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

Abstract

The present application relates to the technical field of pumping unit, and discloses a pumping unit gravity potential energy self-adaptive recovery power generation system and a control method thereof, comprising a data extraction module, an energy detection module, an optimization intervention module, a collaborative control module, a structure analysis module and a feedback module, the system integrates multiple functional modules to build an intelligent energy recovery system with data closed loop, autonomous adjustment capability and cross-domain collaborative mechanism. Compared with the system in the prior art which relies on single sensor feedback and cannot realize deep collaboration or real-time response, the system has significantly improved in stability judgment accuracy, energy recovery efficiency, control strategy adaptability and equipment maintenance visualization. By introducing multi-dimensional coupling coefficient and physical structure analysis model, the system has realized the transformation from "passive control" to "active optimization".

Description

Technical Field

[0001] This invention relates to the field of oil pumping unit technology, specifically to an adaptive recovery and power generation system for the gravitational potential energy of an oil pumping unit and its control method. Background Technology

[0002] With the continuous development of modern energy technologies, how to efficiently recover and utilize residual energy in mechanical systems has become an important research direction. Especially in the field of oil and gas machinery, pumping units, as common mechanical equipment widely used in oilfield development, suffer from significant mechanical energy loss. The operation of pumping units relies on electric power, but during operation, some energy is wasted in the form of gravitational potential energy. Therefore, how to efficiently recover and utilize this energy has become a key research focus in this technological field.

[0003] In the operation of traditional oil pumping units, although some energy recovery mechanisms exist, the recovery efficiency is far from ideal due to limitations in equipment design and insufficient energy conversion efficiency. In existing technologies, problems such as low energy recovery efficiency, poor system stability, and significant energy waste during equipment operation remain prevalent. Therefore, although many oil fields have adopted "energy-saving" oil pumping units, the overall energy recovery effect still fails to meet the actual needs of high efficiency and energy saving.

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

[0005] The purpose of this invention is to provide an adaptive gravity potential energy recovery power generation system and control method for oil pumping units, so as to solve the problems that still exist in the prior art, such as low energy recovery efficiency, poor system stability, and large energy waste during equipment operation.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an adaptive recovery and power generation system for the gravitational potential energy of an oil pumping unit, comprising a data extraction module, an energy detection module, an optimization intervention module, a collaborative control module, a structural analysis module, and a feedback module;

[0007] The data extraction module is used to extract the recycled power generation data, preprocess it, and reorganize it into a first data group, a second data group, and a third data group.

[0008] 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 the 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.

[0009] The optimization intervention module is used to map the first data group, the second data group, and the third data group, and couple the extracted data to generate the optimization intervention coefficient YHX. The optimization intervention coefficient YHX is then analyzed to determine whether the optimization intervention and energy recovery are stable.

[0010] The collaborative control module is used to extract data from the first data group, the second data group, and the third data group, and to couple the extracted data to generate collaborative control analysis coefficients XTX. The collaborative control analysis coefficients XTX are then analyzed to determine whether the energy recovery is stable after collaborative analysis.

[0011] The structural analysis module is used to extract data from the first data group, the second data group, and the third data group, and to couple the extracted data to generate the bottleneck coefficient PIX and the disturbance coefficient RDX, and to analyze the bottleneck coefficient PIX and the disturbance coefficient RDX respectively.

[0012] The feedback module is used to feed back various parameters and results to the visualization terminal.

[0013] Preferably, the data extraction module includes a data acquisition unit and a data preprocessing unit;

[0014] The data acquisition unit is used to collect energy recovery-related parameters through various acquisition devices and monitoring software, including:

[0015] The data preprocessing unit preprocesses and dimensionlessly transforms the collected data, and reorganizes it into a first data group, a second data group, and a third data group.

[0016] The first data set includes inverter input and output power A, energy storage charging response time B, and cable voltage drop C;

[0017] The second data set includes the mechanical vibration spectrum D, harmonic content E, and load power impact rate F.

[0018] The third data set includes motor current G, energy storage current H, battery temperature I, motor speed J, power factor K, and bus voltage fluctuation rate L.

[0019] Preferably, the energy detection module includes an energy analysis coefficient extraction unit and an energy analysis coefficient analysis unit;

[0020] 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 the energy stability detection coefficient NLW.

[0021] The energy analysis coefficient analysis unit is used to analyze the energy stability detection coefficient NLW, and the specific method is as follows:

[0022] when When this time, it indicates that the current energy recovery is in a stable state and no adjustment is needed;

[0023] when When the current energy recovery is in a stable state at level two, intervention optimization is performed, and the parameters after intervention optimization are extracted again to generate the optimization intervention coefficient YHX for analysis;

[0024] when At this point, it indicates that the current energy recovery is at a stable level of three, requiring a comprehensive adjustment.

