A kind of oxygen-reducing nitrogen injection fire prevention and control test system and method

Abstract

The application discloses a kind of oxygen-reducing nitrogen injection fire prevention and control test system and method, including control center, video acquisition module, temperature monitoring module, humidity monitoring module, atmospheric pressure monitoring module, oxygen content monitoring module and power module.The application carries out information collection by each module, and input acquisition result into control center, and the effect of fire prevention and control is predicted based on BP neural network prediction algorithm.The application is designed for the oxygen-reducing nitrogen injection fire prevention and control test demand, builds an oxygen-reducing nitrogen injection fire prevention and control test system, realizes the system function that back-end control center real-time monitoring front-end test environment inside video, temperature, humidity, atmospheric pressure, oxygen content and other test data, provides a systematic solution for oxygen-reducing nitrogen injection fire prevention and control technology test environment building, realizes a comprehensive centralized oxygen-reducing nitrogen injection fire prevention and control test environment system.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of fire rescue, and particularly relates to a reduced-oxygen nitrogen injection fire prevention and control test system and method. BACKGROUND

[0002] The reduced-oxygen nitrogen injection fire prevention and control system is a fire prevention and control system for regulating nitrogen and oxygen concentrations at normal temperature and pressure, thereby achieving the purpose of active fire prevention, and is a new fire prevention and control technology. In order to analyze the technical performance of the reduced-oxygen nitrogen injection fire prevention and control system, the reduced-oxygen nitrogen injection fire prevention and control test system is designed. The main function of the system is to test the technical performance of the reduced-oxygen nitrogen injection fire prevention and control system, and to focus on testing the internal temperature, humidity, atmospheric pressure, oxygen content and video data of the reduced-oxygen nitrogen injection test environment, so as to evaluate the application performance of the reduced-oxygen nitrogen injection fire prevention and control system. SUMMARY

[0003] The application is designed for the test demand of the reduced-oxygen nitrogen injection fire prevention and control, and builds a reduced-oxygen nitrogen injection fire prevention and control test system, realizes the system function of the back-end control center for monitoring the front-end test environment internal video, temperature, humidity, atmospheric pressure, oxygen content and other test data in real time, provides a systematic solution for the reduced-oxygen nitrogen injection fire prevention and control test environment, and realizes a comprehensive reduced-oxygen nitrogen injection fire prevention and control test environment system and test method.

[0004] The application is achieved by the following technical scheme: a reduced-oxygen nitrogen injection fire prevention and control test system, a control center connected with a temperature monitoring module, a humidity monitoring module, an atmospheric pressure monitoring module, an oxygen content monitoring module and a video acquisition module;

[0005] The control center controls all modules in terms of instructions and data interaction, and controls the orderly work and data collection of the modules.

[0006] The temperature monitoring module is used for monitoring the temperature data in the reduced-oxygen nitrogen injection fire prevention and control test environment, and monitoring the temperature of the internal test environment. The humidity monitoring module is used for monitoring the humidity data in the reduced-oxygen nitrogen injection fire prevention and control test environment, and monitoring the humidity of the internal test environment. The atmospheric pressure monitoring module is used for monitoring the atmospheric pressure data in the reduced-oxygen nitrogen injection fire prevention and control test environment, and monitoring the atmospheric pressure of the internal test environment. The oxygen content monitoring module is used for monitoring the oxygen environment data in the reduced-oxygen nitrogen injection fire prevention and control test environment, and monitoring the oxygen content of the internal test environment. The video acquisition module is used for acquiring the video data in the reduced-oxygen nitrogen injection fire prevention and control test environment, and monitoring the video of the internal test environment.

[0007] The method for preventing and predicting by using the reduced-oxygen nitrogen injection fire prevention and control test system comprises the following steps:

[0008] 1) Extract data in the database as the training set and test set of neural network; the data include temperature X1, humidity X2, atmospheric pressure X3, oxygen content X4 and fire prevention effect Y;

[0009] 2) Construction of BP neural network:

[0010] 2.1) Establish fire prevention effect prediction model based on BP neural network: set temperature X1, humidity X2, atmospheric pressure X3 and oxygen content X4 as the input value of BP neural network, and fire prevention effect Y as the prediction value of BP neural network, w ij and w j are the weights of BP neural network;

[0011] 2.2) BP neural network initialization: determine the number of network input layer nodes 4, the number of hidden layer nodes 5, the number of output layer nodes 1 according to the system input and output, initialize the weights w ij , w j between the input layer, hidden layer and output layer neural network nodes, initialize the hidden layer threshold a, the output layer threshold b, and give the learning rate γ and the neuron excitation function f;

[0012] 2.3) BP neural network hidden layer output calculation: according to the input data, the weights w ij between the input layer and the hidden layer, calculate the output:

[0013]

[0014] In the formula, j is the number of hidden layer nodes; f is the hidden layer excitation function,

[0015]

[0016] 2.4) BP neural network output layer output calculation: according to the hidden layer output H, the connection weight w j and the threshold value b, calculate the BP neural network prediction output O;

[0017]

[0018] 2.5) BP neural network error calculation: according to the network prediction output O and the expected output Y', calculate the neural network prediction error e;

[0019] e=Y'-O

[0020] 2.6) Neural network weight update: update the network connection weights w ij , w j according to the neural network prediction error e;

[0021] w ij =w ij +γHj (1-H j )x(i)w j e i=1,2,…,4;j=1,2,…,5

[0022] w j =w j +γH j ej=1,2,…,5

[0023] In the formula, γ is a learning rate;

[0024] 2.7) Neural network threshold update: update network node threshold a, b according to neural network prediction error e;

[0025] a j =a j +γH j (1-H j )w j e j=1,2,…,5

[0026] b=b+e

[0027] 2.8) Train the neural network through the training set, and detect the prediction effect of the neural network through the test set; if the prediction error e is less than the set value, it means that the neural network reaches the preset effect, which is the final neural network structure; if the prediction error e is greater than the set value, it means that the neural network has not reached the preset effect, and more data needs to be extracted from the database as a training set to continue training the neural network;

[0028] 3) Prediction: input the collected temperature X1, humidity X2, atmospheric pressure X3 and oxygen content X4 in the actual into the neural network finally obtained in step 2), if the fire prevention and control effect Y output by the neural network is 1, it means that the fire prevention and control effect is successful, and if the fire prevention and control effect Y output by the neural network is 0, it means that the fire prevention and control effect fails.

