Oil gas recovery equipment intelligent management method and system based on Internet of Things

Abstract

The invention discloses an oil gas recovery equipment intelligent management method and system based on the Internet of Things, and belongs to the technical field of the Internet of Things. The method comprises the following steps: acquiring suction capacity, storage capacity and communication relation parameters of all oil and gas recovery equipment in a target area based on a cloud platform and an Internet of Things platform, constructing a schedulable equipment pair set according to a preset communication relation parameter threshold value, and calculating a theoretical collaborative efficiency index in combination with a capacity parameter and a communication parameter; generating a cross-device collaboration path set through the collaboration efficiency index; operation data such as gas flow, a negative pressure value and a recovery rate are collected in real time to calculate an equipment load rate, when the equipment load rises, scheduling equipment pairs are screened based on a collaborative path set and a path weight threshold value, and the equipment load is dynamically scheduled and balanced, so that the collaborative efficiency, the operation stability and the safety in the oil and gas recovery process are improved, and the recovery efficiency is improved. The overload risk is reduced; and the overall recovery performance is improved.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, specifically to an intelligent management method and system for oil and gas recovery equipment based on IoT. Background Technology

[0002] In oil and gas storage and transportation systems and gas station operations, oil and gas recovery technology has long been widely deployed at various terminal sites as a crucial component for controlling volatile organic compound (VOC) emissions, reducing safety hazards, and meeting environmental regulations. With increasingly stringent regulations and rising urban oil and gas emission requirements, various oil and gas recovery devices are gradually becoming more networked, data-driven, and visualized. Especially driven by the development of IoT technology, oil and gas recovery equipment now possesses capabilities such as multi-source sensor data acquisition, communication link topology construction, and real-time equipment status perception, making cross-device information collaboration and regional operational status assessment possible. However, existing technologies primarily focus on independent monitoring of individual device operation data, relying solely on single-dimensional parameters such as gas flow rate, negative pressure, or recovery rate for threshold judgment. Furthermore, traditional equipment scheduling models remain at the stage of manual inspection or static allocation, lacking a dynamic collaborative mechanism based on the overall regional structure, making it difficult to adapt to the complex topological relationships between devices and the rapidly growing data volume. This results in limited overall operating efficiency of the oil and gas recovery system, and the problem of uneven absorption and storage capacity between regions is difficult to systematically identify.

[0003] In large-scale deployments, the connectivity, pumping capacity, and storage capacity differences between devices affect gas migration paths and recovery load distribution within a region. However, existing solutions generally lack a structured parameter system that reflects the degree of coupling between devices, making it impossible to measure collaborative performance based on multi-device relationships. Furthermore, cross-device collaborative paths lack a unified modeling approach, and most systems do not establish quantifiable collaborative path weight models, making device scheduling reliant on experience-based judgment and hindering the rapid identification of suitable scheduling targets in high-load scenarios. Simultaneously, existing management methods lack a complete dynamic scheduling framework, failing to adjust in real-time based on changes in device load rates. This results in some devices remaining under high load for extended periods, increasing equipment wear and failure rates. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent management method and system for oil and gas recovery equipment based on the Internet of Things, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A smart management method for oil and gas recovery equipment based on the Internet of Things (IoT) includes the following steps: Step S1: Obtain all oil and gas recovery equipment within the target area from the cloud platform and construct an oil and gas recovery equipment set; based on the IoT platform, obtain the suction capacity parameters, storage capacity parameters, and connectivity parameters of each oil and gas recovery equipment; Step S2: Preset the connectivity parameter threshold and construct a set of schedulable equipment pairs; calculate the theoretical collaborative efficiency index of the oil and gas recovery equipment; Step S3: Construct a set of cross-equipment collaborative paths for the oil and gas recovery equipment; Step S4: Based on the IoT platform, obtain the gas flow rate data, negative pressure value data, and recovery rate data of each oil and gas recovery equipment, and calculate the load rate of the oil and gas recovery equipment; Step S5: If the load of the oil and gas recovery equipment is high, obtain the schedulable candidate equipment pairs based on the cross-equipment collaborative path set; preset the collaborative path weight threshold, filter the schedulable equipment pairs, and dynamically schedule the load of the oil and gas recovery equipment.

[0007] As a preferred embodiment of the IoT-based intelligent management method for oil and gas recovery equipment described in this invention, all oil and gas recovery equipment within a target area is retrieved from a cloud platform, and an oil and gas recovery equipment set is constructed, denoted as... ,in, Let A represent the a-th oil and gas recovery device within the target area, and let A represent the total number of oil and gas recovery devices within the target area.

[0008] Based on an IoT platform, the pumping capacity parameters, storage capacity parameters, and connectivity parameters between each oil and gas recovery device are acquired, constructing a basic dataset for the a-th oil and gas recovery device, denoted as [database name missing]. ,in, Indicates oil and gas recovery equipment The suction capacity parameter, Indicates oil and gas recovery equipment Storage capacity parameters, Indicates oil and gas recovery equipment With oil and gas recovery equipment The connection parameters between them.

