Remote gas monitoring and early warning method and device based on optical fiber energy transmission

Abstract

The invention discloses a remote gas monitoring and early warning method and device based on optical fiber energy transmission, and relates to the technical field of gas early warning. The method comprises the following steps: collecting a plurality of monitoring point coordinates, a plurality of equipment loads, a plurality of initial sampling frequencies and a plurality of environmental parameters of a plurality of gas monitoring devices, and determining a plurality of optical fiber transmission distances with an optical splitter; acquiring light energy loss characteristics of the optical fiber line, performing light energy demand analysis, and determining an initial light energy distribution proportion of the plurality of gas monitoring devices; the optical splitter is controlled to carry out optical energy configuration according to the initial optical energy distribution proportion, and gas monitoring and early warning are executed through the power management module, the voltage conversion module, the information transmission module and the analog-to-digital conversion module. The technical problem that monitoring data transmission is unstable due to the fact that traditional gas monitoring equipment is insufficient in energy supply in a complex environment is solved, the real-time monitoring requirement of the remote gas monitoring device is met by optimizing optical fiber energy transmission distribution, and the technical effect of improving power supply stability and data transmission reliability is achieved.

Description

Technical Field

[0001] The present invention relates to the field of gas early warning technology, and in particular to a remote gas monitoring and early warning method and device based on optical fiber energy transmission. Background Art

[0002] Existing gas monitoring systems usually use wireless communication technology to achieve data backhaul and power supply. However, in complex environments such as enclosed spaces and industrial sites, wireless transmission is often restricted by factors such as electromagnetic interference and extreme temperature changes, resulting in unstable data transmission and even power outages. This problem is particularly prominent in underground mines, closed warehouses and other scenes where harmful gases exist. Electromagnetic interference in such environments will affect the reliability of data transmission, increase the risk of false alarms or missed alarms, and bring great challenges to real-time monitoring and emergency response. In addition, there is usually a lack of stable DC power supply support in enclosed spaces, and how to provide continuous and stable power supply for the monitoring system has become a problem that needs to be solved urgently. Traditional wired power supply methods require a lot of wiring, which is not only costly and complex to maintain, but also prone to power failures during long-term use, further increasing maintenance costs and risks. Summary of the invention

[0003] The present application provides a remote gas monitoring and early warning method and device based on optical fiber energy transmission, which solves the technical problem that insufficient energy supply of traditional gas monitoring equipment in complex environments leads to unstable monitoring data transmission.

[0004] In view of the above problems, the present application provides a remote gas monitoring and early warning method and device based on optical fiber energy transmission.

[0005] In a first aspect of the present application, a remote gas monitoring and early warning method based on optical fiber energy transmission is provided, the method comprising:

[0006] Collect multiple monitoring point coordinates, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters of multiple gas monitoring devices, and determine multiple optical fiber transmission distances from the optical splitter according to the multiple monitoring point coordinates; obtain the optical energy loss characteristics of the optical fiber line, perform optical energy demand analysis according to the optical energy loss characteristics, multiple optical fiber transmission distances, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters, and determine the initial optical energy allocation ratio of the multiple gas monitoring devices; control the optical splitter to configure optical energy according to the initial optical energy allocation ratio, and perform gas monitoring and early warning through the power management module, voltage conversion module, information transmission module and analog-to-digital conversion module; wherein, performing gas monitoring and early warning also includes: at a predetermined time node, performing data interference analysis and environmental change analysis on the multiple gas monitoring devices, dynamically optimizing the initial optical energy allocation ratio according to the interference analysis results and environmental change characteristics, and determining the optimized optical energy allocation ratio for optical energy configuration.

[0007] The second aspect of the present application provides a remote gas monitoring and early warning device based on optical fiber energy transmission, the device comprising:

[0008] Data acquisition component: collects multiple monitoring point coordinates, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters of multiple gas monitoring devices, and determines multiple optical fiber transmission distances from the splitter based on the multiple monitoring point coordinates; demand analysis component: obtains the optical energy loss characteristics of the optical fiber line, performs optical energy demand analysis based on the optical energy loss characteristics, multiple optical fiber transmission distances, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters, and determines the initial optical energy allocation ratio of the multiple gas monitoring devices; optical energy configuration component: controls the splitter to configure optical energy according to the initial optical energy allocation ratio, and performs gas monitoring and early warning through the power management module, voltage conversion module, information transmission module and analog-to-digital conversion module; wherein, the optical energy configuration component also includes: at a predetermined time node, performing data interference analysis and environmental change analysis on the multiple gas monitoring devices, dynamically optimizing the initial optical energy allocation ratio based on the interference analysis results and environmental change characteristics, and determining the optimized optical energy allocation ratio for optical energy configuration.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] First, multiple monitoring point coordinates, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters of multiple gas monitoring devices are collected, and multiple optical fiber transmission distances with the optical splitter are determined according to the multiple monitoring point coordinates. Then, the optical energy loss characteristics of the optical fiber line are obtained, and the optical energy demand analysis is performed according to the optical energy loss characteristics, multiple optical fiber transmission distances, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters to determine the initial optical energy allocation ratio of multiple gas monitoring devices. Finally, the optical splitter is controlled to configure the optical energy according to the initial optical energy allocation ratio, and the gas monitoring early warning is performed through the power management module, the voltage conversion module, the information transmission module and the analog-to-digital conversion module; wherein, the execution of the gas monitoring early warning also includes: at a predetermined time node, data interference analysis and environmental change analysis are performed on multiple gas monitoring devices, and the initial optical energy allocation ratio is dynamically optimized according to the interference analysis results and the environmental change characteristics, and the optimized optical energy allocation ratio is determined to configure the optical energy. The technical problem of unstable monitoring data transmission caused by insufficient energy supply of traditional gas monitoring equipment in complex environments is solved, and the optical fiber transmission energy distribution is optimized to meet the real-time monitoring needs of remote gas monitoring devices, so as to achieve the technical effect of improving power supply stability and data transmission reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic diagram of a remote gas monitoring and early warning method based on optical fiber energy transmission provided in an embodiment of the present application;

