Liquefied petroleum gas safe gas supply system based on Internet of Things

Abstract

The invention provides a safe liquefied petroleum gas supply system based on the Internet of Things. The safe liquefied petroleum gas supply system comprises a multi-gas-cylinder-set cabinet, a sensor array, an Internet of Things control terminal, a cloud management platform and an execution mechanism. The sensor array is mounted on the multi-gas-cylinder group cabinet body and is connected with the Internet of Things control terminal; the sensor array is used for monitoring state information of the multi-gas-cylinder group cabinet body and the liquefied petroleum gas cylinders placed in the multi-gas-cylinder group cabinet body and sending the state information to the Internet of Things control terminal; and the Internet of Things control terminal is connected with the cloud management platform, the Internet of Things control terminal controls and is connected with the execution mechanism, and the Internet of Things control terminal generates an execution instruction based on the state information and controls the execution mechanism to execute. According to the liquefied petroleum gas safety gas supply system, the Internet of Things system is introduced into the liquefied petroleum gas safety gas supply system, the gas supply industry state is reconstructed, discrete steel cylinders are changed into schedulable energy units, passive first-aid repair is changed into predictive protection, regular inspection is replaced with sensor array real-time monitoring, and the response speed is increased.

Description

Technical Field

[0001] The present invention relates to the field of gas supply technology, and in particular to an Internet of Things (IoT) liquefied petroleum gas safety gas supply system, which is suitable for scenarios such as transitional gas supply before pipeline gas coverage, catering concentrated areas, and industrial backup gas sources. Background Art

[0002] The liquefied petroleum gas supply system is a complex engineering system that converts liquid petroleum gas into gas and safely delivers it to users through gas storage, gasification, pressure regulation, transportation and terminal use.

[0003] At present, the traditional gas supply mode is based on the needs of gas-using units. After the gas supply unit loads the gas cylinder into the transportation vehicle, it transports the gas cylinder to the gas-using unit according to the gas supply plan. However, the current traditional gas supply mode has systemic defects such as strong manual dependence and delayed response. Summary of the Invention

[0004] The purpose of the present invention is to provide an Internet of Things liquefied petroleum gas safety gas supply system to solve the problems raised in the above background technology that the current traditional gas supply mode has systemic defects such as strong manual dependence and delayed response.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An Internet of Things (IoT) liquefied petroleum gas safety supply system, comprising: a multi-gas cylinder cabinet, a sensor array, an IoT control terminal, a cloud management platform, and an actuator;

[0007] The sensor array is mounted on the multi-gas cylinder cabinet and connected to the Internet of Things control terminal; the sensor array is used to monitor the status information of the multi-gas cylinder cabinet and the liquefied petroleum gas cylinders placed therein, and send the status information to the Internet of Things control terminal;

[0008] The Internet of Things control terminal is connected to the cloud management platform, the Internet of Things control terminal controls the connection to the execution mechanism, and the Internet of Things control terminal generates an execution instruction based on the status information and controls the execution of the execution mechanism.

[0009] Optionally, the sensor array includes: a pressure sensor, a liquid level / weighing sensor, a temperature sensor, a leakage detection sensor, a cabinet door status sensor, and a monitoring component;

[0010] The pressure sensor is used to detect the pressure of the gas supply pipeline to the gas terminal; the liquid level / weight sensor is used to detect the weight of the liquefied petroleum gas in the liquefied petroleum gas cylinder; the temperature sensor is used to detect the temperature of the environment in which the liquefied petroleum gas cylinder is located; the leak detection sensor is used to detect whether the liquefied petroleum gas in the liquefied petroleum gas cylinder is leaking; the cabinet door status sensor is used to detect the cabinet door status of the multi-gas cylinder cabinet; the monitoring component is used to identify the operator's identity and the operator's operating specifications for the liquefied petroleum gas cylinder.

[0011] Optionally, the liquid level / weighing sensor detects the weight of the liquefied petroleum gas in the liquefied petroleum gas cylinder using a tilt compensation algorithm:

[0012]

[0013] Where W corrected is the measured value of the weighing sensor (kg), θ is the pitch angle of the cylinder, T weight is the pre-calibrated gas cylinder tare weight (kg), NW corrected is the net mass of liquefied gas after compensation (kg).