[0025] Preferably, the energy stability detection coefficient NLW is calculated using the following formula:

[0026] ;

[0027] Where: inverter power A, harmonic content E, motor current G, energy storage current H, battery temperature I, and bus voltage fluctuation rate L;

[0028] 'a' is a non-linear coefficient, with a value range of [0.5, 2], which is specifically set by the user.

[0029] b is the effect index, with a value range of [0.5, 3], which can be adjusted by the user.

[0030] c is the coupling coefficient, which ranges from [0.5, 2] and is specifically set by the user.

[0031] Preferably, the optimization intervention module includes an optimization coefficient extraction unit and an optimization coefficient analysis unit;

[0032] 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.

[0033] The optimization coefficient analysis unit is used to analyze the optimization intervention coefficient YHX, and the specific method is as follows:

[0034] when When this time, it means that the current energy recovery has returned to a stable state and no adjustment is needed;

[0035] when When the current energy recovery is in a stable state at level two, collaborative optimization is performed, and the parameters after collaborative optimization are extracted again to generate collaborative control analysis coefficients XTX for analysis.

[0036] when At this point, it indicates that the current energy recovery is at a stable level of three, requiring a comprehensive adjustment.

[0037] Preferably, the optimized intervention coefficient YHX is calculated in the following way;

[0038] ;

[0039] In the formula: 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.

[0040] Preferably, the collaborative control module includes a collaborative coefficient extraction unit and a collaborative coefficient analysis unit;

[0041] The collaborative coefficient extraction unit is used to extract the first data group, the second data group and the third data group, including 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 collaborative control analysis coefficient XTX.

[0042] The coordination coefficient analysis unit is used to analyze the coordination control analysis coefficient XTX, and the specific method is as follows:

[0043] when When this time, it means that the current energy recovery has returned to a stable state and no adjustment is needed;

[0044] when When the current energy recovery is in a stable second-order state, characteristic analysis is performed to generate the bottleneck coefficient PIX and the disturbance coefficient RDX, and the two are analyzed to determine the unstable factors of energy recovery.

[0045] Preferably, the specific calculation formula for the collaborative control analysis coefficient XTX is as follows:

[0046] ;

[0047] In the formula: A is the inverter input and output power, 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.

[0048] 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;

[0049] The bottleneck coefficient extraction unit and the disturbance coefficient extraction unit are used to extract data from the first data group, the second data group and the third data group, respectively, including inverter input and output power A, energy storage charging response time B, cable voltage drop C, mechanical vibration spectrum D, harmonic content E, load power impact rate F, motor current G, energy storage current H, battery temperature I, motor speed J, power factor K and bus voltage fluctuation rate L, and perform data coupling to generate bottleneck coefficient PIX and disturbance coefficient RDX;

[0050] The bottleneck coefficient PIX and the disturbance coefficient RDX are calculated using the following formulas:

[0051] ;

[0052] ;

[0053] 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 collaborative control analysis coefficient.

[0054] The bottleneck coefficient analysis unit and the disturbance coefficient analysis unit analyze the bottleneck coefficient PIX and the disturbance coefficient RDX, respectively, using the following methods:

[0055] when This indicates that there are currently no equipment bottlenecks in energy recovery;

[0056] when This indicates that there is a bottleneck problem with the current energy recovery equipment;

[0057] when This indicates that there are currently no disturbances in energy recovery;

[0058] when This indicates a disturbance in the current energy recovery process.

[0059] This application also includes a method for adaptive recovery and power generation of gravitational potential energy from an oil pumping unit, the specific steps of which are as follows:

[0060] S1. Extract the recovered power generation data through the data extraction module, preprocess it, and reorganize it into the first data group, the second data group, and the third data group.

[0061] S2. Extract data from the first data group, the second data group, and the third data group through the energy detection module, and couple the extracted data to generate the energy stability detection coefficient NLW. Analyze the energy stability detection coefficient NLW to determine whether the current energy recovery is in a stable state.

[0062] S3. The first data group, the second data group, and the third data group are plotted by the optimization intervention module, and the extracted data are coupled to generate the optimization intervention coefficient YHX. The optimization intervention coefficient YHX is analyzed to determine whether the optimization intervention and energy recovery are stable.

[0063] S4. Extract data from the first data group, the second data group, and the third data group through the collaborative control module, and couple the extracted data to generate collaborative control analysis coefficients XTX. Analyze the collaborative control analysis coefficients XTX to determine whether the energy recovery is stable after collaborative analysis.

[0064] S5. Extract data from the first data group, the second data group, and the third data group through the structural analysis module, and couple the extracted data to generate the bottleneck coefficient PIX and the disturbance coefficient RDX, and analyze the bottleneck coefficient PIX and the disturbance coefficient RDX respectively.

[0065] S6. Feedback modules are used to send various parameters and results to the visualization terminal.