[0029] The control center, the video acquisition module, the temperature monitoring module, the humidity monitoring module, the atmospheric pressure monitoring module and the oxygen content monitoring module are all connected with the power module, and the power module provides power for the whole system.

[0030] The control center is an electronic computer system, and the video acquisition module is connected with the control center through one network interface; the control center is connected with the temperature monitoring module, the humidity monitoring module, the atmospheric pressure monitoring module and the oxygen content monitoring module through four USB interfaces.

[0031] The temperature monitoring module includes U26, which is a USB connector connected with the control center through a USB data line, pin 1 of U26 is connected with a power signal VCC through a fuse RJ3, pin 5 of U26 is connected with GND, and pins 2 and 3 of U26 are connected with pins 8 and 7 of U24 correspondingly;

[0032] U24 is CH341T, pin 1 is connected with VCC through a light emitting diode L7 and a resistor R22, pins 13 and 20 are connected with VCC, pins 11 and 12 are connected with GND, a capacitor C40, a capacitor C41 and a crystal oscillator Q4 constitute a crystal oscillator circuit connected with pins 9 and 10 of U24, a capacitor C39 is a 3.3VCC power decoupling capacitor, a capacitor C45 and a capacitor C47 are VCC power decoupling capacitors, pin 18 is connected with pins 2 and 3 of U25, and pins 3 and 4 of U25 are connected with pins 4 and 1 of U25 correspondingly;

[0033] U25 is MAX487, pins 1 and 4 are connected with pins 4 and 3 of U24 correspondingly, pin 5 is connected with GND, pin 8 is connected with VCC, a capacitor C37 is a decoupling capacitor, pins 6 and 7 are connected with pins A and B of U28 correspondingly, a resistor R24 and a light emitting diode L8 constitute an indicating circuit connected with pin 4 of U25, which indicates data sending, a resistor R25 and a light emitting diode L9 constitute an indicating circuit connected with pin 1 of U25, which indicates data receiving, one end of a resistor R26 is connected with GND, the other end of the resistor R26 is connected with pin 7 of U25, one end of a resistor R30 is connected with VCC, the other end of the resistor R30 is connected with pin 6 of U25;

[0034] U28 is a temperature sensing module, pins A and B of U28 are connected with pins 6 and 7 of U25 correspondingly, a pin VCC of U28 is connected with a power signal 12VCC, and a pin GND of U28 is connected with a signal GND.

[0035] The humidity monitoring module includes U19, which is a USB connector connected with the control center through a USB data line, pin 1 of U19 is connected with a power signal VCC through a fuse RJ1, pin 5 of U19 is connected with GND, and pins 2 and 3 of U19 are connected with pins 8 and 7 of U1 correspondingly;

[0036] U1 is CH341T, pin 1 of U1 is connected with VCC through a light emitting diode L4 and a resistor R12, pins 13 and 20 of U1 are connected with VCC, pins 11 and 12 of U1 are connected with GND, a capacitor C29, a capacitor C30 and a crystal oscillator Q2 constitute a crystal oscillator circuit connected with pins 9 and 10 of U1, a capacitor C27 is a 3.3VCC power decoupling capacitor, a capacitor C33 and a capacitor C35 are VCC power decoupling capacitors, pin 18 of U1 is connected with pins 2 and 3 of U18, and pins 3 and 4 of U18 are connected with pins 4 and 1 of U18 correspondingly;

[0037] U18 is MAX487, pin 1, 4 respectively corresponding to the connection U1 pin 4, 3, pin 5 connects GND, pin 8 connects VCC, capacitor C25 is its decoupling capacitor, pin 6, 7 respectively corresponding to the connection U22 pin A, B, resistor R14, LED L5 constitute an indication circuit connected to U18 pin 4, indicating its data transmission, resistor R15, LED L6 constitute an indication circuit connected to U18 pin 1, indicating its data reception, resistor R16 one end connects GND, the other end connects U18 pin 7, resistor R20 one end connects VCC, the other end connects U18 pin 6;

[0038] U22 is a humidity sensing module, pin A, B respectively corresponding to the connection U18 pin 6, 7, pin VCC connects the power signal 12VCC, pin GND connects the signal GND.

[0039] The atmospheric pressure monitoring module comprises U30, which is a USB connector connected with the control center through the USB data line, U30 pin 1 connects the power signal VCC through the fuse RJ4, U30 pin 5 connects GND, U30 pin 2, 3 and U27 pin 8, 7 are connected correspondingly;

[0040] U27 is CH341T, pin 1 connects VCC through LED L10, resistor R23, pin 13, 20 connects VCC, pin 11, 12 connects GND, capacitor C43, C44 and crystal oscillator Q5 constitute a crystal oscillator circuit connected to U27 pin 9, 10, capacitor C42 is a 3.3VCC power decoupling capacitor, capacitor C46, C48 is a VCC power decoupling capacitor, its pin 18 connects U29 pin 2, 3, pin 3, 4 respectively corresponding to the connection U29 pin 4, 1;

[0041] U29 is MAX487, pin 1, 4 respectively corresponding to the connection U27 pin 4, 3, pin 5 connects GND, pin 8 connects VCC, capacitor C38 is its decoupling capacitor, pin 6, 7 respectively corresponding to the connection U31 pin A, B, resistor R27, LED L11 constitute an indication circuit connected to U29 pin 4, indicating its data transmission, resistor R28, LED L12 constitute an indication circuit connected to U29 pin 1, indicating its data reception, resistor R29 one end connects GND, the other end connects U29 pin 7, resistor R31 one end connects VCC, the other end connects U29 pin 6;

[0042] U31 is an atmospheric pressure sensing module, pin A, B respectively corresponding to the connection U29 pin 6, 7, pin VCC connects the power signal 12VCC, its pin GND connects the signal GND.