[0009] As a preferred embodiment of the intelligent management method for oil and gas recovery equipment based on the Internet of Things described in this invention, a preset connection relationship parameter threshold is used to start from the a-th oil and gas recovery equipment. Basic dataset Screening for oil and gas recovery equipment All oil and gas recovery devices whose connectivity parameters are greater than or equal to the threshold of the connectivity parameters are sequentially connected to the oil and gas recovery devices. Construct device pairs, and based on all device pairs, construct a set of schedulable device pairs;

[0010] Based on oil and gas recovery equipment suction capacity parameters Storage capacity parameters Connection parameters Calculation of oil and gas recovery equipment The theoretical synergistic efficiency index, and the oil and gas recovery equipment The theoretical synergistic efficiency index is incorporated into oil and gas recovery equipment. Basic dataset The calculation formula is as follows:

[0011] ;

[0012] in, Indicates oil and gas recovery equipment The theoretical synergistic effectiveness index, This represents the maximum value of the suction capacity parameter for all oil and gas recovery devices in the set of oil and gas recovery devices. This represents the maximum storage capacity parameter of all oil and gas recovery devices in the set of oil and gas recovery devices. This represents the maximum value of the connectivity parameter among all oil and gas recovery devices in the set of oil and gas recovery devices. , and These represent the weighting coefficients of the preset suction capacity parameter, storage capacity parameter, and connectivity parameter, respectively. This represents a set of schedulable device pairs. This indicates the number of devices in the set of schedulable device pairs.

[0013] As a preferred embodiment of the intelligent management method for oil and gas recovery equipment based on the Internet of Things described in this invention, an oil and gas recovery equipment is constructed. The set of cross-device collaboration paths is as follows:

[0014] Create oil and gas recovery equipment Given an empty set of cross-device collaborative paths, traverse each device pair in the set of schedulable device pairs, and calculate the collaborative path weights between device pairs based on the theoretical collaborative efficiency index of the devices. The calculation formula is as follows: ,in, This represents the a-th oil and gas recovery device. With the b-th oil and gas recovery device Cooperative path weights between Indicates oil and gas recovery equipment Theoretical synergistic effectiveness index;

[0015] The a-th oil and gas recovery device With the b-th oil and gas recovery device The path between them is denoted as It is added to the cross-device collaborative path empty set, the path of each device pair is obtained, and the oil and gas recovery device is constructed. A set of cross-device collaborative paths.

[0016] As a preferred embodiment of the intelligent management method for oil and gas recovery equipment based on the Internet of Things (IoT) described in this invention, the gas flow rate data, negative pressure value data, and recovery rate data of each oil and gas recovery device are acquired based on an IoT platform, and the oil and gas recovery equipment is then managed accordingly. The gas flow rate data, negative pressure data, and recovery rate data are denoted as , and And calculate the oil and gas recovery equipment The load rate is calculated using the following formula:

[0017] ;

[0018] in, Indicates oil and gas recovery equipment load rate, Indicates the preset oil and gas recovery equipment The rated maximum gas flow rate, Indicates the preset oil and gas recovery equipment The rated maximum negative pressure value, Indicates the preset oil and gas recovery equipment The rated maximum recovery rate, , and Indicates the preset oil and gas recovery equipment Weighting factors for gas flow rate data, negative pressure data, and recovery rate data;

[0019] Preset load rate threshold, if oil and gas recovery equipment load rate If the load rate is greater than or equal to the aforementioned threshold, then the oil and gas recovery equipment is deemed to be... The load is high, and a high load label is attached, if the oil and gas recovery equipment... load rate If the load rate is less than the aforementioned threshold, the oil and gas recovery equipment is deemed to be faulty. The load is low, and a low load label is attached.

[0020] As a preferred embodiment of the intelligent management method for oil and gas recovery equipment based on the Internet of Things described in this invention, if the oil and gas recovery equipment If the load of a device is high, then based on the cross-device collaborative path set, another oil and gas recovery device with a low load tag is obtained and recorded as a scheduling candidate device pair.

[0021] All candidate equipment pairs for scheduling are obtained, and a cooperative path weight threshold is preset. If the cooperative path weight of a candidate equipment pair is greater than or equal to the cooperative path weight threshold, the candidate equipment pair is marked as a scheduling equipment pair, and the oil and gas recovery equipment is... The load is dynamically scheduled;

[0022] Real-time acquisition of cross-device collaborative path sets and load rates for each oil and gas recovery device enables dynamic intelligent management.

[0023] An intelligent management system for oil and gas recovery equipment based on the Internet of Things (IoT) includes: a data acquisition and set construction module, a device pair set construction and index calculation module, a path set construction module, a load rate calculation module, and an intelligent management module.

[0024] The data acquisition and collection construction module: acquires all oil and gas recovery devices in the target area from the cloud platform and constructs an oil and gas recovery device collection; based on the Internet of Things platform, it acquires the suction capacity parameters, storage capacity parameters, and connectivity parameters of each oil and gas recovery device;

[0025] The device pair set construction and index calculation module: presets connectivity parameter thresholds, constructs a set of schedulable device pairs; and calculates the theoretical collaborative efficiency index of oil and gas recovery equipment.

[0026] The path set construction module: constructs a cross-device collaborative path set for oil and gas recovery equipment;

[0027] The load rate calculation module: Based on the Internet of Things platform, it acquires gas flow rate data, negative pressure value data and recovery rate data of each oil and gas recovery device, and calculates the load rate of the oil and gas recovery device.

[0028] The intelligent management module: if the load of the oil and gas recovery equipment is high, it obtains the pair of alternative equipment to be scheduled based on the cross-equipment collaborative path set; it presets the collaborative path weight threshold, filters the pair of equipment to be scheduled, and dynamically schedules the load of the oil and gas recovery equipment.