[0014] Optionally, the method further includes: predicting gas demand at the gas-consuming end and optimizing the transportation route of a cabinet with multiple gas cylinders.

[0015] Optionally, the gas demand prediction algorithm is:

[0016]

[0017] Where, f i (t) is the time characteristic (hour / week / holiday), weather characteristics, and historical gas consumption rate; w i is the feature weight obtained from model training; b is the bias term; σ is the sigmoid activation function;

[0018] Path optimization objective function:

[0019]

[0020] Where, d i is the distance of the i-th path (km); v i is the estimated vehicle speed under real-time road conditions (km / h);

[0021] q i is the vehicle's load at the i-th node (kg); Q is the vehicle's maximum load (kg); α and β are weight coefficients.

[0022] Optionally, the method further includes: utilizing blockchain storage technology to store gas usage data, and the cloud management platform connects to the ERP system of the gas-using enterprise to obtain the gas usage data.

[0023] Optionally, the method for storing gas usage data using blockchain storage technology is as follows:

[0024] In the formula, Block j Block j ;t i is the event timestamp; Data i For cylinder ID, operation type, sensor reading; Sig i Digital signature for IoT terminals.

[0025] Optionally, a pressure protection system corresponding to the pressure sensor is configured in the Internet of Things control terminal. The pressure protection system is used to control the actuator to open the relief valve of the liquefied petroleum gas cylinder when the gas pressure in the liquefied petroleum gas cylinder exceeds a predetermined threshold value.

[0026] Optionally, the pressure protection system meets:

[0027]

[0028] Where, P tank is the real-time pressure sensor reading; P rated is the rated working pressure; P release It is the trigger signal of the relief valve.

[0029] Optionally, the step monitoring component is used to identify the operator's identity and the operator's operating specifications for the LPG cylinder, using a violation detection model to achieve:

[0030]

[0031] Where A k is the feature vector of operation action k; A std is the standard operation feature library; γ k Action weight coefficient; τ is the alarm threshold.

[0032] Compared with the existing technology: This application provides an Internet of Things (IoT) liquefied petroleum gas safety gas supply system, comprising: a multi-gas cylinder cabinet, a sensor array, an IoT control terminal, a cloud management platform, and an actuator; the sensor array is installed on the multi-gas cylinder cabinet, and the sensor array is connected to the IoT control terminal; the sensor array is used to monitor the status information of the multi-gas cylinder cabinet and the liquefied petroleum gas cylinders placed therein, and send the status information to the IoT control terminal; the IoT control terminal is connected to the cloud management platform, and the IoT control terminal controls the actuator, and the IoT control terminal generates execution instructions based on the status information and controls the execution of the actuator. This application introduces the IoT system into the liquefied petroleum gas safety gas supply system, reconstructing the gas supply business model: turning discrete cylinders into dispatchable energy units, turning passive repairs into predictive protection, and using sensor arrays for real-time monitoring instead of regular inspections to improve response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a module connection diagram of an Internet of Things liquefied petroleum gas safety supply system. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0035] In addition, when an element in the present invention is referred to as being "fixed to" or "disposed on" another element, it may be directly on the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only implementation method.

[0036] The product provides IoT-based liquefied petroleum gas (LPG) safety gas supply devices for transitional gas supply before pipeline gas coverage in newly built residential areas, catering concentrated areas, and industrial backup gas sources. The details are as follows:

[0037] The essence of IoT solutions goes beyond simple digitization; rather, they reshape the gas supply industry: transforming discrete cylinders into dispatchable energy units and transitioning reactive repairs to predictive protection. First, it represents a qualitative shift in safety, replacing periodic inspections with real-time monitoring. Second, it upgrades economic models, reducing idle cycles across the supply chain through data sharing. Third, it enhances policy compliance, meeting increasingly stringent gas traceability requirements. Leak response times have been improved from hours to seconds, a game-changing improvement for back-of-house safety.