[0043] The oxygen content monitoring module comprises a U21, a USB connector, connected with the control center through a USB data line,

[0044] The U21 is a USB connector, pin 1 is connected with a power signal VCC through an insurance RJ2, pin 5 is connected with GND, and pins 2 and 3 are connected with pins 8 and 7 of the U17 correspondingly.

[0045] The U17 is a CH341T, pin 1 is connected with VCC through a light emitting diode L1 and a resistor R13, pins 13 and 20 are connected with VCC, pins 11 and 12 are connected with GND, a capacitor C31, a capacitor C32 and a crystal oscillator Q3 constitute a crystal oscillator circuit connected to pins 9 and 10 of the U17, a capacitor C28 is a 3.3VCC power decoupling capacitor, a capacitor C34 and a capacitor C36 are VCC power decoupling capacitors, pin 18 is connected with pins 2 and 3 of the U20, and pins 3 and 4 are connected with pins 4 and 1 of the U20 correspondingly.

[0046] The U20 is a MAX487, pins 1 and 4 are connected with pins 4 and 3 of the U20 correspondingly, pin 5 is connected with GND, pin 8 is connected with VCC, a capacitor C26 is a decoupling capacitor thereof, pins 6 and 7 are connected with pins A and B of the U23 correspondingly, a resistor R17 and a light emitting diode L2 constitute an indicating circuit connected to pin 4 of the U20, indicating data transmission, a resistor R18 and a light emitting diode L3 constitute an indicating circuit connected to pin 1 of the U20, indicating data reception, one end of a resistor R19 is connected with GND, and the other end is connected with pin 7 of the U20, one end of a resistor R21 is connected with VCC, and the other end is connected with pin 6 of the U20.

[0047] The U23 is an oxygen content sensing module, a BEE-4100L, pins A and B are connected with pins 6 and 7 of the U20 correspondingly, and a pin VCC is connected with a power signal 12VCC, and a pin GND is connected with a signal GND.

[0048] The video acquisition module comprises a U32 and a U33, the U32 is a video acquisition unit, pins 3, 4, 5 and 6 are connected with pins 6, 3, 2 and 1 of the U33 correspondingly, for data transmission and reception, pin 8 is connected with 12VCC, and pin 7 is connected with a GND signal; the U33 is a network interface, pins 6, 3, 2 and 1 are connected with pins 3, 4, 5 and 6 of the U32 correspondingly, the U33 is connected with the control center through a network cable, realizing transmission of video data and interaction of control instructions.

[0049] The power module comprises a connector U36, a chip U35, a chip U37, a chip U34, the U35 is of a model VRB2412D_20W, the U37 is of a model VRB2405D_20W, and the U34 is of a model LT1529IQ-3.3;The switch S3 is a power signal switch, the resistors R33 and R34 form a matching resistor, the capacitors C53 and C54 form an output end filter rectifier circuit, and an output signal 12VCC;The 12VCC is used as an input signal of the U37, the capacitors C55 and C56 form an output end filter rectifier circuit, and an output signal VCC;The VCC is used as an input signal of the U34, the resistor R32, the capacitors C49 and C51 are used as an input end filter circuit, the capacitors C50 and C52 form a U34 output end filter rectifier circuit, and an output signal 3.3VCC;Thus, the power conversion module forms 12VCC, 5V and 3.3VCC to supply power to each module.

[0050] The beneficial effects of the application are that: through the system, the oxygen-reducing nitrogen-injection fire prevention and control system test environment can be built, the temperature, humidity, atmospheric pressure, oxygen content and video data of the oxygen-reducing nitrogen-injection fire prevention and control system are comprehensively tested, the comprehensive analysis of the fire prevention performance technical platform of the oxygen-reducing nitrogen-injection fire prevention and control system is constructed, and the oxygen-reducing nitrogen-injection fire prevention and control technology and the test system development have a great promoting effect. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 It is a structural block diagram of the application;

[0052] Figure 2 It is a temperature monitoring module circuit schematic diagram;

[0053] Figure 3 It is a humidity monitoring module circuit schematic diagram;

[0054] Figure 4 It is an atmospheric pressure monitoring module circuit schematic diagram;

[0055] Figure 5 It is an oxygen content monitoring module circuit schematic diagram;

[0056] Figure 6 It is a video acquisition module circuit schematic diagram;

[0057] Figure 7 It is a power module circuit schematic diagram;

[0058] Figure 8 It is a fire prevention effect prediction algorithm schematic diagram based on a BP neural network. DETAILED DESCRIPTION

[0059] The application will be further described below with reference to the drawings and examples. In describing the drawings, identical numbers represent the same or similar elements, unless otherwise indicated. The implementations described in the following example embodiments are not meant to represent all implementations consistent with the application. Rather, they are simply examples of apparatuses consistent with some aspects of the application, as detailed in the appended claims.

