Wide-area wireless gas risk early warning method, system, equipment and medium

Abstract

The invention relates to a wide-area wireless gas risk early warning method, system and device and a medium, and the method comprises the steps: continuously collecting gas concentration data through a gas detector, and transmitting the data to a gas alarm controller through a bus communication link; the gas alarm controller compares a local early warning threshold value with the gas concentration data, and if the local early warning threshold value is exceeded, a local sound-light alarm is started, and alarm state information is generated; sending the gas concentration data and the alarm state information to a cloud platform through a network access device; and determining the concentration change trend of the corresponding gas detector based on the gas concentration data, fusing the concentration change trend, the current gas concentration value and the corresponding scene attribute information to carry out risk analysis, generating early warning information, and pushing the early warning information to the user terminal. The purpose of constructing a three-dimensional safety protection system with local rapid disposal and cloud intelligent decision collaboration is achieved.

Description

Technical Field

[0001] This invention relates to the fields of Internet of Things and public safety monitoring technology, and in particular to a wide-area wireless gas risk early warning method, system, device and medium. Background Technology

[0002] Against the backdrop of rapid urbanization and industrial and commercial development, the number of small businesses and various decentralized production and operation units has surged. These units, with their complex spatial structures, weak safety awareness, and high regulatory difficulty, have consistently been high-risk areas for gas leaks, fires, and even explosions. Existing gas safety monitoring methods suffer from two main limitations: First, most systems remain at the stage of local fixed threshold alarms, lacking the ability to perceive dynamic trends in risk evolution and provide early warnings, thus failing to provide effective alerts in the early stages of concentration accumulation. Second, even if some systems have network connectivity, their warning modes are often singular and rigid, failing to deeply integrate with the specific risk characteristics of the location (such as business type, personnel density, and building structure), resulting in a lack of targeted warning information, unclear emergency response instructions, and difficulty in achieving precise and efficient remote intervention and collaborative management. Summary of the Invention

[0003] The main objective of this invention is to provide a wide-area wireless gas risk early warning method, system, device, and medium to upgrade the gas risk assessment of widely distributed monitoring points from post-event alarms to pre-event dynamic early warnings, and from single threshold responses to scenario-based intelligent analysis, thereby constructing a three-dimensional security protection system that integrates local rapid response and cloud-based intelligent decision-making.

[0004] To achieve the above objectives, the present invention provides a wide-area wireless gas risk early warning method, comprising the following steps: Gas concentration data is continuously collected using gas detectors deployed within the monitoring area, and the gas concentration data is transmitted to the gas alarm controller via a bus communication link. The gas alarm controller compares the local warning threshold with the gas concentration data. If the local warning threshold is exceeded, the local audible and visual alarm is activated, and alarm status information containing the concentration value is generated. The gas concentration data and the alarm status information are sent to the cloud platform via a network access device; The cloud platform receives data, determines the concentration change trend of the corresponding gas detector based on the gas concentration data, integrates the concentration change trend, the current gas concentration value and the corresponding scene attribute information to perform risk analysis, generates early warning information including risk level and response guidance, and pushes the early warning information to the corresponding user terminal.

[0005] Furthermore, the step of continuously collecting gas concentration data using gas detectors deployed within the monitoring area and transmitting the gas concentration data to the gas alarm controller via a bus communication link includes: Gas concentration data is continuously collected by gas detectors deployed at multiple monitoring points at a preset basic sampling frequency; When the gas concentration data collected by one or more gas detectors exceeds the preset attention threshold, the sampling frequency of the corresponding gas detector is switched to an enhanced sampling frequency that is higher than the basic sampling frequency. The gas detector uses the enhanced sampling frequency to collect gas concentration data and sends it to the gas alarm controller via a bus communication link.

[0006] Further, the gas alarm controller compares the local warning threshold with the gas concentration data. If the local warning threshold is exceeded, a local audible and visual alarm is activated, and alarm status information containing the concentration value is generated. This step includes: The gas alarm controller is equipped with a first warning threshold and a second warning threshold, wherein the second warning threshold is greater than the first warning threshold. When the gas concentration data exceeds the first warning threshold and is lower than the second warning threshold, the gas alarm controller activates the first level of local audible and visual alarm. When the gas concentration data exceeds the second warning threshold, the gas alarm controller activates the second level of local audible and visual alarm and sends a forced control command to the emergency shut-off valve or fan connected to the gas alarm controller. Upon activating any alarm level, the gas alarm controller generates alarm status information including the current alarm level, real-time concentration value, and detector identifier.

[0007] Further, the step of sending the gas concentration data and the alarm status information to the cloud platform via a network access device includes: The network access device receives data packets uploaded from one or more gas alarm controllers, the data packets containing at least gas concentration data and alarm status information; When alarm status information is present in one or more data packets, the network access device prioritizes scheduling transmission resources to send the corresponding data packet and the preset scene attribute information of the corresponding monitoring point. When there is no alarm status information in the data packet, the network access device sends gas concentration data and the preset scene attribute information of the corresponding monitoring point in sequence.