[0038] See also Figure 1 An embodiment of the present invention provides an Internet of Things (IoT) liquefied petroleum gas safety gas supply system, comprising: a multi-gas cylinder cabinet, a sensor array, an IoT control terminal, a cloud management platform, and an actuator; the sensor array is installed on the multi-gas cylinder cabinet, and the sensor array is connected to the IoT control terminal; the sensor array is used to monitor the status information of the multi-gas cylinder cabinet and the liquefied petroleum gas cylinders placed therein, and send the status information to the IoT control terminal; the IoT control terminal is connected to the cloud management platform, and the IoT control terminal controls the actuator, generates an execution instruction based on the status information, and controls the actuator to execute.

[0039] In this embodiment, the sensor array includes: a pressure sensor, a liquid level / weighing sensor, a temperature sensor, a leakage detection sensor, a cabinet door status sensor, and a monitoring component; the pressure sensor is used to detect the pressure of the gas supply pipeline to the gas terminal; the liquid level / weight sensor is used to detect the weight of the liquefied petroleum gas in the liquefied petroleum gas cylinder; the temperature sensor is used to detect the temperature of the environment in which the liquefied petroleum gas cylinder is located; the leakage detection sensor is used to detect whether the liquefied petroleum gas in the liquefied petroleum gas cylinder is leaking; the cabinet door status sensor is used to detect the cabinet door status of the multi-gas cylinder group cabinet; the monitoring component is used to identify the operator's identity and the operator's operating specifications for the liquefied petroleum gas cylinder.

[0040] Furthermore, an IoT-based intelligent LPG cylinder group gas supply cabinet is used as the core gas supply unit. The system mainly includes: a multi-cylinder group cabinet (including the frame, cylinder position, pipeline valve system, leak alarm system, forced exhaust system, etc.), an IoT monitoring terminal (RTU / DTU / PLC, etc.), various sensors mentioned above (pressure, liquid level / weight, temperature, leak detector, cabinet door status, monitoring, etc.), actuators (emergency shut-off valve, exhaust fan control, etc.), communication equipment (4G / 5G / NB-IoT / LoRa wireless modules or wired network interfaces), a cloud monitoring platform and client (Web / App) management software, and supporting fire protection and security facilities (according to regulatory requirements).

[0041] In this embodiment, the application uses real-time gas concentration monitoring + multi-level linkage, sets up real-time monitoring combustible gas detectors, uses secondary alarms to automatically close valves + exhaust + platform push, and simultaneously pushes leakage alarms to fire protection / property / gas companies, automatically generates evacuation range maps and starts surrounding broadcasts; gas cylinder life cycle management: uses RFID / NFC electronic tags to bind cylinders, and the platform automatically intercepts the refilling of expired and scrapped bottles; uses monitoring components to monitor and identify identity and operations: monitors and identifies whether the gas cylinders are replaced by professional gas delivery personnel, and AI analysis of the operation process video (identification of valves not closed, illegal handling, etc.) Behavior), monitor the surrounding environment for dangerous factors (such as smoking and lighting fires) and alarm to drive them away; Environmental adaptive protection mechanism: automatic valve closing when temperature > 50℃, safety cut-off when pressure fluctuation > 20%, and vibration sensor triggering anti-theft alarm; Weighing sensor + liquid level algorithm: high-precision weighing module (±0.2% FS) + liquid level compensation algorithm (tilt calibration) greatly reduces inventory monitoring errors; Intelligent distribution prediction engine: AI prediction model based on gas consumption rate and calendar events (holidays / weather) generates the optimal distribution route 8 hours in advance; Blockchain evidence storage + automatic settlement: gas consumption data is uploaded The chain cannot be tampered with, and the API is connected to the enterprise ERP system to generate electronic bills; pressure sensor + relief valve: real-time collection of pressure sensor values, providing overpressure protection for downstream equipment, reducing the risk of explosion; thermal insulation layer + automatic temperature control: isolate the temperature inside the cabinet from the outdoor temperature, collect the temperature inside the cabinet in real time and automatically adjust the temperature inside the cabinet suitable for liquefied petroleum gas gasification, improving gas supply stability; automatic switching valve: based on the working principle of the automatic switching valve, the use speed of the liquefied gas in the use bottle and the spare bottle at both ends of the automatic switching valve is different, which can achieve uninterrupted gas supply when the pressure of the use bottle is insufficient and needs to be replaced; explosion relief port : The explosion pressure relief valve should be installed on the upper cover of the cabinet, and the pressure relief valve installed on the side wall of the cabinet should take measures to prevent the explosion pressure relief from causing harm to the surrounding public safety; the automatic switching valve + two-stage pressure regulating valve solves different pressure requirements; based on the working principle of the automatic switching valve, the automatic switching valve has a first-level pressure reducing function, and the two-stage pressure regulating valve is at the rear end of the automatic switching valve and at the front end of the combustion appliance with the same rated working pressure; for combustion appliances with different rated working pressures, the outlet pressure of the two-stage pressure regulating valve may be different, and the number of two-stage pressure regulators may be multiple. The two-stage pressure regulating valve should be at the rear end of the automatic switching valve and at the front end of the combustion appliance with the corresponding rated working pressure.

