Pumping unit gravitational potential energy self-adaptive recovery power generation system and control method thereof

Abstract

The invention relates to the technical field of pumping units, and discloses a pumping unit gravitational potential energy self-adaptive recovery power generation system and a control method thereof.The pumping unit gravitational potential energy self-adaptive recovery power generation system comprises a data extraction module, an energy detection module, an optimization intervention module, a cooperative control module, a structure analysis module and a feedback module; and an intelligent energy recovery system with a data closed loop, an autonomous adjustment capability and a cross-domain cooperation mechanism is constructed. Compared with a system which depends on single sensor feedback and cannot realize deep collaboration or real-time response in the prior art, the system has the advantages that the stability judgment precision, the energy recovery efficiency, the control strategy self-adaptability, the equipment maintenance visualization and the like are remarkably improved, and by introducing a multi-dimensional coupling coefficient and a physical structure analysis model, the stability of the system is greatly improved. And the conversion of the system from'passive control 'to'active optimization' is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of oil pumping units, in particular to an oil pumping unit gravity potential energy adaptive recovery power generation system and a control method thereof. Background Art

[0002] With the continuous development of modern energy technologies, the efficient recovery and utilization of excess energy in mechanical systems has become a key research topic. This is particularly true in the field of oil and gas machinery. Pumping units, a common piece of machinery widely used in oilfield development, experience significant mechanical energy losses. Pumping units rely on electricity for operation, but during operation, some of this energy is wasted as gravitational potential energy. Therefore, the efficient recovery and utilization of this energy has become a key research topic in this field.

[0003] While conventional pumping units already have some energy recovery mechanisms, these are far from ideal due to limitations in equipment design and insufficient energy conversion efficiency. Current technologies still suffer from low energy recovery efficiency, poor system stability, and significant energy waste during operation. Consequently, despite the deployment of "energy-saving" pumping units in many oil fields, overall energy recovery still fails to meet the practical needs for high efficiency and energy conservation.

[0004] Therefore, we proposed an adaptive recovery and power generation system of the gravity potential energy of the oil pumping unit to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide an adaptive recovery and power generation system and control method for the gravity potential energy of an oil pump, so as to solve the problems proposed in the above background technology, such as low energy recovery efficiency, poor system stability, and large energy waste during equipment operation.

[0006] To achieve the above-mentioned object, the present invention provides the following technical solutions: a pumping unit gravity potential energy adaptive recovery power generation system, comprising a data extraction module, an energy detection module, an optimization intervention module, a coordinated control module, a structural analysis module and a feedback module; The data extraction module is used to extract the recovery power generation data, perform pre-processing, and reorganize it into a first data group, a second data group, and a third data group; The energy detection module extracts data from the first data group, the second data group, and the third data group, and couples the extracted data to generate an energy stability detection coefficient NLW, and analyzes the energy stability detection coefficient NLW to determine whether the current energy recovery is in a stable state; The optimization intervention module is used to perform data plotting on the first data group, the second data group, and the third data group, and couple the extracted data to generate an optimization intervention coefficient YHX, and analyze the optimization intervention coefficient YHX to determine whether the optimization intervention and energy recovery are stable; The collaborative control module is used to extract data from the first data group, the second data group, and the third data group, and couple the extracted data to generate a collaborative control analysis coefficient XTX, and analyze the collaborative control analysis coefficient XTX to determine whether energy recovery is stable after the collaborative analysis; The structural analysis module is used to extract data from the first data group, the second data group, and the third data group, and couple the extracted data to generate a bottleneck coefficient PIX and a disturbance coefficient RDX, and analyze the bottleneck coefficient PIX and the disturbance coefficient RDX respectively; The feedback module is used to feed back various parameters and results to the visualization terminal.

[0007] Preferably, the data extraction module includes a data acquisition unit and a data preprocessing unit; The data acquisition unit is used to collect energy recovery related parameters through various acquisition devices and monitoring software, including: The data preprocessing unit preprocesses and dimensionlessly converts the collected data, and reorganizes the data into a first data group, a second data group, and a third data group; The first data group includes the inverter input and output power A, the energy storage charging response time B, and the cable voltage drop C; The second data set includes the mechanical vibration spectrum D, harmonic content E, and load power impact rate F; The third data group includes motor current G, energy storage current H, battery temperature I, motor speed J, power factor K, and bus voltage fluctuation rate L.

[0008] Preferably, the energy detection module includes an energy analysis coefficient extraction unit and an energy analysis coefficient analysis unit; The energy analysis coefficient extraction unit is used to extract data from the first data group, the second data group, and the third data group, including inverter power A, harmonic content E, motor current G, energy storage current H, battery temperature I, and bus voltage fluctuation rate L, and couple the data to generate an energy stability detection coefficient NLW; The energy analysis coefficient analysis unit is used to analyze the energy stability detection coefficient NLW, and the specific method is as follows: when When , it means that the current energy recovery is in the first-level stable state and no adjustment is required; when When , it means that the current energy recovery is in the secondary stable state, and intervention optimization is performed. The parameters after intervention optimization are then extracted to generate the optimized intervention coefficient YHX for analysis. when , it means that the current energy recovery is at the third level of stability and needs to be fully adjusted.

[0009] Preferably, the energy stability detection coefficient NLW is calculated by the following formula: ; Where: inverter power A, harmonic content E, motor current G, energy storage current H, battery temperature I and bus voltage fluctuation rate L; a is the nonlinear coefficient, and its value range is [0.5, 2]. It is specifically set by the user; b is the effect index, which ranges from [0.5, 3] and is set by the user; c is the coupling coefficient, which ranges from [0.5, 2] and is adjusted by the user.