[0008] Furthermore, the cloud platform receives data and determines the concentration change trend of the corresponding gas detector based on the gas concentration data, including: A circular cache queue is allocated to each gas detector using a cloud platform to store multiple historical gas concentration data reported by each gas detector in chronological order. After receiving new gas concentration data, the cloud platform combines the historical gas concentration data stored in the data cache queue and uses a trend analysis algorithm to calculate a trend value that characterizes the direction of gas concentration change in the detector. The cloud platform compares the calculated trend value with multiple preset trend classification thresholds to determine the concentration change trend level of the corresponding gas detector.

[0009] Furthermore, the step of integrating the concentration change trend, current gas concentration value, and corresponding scenario attribute information for risk analysis includes: Based on the location type in the scene attribute information, call the pre-configured risk assessment matrix that matches the corresponding location type; The concentration range to which the current gas concentration value belongs, and the level of the concentration change trend, are used as indexes and input into the risk assessment matrix; The cloud platform outputs a comprehensive risk index through a risk assessment matrix, and then maps the risk level based on the comprehensive risk index.

[0010] Furthermore, the step of generating early warning information containing risk levels and response guidelines, and pushing the early warning information to the corresponding user terminals, includes: Based on the determined risk level, select the corresponding basic response framework from the pre-stored strategy library; Based on the scene attribute information of the monitoring point that triggered this warning, the elements in the basic response framework are instantiated and populated to generate detailed response guidelines containing specific operation instructions; The risk level, detailed response guidelines, associated monitoring point identifiers, real-time gas concentration values, and concentration change trend levels are combined and assembled into structured early warning information. According to the notification rules set in the scene attribute information, the assembled early warning information is sent to one or more designated user terminals.

[0011] The present invention also provides a wide-area wireless gas risk early warning system, comprising: The data acquisition unit is used to continuously collect gas concentration data using gas detectors deployed in the monitoring area, and to send the gas concentration data to the gas alarm controller via a bus communication link. The data comparison unit is used by the gas alarm controller to compare the local warning threshold with the gas concentration data. If the local warning threshold is exceeded, the local audible and visual alarm is activated, and alarm status information containing the concentration value is generated. The data uploading unit is used to send the gas concentration data and the alarm status information to the cloud platform via a network access device; The analysis and early warning unit is used to receive data from the cloud platform, determine the concentration change trend of the corresponding gas detector based on the gas concentration data, perform risk analysis by integrating the concentration change trend, the current gas concentration value and the corresponding scene attribute information, generate early warning information including risk level and response guidance, and push the early warning information to the corresponding user terminal.

[0012] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described wide-area wireless gas risk early warning method.

[0013] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described wide-area wireless gas risk early warning method.

[0014] The wide-area wireless gas risk early warning method, system, device, and medium provided by this invention have the following beneficial effects: The wide-area wireless gas risk early warning method and system provided by this invention effectively solves the core problems of traditional monitoring systems, such as response delay, crude early warning, and insufficient decision support, by constructing a two-layer architecture of "local rapid linkage + cloud intelligent analysis". The local controller implements audible and visual alarms and forced linkage (such as valve closure) based on multi-level thresholds, ensuring instantaneous physical intervention in critical situations. The cloud platform, by integrating real-time concentration, trend, and scene attribute information, performs dynamic risk assessment, achieving a leap from "alarm after exceeding the standard" to "trend prediction and graded early warning". This invention can output differentiated risk levels and specific operational guidelines based on different location types (such as restaurant kitchens and school laboratories), and accurately push these guidelines to relevant responsible persons through intelligent rules, improving the targeting of early warnings and the efficiency of emergency response. Simultaneously, the front-end detector can adaptively adjust the sampling frequency according to the concentration, combined with the network's priority transmission mechanism for alarm data, optimizing the overall system energy consumption and communication resource utilization while ensuring the real-time nature of key data. This provides a feasible technical path for long-term, reliable, and intelligent monitoring of a large number of dispersed nodes over a wide area. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a wide-area wireless gas risk early warning method according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a wide-area wireless gas risk early warning system according to an embodiment of the present invention; Figure 3This is a structural block diagram of the bus-based system construction and networking of a wide-area wireless gas risk early warning method according to an embodiment of the present invention; Figure 4 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0016] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] Reference Figure 1 The diagram below illustrates a process for a wide-area wireless gas risk early warning method proposed in this invention, comprising the following steps: S1, continuously collect gas concentration data using gas detectors deployed in the monitoring area, and send the gas concentration data to the gas alarm controller via a bus communication link; S2, the gas alarm controller compares the local warning threshold with the gas concentration data. If the local warning threshold is exceeded, the local audible and visual alarm is activated, and alarm status information containing the concentration value is generated. S3, the gas concentration data and the alarm status information are sent to the cloud platform via the network access device; S4, the cloud platform receives data, determines the concentration change trend of the corresponding gas detector based on the gas concentration data, integrates the concentration change trend, the current gas concentration value and the corresponding scene attribute information to perform risk analysis, generates early warning information including risk level and response guidance, and pushes the early warning information to the corresponding user terminal.