[0029] Furthermore, the device pair set construction and index calculation module includes a device pair set construction unit and an index calculation unit;

[0030] The device pair set construction unit: presets a connection relationship parameter threshold, selects all oil and gas recovery devices from the basic data of the a-th oil and gas recovery device that satisfy the connection relationship parameter with the oil and gas recovery device being greater than or equal to the connection relationship parameter threshold, and constructs device pairs with the oil and gas recovery devices in sequence, and constructs a schedulable device pair set based on all device pairs;

[0031] The index calculation unit calculates the theoretical synergistic efficiency index of the oil and gas recovery equipment based on the pumping capacity parameters, storage capacity parameters, and connectivity parameters of the oil and gas recovery equipment, and writes the theoretical synergistic efficiency index of the oil and gas recovery equipment into the basic dataset of the oil and gas recovery equipment.

[0032] Furthermore, the path set construction module includes a path set construction unit;

[0033] The path set construction unit constructs a cross-device collaborative path set for oil and gas recovery equipment, specifically as follows: creating an empty set of cross-device collaborative paths for oil and gas recovery equipment; traversing each device pair in the set of schedulable device pairs; and calculating the collaborative path weights between device pairs based on the theoretical collaborative efficiency index of the devices; obtaining the path for each device pair; and constructing a cross-device collaborative path set for oil and gas recovery equipment.

[0034] Furthermore, the load rate calculation module includes a load rate calculation unit;

[0035] The load rate calculation unit: Based on the Internet of Things platform, it acquires gas flow rate data, negative pressure value data, and recovery rate data of each oil and gas recovery device, and calculates the load rate of the oil and gas recovery device; it presets a load rate threshold. If the load rate of the oil and gas recovery device is greater than or equal to the load rate threshold, it is determined that the load of the oil and gas recovery device is high and a high load label is attached. If the load rate of the oil and gas recovery device is less than the load rate threshold, it is determined that the load of the oil and gas recovery device is low and a low load label is attached.

[0036] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: The intelligent management method and system for oil and gas recovery equipment based on the Internet of Things provided by this invention acquires the pumping capacity parameters, storage capacity parameters, and connectivity parameters of all oil and gas recovery equipment within a target area, constructing a structured equipment basic dataset. This allows for a unified expression of equipment capabilities, resource limits, and interconnection conditions within the system, providing a quantifiable data foundation for subsequent collaborative screening and scheduling. By constructing a set of schedulable equipment pairs based on connectivity parameter thresholds, and calculating a theoretical collaborative efficiency index based on pumping capacity, storage capacity, and connectivity, the system can distinguish and rank the potential collaborative value of equipment, thereby reducing blind scheduling and lowering the risk of transmission failure. Furthermore, by using equipment collaboration... The system constructs a cross-device collaborative path set using the same efficiency index, transforming the potential collaborative relationships between devices from isolated connections into a path set usable for priority analysis. This provides a stable and weighted basis for subsequent load transfer. By acquiring gas flow rate, negative pressure value, and recovery rate and calculating load rate, the system can identify the operating status of devices in real time, transforming abstract operating pressure into classification labels that can trigger scheduling logic, providing immediate conditions for dynamic scheduling. Under high load conditions, by combining the cross-device collaborative path set and path weight thresholds to screen and schedule device pairs, the system achieves automated load allocation and transfer. This ensures that scheduling behavior is based on real-time status and constrained by collaborative capabilities and path quality, avoiding the transfer of load to devices with insufficient capacity or unstable paths. This forms a closed-loop management system from device modeling, collaborative screening, path construction to dynamic scheduling, improving the efficiency, stability, and safety of the oil and gas recovery process. Overall, it achieves the comprehensive benefits of reducing overload risk, increasing recovery rate, reducing energy consumption, and enhancing the long-term operational reliability of the system. Attached Figure Description

[0037] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0038] Figure 1 This is a schematic diagram illustrating the steps of an intelligent management method for oil and gas recovery equipment based on the Internet of Things according to the present invention.

[0039] Figure 2 This is a schematic diagram of the structure of an intelligent management system for oil and gas recovery equipment based on the Internet of Things according to the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Please see Figure 1 In this first embodiment: a smart management method for oil and gas recovery equipment based on the Internet of Things is provided, the method including the following steps:

[0042] Step S1: Obtain all oil and gas recovery devices in the target area from the cloud platform and construct an oil and gas recovery device set; based on the Internet of Things platform, obtain the suction capacity parameters, storage capacity parameters and connectivity parameters of each oil and gas recovery device.

[0043] Specifically, all oil and gas recovery devices within the target area are retrieved from the cloud platform, and a set of oil and gas recovery devices is constructed, denoted as... ,in, Let A represent the a-th oil and gas recovery device within the target area, and let A represent the total number of oil and gas recovery devices within the target area.

[0044] Based on an IoT platform, the pumping capacity parameters, storage capacity parameters, and connectivity parameters between each oil and gas recovery device are acquired, constructing a basic dataset for the a-th oil and gas recovery device, denoted as [database name missing]. ,in, Indicates oil and gas recovery equipment The suction capacity parameter, Indicates oil and gas recovery equipment Storage capacity parameters, Indicates oil and gas recovery equipment With oil and gas recovery equipment The connection parameters between them.

