Method and device for determining gas risk parameters of limited space and electronic equipment

Abstract

The invention discloses a method and a device for determining gas risk parameters of a limited space and electronic equipment. The method comprises the following steps: acquiring environment data of a limited space; determining a first concentration characteristic corresponding to the target gas according to the gas data; determining a second concentration characteristic corresponding to the target gas according to the gas data, the first concentration characteristic and associated data corresponding to the associated gas; determining a target concentration characteristic corresponding to the target gas according to the temperature data, the humidity data and the second concentration characteristic; and determining a gas risk parameter corresponding to the limited space according to the target concentration characteristic. According to the method and the device, the technical problem that potential gas potential safety hazards in the limited space are difficult to identify in time due to inaccurate determination of the gas risk parameters when the gas risk parameters in the limited space are determined in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of gas detection, and more specifically, to a method, apparatus, and electronic device for determining gas risk parameters in a confined space. Background Technology

[0002] In related technologies, confined spaces refer to enclosed or partially enclosed spaces with restricted access but accessible to personnel. These spaces are typically not designed as fixed workplaces and are poorly ventilated, making them prone to the accumulation of toxic, harmful, flammable, and explosive substances or insufficient oxygen content. To avoid serious safety accidents such as personnel poisoning, fires, explosions, and equipment corrosion caused by excessive gas concentrations, and to ensure the safety of operation and maintenance, it is necessary to determine the gas risk parameters of confined spaces. However, in related technologies, there are technical problems in determining the gas risk parameters of confined spaces, such as inaccurate determination of gas risk parameters, which makes it difficult to identify potential gas safety hazards in confined spaces in a timely manner.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, and electronic device for determining gas risk parameters in a confined space, to at least solve the technical problem in the related art where the determination of gas risk parameters in a confined space is inaccurate, making it difficult to identify potential gas safety hazards in a confined space in a timely manner.

[0005] According to one aspect of the present invention, a method for determining gas risk parameters in a confined space is provided, comprising: acquiring environmental data of the confined space, wherein the environmental data includes temperature data, humidity data, and gas data corresponding to a target gas within the confined space; determining a first concentration characteristic corresponding to the target gas based on the gas data; determining a second concentration characteristic corresponding to the target gas based on the gas data, the first concentration characteristic, and correlation data corresponding to a related gas, wherein the related gas is a gas within the confined space whose correlation index with the target gas is greater than a correlation threshold; determining a target concentration characteristic corresponding to the target gas based on the temperature data, the humidity data, and the second concentration characteristic; and determining a gas risk parameter corresponding to the confined space based on the target concentration characteristic.

[0006] Optionally, determining the second concentration feature corresponding to the target gas based on the gas data, the first concentration feature, and the associated data of the associated gas includes: determining the gas correlation relationship between the target gas and the associated gas; determining the first deviation feature between the gas data and the associated data; and determining the second concentration feature corresponding to the target gas based on the gas correlation relationship, the first deviation feature, and the first concentration feature.

[0007] Optionally, determining the second concentration feature corresponding to the target gas based on the gas correlation, the first deviation feature, and the first concentration feature includes: when the gas data includes multiple sub-data, wherein the multiple sub-data correspond to different data acquisition devices, and the different data acquisition devices are used to acquire data of the target gas; determining the second deviation feature between the multiple sub-data; and determining the second concentration feature corresponding to the target gas based on the second deviation feature, the gas correlation, the first deviation feature, and the first concentration feature.

[0008] Optionally, determining the gas risk parameter corresponding to the confined space based on the target concentration characteristics includes: if the target concentration characteristics include a gas concentration index, comparing the gas concentration index with a predetermined concentration index to obtain a first comparison result; and determining the gas risk parameter corresponding to the confined space based on the first comparison result.

[0009] Optionally, determining the gas risk parameter corresponding to the confined space based on the first comparison result includes: when the target concentration feature includes the gas change rate, and the first comparison result is that the gas concentration index is less than or equal to the predetermined concentration index, comparing the gas change rate with the predetermined change rate to obtain a second comparison result; and determining the gas risk parameter corresponding to the confined space based on the second comparison result.

[0010] Optionally, before acquiring the environmental data of the limited space, the method includes: determining the number of packets in the data buffer, wherein the number of packets is the number of multiple cached data packets in the data buffer; if the number of packets is less than a predetermined number, determining the packet difference between the number of packets and the predetermined number; determining multiple new acquisition time points based on the packet difference and the data acquisition frequency corresponding to the limited space; acquiring the new data packets corresponding to the multiple new acquisition time points respectively; and determining the environmental data based on the multiple cached data packets and the new data packets corresponding to the multiple new acquisition time points respectively.

[0011] Optionally, after determining the gas risk parameters corresponding to the confined space based on the target concentration characteristics, the method further includes: when the gas risk parameters include a target risk type and a target risk index, determining a plurality of candidate early warning methods corresponding to the target risk type, wherein the plurality of candidate early warning methods correspond to different risk index ranges; and determining a target early warning method from the plurality of candidate early warning methods based on the target risk index for performing gas risk early warning operations, wherein the risk index range corresponding to the target early warning method includes the target risk index.

[0012] According to one aspect of the present invention, a device for determining gas risk parameters in a confined space is provided, comprising: an acquisition module for acquiring environmental data of the confined space, wherein the environmental data includes temperature data, humidity data, and gas data corresponding to a target gas within the confined space; a first determination module for determining a first concentration characteristic corresponding to the target gas based on the gas data; a second determination module for determining a second concentration characteristic corresponding to the target gas based on the gas data, the first concentration characteristic, and correlation data corresponding to a related gas, wherein the related gas is a gas within the confined space whose correlation index with the target gas is greater than a correlation threshold; a third determination module for determining a target concentration characteristic corresponding to the target gas based on the temperature data, the humidity data, and the second concentration characteristic; and a fourth determination module for determining a gas risk parameter corresponding to the confined space based on the target concentration characteristic.

[0013] According to one aspect of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the gas risk parameter determination method for a confined space as described in any of the preceding claims.

[0014] According to one aspect of the present invention, a computer-readable storage medium is provided, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the gas risk parameter determination method for a confined space as described in any of the preceding claims.