[0019] In one embodiment, for step S1, The steps of continuously collecting gas concentration data using gas detectors deployed within the monitoring area and transmitting the gas concentration data to a gas alarm controller via a bus communication link include: Gas concentration data is continuously collected by gas detectors deployed at multiple monitoring points at a preset basic sampling frequency; When the gas concentration data collected by one or more gas detectors exceeds the preset attention threshold, the sampling frequency of the corresponding gas detector is switched to an enhanced sampling frequency that is higher than the basic sampling frequency. The gas detector uses the enhanced sampling frequency to collect gas concentration data and sends it to the gas alarm controller via a bus communication link.

[0020] In its implementation, step S1 aims to address the difficulty in balancing energy efficiency and emergency response in traditional fixed-frequency data acquisition modes, as well as the potential link congestion caused by sudden high-priority data in bus communication. Specifically, the gas detector is pre-configured with a basic sampling frequency (e.g., once per minute), which is sufficient for periodic inspections of the monitoring environment under normal conditions, while maintaining low device power consumption and bus communication load. Simultaneously, the system sets a preset attention threshold, lower than the local early warning threshold required for subsequent alarms. This aims to define a "risk attention zone" to trigger early adaptive adjustments to the acquisition strategy.

[0021] When the concentration data collected by a gas detector deployed at a monitoring point exceeds the preset threshold for concern for the first time, the microprocessor built into the corresponding detector will automatically execute control logic to switch its operating mode from routine inspection to enhanced monitoring. Specifically, this means increasing the sampling frequency from the basic once per minute to an enhanced sampling frequency (e.g., once per second or once every 5 seconds). This frequency switch is instantaneous and localized, independent of controller commands, thus achieving the fastest response to the nascent stage of potential risks. With the enhanced sampling frequency, the detector can capture subtle changes and rapid upward trends in gas concentration over a short period, providing a high-temporal-resolution data foundation for subsequent trend analysis, thereby overcoming the potential for missing key inflection points at the basic frequency. When the detector exceeding the preset threshold continues to collect data at the enhanced sampling frequency, and finds that the gas concentration data collected multiple times (e.g., 10 consecutive samples) has fallen back below the preset threshold for concern, and maintains this safe state for a preset stable duration (e.g., 3 minutes), the system determines that the current local risk has been eliminated or effectively controlled. At this point, the gas detector will automatically switch its sampling frequency from the enhanced sampling frequency back to the original basic sampling frequency. Through the frequency reduction and recovery mechanism, the system can dynamically adjust resource allocation according to the real-time risk status, avoiding unnecessary high-power, high-load operation even after the risk has subsided. This higher-density gas concentration data, acquired at the enhanced sampling frequency, is sent to the gas alarm controller via a bus communication link (e.g., a bus network using the RS-485 standard). Preferably, the bus communication design supports a mechanism combining periodic data reporting and event-triggered reporting. In enhanced monitoring mode, the detector not only reports data from each sampling point but also attaches an identifier indicating that the current data originates from the "enhanced sampling" mode, enabling the controller and subsequent cloud platform to identify the special nature and importance of the data stream. This collaborative mechanism of "threshold triggering - frequency adaptation - data tagging" constitutes an intelligent upgrade of the front-end perception layer, ensuring low-power operation of the system during most safe periods, while seamlessly transitioning to high-precision monitoring when risks first emerge, laying a solid data foundation for the timeliness and accuracy of the entire early warning chain. This design is significantly different from existing technologies that either fix a high sampling rate, leading to resource waste, or fix a low sampling rate, leading to response lag.

[0022] In one embodiment, for step S2, The gas alarm controller compares the local warning threshold with the gas concentration data. If the local warning threshold is exceeded, it activates a local audible and visual alarm and generates alarm status information containing the concentration value. This process includes: The gas alarm controller is equipped with a first warning threshold and a second warning threshold, wherein the second warning threshold is greater than the first warning threshold. When the gas concentration data exceeds the first warning threshold and is lower than the second warning threshold, the gas alarm controller activates the first level of local audible and visual alarm. When the gas concentration data exceeds the second warning threshold, the gas alarm controller activates the second level of local audible and visual alarm and sends a forced control command to the emergency shut-off valve or fan connected to the gas alarm controller. Upon activating any alarm level, the gas alarm controller generates alarm status information including the current alarm level, real-time concentration value, and detector identifier.

