Environment monitoring method and system and storage medium

Abstract

The invention provides an environment monitoring method and system and a storage medium, and belongs to the technical field of environment monitoring, the method comprises the following steps: using a gas sensing module to collect gas concentration data of a target space according to a preset frequency, and using a physical environment sensing module to collect physical environment data of the target space according to the preset frequency; judging whether the target space is in an abnormal state or not according to the gas concentration data and the physical environment data collected each time; and when the target space is in the abnormal state, executing an alarm response. The gas concentration data and the physical environment data are collected at the same time and combined to judge the state of the target space, whether the target space is in the abnormal state or not can be judged more accurately, and potential safety hazards are effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to an environmental monitoring method, system and storage medium. Background Art

[0002] With the acceleration of industrialization and urbanization, environmental safety issues are becoming increasingly prominent. This is particularly true in scenarios such as chemical production, underground operations, and warehouse management, where toxic and hazardous gas leaks, abnormal oxygen concentrations, and explosion risks are common. Traditional environmental monitoring technologies typically deploy single, decentralized sensors (such as electrochemical sensors, infrared sensors, and catalytic combustion sensors) to monitor the concentration of specific gases, ignoring the impact of environmental factors on gas states and leading to safety risks.

[0003] Therefore, the existing technology has defects and needs to be improved and developed. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an environmental monitoring method, system and storage medium in response to the above-mentioned defects of the prior art, aiming to solve the safety hazard problem caused by using a single sensor for gas concentration monitoring in the prior art.

[0005] The technical solutions adopted by the present invention to solve the technical problems are as follows:

[0006] In a first aspect, an embodiment of the present invention provides a method for detecting when a user leaves bed, which is applied to an environment monitor, wherein the environment monitor is configured with a gas sensor module and a physical environment sensor module, and the method includes:

[0007] Using the gas sensor module to collect gas concentration data of the target space at a preset frequency, and using the physical environment sensor module to collect physical environment data of the target space at a preset frequency;

[0008] Based on the gas concentration data and physical environment data collected each time, determine whether the target space is in an abnormal state;

[0009] When the target space is in an abnormal state, an alarm response is executed.

[0010] In one embodiment, the gas sensing module includes an electrochemical gas sensor, an infrared gas sensor, a catalytic combustion gas sensor and an oxygen sensor; the physical environment sensing module includes a pressure sensor, an illumination sensor, a temperature and humidity sensor; the gas concentration data includes carbon monoxide concentration, hydrogen sulfide concentration, oxygen concentration, carbon dioxide concentration and methane concentration; and the physical environment data includes air pressure, temperature, humidity and brightness.

[0011] In one embodiment, judging whether the target space is in an abnormal state based on the gas concentration data and physical environment data collected each time includes:

[0012] If any of the gas concentration data exceeds the corresponding maximum gas concentration threshold, or is lower than the corresponding minimum gas concentration threshold, it is determined that the target space is in an abnormal state;

[0013] If any item of the physical environment data exceeds the corresponding highest physical environment threshold, or is lower than the corresponding lowest physical environment threshold, it is determined that the target space is in an abnormal state;

[0014] If the combination of at least one of the gas concentration data and at least one of the physical environment data satisfies a preset combination abnormality condition, it is determined that the target space is in an abnormal state.

[0015] In one embodiment, if any item of the gas concentration data exceeds a corresponding maximum gas concentration threshold, or is lower than a corresponding minimum gas concentration threshold, then determining that the target space is in an abnormal state includes:

[0016] If any one of the carbon monoxide concentration, the hydrogen sulfide concentration, the carbon dioxide concentration, the methane concentration and the oxygen concentration exceeds the corresponding maximum gas concentration threshold, or if the oxygen concentration is lower than the corresponding minimum gas concentration threshold, it is determined that the target space is in an abnormal state.

[0017] In one embodiment, when in an abnormal state, executing an alarm response includes:

[0018] Controlling the warning light on the environmental monitor to flash red;

[0019] generating alarm information, wherein the alarm information includes crowd evacuation information and physical environment data and / or gas concentration data causing the abnormal state;

[0020] The alarm device on the environmental monitor is controlled to broadcast the alarm information by voice, and the LED display screen on the environmental monitor is controlled to display the alarm information.