[0015] In this embodiment of the invention, environmental data of a confined space is acquired, including temperature data, humidity data, and gas data corresponding to a target gas within the confined space. Based on the gas data, a first concentration characteristic corresponding to the target gas is determined. Based on the gas data, the first concentration characteristic, and correlation data corresponding to associated gases, a second concentration characteristic corresponding to the target gas is determined, wherein the associated gas is a gas within the confined space whose correlation index with the target gas is greater than a correlation threshold. Based on the temperature data, humidity data, and the second concentration characteristic, a target concentration characteristic corresponding to the target gas is determined. Based on the target concentration characteristic, a gas risk parameter corresponding to the confined space is determined. By acquiring temperature, humidity, and target gas data within a confined space, comprehensive foundational data support is provided for subsequent feature analysis. Based on the gas data, the first concentration characteristic of the target gas is determined to capture its core fundamental attributes. Combining the gas data, the first concentration characteristic, and the correlation data of related gases with correlation indices exceeding thresholds, the second concentration characteristic is determined. This achieves multi-dimensional complementary verification and optimization of the target gas state. Temperature and humidity data are then incorporated to determine the target concentration characteristic, fully considering the impact of environmental factors on the gas state. Finally, gas risk parameters are determined based on the target concentration characteristics. By comprehensively integrating single gas attributes, correlation characteristics of related gases, and environmental conditions, the accuracy of gas risk parameter determination is improved, effectively identifying potential gas safety hazards in confined spaces. This solves the technical problem in related technologies where inaccurate determination of gas risk parameters in confined spaces leads to difficulty in timely identification of potential gas safety hazards. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 This is a flowchart of a method for determining gas risk parameters in a confined space according to an embodiment of the present invention;

[0018] Figure 2 This is a schematic diagram of the gas risk parameter determination system in a confined space according to an optional embodiment of the present invention;

[0019] Figure 3 This is a structural block diagram of a gas risk parameter determination device in a confined space according to an embodiment of the present invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] Example 1

[0023] According to an embodiment of the present invention, an embodiment of a method for determining gas risk parameters in a confined space is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0024] Figure 1 This is a flowchart of a method for determining gas risk parameters in a confined space according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0025] S102, acquire environmental data of the confined space, including temperature data, humidity data and gas data corresponding to the target gas in the confined space;

[0026] This involves confined spaces, which are enclosed or partially enclosed spaces with restricted access but accessible to personnel. These spaces are typically not designed as fixed workplaces and are poorly ventilated, making them prone to the accumulation of toxic, harmful, flammable, and explosive substances or insufficient oxygen levels. These confined spaces include cable tunnels, utility tunnels, and underground pits, whose internal environments are complex and remain relatively enclosed for extended periods, creating a risk of gas accumulation.

[0027] This involves environmental data, which is comprehensive data that reflects the overall environmental state within a limited space, including temperature data, humidity data, and gas data corresponding to the target gas.

[0028] This includes temperature data, which is a quantitative measure of the temperature of the air within a confined space.

[0029] This includes humidity data, which is a quantitative measure of the moisture level of the air within a confined space.

[0030] This involves target gases, which are specific hazardous gases that need to be monitored within a confined space. Exceeding the permitted concentration of these gases can lead to serious safety accidents such as poisoning, fires, and explosions. These target gases include one or more gases to be detected, such as methane, hydrogen sulfide, carbon monoxide, and hydrogen.

[0031] This involves gas data, which is data used to reflect the state characteristics of the target gas, including instantaneous concentration data, concentration change trend data, etc.

[0032] By acquiring environmental data within a confined space, we can comprehensively reflect the overall environmental status of that space, providing a data foundation for subsequent analysis.

[0033] S104, Based on the gas data, determine the first concentration characteristic corresponding to the target gas;

[0034] This involves the first concentration feature, which is a basic feature that reflects the core concentration attributes of the target gas after preliminary processing of the gas data. It is a preliminary quantitative characterization of the concentration state of the target gas.

[0035] By performing preliminary processing on the gas data of the target gas, the first concentration feature that can reflect its core concentration attributes can be extracted, thus enabling a preliminary quantitative characterization of the concentration state of the target gas.

[0036] S106. Based on the gas data, the first concentration characteristic, and the correlation data corresponding to the associated gas, determine the second concentration characteristic corresponding to the target gas. The associated gas is a gas in a limited space whose correlation index with the target gas is greater than the correlation threshold.

[0037] This involves associated gases, which are gases within a confined space that have a physical or chemical relationship with the target gas, and whose correlation index is greater than a preset correlation threshold. For example, the concentration changes of the associated gas and the target gas may be synchronous or causally related (e.g., in terms of source, trend of change, risk impact, etc.), jointly reflecting the environmental risk status within the confined space.

[0038] This involves associated data, which is the core state data corresponding to the associated gas. The data type is consistent with that of the target gas, including preprocessed quantitative data such as the instantaneous concentration value, concentration change rate, and short-term average concentration of the associated gas. This data serves as a key reference for verifying the state of the target gas.

[0039] This involves a second concentration feature, which is an optimized concentration feature obtained by integrating the original gas data of the target gas and the associated data of the associated gases on the basis of the first concentration feature. It is a more comprehensive and accurate quantitative representation of the concentration state of the target gas, which not only retains the core concentration attributes of the target gas itself, but also incorporates the collaborative verification information of the associated gases, effectively reducing the bias of judgment based on single data.

[0040] This includes a correlation index, which is used to quantify the strength of the correlation between the target gas and other gases in a confined space.

[0041] This involves an association threshold, which is a pre-set critical value used to define whether there is an association relationship between gases. When the association index between the target gas and a certain gas exceeds the threshold, the gas can be determined to be an associated gas of the target gas. The value can be flexibly adjusted according to the type of confined space, gas characteristics, and safety detection requirements.

[0042] By combining the gas data of the target gas with the established primary concentration characteristics for fusion analysis, it is possible to incorporate collaborative verification information from related gases while preserving the core concentration attributes of the target gas, thereby achieving a more comprehensive and accurate quantitative characterization of the target gas concentration state.

[0043] S108, Based on temperature data, humidity data, and the second concentration characteristic, determine the target concentration characteristic corresponding to the target gas;

[0044] This involves target concentration characteristics, which are obtained by fusing temperature data, humidity data, and a second concentration characteristic. These characteristics reflect the concentration state of the target gas and can further offset the interference of environmental factors on gas detection. They accurately reflect the actual concentration level and change pattern of the target gas under the current environmental conditions, providing a direct and reliable core basis for the accurate determination of subsequent gas risk parameters.

[0045] By integrating temperature and humidity data with optimized secondary concentration features for comprehensive analysis, the interference of environmental factors such as temperature and humidity on gas detection can be effectively offset. This accurately restores the actual concentration level and variation pattern of the target gas under the current environmental conditions, avoids the distortion of concentration features caused by environmental interference, and ensures that a direct and reliable core basis is provided for the accurate determination of subsequent gas risk parameters, thereby achieving the most comprehensive and accurate quantitative characterization of the target gas state.

[0046] S110, based on the target concentration characteristics, determines the gas risk parameters corresponding to the confined space.

[0047] This involves gas risk parameters, which are comprehensive parameters used to quantify gas safety risks in confined spaces. These parameters include target risk types (such as poisoning risk, explosion risk, oxygen deficiency risk, and risk of complex gas hazards) and target risk indices (risk level values ​​quantified based on the absolute value of gas concentration, rate of change, and the degree of correlation among multiple parameters). These parameters can accurately reflect the degree of threat posed by the current gas environment in confined spaces to personnel safety and equipment operation, and serve as the core basis for subsequent triggering of graded early warnings and emergency response operations.

[0048] Based on the characteristics of target concentration, gas risk parameters are determined. By comprehensively integrating the properties of single gases, the characteristics of related gases, and environmental conditions, the accuracy of gas risk parameter determination is improved, and potential gas safety hazards in confined spaces are effectively identified.