[0042] It should be noted that this application provides a blockchain-based LPG intelligent settlement system, including: a metering module (real-time collection of gas cylinder weight and delivery volume data), a blockchain evidence storage module (uploading operation data to the chain, including gas cylinder ID, filling volume, delivery time, and receipt information), an intelligent settlement engine (automatically generates settlement bills according to preset rules), and a payment interface module (connecting to banks / third-party payment platforms).

[0043] Furthermore, the specific method includes: the delivery person's terminal scans the RFID tag of the gas cylinder to obtain the current weight W1; S2: when the customer signs for the gas cylinder, the weight is weighed again and recorded W2 to calculate the net delivery volume NW corrected ; S3: Input ΔW, customer contract unit price, and promotion strategy into the smart contract; S4: The blockchain network generates an unalterable settlement certificate after 2-5 blocks are confirmed; S5: Automatically trigger bank transfer or prepaid account deduction.

[0044] In this embodiment, the goal of automated settlement is to automatically complete settlement based on delivery data and pre-set rules, reducing manual intervention. Settlement trigger conditions: 1. Successful delivery (customer signature confirmation); 2. Periodic settlement (e.g., for customers with monthly billing); 3. Prepaid top-up (instant settlement upon top-up arrival). Settlement rules: Actual delivery weight × unit price (unit price may vary by customer type, contract, etc.) - consideration of tiered pricing (e.g., discounts for orders exceeding a certain volume) - consideration of coupons, points, and other deductions.

[0045] In this implementation, by deeply integrating physical liquefied gas distribution data with blockchain evidence storage and intelligent settlement, a trusted energy supply chain system integrating "metering, evidence storage, and settlement" has been established. The system features full cylinder lifecycle tracking, automatic micropayments based on actual usage, and a transparent, regulator-friendly audit channel.

[0046] The technical solution of this application not only solves the settlement pain points of the traditional gas industry, but also provides an infrastructure for business model innovation in the energy Internet of Things, and can be extended to other special logistics fields such as industrial gases and hazardous chemicals.

[0047] In a specific embodiment, the liquid level / weighing sensor detects the weight of the liquefied petroleum gas in the liquefied petroleum gas cylinder using a tilt compensation algorithm:

[0048]

[0049] Where W corrected is the measured value of the weighing sensor (kg), θ is the pitch angle of the cylinder, T weight is the pre-calibrated gas cylinder tare weight (kg), NW corrected is the net mass of liquefied gas after compensation (kg).

[0050] In this embodiment, the algorithm aims to convert the measured gross weight (GW) into an accurate net weight (NW) and compensate for the influence of the tilt angle.

[0051] Basic calculation:

[0052] NW=GW

[0053] Tare Weight

[0054] Tare Weight is the weight of the empty bottle and needs to be calibrated and stored in advance.