[0021] In one embodiment, the method further comprises:

[0022] Obtain the geometric dimension data of the target space and process it into a three-dimensional feature vector;

[0023] When receiving an environmental prediction instruction, obtaining the current wind speed of the target space and processing it into a first eigenvector, obtaining the current gas concentration data and physical environment data of the target space and processing them into a second eigenvector, and concatenating the first eigenvector, the second eigenvector, and the three-dimensional eigenvector into a current eigenvector;

[0024] Acquire historical data of the previous N time steps, wherein the historical data includes historical wind speed, historical gas concentration, and historical physical environment data;

[0025] Processing the historical data of the first N time steps into a time series feature vector, and concatenating each feature vector in the time series feature vector with the three-dimensional feature vector to obtain a historical time series feature vector;

[0026] The current feature vector and the historical time series feature vector are input into a pre-trained LSTM model, and the LSTM model processes the feature vector to output an environmental prediction result.

[0027] In one embodiment, after the LSTM model processes and outputs the environment prediction result, the method further includes:

[0028] Based on the gas concentration distribution heat map and the diffusion rate of each gas in the environmental prediction result, the gas concentration distribution heat map and the diffusion rate of each gas are compared with the preset rules in the preset risk determination rule library;

[0029] If there is a matching preset rule, the corresponding risk level is determined based on the preset rule, and an early warning operation associated with the risk level is triggered;

[0030] If there is no matching preset rule, it is determined that there is no risk, and feedback information indicating that there is no risk is generated and sent to the monitoring platform.

[0031] In one embodiment, the training process of the LSTM model includes:

[0032] Acquiring training data, where the training data is obtained by performing a fluid dynamics simulation on a target space;

[0033] The training data is input into the LSTM model to be trained in batches, and iterative training is performed until the objective function converges to a preset value to obtain a pre-trained LSTM model.

[0034] In a second aspect, an embodiment of the present invention further provides an environmental monitoring system, the system comprising:

[0035] A data acquisition module, configured to collect gas concentration data of the target space at a preset frequency using the gas sensing module, and to collect physical environment data of the target space at a preset frequency using the physical environment sensing module;

[0036] The state judgment module is used to judge whether the target space is in an abnormal state based on the gas concentration data and physical environment data collected each time;

[0037] The alarm response module is used to execute an alarm response when the target space is in an abnormal state.

[0038] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores an environmental monitoring program, and the environmental monitoring program can be executed to implement the steps of the environmental monitoring method described above.

[0039] The present invention has the following beneficial effects: The present invention utilizes a gas sensor module to collect gas concentration data of a target space at a preset frequency, and utilizes a physical environment sensor module to collect physical environment data of the target space at a preset frequency; based on the gas concentration data and physical environment data collected each time, the present invention determines whether the target space is in an abnormal state; and when the target space is in an abnormal state, an alarm response is executed. By simultaneously collecting gas concentration data and physical environment data and combining them to determine the state of the target space, the present invention can more accurately determine whether the target space is in an abnormal state, effectively reducing safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of a preferred embodiment of the environmental monitoring method of the present invention.

[0041] Figure 2 It is a schematic diagram of the types of target spaces in the present invention.

[0042] Figure 3 It is a schematic diagram of the housing of the environmental monitor in the present invention.

[0043] Figure 4 It is a cross-sectional schematic diagram of the environmental monitor in the present invention.

[0044] Figure 5 It is a functional principle block diagram of a preferred embodiment of the environmental monitoring system in the present invention.

[0045] Description of reference numerals:

[0046] 10. Shell; 11. Ventilation hole; 12. Warning light; 13. Fixing plate; 20. First inner shell; 30. Second inner shell; 101. Pressure sensor; 102. Illumination sensor; 201. Oxygen sensor; 202. Infrared gas sensor; 203. Electrochemical gas sensor; 204. Catalytic combustion gas sensor; 301. Computing module; 302. Communication module; 303. Self-test and calibration module; 304. Power supply module. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present invention more clear and distinct, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0048] With the acceleration of industrialization and urbanization, environmental safety issues are becoming increasingly prominent. This is particularly true in scenarios such as chemical production, underground operations, and warehouse management, where toxic and hazardous gas leaks, abnormal oxygen concentrations, and explosion risks are common. Traditional environmental monitoring technologies typically deploy single, decentralized sensors (such as electrochemical sensors, infrared sensors, and catalytic combustion sensors) to monitor the concentration of specific gases, ignoring the impact of environmental factors on gas states and leading to safety risks.