[0060] As Figure 1 The oxygen-reducing nitrogen-injection fire prevention and control test system includes a control center, a temperature monitoring module, a humidity monitoring module, an atmospheric pressure monitoring module, an oxygen content monitoring module, a video acquisition module, and a power module. The control center controls and exchanges data with all modules, controls the orderly work of each module, and collects data. The temperature monitoring module is used to monitor the temperature data inside the oxygen-reducing nitrogen-injection fire prevention and control test environment, and monitor the temperature of the internal test environment. The humidity monitoring module is used to monitor the humidity data inside the oxygen-reducing nitrogen-injection fire prevention and control test environment, and monitor the humidity of the internal test environment. The atmospheric pressure monitoring module is used to monitor the atmospheric pressure data inside the oxygen-reducing nitrogen-injection fire prevention and control test environment, and monitor the atmospheric pressure of the internal test environment. The oxygen content monitoring module is used to monitor the oxygen environment data inside the oxygen-reducing nitrogen-injection fire prevention and control test environment, and monitor the oxygen content of the internal test environment. The video acquisition module is used to acquire the video data inside the oxygen-reducing nitrogen-injection fire prevention and control test environment, and monitor the video of the internal test environment. The power module provides power for the control center, the video acquisition module, the temperature monitoring module, the humidity monitoring module, the atmospheric pressure monitoring module, and the oxygen content monitoring module.

[0061] The control center is an electronic computer system connected with the temperature monitoring module, the humidity monitoring module, the atmospheric pressure monitoring module, the oxygen content monitoring module, and the video acquisition module through one network interface and four USB interfaces, realizing the instruction control and data exchange of each module, controlling the orderly work of each module and collecting data. Specifically, it includes controlling and receiving the temperature data inside the test environment obtained by the temperature monitoring module, controlling and receiving the humidity data inside the test environment obtained by the humidity monitoring module, controlling and receiving the atmospheric pressure data inside the test environment obtained by the atmospheric pressure monitoring module, controlling and receiving the oxygen content data inside the test environment obtained by the oxygen content monitoring module, and controlling and receiving the video data inside the test environment obtained by the video acquisition module.

[0062] The specific working principle is as follows: first, the control center sends video acquisition, temperature monitoring, humidity monitoring, atmospheric pressure monitoring, oxygen content monitoring start and related setting instructions to the video acquisition module, temperature monitoring module, humidity monitoring module, atmospheric pressure monitoring module and oxygen content monitoring module respectively; then, the video acquisition module, temperature monitoring module, humidity monitoring module, atmospheric pressure monitoring module and oxygen content monitoring module perform corresponding work according to the instruction requirements; next, the video acquisition module continuously transmits the video data collected after starting to the control center, the temperature monitoring module continuously transmits the temperature data obtained to the control center, the humidity monitoring module continuously transmits the humidity data obtained to the control center, the atmospheric pressure monitoring module continuously transmits the atmospheric pressure data obtained to the control center, and the oxygen content monitoring module continuously transmits the oxygen content data obtained to the control center; at the same time, the control center can adjust the control and corresponding setting instructions of each module at any time, and the power module supplies power to the video acquisition module, temperature monitoring module, humidity monitoring module, atmospheric pressure monitoring module and oxygen content monitoring module respectively.

[0063] As shown in Figure 2 The temperature monitoring module includes U26, which is a USB connector connected with the control center through a USB data line, pin 1 of U26 is connected with a power signal VCC through a fuse RJ3, pin 5 of U26 is connected with GND, pins 2 and 3 of U26 are connected with pins 8 and 7 of U24 respectively; U24 is a CH341T, pin 1 of U24 is connected with VCC through a light-emitting diode L7 and a resistor R22, pins 13 and 20 of U24 are connected with VCC, pins 11 and 12 of U24 are connected with GND, a crystal oscillator circuit composed of a capacitor C40, a capacitor C41 and a crystal oscillator Q4 is connected to pins 9 and 10 of U24, a capacitor C39 is a 3.3VCC power decoupling capacitor, a capacitor C45 and a capacitor C47 are VCC power decoupling capacitors, pin 18 of U24 is connected with pins 2 and 3 of U25, pins 3 and 4 of U24 are connected with pins 4 and 1 of U25 respectively; U25 is a MAX487, pins 1 and 4 of U25 are connected with pins 4 and 3 of U24 respectively, pin 5 of U25 is connected with GND, pin 8 of U25 is connected with VCC, a capacitor C37 is a decoupling capacitor of U25, pins 6 and 7 of U25 are connected with pins A and B of U28 respectively, a resistor R24 and a light-emitting diode L8 constitute an indicating circuit connected to pin 4 of U25 to indicate data sending, a resistor R25 and a light-emitting diode L9 constitute an indicating circuit connected to pin 1 of U25 to indicate data receiving, one end of a resistor R26 is connected with GND, the other end of the resistor R26 is connected with pin 7 of U25, one end of a resistor R30 is connected with VCC, the other end of the resistor R30 is connected with pin 6 of U25; U28 is a temperature sensing module, pins A and B of U28 are connected with pins 6 and 7 of U25 respectively, pin VCC of U28 is connected with a power signal 12VCC, pin GND of U28 is connected with a signal GND.

