Oil gas recovery remote monitoring and management system based on Internet of Things

Abstract

The invention discloses an oil and gas recovery remote monitoring and management system based on the Internet of Things, and the system comprises a data collection module which is used for collecting the working condition parameters of oil and gas recovery equipment through a multi-dimensional sensor network when the operation state of the oil and gas recovery equipment is monitored; the analysis processing module is used for analyzing the working condition parameters based on a preset working condition threshold value and an operation model to obtain an operation state analysis result, and the operation state analysis result comprises the operation state and efficiency of the oil and gas recovery system; and the remote management module is used for establishing a remote management mechanism according to the operation state analysis result and realizing intelligent control and safety monitoring of the oil gas recovery system. An efficient remote management mechanism is established, and the system has important significance in improving the operation efficiency, safety and environmental protection performance of the oil gas recovery system.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas recovery, and in particular to an oil and gas recovery remote monitoring and management system based on the Internet of Things. Background Art

[0002] Currently, oil and gas recovery systems are widely used in the petrochemical industry. Their main purpose is to reduce volatile organic compound (VOC) emissions, protect the environment, and recover valuable oil and gas resources. However, traditional oil and gas recovery systems have obvious shortcomings in monitoring and management.

[0003] Traditional oil and vapor recovery systems are typically managed through manual inspections and regular maintenance, relying on on-site personnel and their own experience. Due to the limited frequency of manual inspections, real-time monitoring of equipment operating status is impossible, resulting in a delay in identifying and addressing equipment anomalies, increasing safety risks. Furthermore, manually recorded data often suffers from lags and incompleteness, making it difficult to fully and accurately reflect the equipment's actual operating parameters. Furthermore, existing oil and vapor recovery systems generally lack the integrated use of multi-dimensional sensor networks, preventing the simultaneous collection and analysis of multiple parameters such as temperature, pressure, flow rate, and concentration. This results in an incomplete and inaccurate assessment of system operating status. This single or limited parameter monitoring approach makes it difficult for the system to identify and process complex operating conditions. Regarding data analysis and processing, traditional systems often remain limited to simple data recording and display, lacking in-depth analytical capabilities based on preset operating thresholds and operational models. This makes it difficult to accurately assess oil and vapor recovery efficiency and predict potential equipment failures. This lack of analytical capabilities limits system optimization and reduces the overall effectiveness of oil and vapor recovery. Crucially, most existing technologies lack effective remote management mechanisms, making them incapable of intelligent control and safety monitoring of oil and gas recovery systems. This makes it difficult for managers to remotely access system operating information and conduct remote adjustments and controls. This not only increases management costs but also reduces response speed and ability to handle emergencies.

[0004] With the rapid development of the Internet of Things, big data, and artificial intelligence technologies, how to effectively apply these advanced technologies to oil and gas recovery systems to achieve real-time monitoring, intelligent analysis, and remote management of equipment operating status has become a key issue that needs to be addressed urgently. Summary of the Invention

[0005] The present invention provides an oil and gas recovery remote monitoring and management system based on the Internet of Things to solve the above-mentioned problems existing in the prior art.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] An oil and gas recovery remote monitoring and management system based on the Internet of Things, including:

[0008] The data acquisition module is used to collect the operating parameters of the oil and gas recovery equipment through a multi-dimensional sensor network when monitoring the operating status of the oil and gas recovery equipment;

[0009] The analysis and processing module is used to analyze the operating parameters based on the preset operating thresholds and operating models to obtain the operating status analysis results, including: the operating status and efficiency of the oil and gas recovery system;

[0010] The remote management module is used to establish a remote management mechanism based on the operating status analysis results to achieve intelligent control and safety monitoring of the oil and gas recovery system.

[0011] The data acquisition module includes:

[0012] A sensor node scanning submodule is used to sequentially scan multiple sensor nodes in a multi-dimensional sensor network;

[0013] The parameter determination submodule is used to determine the type of the scanned sensor node and the corresponding parameter to be measured each time the sensor node scanning submodule scans a sensor node;

[0014] The data integration submodule is used to integrate various parameters to be measured to form comprehensive operating data of the oil and gas recovery system after the sensor node scanning submodule finishes scanning multiple sensor nodes.

[0015] The analysis and processing module includes:

[0016] The parameter monitoring submodule is used to determine the key parameters to be monitored based on the pre-matched operation monitoring standards of the oil and gas recovery equipment and the historical operation records of the equipment;

[0017] The anomaly identification submodule is used to identify unresolved operation anomalies in the historical operation records of the equipment;

[0018] The demand generation submodule is used to generate monitoring requirements based on the determined key parameters and identified unresolved operating anomalies. The monitoring requirements include: the need to prompt the oil and gas recovery system to enter the next optimal operating state, and the need to prompt the identified unresolved operating anomalies to be resolved.

[0019] Among them, the abnormality identification submodule includes:

[0020] Deviation identification unit, used to identify operation deviations in the historical operation records of the oil and gas recovery equipment;

[0021] a deviation set determining unit, configured to determine a deviation set from the identified operating deviations, wherein multiple operating deviations of the same type or having a progressive relationship are included in the same deviation set;

[0022] a target determination unit, configured to use the last operating deviation in each determined deviation set as a target for resolution;

[0023] an abnormality determination unit, configured to, when a standard operating stage representing that the resolution target has been resolved does not appear after the operating stage where the resolution target is located, treat the corresponding resolution target as an unresolved operating abnormality;

[0024] Among them, unresolved operational abnormalities include temperature abnormalities, pressure abnormalities, flow abnormalities and equipment failures.