[0023] In its implementation, the gas alarm controller internally presets a first warning threshold and a second warning threshold, with the second warning threshold being numerically greater than the first. This dual-threshold mechanism distinguishes between "pre-warning" and "emergency action" for risks, as gas leaks or accumulation events typically have a developmental process rather than an instantaneous outburst. When the gas alarm controller receives and processes real-time concentration data from the detector and determines that it exceeds the first warning threshold but has not yet reached the second warning threshold, the controller determines that the site has entered a Level 1 risk state (or "warning state"). At this time, the controller immediately activates the first-level local audible and visual alarm. The technical characteristics of this level of alarm may include intermittent beeping and flashing lights at a specific frequency. Its main purpose is to issue a clear, explicit, but not the highest level of urgency warning signal to on-site personnel, indicating the presence of gas anomalies that require attention and preparation for manual inspection or preliminary handling, but not yet reaching the severity requiring immediate evacuation or automatic intervention. Once the real-time concentration data reaches or exceeds the higher second warning threshold, the controller determines that the site has entered a Level 2 risk state (or "accident alarm state"). At this time, the controller will simultaneously perform two key operations: first, activate the second-level local audible and visual alarm. This alarm level differs from the first level in sound intensity, light intensity, and / or alarm mode (e.g., changing to rapid continuous sound or high-frequency strong light), aiming to convey the highest level of urgency, forcibly attracting the attention of on-site personnel and instructing immediate evacuation. Secondly, and most substantially intervention-capable of this step, the controller automatically sends a forced control command to the emergency shut-off valve or exhaust fan directly connected to it via hardwire. This command is a zero-delay switch or digital signal that directly drives the shut-off valve to shut off the gas supply or starts the fan for powerful exhaust, thus physically intervening in the hazardous environment and preventing further escalation. Through local hard-linkage that "takes action when the threshold is exceeded," the system ensures the ultimate response speed in the most critical situations. Its reliability does not depend on network communication, forming a crucial safety baseline for the entire early warning system. Regardless of the alarm level triggered, the gas alarm controller simultaneously generates a structured alarm status information data packet containing several key data fields: the current alarm level (clearly distinguishing between a Level 1 warning and a Level 2 alert), the real-time concentration value (providing a specific quantitative risk level), and the detector identifier (used to accurately locate the risk source). This data packet informs the cloud platform that "an alarm has occurred," specifying "where," "at what severity," and "what the current accurate concentration is," providing a precise snapshot of the scene for cross-regional risk analysis, tracing the root cause of the alarm, and assessing the development of the event in the cloud. Step S2, through the rapid judgment and execution of the local controller, solves the network latency problem that may exist if solely relying on cloud responses, and avoids false alarms and disturbances caused by overly strict single threshold settings or insufficient responses caused by overly lenient ones through a hierarchical strategy.

[0024] In one embodiment, for step S3, The step of sending the gas concentration data and the alarm status information to the cloud platform via a network access device includes: The network access device receives data packets uploaded from one or more gas alarm controllers, the data packets containing at least gas concentration data and alarm status information; When alarm status information is present in one or more data packets, the network access device prioritizes scheduling transmission resources to send the corresponding data packet and the preset scene attribute information of the corresponding monitoring point. When there is no alarm status information in the data packet, the network access device sends gas concentration data and the preset scene attribute information of the corresponding monitoring point in sequence.

[0025] In specific implementation, refer to Figure 3 Step S3 involves data aggregation and intelligent uploading. A differentiated network transmission mechanism based on data priority and content awareness is designed: a network access device (such as an industrial protocol gateway) located at the boundary between the fieldbus network and the wide area internet acts as a converter between physical protocols and data formats, as well as an intelligent scheduler of network communication resources. This effectively solves the engineering problem of ensuring the timely delivery of critical alarm information while simultaneously transmitting massive amounts of routine monitoring data under limited uplink bandwidth. Specifically, the network access device continuously listens for and receives data packets uploaded from one or more downstream gas alarm controllers via a bus (such as RS-485). This data packet has a standardized structure, and its payload encapsulates at least the core gas concentration data (timestamp, concentration value) and possible alarm status information (such as the structured information generated in step S2, containing alarm level, concentration value, and detector identifier). After receiving the data packet, the network access device performs content parsing and priority judgment. Its core logic is as follows: Prioritizing alarm data transmission: When parsing reveals alarm status information in one or more data packets, the device immediately determines that the data stream belongs to a high-priority event stream. At this time, the device will activate its built-in priority scheduling module to prioritize transmission resources. Specifically, it will immediately interrupt or postpone the currently ongoing regular data transmission queue, opening an independent, high-priority communication channel for the alarm data packet. Simultaneously, based on the detector or controller identifier in the alarm data packet, the device will quickly index and associate the corresponding monitoring point's preset scene attribute information (such as location type "small restaurant kitchen," specific address "XX Road XX Number," contact information of the person in charge, building structural characteristics, etc.) from its local storage or associated configuration database. The network access device binds and encapsulates the alarm data packet with the retrieved scene attribute information, forming an enhanced alarm data unit, and immediately sends it to the cloud platform via a wireless network (such as 4G / 5G, NB-IoT) or Ethernet uplink. Through the pipeline operation of "instant identification - resource preemption - information association - priority transmission," key alarm information containing the complete on-site context is delivered to the cloud with the shortest communication latency, gaining valuable time for initiating remote emergency response.

