An electrical automation intelligent control system

Abstract

The application relates to the technical field of electrical control, in particular to an electrical automation intelligent control system, which comprises a database, a model generation module, a data monitoring module, a data analysis module and a control adjustment module; the model generation module is used for determining a plurality of associated electrical equipment of a target device in a target area and generating an electrical twin model corresponding to the target device; the data monitoring module acquires current electrical parameters of each electrical equipment and current environmental parameters corresponding to each electrical equipment; the data analysis module is used for determining electrical operation deviation and environmental operation deviation and determining predicted electrical parameters corresponding to the target device based on the electrical twin model and the current electrical parameters of the target device; the control adjustment module is used for determining key electrical parameters corresponding to the target device based on the electrical operation deviation and the environmental operation deviation and determining adjusted electrical parameters based on the key electrical parameters and the predicted electrical parameters; the application can improve the control efficiency and accuracy of electrical equipment control and effectively ensure the operation stability of the electrical equipment.

Description

Technical Field

[0001] This invention relates to the field of electrical control technology, and in particular to an intelligent electrical automation control system. Background Technology

[0002] Electrical automation control systems play a crucial role in modern industrial production, aiming to achieve efficient, safe, and stable operation management of electrical equipment through automation. On the one hand, the number of electrical devices in large-scale production has surged, and their interconnections have become complex, making it difficult for traditional manual control or simple automation systems to achieve precise regulation. On the other hand, demands for energy conservation and fault early warning are driving the upgrading of control systems towards intelligence to cope with dynamically changing production environments and complex equipment operating states, and to meet the requirements of efficient, stable, and low-consumption production.

[0003] Traditional electrical automation control systems often employ a single-device, independent control mode, relying primarily on preset control logic and manual intervention. They collect equipment operating data through sensors and implement simple alarms and controls based on preset thresholds. However, with the increasing complexity of industrial production and the ever-increasing demands for equipment operating efficiency, traditional systems have gradually revealed some significant shortcomings. For example, traditional systems lack in-depth analysis and prediction capabilities of equipment operating states, cannot dynamically adjust control strategies based on actual operating conditions, and suffer from fixed models that fail to optimize control strategies based on real-time electrical parameters and predicted data, resulting in low equipment operating efficiency. Furthermore, traditional systems neglect the mutual influence of related devices, exhibiting weak data interaction and collaborative control capabilities between different devices, making it difficult to achieve coordinated and optimized operation of multiple devices. This leads to low overall control efficiency and accuracy, as well as poor stability.

[0004] Chinese Patent Application Publication No. CN119247811A discloses an intelligent control method and system for electrical equipment, including: outputting decision data for the next moment based on user-input demand commands; performing fault detection based on the operating status, and controlling the electrical equipment based on the decision data if no fault is detected; if a fault is detected, detecting the fault area and generating fault control data based on the fault area; generating fault decision data based on the fault control data and the decision data for the next moment, and controlling the electrical equipment based on the fault decision data. Fault decision data is generated by combining the decision data for the next moment with the fault control data.

[0005] The existing technology has the following problems: it only considers the impact of faults and demand decisions on the control of electrical equipment, and cannot optimize the control strategy based on real-time electrical parameters and predictive data. Furthermore, it is difficult to achieve coordinated optimization of multiple devices, resulting in low control efficiency and accuracy of the overall control system and poor operational stability. Summary of the Invention

[0006] To address this, the present invention provides an intelligent electrical automation control system to overcome the problems in the prior art, which cannot optimize control strategies based on real-time electrical parameters and predicted data, and is difficult to achieve coordinated optimization of multiple devices, resulting in low control efficiency and accuracy of the overall control system and poor operational stability.

[0007] To achieve the above objectives, the present invention provides an intelligent electrical automation control system, comprising:

[0008] The database is used to store historical electrical parameters of several electrical devices in the target area during operation, as well as historical environmental parameters corresponding to each electrical device.