[0055] Compensation target:

[0056] Goal 1 (Precise Mass): If only the precise mass NW is required and the Tare Weight is stable, then the effect of temperature on the weighing result itself (mass) is minimal and can be ignored. The essence of weighing is to measure mass.

[0057] Tilt compensation:

[0058] Problem: When a cylinder is tilted, the center of gravity of the liquid inside shifts, causing the force acting on the load cell (measured weight) to be less than the actual weight of the liquid (true weight), resulting in a negative deviation. The greater the tilt angle and the higher the liquid level, the greater the error.

[0059] Compensation goal: Based on the inclination information, correct the measured weight GW to obtain a weight GW_corrected that is closer to the cylinder's vertical state.

[0060] Compensation model (geometry based):

[0061] Measuring the tilt angle: Calculate the pitch angle θ (forward and backward tilt) and the roll angle φ (left and right tilt). Assuming the roll angle has a small or symmetrical effect, compensate primarily for θ.

[0062] The actual gravity GW_true = m*g. The measured value W_measured = GW_true*cos(β), where β is the angle between the gravity direction and the force direction of the sensor (perpendicular to the mounting plane). When the sensor platform is mounted horizontally with a single axis tilt (θ), β≈θ. Therefore:

[0063] W_measured = GW_true*cos(θ)

[0064] Compensated measurements:

[0065] GW_true≈W_measured / cos(θ)(small angle approximation cos(θ)≈1-θ 2 / 2, but when θ is large, it is more accurate to use 1 / cos(θ) directly).

[0066] NW_corrected=GW_corrected-TareWeight.

[0067] The NW_corrected obtained at this time is the current quality after compensating for the influence of the tilt angle.

[0068] In a specific embodiment, the method further includes: predicting gas demand at the gas-consuming end and optimizing the transportation route of a cabinet with multiple gas cylinders.

[0069] In this embodiment, an AI-based liquefied gas cylinder distribution optimization system is used; accurate prediction: predicting each customer's gas demand in the next 8 hours (whether it is about to run out); route optimization: generating the optimal delivery route based on predicted demand, customer geographic location, traffic conditions, vehicle load, etc.; dynamic adjustment: the system can respond to emergencies (such as temporary orders, traffic congestion, etc.) in real time and replan routes.

[0070] In a specific embodiment, the gas demand prediction algorithm is:

[0071]

[0072] Where, f i (t) is the time characteristic (hour / week / holiday), weather characteristics, and historical gas consumption rate; w i is the feature weight obtained from model training; b is the bias term; σ is the sigmoid activation function;

[0073] Path optimization objective function:

[0074]

[0075] Where, d i is the distance of the i-th path (km); v i is the estimated vehicle speed under real-time road conditions (km / h);

[0076] q i is the vehicle's load at the i-th node (kg); Q is the vehicle's maximum load (kg); α and β are weight coefficients.

[0077] In this embodiment, the system architecture of the above-mentioned route optimization and gas demand forecasting is as follows:

[0078] Data layer - prediction model - path optimization engine - output and execution

[0079] Data layer: collects and stores historical gas usage data, customer information, calendar events (holidays, weather), real-time traffic data, etc.; prediction model: predicts each customer's gas usage status (whether delivery is required) in the next 8 hours based on historical data and calendar events; route optimization engine: uses optimization algorithms to generate delivery routes based on prediction results and real-time constraints; output and execution: pushes the optimal route to the delivery person and supports real-time monitoring and adjustment.

[0080] Specifically: historical gas usage data: the filling volume of each gas cylinder, filling time, delivery time, customer gas usage habits (such as the catering industry consumes a lot of gas at meal times), etc.; customer information: customer location, gas cylinder specifications, gas usage type (household, catering, industry), etc.; calendar events: holidays (especially the catering industry increases gas consumption during holidays); weather (temperature affects gas consumption, rain and snow affect traffic and delivery efficiency); real-time data: traffic conditions, current vehicle location, remaining gas cylinder inventory, etc.; others: special customer requirements (such as delivery time window), road restrictions, etc.

[0081] Data processing:

[0082] Time characteristics: hour, day of the week, holiday, season, etc.