[0026] For the orderly and efficient transmission of routine monitoring data: For the vast majority of routine data packets containing only periodic gas concentration data and no alarm status information, the network access device classifies them as low-priority background streams. The device employs a buffer queue mechanism to arrange these data packets sequentially (e.g., first-in, first-out), and sends them in batches and in an orderly manner when network bandwidth is available or within a preset time window. Similarly, when sending each routine concentration data packet, the device also uploads it along with the preset scene attribute information of its corresponding monitoring point. This processing method avoids interference with high-priority alarm channels while ensuring that historical concentration data from all monitoring points is continuously and completely recorded in the cloud, providing a data foundation for long-term trend analysis, equipment health assessment, and risk assessment model optimization.

[0027] Step S3 improves the reliability and efficiency of the entire system from a communication perspective, ensuring rapid and direct transmission of alarm information in emergencies. This overcomes the shortcomings of traditional polling or equal transmission modes, where alarm information may be delayed due to blockage by regular data. Simultaneously, this strategy optimizes network bandwidth utilization, smoothly completing the transmission of large volumes of daily monitoring information during non-emergency periods. This aims to demonstrate the optimization of the IoT data upload process, enabling the system to meet both the immediate requirements of emergencies and the continuous needs of routine monitoring, thus establishing an efficient and reliable data connection between rapid on-site response and cloud-based intelligent analysis.

[0028] In one embodiment, for step S4, The step of the cloud platform receiving data and determining the concentration change trend of the corresponding gas detector based on the gas concentration data includes: A circular cache queue is allocated to each gas detector using a cloud platform to store multiple historical gas concentration data reported by each gas detector in chronological order. After receiving new gas concentration data, the cloud platform combines the historical gas concentration data stored in the data cache queue and uses a trend analysis algorithm to calculate a trend value that characterizes the direction of gas concentration change in the detector. The cloud platform compares the calculated trend value with multiple preset trend classification thresholds to determine the concentration change trend level of the corresponding gas detector.

[0029] In implementation, the cloud platform sets up an independent data structure, namely a circular cache queue, for each gas detector connected to the system. Logically, this queue is a fixed-length First-In-First-Out (FIFO) storage area, designed to continuously store multiple historical gas concentration data points recently reported by the detector in strict chronological order. The circular structure aims to efficiently utilize storage space and achieve automatic rolling updates of data: when the queue is full, the latest data automatically overwrites the oldest data, thus always preserving the concentration history within the most recent key time window (e.g., the past 30 minutes or 100 sampling points). This ensures that the data relied upon for trend analysis is always up-to-date and continuous, avoiding resource waste caused by storing infinitely growing historical data and highlighting the focus on recent dynamics. When the cloud platform receives new gas concentration data reported by a detector, the trend analysis process is triggered. The platform retrieves the stored historical gas concentration data from the circular cache queue corresponding to that detector, combining these historical data points with the newly received data points to form a complete and up-to-date time series. The platform uses its built-in trend analysis algorithm to process this time series: by mathematically modeling and analyzing the distribution characteristics of data points changing over time, it calculates a quantitative indicator characterizing the direction and rate of recent changes in the gas concentration of the detector, i.e., a trend value. For example, this trend value can be a slope coefficient; a positive value indicates an upward trend in concentration, with the magnitude reflecting the intensity of the increase; a value near zero indicates stability; and a negative value indicates a decrease in concentration. Through this calculation, the system transforms the originally discrete concentration readings into a continuous trend signal that intuitively reflects "how the situation is developing." To standardize the understanding and subsequent logical judgment of the calculated continuous trend value, the cloud platform introduces several preset trend grading thresholds. These thresholds divide the continuous trend value range into several discrete trend level intervals. For example, thresholds can be set to divide the trend value into multiple levels from "rapid decline," "slow decline," "stable," "slow rise," "rapid rise" to "sharp rise." By comparing the calculated actual trend value with these trend grading thresholds, the cloud platform can determine the specific concentration change trend level currently occupied by the detector. This embodiment transforms vague trend perception into precise, programmable trend level determination, thereby enabling continuous and automated tracking of the situation at each monitoring point through cyclic caching and real-time calculation. The division of trend levels provides standardized input for subsequent steps (such as risk matrix analysis), enabling the system to distinguish between two distinctly different danger modes: "slow concentration accumulation" and "explosive rapid growth," thus providing key decision-making basis for adopting differentiated early warning and response strategies.