[0010] Preferably, the optimization intervention module includes an optimization coefficient extraction unit and an optimization coefficient analysis unit; The optimization coefficient extraction unit is used to extract data from the first data group, the second data group, and the third data group, including harmonic content E, battery temperature I, power factor K, and energy storage response time B, and couple the data to generate the optimization intervention coefficient YHX; The optimization coefficient analysis unit is used to analyze the optimization intervention coefficient YHX, and the specific method is as follows: when When , it means that the current energy recovery has returned to the first-level stable state and no adjustment is needed; when When , it means that the current energy recovery is in the secondary stable state, and collaborative optimization is performed. The parameters after collaborative optimization are extracted again to generate the collaborative control analysis coefficient XTX for analysis; when , it means that the current energy recovery is at the third level of stability and needs to be fully adjusted.

[0011] Preferably, the optimized intervention coefficient YHX is specifically calculated as follows: ; Where: E is the harmonic content, I is the battery temperature, K is the power factor, B is the energy storage response time, and NLW is the energy stability detection coefficient.

[0012] Preferably, the collaborative control module includes a collaborative coefficient extraction unit and a collaborative coefficient analysis unit; The synergy coefficient extraction unit is used to extract the first data group, the second data group, and the third data group, including the inverter input and output power A, harmonic content E, motor current G, energy storage current H, battery temperature I, and power factor K, and couple the data to generate a synergy control analysis coefficient XTX; The coordination coefficient analysis unit is used to analyze the coordination control analysis coefficient XTX, and the specific method is as follows: when When , it means that the current energy recovery has returned to the first-level stable state and no adjustment is needed; when When , it means that the current energy recovery is in the secondary stable state. Characteristic analysis is performed to generate the bottleneck coefficient PIX and the disturbance coefficient RDX. The two are analyzed to determine the unstable factors of energy recovery.

[0013] Preferably, the specific calculation formula of the collaborative control analysis coefficient XTX is as follows: ; Where: A is the input and output power of the inverter, E is the harmonic content, G is the motor current, H is the energy storage current, I is the battery temperature, K is the power factor, and YHX is the optimization intervention coefficient.

[0014] Preferably, the structural analysis module includes a bottleneck coefficient extraction unit, a bottleneck coefficient analysis unit, a disturbance coefficient extraction unit and a disturbance coefficient analysis unit; The bottleneck coefficient extraction unit and the disturbance coefficient extraction unit are respectively used to extract data from the first data group, the second data group, and the third data group, including the inverter input and output power A, the energy storage charging response time B, the cable voltage drop C, the mechanical vibration spectrum D, the harmonic content E, the load power impact rate F, the motor current G, the energy storage current H, the battery temperature I, the motor speed J, the power factor K, and the bus voltage fluctuation rate L, and respectively perform data coupling to generate the bottleneck coefficient PIX and the disturbance coefficient RDX; The bottleneck coefficient PIX and the disturbance coefficient RDX are calculated by the following formulas: ; ; Where: A is the inverter input and output power, B is the energy storage charging response time, C is the cable voltage drop, D is the mechanical vibration spectrum, E is the harmonic content, F is the load power impact rate, G is the motor current, H is the energy storage current, I is the battery temperature, J is the motor speed, K is the power factor, L is the bus voltage fluctuation rate, and XTX is the cooperative control analysis coefficient; The bottleneck coefficient analysis unit and the disturbance coefficient analysis unit analyze the bottleneck coefficient PIX and the disturbance coefficient RDX respectively. The specific methods are as follows: when When , it means that there is no equipment bottleneck problem in the current energy recovery; when When , it means that the current energy recovery equipment has a bottleneck problem; when When , it means that there is no disturbance problem in the current energy recovery; when , it means that there is a disturbance problem in the current energy recovery.

[0015] This application also includes a method for adaptively recovering the gravity potential energy of an oil pumping unit for power generation, the specific steps of which are as follows: S1. Extracting the recycled power generation data through a data extraction module, performing preprocessing, and reorganizing the data into a first data group, a second data group, and a third data group; S2. Extracting data from the first data group, the second data group, and the third data group through the energy detection module, coupling the extracted data to generate an energy stability detection coefficient NLW, and analyzing the energy stability detection coefficient NLW to determine whether the current energy recovery is in a stable state; S3. Performing data plotting on the first data group, the second data group, and the third data group through the optimization intervention module, and coupling the extracted data to generate an optimization intervention coefficient YHX. The optimization intervention coefficient YHX is analyzed to determine whether the optimization intervention and energy recovery are stable. S4. Extracting data from the first data group, the second data group, and the third data group through the collaborative control module, coupling the extracted data, thereby generating a collaborative control analysis coefficient XTX, and analyzing the collaborative control analysis coefficient XTX to determine whether energy recovery is stable after the collaborative analysis; S5. Extracting data from the first data group, the second data group, and the third data group through a structural analysis module, coupling the extracted data to generate a bottleneck coefficient PIX and a disturbance coefficient RDX, and analyzing the bottleneck coefficient PIX and the disturbance coefficient RDX respectively; S6. Feedback various parameters and results to the visualization terminal through the feedback module.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. By integrating multiple functional modules, this system builds an intelligent energy recovery system with a closed-loop data loop, autonomous regulation capabilities, and cross-domain collaborative mechanisms. Compared to existing systems that rely on single sensor feedback and lack deep collaboration or real-time response, this system achieves significant improvements in stability judgment accuracy, energy recovery efficiency, control strategy adaptability, and equipment maintenance visualization. By introducing multidimensional coupling coefficients and physical structure analysis models, the system transitions from "passive control" to "active optimization."

[0017] 2. The introduction of the structural analysis module empowers the system with proactive fault detection and hidden danger warning capabilities. Independent identification mechanisms for both bottlenecks and disturbances promptly indicate the source of potential problems, enabling subsequent optimization intervention modules or collaborative control modules to adopt targeted strategies, thereby promoting the establishment of an intelligent O&M closed loop of "self-diagnosis, self-adjustment, and self-optimization." BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a system step diagram of the present invention.

