m+1)＝Wjk(m)+ΔWjk(m)＝Wjk(m)+ηgδ1(m)gOoj(m)
updating the weight between the input layer and the hidden layer:
Vij(m+1)＝Vij(m)+ΔVij(m)＝Vij(m)+ηgδ2(m)gdi(m)。
i is the input layer neuron number, j is the hidden layer neuron number, k is the output layer neuron number, and η is the learning rate, which is set to 0.01 in this example. Vij，WjkRespectively, the connection weights between the input layer and the hidden layer and between the hidden layer and the output layer, and f is a continuously derivable Sigmoid function.
And (3) error judgment: and judging whether the error reaches the upper limit of the preset precision or the learning times. If the requirement is met, the neural network training is finished, otherwise, the learning is returned again.
Through the global optimization of the genetic algorithm, the problem that the BP neural network is easy to fall into local optimization can be solved to a great extent.
The test set data is used for verifying the neural network prediction model, and the result shows that the difference between the predicted value and the true value output by the neural network to the pressure gauge is small, and the relative error is small, so that the model can accurately reflect the nonlinear mapping relation between input and output, can accurately illustrate the internal relation between the surrounding rock stress and the pressure gauge output data, and can accurately predict the pressure gauge output data.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make various changes, modifications, additions and substitutions within the spirit and scope of the present invention.
Claims (9)
1. A flexible liquid injection sensor detection method based on genetic algorithm optimization neural network is characterized by comprising the steps of setting a plurality of flexible liquid injection sensors, constructing and training a neural network prediction model, optimizing the neural network prediction model by genetic algorithm and setting a control system, arranging a plurality of drill holes on surrounding rock, installing the flexible liquid injection sensors in each drill hole, detecting the stress at different positions on the surrounding rock, dividing each flexible liquid injection sensor into 4 sections, applying different pressures respectively, performing simulation acquisition on the stress of the surrounding rock, taking the stress value of the surrounding rock measured in each section 1 as the input value of the neural network prediction model, taking the reading of a pressure gauge as the output value of the neural network prediction model, obtaining the high-precision approximate nonlinear function relation between the stress change of the surrounding rock and the deformation of the pressure gauge, and performing parameter optimization on the neural network prediction model by the genetic algorithm, the control system adopts a high-performance chip as a control center controller, and a plurality of flexible liquid injection sensors are in communication connection with a data bus of a field bus network to realize communication between an output pressure gauge and the control center.
2. The flexible liquid injection sensor detection method based on the genetic algorithm optimization neural network is characterized in that the construction and training method of the neural network prediction model is realized by the following steps:
the first stage is as follows: the method comprises the steps of building a neural network prediction model and collecting data, wherein the neural network prediction model is composed of an input layer, a hidden layer and an output layer, neurons of the input layer are set to have four diameters of d1, d2, d3 and d4 which correspond to 4 sections of flexible liquid injection sensors, neurons of the output layer are set to be pressure readings p output by a pressure gauge, the number of the neurons of the hidden layer is generally determined by an empirical formula, different surrounding rock stresses are simulated and applied through experimental equipment, required input and output data are collected, n groups of data are recorded, and corresponding sample data are obtained;
and a second stage: and training the neural network prediction model, wherein the training process mainly comprises two processes of forward propagation of signals and backward propagation of errors, when the error between actual output and target output does not meet the preset precision requirement, the neural network can continuously adjust the weight, update the network until the error is smaller than the preset precision, and the training is finished.
3. The flexible liquid injection sensor detection method based on the genetic algorithm optimization neural network as claimed in claim 2, wherein the number of hidden layers is set to 1, and the number of hidden layer neurons is set to 9.
4. The flexible liquid injection sensor detection method based on the genetic algorithm optimization neural network is characterized in that the genetic algorithm is designed and realized through the following steps:
s1, selecting binary codes;
s2, initializing a group;
s3, selecting an error performance index function to define a fitness function, evaluating the fitness value and storing the optimal chromosome;
s4, selecting operation by roulette, single-point crossing operation and variation operation with variation rate of 0.000-0.1;
s5, generating a new population, and recalculating the adaptive value of each chromosome;
and S6, judging whether the termination condition is met, when the algorithm reaches the termination condition, terminating the algorithm, outputting a result, and if the termination condition is not met, returning to S3.
5. The flexible liquid injection sensor detection method based on the genetic algorithm optimization neural network is characterized by comprising at least one flexible pressure sensing device (1), a liquid injection device (2), a conveying device (3) and a pressure measuring device (4), wherein the liquid injection device (1) is connected with the flexible pressure sensing device (1) through the conveying device (2) and used for injecting emulsion into the flexible pressure sensing device (1), and the pressure measuring device (4) is respectively communicated with the conveying device (2) and the flexible pressure sensing device (1) and used for displaying the pressure of the emulsion in the flexible pressure sensing device through a pressure gauge.
6. The flexible liquid injection sensor detection method based on the genetic algorithm optimization neural network is characterized in that the flexible pressure sensing device (1) comprises a pipe body (11), a sealing layer (12), a rubber layer (13) and a joint (14), the sealing layer (12) and the rubber layer (13) are respectively arranged outside the pipe body (11), one end of the pipe body (11) is closed, the other end of the pipe body is connected with the joint (14), a liquid injection hole is formed in the middle of the joint (14), an exhaust hole is formed in the side face of the joint (14), and the liquid injection hole and the exhaust hole are both communicated with an inner cavity of the pipe body (11).
7. The flexible liquid injection sensor detection method based on the genetic algorithm optimization neural network is characterized in that the liquid injection device (2) comprises a pump (21), a flow meter (22) and a liquid injection gun (23), the conveying device (3) is arranged to be a rubber tube, and the pump (21), the flow meter (22), the liquid injection gun (23) and the rubber tube are connected in sequence and used for conveying emulsion.
8. The flexible liquid injection sensor detection method based on the genetic algorithm optimization neural network is characterized in that the pressure measurement device (4) comprises a pressure gauge and a three-way valve (41), one end of the three-way valve (41) is communicated with the pressure gauge, and the other two ends of the three-way valve are respectively communicated with the flexible pressure sensing device (3) and the liquid injection device (2).
9. The flexible liquid injection sensor detection method based on the genetic algorithm optimization neural network is characterized in that an STM32, DSP, CPLD or FPGA high-performance chip is used as a controller.
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