[0030] In one embodiment, the step of performing risk analysis by integrating the concentration change trend, the current gas concentration value, and the corresponding scene attribute information includes: Based on the location type in the scene attribute information, call the pre-configured risk assessment matrix that matches the corresponding location type; The concentration range to which the current gas concentration value belongs, and the level of the concentration change trend, are used as indexes and input into the risk assessment matrix; The cloud platform outputs a comprehensive risk index through a risk assessment matrix, and then maps the risk level based on the comprehensive risk index.

[0031] In its implementation, step S4 employs an intelligent decision-making model based on multi-dimensional information fusion and matrix-based evaluation to conduct risk analysis. This aims to overcome the limitations of traditional gas alarm systems that rely solely on fixed concentration thresholds for binary "yes / no" judgments. By introducing two key dimensions—scene attribute information and concentration change trends—it constructs a dynamic risk assessment framework capable of understanding "where it occurs" and "how it is developing," thereby achieving a fundamental improvement from "uniform alarms" to "scenario-based and trend-based early warnings." Specifically, when initiating risk analysis, the cloud platform first parses the scene attribute information obtained from the data and extracts key fields: location type (e.g., "kitchen of a small restaurant," "laboratory in a primary school," "kitchen of a small hotel," or "workshop of a small manufacturing enterprise"). Different types of locations exhibit significant differences in the types of combustible / toxic gases, background normal concentrations, ventilation conditions, personnel density, fire load, and the severity of potential accident consequences. Therefore, this invention adopts a differentiated assessment strategy: the platform, based on the identified location type, retrieves a precisely matched risk assessment matrix from a pre-built rule base. This matrix is ​​a predefined and configured two-dimensional decision table based on a large amount of historical accident data, industry safety standards, and expert experience. Its rows and columns represent different levels of concentration and trend states, respectively, while each cell stores a pre-calculated comprehensive risk index. For example, for a high-risk and sensitive scenario like a small restaurant kitchen, the matrix might assign a higher risk index to the combination of "medium concentration" and "rapidly rising" trends; while for a well-ventilated small manufacturing warehouse, the same concentration and trend combination might correspond to a relatively lower risk index. After determining the applicable assessment matrix, the cloud platform uses two dynamic indicators obtained from real-time analysis as query inputs: first, the current gas concentration value, which the platform determines based on multiple preset concentration ranges (such as "low," "medium," "high," and "extremely high"); and second, the concentration change trend level calculated in step S4 (such as "stable," "slowly rising," and "rapidly rising"). The system uses the concentration range and trend level as a set of two-dimensional indexes and inputs them into the invoked risk assessment matrix for searching. This operation is similar to locating a point on a coordinate graph: the intersection of the horizontal axis (trend level) and the vertical axis (concentration range), corresponding to a specific cell in the matrix. The value stored in this cell is the comprehensive risk index output by the system through matrix operations. This index is a normalized quantitative score (e.g., ranging from 0 to 100), comprehensively reflecting the overall risk level determined by both the "current severity" and the "future rate of deterioration" in the current specific context. A "high concentration + stable" state and a "medium concentration + rapidly rising" state may calculate similar high-risk indices, demonstrating the model's sensitivity to the dynamic evolution of risk.The cloud platform uses preset mapping rules (which define differentiated index ranges based on different location types. For example, for high-risk sensitive locations like small restaurant kitchens, the mapping rules set stricter thresholds to make the system more "sensitive" to risks and trigger higher-level warnings earlier; while for low-risk sensitive locations like well-ventilated warehouses, relatively lenient thresholds are set to avoid over-warning) to map the calculated comprehensive risk index to several discrete risk levels (such as "low risk," "moderate risk," "relatively high risk," and "high risk"), resulting in a final, easily understood, and operable qualitative risk conclusion. This embodiment transforms the complex risk assessment process into a rapid and repeatable calculation process based on a scenario-based knowledge base (matrix) and standardized inputs (concentration, trend), improving the scientific rigor and accuracy of risk assessment. This gives the warning system "contextual awareness" and "predictability," enabling it to provide more accurate and appropriate warning levels for specific risk patterns and accident evolution patterns in different locations.

[0032] In one embodiment, the step of generating early warning information containing risk level and response guidelines, and pushing the early warning information to the corresponding user terminal, includes: Based on the determined risk level, select the corresponding basic response framework from the pre-stored strategy library; Based on the scene attribute information of the monitoring point that triggered this warning, the elements in the basic response framework are instantiated and populated to generate detailed response guidelines containing specific operation instructions; The risk level, detailed response guidelines, associated monitoring point identifiers, real-time gas concentration values, and concentration change trend levels are combined and assembled into structured early warning information. According to the notification rules set in the scene attribute information, the assembled early warning information is sent to one or more designated user terminals.