[0083] Weather characteristics: temperature, rainfall, snow amount, etc.

[0084] Customer characteristics: historical average gas consumption rate, gas consumption fluctuation rate, customer type, etc.

[0085] Data cleaning: processing missing values ​​and outliers (such as negative gas usage).

[0086] Data fusion: Link data from different sources by timestamp and customer ID.

[0087] Gas demand forecasting model

[0088] Objective: Predict each customer's gas usage status within the next 8 hours (whether gas will run out, i.e., whether gas delivery is needed); Model selection: Time series prediction + classification model; Time series prediction model (predicting remaining gas usage time): For each gas cylinder, establish a time series model (such as ARIMA, LSTM, Prophet) based on the historical gas usage rate.

[0089] Input: historical gas consumption rate, time characteristics, and weather characteristics.

[0090] Output: Predict the remaining gas volume (or remaining time) in the next 8 hours.

[0091] Classification model (determine whether delivery is required): Convert the problem into a binary classification problem (delivery required / not required).

[0092] Features: current remaining gas volume (or remaining time), predicted gas usage rate, customer type, whether it is a holiday, etc.

[0093] Model: XGBoost, LightGBM, or neural network.

[0094] Output: Probability value (probability of needing delivery). Set a threshold (e.g., probability > 0.7) to trigger a delivery request. Model training and updating: Use historical data to train the model and regularly (e.g., daily) update the model with new data.

[0095] Evaluation indicators: accuracy, recall rate, F1 value (especially for the "need delivery" category, the recall rate should be high to avoid customers being out of breath).

[0096] Further: Delivery route optimization input:

[0097] Forecast results: list of customers who need delivery and demand (number of gas cylinders).

[0098] Constraints: Vehicles: number of available vehicles, load limit (total weight of gas cylinders); Time: delivery time window (e.g., customer requirement 9:00-17:00), delivery completed within 8 hours; Geography: customer location, warehouse location, real-time road conditions (drive time matrix); Others: priority customers (e.g., hospitals), traffic control, etc.

[0099] Objectives: Minimize total driving distance / time, reduce idle mileage, and improve vehicle utilization; maximize customer satisfaction (such as on-time delivery and avoiding gas outages); balance the workload of delivery personnel; and avoid the risks brought by emergency delivery (such as night delivery).

[0100] In a specific embodiment, the method further includes: using blockchain storage technology to store gas usage data, and the cloud management platform connects to the ERP system of the gas-using enterprise to obtain the gas usage data.

[0101] In a specific embodiment, the method for storing gas usage data using blockchain storage technology is as follows:

[0102] In the formula, Block j Block j ;t i is the event timestamp; Data i For cylinder ID, operation type, sensor reading; Sig i Digital signature for IoT terminals.

[0103] In this embodiment, key operations and data are put on the chain to ensure that they cannot be tampered with and improve trust. The data that needs to be put on the chain include: 1. Gas cylinder status change records: filling, delivery, installation, recycling, maintenance, etc.; 2. Delivery transaction records: detailed information of each delivery (time, customer, gas cylinder ID, weight, etc.); 3. Settlement vouchers: payment vouchers generated by automatic settlement; 4. Abnormal events: such as leak alarms, overdue inspections, etc. Specifically, the data shown in the following table can be stored according to the trigger conditions:

[0104]

[0105] In a specific embodiment, a pressure protection system corresponding to the pressure sensor is configured in the Internet of Things control terminal. The pressure protection system is used to control the actuator to open the relief valve of the liquefied petroleum gas cylinder when the gas pressure in the liquefied petroleum gas cylinder exceeds a predetermined threshold value.

[0106] In one embodiment, the pressure protection system satisfies:

[0107]

[0108] Where, P tank is the real-time pressure sensor reading; P rated is the rated working pressure; P release It is the trigger signal of the relief valve.