[0019] Figure 2 Flow chart of the method of the present invention.

[0020] In the figure: 1. Data extraction module; 11. Data acquisition unit; 12. Data preprocessing unit; 2. Energy detection module; 21. Energy analysis coefficient extraction unit; 22. Energy analysis coefficient analysis unit; 3. Optimization intervention module; 31. Optimization coefficient extraction unit; 32. Optimization coefficient analysis unit; 4. Collaborative control module; 41. Collaborative coefficient extraction unit; 42. Collaborative coefficient analysis unit; 5. Structural analysis module; 51. Bottleneck coefficient extraction unit; 52. Bottleneck coefficient analysis unit; 53. Perturbation coefficient extraction unit; 54. Perturbation coefficient analysis unit; 6. Feedback module. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] Example 1: Please refer to Figure 1 The pumping unit gravity potential energy adaptive recovery power generation system includes a data extraction module 1, an energy detection module 2, an optimization intervention module 3, a coordinated control module 4, a structural analysis module 5 and a feedback module 6; The data extraction module 1 is used to extract the recycled power generation data, perform pre-processing, and reorganize it into a first data group, a second data group, and a third data group; The energy detection module 2 extracts data from the first data group, the second data group, and the third data group, and couples the extracted data to generate an energy stability detection coefficient NLW. The energy stability detection coefficient NLW is analyzed to determine whether the current energy recovery is in a stable state. The optimization intervention module 3 is used to perform data plotting on the first data group, the second data group, and the third data group, and couple the extracted data to generate an optimization intervention coefficient YHX, and analyze the optimization intervention coefficient YHX to determine whether the optimization intervention and energy recovery are stable; The collaborative control module 4 is used to extract data from the first data group, the second data group, and the third data group, and couple the extracted data to generate a collaborative control analysis coefficient XTX, and analyze the collaborative control analysis coefficient XTX to determine whether the energy recovery is stable after the collaborative analysis; The structural analysis module 5 is used to extract data from the first data group, the second data group, and the third data group, and couple the extracted data to generate a bottleneck coefficient PIX and a disturbance coefficient RDX, and analyze the bottleneck coefficient PIX and the disturbance coefficient RDX respectively; The feedback module 6 is used to feed back various parameters and results to the visualization terminal.

[0023] In this embodiment, data extraction module 1 is used to collect and preprocess raw data generated during the operation of the oil pumping unit recovery power generation system. This module automatically divides different data sources into first, second, and third data groups, and performs noise reduction, standardization, and format unification. This module effectively improves data quality and analysis efficiency, providing reliable data support for subsequent energy state assessment, optimized control, and structural analysis of the system, enabling efficient perception and refined management of the system's operating status.

[0024] Energy Detection Module 2 is used to extract relevant numerical information and perform multidimensional coupling analysis based on key parameters from the first, second, and third data sets to construct an energy stability detection coefficient (NLW). This coefficient characterizes the stability level of the system's current recovery and power generation phase and is used to determine whether the system is in a primary stable state, a secondary stable state, or an unstable state. By integrating multi-source data, this module achieves a precise assessment of the energy recovery state, providing a scientific basis for subsequent optimization intervention or the selection of collaborative control strategies, significantly improving the system's dynamic response capability and adaptive regulation level.

[0025] When the energy detection module 2 determines that the system is in a secondary stable or unstable state, the optimization intervention module 3 extracts key control parameters from the first, second, and third data sets and generates an optimization intervention coefficient YHX through coupling calculations. This coefficient measures the improvement in system stability after the optimization measures are implemented and guides the system in implementing dynamic optimization strategies such as load adjustment, recovery rhythm reconstruction, and energy flow reallocation. This module enables fine-grained adaptive adjustment without affecting the overall system structure, effectively improving energy recovery efficiency and slowing system aging.

[0026] When optimization intervention measures are insufficient to restore system stability, collaborative control module 4 activates and further extracts key parameters from the first, second, and third data groups to construct a collaborative control analysis coefficient, XTX. This coefficient, XTX, comprehensively reflects the collaborative operation between the system's submodules, assessing the efficiency of the linkage mechanism during the energy recovery process and the presence of control delays, scheduling conflicts, or energy flow anomalies. By analyzing XTX, the system automatically adjusts the control strategy matching and inter-module coordination mechanisms to achieve cross-dimensional linkage optimization. The application of this module significantly enhances the overall coordination of the system, improving operational robustness and energy efficiency under complex operating conditions.

[0027] Structural Analysis Module 5 extracts parameters related to the mechanical structure, physical transmission path, and external disturbances from the three data sets. Through modeling and analysis, it generates the bottleneck coefficient PIX and the disturbance coefficient RDX, respectively. The bottleneck coefficient PIX quantifies the constraints within the system structure, such as rotational resistance, transmission hysteresis, or component wear; the disturbance coefficient RDX reflects the system's sensitivity to external factors such as load fluctuations, vibration interference, and ambient temperature changes. This module provides the system with physical-level fault precursor identification capabilities, assisting in preventive maintenance and iterative structural optimization, thereby fundamentally improving the system's stable operation and structural adaptability.

[0028] Feedback Module 6 provides real-time feedback to the system's visualization terminal on the key coefficients extracted and analyzed by the aforementioned modules. This module supports graphical interface display, abnormality alarm output, historical data comparison, and remote data synchronization, enabling users to fully understand the system's operating status and various performance indicators. This module provides the system with advanced human-computer interaction and intelligent monitoring capabilities, further enabling data-driven remote operation and maintenance and intelligent decision support, improving the system's manageability and informationization.