[0049] Through the above steps S102-S110, environmental data of the confined space is acquired, including temperature data, humidity data, and gas data corresponding to the target gas within the confined space. Based on the gas data, a first concentration characteristic corresponding to the target gas is determined. Based on the gas data, the first concentration characteristic, and the correlation data corresponding to the associated gas, a second concentration characteristic corresponding to the target gas is determined, wherein the associated gas is a gas within the confined space whose correlation index with the target gas is greater than a correlation threshold. Based on the temperature data, humidity data, and the second concentration characteristic, a target concentration characteristic corresponding to the target gas is determined. Based on the target concentration characteristic, a gas risk parameter corresponding to the confined space is determined. By acquiring temperature, humidity, and target gas data within a confined space, comprehensive foundational data support is provided for subsequent feature analysis. Based on the gas data, the first concentration characteristic of the target gas is determined to capture its core fundamental attributes. Combining the gas data, the first concentration characteristic, and the correlation data of related gases with correlation indices exceeding thresholds, the second concentration characteristic is determined. This achieves multi-dimensional complementary verification and optimization of the target gas state. Temperature and humidity data are then incorporated to determine the target concentration characteristic, fully considering the impact of environmental factors on the gas state. Finally, gas risk parameters are determined based on the target concentration characteristics. By comprehensively integrating single gas attributes, correlation characteristics of related gases, and environmental conditions, the accuracy of gas risk parameter determination is improved, effectively identifying potential gas safety hazards in confined spaces. This solves the technical problem in related technologies where inaccurate determination of gas risk parameters in confined spaces leads to difficulty in timely identification of potential gas safety hazards.

[0050] As an optional embodiment, based on gas data, a first concentration feature, and associated data corresponding to associated gases, a second concentration feature corresponding to the target gas is determined, including: determining the gas correlation relationship between the target gas and associated gases; determining a first deviation feature between the gas data and associated data; and determining the second concentration feature corresponding to the target gas based on the gas correlation relationship, the first deviation feature, and the first concentration feature.

[0051] This involves gas correlation, which is an inherent correlation pattern between the target gas and related gases based on their source of generation, change patterns, or risk impact. This includes synchronous generation relationships, causal relationships of concentration changes, and synergistic relationships of risk types.

[0052] Among them, the first deviation feature is involved. This first deviation feature is the difference feature between the gas data of the target gas and the associated gas data, which can reflect the degree of fit between the two types of gas data in characterizing the environmental state.

[0053] By determining the gas correlation between the target gas and related gases, we can reveal their inherent interaction patterns in the environment. By identifying the first deviation feature between gas data and related data, we can achieve collaborative verification, avoid judgment bias caused by relying solely on the target gas's own data, and thus perform multi-dimensional correction and optimization to offset the limitations of single data judgment and achieve a more comprehensive and accurate characterization of the target gas concentration state.

[0054] As an optional embodiment, determining a second concentration feature corresponding to the target gas based on gas correlation, a first deviation feature, and a first concentration feature includes: when the gas data includes multiple sub-data, wherein the multiple sub-data correspond to different data acquisition devices, and the different data acquisition devices are used to acquire data on the target gas; determining a second deviation feature between the multiple sub-data; and determining a second concentration feature corresponding to the target gas based on the second deviation feature, gas correlation, the first deviation feature, and the first concentration feature.

[0055] This involves multiple sub-data sets, which are multiple sets of data collected by different data acquisition devices for the same target gas, representing a multi-source record of the target gas state.

[0056] This involves different data acquisition devices, which are used to collect data related to target gases within a confined space, including multiple sets of the same type of gas sensors.

[0057] This involves a second deviation feature between multiple sub-data sets. This second deviation feature is a quantitative difference feature between multiple sets of sub-data sets of the same target gas, including the numerical dispersion of each set of sub-data sets, the consistency deviation of the change trend, the time difference of peak / valley occurrence, etc. It can reflect the difference in detection accuracy or data synchronization between multi-source acquisition devices and is an important reference for correcting data deviation and improving the reliability of target gas concentration characterization.

[0058] By using different data acquisition devices to acquire multiple sub-data of the target gas, the gas state can be recorded from multiple sources to reduce the risk of single-point failure. By determining the second deviation feature between multiple sub-data, the consistency of multi-device detection can be quantitatively evaluated and abnormal data can be identified. Thus, by combining the second deviation feature, gas correlation, first deviation feature and first concentration feature, sensor error can be dynamically corrected and environmental interference can be eliminated, ensuring that the second concentration feature accurately reflects the actual concentration level of the target gas.

[0059] As an optional embodiment, determining the gas risk parameters corresponding to the confined space based on the target concentration characteristics includes: if the target concentration characteristics include a gas concentration index, comparing the gas concentration index with a predetermined concentration index to obtain a first comparison result; and determining the gas risk parameters corresponding to the confined space based on the first comparison result.

[0060] This involves a gas concentration index, which is an index that reflects the concentration level of the target gas, obtained by quantifying the target concentration characteristics; specifically, it is a concentration value.

[0061] This involves a predetermined concentration index, which is a concentration threshold value set in advance based on the safety requirements of confined spaces, the hazardous characteristics of gases, and actual application scenarios. It includes different levels of standards such as gas safety threshold and hazard threshold, and serves as the core reference benchmark for judging whether the gas concentration exceeds the standard and whether a risk exists. It can be flexibly adjusted according to the type of gas (such as toxic gas or combustible gas).

[0062] This involves a first comparison result, which is a quantitative judgment result obtained by comparing the gas concentration index with a predetermined concentration index. For example, it includes three core scenarios: the gas concentration index is lower than the predetermined concentration index (safe state), equal to the predetermined concentration index (critical state), and higher than the predetermined concentration index (dangerous state). Alternatively, the first comparison result may also include a quantitative degree of difference obtained by comparing the gas concentration index with the predetermined concentration index.

[0063] By comparing the target gas concentration index with the preset concentration threshold, it is possible to determine whether the current gas concentration is in a safe, critical, or dangerous state. Based on this quantitative judgment result, gas risk parameters can be determined, enabling timely early warning and precise control of gas safety risks in confined spaces.

[0064] As an optional embodiment, determining the gas risk parameters corresponding to the confined space based on the first comparison result includes: when the target concentration feature includes the gas change rate, and the first comparison result is that the gas concentration index is less than or equal to a predetermined concentration index, comparing the gas change rate with the predetermined change rate to obtain a second comparison result; and determining the gas risk parameters corresponding to the confined space based on the second comparison result.

[0065] This involves the gas change rate, which is the rate at which the concentration of the target gas changes per unit time. It is calculated using algorithms such as sliding window linear regression and can accurately reflect the speed of gas concentration increase or decrease. It is a core indicator for identifying early potential risks.

[0066] This involves a predetermined rate of change, which is a pre-set critical value for concentration change, such as an increase rate threshold and a decrease rate threshold (e.g., an oxygen concentration decrease rate threshold). These are the core reference benchmarks for judging whether the gas change trend is abnormal.

[0067] This includes a second comparison result, which is a quantitative determination result obtained by comparing the gas change rate with a predetermined change rate.