[0013] Figure 2 A schematic diagram of the structure of a remote gas monitoring and early warning device based on optical fiber energy transmission provided in an embodiment of the present application.

[0014] Explanation of the reference numerals: data collection component 11 , demand analysis component 12 , light energy configuration component 13 . DETAILED DESCRIPTION

[0015] The present application solves the technical problem of unstable monitoring data transmission caused by insufficient energy supply of traditional gas monitoring equipment in complex environments by providing a remote gas monitoring and early warning method and device based on optical fiber energy transmission.

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a remote gas monitoring and early warning method based on optical fiber energy transmission, which is applied to multiple remote gas monitoring and early warning devices based on optical fiber energy transmission, wherein the device includes a power management module, a voltage conversion module, an information transmission module and an analog-to-digital conversion module, wherein the method includes:

[0019] The coordinates of multiple monitoring points of multiple gas monitoring devices, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters are collected, and multiple optical fiber transmission distances from the optical splitter are determined according to the coordinates of the multiple monitoring points.

[0020] By collecting the monitoring point coordinates, equipment load, initial sampling frequency and environmental parameters of multiple gas monitoring devices, the layout and operating status information of these devices in the actual environment is obtained. Specifically, the geographical coordinates or location parameters of each gas monitoring device are collected to determine the length of the optical fiber transmission path between each device and the central splitter; the current load of each gas monitoring device, including power consumption requirements and real-time energy consumption, is collected to evaluate the demand for light energy of each device; the initial sampling frequency of each gas monitoring device is recorded to determine its requirements for data transmission rate and frequency; and the average temperature and humidity in the current environment are collected. The optical fiber transmission distance between each monitoring device and the central splitter is calculated through the monitoring point coordinates, thereby providing accurate basic data for subsequent light energy distribution and configuration.

[0021] Obtain the optical energy loss characteristics of the optical fiber line, perform optical energy demand analysis based on the optical energy loss characteristics, multiple optical fiber transmission distances, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters, and determine the initial optical energy allocation ratio of the multiple gas monitoring devices.

[0022] The optical energy loss characteristics of optical fiber lines refer to the optical energy loss of optical fiber lines at different distances and transmission environments. By obtaining the optical energy loss characteristics of optical fiber lines and combining the optical fiber transmission distance, equipment load, initial sampling frequency and environmental parameters, optical energy demand analysis is performed to determine the initial optical energy allocation ratio for each gas monitoring device. This ratio enables the optical energy to be effectively configured according to the needs of each device and environmental changes, ensuring that each monitoring point can obtain appropriate energy support under different environmental conditions and achieve stable monitoring.

[0023] Furthermore, the optical energy loss characteristics of the optical fiber line are obtained, including:

[0024] The optical fiber type, optical fiber mode and connector type of the optical fiber line are read, and a positive sample search is performed to obtain a plurality of sample optical energy loss coefficients with the optical fiber type, optical fiber mode and connector type as constraints; the mean of the plurality of sample optical energy loss coefficients is calculated to determine the mean of the sample loss coefficients, and a plurality of sample loss coefficient deviations are calculated based on the mean of the sample loss coefficients; the plurality of sample loss coefficient deviations are screened according to a coefficient deviation threshold to determine a plurality of standard sample optical energy loss coefficients, and the mean of the calculated standard loss coefficient is set as the optical energy loss feature.

[0025] Specifically, the basic properties of the optical fiber line are read, including the optical fiber type (such as single-mode or multi-mode optical fiber), the optical fiber mode (transmission mode), and the type of connector used; with the optical fiber type, optical fiber mode, and connector type as constraints, optical fiber loss samples with the same conditions are retrieved from the database to obtain multiple sample optical energy loss coefficients, which represent the optical energy loss under different conditions (such as different optical fiber lengths, different connector qualities, etc.); the multiple sample optical energy loss coefficients retrieved are averaged to obtain the sample loss coefficient mean, which reflects the typical loss level under the constraints; with the sample loss coefficient mean as a benchmark, the deviation of each sample loss coefficient relative to the mean is calculated to obtain multiple sample loss coefficient deviations; a coefficient deviation threshold is set to screen the sample loss coefficient deviations, and samples with deviations within the threshold range are retained to obtain multiple standard sample optical energy loss coefficients; the standard sample optical energy loss coefficients are averaged to obtain the standard loss coefficient mean, and the standard loss coefficient mean is used as the optical energy loss characteristic of the optical fiber line for subsequent optical energy demand analysis and optical energy allocation.