[0009] A model generation module, connected to the database, is used to determine several associated electrical devices of the target device within the target area based on the historical environmental parameters corresponding to each of the electrical devices, and to generate an electrical twin model of the target device based on the historical electrical parameters corresponding to the target device and each of the associated electrical devices, wherein the target device is any electrical device within the target area;

[0010] The data monitoring module is used to monitor the operation of each electrical device in the target area in real time, so as to obtain the current electrical parameters and current environmental parameters of each electrical device;

[0011] The data analysis module, which is connected to the model generation module and the data monitoring module respectively, is used to determine the electrical operation deviation based on the current electrical parameters of the target device and each of the associated electrical devices, determine the environmental operation deviation based on the current environmental parameters of the target device and each of the associated electrical devices, and determine the predicted electrical parameters corresponding to the target device based on the electrical twin model and the current electrical parameters of the target device.

[0012] The control and adjustment module is connected to the model generation module and the data analysis module respectively. It is used to determine the key electrical parameters corresponding to the target equipment based on the electrical operation deviation and the environmental operation deviation, and to determine the adjustment electrical parameters based on the key electrical parameters and the predicted electrical parameters, so as to adjust the electrical parameters and the electrical twin model during the operation of the target equipment.

[0013] Furthermore, the model generation module includes:

[0014] The correlation analysis submodule is connected to the database and is used to construct the environmental parameter change curves corresponding to each electrical device based on the historical environmental parameters corresponding to each electrical device, and to determine several associated electrical devices of the target device in the target area based on the environmental parameter change curves.

[0015] The model generation submodule is connected to the database and the association analysis submodule, respectively, and is used to generate a target twin sub-model based on the historical electrical parameters corresponding to the target device, generate a number of associated twin sub-models based on the historical electrical parameters corresponding to each associated electrical device, and generate an electrical twin model corresponding to the target device based on the target twin sub-model and each of the associated twin sub-models.

[0016] Furthermore, the correlation analysis submodule includes:

[0017] A curve construction unit, which is connected to the database, is used to construct environmental parameter change curves for each electrical device based on the historical environmental parameters corresponding to each electrical device.

[0018] A change analysis unit, which is connected to the curve construction unit, is used to determine key change areas based on the environmental parameter change curves of the target device.

[0019] The correlation analysis unit, which is connected to the change analysis unit, is used to determine several associated electrical devices of the target device within the target area based on the key change area.

[0020] Furthermore, the model generation submodule includes:

[0021] The target equipment analysis unit, which is connected to the database, is used to determine the target important electrical parameters based on the historical electrical parameters corresponding to the target equipment, and to generate a target twin model based on the target important electrical parameters;

[0022] The associated equipment analysis unit is connected to the database and is used to determine the associated important electrical parameters of each associated electrical device based on the historical electrical parameters of each associated electrical device, and to generate associated twin models of each associated electrical device based on the associated important electrical parameters.

[0023] A model generation unit, which is connected to the target device analysis unit and the associated device analysis unit respectively, is used to fuse the target twin sub-model and each of the associated twin sub-models to generate an electrical twin model corresponding to the target device.

[0024] Furthermore, the data analysis module determines the electrical deviation characterization value between the target device and each associated electrical device based on the current electrical parameters of the target device and each associated electrical device, and determines the electrical operating deviation corresponding to the target device based on each electrical deviation characterization value.

[0025] Furthermore, the data analysis module determines the environmental deviation characterization value between the target device and each associated electrical device based on the current environmental parameters of the target device and each associated electrical device, and determines the environmental operation deviation corresponding to the target device based on each environmental deviation characterization value.

[0026] Furthermore, the data analysis module inputs the current electrical parameters of the target device into the electrical twin model to simulate the operation process, and obtains the simulated electrical parameters of each electrical device within a preset time period during the simulation process, so as to determine the predicted electrical parameters corresponding to the target device.

[0027] Furthermore, the control adjustment module includes:

[0028] The parameter analysis submodule, which is connected to the data analysis module, is used to determine a comprehensive deviation index based on the electrical operating deviation and the environmental operating deviation, and to determine the key electrical parameters corresponding to the target equipment based on the comprehensive deviation index and the current electrical parameters of the target equipment.