[0068] When the target concentration characteristics include the gas change rate reflecting the trend of gas concentration change, and the first comparison result shows that the gas concentration index does not exceed the predetermined concentration index, the gas change rate is compared with the predetermined change rate to obtain a second comparison result for quantitative judgment. Based on this result, early potential risks that do not exceed the standard but have abnormal change trends can be captured, avoiding risk omissions caused by relying solely on the absolute value of concentration. This ensures that the gas risk parameters of a limited space are determined comprehensively and accurately, providing reliable support for early warning and risk management.

[0069] As an optional embodiment, before acquiring environmental data in a limited space, the method includes: determining the number of packets in a data buffer, wherein the number of packets is the number of multiple cached data packets in the data buffer; if the number of packets is less than a predetermined number, determining the packet difference between the number of packets and the predetermined number; determining multiple new acquisition time points based on the packet difference and the data acquisition frequency corresponding to the limited space; acquiring the new data packets corresponding to the multiple new acquisition time points; and determining environmental data based on the multiple cached data packets and the new data packets corresponding to the multiple new acquisition time points.

[0070] This includes the number of packets, which is the total number of cached data packets currently stored in the data cache area, used to measure the degree of data accumulation in the cache area.

[0071] This includes a data cache area, which is a storage area used to temporarily store multiple cached data packets generated during data acquisition in a limited space environment.

[0072] This involves multiple cached data packets, which are data packets that have been collected and stored in the data cache area, containing raw data units of environmental parameters in a limited space (such as temperature, humidity, gas concentration, etc.).

[0073] This involves a predetermined quantity, which is the minimum number of data packets that the data buffer should store, pre-set according to the needs of monitoring the confined space environment.

[0074] This involves packet difference, which is the difference between the current number of cached data packets and the predetermined number, used to quantify the degree of data insufficiency.

[0075] This involves the data acquisition frequency, which is the frequency at which data related to the confined space environment is collected.

[0076] This involves several newly added data collection time points, which are specific time points for data collection that need to be added, determined based on packet differences and data collection frequency.

[0077] This includes newly added data packets, which are raw data units containing the latest environmental parameters that are re-collected and generated by sensors at new acquisition time points. These data packets are used to supplement the buffer data to support complete environmental analysis.

[0078] By statistically analyzing the total number of cached data packets stored in the cache, and when this number does not reach the preset minimum data packet threshold, the difference between the current number of packets and the predetermined number is calculated. Combined with the environmental data collection frequency in the limited space, the specific time points that need to be supplemented for collection are determined. The new data packets containing the latest environmental parameters corresponding to these time points are obtained. Then, the new data packets are integrated with the original cached data packets to fill the data gaps in the cache, ensuring that the environmental data used for analysis has sufficient data volume to support it. This avoids distortion of environmental feature extraction or inaccurate analysis results due to insufficient data, and lays a complete and reliable data foundation for subsequent target gas concentration feature analysis and gas risk parameter determination.

[0079] As an optional embodiment, after determining the gas risk parameters corresponding to the confined space based on the target concentration characteristics, the method further includes: when the gas risk parameters include a target risk type and a target risk index, determining multiple candidate early warning methods corresponding to the target risk type, wherein the multiple candidate early warning methods correspond to different risk index ranges; and determining a target early warning method from the multiple candidate early warning methods based on the target risk index, for use in performing gas risk early warning operations, wherein the risk index range corresponding to the target early warning method includes the target risk index.

[0080] This includes target risk types, which are specific risk categories that may be caused by the gas environment in a confined space, determined based on the characteristics of the target concentration. These include, but are not limited to, poisoning risk, explosion risk, oxygen deficiency risk, and the risk of hidden dangers from complex gases.

[0081] This includes a target risk index, which is used to quantify the degree of gas safety risk within a confined space.

[0082] This involves multiple alternative early warning methods, which are pre-set early warning methods of different intensities or forms for specific target risk types, including but not limited to audible and visual alerts, vibration alerts, remote data reporting alerts, and emergency response trigger alerts, providing differentiated early warning options for different risk levels.

[0083] This involves risk index ranges, which represent a pre-defined, continuous range of target risk indices for classifying risk levels. Each range corresponds to a candidate early warning method.

[0084] This includes a target-based early warning method, which is a method selected from multiple candidate early warning methods and precisely matched with the range to which the target risk index belongs.

[0085] This includes gas risk warning operations, which are specific operations performed according to the target warning method to alert relevant personnel or activate related emergency mechanisms. The core purpose is to promptly convey risk warnings and provide support for emergency response.

[0086] By identifying multiple alternative early warning methods corresponding to the target risk type and clarifying the risk index range corresponding to each method, a mapping relationship between risk level and early warning intensity can be established. By accurately matching the target early warning method within the corresponding range based on the target risk index, it is possible to ensure that the early warning operation is dynamically adapted to the actual risk level, thereby achieving the timeliness, pertinence, and effectiveness of tiered early warning and avoiding safety risks or resource waste caused by insufficient or excessive early warning.

[0087] Based on the above embodiments and optional embodiments, an optional implementation method is provided, which is described in detail below.

[0088] In related technologies, confined spaces refer to enclosed or partially enclosed spaces with restricted access but accessible to personnel. These spaces are typically not designed as fixed workplaces and are poorly ventilated, making them prone to the accumulation of toxic, harmful, flammable, and explosive substances or insufficient oxygen content. To avoid serious safety accidents such as personnel poisoning, fires, explosions, and equipment corrosion caused by excessive gas concentrations, and to ensure the safety of operation and maintenance, it is necessary to determine the gas risk parameters of confined spaces. However, in related technologies, there are technical problems in determining the gas risk parameters of confined spaces, such as inaccurate determination of gas risk parameters, which makes it difficult to identify potential gas safety hazards in confined spaces in a timely manner.

[0089] There is currently no effective solution to the above problems.

[0090] In view of this, an optional embodiment of the present invention provides a method for determining gas risk parameters in a confined space, which can effectively solve the above-mentioned technical problems.

[0091] S1, acquire environmental data of the confined space, including temperature data, humidity data and gas data corresponding to the target gas in the confined space;

[0092] For example, before acquiring environmental data for a confined space, the process includes: data acquisition, and preprocessing and calibration of the acquired environmental data. Among these steps:

[0093] Signal acquisition: Various sensors, such as the main control microcontroller unit (MCU), are used to read the raw output values ​​(voltage, current or digital signal) of each sensor in a fixed frequency (e.g., once per second).

[0094] Data filtering: For the raw data stream of each sensor, Kalman filtering or moving average filtering algorithms are used to suppress random noise and improve signal stability.

[0095] Temperature / Humidity Compensation: In the case of various sensors, including temperature and humidity sensors (T / H), electrochemical sensors, and metal-oxide-semiconductor (MOS) sensors, the performance of electrochemical and MOS sensors is affected by ambient temperature and humidity. Real-time data from the temperature and humidity sensors is used, and a built-in compensation model (e.g., a pre-calibrated temperature-sensitivity lookup table or compensation formula) is applied to correct the raw data (such as raw gas concentration readings) collected by the electrochemical and MOS sensors, thus obtaining the concentration values ​​included in the gas data. This concentration value It has been revised and is now more accurate.