[0066] Compared with the prior art, the beneficial effects of the present invention are:

[0067] 1. This system integrates multiple functional modules to construct an intelligent energy recovery system with data closed-loop, autonomous adjustment capabilities, and cross-domain collaborative mechanisms. Compared with existing systems that rely on single sensor feedback and cannot achieve deep collaboration or real-time response, this system has achieved significant improvements in stability judgment accuracy, energy recovery efficiency, control strategy adaptability, and equipment maintenance visualization. By introducing multi-dimensional coupling coefficients and physical structure analysis models, the system has achieved a transformation from "passive control" to "active optimization."

[0068] 2. The introduction of the structural analysis module enables the system to predictively identify faults and provide early warnings of potential problems. The independent identification mechanism for bottlenecks and disturbances can promptly indicate the source of potential problems, supporting subsequent optimization intervention modules or collaborative control modules to take targeted strategies, and promoting the construction of an intelligent operation and maintenance closed loop of "self-diagnosis - self-adjustment - self-optimization". Attached Figure Description

[0069] Figure 1 This is a system step diagram of the present invention.

[0070] Figure 2 This is a flowchart of the method of the present invention.

[0071] In the diagram: 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. Cooperative control module; 41. Cooperative coefficient extraction unit; 42. Cooperative coefficient analysis unit; 5. Structural analysis module; 51. Bottleneck coefficient extraction unit; 52. Bottleneck coefficient analysis unit; 53. Disturbance coefficient extraction unit; 54. Disturbance coefficient analysis unit; 6. Feedback module. Detailed Implementation

[0072] 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.

[0073] Example 1: Please refer to Figure 1 The adaptive recovery and power generation system of gravity potential energy of the oil pumping unit includes a data extraction module 1, an energy detection module 2, an optimization intervention module 3, a collaborative control module 4, a structural analysis module 5, and a feedback module 6.

[0074] The data extraction module 1 is used to extract the recycled power generation data, preprocess it, and reorganize it into a first data group, a second data group, and a third data group.

[0075] 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 the 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.

[0076] The optimized intervention module 3 is used to plot the data of the first data group, the second data group and the third data group, and to couple the extracted data to generate the optimized intervention coefficient YHX. The optimized intervention coefficient YHX is analyzed to determine whether the optimized intervention and energy recovery are stable.

[0077] 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 to couple the extracted data to generate collaborative control analysis coefficients XTX. The collaborative control analysis coefficients XTX are analyzed to determine whether the energy recovery is stable after collaborative analysis.

[0078] 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 to couple the extracted data to generate the bottleneck coefficient PIX and the disturbance coefficient RDX, and to analyze the bottleneck coefficient PIX and the disturbance coefficient RDX respectively.

[0079] Feedback module 6 is used to feed back various parameters and results to the visualization terminal.

[0080] In this embodiment, the data extraction module 1 is used to collect and preprocess the raw data generated during the operation of the oil pumping unit recovery power generation system. This module can automatically classify different types of data sources into three groups: a first data group, a second data group, and a third data group, and perform noise reduction, standardization, and format unification processing. The implementation of this module effectively improves data quality and analysis efficiency, providing reliable data support for subsequent energy status judgment, optimized control, and structural analysis of the system, thus achieving efficient perception and refined management of the system's operating status.

[0081] 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 the energy stability detection coefficient NLW. This coefficient characterizes the stability level of the current energy recovery and power generation stage of the system, and is used to determine whether the system is in a first-order stable state, a second-order stable state, or an unstable state. By fusing multi-source data, this module achieves accurate assessment of the energy recovery status, providing a scientific basis for the selection of subsequent optimization intervention or collaborative control strategies, and significantly improving the system's dynamic response capability and adaptive adjustment level.

[0082] The optimization intervention module 3 is used to extract key control parameters from the first, second, and third data sets when the energy detection module 2 determines that the system is in a level two stable or unstable state, and generates the optimization intervention coefficient YHX through coupling operations. This coefficient is used to measure the degree of improvement in system stability after the implementation of optimization measures, and guides the system to execute dynamic optimization strategies such as load adjustment, recovery rhythm reconfiguration, and energy flow reallocation. Through the configuration of this module, fine-grained adaptive adjustment can be achieved without affecting the overall system structure, effectively improving energy recovery efficiency and delaying the system aging trend.

[0083] The Cooperative Control Module 4 is activated when optimized intervention measures are insufficient to restore system stability. It further extracts key parameters from the first, second, and third data sets to construct the Cooperative Control Analysis Coefficient XTX. The Cooperative Control Analysis Coefficient XTX comprehensively reflects the cooperative operation effect among the system's sub-modules, assessing the efficiency of the linkage mechanism in the energy recovery process and identifying issues such as control delays, scheduling conflicts, or abnormal energy flow. Through XTX analysis, the system can automatically adjust the matching degree of control strategies and the coordination mechanism between modules, achieving cross-dimensional linkage optimization. The application of this module significantly enhances the overall coordination of the system and improves its operational robustness and energy efficiency under complex operating conditions.