[0029] By integrating multiple functional modules, this system creates an intelligent energy recovery system with a closed-loop data system, autonomous regulation capabilities, and cross-domain collaborative mechanisms. Compared to existing systems that rely on single sensor feedback and lack deep collaboration or real-time response, this system significantly improves stability judgment accuracy, energy recovery efficiency, control strategy adaptability, and equipment maintenance visualization. By introducing multidimensional coupling coefficients and physical structure analysis models, the system transitions from "passive control" to "active optimization."

[0030] Example 2: Please refer to Figure 1 , the data extraction module 1 includes a data acquisition unit 11 and a data preprocessing unit 12; The data acquisition unit 11 is used to collect energy recovery related parameters through various acquisition devices and monitoring software, including: The data preprocessing unit 12 preprocesses and dimensionlessly converts the collected data, and reorganizes the data into a first data group, a second data group, and a third data group; The first data group includes the inverter input and output power A, the energy storage charging response time B, and the cable voltage drop C; The second data set includes the mechanical vibration spectrum D, harmonic content E, and load power impact rate F; The third data group includes motor current G, energy storage current H, battery temperature I, motor speed J, power factor K, and bus voltage fluctuation rate L.

[0031] In this embodiment, the system effectively enhances the energy recovery system's comprehensive perception and intelligent processing capabilities for operating data by providing a data extraction module 1, which is further subdivided into a data acquisition unit 11 and a data preprocessing unit 12. Specifically, the data acquisition unit 11 can collect multidimensional parameters related to energy recovery performance in real time through a variety of acquisition devices and monitoring software. These parameters cover electrical parameters, mechanical responses, and environmental variables, with data from a wide range of sources and comprehensive dimensions. The data preprocessing unit 12 standardizes the format, denoises, and dimensionlessly processes the collected data to ensure data consistency and comparability during subsequent analysis, and then divides the data into three data groups based on parameter characteristics.

[0032] Through the systematic classification and preprocessing of three sets of data, this system can achieve efficient normalization and coupling preparation of multi-source heterogeneous data on the basis of ensuring the accuracy of collected data, and provide accurate and real-time basic data support for subsequent energy detection, optimization intervention, collaborative control and other modules. Compared with the traditional energy recovery system that relies only on a single electrical parameter or simplified physical quantity as the basis for feedback, this invention has achieved significant improvements in data dimension, processing depth and logical structure, enhanced the system's state perception ability, operation adaptability and control accuracy, and thus improved the overall energy recovery efficiency and system robustness. Example 3: Please refer to Figure 1 , the energy detection module 2 includes an energy analysis coefficient extraction unit 21 and an energy analysis coefficient analysis unit 22; The energy analysis coefficient extraction unit 21 is used to extract data from the first data group, the second data group, and the third data group, including the inverter power A, harmonic content E, motor current G, energy storage current H, battery temperature I, and bus voltage fluctuation rate L, and couple the data to generate the energy stability detection coefficient NLW; The energy analysis coefficient analysis unit 22 is used to analyze the energy stability detection coefficient NLW. The specific method is as follows: when When , it means that the current energy recovery is in the first-level stable state and no adjustment is required; when When , it means that the current energy recovery is in the secondary stable state, and intervention optimization is performed. The parameters after intervention optimization are then extracted to generate the optimized intervention coefficient YHX for analysis. when , it means that the current energy recovery is at the third level of stability and needs to be fully adjusted.

[0033] In this embodiment, the energy detection module 2 is subdivided into an energy analysis coefficient extraction unit 21 and an energy analysis coefficient analysis unit 22, significantly improving the ability to accurately monitor and dynamically adjust the energy recovery status. The energy analysis coefficient extraction unit 21 extracts and couples key parameters from the first, second, and third data sets to comprehensively generate the energy stability detection coefficient NLW. This coefficient quantitatively reflects the stability level of the entire energy recovery system, providing a comprehensive and accurate health assessment of the system.

[0034] The energy analysis coefficient analysis unit 22 implements a multi-level judgment and dynamic adjustment strategy based on the generated energy stability detection coefficient NLW, thereby ensuring the energy recovery efficiency of the system under different working conditions. By introducing the energy stability detection coefficient NLW based on multi-dimensional data coupling, refined management and adaptive adjustment of the system status are achieved. Compared with the traditional energy recovery system that relies on a single indicator or empirical rule for adjustment, the present invention uses a dynamic adjustment mechanism to be able to optimize and intervene in time when the system has slight fluctuations, avoiding the lagging reaction of the traditional system when encountering large fluctuations. In addition, by combining NLW with specific operational decisions, the predictability of energy recovery efficiency and the long-term stability of the system are improved, thereby providing a strong guarantee for the realization of intelligent energy management and self-healing functions.

[0035] Example 4: Please refer to Figure 1, the energy stability detection coefficient NLW is calculated by the following formula: ; Where: inverter power A, harmonic content E, motor current G, energy storage current H, battery temperature I and bus voltage fluctuation rate L; a is the nonlinear coefficient, and its value range is [0.5, 2]. It is specifically set by the user; b is the effect index, which ranges from [0.5, 3] and is set by the user; c is the coupling coefficient, which ranges from [0.5, 2] and is adjusted by the user.

[0036] In this embodiment, the introduction of exponential power functions and logarithmic functions effectively reflects the nonlinear dynamic response characteristics between key parameters in the energy system, overcoming the problem of poor adaptability of traditional linear indicator evaluation methods to complex systems.

[0037] The a, b, and c coefficients are all set as adjustable parameters, allowing users to flexibly configure them according to system type, load characteristics, or application environment, enhancing the versatility and customizability of the system under different working conditions.

[0038] The formula integrates multiple dimensions of information, including power operation efficiency, energy storage health status, power quality, and system impact response, into the same evaluation system, avoiding the defect of a single parameter being susceptible to noise interference and improving the stability and robustness of the overall evaluation.