[0064] AsFigure 3 The humidity monitoring module contains U19, which is a USB connector, connected to the control center through the USB data line, U19 pin 1 is connected to the power signal VCC through the fuse RJ1, U19 pin 5 is connected to GND, U19 pin 2, 3 is connected to U1 pin 8, 7 respectively; U1 is CH341T, pin 1 is connected to VCC through LED L4 and resistor R12, pin 13, 20 is connected to VCC, pin 11, 12 is connected to GND, capacitor C29, C30 and crystal oscillator Q2 form a crystal oscillator circuit connected to U1 pin 9, 10, capacitor C27 is a 3.3VCC power decoupling capacitor, capacitor C33, C35 is a VCC power decoupling capacitor, pin 18 is connected to U18 pin 2, 3, pin 3, 4 is connected to U18 pin 4, 1 respectively; U18 is MAX487, pin 1, 4 is connected to U1 pin 4, 3 respectively, pin 5 is connected to GND, pin 8 is connected to VCC, capacitor C25 is its decoupling capacitor, pin 6, 7 is connected to U22 pin A, B respectively, resistor R14 and LED L5 form an indication circuit connected to U18 pin 4, indicating data transmission, resistor R15 and LED L6 form an indication circuit connected to U18 pin 1, indicating data reception, resistor R16 one end is connected to GND, the other end is connected to U18 pin 7, resistor R20 one end is connected to VCC, the other end is connected to U18 pin 6; U22 is a humidity sensor module, pin A, B is connected to U18 pin 6, 7 respectively, pin VCC is connected to power signal 12VCC, pin GND is connected to signal GND.

[0065] As Figure 4The shown is the atmospheric pressure monitoring module circuit principle diagram. The atmospheric pressure monitoring module contains U30, which is a USB connector, connected with the control center through the USB data line, U30 pin 1 connects the power signal VCC through the fuse RJ4, U30 pin 5 connects GND, U30 pin 2, 3 and U27 pin 8, 7 are connected correspondingly; U27 is CH341T, its pin 1 connects VCC through the light emitting diode L10, resistor R23, its pin 13, 20 connects VCC, its pin 11, 12 connects GND, capacitor C43, C44 and crystal oscillator Q5 constitute crystal oscillator circuit connected to U27 pin 9, 10, capacitor C42 is 3.3VCC power decoupling capacitor, capacitor C46, C48 is VCC power decoupling capacitor, its pin 18 connects U29 pin 2, 3, its pin 3, 4 connects U29 pin 4, 1 respectively; U29 is MAX487, its pin 1, 4 connects U27 pin 4, 3 respectively, its pin 5 connects GND, its pin 8 connects VCC, capacitor C38 is its decoupling capacitor, its pin 6, 7 connects U31 pin A, B respectively, resistor R27, light emitting diode L11 constitute indicating circuit connected to U29 pin 4, indicating its data sending, resistor R28, light emitting diode L12 constitute indicating circuit connected to U29 pin 1, indicating its data receiving, resistor R29 one end connects GND, the other end connects U29 pin 7, resistor R31 one end connects VCC, the other end connects U29 pin 6; U31 is atmospheric pressure sensor module, its pin A, B connects U29 pin 6, 7 respectively, its pin VCC connects power signal 12VCC, its pin GND connects signal GND.

[0066] As Figure 5The diagram shown is the circuit schematic of the oxygen content monitoring module. The oxygen content monitoring module includes U21, a USB connector that connects to the control center via a USB data cable. Pin 1 of U21 is connected to the power signal VCC via fuse RJ2, pin 5 is connected to GND, and pins 2 and 3 of U21 are connected to pins 8 and 7 of U17 respectively. U17, model CH341T, has pin 1 connected to VCC via LED L1 and resistor R13, pins 13 and 20 connected to VCC, and pins 11 and 12 connected to GND. Capacitors C31 and C32, along with crystal oscillator Q3, form a crystal oscillator circuit connected to pins 9 and 10 of U17. Capacitor C28 is a 3.3VCC power supply decoupling capacitor, and capacitors C34 and C36 are VCC power supply decoupling capacitors. Pin 18 of C28 is connected to pins 2 and 3 of U20, and pins 3 and 4 are connected to pins 4 and 1 of U20 respectively. The model is MAX487. Its pins 1 and 4 are connected to pins 4 and 3 of U20, respectively. Its pin 5 is connected to GND, and its pin 8 is connected to VCC. Capacitor C26 is its decoupling capacitor. Its pins 6 and 7 are connected to pins A and B of U23, respectively. Resistor R17 and LED L2 form an indicator circuit connected to pin 4 of U20 to indicate data transmission. Resistor R18 and LED L3 form an indicator circuit connected to pin 1 of U20 to indicate data reception. One end of resistor R19 is connected to GND, and the other end is connected to pin 7 of U20. One end of resistor R21 is connected to VCC, and the other end is connected to pin 6 of U20. U23 is the oxygen content sensing module. Its pins A and B are connected to pins 6 and 7 of U20, respectively. Its pin VCC is connected to the power signal 12VCC, and its pin GND is connected to the signal GND.

[0067] like Figure 6 The diagram shows the circuit schematic of the video acquisition module. The video acquisition module includes U32 and U33. U32 is the video acquisition unit; its pins 3, 4, 5, and 6 are connected to pins 6, 3, 2, and 1 of U33, respectively, for data transmission and reception. Pin 8 is connected to 12VCC, and pin 7 is connected to the GND signal. U33 is the network interface; its pins 6, 3, 2, and 1 are connected to pins 3, 4, 5, and 6 of U32. U33 connects to the control center via a network cable to achieve video data transmission and control command interaction.

[0068] like Figure 7As shown is a power module circuit schematic. The power module includes connector U36, chip U35 (VRB2412D_20W), chip U37 (VRB2405D_20W), chip U34 (LT1529IQ-3.3), switch S3 is a power signal switch, resistors R33 and R34 constitute a matching resistor, capacitors C53 and C54 constitute an output end filter rectifier circuit, and output signal 12VCC; at the same time, 12VCC is used as an input signal of U37, capacitors C55 and C56 constitute an output end filter rectifier circuit, and output signal VCC; at the same time, VCC is used as an input signal of U34, resistor R32, capacitor C49 and C51 are used as an input end filter circuit, capacitors C50 and C52 constitute a U34 output end filter rectifier circuit, and output signal 3.3VCC; thus, the power conversion module forms 12VCC (12V), VCC (5V) and 3.3VCC (3.3V) to supply power to each module.