[0084] The structural analysis module 5 extracts parameters related to mechanical structure, physical transmission path, and external disturbances from three types of data sets. Through modeling and analysis, it generates bottleneck coefficient PIX and disturbance coefficient RDX. The bottleneck coefficient PIX quantifies the limiting factors in the system structure, such as rotational resistance, transmission hysteresis, or component wear; the disturbance coefficient RDX reflects the system's sensitivity to external load fluctuations, vibration disturbances, or changes in ambient temperature. This module provides the system with physical-level fault precursor identification capabilities, assisting in pre-maintenance and structural iterative optimization, thereby improving the system's stable operation and structural adaptability from the source.

[0085] Feedback module 6 is used to provide real-time feedback of the key coefficients and analysis results extracted by the above modules to the system's visualization terminal. This module supports graphical interface display, anomaly alarm output, historical data comparison, and remote data synchronization, facilitating users' comprehensive understanding of the system's operating status and performance indicators. Through this module, the system possesses excellent human-computer interaction and intelligent monitoring capabilities, further enabling data-driven remote operation and maintenance and intelligent decision support, thereby improving the system's manageability and informatization level.

[0086] This system integrates multiple functional modules to construct an intelligent energy recovery system with data closed-loop, autonomous adjustment capabilities, and cross-domain collaborative mechanisms. Compared with existing systems that rely on single sensor feedback and cannot achieve deep collaboration or real-time response, this system has achieved significant improvements in stability judgment accuracy, energy recovery efficiency, control strategy adaptability, and equipment maintenance visualization. By introducing multi-dimensional coupling coefficients and physical structure analysis models, the system has transformed from "passive control" to "active optimization."

[0087] Example 2: Please refer to Figure 1 The data extraction module 1 includes a data acquisition unit 11 and a data preprocessing unit 12;

[0088] Data acquisition unit 11 is used to acquire energy recovery-related parameters through various acquisition devices and monitoring software, including:

[0089] The data preprocessing unit 12 preprocesses and dimensionlessly transforms the collected data, and reorganizes it into a first data group, a second data group, and a third data group.

[0090] The first data set includes inverter input and output power A, energy storage charging response time B, and cable voltage drop C;

[0091] The second data set includes the mechanical vibration spectrum D, harmonic content E, and load power impact rate F.

[0092] The third data set includes motor current G, energy storage current H, battery temperature I, motor speed J, power factor K, and bus voltage fluctuation rate L.

[0093] In this embodiment, the system effectively enhances the energy recovery system's comprehensive perception and intelligent processing capabilities of operational data by setting up 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 multi-dimensional parameters related to energy recovery performance in real time through various acquisition devices and monitoring software, covering electrical parameters, mechanical responses, and environmental variables, with a wide range of data sources and comprehensive dimensions. The data preprocessing unit 12 performs format unification, noise reduction, and dimensionless processing on the collected data to ensure data consistency and comparability in subsequent analysis processes, and divides the data into three data groups according to parameter characteristics.

[0094] By systematically classifying and preprocessing three sets of data, this system can efficiently normalize and couple multi-source heterogeneous data while ensuring the accuracy of the collected data. This provides accurate and real-time basic data support for subsequent energy detection, optimization intervention, and collaborative control modules. Compared to traditional energy recovery systems that rely solely on a single electrical parameter or simplified physical quantity as feedback, this invention achieves significant improvements in data dimension, processing depth, and logical structure. This enhances the system's state awareness, operational adaptability, and control precision, thereby improving overall energy recovery efficiency and system robustness.

[0095] 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;

[0096] 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 the energy stability detection coefficient NLW.

[0097] The energy analysis coefficient analysis unit 22 is used to analyze the energy stability detection coefficient NLW, and the specific method is as follows:

[0098] when When this time, it indicates that the current energy recovery is in a stable state and no adjustment is needed;

[0099] when When the current energy recovery is in a stable state at level two, intervention optimization is performed, and the parameters after intervention optimization are extracted again to generate the optimization intervention coefficient YHX for analysis;

[0100] when At this point, it indicates that the current energy recovery is at a stable level of three, requiring a comprehensive adjustment.

[0101] In this embodiment, the energy detection module 2 of the present invention, by being refined into an energy analysis coefficient extraction unit 21 and an energy analysis coefficient analysis unit 22, significantly improves 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 status assessment for the system.