[0109] In this embodiment, the relief valve is a safety warning device for pipelines transporting flammable and explosive gases. When the pressure at the control point exceeds a set value (bubble burst pressure), it releases a certain amount of gas. Applicable media include natural gas, manufactured gas, liquefied petroleum gas, and air. It can be used in city gate stations, regional pressure regulating stations, and various industrial and residential users.

[0110] In one embodiment, the step monitoring component is used to identify the operator's identity and the operator's operating specifications for the liquefied petroleum gas cylinder, using a violation detection model to achieve:

[0111]

[0112] Where A k is the feature vector of operation action k; A std is the standard operation feature library; γ k Action weight coefficient; τ is the alarm threshold.

[0113] In this embodiment, the intelligent monitoring system for liquefied gas cylinder replacement safety and multi-modal biometric recognition for professional gas delivery personnel are used to authenticate the identity of the gas delivery personnel:

[0114] 1. Face recognition, 2. Uniform detection, 3. RFID badge verification:

[0115] Dynamic behavior feature library

[0116] Feature Dimension Legal personnel characteristics Characteristics of illegal persons Tool Usage Special cart / anti-static gloves Manual transport / ordinary cart Operation process Close the valve first and then remove the pipe Direct hose removal Position track Gas cylinder area → operation area → loading and unloading area Random Move

[0117] Violation detection model architecture:

[0118]

[0119]

[0120] In this embodiment, a gas environment risk grading response system is provided, including: a multimodal sensor group (including a combustible gas detector, an infrared thermal imager, a sound recognition module, and a visual recognition camera); a risk analysis engine (which analyzes sensor data in real time and determines the risk level); a graded execution mechanism (including an emergency shut-off valve, an audible and visual alarm, a fire extinguishing device, and a directional sound wave transmitter); and a cloud-based collaborative module (which links the fire protection system, the property platform, and the supervision terminal).

[0121] The specific method is as follows:

[0122] Including steps:

[0123] S1: Real-time collection of environmental parameters (gas concentration, temperature, video, audio); S2: Determine the risk level (I / II / III) through a pre-trained model; S3: Trigger the corresponding action combination in the response measure library according to the level; S4: Record the disposal process data and generate a compliance report.

[0124] It should be noted that the risk determination model in step S2 includes:

[0125] Open flame detection: Temperature gradient analysis based on infrared thermal imaging (sensitivity ±2°C);

[0126] Leakage judgment: Combined with the rising rate of gas concentration (> 5% LEL / min will trigger an alarm);

[0127] Behavior recognition: Detect illegal movements of key points on the human body using the OpenPose algorithm.

[0128] In this embodiment, the present application is also configured with an environmental risk intelligent perception system, specifically:

[0129] Hierarchical response mechanism of intelligent warning and intervention system

[0130] The risk levels, trigger conditions, and response measures are as follows:

[0131] Level I, open flame / high-concentration gas leakage, activate the fire extinguishing system + cut off the gas source + evacuation broadcast + notify the fire department; Level II, valve not closed / smoking behavior, directional sound wave repellent + valve locking + on-site strong light warning; Level III, illegal transportation / unqualified personnel, voice warning + operation interruption + background manual review.

[0132] In the embodiment of the present invention, a "behavior-environment" two-dimensional safety assessment model is first created, based on the implicit risk prediction technology of thermal imaging and the multi-modal active intervention mechanism of sound, light and electricity. It complies with the GB / T 33215-2016 "Gas Cylinder Safety Monitoring System" standard. By converting professional operating specifications into AI-recognizable digital rules, it realizes the paradigm shift from passive monitoring to active protection, effectively solving the safety management and control problem of the "last meter" of liquefied gas operations.

[0133] According to one embodiment of the present invention, a server is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to call the instructions stored in the memory to execute the credit system data management method.

[0134] According to one embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the credit system data management method is implemented.

[0135] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0136] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0137] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. An Internet of Things liquefied petroleum gas safety gas supply system, characterized in that: include: Multi-gas cylinder cabinet, sensor array, IoT control terminal, cloud management platform, and actuator; The sensor array is mounted on the multi-gas cylinder cabinet and connected to the Internet of Things control terminal; the sensor array is used to monitor the status information of the multi-gas cylinder cabinet and the liquefied petroleum gas cylinders placed therein, and send the status information to the Internet of Things control terminal; The Internet of Things control terminal is connected to the cloud management platform, the Internet of Things control terminal controls the connection to the execution mechanism, and the Internet of Things control terminal generates an execution instruction based on the status information and controls the execution of the execution mechanism.