[0029] An adjustment analysis submodule is connected to the data analysis module and the parameter analysis submodule, respectively, to determine the adjustment electrical parameters based on the key electrical parameters and the predicted electrical parameters;

[0030] A parameter adjustment submodule, which is connected to the adjustment analysis submodule, is used to adjust the electrical parameters of the target equipment during operation based on the adjusted electrical parameters;

[0031] The model adjustment submodule is connected to both the adjustment analysis submodule and the model generation module, and is used to adjust the electrical twin model based on the adjusted electrical parameters.

[0032] Furthermore, the adjustment analysis submodule determines the adjustment deviation based on the key electrical parameters and the predicted electrical parameters, and determines the adjustment electrical parameters based on the comparison result between the adjustment deviation and the preset deviation.

[0033] Furthermore, the data analysis module determines the predicted electrical parameters corresponding to the target device based on the simulated electrical parameters of each associated electrical device within a preset time period during the simulation process and the simulated electrical parameters of the target device.

[0034] Compared with existing technologies, the beneficial effects of this invention are as follows: By establishing a database for data integration and management, it provides data support for evaluating the operation process of electrical equipment. Through a model generation module, based on the historical environmental parameters of the electrical equipment, it can accurately identify the associated electrical equipment within the target area, facilitating a deeper understanding of the mutual influence relationships between electrical equipment. This provides crucial support for building more accurate electrical twin models, thereby achieving comprehensive and precise simulation of the target equipment's operation process. By utilizing the historical electrical parameters of the target equipment and its associated electrical equipment to generate an electrical twin model corresponding to the target equipment, the efficiency and quality of model construction are improved. The generation of electrical twin models enables digital mapping and virtual simulation of electrical equipment, laying the foundation for intelligent management of electrical equipment and improving the intelligence level and efficiency of electrical equipment management. Furthermore, by setting up a data monitoring module, the operation process of each electrical device within the target area is monitored in real time, acquiring its current electrical and environmental parameters, ensuring the timeliness and accuracy of the data, and providing data support for the refined and intelligent management of electrical equipment. By setting up a data analysis module, based on the current electrical parameters and environmental parameters of the target device and its associated electrical equipment, electrical and environmental operational deviations can be accurately determined. This allows for the timely detection of anomalies and potential problems during equipment operation, facilitating proactive adjustments and optimizations to prevent equipment failures and performance degradation. Combining the electrical twin model with the target device's current electrical parameters, changes in the target device's electrical parameters over a predetermined time period can be accurately predicted. By setting up a control adjustment module, based on electrical and environmental operational deviations, key electrical parameters of the target device can be accurately determined. By comprehensively considering the predicted electrical parameters and key electrical parameters, adjustment electrical parameters are determined. Based on these adjustment parameters, the electrical parameters during the target device's operation, as well as the electrical twin model, are adjusted. This enables precise adjustment and optimization of the target device's electrical parameters during operation, ensuring the stability and reliability of the target device's operation and reducing operational risks.

[0035] Furthermore, the model generation module of this invention constructs environmental parameter change curves for each electrical device by setting up an association analysis submodule. This allows for a visual display of the changing trends in the device's operating environment, more accurate identification of the mutual influence relationships between electrical devices, and dynamic adjustment of the association relationships between the target device and related electrical devices. This identifies several related electrical devices for the target device within the target area at the current moment, enabling collaborative management and optimized control of electrical devices throughout the entire target area. By setting up a model generation submodule to generate a target twin sub-model based on the historical electrical parameters of the target device, and several related twin sub-models based on the historical electrical parameters of each related electrical device, the module accurately reflects the operating characteristics and state changes of each electrical device, providing strong support for refined management and optimized control of electrical devices. Integrating the target twin sub-model and the related twin sub-models to generate an electrical twin model corresponding to the target device achieves model construction from individual devices to the entire system. This comprehensively and accurately simulates the operating state and interactions of the target device and related electrical devices within the target area, improving the stability and reliability of electrical device operation.