[0069] In use, first, a certain amount of training data needs to be collected by the oxygen reduction and nitrogen injection fire prevention and control test system to train the neural network, so that the threshold parameters of the neural network achieve the best matching effect, the more training data, the more accurate the neural network prediction effect; then some test data is collected by the oxygen reduction and nitrogen injection fire prevention and control test system, and the prediction effect of the neural network is detected through the test data, at this time the neural network can accurately predict, and the neural network training reaches a satisfactory effect. Then, the whole system is in a coherent working state, that is, the oxygen reduction and nitrogen injection fire prevention and control test system controls the temperature monitoring module, the humidity monitoring module, the atmospheric pressure monitoring module and the oxygen content monitoring module to work through the control center, and collects temperature, humidity, atmospheric pressure and oxygen content data; next, the control center takes the temperature, humidity, atmospheric pressure and oxygen content data as the neural network algorithm input value, and guides the neural network to perform prediction operation; finally, the neural network outputs the prediction effect.

[0070] The specific method is as follows:

[0071] 1) Extract data in the database as the training set and test set of the neural network; the data includes temperature X1, humidity X2, atmospheric pressure X3, oxygen content X4 and fire prevention and control effect Y;

[0072] 2) Construction of BP neural network:

[0073] 2.1) Establish a fire prevention and control effect prediction model based on BP neural network: let temperature X1, humidity X2, atmospheric pressure X3 and oxygen content X4 be the input values of the BP neural network, and let fire prevention and control effect Y be the prediction value of the BP neural network, w ij and w j be the weight values of the BP neural network;

[0074] 2.2) BP neural network initialization: determine the number of input layer nodes 4, the number of hidden layer nodes 5, the number of output layer nodes 1 according to the system input and output, initialize the weights w between the input layer, hidden layer and output layer neural network nodes ij j , initialize the hidden layer threshold value a, the output layer threshold value b, the given learning rate γ and the neuron excitation function f;

[0075] 2.3) BP neural network hidden layer output calculation: according to the input data, the weights w between the input layer and the hidden layer ij , calculate the output:

[0076]

[0077] where j is the number of hidden layer nodes; f is the hidden layer excitation function,

[0078]

[0079] 2.4) BP neural network output layer output calculation: according to the hidden layer output H, the connection weights w j and the threshold value b, calculate the BP neural network prediction output O;

[0080]

[0081] 2.5) BP neural network error calculation: according to the network prediction output O and the expected output Y', calculate the neural network prediction error e;

[0082] e = Y' - O

[0083] 2.6) Neural network weight update: update the network connection weights w ij , w j according to the neural network prediction error e;

[0084] w ij = w ij + γH j (1 - H j ) x (i) w j e i = 1, 2, …, 4; j = 1, 2, …, 5

[0085] w j = w j + γH j e j = 1, 2, …, 5

[0086] where γ is the learning rate;

[0087] 2.7) Neural network threshold value update: update the network node threshold values a, b according to the neural network prediction error e;

[0088] a​j = a j + yH j (1-H j )w j e j = 1,2, …, 5

[0089] b = b + e

[0090] 2.8) Train the neural network through the training set, and detect the prediction effect of the neural network through the test set; if the prediction error e is less than the set value, it means that the neural network reaches the preset effect, which is the final neural network structure; if the prediction error e is greater than the set value, it means that the neural network does not reach the preset effect, and more data needs to be extracted from the database as the training set to continue training the neural network;

[0091] 3) Prediction: input the collected temperature X1, humidity X2, atmospheric pressure X3 and oxygen content X4 in the actual into the neural network finally obtained in step 2), if the fire prevention and control effect Y output by the neural network is 1, it means that the fire prevention and control effect is successful, and if the fire prevention and control effect Y output by the neural network is 0, it means that the fire prevention and control effect fails.