[0026] Furthermore, performing a light energy demand analysis according to the light energy loss characteristics, multiple optical fiber transmission distances, multiple device loads, multiple initial sampling frequencies, and multiple environmental parameters to determine the initial light energy allocation ratio of the multiple gas monitoring devices includes:

[0027] According to the multiple gas monitoring devices, the optical energy loss characteristics, multiple optical fiber transmission distances, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters are respectively integrated to determine multiple device characteristics; the historical optical energy allocation records of the same type of splitters are queried to collect multiple sample device feature sets and multiple sample optical energy allocation ratios; with the multiple sample device feature sets as input and the multiple sample optical energy allocation ratios as supervision, supervised training is performed on the random decision forest until the convergence conditions are met, and a light energy distributor is output, and the multiple device characteristics are input into the light energy distributor to obtain the initial light energy allocation ratio.

[0028] The light energy demand analysis is performed based on the light energy loss characteristics, fiber optic transmission distance, device load, sampling frequency and environmental parameters. Specifically, for each gas monitoring device, the light energy loss characteristics, fiber optic transmission distance, device load, initial sampling frequency and environmental parameters are integrated to form an independent device feature. Each device feature contains the basic information of the light energy demand of the device under the current environment and transmission conditions; the historical light energy allocation records of similar splitters are queried to collect data containing multiple device feature sets and their corresponding sample light energy allocation ratios. These historical records provide light energy allocation schemes similar to the current situation, which is convenient for predicting the optimal initial light energy allocation ratio; multiple sample device feature sets are used as input and the sample light energy allocation ratio is used as output to supervise the random decision forest model. During the training process, the random decision forest constructs multiple decision trees to conduct multi-level learning on the input features to capture the complex relationship between the device characteristics and the light energy allocation ratio; the training is carried out until the model meets the preset convergence conditions, that is, the output of the decision forest is stable and accurate, ensuring that the model can effectively predict the reasonable light energy allocation ratio under different device characteristics; after the training is completed, the random decision forest model forms the final light energy distributor, which has mastered the association pattern between the device characteristics and the light energy allocation ratio; the device feature set of multiple gas monitoring devices in the current environment is input into the light energy distributor, and the corresponding initial light energy allocation ratio is output. The initial light energy allocation ratio is the optimal light energy allocation scheme for each device under its actual operating conditions, ensuring the energy supply stability and monitoring accuracy of the monitoring device.

[0029] The optical splitter is controlled to configure the optical energy according to the initial optical energy distribution ratio, and the gas monitoring and early warning are performed through the power management module, the voltage conversion module, the information transmission module and the analog-to-digital conversion module.

[0030] According to the initial light energy distribution ratio, the spectrometer distributes the light energy to each gas monitoring device to ensure that each device obtains the appropriate amount of light energy within its required range. After the light energy is transmitted to each monitoring device, the power management module is used for power management. The voltage conversion module converts the light energy into a suitable voltage according to the equipment requirements to ensure the stable operation of the monitoring device. Once the power supply configuration is completed, the gas monitoring device starts to collect data and sends the monitoring results through the information transmission module. The data collected by the monitoring device is an analog signal, and the analog-to-digital conversion module converts these analog signals into digital signals to maintain the integrity and accuracy of the data during the information transmission process. Through the collaborative work of these modules, the gas monitoring and early warning functions can be performed in real time and accurately.

[0031] Furthermore, controlling the optical splitter to configure the optical energy according to the initial optical energy allocation ratio, and performing gas monitoring and early warning through the power management module, the voltage conversion module, the information transmission module and the analog-to-digital conversion module, includes:

[0032] The optical energy is transmitted by means of a laser using an optical cable, the optical splitter is controlled to configure the optical energy of the multiple gas monitoring devices according to the initial optical energy distribution ratio, and the gas monitoring and early warning are performed through the power management module, the voltage conversion module, the information transmission module and the analog-to-digital conversion module, wherein the power management module includes a photoelectric conversion unit and a supercapacitor, the photoelectric conversion unit is used to convert the optical energy transmitted by the laser into electrical energy; the voltage conversion module is used to convert the output voltage of the photoelectric conversion unit into two DC voltages of different specifications, which are respectively supplied to different circuit units and sensors, wherein the voltage conversion module also includes a rectifier, a voltage stabilizer and a dynamic voltage conversion mechanism; the information transmission module is used to realize the mutual conversion between TTL level signals and optical signals, wherein the TTL level signal is converted into an optical signal at the input end, and the optical signal is converted into a TTL level signal at the receiving end; the analog-to-digital conversion module is used to convert the analog signal from the sensor into a digital signal, and transmit it through a TTL level signal.