[0036] Furthermore, the correlation analysis submodule of this invention, by setting the change analysis unit based on the environmental parameter change curve of the target device, can accurately identify the key change area, that is, the time period during which the device's operating environment undergoes significant changes. Based on the key change area, it determines several related electrical devices of the target device within the target area, which can quickly identify electrical devices closely related to the changes in the operating state of the target device. This avoids the complexity and inefficiency of conducting a comprehensive analysis of all electrical devices within the target area, and improves the efficiency and accuracy of correlation analysis.

[0037] Furthermore, the model generation submodule of this invention analyzes the historical electrical parameters corresponding to the target device by setting a target device analysis unit. This accurately identifies important electrical parameters of the target device, reduces data processing volume, and improves data analysis accuracy. Based on the important electrical parameters of the target device, a target twin sub-model is generated, which accurately reflects the operating characteristics and state changes of the target device. By setting an associated device analysis unit, the associated important electrical parameters of each associated electrical device are accurately identified, reducing data processing volume and improving data analysis accuracy. Based on the associated important electrical parameters of each associated electrical device, a corresponding associated twin sub-model is generated, which accurately reflects the operating characteristics and state changes of the associated electrical devices. By setting a model generation unit, the target twin sub-model is fused with each associated twin sub-model to generate an electrical twin model corresponding to the target device. This improves the simulation and prediction accuracy of the electrical twin model, enables collaborative optimization control of the target device and its associated devices, improves the control efficiency and accuracy of the target device, and ensures the operational stability of the target device.

[0038] Furthermore, the control adjustment module of this invention, through the setting of a parameter analysis submodule, determines a comprehensive deviation index based on electrical and environmental operating deviations. This enables a comprehensive assessment of the target equipment's operating status. Based on the comprehensive deviation index and the target equipment's current electrical parameters, it can accurately determine the target equipment's key electrical parameters, ensuring more precise and effective adjustment strategies and avoiding equipment instability caused by blind adjustments. By setting the adjustment analysis submodule, the adjustment electrical parameters are determined based on key electrical parameters and predicted electrical parameters. This considers not only the impact of electrical and environmental operating deviations in historical data but also the simulation prediction results of the electrical twin model, improving the control accuracy of the target equipment and ensuring its operational stability. By setting the parameter adjustment submodule, the electrical parameters of the target equipment are adjusted in real time during operation, enabling rapid response to changes in the equipment's operating status and ensuring that the equipment always operates in a highly efficient and stable state, improving its operational efficiency and reliability. By setting the model adjustment submodule, the electrical twin model is adjusted based on the adjusted electrical parameters, ensuring that the digital twin model always reflects the actual operating status of the equipment, improving the model's accuracy and reliability, and providing stronger support for equipment operation monitoring, fault prediction, and optimized control. Attached Figure Description

[0039] Figure 1 This is a structural block diagram of the electrical automation intelligent control system according to an embodiment of the present invention;

[0040] Figure 2 This is a structural block diagram of the model generation module in an embodiment of the present invention;

[0041] Figure 3 This is a structural block diagram of the association analysis submodule in an embodiment of the present invention;

[0042] Figure 4 This is a structural block diagram of the model generation submodule in an embodiment of the present invention;

[0043] Figure 5 This is a structural block diagram of the control and adjustment module according to an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0045] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0046] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0047] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0048] Please see Figures 1-5 As shown, Figure 1 This is a structural block diagram of the electrical automation intelligent control system according to an embodiment of the present invention; Figure 2 This is a structural block diagram of the model generation module in an embodiment of the present invention; Figure 3 This is a structural block diagram of the association analysis submodule in an embodiment of the present invention; Figure 4 This is a structural block diagram of the model generation submodule in an embodiment of the present invention; Figure 5 This is a structural block diagram of the control and adjustment module according to an embodiment of the present invention; an embodiment of the present invention provides an intelligent electrical automation control system, including:

[0049] The database is used to store historical electrical parameters of several electrical devices in the target area during operation, as well as historical environmental parameters corresponding to each electrical device.