[0039] As the core reference for the subsequent module triggering mechanism, the energy stability detection coefficient NLW index has the characteristics of continuity, quantification and reversibility, providing a solid foundation for the system to achieve state judgment, closed-loop regulation and adaptive optimization.

[0040] Example 5: Please refer to Figure 1 , the optimization intervention module 3 includes an optimization coefficient extraction unit 31 and an optimization coefficient analysis unit 32; The optimization coefficient extraction unit 31 is used to extract data from the first data group, the second data group, and the third data group, including the harmonic content E, the battery temperature I, the power factor K, and the energy storage response time B, and couple the data to generate the optimized intervention coefficient YHX; The optimization coefficient analysis unit 32 is used to analyze the optimization intervention coefficient YHX. The specific method is as follows: when When , it means that the current energy recovery has returned to the first-level stable state and no adjustment is needed; when When , it means that the current energy recovery is in the secondary stable state, and collaborative optimization is performed. The parameters after collaborative optimization are extracted again to generate the collaborative control analysis coefficient XTX for analysis; when , it means that the current energy recovery is at the third level of stability and needs to be fully adjusted.

[0041] In this embodiment, by implementing an optimization intervention module 3, further subdivided into an optimization coefficient extraction unit 31 and an optimization coefficient analysis unit 32, the system's ability to perceive and intervene in energy recovery performance under metastable conditions is significantly enhanced. The optimization coefficient extraction unit 31 extracts key parameters from the first, second, and third data sets and, through a specific coupling calculation method, generates an optimization intervention coefficient, YHX. This coefficient is used to comprehensively measure the system's operational stability and response efficiency under optimization intervention conditions.

[0042] The idea of ​​hierarchical intervention and dynamic progressive control logic in the process of stability assessment and adjustment. Compared with the traditional system's passive response, single-stage adjustment, or reliance on human experience for intervention, the present invention constructs a unified optimization criterion YHX by coupling multi-dimensional key operating parameters, realizing the automation and refinement of stable state identification. At the same time, by matching different control strategies to different levels of stable states, the sensitivity of system response and the efficiency of control resource allocation are significantly improved. In addition, as an intermediary criterion between NLW and XTX, YHX plays an important role in connecting energy detection and collaborative control. It has good system embeddability and linkage coordination, and provides a high-precision input basis for the collaborative decision-making and intelligent control of subsequent modules.

[0043] Example 6: Please refer to Figure 1 , the specific calculation method of the optimized intervention coefficient YHX is as follows; ; Where: E is the harmonic content, I is the battery temperature, K is the power factor, B is the energy storage response time, and NLW is the energy stability detection coefficient.

[0044] In this embodiment, the optimized intervention coefficient YHX formula couples multiple key physical parameters, including harmonic content, energy storage system response time, battery temperature, and power factor, reflecting the complexity and diversity of energy recovery systems. Compared to traditional regulation methods that rely solely on a single indicator, this calculation method more accurately reflects the actual operating status of the system, avoiding the undue influence of changes in a single parameter on the overall judgment.

[0045] In the formula, the ln(1+B) portion uses a logarithmic function, allowing for accurate assessment even when the energy storage response time B is short. This design effectively enhances sensitivity to the energy storage system's rapid response capabilities, avoids neglecting small fluctuations, and improves the immediacy and accuracy of system regulation.

[0046] The energy stability detection coefficient (NLW), as the core factor of the energy stability detection coefficient, represents the current stability of the system. By combining the energy stability detection coefficient (NLW) with other control parameters, the optimization intervention coefficient (YHX) accurately reflects the system's energy recovery efficiency and optimization requirements, providing a reliable decision-making basis for subsequent optimization interventions.

[0047] Based on the output of the optimized intervention coefficient YHX, the system can automatically determine whether it is in a stable state and adopt different levels of control measures such as collaborative optimization or comprehensive adjustment according to actual needs. Through this refined optimization mechanism, the present invention can flexibly adjust the energy recovery strategy in unstable states, ensuring the continuous and efficient operation of the system to the greatest extent possible.

[0048] The proposed calculation method for the optimized intervention coefficient, YHX, leverages multidimensional data coupling and nonlinear function processing, demonstrating significant technical advantages in improving the system's adaptive regulation accuracy, optimizing control efficiency, and reducing energy loss. Compared with existing technologies, this method can better cope with complex operating conditions and dynamic changes, enabling intelligent and refined control of energy recovery systems, and providing new insights for technological innovation in the field of energy management.

[0049] Example 7: Please refer to Figure 1 , the collaborative control module 4 includes a collaborative coefficient extraction unit 41 and a collaborative coefficient analysis unit 42; The synergy coefficient extraction unit 41 is used to extract the first data group, the second data group, and the third data group, including the inverter input and output power A, harmonic content E, motor current G, energy storage current H, battery temperature I, and power factor K, and couple the data to generate a synergy control analysis coefficient XTX; The coordination coefficient analysis unit 42 is used to analyze the coordination control analysis coefficient XTX. The specific method is as follows: when When , it means that the current energy recovery has returned to the first-level stable state and no adjustment is needed; when When , it means that the current energy recovery is in the secondary stable state. Characteristic analysis is performed to generate the bottleneck coefficient PIX and the disturbance coefficient RDX. The two are analyzed to determine the unstable factors of energy recovery.

[0050] In this embodiment, the collaborative control module 4 implements efficient analysis and intelligent collaborative control of key operating parameters of the energy recovery system by establishing a collaborative coefficient extraction unit 41 and a collaborative coefficient analysis unit 42. The collaborative coefficient extraction unit 41 extracts key information from the first, second, and third data sets and, through precise coupled calculations, generates a collaborative control analysis coefficient XTX. This coefficient provides a quantitative basis for determining the system's current energy recovery status and whether further adjustments are needed.