[0102] 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 operating conditions. By introducing the energy stability detection coefficient NLW based on multi-dimensional data coupling, refined management and adaptive adjustment of the system state are achieved. Compared with the traditional energy recovery system's reliance on a single indicator or empirical rule for adjustment, this invention, through a dynamic adjustment mechanism, can promptly intervene in optimization when the system experiences slight fluctuations, avoiding the lagging response of traditional systems when encountering large fluctuations. Furthermore, by combining NLW with specific operational decisions, the predictability of energy recovery efficiency and the long-term stability of the system are improved, thus providing a strong guarantee for realizing intelligent energy management and self-healing functions.

[0103] Example 4: Please refer to Figure 1 The energy stability detection coefficient NLW is calculated using the following formula:

[0104] ;

[0105] Where: inverter power A, harmonic content E, motor current G, energy storage current H, battery temperature I, and bus voltage fluctuation rate L;

[0106] 'a' is a non-linear coefficient, with a value range of [0.5, 2], which is specifically set by the user.

[0107] b is the effect index, with a value range of [0.5, 3], which can be adjusted by the user.

[0108] c is the coupling coefficient, which ranges from [0.5, 2] and is specifically set by the user.

[0109] In this embodiment, the introduction of exponential 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 index evaluation methods to complex systems.

[0110] The coefficients a, b, and c are all set to adjustable parameters, which allows users to configure them flexibly according to system type, load characteristics, or application environment, thereby enhancing the versatility and customizability of the system under different operating conditions.

[0111] The formula integrates multiple dimensions of information, including power operation efficiency, energy storage health status, power quality, and system impact response, into a unified evaluation system. This avoids the shortcomings of single parameters being susceptible to noise interference and improves the stability and robustness of the overall assessment.

[0112] As the core reference for subsequent module triggering mechanisms, the energy stability detection coefficient NLW index has continuous, quantifiable, and inverse characteristics, providing a solid foundation for the system to realize state judgment, closed-loop regulation, and adaptive optimization.

[0113] 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;

[0114] 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 the optimization intervention coefficient YHX.

[0115] The optimization coefficient analysis unit 32 is used to analyze the optimization intervention coefficient YHX. The specific method is as follows:

[0116] when When this time, it means that the current energy recovery has returned to a stable state and no adjustment is needed;

[0117] when When the current energy recovery is in a stable state at level two, collaborative optimization is performed, and the parameters after collaborative optimization are extracted again to generate collaborative control analysis coefficients XTX for analysis.

[0118] when At this point, it indicates that the current energy recovery is at a stable level of three, requiring a comprehensive adjustment.

[0119] In this embodiment, by setting up an optimization intervention module 3, and further refining it 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 generates the optimization intervention coefficient YHX through a specific coupling calculation method. This coefficient is used to comprehensively measure the system's operational stability and response efficiency under optimized intervention conditions.

[0120] This invention employs a hierarchical intervention approach and dynamic progressive control logic in the stability assessment and adjustment process. Compared to traditional systems that rely on passive response, single-level adjustment, or intervention based on human experience, this invention constructs a unified optimization criterion YHX by coupling multiple key operating parameters, achieving automated and refined stable state identification. Simultaneously, by matching different control strategies to different levels of stable states, it significantly improves the sensitivity of the system response and the efficiency of control resource allocation. Furthermore, YHX, as an intermediary criterion between NLW and XTX, plays a crucial role in connecting energy detection and collaborative control, exhibiting good system embedding and linkage coordination, and providing high-precision input for the collaborative decision-making and intelligent control of subsequent modules.

[0121] Example 6: Please refer to Figure 1 The specific calculation method for the optimized intervention coefficient YHX is as follows;

[0122] ;

[0123] In the formula: 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.

[0124] In this embodiment, the optimized intervention coefficient YHX formula couples and calculates multiple important physical parameters such as harmonic content, energy storage system response time, battery temperature, and power factor, reflecting the complexity and diversity of energy recovery systems. Compared with traditional adjustment methods that rely on only a single indicator, the calculation method of this invention can more accurately reflect the actual operating state of the system and avoid the excessive influence of changes in a single parameter on the overall judgment.

[0125] In the formula, the ln(1+B) part adopts a logarithmic function form, thus providing accurate assessment even when the energy storage response time B is small. This design effectively enhances the sensitivity to the rapid response capability of the energy storage system, avoids ignoring small fluctuations, and improves the immediacy and accuracy of system regulation.

[0126] The energy stability detection coefficient NLW, as the core factor of the energy stability detection coefficient, represents the current stable state of the system. By comprehensively calculating 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, thus providing a reliable decision-making basis for subsequent optimization interventions.

[0127] Based on the output of the optimized intervention coefficient YHX, the system can automatically determine whether it is in a stable state and take different levels of control measures, such as coordinated optimization or comprehensive adjustment, as needed. Through this refined optimization mechanism, the present invention can flexibly adjust the energy recovery strategy in an unstable state, ensuring the continuous and efficient operation of the system to the greatest extent.