[0050] In implementation, the target area can be a production workshop, factory, substation, or any area that requires monitoring and management of electrical equipment. The target area includes several electrical devices, and the types of electrical devices can be the same or different. For example, electrical devices can be any of the following: motors, cooling fans, water pumps, main transformers, cooler groups, circuit breakers, frequency converters, air compressors, air conditioning units, centrifuges, inverters, etc.

[0051] It is understood that electrical parameters include, but are not limited to, voltage, current, power, and operating frequency, while environmental parameters include, but are not limited to, equipment temperature, ambient temperature vibration frequency, and vibration amplitude.

[0052] A model generation module, connected to the database, is used to determine several associated electrical devices of the target device within the target area based on the historical environmental parameters corresponding to each of the electrical devices, and to generate an electrical twin model of the target device based on the historical electrical parameters corresponding to the target device and each of the associated electrical devices, wherein the target device is any electrical device within the target area;

[0053] Specifically, the model generation module includes:

[0054] The correlation analysis submodule is connected to the database and is used to construct the environmental parameter change curves corresponding to each electrical device based on the historical environmental parameters corresponding to each electrical device, and to determine several associated electrical devices of the target device in the target area based on the environmental parameter change curves.

[0055] Specifically, the association analysis submodule includes:

[0056] A curve construction unit, which is connected to the database, is used to construct environmental parameter change curves for each electrical device based on the historical environmental parameters corresponding to each electrical device.

[0057] A change analysis unit, which is connected to the curve construction unit, is used to determine key change areas based on the environmental parameter change curves of the target device.

[0058] The correlation analysis unit, which is connected to the change analysis unit, is used to determine several associated electrical devices of the target device within the target area based on the key change area.

[0059] In practice, for any electrical device, with the data collection time as the independent variable and the environmental parameters as the dependent variable, a corresponding environmental parameter change curve is constructed. The data collection period for the historical environmental parameters of each electrical device is the same.

[0060] It is understandable that, for the target device, candidate change areas are determined based on the slope of any environmental parameter change curve. The absolute value of the slope in the candidate change area is greater than the preset slope. For other environmental parameter change curves of the target device, if the absolute value of the slope in the corresponding area of ​​the candidate change area is greater than the preset slope, then the candidate change area is determined as the key change area. The actual implementer can set the preset slope based on the actual situation. Preferably, the preset slope value range is set to 0.8 to 1.

[0061] It is understandable that if the absolute value of the slope of the environmental parameter change curve of any electrical device other than the target device is greater than the preset slope in the same time period as the critical change area, then the electrical device is identified as an associated electrical device of the target device.

[0062] The correlation analysis submodule of this invention can accurately identify key change areas, i.e. time periods when significant changes occur in the operating environment of the equipment, by setting the change analysis unit based on the environmental parameter change curve of the target equipment. Based on the key change areas, it can identify several related electrical devices of the target equipment within the target area, and quickly identify electrical devices closely related to the changes in the operating state of the target equipment. This avoids the complexity and inefficiency of conducting a comprehensive analysis of all electrical devices within the target area, and improves the efficiency and accuracy of correlation analysis.

[0063] The model generation submodule is connected to the database and the association analysis submodule, respectively, and is used to generate a target twin sub-model based on the historical electrical parameters corresponding to the target device, generate a number of associated twin sub-models based on the historical electrical parameters corresponding to each associated electrical device, and generate an electrical twin model corresponding to the target device based on the target twin sub-model and each of the associated twin sub-models.

[0064] Specifically, the model generation submodule includes:

[0065] The target equipment analysis unit, which is connected to the database, is used to determine the target important electrical parameters based on the historical electrical parameters corresponding to the target equipment, and to generate a target twin model based on the target important electrical parameters;

[0066] During implementation, the historical electrical parameters of the target equipment are preprocessed to remove outliers and noise data, and the data is standardized. Statistical analysis is then performed on the preprocessed historical electrical parameters to determine the key electrical parameters of the target equipment.