[0025] The remote management module includes:

[0026] Intelligent control submodule, used to realize frequency conversion regulation, automatic switching control and interlocking protection functions based on the operating status analysis results;

[0027] Safety monitoring submodule, used to monitor the safety status of the oil and gas recovery system, and realize combustible gas concentration monitoring, sound and light alarm and emergency discharge interlock functions;

[0028] Among them, the automatic switching control is triggered when the temperature difference of the adsorption tank reaches the preset threshold, and the interlock protection is activated when the vacuum pump pressure is lower than the safety threshold.

[0029] The remote management module also includes:

[0030] The state sorting submodule is used to sort the operation states from high to low according to their severity to obtain the operation state sequence;

[0031] A sequence division submodule is used to divide the operating state sequence into multiple local sequences, wherein the operating states whose severity differences do not exceed a preset value are included in the same local sequence;

[0032] The information transmission submodule is used to transmit the management information of the operating status in the same local sequence to the personnel group in sequence;

[0033] Among them, management information includes equipment status, alarm information and processing suggestions.

[0034] Among them, also include:

[0035] Equipment management module, used to realize equipment identification, maintenance cycle reminder and fault self-diagnosis functions;

[0036] Among them, equipment identification is achieved through radio frequency identification technology, maintenance cycle reminders are generated based on the key component service life prediction model, and fault self-diagnosis is matched and judged based on the preset fault code library.

[0037] Among them, also include:

[0038] The communication matching module is used to assist the requesting personnel in matching the corresponding communication mode based on the data of each monitoring module of the oil and gas recovery system when any personnel in the personnel group requests fault support for the oil and gas recovery system;

[0039] a communication establishment module for establishing a communication connection between each monitoring module of the oil and gas recovery system and a requesting person based on speed matching communication;

[0040] Among them, communication methods include instant messaging, SMS reminders and emails.

[0041] Among them, also include:

[0042] Data storage module, used to realize local storage and cloud backup functions;

[0043] Data analysis module, used to store historical data and generate oil and gas recovery efficiency analysis reports;

[0044] Among them, local storage uses industrial-grade storage media, cloud backup supports access to the Internet of Things platform, and analysis reports include daily reports, weekly reports and monthly reports.

[0045] Among them, the communication matching module includes:

[0046] The demand determination submodule is used to receive fault keywords input by managers and determine multiple fault support requirements and priorities based on the real-time data of each monitoring module of the oil and gas recovery system;

[0047] The demand traversal submodule is used to receive fault support requirements and traverse each fault support requirement in descending order of priority;

[0048] The sequence generation submodule is used to receive the results of the traversal of the demand traversal submodule. During each traversal, based on the relevant parameters of the traversed fault support requirements and the previous fault support requirements that have not been traversed, a sequence of fault handling solutions is generated;

[0049] The result display submodule is used to receive the fault handling solution sequence and display the execution steps of the fault handling solution sequence to the requesting person;

[0050] The matching determination submodule is used to receive the displayed content and the personnel selection result, use the processing solution selected by the requesting personnel during the display of the fault processing solution sequence as the matching basis, and continue traversing from the next requirement of the fault support requirement corresponding to the matching basis;

[0051] The communication matching submodule is used to receive the determined matching basis, and after traversing each fault support requirement, match the corresponding communication mode based on the matching basis obtained in each traversal.

[0052] Compared with the prior art, the present invention has the following advantages:

[0053] An IoT-based oil and gas recovery remote monitoring and management system includes a data acquisition module, which uses a multi-dimensional sensor network to collect operating parameters of the oil and gas recovery equipment when monitoring its operating status. An analysis and processing module analyzes these operating parameters based on preset operating thresholds and operating models, obtaining operational status analysis results, including the operating status and performance of the oil and gas recovery system. A remote management module establishes a remote management mechanism based on the operational status analysis results, enabling intelligent control and safety monitoring of the oil and gas recovery system. Establishing an efficient remote management mechanism is crucial for improving the operational efficiency, safety, and environmental performance of the oil and gas recovery system.

[0054] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the present invention.

[0055] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0057] Figure 1 This is a structural diagram of an oil and gas recovery remote monitoring and management system based on the Internet of Things in an embodiment of the present invention;

[0058] Figure 2 This is a structural diagram of a data acquisition module in an embodiment of the present invention;

[0059] Figure 3 2 is a structural diagram of the analysis and processing module in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0061] The embodiment of the present invention provides an oil and gas recovery remote monitoring and management system based on the Internet of Things, including:

[0062] The data acquisition module is used to collect the operating parameters of the oil and gas recovery equipment through a multi-dimensional sensor network when monitoring the operating status of the oil and gas recovery equipment;

[0063] The analysis and processing module is used to analyze the operating parameters based on the preset operating thresholds and operating models to obtain the operating status analysis results, including: the operating status and efficiency of the oil and gas recovery system;

[0064] The remote management module is used to establish a remote management mechanism based on the operating status analysis results to achieve intelligent control and safety monitoring of the oil and gas recovery system.

[0065] The working principle of the above technical solution is as follows: Data acquisition module: operating parameters are collected through a multi-dimensional sensor network installed at key nodes of the oil and gas recovery system; the multi-dimensional sensor network includes temperature sensors, pressure sensors, liquid level sensors and flow sensors, etc.; the data collected by the sensors include: compressor operating conditions, adsorption tank temperature, vacuum pump pressure, oil and gas flow and other key indicators; these data are transmitted to the data processing center in real time through the built-in communication interface of the explosion-proof control cabinet using industrial communication protocols.