[0045] It should be noted that the connectivity parameter is a comprehensive indicator that quantifies the collaborative working capability and data transmission reliability between two oil and gas recovery devices. It reflects the tightness of the physical connection, communication link and functional collaboration between the devices. It is calculated by weighting three dimensions: physical connection strength (the tightness and transmission capacity of the physical pipeline connection between the devices), communication link quality (the stability and real-time performance of the IoT communication link), and functional collaboration history (the record of the effect of historical collaborative work between the devices).

[0046] In this invention, by acquiring all oil and gas recovery equipment within a target area from a cloud platform and constructing an equipment set, and simultaneously acquiring and recording the suction capacity parameters, storage capacity parameters, and connectivity parameters of each device based on an IoT platform, a structured model of the basic capabilities and interconnection attributes of the equipment within the target area is achieved. This structured basic dataset can serve as a data source for subsequent screening, performance calculation, and scheduling decisions, facilitating quantitative comparisons of equipment capability differences, bottlenecks, and potential collaborative pairs. It can reduce errors in manual inventory and judgment, improve the data traceability and consistency of scheduling decisions, and enable subsequent automated scheduling to be based on accurate equipment capability and connectivity information, thereby laying a reliable foundation for systematic collaborative optimization (reducing missed detections, improving response speed, and supporting historical record auditing).

[0047] Step S2: Preset the threshold values ​​for connectivity parameters and construct a set of schedulable equipment pairs; calculate the theoretical collaborative efficiency index of the oil and gas recovery equipment.

[0048] Specifically, preset connection relationship parameter thresholds are used to start from the a-th oil and gas recovery device. Basic dataset Screening for oil and gas recovery equipment All oil and gas recovery devices whose connectivity parameters are greater than or equal to the threshold of the connectivity parameters are sequentially connected to the oil and gas recovery devices. Construct device pairs, and based on all device pairs, construct a set of schedulable device pairs;

[0049] Based on oil and gas recovery equipment suction capacity parameters Storage capacity parameters Connection parameters Calculation of oil and gas recovery equipment The theoretical synergistic efficiency index, and the oil and gas recovery equipment The theoretical synergistic efficiency index is incorporated into oil and gas recovery equipment. Basic dataset The calculation formula is as follows:

[0050] ;

[0051] in, Indicates oil and gas recovery equipment The theoretical synergistic effectiveness index, This represents the maximum value of the suction capacity parameter for all oil and gas recovery devices in the set of oil and gas recovery devices. This represents the maximum storage capacity parameter of all oil and gas recovery devices in the set of oil and gas recovery devices. This represents the maximum value of the connectivity parameter among all oil and gas recovery devices in the set of oil and gas recovery devices. , and These represent the weighting coefficients of the preset suction capacity parameter, storage capacity parameter, and connectivity parameter, respectively. This represents a set of schedulable device pairs. This indicates the number of devices in the set of schedulable device pairs.

[0052] It should be noted that each parameter in this formula is divided by the maximum value of the corresponding region, uniformly mapping parameters of different dimensions to the [0,1] interval to ensure horizontal comparability. In traditional oil and gas recovery management, the value of equipment is often judged based on a single parameter such as "pumping capacity" or "storage capacity," leading to the misselection of "weak equipment" (for example, a piece of equipment with strong pumping capacity but full storage capacity will still cause oil and gas overflow after coordination). This formula accurately matches the core requirements of oil and gas recovery—"pumping capacity, storage capacity, and interconnectivity"—by weighting and integrating three parameters: pumping capacity, storage capacity, and interconnectivity.

[0053] This formula addresses the shortcomings of traditional "single-device evaluation": traditional methods only consider a single parameter (such as suction capacity), while this formula integrates three key attributes. For example, if a device has strong suction capacity but insufficient storage capacity, the formula will reduce its synergistic efficiency index through weight balancing, avoiding the "weakest link" effect. The calculation results are directly written into the device's basic dataset, allowing the system to quickly sort and filter them. High-value equipment is prioritized for inclusion in the collaborative scheduling pool to reduce ineffective connections (such as equipment combinations with poor connectivity or mismatched capabilities). In oil and gas recovery, "extraction, storage, and connectivity" are all indispensable. Formulas precisely match this requirement; for example, in oil and gas storage and transportation terminals, this can improve… Weighting prioritizes devices with sufficient storage capacity as collaborating partners.

[0054] In this invention, devices that meet the connectivity conditions are screened by pre-setting connectivity parameter thresholds, and a set of schedulable device pairs is constructed. At the same time, a theoretical collaborative efficiency index is calculated based on the suction capacity, storage capacity, and connectivity of each device, realizing a quantitative description of "candidate devices that can participate in collaborative scheduling" and "potential collaborative value of each device". The set of schedulable device pairs and the collaborative efficiency index of each device provide criteria for priority ranking, resource allocation, and fault-tolerant scheduling, supporting the rapid identification of devices that can bear additional load or receive transfer when the device load fluctuates.

[0055] This step improves the accuracy and interpretability of scheduling selection, avoids blindly transferring loads to equipment with insufficient capacity or poor connectivity, and reduces the risk of recycling interruptions and the probability of oil spills / leaks. At the same time, standardized thresholds and index calculations facilitate cross-time period / cross-regional comparisons and policy consistency maintenance.