[0096] Specifically, before S1, the steps include: determining the number of packets in the data buffer, where the number of packets refers to the number of multiple cached data packets in the data buffer; if the number of packets is less than a predetermined number, determining the packet difference between the number of packets and the predetermined number; determining multiple new acquisition time points based on the packet difference and the data acquisition frequency corresponding to the limited space; obtaining the new data packets corresponding to each of the multiple new acquisition time points; and determining the environmental data based on the multiple cached data packets and the new data packets corresponding to each of the multiple new acquisition time points. When the environmental data includes concentration values, this can be achieved in the following way:

[0097] Data buffer:

[0098] For the number of packets of target gas i, a fixed-length FIFO (first-in, first-out) data buffer Buffer_i (i.e., data buffer) with a length of N (e.g., N=10, corresponding to the data of the most recent 10 seconds) is determined to store the historical concentration value C_t of the target gas after preprocessing.

[0099] Calculation of rate of change:

[0100] At each new sampling time t among multiple newly added data collection time points, perform the following steps:

[0101] Store the new concentration value C_t (i.e., the new data packet) into Buffer_i.

[0102] If the buffer is not full (i.e., the number of packets is less than the predetermined number), then wait; if it is full, then proceed with the subsequent calculations.

[0103] Linear regression was used to fit N data points within the buffer buffer_i (with time as the independent variable and concentration as the dependent variable). The slope k_i of the fitted line is the average rate of change of the gas within the current time window, expressed in ppm / s or %LEL / s.

[0104] By employing linear regression to calculate the average rate of change over the entire time window, rather than simply subtracting the first and last data points, misjudgments caused by fluctuations in single-point data can be effectively smoothed out, making trend assessments more robust. Furthermore, by appropriately setting the sliding window length N, an optimal balance can be achieved between "rapid response" and "stability." A shorter window (e.g., 5-10 seconds) ensures that anomalies are detected within seconds of a sudden change in concentration.

[0105] S2, Based on the gas data, determine the first concentration characteristic corresponding to the target gas;

[0106] For example, feature extraction can be performed based on gas data: extract the first concentration feature from the gas data (which is preprocessed data). The first concentration feature includes instantaneous concentration value, concentration change rate (first derivative), average concentration (such as short-term average concentration), etc.

[0107] S3. Based on the gas data, the first concentration feature, and the correlation data corresponding to the associated gas, determine the second concentration feature corresponding to the target gas. The associated gas is a gas in a limited space whose correlation index with the target gas is greater than the correlation threshold.

[0108] Specifically, S3 includes:

[0109] S31, determine the gas correlation relationship between the target gas and the associated gas;

[0110] S32, determine the first deviation characteristic between the gas data and the associated data;

[0111] S33. Based on the gas correlation, the first deviation characteristic, and the first concentration characteristic, determine the second concentration characteristic corresponding to the target gas.

[0112] For example, based on the associated data corresponding to the associated gas (a gas that is strongly associated with the target gas), data cross-calibration and weighted averaging are performed to obtain the first deviation feature between the gas data and the associated data, so as to identify abnormal data in the gas data of the target gas. Then, based on the gas correlation, the first deviation feature, and the first concentration feature, the second concentration feature corresponding to the target gas is determined.

[0113] Specifically, S33 includes:

[0114] When the gas data includes multiple sub-data, each sub-data corresponds to a different data acquisition device, and the different data acquisition devices are used to acquire data on the target gas; a second deviation feature is determined among the multiple sub-data; based on the second deviation feature, the gas correlation, the first deviation feature, and the first concentration feature, a second concentration feature corresponding to the target gas is determined.

[0115] For example, for the target gas, the sub-data collected by multiple sensors (i.e., different data acquisition devices) are cross-calibrated (pairwise cross-calibration between sub-data) and weighted averaged to obtain the second deviation feature between the multiple sub-data. Based on the second deviation feature, gas correlation, first deviation feature, and first concentration feature, the second concentration feature corresponding to the target gas is determined to further improve the reliability of single gas data.

[0116] S4. Based on temperature data, humidity data, and the second concentration characteristic, determine the target concentration characteristic corresponding to the target gas.

[0117] Specifically, temperature and humidity compensation is performed based on temperature and humidity data, including: calling real-time data from temperature and humidity sensors to determine temperature and humidity characteristics; determining perturbation characteristics based on temperature and humidity characteristics, wherein the perturbation characteristics are used to characterize the changes in the concentration state of the target gas caused by changes in temperature and humidity; and correcting the second concentration characteristics based on the perturbation characteristics to obtain the target concentration characteristics corresponding to the target gas.

[0118] Optionally, based on temperature data, humidity data, and the second concentration feature, the target concentration feature corresponding to the target gas can be determined. This can be achieved by treating all parameters (e.g., temperature, humidity, and gas concentration) as a whole feature vector and inputting it into a fusion judgment engine based on threshold logic or a lightweight machine learning model to obtain the target concentration feature corresponding to the target gas.

[0119] For example, when the target gas includes CO (carbon monoxide), determining whether CO exceeds the limit will also involve... The decreasing trend of oxygen concentration and the abnormal increase in temperature are used to comprehensively determine whether there is a risk of smoldering. This multi-parameter collaborative judgment can identify early and complex risks that cannot be detected by a single parameter alarm.

[0120] S5. Based on the target concentration characteristics, determine the gas risk parameters corresponding to the confined space.

[0121] Specifically, S5 includes:

[0122] S51, if the target concentration feature includes a gas concentration index, compare the gas concentration index with a predetermined concentration index to obtain a first comparison result;

[0123] S52, based on the first comparison result, determine the gas risk parameters corresponding to the confined space.

[0124] Specifically, S52 includes:

[0125] When the target concentration feature includes the gas change rate, and the first comparison result is that the gas concentration index is less than or equal to a predetermined concentration index, the gas change rate is compared with the predetermined change rate to obtain a second comparison result. Based on the second comparison result, the gas risk parameter corresponding to the confined space is determined, which can be achieved through a sliding window weighted change rate early warning algorithm, including:

[0126] The calculated rate of change k_i (i.e., the gas change rate) is compared with a preset rate of change threshold K_threshold_i (i.e., the predetermined rate of change). This threshold is set individually according to the hazardous characteristics of different gases.

[0127] Judgment logic: If |k_i|>K_threshold_i (for oxygen, this means the concentration decrease rate is too fast, k_i is too fast). <-K_threshold_ If the gas i (i.e., the target gas) is determined to be in an "abnormally rapid change" state, then the gas i is immediately determined to be in an "abnormally rapid change" state.

[0128] Warning Trigger: Once any one or more of the target gases enter this state, an early risk warning will be immediately activated. Specifically, this manifests as follows:

[0129] Display interface: On the display screen, an arrow icon indicating "rapid rise" or "rapid fall" is dynamically displayed next to the gas reading, and the background of the numerical area flashes amber (yellow).

[0130] Audible and visual alert: The device emits an intermittent, alert beeping sound, distinct from the over-limit alarm, while the LED indicator flashes yellow.

[0131] Vibration alert: Triggers a short vibration to alert the user in environments where it is inconvenient to view the screen.