[0066] The analysis and processing module analyzes operating parameters based on preset operating thresholds and operating models, generating operational status analysis results. These include the vapor recovery system's operating efficiency, equipment health, energy consumption, and potential fault warnings. This analysis and processing involves comparing collected operating parameters with preset operating thresholds to identify abnormal conditions. This data is then fed into a pre-established vapor recovery system operating model to generate equipment operating trend and performance evaluation reports.

[0067] The remote management module establishes a remote management mechanism based on operational status analysis results, enabling intelligent control and safety monitoring of the oil and vapor recovery system. This remote management mechanism includes remote implementation of functions such as automatic frequency conversion equipment adjustment, dual adsorption tank switching control, condensing system defrost control, and safety interlock protection. Intelligent control primarily adjusts equipment operating status based on parameters such as inlet pressure. Automatic switching is triggered when the adsorption tank temperature differential reaches a set threshold, automatically switches channels when the condensing system pressure differential exceeds a set threshold, and initiates shutdown protection when the vacuum pump absolute pressure falls below a safety threshold.

[0068] It also includes: adjusting the frequency of intelligent data collection based on the operating cycle and data change trend of the oil and gas recovery system; and configuring the time interval for system data collection based on the intelligent data collection frequency adjustment.

[0069] The beneficial effects of this technical solution include adaptively adjusting the data collection frequency by analyzing the oil and gas recovery system's operating cycle and data change trends. Specifically, the system dynamically adjusts the sampling interval based on the severity of operating parameter changes. When parameter changes are slow and stable, the sampling frequency is reduced to save bandwidth and storage resources. When parameters change rapidly or approach warning thresholds, the sampling frequency is increased to ensure monitoring accuracy. Specifically, this can be achieved by establishing a mapping between data change rate and sampling frequency to achieve optimal allocation of system resources.

[0070] In another embodiment, the data acquisition module includes:

[0071] A sensor node scanning submodule is used to sequentially scan multiple sensor nodes in a multi-dimensional sensor network;

[0072] The parameter determination submodule is used to determine the type of the scanned sensor node and the corresponding parameter to be measured each time the sensor node scanning submodule scans a sensor node;

[0073] The data integration submodule is used to integrate various parameters to be measured to form comprehensive operating data of the oil and gas recovery system after the sensor node scanning submodule finishes scanning multiple sensor nodes.

[0074] The working principle of the above technical solution is as follows: the sensor node scanning submodule performs multi-dimensional monitoring of the oil and gas recovery system to determine the current operating environment parameters; the multi-dimensional monitoring can be achieved through a multi-dimensional sensor network configured at key locations in the oil and gas recovery system; the multi-dimensional sensor network can include various types of sensors such as temperature sensors, pressure sensors, liquid level sensors, and flow meters; the scanning process mainly visits each sensor node in turn to collect sensor status information and data, wherein the status information includes: the sensor's online status, power status, communication quality, etc.; the data information includes: compressor operating conditions, adsorption tank temperature, vacuum pump pressure, oil and gas flow, and other key indicators;

[0075] The parameter determination submodule identifies and processes data based on the type of sensor nodes scanned, and determines the various parameters to be measured. Sensor node types include temperature sensors, pressure sensors, liquid level sensors, flow sensors, and other types. Determining the parameters to be measured involves converting the units of the raw data collected by the sensor and inputting the extracted data into a pre-configured processing algorithm to obtain standardized parameter values. The processing algorithm can perform corresponding data calibration and filtering based on the characteristics of different sensor types to eliminate possible noise interference. Different sensors may have different acquisition frequencies, and the parameter determination submodule is also responsible for synchronizing the data timestamps to ensure the time consistency of various parameters.

[0076] The data integration submodule aggregates data from each sensor node to generate comprehensive system operational data reports. This comprehensive operational data includes a combination of key indicators such as the oil and gas recovery system's operating status, recovery efficiency, equipment health, and energy consumption. Data integration involves categorizing and organizing various sensor data, establishing logical associations between parameters, and comprehensively analyzing these associated parameters using a pre-defined calculation model. This calculation model extracts key performance indicators from multi-dimensional monitoring data based on the process flow and physical characteristics of the oil and gas recovery system.

[0077] The beneficial effects of the above technical solution are: by analyzing the operating status and environmental changes, dynamically adjusting the data collection strategy, and adjusting the collection frequency according to the different stages of system operation. The specific implementation can be through monitoring the system load and the change rate of key parameters, and selecting the collection solution that best suits the current scenario from the preset collection strategy library.

[0078] In another embodiment, the analysis and processing module includes:

[0079] The parameter monitoring submodule is used to determine the key parameters to be monitored based on the pre-matched operation monitoring standards of the oil and gas recovery equipment and the historical operation records of the equipment;

[0080] The anomaly identification submodule is used to identify unresolved operation anomalies in the historical operation records of the equipment;

[0081] The demand generation submodule is used to generate monitoring requirements based on the determined key parameters and identified unresolved operating anomalies. The monitoring requirements include: the need to prompt the oil and gas recovery system to enter the next optimal operating state, and the need to prompt the identified unresolved operating anomalies to be resolved.