[0056] Step S3: Construct a set of cross-device collaborative paths for oil and gas recovery equipment.

[0057] Specifically, constructing oil and gas recovery equipment The set of cross-device collaboration paths is as follows:

[0058] Create oil and gas recovery equipment Given an empty set of cross-device collaborative paths, traverse each device pair in the set of schedulable device pairs, and calculate the collaborative path weights between device pairs based on the theoretical collaborative efficiency index of the devices. The calculation formula is as follows: ,in, This represents the a-th oil and gas recovery device. With the b-th oil and gas recovery device Cooperative path weights between Indicates oil and gas recovery equipment Theoretical synergistic effectiveness index;

[0059] It should be noted that a geometric mean is used instead of an arithmetic mean to avoid misclassifying a combination of equipment with "stronger aspects and weaker aspects" as a superior path. For example, equipment... of Value = 0.9, Equipment of The value is 0.1, the arithmetic mean is 0.5, and the geometric mean is only 0.3, which is more in line with the law that "the weakest link determines the overall effect" in actual collaboration. This formula transforms the collaboration efficiency index of two devices into path weights. The higher the weight, the stronger the collaboration potential of the device pair and the more stable the cooperation.

[0060] When traversing schedulable device pairs, the path weight of each pair of devices is calculated using this formula, forming a "device" weight. -equipment —weight The structured path data avoids chaotic paths, and in high-load scheduling scenarios, the system will prioritize the path with a collaborative path weight greater than or equal to a preset threshold. For example, when a certain device at a gas station is overloaded, the load will be transferred to the low-load device with the highest path weight to ensure that the collaborative efficiency does not decrease after the transfer.

[0061] The a-th oil and gas recovery device With the b-th oil and gas recovery device The path between them is denoted as It is added to the cross-device collaborative path empty set, the path of each device pair is obtained, and the oil and gas recovery device is constructed. A set of cross-device collaborative paths.

[0062] In this invention, a cross-device collaborative path set is constructed by traversing schedulable device pairs and collaboratively calculating path weights using the theoretical collaborative efficiency index between devices. This achieves a collaborative capability mapping from point pairs to path levels and a weighted representation of paths. This path set formalizes feasible transmission / collaboration paths "from high-load devices to low-load receptive devices." Path weights can be directly used to select priority scheduling paths, calculate redundant paths, and evaluate transfer costs, facilitating the implementation of path-based dynamic scheduling strategies (e.g., selecting paths with high weights and stable connectivity to reduce transfer losses).

[0063] This step significantly improves the controllability and stability of scheduling, avoiding systemic failures caused by single direct docking; by prioritizing path weights, energy consumption and time delays on the gas transmission path can be reduced, improving overall recovery efficiency and reducing secondary losses (such as decreased recovery rate or container overrun).

[0064] Step S4: Based on the IoT platform, acquire gas flow rate data, negative pressure value data, and recovery rate data for each oil and gas recovery device, and calculate the load rate of the oil and gas recovery device.

[0065] Specifically, based on an IoT platform, gas flow rate data, negative pressure data, and recovery rate data of each oil and gas recovery device are acquired, and the oil and gas recovery devices are... The gas flow rate data, negative pressure data, and recovery rate data are denoted as , and And calculate the oil and gas recovery equipment The load rate is calculated using the following formula:

[0066] ;

[0067] in, Indicates oil and gas recovery equipment load rate, Indicates the preset oil and gas recovery equipment The rated maximum gas flow rate, Indicates the preset oil and gas recovery equipment The rated maximum negative pressure value, Indicates the preset oil and gas recovery equipment The rated maximum recovery rate, , and Indicates the preset oil and gas recovery equipment Weighting factors for gas flow rate data, negative pressure data, and recovery rate data;

[0068] It should be noted that traditional methods only determine load based on "gas flow rate" (e.g., exceeding the rated value indicates a high load). However, in oil and gas recovery, "abnormal negative pressure" (e.g., excessively high negative pressure leading to equipment wear) and "decreased recovery rate" (e.g., low rate leading to oil and gas accumulation) can also cause equipment overload. This formula uses the rated maximum value of a single unit as the denominator, which better reflects the individual operating limits of each piece of equipment. Gas flow rate reflects real-time processing capacity, negative pressure reflects suction stability, and recovery rate reflects the final effect. These three parameters cover the entire "input-process-output" chain, avoiding misjudgment based on a single parameter.

[0069] During peak hours at gas stations, the gas flow of oil and gas recovery equipment may surge. This formula can quickly calculate the load rate and promptly detect equipment that is "overloaded". When high-load equipment needs to transfer its load, the system will screen the recipient based on the "low load tag" to avoid transferring the load to equipment that is already close to full load, thus achieving regional load balance.

[0070] Traditional manual inspections struggle to detect overloads in real time. This formula can automatically trigger scheduling. For example, if a device's load rate exceeds the limit due to excessively high negative pressure (close to the rated maximum value), the system will immediately transfer part of the load to devices with sufficient negative pressure, reducing equipment wear and the risk of failure.

[0071] Preset load rate threshold, if oil and gas recovery equipment load rate If the load rate is greater than or equal to the aforementioned threshold, then the oil and gas recovery equipment is deemed to be... The load is high, and a high load label is attached, if the oil and gas recovery equipment... load rate If the load rate is less than the aforementioned threshold, the oil and gas recovery equipment is deemed to be faulty. The load is low, and a low load label is attached.