[0128] The proposed method for calculating the optimized intervention coefficient YHX, relying on multidimensional data coupling and nonlinear function processing, demonstrates significant technical advantages in improving the system's adaptive adjustment accuracy, optimizing control efficiency, and reducing energy loss. Compared with existing technologies, this method can better cope with complex operating conditions and dynamic changes, realizing intelligent and refined control of energy recovery systems, and providing new ideas for technological innovation in the field of energy management.

[0129] 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;

[0130] The collaborative 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 collaborative control analysis coefficient XTX.

[0131] The synergy coefficient analysis unit 42 is used to analyze the synergy control analysis coefficient XTX. The specific method is as follows:

[0132] when When this time, it means that the current energy recovery has returned to a stable state and no adjustment is needed;

[0133] when When the current energy recovery is in a stable second-order state, characteristic analysis is performed to generate the bottleneck coefficient PIX and the disturbance coefficient RDX, and the two are analyzed to determine the unstable factors of energy recovery.

[0134] In this embodiment, the collaborative control module 4, by establishing a collaborative coefficient extraction unit 41 and a collaborative coefficient analysis unit 42, achieves efficient analysis and intelligent collaborative control of key operating parameters of the energy recovery system. The collaborative coefficient extraction unit 41 extracts key information from the first, second, and third data sets and generates the collaborative control analysis coefficient XTX through precise coupling calculation. This coefficient provides a quantitative basis for judging the current energy recovery status of the system and whether further adjustments are needed.

[0135] By introducing the collaborative control analysis coefficient XTX and combining it with different numerical thresholds to assess the system's state, the system's stability can be accurately determined, and it can be decided whether further adjustments or optimizations are needed. Compared to the traditional system's single stable state assessment, this hierarchical management approach can more flexibly respond to dynamic changes and load fluctuations within the system.

[0136] When the system is in a second-order stable state, by generating and analyzing the bottleneck coefficient PIX and the disturbance coefficient RDX, the system can automatically identify the key factors leading to instability, providing a scientific basis for subsequent optimization decisions. This feature recognition capability greatly enhances the system's adaptive optimization capability.

[0137] The collaborative control module couples and analyzes key parameters of multiple subsystems, including the inverter, energy storage system, and motor, forming a collaborative optimization mechanism between the overall system and its various subsystems. Unlike traditional systems that solve single problems through local optimization, this invention's collaborative control mechanism optimizes system operation from a global perspective, better coordinating the operation of various subsystems and further improving overall energy recovery efficiency. Through precise energy recovery stability assessment and characteristic analysis, the system can allocate resources more efficiently, avoiding unnecessary adjustments and interventions, while ensuring high robustness and stability in the face of different load fluctuations and environmental changes.

[0138] Example 8: Please refer to Figure 1 The specific calculation formula for the collaborative control analysis coefficient XTX is as follows:

[0139] ;

[0140] In the formula: A is the inverter input and output power, 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.

[0141] In this embodiment, by integrating the core operating parameters of the inverter, motor, and energy storage system, the formula achieves a mathematical modeling of the coupling relationships between the various sub-units within the system. Compared with traditional schemes that rely on single-dimensional indicators for adjustment, this method can comprehensively consider the influence of multiple sources, forming evaluation factors that are closer to the actual system behavior, thus improving the accuracy and representativeness of the judgment.

[0142] The calculation of the collaborative control analysis coefficient XTX uses the optimization intervention coefficient YHX as the overall amplification factor, realizing deep linkage between the front-end 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 optimization intervention when performing collaborative analysis, thereby enhancing the consistency and response coordination of the upstream and downstream control strategy.

[0143] The fractional structure in the formula and the nonlinear function of the motor current G can amplify the coupling abrupt 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 refined coordinated regulation strategy.

[0144] By coupling local efficiency indices such as power factor K and harmonic content E, and combining this with the amplification mechanism of the overall coefficient YHX, XTX can not only accurately characterize the current cooperative state of the system, but also take into account the long-term stable operating trend. This structural design makes system control more flexible and interpretable, which helps to realize the design and implementation of a closed-loop control system in both hardware and software.

[0145] The collaborative control analysis coefficient XTX serves as the core criterion in the collaborative control module. Its component structure can be used as an important basis for the subsequent extraction of the bottleneck coefficient PIX and disturbance coefficient RDX, which helps to achieve rapid identification and quantitative analysis of internal constraints and external disturbance sources of the system.

[0146] 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.

[0147] The bottleneck coefficient extraction unit 51 and the disturbance coefficient extraction unit 53 are used to extract data from the first data group, the second data group and the third data group, respectively, including inverter input and output power A, energy storage charging response time B, cable voltage drop C, mechanical vibration spectrum D, harmonic content E, load power impact rate F, motor current G, energy storage current H, battery temperature I, motor speed J, power factor K and bus voltage fluctuation rate L, and perform data coupling to generate bottleneck coefficient PIX and disturbance coefficient RDX;

[0148] The bottleneck coefficient PIX and the disturbance coefficient RDX are calculated using the following formulas:

[0149] ;

[0150] ;

[0151] 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 collaborative control analysis coefficient.