[0082] The working principle of the above technical solution is as follows: the parameter monitoring submodule determines the key parameters to be monitored based on the pre-matched operation monitoring standards of the oil and gas recovery equipment; among them, the pre-matched operation monitoring standards are a standardized parameter set formulated according to industry specifications and equipment technical characteristics; the equipment's historical operation records can be formed through data collected by a multi-dimensional sensor network, including the historical changes in key indicators such as compressor operating conditions, adsorption tank temperature, vacuum pump pressure, and oil and gas flow; the determination of key parameters mainly considers factors such as the degree of influence of parameters on system operating efficiency, the correlation between parameter fluctuations and system anomalies, and the impact of parameter changes on equipment life, so as to screen out the parameter set with the most monitoring value.

[0083] The anomaly identification submodule identifies unresolved operational anomalies in the historical operation records of the equipment. Operational anomaly identification is achieved by comparing the deviation between the actual operating parameters and the standard parameter range. The system will perform trend analysis on historical data to identify recurring but unresolved abnormal patterns. Anomaly types may include: equipment overheating, abnormal pressure fluctuations, unstable flow, decreased adsorption efficiency, etc. The identification process combines threshold judgment and pattern recognition methods. When a parameter continuously exceeds the preset threshold or a combination of multiple parameters presents a specific abnormal pattern, the system will mark it as an unresolved operational anomaly.

[0084] The demand generation submodule generates monitoring requirements based on the determined key parameters and identified unresolved operating anomalies. Monitoring requirements are divided into two categories: one is the requirement to prompt the oil and gas recovery system to enter the next optimal operating state, and the other is the requirement to prompt the identified unresolved operating anomalies to be resolved. The demand generation for the optimal operating state is based on the system energy efficiency model. By analyzing the difference between the current operating conditions and the ideal operating conditions, the direction and magnitude of parameter adjustment are calculated. The anomaly resolution requirement is based on fault diagnosis logic, combined with the equipment maintenance knowledge base, to generate specific processing suggestions and operating instructions. The system will prioritize the requirements according to their urgency and scope of impact to ensure that important issues are handled in a timely manner.

[0085] The beneficial effects of this technical solution include: intelligent monitoring and management of the oil and gas recovery system. The system proactively identifies potential problems and provides optimization suggestions, significantly improving oil and gas recovery efficiency and equipment operational reliability.

[0086] In another embodiment, the anomaly identification submodule includes:

[0087] Deviation identification unit, used to identify operation deviations in the historical operation records of the oil and gas recovery equipment;

[0088] a deviation set determining unit, configured to determine a deviation set from the identified operating deviations, wherein multiple operating deviations of the same type or having a progressive relationship are included in the same deviation set;

[0089] a target determination unit, configured to use the last operating deviation in each determined deviation set as a target for resolution;

[0090] an abnormality determination unit, configured to, when a standard operating stage representing that the resolution target has been resolved does not appear after the operating stage where the resolution target is located, treat the corresponding resolution target as an unresolved operating abnormality;

[0091] Among them, unresolved operational abnormalities include temperature abnormalities, pressure abnormalities, flow abnormalities and equipment failures.

[0092] The working principle of the above technical solution is as follows: the deviation identification unit obtains the historical operation records of the oil and gas recovery equipment through the data acquisition system and performs operation deviation identification and analysis. The historical operation records include the temperature data, pressure data, flow data and equipment status information of the oil and gas recovery equipment. The operation deviation identification and analysis mainly compares the difference between the actual operating parameters of the equipment and the standard parameters to determine whether there is any deviation from the normal operating range. The deviation types include temperature deviation, pressure deviation, flow deviation and equipment abnormal status.

[0093] The deviation set determination unit performs correlation analysis on multiple identified operational deviations to determine deviation sets. Deviation sets are determined based on the principle that deviations of the same type or with a progressive relationship are grouped together. A progressive relationship means that some deviations are derivative or escalated from other deviations, such as a minor temperature fluctuation developing into a persistent temperature anomaly. Deviation set construction helps track the evolution of problems and identify the root causes and development paths of anomalies.

[0094] The target determination unit analyzes the development trend of each deviation set and determines the resolution target. The resolution target is determined based on the last operational deviation in each deviation set. The last deviation usually represents the current state of the problem and is the target that needs to be addressed. The resolution target is determined by considering the severity, duration, and impact of the deviation on the overall system, providing a clear direction for subsequent processing.

[0095] The anomaly determination unit tracks the processing status of the target to be resolved and determines whether it constitutes an unresolved operational anomaly. The anomaly determination is based on the equipment's operational phase. If the standard operational phase, representing the resolution of the target, does not occur after the operational phase in which the target is resolved, the system determines the target as an unresolved operational anomaly. The standard operational phase refers to the phase in which the equipment's various parameters return to normal. The system determines whether the problem has been resolved by comparing the current operational parameters with the preset standard parameter range.

[0096] Among them, unresolved operational anomalies mainly include four types: temperature anomalies, pressure anomalies, flow anomalies and equipment failures; temperature anomalies mainly occur in key parts such as adsorption tanks and condensation systems, which may lead to decreased adsorption efficiency or insufficient condensation; pressure anomalies are mainly manifested as system pressure that is too high or too low, affecting the overall efficiency of oil and gas recovery; flow anomalies include unstable oil and gas flow, decreased recovery rate, etc.; equipment failures involve operational abnormalities of core equipment such as compressors, vacuum pumps, and variable frequency fans.

[0097] The beneficial effect of this technical solution is that the IoT monitoring system transmits anomaly identification results to the remote monitoring platform in real time, enabling operations and maintenance personnel to promptly understand equipment status and initiate appropriate processing based on the anomaly type. The intelligent operation of the anomaly identification submodule not only improves the operational stability of the oil and gas recovery equipment but also reduces the frequency of manual inspections, achieving refined and efficient equipment management.