[0132] Based on the above approach, not only is a binary judgment of "present" or "absent" provided, but also a quantitative indicator of the rate of change, k_i. In the future, this value can be combined with the absolute concentration value to construct more complex risk assessment models, achieving a more refined distinction between risk levels. Even if all current gas concentration readings are within safe thresholds, potential, rapidly developing risk sources can be identified by tracking and analyzing the instantaneous rate of change of various gas concentrations in real time, thus issuing early warnings. This function greatly enhances the equipment's proactive early warning capabilities, providing inspection personnel with emergency preparation time. For example, in cable tunnels, although the concentration of methane or carbon monoxide may not have reached the lower limit for explosion or poisoning, a sharp increase in its concentration often indicates localized overheating or the occurrence of a hidden open flame, thus enabling an alarm to be issued in the early stages of such accidents.

[0133] Specifically, after S5, it also includes:

[0134] Given that the gas risk parameters include the target risk type and the target risk index, multiple candidate early warning methods corresponding to the target risk type are determined, wherein each candidate early warning method corresponds to a different risk index range; based on the target risk index, a target early warning method is determined from the multiple candidate early warning methods for use in executing gas risk early warning operations, wherein the risk index range corresponding to the target early warning method includes the target risk index.

[0135] For example, when gas risk parameters include target risk type and target risk index, intelligent early warning can be implemented, including multi-level early warning mechanisms and fault self-diagnosis.

[0136] The multi-level early warning mechanism includes:

[0137] Warning level: When the concentration of at least one gas to be detected in the target gas reaches 80% of the preset threshold, or the concentration change rate is continuously positive and large, it is judged as an "abnormal trend" and a warning message can be sent, but no strong alarm will be triggered on site.

[0138] Alarm level: When the concentration of any gas exceeds the safety threshold, an on-site audible and visual alarm is immediately triggered, and the highest priority alarm information is sent to the monitoring center via the communication module.

[0139] Emergency Response Level: When the fusion algorithm determines that the risk is extremely high (e.g., methane), ) and hydrogen ( (If the temperature exceeds the standard and rises sharply, in addition to sending an alarm, a linkage signal can also be output through the communication module to remotely activate the emergency ventilation system in the tunnel.)

[0140] Self-diagnosis of faults: Continuously monitors the working status of each sensor (such as signal baseline drift, abnormal response, etc.). When it is determined that a sensor may fail or needs calibration, it will actively report the equipment fault status and remind maintenance.

[0141] The above steps can be achieved using a gas risk parameter determination system for confined spaces (hereinafter referred to as the system). Figure 2 This is a schematic diagram of a gas risk parameter determination system in a confined space according to an optional embodiment of the present invention, as shown below. Figure 2 As shown, the system includes: a sensor array module, a main control processing module, a communication transmission module, a power management module, and a human-machine interaction and alarm module. The connection relationships and working principles between the modules are as follows.

[0142] For sensor array modules:

[0143] Function: Responsible for collecting target gas concentration and temperature and humidity data in the environment.

[0144] Composition and Connection: This sensor array module is a multi-sensor integrated board, connected to the main control processing module via a unified hardware interface (serial port). Specifically, it includes:

[0145] (1) Gas sensing unit: For the target gas, a high-precision, long-life sensor is preferred, wherein the target gas includes:

[0146] methane ( It employs a non-dispersive infrared (NDIR) sensor, which measures based on the principle of gas absorption of specific infrared bands. It has strong anti-interference capabilities and a long lifespan.

[0147] Carbon monoxide (CO), hydrogen sulfide ( ),oxygen( ), nitrogen oxides ( ): It adopts a high-performance electrochemical sensor, which has the advantages of high sensitivity and fast response.

[0148] hydrogen( ): Employs a thermal conductivity (TCD) sensor or a dedicated one An electrochemical sensor for detecting the properties of hydrogen.

[0149] Volatile organic compounds (VOCs) ): Employs a metal oxide semiconductor (MOS) sensor, which exhibits high sensitivity to a wide range of organic gases.

[0150] (2) Environmental sensing unit:

[0151] Temperature sensor: A digital temperature sensor is used (which can be integrated into other chips).

[0152] Humidity sensor: A capacitive digital humidity sensor is used.

[0153] Spatial arrangement: All sensors are centrally located in the gas sampling chamber of the system casing. The sampling chamber is designed with a windproof and dustproof structure, and has a built-in miniature air intake pump or utilizes natural air convection to ensure that the gas to be measured can fully and evenly contact the sensing elements of each sensor.

[0154] For the main control processing module:

[0155] Function: Responsible for controlling sensor sampling, running core algorithms, decision-making and early warning, and managing the entire system's workflow.

[0156] Composition and Connection:

[0157] Core Microcontroller (MCU): Employs a microcontroller with strong computing power and rich peripheral interfaces.

[0158] Memory: A secure digital card (SD) is used to store historical data, alarm records, and algorithm model parameters.

[0159] Connection relationships: The MCU connects to the sensor array module, communication module, and human-machine interaction module through its general purpose input / output ports (GPIO), analog-to-digital converter (ADC), inter-integrated circuit bus (I2C), serial peripheral interface (SPI), and universal asynchronous transceiver (UART) to realize data acquisition and command control.

[0160] For the communication transmission module:

[0161] Function: Responsible for wirelessly transmitting processed data and alarm information to the remote monitoring center.

[0162] Composition and Connection:

[0163] Core components: Integrated fourth-generation / fifth-generation mobile communication technology (4G / 5G) communication category 1 (CAT.1) communication module or low-power long-range wireless (LoRa) module. 4G / 5G is suitable for areas with network coverage, enabling long-distance data transmission; LoRa is suitable for tunnel environments with severe signal shielding and requiring self-organizing networks.

[0164] Connection relationship: The communication module is connected to the main control MCU through the UART interface and is controlled by the MCU's attention commands (AT) to realize the sending and receiving of data.

[0165] For the power management module:

[0166] Function: Provides a stable and reliable power supply for the entire system and enables low-power management.

[0167] Composition and Connection:

[0168] Power supply source: It can be a built-in high-capacity lithium battery with a solar charging panel; or it can be the existing DC safe power supply in the tunnel.

[0169] Core circuitry includes a lithium battery charging management circuit, a DC-DC step-up / step-down regulator circuit, a power path management circuit, and a power consumption switching switch.

[0170] Working principle: The main control MCU can switch between multiple modes such as "high frequency acquisition", "intermittent operation" and "sleep" by controlling the power consumption switching switch according to the preset strategy, which greatly extends the battery life.

[0171] For the human-computer interaction and alarm module:

[0172] Functions: Enables on-site status display, parameter setting, and audible and visual alarms.

[0173] Composition and Connection:

[0174] Display unit: High-brightness light-emitting diode (LED) display screen, connected to the MCU via serial port, to display the concentration of each gas and the status of the device in real time.

[0175] Audible and visual alarm unit: High-brightness LED indicator and high-decibel buzzer, directly driven by the MCU's GPIO port. When any monitored parameter exceeds the limit or the algorithm determines it to be dangerous, the MCU will trigger an audible and visual alarm.

[0176] The following description, with specific examples, will further illustrate this point.