[0051] By introducing the collaborative control analysis coefficient XTX and combining it with different numerical thresholds to assess system status, we can accurately determine system stability and decide whether further adjustments or optimizations are needed. This hierarchical management approach is more flexible in responding to dynamic changes and load fluctuations in the system, compared to the single stable state assessment used in traditional systems.

[0052] When the system is in a secondary stable state, by generating and analyzing the bottleneck coefficient PIX and the disturbance coefficient RDX, the system can automatically identify the key factors that lead to instability, providing a scientific basis for subsequent optimization decisions. This feature recognition capability greatly enhances the system's adaptive optimization capabilities.

[0053] The collaborative control module couples and analyzes the key parameters of multiple subsystems, such as the inverter, energy storage system, and motor, to form a collaborative optimization mechanism between the overall system and each subsystem. Unlike traditional systems that solve single problems through local optimization, the collaborative control mechanism of the present invention optimizes system operation from a global perspective, which can better coordinate the operation of each subsystem and further improve the overall energy recovery efficiency. Through precise energy recovery stability assessment and feature analysis, the system can allocate resources more efficiently, avoid unnecessary adjustment interventions, and ensure the system's high robustness and stability when facing different load fluctuations and environmental changes.

[0054] Example 8: Please refer to Figure 1 , the specific calculation formula of the collaborative control analysis coefficient XTX is as follows: ; Where: A is the input and output power of the inverter, E is the harmonic content, G is the motor current, H is the energy storage current, I is the battery temperature, K is the power factor, and YHX is the optimization intervention coefficient.

[0055] In this embodiment, by integrating the core operating parameters of the inverter, motor, and energy storage system, a formula mathematically models the coupling relationships between the various subunits within the system. Compared to traditional approaches that rely on single-dimensional indicators for adjustment, this method comprehensively considers the influence of multiple sources, forming an evaluation factor that is more closely aligned with actual system behavior, improving the accuracy and representativeness of the judgment.

[0056] The calculation of the collaborative control analysis coefficient XTX uses the optimization intervention coefficient YHX as the overall amplification factor, realizing the deep linkage between the front-stage optimization module and the collaborative control module, effectively opening up the data chain, and enabling the system to simultaneously consider the overall change trend before and after the optimization intervention when conducting collaborative analysis, thereby enhancing the upstream and downstream consistency and response coordination of the control strategy.

[0057] The fractional structure in the formula and the nonlinear function of the motor current G are constructed to amplify the coupled mutation effect of key physical quantities, and have a higher ability to identify sudden disturbances and nonlinear fluctuations in the energy system, thereby realizing a more detailed coordinated regulation strategy.

[0058] By coupling local efficiency metrics such as power factor K and harmonic content E, combined with the amplification mechanism of the overall coefficient YHX, XTX not only accurately depicts the current state of system coordination but also considers long-term stable operating trends. This structural design makes system control more flexible and explainable, facilitating the design and implementation of closed-loop software and hardware control systems.

[0059] The collaborative control analysis coefficient XTX is the core criterion in the collaborative control module. Its sub-item structure can serve as an important basis for the subsequent extraction of the bottleneck coefficient PIX and the disturbance coefficient RDX, which helps to achieve rapid identification and quantitative analysis of the system's internal constraints and external interference sources.

[0060] Example 9: Please refer to Figure 1 , the structural analysis module 5 includes a bottleneck coefficient extraction unit 51, a bottleneck coefficient analysis unit 52, a disturbance coefficient extraction unit 53 and a disturbance coefficient analysis unit 54; The bottleneck coefficient extraction unit 51 and the disturbance coefficient extraction unit 53 are respectively used to extract data from the first data group, the second data group, and the third data group, including the inverter input and output power A, the energy storage charging response time B, the cable voltage drop C, the mechanical vibration spectrum D, the harmonic content E, the load power impact rate F, the motor current G, the energy storage current H, the battery temperature I, the motor speed J, the power factor K, and the bus voltage fluctuation rate L, and respectively perform data coupling to generate the bottleneck coefficient PIX and the disturbance coefficient RDX; The bottleneck coefficient PIX and the disturbance coefficient RDX are calculated by the following formulas: ; ; Where: A is the inverter input and output power, B is the energy storage charging response time, C is the cable voltage drop, D is the mechanical vibration spectrum, E is the harmonic content, F is the load power impact rate, G is the motor current, H is the energy storage current, I is the battery temperature, J is the motor speed, K is the power factor, L is the bus voltage fluctuation rate, and XTX is the cooperative control analysis coefficient; The bottleneck coefficient analysis unit 52 and the disturbance coefficient analysis unit 54 analyze the bottleneck coefficient PIX and the disturbance coefficient RDX respectively. The specific methods are as follows: when When , it means that there is no equipment bottleneck problem in the current energy recovery; when When , it means that the current energy recovery equipment has a bottleneck problem; when When , it means that there is no disturbance problem in the current energy recovery; when , it means that there is a disturbance problem in the current energy recovery.

[0061] In this embodiment: by constructing the bottleneck coefficient PIX and the disturbance coefficient RDX respectively, and combining the collaborative control analysis coefficient XTX weighting factor, "structural problems" and "disturbance factors" are effectively distinguished and quantitatively identified, breaking through the limitations of fuzzy fault judgment in traditional energy recovery systems.

[0062] The calculation of the bottleneck coefficient PIX and the disturbance coefficient RDX not only considers the actual coupling relationship between variables but also enhances sensitivity to sudden anomalies through reasonable logarithmic and fractional structures. Preset judgment thresholds enable rapid judgment of operating status and automated triggering of intervention mechanisms.

[0063] The bottleneck coefficient PIX and the disturbance coefficient RDX, output as the final structural analysis results, form a closed loop with the collaborative control analysis coefficient XTX, ensuring a clear and logically logical analysis chain and smooth data flow for the entire energy recovery system. Compared to traditional methods that rely solely on total power or a single indicator for fluctuation monitoring, the proposed analysis method offers greater interpretability and traceability, facilitating targeted maintenance by maintenance personnel.