[0049] To address the above-mentioned shortcomings of the prior art, the present invention provides an environmental monitoring method, system, and storage medium. The method comprises: utilizing the gas sensor module to collect gas concentration data of the target space at a preset frequency, and utilizing the physical environment sensor module to collect physical environment data of the target space at a preset frequency; determining whether the target space is in an abnormal state based on the gas concentration data and physical environment data collected each time; and executing an alarm response when the target space is in an abnormal state. By simultaneously collecting gas concentration data and physical environment data and combining them to determine the state of the target space, the present invention can more accurately determine whether the target space is in an abnormal state, effectively reducing safety hazards.

[0050] See Figure 1 The environmental monitoring method according to an embodiment of the present invention is applied to an environmental monitor, which is equipped with a gas sensor module and a physical environment sensor module, and includes the following steps:

[0051] Step S100: using the gas sensor module to collect gas concentration data of the target space at a preset frequency, and using the physical environment sensor module to collect physical environment data of the target space at a preset frequency.

[0052] Specifically, the target space is a finite space, such as Figure 2 As shown, the target space can be any one of a closed equipment, a sump, an elevator shaft, a septic tank, a pipeline, an underground confined space, a sewage well, and a warehouse. In a confined space, there are many types of gases, some of which are harmful to the human body or pose a safety hazard. In order to accurately monitor the target space, at least one environmental monitor can be set in the target space. For example, environmental monitors are deployed at the top, bottom, and ventilation blind spots of the target space. By collecting gas concentration data and physical environment data through environmental monitors at different locations, it is possible to judge the risks of the target space and effectively reduce safety hazards.

[0053] In one implementation, the gas sensing module includes an electrochemical gas sensor, an infrared gas sensor, a catalytic combustion gas sensor, and an oxygen sensor; the physical environment sensing module includes a pressure sensor, an illumination sensor, and a temperature and humidity sensor; the gas concentration data includes carbon monoxide concentration, hydrogen sulfide concentration, oxygen concentration, carbon dioxide concentration, and methane concentration; and the physical environment data includes air pressure, temperature, humidity, and brightness.

[0054] Specifically, if Figure 3-Figure 4 As shown, the environmental detector includes a housing 10, a first inner housing 20, and a second inner housing 30. A pressure sensor 101, an illumination sensor 102, and a temperature and humidity sensor (not shown) are disposed between the housing and the first inner housing. An oxygen sensor 201, an infrared gas sensor 202, an electrochemical gas sensor 203, and a catalytic combustion gas sensor 204 are disposed between the first and second inner housings. A computing module 301, a communication module 302, a self-test and calibration module 303, and a power supply module 304 are disposed within the second inner housing. Housing 10 is provided with a plurality of ventilation holes 11, a warning light 12, an LED display (not shown), and an alarm (not shown), as well as four mounting plates 13.

[0055] The computing module 301 performs logical analysis based on collected data, using the NVIDIA Jetson Nano chip for calculations. The communication module 302, which is used to send and receive instructions and interact with external devices, includes a primary communication submodule and a backup communication submodule. The primary communication submodule uses 5G communication technology to transmit data to external devices, while the backup communication submodule uses LoRa physical layer modulation technology. Switching between the primary and backup communication submodules is based on 5G signal strength. Specifically, when the 5G signal strength is equal to or greater than a preset signal strength threshold, communication with the external device is performed using the primary communication submodule; when the 5G signal strength is lower than the preset signal strength threshold, communication switches to the backup communication submodule. The self-test and calibration module 303 calibrates each sensor at midnight each day to eliminate drift caused by interference. If the self-test and calibration module 303 detects a sensor failure, it generates a fault notification and sends it to the monitoring platform. This notification includes a request for manual intervention for the faulty sensor. In addition, the data collected by the environmental monitor will be transmitted to the cloud server through the communication module. The self-test and calibration module 303 will compare the data with the baseline data of the cloud server on a fixed date every month. If a sensor is found to deviate from the baseline data, a calibration alarm will be triggered and sent to the monitoring platform. The cloud server stores two months of data to facilitate accident tracing. The calibration alarm includes a suggestion to manually calibrate sensors that deviate from the historical average. In this way, the accuracy of each sensor in the environmental monitor can be ensured to meet the JJG 693 standard. The power supply module 304 is used to power the environmental monitor. It includes a main power supply and a backup power supply. The main power supply uses a 3.7V / 10000mAh lithium battery, which can provide continuous power support for the environmental monitor for 72 hours. This allows the monitor to work for a long time after a single charge, reducing the trouble of frequent charging. The backup power supply uses a supercapacitor, which can provide 30 minutes of power support for the environmental monitor.