[0033] In practice, after completing risk analysis and determining the risk level, the cloud platform selects a corresponding basic response framework from a pre-stored strategy library based on that level. This framework is a standardized response template for different risk levels. For example, for a "higher risk" level, the basic framework might include general action items such as "on-site inspection," "enhanced ventilation," and "prepared evacuation." However, to generate truly actionable instructions, the platform will instantiate and populate the above basic framework by combining the scenario attribute information of the monitoring point that triggered the warning. For example, combining information such as "venue type: small restaurant kitchen" and "specific address: XX Road, XX No. 1, kitchen," the system will specifically fill in the general item "enhanced ventilation" as "immediately start the exhaust fan located on the east side of the kitchen (equipment number: FAN-001)"; and fill in "prepared evacuation" as "guide customers in the lobby and private rooms to evacuate through the main entrance and safety exits." Through this process, a detailed response guide containing specific operational instructions, contact persons, contact numbers, equipment numbers, and other details is generated, enabling the recipient to obtain a practical action plan. The platform combines risk levels, detailed response guidelines, and key data such as associated monitoring point identifiers, real-time gas concentration values, and concentration change trend levels—all used as the basis for analysis—into a structured early warning information data package. This data package has a standard format, facilitating parsing and display by various terminals (such as mobile apps, web backends, and SMS gateways). The system precisely pushes notifications based on the notification rules set in the scenario attribute information, defining one or more designated user terminals to which early warning information should be sent under different risk levels and time periods. For example, for "general risk," the rule might be set to only notify the safety officer's mobile app at that location; for "high risk," the rule might be expanded to simultaneously notify the location manager's and area safety supervisor's apps, triggering SMS and telephone voice calls. This rule-based differentiated notification mechanism ensures that the appropriate level of early warning information is delivered to the responsible party most in need of action at the right time, avoiding information overload and ensuring that critical early warnings are received and processed promptly. This embodiment completes a closed loop from risk perception to action delivery. Through intelligent and personalized information generation and precise push, the original concentration data is transformed into decision support information that drives effective emergency response, realizing the early warning value of the technical solution of this invention and improving the level of safety management.

[0034] Reference Figure 2 Here is a structural block diagram of a wide-area wireless gas risk early warning system according to an embodiment of the present invention, comprising: The data acquisition unit is used to continuously collect gas concentration data using gas detectors deployed in the monitoring area, and to send the gas concentration data to the gas alarm controller via a bus communication link. The data comparison unit is used by the gas alarm controller to compare the local warning threshold with the gas concentration data. If the local warning threshold is exceeded, the local audible and visual alarm is activated, and alarm status information containing the concentration value is generated. The data uploading unit is used to send the gas concentration data and the alarm status information to the cloud platform via a network access device; The analysis and early warning unit is used to receive data from the cloud platform, determine the concentration change trend of the corresponding gas detector based on the gas concentration data, perform risk analysis by integrating the concentration change trend, the current gas concentration value and the corresponding scene attribute information, generate early warning information including risk level and response guidance, and push the early warning information to the corresponding user terminal.

[0035] For the specific implementation of each unit in the above device example, please refer to the method embodiments described above, and will not be repeated here.

[0036] Reference Figure 4 This invention also provides a computer device, which can be a server, and its internal structure can be as follows: Figure 4 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0037] Those skilled in the art will understand that Figure 4 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0038] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0039] In summary, this invention utilizes gas detectors deployed within the monitoring area to continuously collect gas concentration data, which is then transmitted to a gas alarm controller via a bus communication link. The gas alarm controller compares the local warning threshold with the gas concentration data; if the threshold is exceeded, a local audible and visual alarm is activated, and alarm status information containing the concentration value is generated. The gas concentration data and alarm status information are then transmitted to a cloud platform via a network access device. The cloud platform receives the data, determines the concentration change trend of the corresponding gas detector based on the gas concentration data, and performs risk analysis by integrating the concentration change trend, the current gas concentration value, and corresponding scene attribute information. It generates warning information containing risk levels and response guidelines, and pushes the warning information to the corresponding user terminals. This achieves an upgrade in gas risk management from post-event alarms to pre-event dynamic warnings and from single threshold responses to scenario-based intelligent assessment at widely distributed monitoring points, aiming to construct a three-dimensional security protection system that integrates local rapid response and cloud-based intelligent decision-making collaboration.

[0040] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0041] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0042] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A wide-area wireless gas risk early warning method, characterized in that, Includes the following steps: Gas concentration data is continuously collected using gas detectors deployed within the monitoring area, and the gas concentration data is transmitted to the gas alarm controller via a bus communication link. The gas alarm controller compares the local warning threshold with the gas concentration data. If the local warning threshold is exceeded, the local audible and visual alarm is activated, and alarm status information containing the concentration value is generated. The gas concentration data and the alarm status information are sent to the cloud platform via a network access device; The cloud platform receives data, determines the concentration change trend of the corresponding gas detector based on the gas concentration data, integrates the concentration change trend, the current gas concentration value and the corresponding scene attribute information to perform risk analysis, generates early warning information including risk level and response guidance, and pushes the early warning information to the corresponding user terminal.