[0033] The laser is used to transmit light energy to the spectrometer through an optical cable, and the spectrometer distributes the received light energy to each gas monitoring device as needed according to the preset initial light energy distribution ratio. In each monitoring device, the power management module is responsible for converting the light energy transmitted by the laser into electrical energy through the photoelectric conversion unit. The output electrical energy of the photoelectric conversion unit is stored in the supercapacitor to maintain the continuous power supply of the system. In particular, when the light energy transmission is temporarily interrupted, the supercapacitor can provide backup power to avoid monitoring interruption, and at the same time reduce the power loss caused by environmental fluctuations. The voltage conversion module adjusts the voltage output by the photoelectric conversion unit into two different DC voltage specifications to supply power to different circuit units and sensors respectively, so as to meet the different working voltage requirements of each component. For example, the voltage conversion module converts the 3.3V voltage output by the photoelectric conversion unit into two DC voltages of 5V and 3.3V.

[0034] The voltage conversion module integrates a rectifier and a voltage regulator, which can control the input voltage fluctuation within a very small range to prevent voltage instability from damaging the equipment. Specifically, the rectifier converts the AC signal into a DC signal, while the voltage regulator is used to adjust the output voltage to ensure that each module can obtain a constant DC voltage. In addition, in order to improve the energy utilization of the equipment, the voltage conversion module also has a dynamic voltage conversion mechanism, which can automatically adjust the output voltage according to the real-time load conditions and optimize the power consumption distribution; in low-power mode, non-essential circuit units can be selectively turned off, thereby effectively reducing unnecessary power consumption and extending the working time of the equipment; this flexible voltage conversion mechanism enables the device to dynamically respond to complex power requirements in different environments, while providing efficient and reliable power supply support, providing guarantee for the long-term stable operation of the entire monitoring system.

[0035] The information transmission module is responsible for transmitting data between the monitoring device and the data processing center, and realizing the mutual conversion between TTL level signals and optical signals. At the input end of data transmission, the information transmission module converts the TTL level signal into an optical signal; at the receiving end, the optical signal is converted back to a TTL level signal to ensure the integrity and anti-interference of the data in long-distance optical fiber transmission.

[0036] The analog-to-digital conversion module converts the analog signal collected by the sensor into a digital signal to facilitate digital processing of the data; after the analog-to-digital conversion is completed, the digital signal is transmitted through the TTL level, thereby ensuring the accuracy and consistency of the signal during the transmission process.

[0037] Among them, the implementation of gas monitoring and early warning also includes:

[0038] At a predetermined time point, data interference analysis and environmental change analysis are performed on the multiple gas monitoring devices, the initial light energy allocation ratio is dynamically optimized according to the interference analysis results and environmental change characteristics, and the optimized light energy allocation ratio is determined for light energy configuration.

[0039] At predetermined time points, real-time data transmission indicators (such as bit error rate, signal strength, packet loss rate and signal-to-noise ratio) of each gas monitoring device are collected to determine the stability of data transmission; by analyzing these indicators, the degree of interference in the transmission process is evaluated and the interference coefficient of each monitoring device is determined. The higher the interference coefficient, the stronger the interference to the data transmission.

[0040] At predetermined time nodes, environmental data within adjacent time nodes are collected, including temperature and humidity changes in the area where the monitoring device is located; by calculating temperature deviation and humidity deviation, the dynamic changes of environmental parameters are obtained, which may affect the transmission efficiency of the optical fiber.

[0041] The interference coefficient and environmental change parameters are input into the system's light energy ratio corrector, which will combine these data with the initial light energy allocation ratio to generate an optimized light energy allocation ratio. This optimized ratio takes into account the real-time interference level and environmental conditions, so that the light energy configuration can better adapt to the current conditions and avoid insufficient energy supply or unstable transmission due to environmental changes and interference. According to the optimized light energy allocation ratio, the control splitter is used to redistribute light energy and transmit appropriate light energy to each monitoring device; by dynamically adjusting the light energy configuration, it is ensured that each monitoring device can obtain stable energy support under different environmental and interference conditions to ensure the real-time and accuracy of the monitoring data.

[0042] Furthermore, data interference analysis and environmental change analysis are performed on the multiple gas monitoring devices, and the initial light energy allocation ratio is dynamically optimized according to the interference analysis results and environmental change characteristics, including:

[0043] At a predetermined time node, multiple data transmission indicators of the multiple gas monitoring devices are read, wherein the data transmission indicators include bit error rate, signal strength, packet loss rate and signal-to-noise ratio; data interference evaluation is performed according to the bit error rate, signal strength, packet loss rate and signal-to-noise ratio, a plurality of interference coefficients are determined, and a plurality of signal redundancy ratios are configured according to the plurality of interference coefficients, wherein the signal redundancy ratio and the interference coefficient are positively correlated; a plurality of temperature deviations and a plurality of humidity deviations of the areas where the multiple gas monitoring devices are located in adjacent time nodes are collected; the plurality of signal redundancy ratios, the plurality of temperature deviations, the plurality of humidity deviations and the initial light energy distribution ratio are input into a light energy ratio corrector, and the optimized light energy distribution ratio is output, wherein the light energy ratio corrector is constructed based on a BP neural network and is trained to convergence through a sample data set.