[0072] In this invention, by acquiring the gas flow rate, negative pressure value, and recovery rate of each device from the Internet of Things platform in real time (or periodically), and calculating the device load rate in a normalized weighted form, on-site and quantitative monitoring of the device's operating status and load classification (high / low load) are realized. The load rate, as an instant operating indicator, can directly trigger scheduling strategies, alarms, and maintenance work orders, support one-click decision-making based on operating status (e.g., automatically seeking transfer / grid connection when LR exceeds the threshold), and can help determine whether the device is in a performance degradation or abnormal operating point.

[0073] This step improves the system's response speed and automated scheduling capabilities to sudden high loads, reducing the risk of equipment failure and performance degradation caused by overload. In addition, by using a combination of multiple parameters (flow rate, negative pressure, and recovery rate), it avoids misjudgment based on a single indicator, improving the robustness of the judgment and its applicability to the field.

[0074] Step S5: If the load of the oil and gas recovery equipment is high, obtain the scheduling candidate equipment pair based on the cross-equipment collaborative path set; preset the collaborative path weight threshold, filter the scheduling equipment pair, and dynamically schedule the load of the oil and gas recovery equipment.

[0075] Specifically, if oil and gas recovery equipment If the load of a device is high, then based on the cross-device collaborative path set, another oil and gas recovery device with a low load tag is obtained and recorded as a scheduling candidate device pair.

[0076] All candidate equipment pairs for scheduling are obtained, and a cooperative path weight threshold is preset. If the cooperative path weight of a candidate equipment pair is greater than or equal to the cooperative path weight threshold, the candidate equipment pair is marked as a scheduling equipment pair, and the oil and gas recovery equipment is... The load is dynamically scheduled;

[0077] Real-time acquisition of cross-device collaborative path sets and load rates for each oil and gas recovery device enables dynamic intelligent management.

[0078] In this invention, when the equipment load is high, candidate scheduling equipment pairs marked as low load are selected based on the cross-device collaborative path set, and feasible scheduling equipment pairs are further screened using collaborative path weight thresholds to perform dynamic load transfer or grid connection operations. This realizes the transformation from passive alarm to proactive, path- and capacity-based intelligent scheduling. This step enables the system to selectively redistribute load or parallel recover equipment while ensuring connectivity reliability and equipment carrying capacity, reducing human intervention and decision-making delays. Furthermore, the path weight and threshold mechanism can be used to flexibly configure safety margins (e.g., increasing the threshold to ensure higher redundancy in high-risk situations).

[0079] This step reduces the probability of recovery interruption or leakage caused by single-point overload, optimizes resource utilization and energy consumption (by allocating airflow to more efficient equipment), and improves the overall recovery rate and system continuous operation time; at the same time, it enables traceable scheduling records and configurable risk control strategies, which facilitates supervision and subsequent optimization.

[0080] Please see Figure 2 In this second embodiment: an intelligent management system for oil and gas recovery equipment based on the Internet of Things is provided. The system includes: a data acquisition and set construction module, a device pair set construction and index calculation module, a path set construction module, a load rate calculation module, and an intelligent management module.

[0081] The data acquisition and collection construction module: acquires all oil and gas recovery devices in the target area from the cloud platform and constructs an oil and gas recovery device collection; based on the Internet of Things platform, it acquires the suction capacity parameters, storage capacity parameters, and connectivity parameters of each oil and gas recovery device;

[0082] The device pair set construction and index calculation module: presets connectivity parameter thresholds, constructs a set of schedulable device pairs; and calculates the theoretical collaborative efficiency index of oil and gas recovery equipment.

[0083] The path set construction module: constructs a cross-device collaborative path set for oil and gas recovery equipment;

[0084] The load rate calculation module: Based on the Internet of Things platform, it acquires gas flow rate data, negative pressure value data and recovery rate data of each oil and gas recovery device, and calculates the load rate of the oil and gas recovery device.

[0085] The intelligent management module: if the load of the oil and gas recovery equipment is high, it obtains the pair of alternative equipment to be scheduled based on the cross-equipment collaborative path set; it presets the collaborative path weight threshold, filters the pair of equipment to be scheduled, and dynamically schedules the load of the oil and gas recovery equipment.

[0086] Furthermore, the device pair set construction and index calculation module includes a device pair set construction unit and an index calculation unit;

[0087] The device pair set construction unit: presets a connection relationship parameter threshold, selects all oil and gas recovery devices from the basic data of the a-th oil and gas recovery device that satisfy the connection relationship parameter with the oil and gas recovery device being greater than or equal to the connection relationship parameter threshold, and constructs device pairs with the oil and gas recovery devices in sequence, and constructs a schedulable device pair set based on all device pairs;

[0088] The index calculation unit calculates the theoretical synergistic efficiency index of the oil and gas recovery equipment based on the pumping capacity parameters, storage capacity parameters, and connectivity parameters of the oil and gas recovery equipment, and writes the theoretical synergistic efficiency index of the oil and gas recovery equipment into the basic dataset of the oil and gas recovery equipment.

[0089] Furthermore, the path set construction module includes a path set construction unit;

[0090] The path set construction unit constructs a cross-device collaborative path set for oil and gas recovery equipment, specifically as follows: creating an empty set of cross-device collaborative paths for oil and gas recovery equipment; traversing each device pair in the set of schedulable device pairs; and calculating the collaborative path weights between device pairs based on the theoretical collaborative efficiency index of the devices; obtaining the path for each device pair; and constructing a cross-device collaborative path set for oil and gas recovery equipment.