[0056] The housing 10 is made of 316L stainless steel, with the ventilation holes 11 covered in sequence with a HEPA filter and a hydrophobic membrane. The explosion-proof certification level is Ex dIIB T4, making it suitable for extreme environments. The electrochemical gas sensor can detect carbon monoxide and hydrogen sulfide concentrations, the infrared sensor can detect carbon dioxide concentration, the oxygen sensor can detect oxygen concentration, and the catalytic combustion gas sensor can detect methane concentration. In the present invention, the explosion-proof level of the electrochemical gas sensor is equal to or greater than Ex ia IIC T6. The infrared sensor has a detection accuracy of ±2% FS. The catalytic combustion gas sensor has a built-in temperature compensation module to eliminate environmental interference and ensure the accuracy of detection results. The temperature and humidity sensor can accurately measure ambient temperature and humidity with an accuracy of ±0.5°C / ±3% RH. The present invention integrates multiple sensors into a single environmental monitor and simultaneously collects data at a preset frequency. This not only effectively reduces equipment maintenance costs but also ensures that the collected data is in the same time dimension, eliminating correlation analysis errors caused by asynchronous sampling and providing a data foundation for subsequent determination of the target space's status.

[0057] See Figure 1 The environmental monitoring method according to the embodiment of the present invention further includes the following steps:

[0058] Step S200: Determine whether the target space is in an abnormal state based on the gas concentration data and physical environment data collected each time.

[0059] Specifically, the present invention combines gas concentration data and physical environment data to consider the state of the target space in multiple dimensions, which can more effectively determine the state of the target space.

[0060] In one implementation, judging whether the target space is in an abnormal state based on the gas concentration data and physical environment data collected each time includes:

[0061] If any of the gas concentration data exceeds the corresponding maximum gas concentration threshold, or is lower than the corresponding minimum gas concentration threshold, it is determined that the target space is in an abnormal state;

[0062] If any item of the physical environment data exceeds the corresponding maximum physical environment threshold, it is determined that the target space is in an abnormal state;

[0063] If the combination of at least one of the gas concentration data and at least one of the physical environment data satisfies a preset combination abnormality condition, it is determined that the target space is in an abnormal state.

[0064] Specifically, if any one of the carbon monoxide concentration, hydrogen sulfide concentration, carbon dioxide concentration, methane concentration and oxygen concentration exceeds the corresponding maximum gas concentration threshold, or if the oxygen concentration is lower than the corresponding minimum gas concentration threshold, it is determined that the target space is in an abnormal state.

[0065] In one embodiment, the maximum gas concentration threshold corresponding to the methane concentration is 1% LEL, the maximum gas concentration threshold corresponding to the hydrogen sulfide concentration is 10 ppm, the maximum gas concentration threshold corresponding to the carbon monoxide concentration is 25 ppm, the maximum gas concentration threshold corresponding to the oxygen concentration is 23.5%, and the minimum gas concentration threshold is 19.5%.

[0066] In addition, the present invention also sets corresponding maximum physical environment thresholds for air pressure, temperature, and humidity. When any one of the air pressure, temperature, and humidity exceeds the corresponding maximum physical environment threshold, the target space is determined to be in an abnormal state. The present invention presets a combination abnormality condition for multiple gas concentration data and associated physical environment data. When the gas concentration data and the physical environment data meet the preset combination abnormality condition, the target space is determined to be in an abnormal state. For example, if the methane concentration is non-zero and the temperature is higher than 30 degrees Celsius, the preset combination abnormality condition is met, and the target space is determined to be in an abnormal state. The above method breaks through the traditional single gas concentration monitoring mode, analyzes the environment based on a multi-dimensional parameter coupling analysis mechanism, and can effectively improve the accuracy of judging the state of the target space.

[0067] In one implementation, the method further includes:

[0068] When the illuminance collected by the illuminance sensor in the environmental sensor is lower than a preset illuminance threshold, it is determined that the visibility of the target space is abnormal;

[0069] Generate abnormal event alarm information and send it to the monitoring platform. The abnormal event alarm information includes the location of the target space, the illumination of the target space, and the conclusion that the visibility is abnormal.