Claims

1. A fire prevention and control testing system for oxygen reduction and nitrogen injection, characterized in that: The control center is connected to the temperature monitoring module, humidity monitoring module, atmospheric pressure monitoring module, oxygen content monitoring module, and video acquisition module; The control center controls all modules by giving commands and interacting with them by sending data, ensuring the orderly operation of each module and the collection of data. The temperature monitoring module is used to monitor the temperature data inside the oxygen reduction and nitrogen injection fire prevention test environment, and to perform temperature monitoring of the internal test environment; the humidity monitoring module is used to monitor the humidity data inside the oxygen reduction and nitrogen injection fire prevention test environment, and to perform humidity monitoring of the internal test environment; the atmospheric pressure monitoring module is used to monitor the atmospheric pressure data inside the oxygen reduction and nitrogen injection fire prevention test environment, and to perform atmospheric pressure monitoring of the internal test environment; the oxygen content monitoring module is used to monitor the oxygen content data inside the oxygen reduction and nitrogen injection fire prevention test environment, and to perform oxygen content monitoring of the internal test environment; the video acquisition module is used to acquire video data inside the oxygen reduction and nitrogen injection fire prevention test environment, and to perform video monitoring of the internal test environment. The method for fire prevention and control prediction using the aforementioned oxygen reduction and nitrogen injection fire prevention and control testing system comprises the following steps: 1) Extract data from the database to serve as the training and testing sets for the neural network; the data includes temperature X1, humidity X2, atmospheric pressure X3, oxygen content X4, and fire prevention and control effectiveness Y; 2) Construction of BP neural network: 2.1) Establish a fire prevention and control effect prediction model based on BP neural network: Let temperature X1, humidity X2, atmospheric pressure X3, and oxygen content X4 be the input values ​​of the BP neural network, and the fire prevention and control effect Y be the predicted value of the BP neural network. ij and w j These are the weights of a BP neural network; 2.2) BP Neural Network Initialization: Based on the system's input and output conditions, determine the number of nodes in the input layer (4), the number of nodes in the hidden layer (5), and the number of nodes in the output layer (1). Initialize the weights w between the nodes in the input, hidden, and output layers of the neural network. ij w j Initialize the hidden layer threshold a, the output layer threshold b, given the learning rate γ and the neuron activation function f; 2.3) Calculation of hidden layer output in a BP neural network: Based on the input data, the weights w between the input layer and the hidden layer... ij Calculation output: ； In the formula, j is the number of hidden layer nodes; f is the hidden layer activation function. ； 2.4) Output calculation of the BP neural network output layer: Based on the hidden layer output H, the connection weights w j Given a threshold b, calculate the predicted output O of the BP neural network; ； 2.5) BP Neural Network Error Calculation: Calculate the neural network prediction error e based on the network's predicted output O and expected output Y'; e = Y' - O 2.6) Neural Network Weight Update: Update the network connection weights w based on the neural network prediction error e. ij w j ; w ij = w ij + γH j (1 - H j )x(i)w j e i = 1,2,…,4; j = 1,2，…，5 w j = w j + γH j ej = 1,2,...,5 In the formula, γ is the learning rate; 2.7) Neural Network Threshold Update: Update the network node thresholds a and b based on the neural network prediction error e; a j = a j + γH j (1 - H j )w j e j = 1,2,…,5 b = b + e 2.8) Train the neural network using the training set and test the prediction effect of the neural network using the test set. If the prediction error e is less than the set value, it means that the neural network has achieved the preset effect and is the final neural network structure. If the prediction error e is greater than the set value, it means that the neural network has not achieved the preset effect and more data needs to be extracted from the database as a training set to continue training the neural network. 3) Prediction: Input the actual collected temperature X1, humidity X2, atmospheric pressure X3, and oxygen content X4 into the neural network obtained in step 2). If the fire prevention effect Y=1 output by the neural network, it means that the fire prevention effect is successful. If the fire prevention effect Y=0 output by the neural network, it means that the fire prevention effect is unsuccessful.

2. The oxygen reduction and nitrogen injection fire prevention and control testing system according to claim 1, characterized in that: The control center, video acquisition module, temperature monitoring module, humidity monitoring module, atmospheric pressure monitoring module, and oxygen content monitoring module are all connected to the power supply module, which provides power to the entire system.

3. The oxygen reduction and nitrogen injection fire prevention and control testing system according to claim 1, characterized in that: The control center is an electronic computer system, which is connected to the video acquisition module through one network interface. The control center is connected to the temperature monitoring module, humidity monitoring module, atmospheric pressure monitoring module, and oxygen content monitoring module through four USB interfaces.

4. The oxygen reduction and nitrogen injection fire prevention and control testing system according to claim 1, characterized in that: The temperature monitoring module includes U26, which is a USB connector that connects to the control center via a USB data cable. Pin 1 of U26 is connected to the power signal VCC via fuse RJ3, pin 5 of U26 is connected to GND, and pins 2 and 3 of U26 are connected to pins 8 and 7 of U24 respectively. U24 is model CH341T. Pin 1 is connected to VCC through LED L7 and resistor R22. Pins 13 and 20 are connected to VCC. Pins 11 and 12 are connected to GND. Capacitors C40 and C41 and crystal oscillator Q4 form a crystal oscillator circuit connected to pins 9 and 10 of U24. Capacitor C39 is a 3.3VCC power supply decoupling capacitor. Capacitors C45 and C47 are VCC power supply decoupling capacitors. Pin 18 is connected to pins 2 and 3 of U25. Pins 3 and 4 are connected to pins 4 and 1 of U25 respectively. U25 is model MAX487. Pins 1 and 4 are connected to pins 4 and 3 of U24 respectively. Pin 5 is connected to GND, and pin 8 is connected to VCC. Capacitor C37 is a decoupling capacitor. Pins 6 and 7 are connected to pins A and B of U28 respectively. Resistor R24 ​​and LED L8 form an indicator circuit connected to pin 4 of U25 to indicate data transmission. Resistor R25 and LED L9 form an indicator circuit connected to pin 1 of U25 to indicate data reception. One end of resistor R26 is connected to GND, and the other end is connected to pin 7 of U25. One end of resistor R30 is connected to VCC, and the other end is connected to pin 6 of U25. U28 is a temperature sensing module. Its pins A and B are connected to pins 6 and 7 of U25, respectively. Its pin VCC is connected to the power supply signal 12VCC, and its pin GND is connected to the signal GND.

5. The oxygen reduction and nitrogen injection fire prevention and control testing system according to claim 1, characterized in that: The humidity monitoring module includes U19, which is a USB connector that connects to the control center via a USB data cable. Pin 1 of U19 is connected to the power signal VCC via fuse RJ1, pin 5 of U19 is connected to GND, and pins 2 and 3 of U19 are connected to pins 8 and 7 of U1 respectively. U1 is model CH341T. Pin 1 is connected to VCC via LED L4 and resistor R12. Pins 13 and 20 are connected to VCC. Pins 11 and 12 are connected to GND. Capacitors C29 and C30 and crystal oscillator Q2 form a crystal oscillator circuit connected to pins 9 and 10 of U1. Capacitor C27 is a 3.3VCC power supply decoupling capacitor. Capacitors C33 and C35 are VCC power supply decoupling capacitors. Pin 18 is connected to pins 2 and 3 of U18. Pins 3 and 4 are connected to pins 4 and 1 of U18, respectively. U18 is model MAX487. Pins 1 and 4 are connected to pins 4 and 3 of U1 respectively. Pin 5 is connected to GND, and pin 8 is connected to VCC. Capacitor C25 is its decoupling capacitor. Pins 6 and 7 are connected to pins A and B of U22 respectively. Resistor R14 and LED L5 form an indicator circuit connected to pin 4 of U18 to indicate data transmission. Resistor R15 and LED L6 form an indicator circuit connected to pin 1 of U18 to indicate data reception. One end of resistor R16 is connected to GND, and the other end is connected to pin 7 of U18. One end of resistor R20 is connected to VCC, and the other end is connected to pin 6 of U18. U22 is a humidity sensing module. Pins A and B are connected to pins 6 and 7 of U18, respectively. Pin VCC is connected to the power signal 12VCC, and pin GND is connected to the signal GND.