[0091] Furthermore, the load rate calculation module includes a load rate calculation unit;

[0092] The load rate calculation unit: Based on the Internet of Things platform, it acquires gas flow rate data, negative pressure value data, and recovery rate data of each oil and gas recovery device, and calculates the load rate of the oil and gas recovery device; it presets a load rate threshold. If the load rate of the oil and gas recovery device is greater than or equal to the load rate threshold, it is determined that the load of the oil and gas recovery device is high and a high load label is attached. If the load rate of the oil and gas recovery device is less than the load rate threshold, it is determined that the load of the oil and gas recovery device is low and a low load label is attached.

[0093] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0094] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart management method for oil and gas recovery equipment based on the Internet of Things, characterized in that, The method includes the following steps: Step S1: Obtain all oil and gas recovery devices in the target area from the cloud platform and construct an oil and gas recovery device set; based on the Internet of Things platform, obtain the suction capacity parameters, storage capacity parameters and connectivity parameters of each oil and gas recovery device; Step S2: Preset connectivity parameter thresholds and construct a set of schedulable device pairs; calculate the theoretical collaborative efficiency index of the oil and gas recovery equipment; Step S3: Construct a set of cross-device collaborative paths for oil and gas recovery equipment; Step S4: Based on the IoT platform, acquire gas flow rate data, negative pressure value data and recovery rate data for each oil and gas recovery device, and calculate the load rate of the oil and gas recovery device; Step S5: If the load of the oil and gas recovery equipment is high, obtain the scheduling candidate equipment pair based on the cross-equipment collaborative path set; preset the collaborative path weight threshold, filter the scheduling equipment pair, and dynamically schedule the load of the oil and gas recovery equipment.

2. The intelligent management method for oil and gas recovery equipment based on the Internet of Things according to claim 1, characterized in that, The specific implementation process of step S1 includes: Retrieve all oil and gas recovery devices within the target area from the cloud platform and construct an oil and gas recovery device set, denoted as... ,in, Let A represent the a-th oil and gas recovery device within the target area, and let A represent the total number of oil and gas recovery devices within the target area. Based on an IoT platform, the pumping capacity parameters, storage capacity parameters, and connectivity parameters between each oil and gas recovery device are acquired, constructing a basic dataset for the a-th oil and gas recovery device, denoted as [database name missing]. ,in, Indicates oil and gas recovery equipment The suction capacity parameter, Indicates oil and gas recovery equipment Storage capacity parameters, Indicates oil and gas recovery equipment With oil and gas recovery equipment The connection parameters between them.

3. The intelligent management method for oil and gas recovery equipment based on the Internet of Things according to claim 2, characterized in that, The specific implementation process of step S2 includes: Preset connection relationship parameter thresholds, starting from the a-th oil and gas recovery device Basic dataset Screening for oil and gas recovery equipment All oil and gas recovery devices whose connectivity parameters are greater than or equal to the threshold of the connectivity parameters are sequentially connected to the oil and gas recovery devices. Construct device pairs, and based on all device pairs, construct a set of schedulable device pairs; Based on oil and gas recovery equipment suction capacity parameters Storage capacity parameters Connection parameters Calculation of oil and gas recovery equipment The theoretical synergistic efficiency index, and the oil and gas recovery equipment The theoretical synergistic efficiency index is incorporated into oil and gas recovery equipment. Basic dataset The calculation formula is as follows: ； in, Indicates oil and gas recovery equipment The theoretical synergistic effectiveness index, This represents the maximum value of the suction capacity parameter for all oil and gas recovery devices in the set of oil and gas recovery devices. This represents the maximum storage capacity parameter of all oil and gas recovery devices in the set of oil and gas recovery devices. This represents the maximum value of the connectivity parameter among all oil and gas recovery devices in the set of oil and gas recovery devices. , and These represent the weighting coefficients of the preset suction capacity parameter, storage capacity parameter, and connectivity parameter, respectively. This represents a set of schedulable device pairs. This indicates the number of devices in the set of schedulable device pairs.

4. The intelligent management method for oil and gas recovery equipment based on the Internet of Things according to claim 3, characterized in that, The specific implementation process of step S3 includes: Build oil and gas recovery equipment The set of cross-device collaboration paths is as follows: Create oil and gas recovery equipment Given an empty set of cross-device collaborative paths, traverse each device pair in the set of schedulable device pairs, and calculate the collaborative path weights between device pairs based on the theoretical collaborative efficiency index of the devices. The calculation formula is as follows: ,in, This represents the a-th oil and gas recovery device. With the b-th oil and gas recovery device Cooperative path weights between Indicates oil and gas recovery equipment Theoretical synergistic effectiveness index; The a-th oil and gas recovery device With the b-th oil and gas recovery device The path between them is denoted as It is added to the cross-device collaborative path empty set, the path of each device pair is obtained, and the oil and gas recovery device is constructed. A set of cross-device collaborative paths.