[0070] Specifically, this invention also supports illumination-based alarms, enabling the monitoring platform to respond promptly and ensure construction safety for workers. While generating and sending abnormal event alarm information to the monitoring platform, this information is also pushed to the monitoring platform app via a push notification mechanism, allowing supervisors to be kept informed of the situation. When supervisors receive abnormal event alarm information, if the target space is monitored, they can access on-site monitoring to gain immediate insight.

[0071] In one implementation, the method further includes:

[0072] When the main power supply is cut off, it switches to the backup power supply and generates a power alarm message which is sent to the monitoring platform. The power alarm message includes the location of the target space and the time of the power outage.

[0073] Specifically, when the monitoring platform receives the power alarm information, it can arrange staff to charge the main power supply according to the specific conditions of the target space, and the charging method can be wireless induction charging.

[0074] See Figure 1 The environmental monitoring method according to the embodiment of the present invention further includes the following steps:

[0075] Step S300: When the target space is in an abnormal state, execute an alarm response.

[0076] Specifically, the system controls the warning light on the environmental monitor to flash red; generates an alarm message containing crowd evacuation information and physical environment data and / or gas concentration data that caused the abnormal state; and controls the alarm device on the environmental monitor to broadcast the alarm message via voice, and controls the LED display on the environmental monitor to display the alarm message. This allows staff to be informed of the on-site situation in a timely manner and evacuate in a timely manner.

[0077] In one implementation, after generating the alarm information, the method further includes:

[0078] A ventilation control instruction is generated and transmitted to the corresponding ventilation equipment in the target space to control the ventilation equipment to perform ventilation operations.

[0079] Specifically, when an alarm message is generated, the environmental monitor can generate ventilation control instructions to control the ventilation equipment in the target space to perform ventilation. In this way, the concentration of harmful gases or flammable gases can be effectively reduced, and the concentration of oxygen can be increased, thereby reducing the risk of the target space.

[0080] In one implementation, after generating the alarm information, the method further includes:

[0081] The target space geographic location and the physical environment data and / or gas concentration data that cause the abnormal state are combined into environmental alarm information and sent to the monitoring platform.

[0082] Specifically, the present invention also generates environmental warning information and sends it to the monitoring platform, which can promptly know the specific situation of the target space. In addition, the environmental warning information is also pushed to the monitoring platform app in the form of a message push, so that monitoring personnel can understand the situation in time and intervene.

[0083] In one implementation, the method further includes:

[0084] Obtain the geometric dimension data of the target space and process it into a three-dimensional feature vector;

[0085] When receiving an environmental prediction instruction, obtaining the current wind speed of the target space and processing it into a first eigenvector, obtaining the current gas concentration data and physical environment data of the target space and processing them into a second eigenvector, and concatenating the first eigenvector, the second eigenvector, and the three-dimensional eigenvector into a current eigenvector;

[0086] Acquire historical data of the previous N time steps, wherein the historical data includes historical wind speed, historical gas concentration, and historical physical environment data;

[0087] Processing the historical data of the first N time steps into a time series feature vector, and concatenating each feature vector in the time series feature vector with the three-dimensional feature vector to obtain a historical time series feature vector;

[0088] The current feature vector and the historical time series feature vector are input into a pre-trained LSTM model, and the LSTM model processes the feature vector to output an environmental prediction result.

[0089] Specifically, each time step is 1 minute. The range of N can be 20-30. Preferably, N is 24. The present invention uses a pre-trained LSTM model to predict the environmental conditions in the next 10 minutes, which can quickly predict the trend of gas diffusion and provide data support for emergency decision-making. The environmental monitor of the present invention also has a built-in wind speed sensor (not shown), through which the current wind speed can be collected. The processing of this part is performed in the calculation module 301 of the environmental monitor. In the traditional way, data processing relies on the cloud, which may cause a delay in response speed when the network is unstable. The present invention can improve the response speed and provide timely warnings of dangers by performing environmental predictions in the local calculation module 301. In the present invention, the LSTM model includes a spatial channel and a temporal channel. The spatial channel models the spatial correlation of adjacent sensor nodes through a graph convolutional network. The temporal channel captures the temporal dependency of gas diffusion by stacking 2 layers of LSTM.