[0098] In another embodiment, the remote management module includes:

[0099] Intelligent control submodule, used to realize frequency conversion regulation, automatic switching control and interlocking protection functions based on the operating status analysis results;

[0100] Safety monitoring submodule, used to monitor the safety status of the oil and gas recovery system, and realize combustible gas concentration monitoring, sound and light alarm and emergency discharge interlock functions;

[0101] Among them, the automatic switching control is triggered when the temperature difference of the adsorption tank reaches the preset threshold, and the interlock protection is activated when the vacuum pump pressure is lower than the safety threshold.

[0102] The working principle of the above technical solution is: through the operating status data, the preset control strategy is executed. Among them, the frequency conversion adjustment device integrated in the intelligent control submodule forms a closed control loop with the system fan. By continuously collecting the inlet pressure signal and performing real-time analysis, the fan operating frequency is dynamically adjusted to maintain the optimal working state of the system. When the inlet pressure increases, the fan frequency increases synchronously, and when the pressure decreases, the frequency is correspondingly reduced, establishing a dynamic balance mechanism between energy consumption control and recovery efficiency. The dual adsorption tank system is equipped with an automatic switching control device to monitor the temperature difference changes of each adsorption tank. When the temperature difference of a single adsorption tank reaches the preset critical value, the control program starts the working state transfer process, and smoothly transitions the operating load from the current adsorption tank to the standby adsorption tank, maximizing the adsorption treatment efficiency while ensuring the continuous operation of the system. The interlocking protection mechanism immediately triggers the shutdown protection program when it detects that the vacuum pump pressure is lower than the safety lower limit.

[0103] The safety monitoring submodule establishes a comprehensive system operation status monitoring network, ensuring operational safety through multiple safeguards. Catalytic combustion sensors distributed at key nodes of the system continuously monitor the concentration of combustible gases, and the detection data is processed and transmitted to the central control unit. When the concentration value approaches the warning standard, the system issues a primary warning signal, and when it exceeds the safety limit, the sound and light alarm devices are activated simultaneously. The alarm system adopts a high sound pressure level design with all-round visual warning lights to ensure effective warning communication under various environmental conditions. When a major safety hazard is detected, the system automatically executes the system shutdown procedure, and at the same time, the bypass valve is opened in a very short time to safely discharge the residual gas in the pipeline to prevent accidents.

[0104] The beneficial effect of this technical solution is that it forms a complete closed-loop management architecture through information exchange, jointly ensuring the safe and efficient operation of the oil and gas recovery system. When the safety monitoring module detects an abnormal situation, it sends a command signal to the intelligent control module, triggering the corresponding protection operation, achieving comprehensive system safety management.

[0105] In another embodiment, the remote management module further includes:

[0106] The state sorting submodule is used to sort the operation states from high to low according to their severity to obtain the operation state sequence;

[0107] A sequence division submodule is used to divide the operating state sequence into multiple local sequences, wherein the operating states whose severity differences do not exceed a preset value are included in the same local sequence;

[0108] The information transmission submodule is used to transmit the management information of the operating status in the same local sequence to the personnel group in sequence;

[0109] Among them, management information includes equipment status, alarm information and processing suggestions.

[0110] The working principle of the above technical solution is as follows: the status sorting submodule sorts the equipment operating status by severity to form an ordered status sequence; the equipment operating status can be collected by a sensor network distributed at various key points in the oil and gas recovery device; the equipment operating status includes key operating parameters such as compressor operating conditions, adsorption tank temperature, vacuum pump pressure, oil and gas flow rate, etc. The severity sorting is mainly based on the deviation between the preset safety threshold and the current measured value, and the greater the deviation, the more serious the fault or abnormality. The sorting process first divides each type of equipment status into emergency level, warning level, caution level and normal level according to the degree of impact on the safe operation of the system, and then performs a secondary sorting within each level according to the deviation ratio;

[0111] The sequence division submodule divides the sorted operating status sequence into multiple local sequences for easy hierarchical management and processing. The basis for the division of local sequences is to classify operating states whose severity differences do not exceed a preset value into the same category. The preset value is a threshold determined based on expert experience and historical data analysis, and is usually set as the difference between safety levels. The division process adopts an adaptive clustering algorithm, which first calculates the severity difference between adjacent states and then automatically determines the boundary points of the local sequence based on the preset threshold as the boundary condition. The advantage of local sequence division is that it enables management personnel to concentrate on handling problems of similar severity, thereby improving fault handling efficiency. The system will dynamically adjust the preset value according to the operating environment and operating characteristics of the oil and gas recovery equipment to ensure that the division of local sequences is more reasonable.

[0112] The beneficial effect of the above technical solution is that the management information in the same local sequence is transmitted to the corresponding personnel group in order of severity through the information transmission submodule, ensuring that the most urgent problems can be handled first, thereby improving the safe operation level and processing efficiency of the oil and gas recovery system.

[0113] In another embodiment, further comprising:

[0114] Equipment management module, used to realize equipment identification, maintenance cycle reminder and fault self-diagnosis functions;

[0115] Among them, equipment identification is achieved through radio frequency identification technology, maintenance cycle reminders are generated based on the key component service life prediction model, and fault self-diagnosis is matched and judged based on the preset fault code library.

[0116] The working principle of the above technical solution is: device identification is achieved through radio frequency identification technology. When the device is installed or replaced, the management personnel can use the radio frequency reader equipped with the system to scan and identify the device.