5. The intelligent management method for oil and gas recovery equipment based on the Internet of Things according to claim 4, characterized in that, The specific implementation process of step S4 includes: Based on an IoT platform, gas flow rate data, negative pressure data, and recovery rate data of each oil and gas recovery device are acquired, and the oil and gas recovery devices are... The gas flow rate data, negative pressure data, and recovery rate data are denoted as , and And calculate the oil and gas recovery equipment The load rate is calculated using the following formula: ； in, Indicates oil and gas recovery equipment load rate, Indicates the preset oil and gas recovery equipment The rated maximum gas flow rate, Indicates the preset oil and gas recovery equipment The rated maximum negative pressure value, Indicates the preset oil and gas recovery equipment The rated maximum recovery rate, , and Indicates the preset oil and gas recovery equipment Weighting factors for gas flow rate data, negative pressure data, and recovery rate data; Preset load rate threshold, if oil and gas recovery equipment load rate If the load rate is greater than or equal to the aforementioned threshold, then the oil and gas recovery equipment is deemed to be... The load is high, and a high load label is attached, if the oil and gas recovery equipment... load rate If the load rate is less than the aforementioned threshold, the oil and gas recovery equipment is deemed to be faulty. The load is low, and a low load label is attached.

6. The intelligent management method for oil and gas recovery equipment based on the Internet of Things according to claim 5, characterized in that, The specific implementation process of step S5 includes: If oil and gas recovery equipment If the load of a device is high, then based on the cross-device collaborative path set, another oil and gas recovery device with a low load tag is obtained and recorded as a scheduling candidate device pair. All candidate equipment pairs for scheduling are obtained, and a cooperative path weight threshold is preset. If the cooperative path weight of a candidate equipment pair is greater than or equal to the cooperative path weight threshold, the candidate equipment pair is marked as a scheduling equipment pair, and the oil and gas recovery equipment is... The load is dynamically scheduled; Real-time acquisition of cross-device collaborative path sets and load rates for each oil and gas recovery device enables dynamic intelligent management.

7. An intelligent management system for oil and gas recovery equipment based on the Internet of Things (IoT), executing the intelligent management method for oil and gas recovery equipment based on the IoT as described in any one of claims 1-6, characterized in that, The system includes: a data acquisition and set construction module, a device pair set construction and index calculation module, a path set construction module, a load rate calculation module, and an intelligent management module; The data acquisition and collection construction module: acquires all oil and gas recovery devices in the target area from the cloud platform and constructs an oil and gas recovery device collection; based on the Internet of Things platform, it acquires the suction capacity parameters, storage capacity parameters, and connectivity parameters of each oil and gas recovery device; The device pair set construction and index calculation module: presets connectivity parameter thresholds, constructs a set of schedulable device pairs; and calculates the theoretical collaborative efficiency index of oil and gas recovery equipment. The path set construction module: constructs a cross-device collaborative path set for oil and gas recovery equipment; The load rate calculation module: Based on the Internet of Things platform, it acquires gas flow rate data, negative pressure value data and recovery rate data of each oil and gas recovery device, and calculates the load rate of the oil and gas recovery device. The intelligent management module: if the load of the oil and gas recovery equipment is high, it obtains the pair of alternative equipment to be scheduled based on the cross-equipment collaborative path set; it presets the collaborative path weight threshold, filters the pair of equipment to be scheduled, and dynamically schedules the load of the oil and gas recovery equipment.

8. The intelligent management system for oil and gas recovery equipment based on the Internet of Things according to claim 7, characterized in that: The device pair set construction and index calculation module includes a device pair set construction unit and an index calculation unit; The device pair set construction unit: presets a connection relationship parameter threshold, selects all oil and gas recovery devices from the basic data of the a-th oil and gas recovery device that satisfy the connection relationship parameter with the oil and gas recovery device being greater than or equal to the connection relationship parameter threshold, and constructs device pairs with the oil and gas recovery devices in sequence, and constructs a schedulable device pair set based on all device pairs; The index calculation unit calculates the theoretical synergistic efficiency index of the oil and gas recovery equipment based on the pumping capacity parameters, storage capacity parameters, and connectivity parameters of the oil and gas recovery equipment, and writes the theoretical synergistic efficiency index of the oil and gas recovery equipment into the basic dataset of the oil and gas recovery equipment.

9. The intelligent management system for oil and gas recovery equipment based on the Internet of Things according to claim 8, characterized in that: The path set construction module includes a path set construction unit; The path set construction unit constructs a cross-device collaborative path set for oil and gas recovery equipment, specifically as follows: creating an empty set of cross-device collaborative paths for oil and gas recovery equipment; traversing each device pair in the set of schedulable device pairs; and calculating the collaborative path weights between device pairs based on the theoretical collaborative efficiency index of the devices; obtaining the path for each device pair; and constructing a cross-device collaborative path set for oil and gas recovery equipment.

10. The intelligent management system for oil and gas recovery equipment based on the Internet of Things according to claim 9, characterized in that: The load rate calculation module includes a load rate calculation unit; The load rate calculation unit: Based on the Internet of Things platform, it acquires gas flow rate data, negative pressure value data, and recovery rate data of each oil and gas recovery device, and calculates the load rate of the oil and gas recovery device; it presets a load rate threshold. If the load rate of the oil and gas recovery device is greater than or equal to the load rate threshold, it is determined that the load of the oil and gas recovery device is high and a high load label is attached. If the load rate of the oil and gas recovery device is less than the load rate threshold, it is determined that the load of the oil and gas recovery device is low and a low load label is attached.

Images