[0177] Quick Start and Self-Test Procedure: Turn on the device power and the system initializes. The main control module controls the preheating of each sensor and reads the current environmental parameters to perform zero-point and baseline self-calibration. The self-test status (normal / fault) is displayed on the screen.

[0178] Active sampling and real-time monitoring steps: Align the device's air inlet with the area to be measured. Activate the miniature vacuum pump via the human-machine interface (e.g., a button), which actively draws ambient gas into the multi-sensor chamber. The gas simultaneously flows through all sensor units, and the main control module begins high-speed, synchronous acquisition of raw data from each sensor.

[0179] Data processing and intelligent analysis steps:

[0180] Data preprocessing: The main control module sequentially performs moving average filtering and real-time compensation based on temperature and humidity readings on the collected raw data to calculate accurate, environmentally compensated concentration values ​​of various gases.

[0181] Trend analysis and fusion judgment: Real-time calculation of the rate of change of each gas concentration and judgment based on preset multi-level early warning logic. At the same time, the multi-parameter fusion judgment engine comprehensively analyzes all nine parameters to identify complex risk patterns.

[0182] Multimodal warning and display steps: Based on the analysis results, if everything is normal, the display screen dynamically updates the real-time values ​​and curves of all parameters, and the indicator light shows green. If an abnormal trend such as a rapid increase in concentration is detected, a warning is triggered: the relevant value area on the screen flashes yellow, the indicator light turns yellow, and the buzzer emits an intermittent warning sound.

[0183] If the concentration of any parameter exceeds the safety threshold or a combined risk is identified, an alarm will be triggered immediately: the relevant value area on the screen will be highlighted in red and a warning message will pop up, the indicator light will turn red and flash, the buzzer will emit a continuous loud sound, and the vibrator will start simultaneously.

[0184] Data recording and remote communication steps: All data from the entire testing process, along with timestamp information, is automatically stored in the memory. Users can use the communication module to send test reports, alarm records, and other data to a remote server or backend monitoring center with a single click, completing data archiving and reporting.

[0185] Furthermore, the gas risk parameter determination system includes the following core components:

[0186] Fast sampling and sensor array module:

[0187] Sensor arrays: These are arranged compactly in a ring or matrix within a multi-sensor gas chamber. The chamber is made of chemically inert materials (such as stainless steel or polytetrafluoroethylene), and its internal flow channels are optimized to ensure that the gas can make uniform and sufficient contact with each sensor, avoiding detection blind spots or response differences.

[0188] Miniature vacuum pump: Fixed inside the device, connected to the gas chamber inlet via a hose. Its function is to provide active suction, drawing external gas into the gas chamber through the inlet. The pump's power is precisely calculated to achieve rapid response while minimizing overall power consumption.

[0189] Working principle: After the pump starts, gas is drawn in through the inlet and diffuses in the gas chamber. Each sensor detects the concentration of its corresponding gas and generates an electrical signal. After sampling is completed, the pump stops, and the residual gas can be discharged through natural diffusion or a micro exhaust port.

[0190] Main control processing module:

[0191] Core Microcontroller (MCU): As the control center of the system, it coordinates the operation of all hardware through its internal program (firmware). It reads data from the gas sensor and temperature and humidity sensor via the I2C bus; controls the switching of the pump, indicator lights, buzzer, and vibrator via GPIO ports; drives the display screen and reads and writes memory via the Serial Peripheral Interface (SPI); and exchanges data with the communication module via the UART interface.

[0192] Memory: SPI flash memory chip (FLASH) or micro SD card is soldered or plugged into the motherboard to store program code, sensor calibration parameters, historical detection data and alarm logs.

[0193] Human-computer interaction and alarm module:

[0194] Display: A 3.5-inch LCD screen is used, connected to the motherboard via a flexible printed circuit board (FPC) cable and fixed to the front of the device casing. It is used to display a graphical interface, including numbers, curves, battery level, and signal strength.

[0195] Audible, visual, and vibration alarm unit: High-brightness RGB LEDs are installed next to the screen or on the top of the device, indicating status through different colors. Both the buzzer and vibration motor are mounted flush against the inside of the device housing to ensure effective sound and vibration transmission.

[0196] Communication module and power module:

[0197] Communication module: Adopts fourth-generation mobile communication technology (4G) communication category 1 (CAT.1) module, equipped with a user identification module (SIM) card slot and a standard antenna interface, and is fixed to the motherboard through a stamp hole or slot.

[0198] Portable power module: It uses a 3.7V lithium polymer battery pack, which is connected to the charging management chip and DC-DC voltage regulator circuit on the motherboard via wires. The device casing has a USB-C charging port. USB-C is short for USB Type-C, a physical interface / connector standard for the Universal Serial Bus (USB).

[0199] Based on the above, verification and analysis were conducted through a simulated cable overheating fault experiment.

[0200] Experimental objective: To verify the early warning capability of the gas risk parameter determination system before the gas concentration reaches the safety threshold, and to compare its performance with that of traditional equipment.

[0201] Experimental environment and equipment:

[0202] The experiment was conducted inside a 1-cubic-meter sealed experimental chamber.

[0203] Control group: Existing detection system (detection) , , , It only has a threshold alarm function.

[0204] Experimental group: Gas risk parameter determination system.

[0205] Experimental procedure:

[0206] An overload current was applied to a section of cable inside the experimental chamber to simulate a cable overheating fault.

[0207] Two sets of detection equipment were activated simultaneously to continuously monitor the gas environment inside the cabin.

[0208] Table 1 shows the experimental results and data.

[0209] Table 1

[0210]

[0211] Experimental Conclusion: Based on the above, compared to traditional detection systems that can only issue "post-event alarms" after a hazard has occurred, the gas risk parameter determination system, through its dynamic rate of change tracking and multi-parameter fusion judgment capabilities, successfully advances the warning time by more than 2 minutes and can also make a preliminary diagnosis of the nature of the risk. This provides effective emergency response time and significantly improves the safety level of confined space operations.

[0212] The above optional implementation methods can achieve at least the following beneficial effects:

[0213] (1) Compared with related technologies, by acquiring temperature, humidity and gas data of the target gas in a confined space, comprehensive basic data support is provided for subsequent feature analysis. The first concentration feature of the target gas is determined based on the gas data to capture its core basic attributes. The second concentration feature is determined by combining the gas data, the first concentration feature and the correlation data of the related gas with the correlation index exceeding the threshold. This achieves multi-dimensional complementary verification and optimization of the target gas state. Temperature and humidity data are then integrated to determine the target concentration feature. The influence of environmental factors on the gas state is fully considered. Finally, the gas risk parameters are determined based on the target concentration features. The single gas attribute, the correlation characteristics of the related gas and the environmental conditions are fully integrated to improve the accuracy of the determination of the gas risk parameters. This effectively identifies potential gas safety hazards in confined spaces and solves the technical problem in related technologies where the determination of gas risk parameters in confined spaces is inaccurate, making it difficult to identify potential gas safety hazards in confined spaces in a timely manner.