[0117] A key component lifespan prediction model is constructed based on a multi-dimensional dataset including cumulative equipment operating time, operating environment parameters, historical load conditions, and manufacturer-recommended maintenance cycles. The system continuously records the actual operating status of each key component and dynamically assesses its remaining service life using a predictive algorithm. When a component's estimated lifespan approaches its warning threshold, the system automatically sends a maintenance reminder to management, including component type, location, recommended replacement time, and spare parts preparation recommendations. This shifts maintenance from a reactive response to a proactive one.

[0118] Fault self-diagnosis monitors deviations between equipment operating parameters and their normal operating range in real time. When an abnormal indicator is detected, the device management module compares and analyzes the collected abnormal data with characteristic patterns in the fault code library to determine the fault type and possible cause. The fault code library covers a variety of fault scenarios, including pressure anomalies, temperature deviations, communication interruptions, and sensor failures, each with detailed treatment recommendations. Diagnostic results are pushed to maintenance personnel via the remote monitoring platform, triggering the corresponding level of alarm mechanism. For serious faults that may pose a safety hazard, the system will initiate emergency protection measures.

[0119] The beneficial effects of this technical solution include: equipment identification information interacting with the data storage and analysis module, maintenance cycle predictions referencing historical operating data, and fault diagnosis results being communicated to relevant personnel via the safety monitoring system. In practical applications, the equipment management module is both a fundamental guarantee for reliable system operation and a key means of improving oil and gas recovery efficiency and extending equipment life.

[0120] In another embodiment, further comprising:

[0121] The communication matching module is used to assist the requesting personnel in matching the corresponding communication mode based on the data of each monitoring module of the oil and gas recovery system when any personnel in the personnel group requests fault support for the oil and gas recovery system;

[0122] a communication establishment module for establishing a communication connection between each monitoring module of the oil and gas recovery system and a requesting person based on speed matching communication;

[0123] Among them, communication methods include instant messaging, SMS reminders and emails.

[0124] The working principle of the above technical solution is as follows: When any member of a team requests support for a gas recovery system fault, the communication matching module quickly matches the requesting member with a corresponding communication method based on data from the system's various monitoring modules. This communication matching determines the most appropriate communication method by analyzing the fault type and urgency. The fault type can be comprehensively determined using data from sensors installed at key points in the gas recovery system. Fault types include compressor abnormalities, adsorption tank temperature exceeding limits, vacuum pump pressure abnormalities, and fluctuating gas flow rates. Urgency levels range from critical faults to general faults and warning messages. The communication matching module first receives abnormal data from the gas recovery system's monitoring modules, extracts fault features, and inputs these features into a preconfigured judgment model to determine the corresponding fault type and urgency. The judgment model is trained based on historical fault data and can analyze and identify specific fault conditions from various sensor data. Based on the fault type and urgency, the communication matching module automatically selects the appropriate communication method: for critical faults, a combination of instant messaging and SMS notifications is preferred; for general faults, either instant messaging or SMS notifications are preferred; and for warning messages, email is primarily used.

[0125] The communication establishment module is used to establish a communication connection between each monitoring module of the oil and vapor recovery system and the requesting personnel based on a quick-matching communication method. The communication establishment process determines the specific personnel receiving notifications based on the personnel group's permissions and duty roster. Personnel groups include different roles, such as field operators, technical support personnel, and management personnel. Permission configurations include the fault types, notification time periods, and system data scopes that each role can access. The communication establishment module queries the permission configuration table based on the fault type to select a subset of personnel with permission to handle that fault. This is then combined with the current duty roster information to determine the final notification recipient. Finally, based on the quick-matching communication method, the communication connection is automatically established between the system and the receiving personnel. Once the communication connection is established, the system automatically pushes information including a description of the fault, key parameters, historical similar cases, and possible solution recommendations. For instant messaging, personnel can also remotely execute some control commands, such as emergency shutdown and switching to backup equipment, through the communication connection, improving fault response efficiency.

[0126] The beneficial effect of the above technical solution is to provide data support for subsequent system optimization. When the fault is resolved, the communication establishment module will send a fault resolution confirmation message to the relevant personnel according to the initial communication method and automatically close the communication connection.

[0127] In another embodiment, further comprising:

[0128] Data storage module, used to realize local storage and cloud backup functions;

[0129] Data analysis module, used to store historical data and generate oil and gas recovery efficiency analysis reports;

[0130] Among them, local storage uses industrial-grade storage media, cloud backup supports access to the Internet of Things platform, and analysis reports include daily reports, weekly reports and monthly reports.

[0131] The working principle of the above technical solution is: local storage and cloud backup functions are realized through the data storage module; the stored historical data is analyzed through the data analysis module to generate oil and gas recovery efficiency analysis reports; among them, local storage uses industrial-grade storage media, cloud backup supports Internet of Things platform access, and analysis reports include daily reports, weekly reports and monthly reports.

[0132] The beneficial effects of the above technical solution are: it not only ensures the automated execution of routine analysis, but also provides flexible on-demand analysis capabilities to meet application needs in different scenarios.