[0044] At a predetermined time point, multiple data transmission indicators of each gas monitoring device are read, including bit error rate, signal strength, packet loss rate and signal-to-noise ratio. These indicators reflect the quality of the current transmitted data and the degree of interference it is subject to. Data interference analysis is performed based on the bit error rate, signal strength, packet loss rate and signal-to-noise ratio. That is, the interference factor of each indicator is calculated based on the expert team, and the interference coefficient of each device is obtained through weighted calculation. The interference coefficient reflects the interference intensity in the environment in which each monitoring device is located. The stronger the interference, the greater the interference coefficient. Based on these interference coefficients, the signal redundancy ratio is configured. The signal redundancy ratio is proportional to the interference coefficient, that is, the greater the interference, the higher the allocated redundancy ratio, thereby enhancing the signal stability and transmission reliability.

[0045] The temperature deviation (the difference between the current temperature and the reference temperature) and humidity deviation (the difference between the current humidity and the reference humidity) of the area where the monitoring device is located in adjacent time nodes are collected. These environmental deviations reflect the changes in temperature and humidity in different time periods. These parameters will affect the loss characteristics of optical fiber transmission.

[0046] The signal redundancy ratio, temperature deviation, humidity deviation and initial light energy distribution ratio are input into the light energy ratio corrector. The light energy ratio corrector is a model built on the BP neural network. Specifically, a sample data set containing various environmental conditions and data transmission quality is used to train the BP neural network until it reaches a convergence state, so that the network can learn the complex relationship between environmental changes, data transmission quality and light energy distribution ratio; after the training is completed, the light energy ratio corrector is obtained, and the real-time collected data is input into the light energy ratio corrector. The light energy ratio corrector will automatically adjust the light energy distribution ratio according to these data to optimize the data transmission quality and light energy utilization efficiency, and finally output an optimized light energy distribution ratio, which can better adapt to the current environmental conditions and data transmission requirements.

[0047] Furthermore, the optimal light energy distribution ratio is determined to configure the light energy, and then the following is included:

[0048] Receive feedback monitoring information from the multiple gas monitoring devices, and issue early warnings and identifications to devices that monitor harmful gases, and determine multiple identification monitoring devices; perform gas concentration change analysis on the areas where the multiple identification monitoring devices are located based on the continuous feedback monitoring information, and determine multiple concentration change characteristics; set multiple adaptive sampling parameters based on the multiple concentration change characteristics, determine multiple required energy supply powers based on the multiple adaptive sampling parameters, and adaptively adjust the optimized light energy distribution ratio.

[0049] In industrial environments, the demand for gas concentration monitoring fluctuates according to workload, operation time or gas leakage. Gas monitoring devices can add dynamic energy supply adjustment functions to automatically adjust the energy supply power according to the changes in gas concentration detected by the sensor. Specifically, the monitoring data from multiple gas monitoring devices are received in real time; when harmful gases are detected, the early warning mechanism should be triggered immediately, and the devices that monitor harmful gases should be identified in a specific way (such as color change, sound alarm, etc.). These identified devices are called identification monitoring devices; for each identification monitoring device, it is necessary to continuously collect its feedback monitoring information to form a continuous data stream. Using this data, gas concentration change analysis can be performed to identify key features such as the changing trend, peak value, and valley value of gas concentration. These features are called concentration change features. Based on the concentration change features, sampling parameters such as sampling frequency and sampling time can be intelligently adjusted to adapt to the changes in gas concentration in different areas. These adjusted parameters are called adaptive sampling parameters. Adjustment of sampling parameters will directly affect the workload and energy consumption of the gas monitoring device. Therefore, it is necessary to adaptively adjust the previously determined optimized light energy allocation ratio according to the required power supply of each identification monitoring device, including increasing or decreasing the light energy allocated to specific devices, to ensure that they can perform efficient and accurate monitoring work according to the new sampling parameters.

[0050] Furthermore, setting a plurality of adaptive sampling parameters based on the plurality of concentration variation characteristics includes:

[0051] Randomly selecting a first concentration change feature, wherein the first concentration change feature includes a concentration change rate, a concentration fluctuation amplitude, and a concentration change trend;

[0052] The sampling frequency and the data transmission frequency are set according to the concentration change rate, the concentration fluctuation amplitude and the concentration change trend, and a first adaptive sampling frequency and a first adaptive data transmission frequency are determined, set as a first adaptive sampling parameter, and added to the multiple adaptive sampling parameters.

[0053] Preferably, a concentration change feature is randomly selected as the first concentration change feature, and the first concentration change feature includes the concentration change rate, the concentration fluctuation amplitude and the concentration change trend; the sampling frequency and the data transmission frequency are set according to the concentration change rate, the concentration fluctuation amplitude and the concentration change trend. For example, if the concentration change rate is fast, it means that the gas concentration has changed significantly in a short time, so a higher sampling frequency is required to capture these changes; the larger the concentration fluctuation amplitude, the more drastic the change in gas concentration, and the sampling frequency also needs to be increased to ensure the accuracy of the data; if the concentration change trend is obvious (such as continuous increase or decrease), the sampling frequency can be set according to the trend. The sampling frequency is adjusted according to the urgency (increase the sampling frequency when the trend changes sharply, and reduce the sampling frequency when the trend is stable); based on the above analysis, a sampling frequency that matches the concentration change characteristics can be determined, that is, the first adaptive sampling frequency; an increase in the sampling frequency may mean an increase in data transmission requirements, therefore, it is necessary to set the data transmission frequency according to the sampling frequency and the importance of the data, that is, the first adaptive data transmission frequency; the determined first adaptive sampling frequency and the first adaptive data transmission frequency are combined to form the first adaptive sampling parameters; the above process is repeated for continuous analysis, and multiple adaptive sampling parameters are set for different concentration change characteristics or different monitoring areas.