[0214] (2) Compared with related technologies, by using different data acquisition devices to acquire multiple sub-data of the target gas, the gas state can be recorded from multiple sources to reduce the risk of single-point failure; by determining the second deviation feature between multiple sub-data, the consistency of multi-device detection can be quantitatively evaluated and abnormal data can be identified; thus, by combining the second deviation feature, gas correlation, first deviation feature and first concentration feature, sensor error can be dynamically corrected and environmental interference can be eliminated, ensuring that the second concentration feature accurately reflects the actual concentration level of the target gas.

[0215] (3) Compared with related technologies, by including the gas change rate reflecting the trend of gas concentration change in the target concentration characteristics, and when the first comparison result shows that the gas concentration index does not exceed the predetermined concentration index, the gas change rate is compared with the predetermined change rate to obtain a second comparison result for quantitative judgment. Based on this result, early potential risks that do not exceed the standard but have abnormal change trends can be captured, avoiding risk omissions caused by relying solely on the absolute value of concentration, ensuring comprehensive and accurate determination of gas risk parameters in a limited space, and providing reliable support for early warning and risk management.

[0216] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0217] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0218] Example 2

[0219] According to embodiments of the present invention, an apparatus for implementing the above-described method for determining gas risk parameters in a confined space is also provided. Figure 3 This is a structural block diagram of a gas risk parameter determination device for a confined space according to an embodiment of the present invention, as shown below. Figure 3 As shown, the device includes: an acquisition module 302, a first determination module 304, a second determination module 306, a third determination module 308, and a fourth determination module 310. The device will be described in detail below.

[0220] The acquisition module 302 is used to acquire environmental data of a confined space, including temperature data, humidity data, and gas data corresponding to the target gas within the confined space. A first determination module 304, connected to the acquisition module 302, is used to determine a first concentration characteristic corresponding to the target gas based on the gas data. A second determination module 306, connected to the first determination module 304, is used to determine a second concentration characteristic corresponding to the target gas based on the gas data, the first concentration characteristic, and correlation data corresponding to the associated gas, wherein the associated gas is a gas within the confined space whose correlation index with the target gas is greater than a correlation threshold. A third determination module 308, connected to the second determination module 306, is used to determine a target concentration characteristic corresponding to the target gas based on the temperature data, humidity data, and the second concentration characteristic. A fourth determination module 310, connected to the third determination module 308, is used to determine gas risk parameters corresponding to the confined space based on the target concentration characteristic.

[0221] It should be noted here that the above-mentioned acquisition module 302, first determination module 304, second determination module 306, third determination module 308, and fourth determination module 310 correspond to steps S102 to S110 in the method for determining gas risk parameters in a confined space. The multiple modules and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0222] Example 3

[0223] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to execute the instructions to implement the gas risk parameter determination method for a confined space as described above.

[0224] Example 4

[0225] According to another aspect of the present invention, a computer-readable storage medium is also provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the gas risk parameter determination method for a confined space described above.

[0226] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0227] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0228] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0229] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0230] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0231] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0232] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining gas risk parameters in a confined space, characterized in that, include: Acquire environmental data of a confined space, wherein the environmental data includes temperature data, humidity data, and gas data corresponding to the target gas within the confined space; Based on the gas data, determine the first concentration characteristic corresponding to the target gas; Based on the gas data, the first concentration feature, and the correlation data corresponding to the associated gas, a second concentration feature corresponding to the target gas is determined, wherein the associated gas is a gas within the limited space whose correlation index with the target gas is greater than the correlation threshold; Based on the temperature data, the humidity data, and the second concentration characteristic, a target concentration characteristic corresponding to the target gas is determined; Based on the target concentration characteristics, the gas risk parameters corresponding to the limited space are determined.

2. The method according to claim 1, characterized in that, The step of determining the second concentration feature corresponding to the target gas based on the gas data, the first concentration feature, and the associated data of the associated gas includes: Determine the gas correlation relationship between the target gas and the associated gas; Determine a first deviation characteristic between the gas data and the associated data; Based on the gas correlation, the first deviation feature, and the first concentration feature, a second concentration feature corresponding to the target gas is determined.

3. The method according to claim 2, characterized in that, The step of determining the second concentration feature corresponding to the target gas based on the gas correlation, the first deviation feature, and the first concentration feature includes: In the case where the gas data includes multiple sub-data, each sub-data corresponds to a different data acquisition device, and the different data acquisition devices are used to acquire data on the target gas; Determine the second deviation feature among the plurality of sub-data; Based on the second deviation feature, the gas correlation, the first deviation feature, and the first concentration feature, a second concentration feature corresponding to the target gas is determined.

4. The method according to claim 1, characterized in that, The step of determining the gas risk parameters corresponding to the confined space based on the target concentration characteristics includes: If the target concentration feature includes a gas concentration index, the gas concentration index is compared with a predetermined concentration index to obtain a first comparison result; Based on the first comparison result, the gas risk parameters corresponding to the finite space are determined.

5. The method according to claim 4, characterized in that, The step of determining the gas risk parameters corresponding to the confined space based on the first comparison result includes: If the target concentration feature includes the gas change rate, and the first comparison result is that the gas concentration index is less than or equal to the predetermined concentration index, the gas change rate is compared with the predetermined change rate to obtain a second comparison result. Based on the second comparison result, the gas risk parameters corresponding to the finite space are determined.

6. The method according to claim 1, characterized in that, Before acquiring environmental data for the limited space, the following steps are included: Determine the number of packets in the data buffer, wherein the number of packets is the number of multiple cached data packets in the data buffer; If the number of packages is less than a predetermined number, determine the package difference between the number of packages and the predetermined number; Based on the packet difference value and the data acquisition frequency corresponding to the limited space, multiple new acquisition time points are determined; Retrieve the newly added data packets corresponding to multiple newly added collection time points; The environmental data is determined based on the multiple cached data packets and the new data packets corresponding to the multiple new collection time points.

7. The method according to any one of claims 1 to 6, characterized in that, After determining the gas risk parameters corresponding to the confined space based on the target concentration characteristics, the method further includes: When the gas risk parameters include a target risk type and a target risk index, multiple candidate early warning methods corresponding to the target risk type are determined, wherein the multiple candidate early warning methods correspond to different risk index ranges. Based on the target risk index, a target early warning method is determined from the plurality of candidate early warning methods for use in executing gas risk early warning operations, wherein the risk index range corresponding to the target early warning method includes the target risk index.

8. A device for determining gas risk parameters in a confined space, characterized in that, include: An acquisition module is used to acquire environmental data of a confined space, wherein the environmental data includes temperature data, humidity data, and gas data corresponding to the target gas in the confined space; The first determining module is used to determine the first concentration characteristic corresponding to the target gas based on the gas data; The second determining module is used to determine the second concentration feature corresponding to the target gas based on the gas data, the first concentration feature, and the correlation data corresponding to the associated gas, wherein the associated gas is a gas in the limited space whose correlation index with the target gas is greater than the correlation threshold. The third determining module is used to determine the target concentration feature corresponding to the target gas based on the temperature data, the humidity data, and the second concentration feature; The fourth determining module is used to determine the gas risk parameters corresponding to the confined space based on the target concentration characteristics.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the method for determining gas risk parameters in a confined space as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method for determining gas risk parameters in a confined space as described in any one of claims 1 to 7.

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