[0152] Bottleneck coefficient analysis unit 52 and disturbance coefficient analysis unit 54 analyze the bottleneck coefficient PIX and disturbance coefficient RDX respectively, using the following methods:

[0153] when This indicates that there are currently no equipment bottlenecks in energy recovery;

[0154] when This indicates that there is a bottleneck problem with the current energy recovery equipment;

[0155] when This indicates that there are currently no disturbances in energy recovery;

[0156] when This indicates a disturbance in the current energy recovery process.

[0157] In this embodiment, by constructing two coefficients, the bottleneck coefficient PIX and the disturbance coefficient RDX, and combining them with the collaborative control analysis coefficient XTX weighting factor, the "structural problems" and "disturbance factors" can be effectively distinguished and quantitatively identified, thus overcoming the limitations of fuzzy fault judgment in traditional energy recovery systems.

[0158] The calculation of the bottleneck coefficient PIX and the disturbance coefficient RDX not only considers the actual coupling relationship between variables, but also enhances the sensitivity to sudden anomalies through reasonable logarithmic and fractional structures. By setting preset judgment thresholds, rapid judgment of operating status and automated triggering of intervention mechanisms can be achieved.

[0159] The bottleneck coefficient PIX and disturbance coefficient RDX, as the final structural analysis results, form a closed loop with the collaborative control analysis coefficient XTX, making the analysis chain of the entire energy recovery system logically clear and the data flow smooth. Compared with traditional methods that rely solely on total power or a single indicator for fluctuation monitoring, the analysis method proposed in this invention has stronger interpretability and traceability, facilitating targeted operation and maintenance by maintenance personnel.

[0160] The introduction of structural analysis module 5 enables the system to predictively identify faults and provide early warnings of potential hazards. The independent identification mechanisms for bottlenecks and disturbances can promptly identify the source of potential problems, supporting targeted strategies from subsequent optimization intervention module 3 or the collaborative control module, thus promoting the construction of an intelligent operation and maintenance closed loop of "self-diagnosis—self-adjustment—self-optimization".

[0161] This application also includes, please refer to, Figure 2 The adaptive recovery and power generation method of gravitational potential energy of oil pumping unit is described in the following steps;

[0162] S1. Extract the recovered power generation data through data extraction module 1, preprocess it, and reorganize it into the first data group, the second data group, and the third data group.

[0163] S2. Data is extracted from the first data group, the second data group and the third data group through the energy detection module 2, and the extracted data is coupled to generate the 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.

[0164] S3. The first data group, the second data group and the third data group are plotted by the optimization intervention module 3, and the extracted data are coupled to generate the optimization intervention coefficient YHX. The optimization intervention coefficient YHX is analyzed to determine whether the optimization intervention and energy recovery are stable.

[0165] S4. Data is extracted from the first data group, the second data group and the third data group through the collaborative control module 4, and the extracted data is coupled to generate collaborative control analysis coefficient XTX. The collaborative control analysis coefficient XTX is analyzed to determine whether the energy recovery is stable after collaborative analysis.

[0166] S5. Data is extracted from the first data group, the second data group and the third data group through the structural analysis module 5, and the extracted data is coupled to generate the bottleneck coefficient PIX and the disturbance coefficient RDX, and the bottleneck coefficient PIX and the disturbance coefficient RDX are analyzed respectively.

[0167] S6. Feedback modules 6 feed back various parameters and results to the visualization terminal.

[0168] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0169] 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. An adaptive recovery and power generation system for the gravitational potential energy of an oil pumping unit, characterized in that: It includes a data extraction module (1), an energy detection module (2), an optimization intervention module (3), a collaborative 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, preprocess it, 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 the 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 optimized intervention module (3) is used to perform data mapping on the first data group, the second data group and the third data group, and to couple the extracted data to generate the optimized intervention coefficient YHX. The optimized intervention coefficient YHX is analyzed to determine whether the optimized 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 collaborative control analysis coefficient XTX. The collaborative control analysis coefficient XTX is analyzed to determine whether the energy recovery is stable after 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 the bottleneck coefficient PIX and the disturbance coefficient RDX, and analyze the bottleneck coefficient PIX and the disturbance coefficient RDX respectively. 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 used to extract data from the first data group, the second data group and the third data group, respectively, including inverter input and output power A, energy storage charging response time B, cable voltage drop C, mechanical vibration spectrum D, harmonic content E, load power impact rate F, motor current G, energy storage current H, battery temperature I, motor speed J, power factor K and bus voltage fluctuation rate L, and respectively perform data coupling to generate bottleneck coefficient PIX and disturbance coefficient RDX; The bottleneck coefficient PIX and the disturbance coefficient RDX are calculated using 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 collaborative 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, and the specific methods are as follows: when This indicates that there are currently no equipment bottlenecks in energy recovery; when This indicates that there is a bottleneck problem with the current energy recovery equipment; when This indicates that there are currently no disturbances in energy recovery; when This indicates a disturbance in the current energy recovery process; The feedback module (6) is used to feed back various parameters and results to the visualization terminal.