[0067] In one specific embodiment, the key electrical parameters of the target device can be processed based on digital twin technology to fit the operation process of the target device and obtain a target twin sub-model. This is existing technology and will not be elaborated further.

[0068] The associated equipment analysis unit is connected to the database and is used to determine the associated important electrical parameters of each associated electrical device based on the historical electrical parameters of each associated electrical device, and to generate associated twin models of each associated electrical device based on the associated important electrical parameters.

[0069] During implementation, the historical electrical parameters of each associated electrical device are preprocessed, including removing outliers and noise data, and the data is standardized. Statistical analysis is then performed on the preprocessed historical electrical parameters to determine the associated important electrical parameters corresponding to each associated electrical device.

[0070] It is understandable that the important electrical parameters associated with each related electrical device can be processed based on digital twin technology, and the operating process of each related electrical device can be fitted to obtain the associated twin sub-models of each related electrical device. This is existing technology and will not be elaborated further.

[0071] A model generation unit, which is connected to the target device analysis unit and the associated device analysis unit respectively, is used to fuse the target twin sub-model and each of the associated twin sub-models to generate an electrical twin model corresponding to the target device.

[0072] In implementation, the target device is the core, and integrated learning methods or model coupling methods are used to integrate the target twin sub-model and each associated twin sub-model into a whole model, namely the electrical twin model corresponding to the target device.

[0073] The model generation submodule of this invention analyzes the historical electrical parameters of the target device by setting up a target device analysis unit. This accurately identifies important electrical parameters of the target device, reduces data processing volume, and improves data analysis accuracy. Based on the important electrical parameters of the target device, a target twin sub-model is generated, which accurately reflects the operating characteristics and state changes of the target device. By setting up an associated device analysis unit, the associated important electrical parameters of each associated electrical device are accurately identified, reducing data processing volume and improving data analysis accuracy. Based on the associated important electrical parameters of each associated electrical device, a corresponding associated twin sub-model is generated, which accurately reflects the operating characteristics and state changes of the associated electrical devices. By setting up a model generation unit, the target twin sub-model is fused with each associated twin sub-model to generate an electrical twin model corresponding to the target device. This improves the simulation and prediction accuracy of the electrical twin model, enables coordinated optimization control of the target device and its associated devices, improves the control efficiency and accuracy of the target device, and ensures the operational stability of the target device.

[0074] Specifically, the model generation module of this invention constructs environmental parameter change curves for each electrical device by setting up an association analysis submodule. This allows for a visual display of the changing trends in the device's operating environment, more accurate identification of the mutual influence relationships between electrical devices, and dynamic adjustment of the association relationships between the target device and related electrical devices. This identifies several related electrical devices for the target device within the target area at the current moment, enabling collaborative management and optimized control of electrical devices throughout the entire target area. By setting up a model generation submodule to generate a target twin sub-model based on the historical electrical parameters of the target device, and several related twin sub-models based on the historical electrical parameters of each related electrical device, the module accurately reflects the operating characteristics and state changes of each electrical device, providing strong support for refined management and optimized control of electrical devices. Integrating the target twin sub-model and the related twin sub-models to generate an electrical twin model corresponding to the target device achieves model construction from individual devices to the entire system. This comprehensively and accurately simulates the operating state and interactions of the target device and related electrical devices within the target area, improving the stability and reliability of electrical device operation.

[0075] The data monitoring module is used to monitor the operation of each electrical device in the target area in real time, so as to obtain the current electrical parameters and current environmental parameters of each electrical device;

[0076] In practice, no specific equipment or method is limited for obtaining the current electrical parameters of each electrical device and the current environmental parameters, as this is existing technology and will not be elaborated upon.

[0077] The data analysis module, which is connected to the model generation module and the data monitoring module respectively, is used to determine the electrical operation deviation based on the current electrical parameters of the target device and each of the associated electrical devices, determine the environmental operation deviation based on the current environmental parameters of the target device and each of the associated electrical devices, and determine the predicted electrical parameters corresponding to the target device based on the electrical twin model and the current electrical parameters of the target device.