[0054] In summary, the embodiments of the present application have at least the following technical effects:

[0055] First, multiple monitoring point coordinates, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters of multiple gas monitoring devices are collected, and multiple optical fiber transmission distances with the optical splitter are determined according to the multiple monitoring point coordinates. Then, the optical energy loss characteristics of the optical fiber line are obtained, and the optical energy demand analysis is performed according to the optical energy loss characteristics, multiple optical fiber transmission distances, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters to determine the initial optical energy allocation ratio of multiple gas monitoring devices. Finally, the optical splitter is controlled to configure the optical energy according to the initial optical energy allocation ratio, and the gas monitoring early warning is performed through the power management module, the voltage conversion module, the information transmission module and the analog-to-digital conversion module; wherein, the execution of the gas monitoring early warning also includes: at a predetermined time node, data interference analysis and environmental change analysis are performed on multiple gas monitoring devices, and the initial optical energy allocation ratio is dynamically optimized according to the interference analysis results and the environmental change characteristics, and the optimized optical energy allocation ratio is determined to configure the optical energy. The technical problem of unstable monitoring data transmission caused by insufficient energy supply of traditional gas monitoring equipment in complex environments is solved, and the optical fiber transmission energy distribution is optimized to meet the real-time monitoring needs of remote gas monitoring devices, so as to achieve the technical effect of improving power supply stability and data transmission reliability.

[0056] Embodiment 2 is based on the same inventive concept as the remote gas monitoring and early warning method based on optical fiber energy transmission in the above embodiment. Figure 2As shown, the present application provides a remote gas monitoring and early warning device based on optical fiber energy transmission, wherein the device includes a power management module, a voltage conversion module, an information transmission module and an analog-to-digital conversion module, and also includes:

[0057] The data acquisition component 11 collects the coordinates of multiple monitoring points, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters of multiple gas monitoring devices, and determines the multiple optical fiber transmission distances from the optical splitter according to the coordinates of the multiple monitoring points; the demand analysis component 12 obtains the optical energy loss characteristics of the optical fiber line, performs optical energy demand analysis according to the optical energy loss characteristics, multiple optical fiber transmission distances, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters, and determines the initial optical energy allocation ratio of the multiple gas monitoring devices; the optical energy configuration component 13 controls the optical splitter to configure the optical energy according to the initial optical energy allocation ratio, and performs gas monitoring and early warning through the power management module, the voltage conversion module, the information transmission module and the analog-to-digital conversion module; wherein the optical energy configuration component 13 also includes: at a predetermined time node, performing data interference analysis and environmental change analysis on the multiple gas monitoring devices, dynamically optimizing the initial optical energy allocation ratio according to the interference analysis results and the environmental change characteristics, and determining the optimized optical energy allocation ratio for optical energy configuration.

[0058] Furthermore, the light energy configuration component 13 is used to perform the following method:

[0059] Receive feedback monitoring information from the multiple gas monitoring devices, and issue early warnings and identifications to devices that monitor harmful gases, and determine multiple identification monitoring devices; perform gas concentration change analysis on the areas where the multiple identification monitoring devices are located based on the continuous feedback monitoring information, and determine multiple concentration change characteristics; set multiple adaptive sampling parameters based on the multiple concentration change characteristics, determine multiple required energy supply powers based on the multiple adaptive sampling parameters, and adaptively adjust the optimized light energy distribution ratio.

[0060] Furthermore, the demand analysis component 12 is used to perform the following method:

[0061] The optical fiber type, optical fiber mode and connector type of the optical fiber line are read, and a positive sample search is performed to obtain a plurality of sample optical energy loss coefficients with the optical fiber type, optical fiber mode and connector type as constraints; the mean of the plurality of sample optical energy loss coefficients is calculated to determine the mean of the sample loss coefficients, and a plurality of sample loss coefficient deviations are calculated based on the mean of the sample loss coefficients; the plurality of sample loss coefficient deviations are screened according to a coefficient deviation threshold to determine a plurality of standard sample optical energy loss coefficients, and the mean of the calculated standard loss coefficient is set as the optical energy loss feature.

[0062] Furthermore, the demand analysis component 12 is used to perform the following method:

[0063] According to the multiple gas monitoring devices, the optical energy loss characteristics, multiple optical fiber transmission distances, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters are respectively integrated to determine multiple device characteristics; the historical optical energy allocation records of the same type of splitters are queried to collect multiple sample device feature sets and multiple sample optical energy allocation ratios; with the multiple sample device feature sets as input and the multiple sample optical energy allocation ratios as supervision, supervised training is performed on the random decision forest until the convergence conditions are met, and a light energy distributor is output, and the multiple device characteristics are input into the light energy distributor to obtain the initial light energy allocation ratio.