[0090] In one implementation, after the LSTM model processes and outputs the environment prediction result, the method further includes:

[0091] Based on the gas concentration distribution heat map and the diffusion rate of each gas in the environmental prediction result, the gas concentration distribution heat map and the diffusion rate of each gas are compared with the preset rules in the preset risk determination rule library;

[0092] If there is a matching preset rule, the corresponding risk level is determined based on the preset rule, and an early warning operation associated with the risk level is triggered;

[0093] If there is no matching preset rule, it is determined that there is no risk, and feedback information indicating that there is no risk is generated and sent to the monitoring platform.

[0094] Specifically, if the environmental prediction results indicate that at least one of the following concentrations within the target space is greater than 2% LEL within 10 minutes, and the diffusion rate is greater than 0.5 m / s, the risk level is determined to be level one. The environmental detector then generates a first warning message, which includes crowd evacuation information and the predicted gas concentration data that could lead to the risk. The warning light on the environmental monitor flashes red, the alarm on the environmental monitor announces the warning message, and the LED display on the environmental monitor displays the warning message. This approach allows personnel to be promptly informed of the on-site situation and evacuate promptly. If the environmental prediction results indicate that the oxygen concentration within the target space is below 19.5% within 10 minutes and persists for more than 30 seconds, the risk level is determined to be level two. The environmental detector then generates a second warning message, which includes the predicted gas concentration data that could lead to the risk. The alarm on the environmental monitor announces the warning message, and if an oxygen delivery device is present within the target space, a start command is sent to the device. When the environmental prediction result shows that at least one of the hydrogen sulfide gas concentration and carbon monoxide gas concentration in the target space within 10 minutes is greater than 50ppm and the gradient increases, the risk level is determined to be level three. At this time, the environmental detector will generate a third warning message, which contains the gas concentration data that causes the risk and information requesting manual review. The third warning message is sent to the monitoring platform so that the monitoring platform can conduct manual review in a timely manner based on the third warning message. In addition, every time a warning message is generated, it will be pushed to the monitoring platform APP synchronously to facilitate supervisors to follow up on the situation of the target space in a timely manner.

[0095] In one implementation, the training process of the LSTM model includes:

[0096] Acquiring training data, where the training data is obtained by performing a fluid dynamics simulation on a target space;

[0097] The training data is input into the LSTM model to be trained in batches, and iterative training is performed until the objective function converges to a preset value to obtain a pre-trained LSTM model.

[0098] Specifically, the present invention generates training data under different ventilation conditions through fluid dynamics simulation, which allows the LSTM model to learn the physical laws of gas diffusion and accurately predict the environment. The objective function can be expressed as: Where y represents the concentration field, represents the predicted value, Represents the gradient amplitude of the predicted expansion field, and λ is a hyperparameter used to adjust the strength of the gradient.

[0099] After the LSTM model is trained, the SHAP method can be used to analyze the basis for its prediction decisions. This mathematical approach quantifies the weight of each environmental parameter (such as wind speed and spatial geometry) on diffusion velocity, helping personnel better understand the factors influencing gas diffusion. For example, in a long and narrow underground passage, analyzing the SHAP values ​​reveals that wind speed has a significant influence on gas diffusion velocity. Therefore, increasing wind speed to accelerate gas diffusion can be a key consideration during ventilation design.

[0100] In one embodiment, if Figure 5 As shown, based on the above-mentioned environmental monitoring method, the present invention also provides an environmental monitoring system, which includes:

[0101] The data acquisition module 100 is used to collect gas concentration data of the target space at a preset frequency using the gas sensing module, and to collect physical environment data of the target space at a preset frequency using the physical environment sensing module;

[0102] The state judgment module 200 is used to judge whether the target space is in an abnormal state based on the gas concentration data and physical environment data collected each time;

[0103] The alarm response module 300 is configured to execute an alarm response when the target space is in an abnormal state.

[0104] In one embodiment, the status determination module includes:

[0105] a first state judgment unit, configured to determine that the target space is in an abnormal state if any item of the gas concentration data exceeds a corresponding maximum gas concentration threshold, or is lower than a corresponding minimum gas concentration threshold;

[0106] a second state judgment unit, configured to determine that the target space is in an abnormal state if any item of the physical environment data exceeds a corresponding maximum physical environment threshold, or is lower than a corresponding minimum physical environment threshold;

[0107] The third state judgment unit is configured to determine that the target space is in an abnormal state if a combination of at least one of the gas concentration data and at least one of the physical environment data satisfies a preset combination abnormality condition.