6. The oxygen reduction and nitrogen injection fire prevention and control testing system according to claim 1, characterized in that: The atmospheric pressure monitoring module includes U30, which is a USB connector that connects to the control center via a USB data cable. Pin 1 of U30 is connected to the power signal VCC via fuse RJ4, pin 5 of U30 is connected to GND, and pins 2 and 3 of U30 are connected to pins 8 and 7 of U27 respectively. U27 is model CH341T. Pin 1 is connected to VCC via LED L10 and resistor R23. Pins 13 and 20 are connected to VCC. Pins 11 and 12 are connected to GND. Capacitors C43 and C44 and crystal oscillator Q5 form a crystal oscillator circuit connected to pins 9 and 10 of U27. Capacitor C42 is a 3.3VCC power supply decoupling capacitor. Capacitors C46 and C48 are VCC power supply decoupling capacitors. Pin 18 is connected to pins 2 and 3 of U29. Pins 3 and 4 are connected to pins 4 and 1 of U29, respectively. U29 is model MAX487. Pins 1 and 4 are connected to pins 4 and 3 of U27 respectively. Pin 5 is connected to GND, and pin 8 is connected to VCC. Capacitor C38 is its decoupling capacitor. Pins 6 and 7 are connected to pins A and B of U31 respectively. Resistor R27 and LED L11 form an indicator circuit connected to pin 4 of U29 to indicate data transmission. Resistor R28 and LED L12 form an indicator circuit connected to pin 1 of U29 to indicate data reception. One end of resistor R29 is connected to GND, and the other end is connected to pin 7 of U29. One end of resistor R31 is connected to VCC, and the other end is connected to pin 6 of U29. U31 is an atmospheric pressure sensing module. Pins A and B are connected to pins 6 and 7 of U29, respectively. Pin VCC is connected to the power signal 12VCC, and pin GND is connected to the signal GND.

7. The oxygen reduction and nitrogen injection fire prevention and control testing system according to claim 1, characterized in that: The oxygen content monitoring module includes a U21 connector and a USB connector, which connects to the control center via a USB data cable. U21 is a USB connector. Pin 1 is connected to the power signal VCC through fuse RJ2. Pin 5 of U21 is connected to GND. Pins 2 and 3 of U21 are connected to pins 8 and 7 of U17 respectively. U17 is model CH341T. Pin 1 is connected to VCC via LED L1 and resistor R13. Pins 13 and 20 are connected to VCC. Pins 11 and 12 are connected to GND. Capacitors C31 and C32 and crystal oscillator Q3 form a crystal oscillator circuit connected to pins 9 and 10 of U17. Capacitor C28 is a 3.3VCC power supply decoupling capacitor. Capacitors C34 and C36 are VCC power supply decoupling capacitors. Pin 18 is connected to pins 2 and 3 of U20. Pins 3 and 4 are connected to pins 4 and 1 of U20, respectively. The U20 model is MAX487. Pins 1 and 4 are connected to pins 4 and 3 of U20 respectively. Pin 5 is connected to GND, and pin 8 is connected to VCC. Capacitor C26 is its decoupling capacitor. Pins 6 and 7 are connected to pins A and B of U23 respectively. Resistor R17 and LED L2 form an indicator circuit connected to pin 4 of U20 to indicate data transmission. Resistor R18 and LED L3 form an indicator circuit connected to pin 1 of U20 to indicate data reception. One end of resistor R19 is connected to GND, and the other end is connected to pin 7 of U20. One end of resistor R21 is connected to VCC, and the other end is connected to pin 6 of U20. U23 is an oxygen content sensing module, model BEE-4100L. Pins A and B are connected to pins 6 and 7 of U20, respectively. Pin VCC is connected to the power signal 12VCC, and pin GND is connected to the signal GND.

8. The oxygen reduction and nitrogen injection fire prevention and control testing system according to claim 1, characterized in that: The video acquisition module includes U32 and U33. U32 is a video acquisition unit, with pins 3, 4, 5, and 6 connected to pins 6, 3, 2, and 1 of U33 respectively for data transmission and reception. Pin 8 is connected to 12VCC, and pin 7 is connected to the GND signal. U33 is a network interface, with pins 6, 3, 2, and 1 connected to pins 3, 4, 5, and 6 of U32 respectively. U33 is connected to the control center via a network cable to realize the transmission of video data and the interaction of control commands.

9. The oxygen reduction and nitrogen injection fire prevention and control testing system according to claim 2, characterized in that: The power module includes connector U36, chip U35, chip U37, and chip U34. U35 is model VRB2412D_20W, U37 is model VRB2405D_20W, and U34 is model LT1529IQ-3.

3. Switch S3 is a power signal switch. Resistors R33 and R34 form a matching resistor, and capacitors C53 and C54 form an output filter and rectifier circuit, outputting a 12VCC signal. 12VCC serves as the input signal for U37, and capacitors C55 and C56 form an output filter and rectifier circuit, outputting a VCC signal. VCC serves as the input signal for U34, and resistors R32, capacitors C49 and C51 form an input filter circuit, while capacitors C50 and C52 form an output filter and rectifier circuit, outputting a 3.3VCC signal. Thus, the power conversion module provides 12VCC, 5V, and 3.3VCC power to each module.

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