[0133] In another embodiment, the communication matching module includes:

[0134] The demand determination submodule is used to receive fault keywords input by managers and determine multiple fault support requirements and priorities based on the real-time data of each monitoring module of the oil and gas recovery system;

[0135] The demand traversal submodule is used to receive fault support requirements and traverse each fault support requirement in descending order of priority;

[0136] The sequence generation submodule is used to receive the results of the traversal of the demand traversal submodule. During each traversal, based on the relevant parameters of the traversed fault support requirements and the previous fault support requirements that have not been traversed, a sequence of fault handling solutions is generated;

[0137] The result display submodule is used to receive the fault handling solution sequence and display the execution steps of the fault handling solution sequence to the requesting person;

[0138] The matching determination submodule is used to receive the displayed content and the personnel selection result, use the processing solution selected by the requesting personnel during the display of the fault processing solution sequence as the matching basis, and continue traversing from the next requirement of the fault support requirement corresponding to the matching basis;

[0139] The communication matching submodule is used to receive the determined matching basis, and after traversing each fault support requirement, match the corresponding communication mode based on the matching basis obtained in each traversal.

[0140] The working principle of the above technical solution is as follows: the demand determination submodule is responsible for receiving the oil and gas recovery system fault keywords input by the management personnel and determining the fault support needs; among them, the fault keywords can be entered by the management personnel on the human-machine interface of the remote monitoring platform, or submitted through the mobile terminal application; the fault keywords can include equipment name, fault phenomenon, alarm code and other forms; the system real-time data mainly comes from the multi-dimensional sensor network distributed throughout the oil and gas recovery system, including: compressor operating data, adsorption tank temperature parameters, vacuum pump pressure value, oil and gas flow data and other key indicators; the fault support demand determination process includes: semantic analysis of fault keywords, combined with the real-time data of each monitoring module, through the preset fault diagnosis rule library, to identify possible fault types, and determine the priority according to factors such as the fault impact range, equipment importance and safety risks.

[0141] The requirement traversal submodule receives the determined fault support requirement list and performs priority sorting and traversal; the fault support requirement list contains information such as the description of each requirement, associated equipment, fault type and priority; the priority sorting follows the principle of highest priority for safety risk, second highest priority for production impact, and third highest priority for equipment loss risk; the traversal process is processed in order from high to low according to the sorted priority, and the index of the currently processed requirement will be recorded during the traversal so that the processing object can be quickly located and switched during the interaction process; in actual applications, the priority may be dynamically adjusted according to the on-site situation, and the traversal submodule supports receiving priority update instructions during the traversal process.

[0142] The sequence generation submodule generates a corresponding fault handling solution sequence based on the result of the requirement traversal. The generation of the fault handling solution sequence is based on the currently traversed fault support requirements and the first few high-priority requirements that have not been traversed. The solution generation process includes: analyzing the characteristic parameters of the current fault, querying the preset solution library, and combining the process flow and equipment correlation of the oil and gas recovery system to generate targeted processing steps. The processing steps take into account both independent solutions to the fault and the possibility of related processing between multiple faults to improve processing efficiency. The solution sequence is sorted according to the execution difficulty, required resources and expected results to ensure that the optimal solution is displayed first.

[0143] The result display submodule is responsible for presenting the generated sequence of fault handling solutions to the requester in an intuitive manner. The display content includes: description of the fault type, analysis of possible causes, steps of the solution, required tools and materials, estimated completion time, risk warnings, and other information. The display format can be a combination of structured text, flow charts, 3D process flow chart visualization, and other forms. On the remote monitoring platform, the display content will adaptively adjust the layout according to the different terminal devices (computers, tablets, mobile phones, etc.). The interactive display allows the requester to view detailed instructions, zoom in on key areas, switch between different solutions, and perform other operations.

[0144] The matching determination submodule receives the personnel selection results and updates the subsequent traversal strategy; among them, the personnel selection result refers to the specific processing solution selected by the requesting personnel after reviewing the sequence of fault handling solutions; the selection result will be recorded as the matching basis for subsequent communication method determination and knowledge base update; updating the traversal strategy means that the system will continue traversing from the next requirement of the fault support requirement corresponding to the currently selected solution to avoid repeated processing of solved problems; the matching determination process also includes execution feedback records for the selected solution to continuously optimize the accuracy of the system's solution recommendations.

[0145] The communication matching submodule selects the best communication method based on the determined matching criteria. Communication methods include SMS notifications, emails, mobile application push notifications, video conference invitations, remote assistance requests, and other forms. The matching process is based on a comprehensive consideration of factors such as the type of fault, urgency, processing complexity, and the skill level of on-site personnel. Different communication content templates are used for different levels of recipients (on-site operators, technical supervisors, system experts, etc.) to ensure the accuracy and effectiveness of information transmission. A two-way feedback mechanism is also supported during the communication process, and the recipient can reply to the execution status or raise further questions through the same channel.

[0146] The beneficial effects of this technical solution include: by monitoring the oil and gas recovery system's operating status and network quality in real time, adaptively adjusting communication strategies to ensure critical information transmission even under poor network conditions and reducing unnecessary data transmission during normal system operation. Specifically, this analysis can be done by studying historical communication records and network fluctuation patterns to establish a predictive model. This allows for the configuration of optimal communication parameters for different time periods and operating modes, achieving optimal utilization of communication resources.

[0147] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the invention.

Claims

1. An oil and gas recovery remote monitoring and management system based on the Internet of Things, characterized in that: include: The data acquisition module is used to collect the operating parameters of the oil and gas recovery equipment through a multi-dimensional sensor network when monitoring the operating status of the oil and gas recovery equipment; The analysis and processing module is used to analyze the operating parameters based on the preset operating thresholds and operating models to obtain the operating status analysis results, including: the operating status and efficiency of the oil and gas recovery system; The remote management module is used to establish a remote management mechanism based on the operating status analysis results to achieve intelligent control and safety monitoring of the oil and gas recovery system.