[0108] In one embodiment, the system further comprises:

[0109] The gas concentration abnormality judgment subunit is used to determine that the target space is in an abnormal state if any one of the carbon monoxide concentration, the hydrogen sulfide concentration, the carbon dioxide concentration, the methane concentration and the oxygen concentration exceeds the corresponding maximum gas concentration threshold, or if the oxygen concentration is lower than the corresponding minimum gas concentration threshold.

[0110] In one embodiment, the alarm response module includes:

[0111] a warning light control unit, used to control the warning light on the environmental monitor to flash red;

[0112] an alarm information generating unit, configured to generate alarm information, wherein the alarm information includes crowd evacuation information and physical environment data and / or gas concentration data causing the abnormal state;

[0113] The alarm unit is used to control the alarm device on the environmental monitor to broadcast the alarm information by voice and control the LED display screen on the environmental monitor to display the alarm information.

[0114] In one embodiment, the system further comprises:

[0115] A three-dimensional feature vector generating unit, used for acquiring geometric dimension data of the target space and processing it into a three-dimensional feature vector;

[0116] a current eigenvector generating unit, configured to, upon receiving an environmental prediction instruction, obtain the current wind speed of the target space and process it into a first eigenvector, process the current gas concentration data and physical environment data of the target space into a second eigenvector, and concatenate the first eigenvector, the second eigenvector, and the three-dimensional eigenvector into a current eigenvector;

[0117] A historical data acquisition unit, configured to acquire historical data of the previous N time steps, wherein the historical data includes historical wind speed, historical gas concentration, and historical physical environment data;

[0118] a historical time series characteristic vector generating unit, configured to process the historical data of the first N time steps into a time series characteristic vector, and concatenate each characteristic vector in the time series characteristic vector with the three-dimensional characteristic vector to obtain a historical time series characteristic vector;

[0119] The environment prediction unit is used to input the current feature vector and the historical time series feature vector into a pre-trained LSTM model, process them through the LSTM model, and output an environment prediction result.

[0120] In one embodiment, the system further comprises:

[0121] A rule comparison unit, configured to compare the gas concentration distribution heat map and the diffusion rate of each gas in the environmental prediction result with preset rules in a preset risk determination rule library;

[0122] an early warning unit, configured to, if a matching preset rule exists, determine a corresponding risk level based on the preset rule and trigger an early warning operation associated with the risk level;

[0123] The information feedback unit is used to determine that there is no risk if there is no matching preset rule, and generate feedback information indicating that there is no risk and send it to the monitoring platform.

[0124] In one embodiment, the system further comprises:

[0125] A training data acquisition unit, configured to acquire training data, wherein the training data is obtained by performing a fluid dynamics simulation on a target space;

[0126] The model training unit is used to input the training data into the LSTM model to be trained in batches, and iteratively train until the objective function converges to a preset value to obtain a pre-trained LSTM model.

[0127] An embodiment of the present invention further provides a computer-readable storage medium, on which an environmental monitoring program is stored. When the environmental monitoring program is executed by a processor, the steps of any one of the environmental monitoring methods provided in the embodiment of the present invention are implemented.

[0128] It should be understood that the sequence numbers of the steps in the above embodiments do not imply a specific order of execution; the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0129] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the above-mentioned device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0130] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0131] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0132] In the embodiments provided by the present invention, it should be understood that the disclosed apparatus / terminal device and method can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For example, the division of the modules or units described above is merely a logical functional division. In actual implementation, other division methods may be used. For example, multiple units or components may be combined or integrated into another device, or some features may be omitted or not implemented.

[0133] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. An environmental monitoring method, applied to an environmental monitor, wherein the environmental monitor is equipped with a gas sensor module and a physical environment sensor module, characterized in that: The method comprises: Using the gas sensor module to collect gas concentration data of the target space at a preset frequency, and using the physical environment sensor module to collect physical environment data of the target space at a preset frequency; Based on the gas concentration data and physical environment data collected each time, determine whether the target space is in an abnormal state; When the target space is in an abnormal state, an alarm response is executed.