[0064] Furthermore, the light energy configuration component 13 is used to perform the following method:

[0065] The optical energy is transmitted by means of a laser using an optical cable, the optical splitter is controlled to configure the optical energy of the multiple gas monitoring devices according to the initial optical energy distribution ratio, and the gas monitoring and early warning are performed through the power management module, the voltage conversion module, the information transmission module and the analog-to-digital conversion module, wherein the power management module includes a photoelectric conversion unit and a supercapacitor, the photoelectric conversion unit is used to convert the optical energy transmitted by the laser into electrical energy; the voltage conversion module is used to convert the output voltage of the photoelectric conversion unit into two DC voltages of different specifications, which are respectively supplied to different circuit units and sensors, wherein the voltage conversion module also includes a rectifier, a voltage stabilizer and a dynamic voltage conversion mechanism; the information transmission module is used to realize the mutual conversion between TTL level signals and optical signals, wherein the TTL level signal is converted into an optical signal at the input end, and the optical signal is converted into a TTL level signal at the receiving end; the analog-to-digital conversion module is used to convert the analog signal from the sensor into a digital signal, and transmit it through a TTL level signal.

[0066] Furthermore, the light energy configuration component 13 is used to perform the following method:

[0067] At a predetermined time node, multiple data transmission indicators of the multiple gas monitoring devices are read, wherein the data transmission indicators include bit error rate, signal strength, packet loss rate and signal-to-noise ratio; data interference evaluation is performed according to the bit error rate, signal strength, packet loss rate and signal-to-noise ratio, a plurality of interference coefficients are determined, and a plurality of signal redundancy ratios are configured according to the plurality of interference coefficients, wherein the signal redundancy ratio and the interference coefficient are positively correlated; a plurality of temperature deviations and a plurality of humidity deviations of the areas where the multiple gas monitoring devices are located in adjacent time nodes are collected; the plurality of signal redundancy ratios, the plurality of temperature deviations, the plurality of humidity deviations and the initial light energy distribution ratio are input into a light energy ratio corrector, and the optimized light energy distribution ratio is output, wherein the light energy ratio corrector is constructed based on a BP neural network and is trained to convergence through a sample data set.

[0068] Furthermore, the light energy configuration component 13 is used to perform the following method:

[0069] A first concentration change characteristic is randomly selected, wherein the first concentration change characteristic includes a concentration change rate, a concentration fluctuation amplitude and a concentration change trend; a sampling frequency and a data transmission frequency are set according to the concentration change rate, the concentration fluctuation amplitude and the concentration change trend, and a first adaptation sampling frequency and a first adaptation data transmission frequency are determined, which are set as a first adaptation sampling parameter and added to the multiple adaptation sampling parameters.

[0070] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0072] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A remote gas monitoring and early warning method based on optical fiber energy transmission, characterized in that: The method is applied to multiple remote gas monitoring and early warning devices based on optical fiber energy transmission, the devices include a power management module, a voltage conversion module, an information transmission module and an analog-to-digital conversion module, including: Collecting multiple monitoring point coordinates, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters of multiple gas monitoring devices, and determining multiple optical fiber transmission distances from the optical splitter according to the multiple monitoring point coordinates; Obtaining optical energy loss characteristics of the optical fiber line, performing optical energy demand analysis according to the optical energy loss characteristics, multiple optical fiber transmission distances, multiple device loads, multiple initial sampling frequencies and multiple environmental parameters, and determining the initial optical energy allocation ratio of the multiple gas monitoring devices; Controlling the optical splitter to configure the light energy according to the initial light energy distribution ratio, and performing gas monitoring and early warning through the power management module, the voltage conversion module, the information transmission module and the analog-to-digital conversion module; Among them, the implementation of gas monitoring and early warning also includes: At a predetermined time point, data interference analysis and environmental change analysis are performed on the multiple gas monitoring devices, the initial light energy allocation ratio is dynamically optimized according to the interference analysis results and environmental change characteristics, and the optimized light energy allocation ratio is determined for light energy configuration.

2. The remote gas monitoring and early warning method based on optical fiber energy transmission according to claim 1 is characterized in that: Determine the optimal light energy distribution ratio for light energy configuration, and then include: Receive feedback monitoring information from the multiple gas monitoring devices, and issue early warnings and identification to the devices that monitor harmful gases, and determine multiple identification monitoring devices; Analyze the gas concentration changes in the areas where multiple identification monitoring devices are located based on the continuous feedback monitoring information to determine multiple concentration change characteristics; A plurality of adaptive sampling parameters are set based on the plurality of concentration variation characteristics, a plurality of required energy supply powers are determined according to the plurality of adaptive sampling parameters, and the optimized light energy distribution ratio is adaptively adjusted.

3. The remote gas monitoring and early warning method based on optical fiber energy transmission according to claim 1 is characterized in that: Obtain optical energy loss characteristics of optical fiber lines, including: Reading the fiber type, fiber mode and connector type of the optical fiber line, and performing positive sample retrieval to obtain multiple sample light energy loss coefficients based on the fiber type, fiber mode and connector type as constraints; Calculate the average of the light energy loss coefficients of the multiple samples to determine the average of the sample loss coefficients, and calculate the deviations of the multiple sample loss coefficients based on the average of the sample loss coefficients; The loss coefficient deviations of the multiple samples are screened according to the coefficient deviation threshold, multiple standard sample light energy loss coefficients are determined, and the average value of the standard loss coefficients is calculated and set as the light energy loss feature.