2. The wide-area wireless gas risk early warning method according to claim 1, characterized in that, The step of continuously collecting gas concentration data using gas detectors deployed within the monitoring area and transmitting the gas concentration data to the gas alarm controller via a bus communication link includes: Gas concentration data is continuously collected by gas detectors deployed at multiple monitoring points at a preset basic sampling frequency; When the gas concentration data collected by one or more gas detectors exceeds the preset attention threshold, the sampling frequency of the corresponding gas detector is switched to an enhanced sampling frequency that is higher than the basic sampling frequency. The gas detector uses the enhanced sampling frequency to collect gas concentration data and sends it to the gas alarm controller via a bus communication link.

3. The wide-area wireless gas risk early warning method according to claim 1, characterized in that, The gas alarm controller compares the local warning threshold with the gas concentration data. If the local warning threshold is exceeded, it activates a local audible and visual alarm and generates alarm status information containing the concentration value. This process includes: The gas alarm controller is equipped with a first warning threshold and a second warning threshold, wherein the second warning threshold is greater than the first warning threshold. When the gas concentration data exceeds the first warning threshold and is lower than the second warning threshold, the gas alarm controller activates the first level of local audible and visual alarm. When the gas concentration data exceeds the second warning threshold, the gas alarm controller activates the second level of local audible and visual alarm and sends a forced control command to the emergency shut-off valve or fan connected to the gas alarm controller. Upon activating any alarm level, the gas alarm controller generates alarm status information including the current alarm level, real-time concentration value, and detector identifier.

4. The wide-area wireless gas risk early warning method according to claim 1, characterized in that, The step of sending the gas concentration data and the alarm status information to the cloud platform via a network access device includes: The network access device receives data packets uploaded from one or more gas alarm controllers, the data packets containing at least gas concentration data and alarm status information; When alarm status information is present in one or more data packets, the network access device prioritizes scheduling transmission resources to send the corresponding data packet and the preset scene attribute information of the corresponding monitoring point. When there is no alarm status information in the data packet, the network access device sends gas concentration data and the preset scene attribute information of the corresponding monitoring point in sequence.

5. The wide-area wireless gas risk early warning method according to claim 1, characterized in that, The step of the cloud platform receiving data and determining the concentration change trend of the corresponding gas detector based on the gas concentration data includes: A circular cache queue is allocated to each gas detector using a cloud platform to store multiple historical gas concentration data reported by each gas detector in chronological order. After receiving new gas concentration data, the cloud platform combines the historical gas concentration data stored in the data cache queue and uses a trend analysis algorithm to calculate a trend value that characterizes the direction of gas concentration change in the detector. The cloud platform compares the calculated trend value with multiple preset trend classification thresholds to determine the concentration change trend level of the corresponding gas detector.

6. The wide-area wireless gas risk early warning method according to claim 5, characterized in that, The step of performing risk analysis by integrating the concentration change trend, current gas concentration value, and corresponding scenario attribute information includes: Based on the location type in the scene attribute information, call the pre-configured risk assessment matrix that matches the corresponding location type; The concentration range to which the current gas concentration value belongs, and the level of the concentration change trend, are used as indexes and input into the risk assessment matrix; The cloud platform outputs a comprehensive risk index through a risk assessment matrix, and then maps the risk level based on the comprehensive risk index.

7. The wide-area wireless gas risk early warning method according to claim 1 or 6, characterized in that, The step of generating early warning information containing risk levels and response guidelines, and pushing the early warning information to the corresponding user terminals, includes: Based on the determined risk level, select the corresponding basic response framework from the pre-stored strategy library; Based on the scene attribute information of the monitoring point that triggered this warning, the elements in the basic response framework are instantiated and populated to generate detailed response guidelines containing specific operation instructions; The risk level, detailed response guidelines, associated monitoring point identifiers, real-time gas concentration values, and concentration change trend levels are combined and assembled into structured early warning information. According to the notification rules set in the scene attribute information, the assembled early warning information is sent to one or more designated user terminals.

8. A wide-area wireless gas risk early warning system, characterized in that, include: The data acquisition unit is used to continuously collect gas concentration data using gas detectors deployed in the monitoring area, and to send the gas concentration data to the gas alarm controller via a bus communication link. The data comparison unit is used by the gas alarm controller to compare the local warning threshold with the gas concentration data. If the local warning threshold is exceeded, the local audible and visual alarm is activated, and alarm status information containing the concentration value is generated. The data uploading unit is used to send the gas concentration data and the alarm status information to the cloud platform via a network access device; The analysis and early warning unit is used to receive data from the cloud platform, determine the concentration change trend of the corresponding gas detector based on the gas concentration data, perform risk analysis by integrating the concentration change trend, the current gas concentration value and the corresponding scene attribute information, generate early warning information including risk level and response guidance, and push the early warning information to the corresponding user terminal.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the wide-area wireless gas risk early warning method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the wide-area wireless gas risk early warning method according to any one of claims 1 to 7.

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