2. The oil and gas recovery remote monitoring and management system based on the Internet of Things according to claim 1 is characterized in that: The data acquisition module includes: A sensor node scanning submodule is used to sequentially scan multiple sensor nodes in a multi-dimensional sensor network; The parameter determination submodule is used to determine the type of the scanned sensor node and the corresponding parameter to be measured each time the sensor node scanning submodule scans a sensor node; The data integration submodule is used to integrate various parameters to be measured to form comprehensive operating data of the oil and gas recovery system after the sensor node scanning submodule finishes scanning multiple sensor nodes.

3. The oil and gas recovery remote monitoring and management system based on the Internet of Things according to claim 1 is characterized in that: The analysis and processing modules include: The parameter monitoring submodule is used to determine the key parameters to be monitored based on the pre-matched operation monitoring standards of the oil and gas recovery equipment and the historical operation records of the equipment; The anomaly identification submodule is used to identify unresolved operation anomalies in the historical operation records of the equipment; The demand generation submodule is used to generate monitoring requirements based on the determined key parameters and identified unresolved operating anomalies. The monitoring requirements include: the need to prompt the oil and gas recovery system to enter the next optimal operating state, and the need to prompt the identified unresolved operating anomalies to be resolved.

4. The oil and gas recovery remote monitoring and management system based on the Internet of Things according to claim 3 is characterized in that: The anomaly identification submodule includes: Deviation identification unit, used to identify operation deviations in the historical operation records of the oil and gas recovery equipment; a deviation set determining unit, configured to determine a deviation set from the identified operating deviations, wherein multiple operating deviations of the same type or having a progressive relationship are included in the same deviation set; a target determination unit, configured to use the last operating deviation in each determined deviation set as a target for resolution; an abnormality determination unit, configured to, when a standard operating stage representing that the resolution target has been resolved does not appear after the operating stage where the resolution target is located, treat the corresponding resolution target as an unresolved operating abnormality; Among them, unresolved operational abnormalities include temperature abnormalities, pressure abnormalities, flow abnormalities and equipment failures.

5. The oil and gas recovery remote monitoring and management system based on the Internet of Things according to claim 1 is characterized in that: The remote management module includes: Intelligent control submodule, used to realize frequency conversion regulation, automatic switching control and interlocking protection functions based on the operating status analysis results; Safety monitoring submodule, used to monitor the safety status of the oil and gas recovery system, and realize combustible gas concentration monitoring, sound and light alarm and emergency discharge interlock functions; Among them, the automatic switching control is triggered when the temperature difference of the adsorption tank reaches the preset threshold, and the interlock protection is activated when the vacuum pump pressure is lower than the safety threshold.

6. The oil and gas recovery remote monitoring and management system based on the Internet of Things according to claim 5 is characterized in that: The remote management module also includes: The state sorting submodule is used to sort the operation states from high to low according to their severity to obtain the operation state sequence; A sequence division submodule is used to divide the operating state sequence into multiple local sequences, wherein the operating states whose severity differences do not exceed a preset value are included in the same local sequence; The information transmission submodule is used to transmit the management information of the operating status in the same local sequence to the personnel group in sequence; Among them, management information includes equipment status, alarm information and processing suggestions.

7. The oil and gas recovery remote monitoring and management system based on the Internet of Things according to claim 6 is characterized in that: Also includes: Equipment management module, used to realize equipment identification, maintenance cycle reminder and fault self-diagnosis functions; Among them, equipment identification is achieved through radio frequency identification technology, maintenance cycle reminders are generated based on the key component service life prediction model, and fault self-diagnosis is matched and judged based on the preset fault code library.

8. The oil and gas recovery remote monitoring and management system based on the Internet of Things according to claim 1 is characterized in that: Also includes: The communication matching module is used to assist the requesting personnel in matching the corresponding communication mode based on the data of each monitoring module of the oil and gas recovery system when any personnel in the personnel group requests fault support for the oil and gas recovery system; a communication establishment module for establishing a communication connection between each monitoring module of the oil and gas recovery system and a requesting person based on speed matching communication; Among them, communication methods include instant messaging, SMS reminders and emails.

9. The oil and gas recovery remote monitoring and management system based on the Internet of Things according to claim 1 is characterized in that: Also includes: Data storage module, used to realize local storage and cloud backup functions; Data analysis module, used to store historical data and generate oil and gas recovery efficiency analysis reports; Among them, local storage uses industrial-grade storage media, cloud backup supports access to the Internet of Things platform, and analysis reports include daily reports, weekly reports and monthly reports.

10. The oil and gas recovery remote monitoring and management system based on the Internet of Things according to claim 8 is characterized in that: The communication matching module includes: The demand determination submodule is used to receive fault keywords input by managers and determine multiple fault support requirements and priorities based on the real-time data of each monitoring module of the oil and gas recovery system; The demand traversal submodule is used to receive fault support requirements and traverse each fault support requirement in descending order of priority; The sequence generation submodule is used to receive the results of the traversal of the demand traversal submodule. During each traversal, based on the relevant parameters of the traversed fault support requirements and the previous fault support requirements that have not been traversed, a sequence of fault handling solutions is generated; The result display submodule is used to receive the fault handling solution sequence and display the execution steps of the fault handling solution sequence to the requesting person; The matching determination submodule is used to receive the displayed content and the personnel selection result, use the processing solution selected by the requesting personnel during the display of the fault processing solution sequence as the matching basis, and continue traversing from the next requirement of the fault support requirement corresponding to the matching basis; The communication matching submodule is used to receive the determined matching basis, and after traversing each fault support requirement, match the corresponding communication mode based on the matching basis obtained in each traversal.

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