4. The remote gas monitoring and early warning method based on optical fiber energy transmission according to claim 3 is characterized in that: The light energy demand analysis is performed according to the light energy loss characteristics, multiple optical fiber transmission distances, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters to determine the initial light energy allocation ratio of the multiple gas monitoring devices, including: Integrate the optical energy loss characteristics, multiple optical fiber transmission distances, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters according to the multiple gas monitoring devices to determine multiple device characteristics; Query the historical light energy allocation records of similar spectrometers, collect multiple sample device feature sets and multiple sample light energy allocation ratios; The plurality of sample device feature sets are used as input and the plurality of sample light energy allocation ratios are used as supervision to perform supervised training on the random decision forest until convergence conditions are met, and a light energy distributor is output, and the plurality of device features are input into the light energy distributor to obtain the initial light energy allocation ratio.

5. The remote gas monitoring and early warning method based on optical fiber energy transmission according to claim 1 is characterized in that: Controlling the optical splitter to configure the light energy according to the initial light energy allocation ratio, and performing gas monitoring and early warning through the power management module, the voltage conversion module, the information transmission module and the analog-to-digital conversion module, including: The optical energy is transmitted by the laser using the optical cable, the optical splitter is controlled to configure the optical energy of the multiple gas monitoring devices according to the initial optical energy distribution ratio, and the gas monitoring early warning is performed by the power management module, the voltage conversion module, the information transmission module and the analog-to-digital conversion module, wherein the power management module includes a photoelectric conversion unit and a super capacitor, and the photoelectric conversion unit is used to convert the optical energy transmitted by the laser into electrical energy; The voltage conversion module is used to convert the output voltage of the photoelectric conversion unit into two DC voltages of different specifications, which are respectively supplied to different circuit units and sensors, wherein the voltage conversion module also includes a rectifier, a voltage stabilizer and a dynamic voltage conversion mechanism; The information transmission module is used to realize the mutual conversion between TTL level signal and optical signal, wherein the TTL level signal is converted into optical signal at the input end, and the optical signal is converted into TTL level signal at the receiving end; The analog-to-digital conversion module is used to convert the analog signal from the sensor into a digital signal and transmit it via a TTL level signal.

6. The remote gas monitoring and early warning method based on optical fiber energy transmission according to claim 1 is characterized in that: Performing data interference analysis and environmental change analysis on the multiple gas monitoring devices, and dynamically optimizing the initial light energy distribution ratio according to the interference analysis results and environmental change characteristics, including: At a predetermined time node, reading multiple data transmission indicators of the multiple gas monitoring devices, wherein the data transmission indicators include bit error rate, signal strength, packet loss rate and signal-to-noise ratio; Performing data interference evaluation according to the bit error rate, signal strength, packet loss rate and signal-to-noise ratio, determining multiple interference coefficients, and configuring multiple signal redundancy ratios according to the multiple interference coefficients, wherein the signal redundancy ratio is positively correlated with the interference coefficient; Collect multiple temperature deviations and multiple humidity deviations of areas where multiple gas monitoring devices are located at adjacent time nodes; The multiple signal redundancy ratios, multiple temperature deviations, multiple humidity deviations and initial light energy distribution ratio are input into a light energy ratio corrector, and the optimized light energy distribution ratio is output, wherein the light energy ratio corrector is constructed based on a BP neural network and is trained to convergence through a sample data set.

7. The remote gas monitoring and early warning method based on optical fiber energy transmission according to claim 2 is characterized in that: Setting a plurality of adaptive sampling parameters based on the plurality of concentration variation characteristics includes: Randomly selecting a first concentration change feature, wherein the first concentration change feature includes a concentration change rate, a concentration fluctuation amplitude, and a concentration change trend; The sampling frequency and the data transmission frequency are set according to the concentration change rate, the concentration fluctuation amplitude and the concentration change trend, and a first adaptive sampling frequency and a first adaptive data transmission frequency are determined, set as a first adaptive sampling parameter, and added to the multiple adaptive sampling parameters.

8. A remote gas monitoring and early warning device based on optical fiber energy transmission, characterized in that: The remote gas monitoring and early warning method based on optical fiber energy transmission for implementing any one of claims 1 to 7 comprises: Data acquisition component: collects multiple monitoring point coordinates of multiple gas monitoring devices, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters, and determines multiple optical fiber transmission distances with the optical splitter according to the multiple monitoring point coordinates; Demand analysis component: obtaining the optical energy loss characteristics of the optical fiber line, performing optical energy demand analysis according to the optical energy loss characteristics, multiple optical fiber transmission distances, multiple equipment loads, multiple initial sampling frequencies and multiple environmental parameters, and determining the initial optical energy allocation ratio of the multiple gas monitoring devices; Light energy configuration component: controls the optical splitter to configure light energy according to the initial light energy allocation ratio, and performs gas monitoring and early warning through a power management module, a voltage conversion module, an information transmission module and an analog-to-digital conversion module; The light energy configuration component also includes: At a predetermined time point, data interference analysis and environmental change analysis are performed on the multiple gas monitoring devices, the initial light energy allocation ratio is dynamically optimized according to the interference analysis results and environmental change characteristics, and the optimized light energy allocation ratio is determined for light energy configuration.

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