2. The environmental monitoring method according to claim 1, characterized in that: The gas sensing module includes an electrochemical gas sensor, an infrared gas sensor, a catalytic combustion gas sensor and an oxygen sensor; the physical environment sensing module includes a pressure sensor and a temperature and humidity sensor; the gas concentration data includes carbon monoxide concentration, hydrogen sulfide concentration, oxygen concentration, carbon dioxide concentration and methane concentration; and the physical environment data includes air pressure, temperature and humidity.

3. The environmental monitoring method according to claim 2, characterized in that: Based on the gas concentration data and physical environment data collected each time, determine whether the target space is in an abnormal state, including: If any of the gas concentration data exceeds the corresponding maximum gas concentration threshold, or is lower than the corresponding minimum gas concentration threshold, it is determined that the target space is in an abnormal state; If any item of the physical environment data exceeds the corresponding maximum physical environment threshold, it is determined that the target space is in an abnormal state; If the combination of at least one of the gas concentration data and at least one of the physical environment data satisfies a preset combination abnormality condition, it is determined that the target space is in an abnormal state.

4. The environmental monitoring method according to claim 3, characterized in that: If any of the gas concentration data exceeds the corresponding maximum gas concentration threshold, or is lower than the corresponding minimum gas concentration threshold, it is determined that the target space is in an abnormal state, including: If any one of the carbon monoxide concentration, the hydrogen sulfide concentration, the carbon dioxide concentration, the methane concentration and the oxygen concentration exceeds the corresponding maximum gas concentration threshold, or if the oxygen concentration is lower than the corresponding minimum gas concentration threshold, it is determined that the target space is in an abnormal state.

5. The environmental monitoring method according to claim 1, characterized in that: When in an abnormal state, execute alarm response, including: Controlling the warning light on the environmental monitor to flash red; generating alarm information, wherein the alarm information includes crowd evacuation information and physical environment data and / or gas concentration data causing the abnormal state; The alarm device on the environmental monitor is controlled to broadcast the alarm information by voice, and the LED display screen on the environmental monitor is controlled to display the alarm information.

6. The environmental monitoring method according to claim 1, characterized in that: The method further comprises: Obtain the geometric dimension data of the target space and process it into a three-dimensional feature vector; When receiving an environmental prediction instruction, obtaining the current wind speed of the target space and processing it into a first eigenvector, obtaining the current gas concentration data and physical environment data of the target space and processing them into a second eigenvector, and concatenating the first eigenvector, the second eigenvector, and the three-dimensional eigenvector into a current eigenvector; Acquire historical data of the previous N time steps, wherein the historical data includes historical wind speed, historical gas concentration, and historical physical environment data; Processing the historical data of the first N time steps into a time series feature vector, and concatenating each feature vector in the time series feature vector with the three-dimensional feature vector to obtain a historical time series feature vector; The current feature vector and the historical time series feature vector are input into a pre-trained LSTM model, and the LSTM model processes the feature vector to output an environmental prediction result.

7. The environmental monitoring method according to claim 6, characterized in that: After the LSTM model processes and outputs the environmental prediction results, it also includes: Based on the gas concentration distribution heat map and the diffusion rate of each gas in the environmental prediction result, the gas concentration distribution heat map and the diffusion rate of each gas are compared with the preset rules in the preset risk determination rule library; If there is a matching preset rule, the corresponding risk level is determined based on the preset rule, and an early warning operation associated with the risk level is triggered; If there is no matching preset rule, it is determined that there is no risk, and feedback information indicating that there is no risk is generated and sent to the monitoring platform.

8. The environmental monitoring method according to claim 6, characterized in that: The training process of the LSTM model includes: Acquiring training data, where the training data is obtained by performing a fluid dynamics simulation on a target space; The training data is input into the LSTM model to be trained in batches, and iterative training is performed until the objective function converges to a preset value to obtain a pre-trained LSTM model.

9. An environmental monitoring system, characterized in that: include: A data acquisition module, configured to collect gas concentration data of the target space at a preset frequency using the gas sensing module, and to collect physical environment data of the target space at a preset frequency using the physical environment sensing module; The state judgment module is used to judge whether the target space is in an abnormal state based on the gas concentration data and physical environment data collected each time; The alarm response module is used to execute an alarm response when the target space is in an abnormal state.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which can be executed to implement the steps of the environment monitoring method according to any one of claims 1 to 8.

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