[0064] The introduction of Structural Analysis Module 5 empowers the system with proactive fault detection and hidden danger warning capabilities. Independent identification mechanisms for both bottlenecks and disturbances promptly indicate the source of potential problems, enabling subsequent optimization intervention modules 3 or collaborative control modules to implement targeted strategies, fostering the establishment of an intelligent O&M closed loop of "self-diagnosis, self-adjustment, and self-optimization."

[0065] This application also includes, see Figure 2 , the adaptive recovery and power generation method of the gravity potential energy of the pumping unit, the specific steps are as follows; S1. Extracting and preprocessing the recycled power generation data through the data extraction module 1, and reorganizing the data into a first data group, a second data group, and a third data group; S2. Extracting data from the first data group, the second data group, and the third data group through the energy detection module 2, and coupling the extracted data to generate an energy stability detection coefficient NLW. Analyzing the energy stability detection coefficient NLW to determine whether the current energy recovery is in a stable state; S3, performing data plotting on the first data group, the second data group, and the third data group by the optimization intervention module 3, and coupling the extracted data to generate an optimization intervention coefficient YHX, and analyzing the optimization intervention coefficient YHX to determine whether the optimization intervention and energy recovery are stable; S4. Extracting data from the first data group, the second data group, and the third data group through the collaborative control module 4, and coupling the extracted data to generate a collaborative control analysis coefficient XTX. Analyzing the collaborative control analysis coefficient XTX to determine whether energy recovery is stable after the collaborative analysis; S5. Extracting data from the first data group, the second data group, and the third data group through the structure analysis module 5, coupling the extracted data to generate a bottleneck coefficient PIX and a disturbance coefficient RDX, and analyzing the bottleneck coefficient PIX and the disturbance coefficient RDX respectively; S6. Feedback various parameters and results to the visualization terminal through the feedback module 6.

[0066] The contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0067] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The adaptive recovery and power generation system of gravity potential energy of the oil pumping unit is characterized by: It includes a data extraction module (1), an energy detection module (2), an optimization intervention module (3), a collaborative control module (4), a structure analysis module (5) and a feedback module (6); The data extraction module (1) is used to extract the recovered power generation data, perform pre-processing, and reorganize it into a first data group, a second data group, and a third data group; The energy detection module (2) extracts data from the first data group, the second data group, and the third data group, and couples the extracted data to generate an energy stability detection coefficient NLW, and analyzes the energy stability detection coefficient NLW to determine whether the current energy recovery is in a stable state; The optimization intervention module (3) is used to perform data plotting on the first data group, the second data group, and the third data group, and couple the extracted data to generate an optimization intervention coefficient YHX, and analyze the optimization intervention coefficient YHX to determine whether the optimization intervention and energy recovery are stable; The collaborative control module (4) is used to extract data from the first data group, the second data group, and the third data group, and couple the extracted data to generate a collaborative control analysis coefficient XTX, and analyze the collaborative control analysis coefficient XTX to determine whether the energy recovery is stable after the collaborative analysis; The structural analysis module (5) is used to extract data from the first data group, the second data group, and the third data group, and couple the extracted data to generate a bottleneck coefficient PIX and a disturbance coefficient RDX, and analyze the bottleneck coefficient PIX and the disturbance coefficient RDX respectively; The feedback module (6) is used to feed back various parameters and results to the visualization terminal.

2. The pumping unit gravity potential energy adaptive recovery power generation system according to claim 1 is characterized by: The data extraction module (1) includes a data acquisition unit (11) and a data preprocessing unit (12); The data acquisition unit (11) is used to collect energy recovery related parameters through a variety of acquisition devices and monitoring software, including: The data preprocessing unit (12) preprocesses and dimensionlessly converts the collected data, and reorganizes the data into a first data group, a second data group, and a third data group; The first data group includes the inverter input and output power A, the energy storage charging response time B, and the cable voltage drop C; The second data set includes the mechanical vibration spectrum D, harmonic content E, and load power impact rate F; The third data group includes motor current G, energy storage current H, battery temperature I, motor speed J, power factor K, and bus voltage fluctuation rate L.

3. The pumping unit gravity potential energy adaptive recovery and power generation system according to claim 2 is characterized by: The energy detection module (2) includes an energy analysis coefficient extraction unit (21) and an energy analysis coefficient analysis unit (22); The energy analysis coefficient extraction unit (21) is used to extract data from the first data group, the second data group, and the third data group, including inverter power A, harmonic content E, motor current G, energy storage current H, battery temperature I, and bus voltage fluctuation rate L, and couple the data to generate an energy stability detection coefficient NLW; The energy analysis coefficient analysis unit (22) is used to analyze the energy stability detection coefficient NLW, and the specific method is as follows: when When , it means that the current energy recovery is in the first-level stable state and no adjustment is required; when When , it means that the current energy recovery is in the secondary stable state, and intervention optimization is performed. The parameters after intervention optimization are then extracted to generate the optimized intervention coefficient YHX for analysis. when , it means that the current energy recovery is at the third level of stability and needs to be fully adjusted.

4. The pumping unit gravity potential energy adaptive recovery power generation system according to claim 3 is characterized by: The energy stability detection coefficient NLW is calculated by the following formula: ； Where: inverter power A, harmonic content E, motor current G, energy storage current H, battery temperature I and bus voltage fluctuation rate L; a is the nonlinear coefficient, and its value range is [0.5, 2]. It is specifically set by the user; b is the effect index, which ranges from [0.5, 3] and is set by the user; c is the coupling coefficient, which ranges from [0.5, 2] and is adjusted by the user.