2. The Internet of Things liquefied petroleum gas safety gas supply system according to claim 1 is characterized in that: The sensor array includes: a pressure sensor, a liquid level / weighing sensor, a temperature sensor, a leakage detection sensor, a cabinet door status sensor, and a monitoring component; The pressure sensor is used to detect the pressure of the gas supply pipeline to the gas terminal; the liquid level / weight sensor is used to detect the weight of the liquefied petroleum gas in the liquefied petroleum gas cylinder; the temperature sensor is used to detect the temperature of the environment in which the liquefied petroleum gas cylinder is located; the leak detection sensor is used to detect whether the liquefied petroleum gas in the liquefied petroleum gas cylinder is leaking; the cabinet door status sensor is used to detect the cabinet door status of the multi-gas cylinder cabinet; the monitoring component is used to identify the operator's identity and the operator's operating specifications for the liquefied petroleum gas cylinder.

3. The Internet of Things liquefied petroleum gas safety gas supply system according to claim 2, characterized in that: The liquid level / weighing sensor detects the weight of the LPG in the LPG cylinder and adopts the tilt compensation algorithm: Where W corrected is the measured value of the weighing sensor (kg), θ is the pitch angle of the cylinder, T weight is the pre-calibrated gas cylinder tare weight (kg), NW corrected is the net mass of liquefied gas after compensation (kg).

4. The Internet of Things liquefied petroleum gas safety gas supply system according to claim 1, characterized in that: The method also includes: predicting gas demand at the gas-consuming end and optimizing the transportation route of a cabinet with multiple gas cylinders.

5. The Internet of Things liquefied petroleum gas safety gas supply system according to claim 4, characterized in that: The gas demand prediction algorithm is: Where, f i (t) is the time characteristic (hour / week / holiday), weather characteristics, and historical gas consumption rate; w i is the feature weight obtained from model training; b is the bias term; σ is the sigmoid activation function; Path optimization objective function: Where, d i is the distance of the i-th path (km); v i is the estimated vehicle speed under real-time road conditions (km / h); q i is the vehicle's load at the i-th node (kg); Q is the vehicle's maximum load (kg); α and β are weight coefficients.

6. The Internet of Things liquefied petroleum gas safety gas supply system according to claim 1, characterized in that: The method further includes: utilizing blockchain storage technology to store gas usage data, and the cloud management platform connects to the ERP system of the gas-using enterprise to obtain the gas usage data.

7. The Internet of Things liquefied petroleum gas safety gas supply system according to claim 6, characterized in that: Using blockchain storage technology, the method for storing gas usage data is as follows: In the formula, Block j Block j ;t i is the event timestamp; Data i For cylinder ID, operation type, sensor reading; Sig i Digital signature for IoT terminals.

8. The Internet of Things liquefied petroleum gas safety gas supply system according to claim 1, characterized in that: A pressure protection system corresponding to the pressure sensor is configured in the Internet of Things control terminal. The pressure protection system is used to control the actuator to open the relief valve of the liquefied petroleum gas cylinder when the gas pressure in the liquefied petroleum gas cylinder exceeds a predetermined threshold.

9. The Internet of Things liquefied petroleum gas safety gas supply system according to claim 8, characterized in that: The pressure protection system meets: Where, P tank is the real-time pressure sensor reading; P rated is the rated working pressure; P release It is the trigger signal of the relief valve.

10. The Internet of Things liquefied petroleum gas safety gas supply system according to claim 2, characterized in that: The step monitoring component is used to identify the operator's identity and the operator's operating specifications for the LPG cylinder, using the violation detection model to achieve: Where A k is the feature vector of operation action k; A std is the standard operation feature library; γ k Action weight coefficient; τ is the alarm threshold.

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