2. The adaptive recovery and power generation system for the gravitational potential energy of an oil pumping unit according to claim 1, characterized in that: 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 acquire energy recovery-related parameters through various acquisition devices and monitoring software, including: The data preprocessing unit (12) preprocesses and dimensionless the collected data, and reorganizes it into a first data group, a second data group, and a third data group. The first data set includes inverter input and output power A, energy storage charging response time B, and 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 set 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 adaptive recovery and power generation system for the gravitational potential energy of an oil pumping unit according to claim 2, characterized in that: 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 to couple the data to generate 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 this time, it indicates that the current energy recovery is in a stable state and no adjustment is needed; when When the current energy recovery is in a stable state at level two, intervention optimization is performed, and the parameters after intervention optimization are extracted again to generate the optimization intervention coefficient YHX for analysis; when At this point, it indicates that the current energy recovery is at a stable level of three, requiring a comprehensive adjustment.

4. The adaptive recovery and power generation system for the gravitational potential energy of an oil pumping unit according to claim 3, characterized in that: The energy stability detection coefficient NLW is calculated using 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 a non-linear coefficient, with a value range of [0.5, 2], which is specifically set by the user. b is the effect index, with a value range of [0.5, 3], which can be adjusted by the user. c is the coupling coefficient, which ranges from [0.5, 2] and is specifically set by the user.

5. The adaptive recovery and power generation system for the gravitational potential energy of an oil pumping unit according to claim 4, 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 to couple the data to generate the 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 this time, it means that the current energy recovery has returned to a stable state and no adjustment is needed; when When the current energy recovery is in a stable state at level two, collaborative optimization is performed, and the parameters after collaborative optimization are extracted again to generate collaborative control analysis coefficients XTX for analysis. when At this point, it indicates that the current energy recovery is at a stable level of three, requiring a comprehensive adjustment.

6. The adaptive recovery and power generation system for the gravitational potential energy of an oil pumping unit according to claim 5, characterized in that: The specific calculation method for the optimized intervention coefficient YHX is as follows; ； In the formula: 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 adaptive recovery and power generation system for the gravitational potential energy of an oil pumping unit according to claim 6, characterized in that: The collaborative control module (4) includes a collaborative coefficient extraction unit (41) and a collaborative coefficient analysis unit (42). The collaborative 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 to couple the data to generate collaborative 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 this time, it means that the current energy recovery has returned to a stable state and no adjustment is needed; when When the current energy recovery is in a stable second-order state, characteristic analysis is performed to generate the bottleneck coefficient PIX and the disturbance coefficient RDX, and the two are analyzed to determine the unstable factors of energy recovery.

8. The adaptive recovery and power generation system for the gravitational potential energy of an oil pumping unit according to claim 7, characterized in that: The specific calculation formula for the collaborative control analysis coefficient XTX is as follows: ； In the formula: A is the inverter input and output power, 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. A method for adaptive recovery and power generation of gravitational potential energy from an oil pumping unit, characterized in that: The adaptive recovery and power generation method for the gravitational potential energy of the oil pumping unit is used to implement the adaptive recovery and power generation system for the gravitational potential energy of the oil pumping unit as described in any one of claims 1 to 8. The specific steps are as follows: S1. Extract the recycled power generation data through the data extraction module (1), preprocess it, and reorganize it into the first data group, the second data group and the third data group. S2. Data is extracted from the first data group, the second data group and the third data group through the energy detection module (2), and the extracted data is coupled to generate the 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. S3. The first data group, the second data group and the third data group are processed by the optimized intervention module (3), and the extracted data are coupled to generate the optimized intervention coefficient YHX. The optimized intervention coefficient YHX is analyzed to determine whether the optimized intervention and energy recovery are stable. S4. Data is extracted from the first data group, the second data group and the third data group through the collaborative control module (4), and the extracted data is coupled to generate collaborative control analysis coefficient XTX. The collaborative control analysis coefficient XTX is analyzed to determine whether the energy recovery is stable after collaborative analysis. S5. The first data group, the second data group and the third data group are extracted by the structural analysis module (5), and the extracted data are coupled to generate the bottleneck coefficient PIX and the disturbance coefficient RDX, and the bottleneck coefficient PIX and the disturbance coefficient RDX are analyzed respectively. S6. Feedback of various parameters and results to the visualization terminal through the feedback module (6).

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