5. The pumping unit gravity potential energy adaptive recovery and power generation system according to claim 4 is characterized in that: The optimization intervention module (3) includes an optimization coefficient extraction unit (31) and an optimization coefficient analysis unit (32); The optimization coefficient extraction unit (31) is used to extract data from the first data group, the second data group, and the third data group, including harmonic content E, battery temperature I, power factor K, and energy storage response time B, and couple the data to generate an optimization intervention coefficient YHX; The optimization coefficient analysis unit (32) is used to analyze the optimization intervention coefficient YHX, and the specific method is as follows: when When , it means that the current energy recovery has returned to the first-level stable state and no adjustment is needed; when When , it means that the current energy recovery is in the secondary stable state, and collaborative optimization is performed. The parameters after collaborative optimization are extracted again to generate the collaborative control analysis coefficient XTX for analysis; when , it means that the current energy recovery is at the third level of stability and needs to be fully adjusted.

6. The pumping unit gravity potential energy adaptive recovery and power generation system according to claim 5 is characterized by: The specific calculation method of the optimized intervention coefficient YHX is as follows: ； Where: E is the harmonic content, I is the battery temperature, K is the power factor, B is the energy storage response time, and NLW is the energy stability detection coefficient.

7. The pumping unit gravity potential energy adaptive recovery and power generation system according to claim 6 is characterized by: The collaborative control module (4) includes a collaborative coefficient extraction unit (41) and a collaborative coefficient analysis unit (42); The synergy coefficient extraction unit (41) is used to extract the first data group, the second data group and the third data group, including the inverter input and output power A, the harmonic content E, the motor current G, the energy storage current H, the battery temperature I and the power factor K, and couple the data to generate a synergy control analysis coefficient XTX; The synergy coefficient analysis unit (42) is used to analyze the synergy control analysis coefficient XTX, and the specific method is as follows: when When , it means that the current energy recovery has returned to the first-level stable state and no adjustment is needed; when When , it means that the current energy recovery is in the secondary stable state. Characteristic analysis is performed to generate the bottleneck coefficient PIX and the disturbance coefficient RDX. The two are analyzed to determine the unstable factors of energy recovery.

8. The pumping unit gravity potential energy adaptive recovery and power generation system according to claim 7 is characterized in that: The specific calculation formula of the collaborative control analysis coefficient XTX is as follows: ； Where: A is the input and output power of the inverter, E is the harmonic content, G is the motor current, H is the energy storage current, I is the battery temperature, K is the power factor, and YHX is the optimization intervention coefficient.

9. The pumping unit gravity potential energy adaptive recovery and power generation system according to claim 8, characterized in that: The structural analysis module (5) includes a bottleneck coefficient extraction unit (51), a bottleneck coefficient analysis unit (52), a disturbance coefficient extraction unit (53), and a disturbance coefficient analysis unit (54); The bottleneck coefficient extraction unit (51) and the disturbance coefficient extraction unit (53) are respectively used to extract data from the first data group, the second data group and the third data group, including the inverter input and output power A, the energy storage charging response time B, the cable voltage drop C, the mechanical vibration spectrum D, the harmonic content E and the load power impact rate F, the motor current G, the energy storage current H, the battery temperature I, the motor speed J, the power factor K and the bus voltage fluctuation rate L, and respectively perform data coupling to generate the bottleneck coefficient PIX and the disturbance coefficient RDX; The bottleneck coefficient PIX and the disturbance coefficient RDX are calculated by the following formulas: ； ； Where: A is the inverter input and output power, B is the energy storage charging response time, C is the cable voltage drop, D is the mechanical vibration spectrum, E is the harmonic content, F is the load power impact rate, G is the motor current, H is the energy storage current, I is the battery temperature, J is the motor speed, K is the power factor, L is the bus voltage fluctuation rate, and XTX is the cooperative control analysis coefficient; The bottleneck coefficient analysis unit (52) and the disturbance coefficient analysis unit (54) analyze the bottleneck coefficient PIX and the disturbance coefficient RDX respectively. The specific methods are as follows: when When , it means that there is no equipment bottleneck problem in the current energy recovery; when When , it means that the current energy recovery equipment has a bottleneck problem; when When , it means that there is no disturbance problem in the current energy recovery; when , it means that there is a disturbance problem in the current energy recovery.

10. A method for adaptively recovering and generating electricity from the gravity potential energy of an oil pumping unit, characterized by: The method for adaptively recovering the gravity potential energy of an oil pumping unit for power generation is used to implement the adaptively recovering the gravity potential energy of an oil pumping unit for power generation described in any one of claims 1 to 9. The specific steps are as follows: S1, extracting the recycled power generation data through the data extraction module (1), performing pre-processing, and reorganizing the data into a first data group, a second data group, and a third data group; S2, extracting data from the first data group, the second data group, and the third data group through the energy detection module (2), and coupling the extracted data to generate an energy stability detection coefficient NLW, and analyzing the energy stability detection coefficient NLW to determine whether the current energy recovery is in a stable state; S3, performing data plotting on the first data group, the second data group, and the third data group through the optimization intervention module (3), and coupling the extracted data to generate an optimization intervention coefficient YHX, and analyzing the optimization intervention coefficient YHX to determine whether the optimization intervention and energy recovery are stable; S4, extracting data from the first data group, the second data group, and the third data group through the collaborative control module (4), and coupling the extracted data to generate a collaborative control analysis coefficient XTX, analyzing the collaborative control analysis coefficient XTX, and determining whether energy recovery is stable after the collaborative analysis; S5, extracting data from the first data group, the second data group, and the third data group through the structure analysis module (5), coupling the extracted data, thereby generating a bottleneck coefficient PIX and a disturbance coefficient RDX, and analyzing the bottleneck coefficient PIX and the disturbance coefficient RDX respectively; S6. Feedback various parameters and results to the visualization terminal through the feedback module (6).

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