[0078] Specifically, the data analysis module determines the electrical deviation characterization value between the target device and each associated electrical device based on the current electrical parameters of the target device and each associated electrical device, and determines the electrical operating deviation corresponding to the target device based on each electrical deviation characterization value.

[0079] During implementation, the current electrical parameters of the target equipment and its associated electrical equipment undergo data preprocessing, including removing outliers and noise data, and standardizing the data to eliminate dimensions, normalizing the data to between 0 and 1. For the target equipment and its associated electrical equipment, the current electrical parameters of the target equipment are MY1, MY2, ..., MY1. j , ...,MYm The current electrical parameters ME of the i-th associated electrical device of the target device i,1 ME i,2 , ..., ME i,j , ..., ME i,m The electrical deviation characterization value MP of the i-th associated electrical device i =(∑ m j=1 MY j ×ME i,j ) / (sqrt(∑ m j=1 (MY j ) 2 )×sqrt(∑ m j=1 (ME i,j ) 2 )); where j=1, 2, ..., m, i=1, 2, ..., n, m is the number of electrical parameters, n is the number of associated electrical devices, sqrt() is the preset square root determination function, and the electrical operating deviation MP corresponding to the target device is (∑ n i=1 (MP i )) / n.

[0080] Specifically, the data analysis module determines the environmental deviation characterization value between the target device and each associated electrical device based on the current environmental parameters of the target device and each associated electrical device, and determines the environmental operation deviation corresponding to the target device based on each environmental deviation characterization value.

[0081] During implementation, environmental parameters of the target equipment and its associated electrical equipment undergo data preprocessing, including removing outliers and noise data, and standardizing the data to eliminate dimensions, normalizing the data to between 0 and 1. For the target equipment and its associated electrical equipment, the current environmental parameters of the target equipment are HY1, HY2, ..., HY... g , ..., HY h The current environmental parameters HE of the i-th associated electrical device of the target device i,1 HE i,2 HE i,g HE i,h The electrical deviation characterization value HP of the i-th associated electrical device i =(∑ h g=1 HY g ×HE i,g ) / (sqrt(∑ h g=1 (HY g ) 2 )×sqrt(∑h g=1 (HE i,g ) 2 )); where g=1, 2, ..., h, i=1, 2, ..., n, h is the number of environmental parameters, n is the number of associated electrical devices, sqrt() is the preset square root determination function, and the environmental operating deviation HP corresponding to the target device is (∑ n i=1 HP i )) / n.

[0082] Specifically, the data analysis module inputs the current electrical parameters of the target device into the electrical twin model to simulate the operation process, and obtains the simulated electrical parameters of each electrical device within a preset time period during the simulation process, so as to determine the predicted electrical parameters corresponding to the target device.

[0083] Specifically, the data analysis module determines the predicted electrical parameters of the target device based on the simulated electrical parameters of each associated electrical device within a preset time period during the simulation process and the simulated electrical parameters of the target device.

[0084] In practice, a training dataset can be constructed based on the historical electrical parameters of the target equipment that has passed the qualification inspection and its associated electrical equipment in historical data. The initial prediction model can then be trained based on the training dataset to obtain the target prediction model. It should be noted that those skilled in the art know that any prediction model in the prior art that can predict the predicted electrical parameters corresponding to the target equipment falls within the protection scope of this invention, and will not be elaborated further here.

[0085] The control and adjustment module is connected to the model generation module and the data analysis module respectively. It is used to determine the key electrical parameters corresponding to the target equipment based on the electrical operation deviation and the environmental operation deviation, and to determine the adjustment electrical parameters based on the key electrical parameters and the predicted electrical parameters, so as to adjust the electrical parameters and the electrical twin model during the operation of the target equipment.

[0086] Specifically, the control adjustment module includes:

[0087] The parameter analysis submodule, which is connected to the data analysis module, is used to determine a comprehensive deviation index based on the electrical operating deviation and the environmental operating deviation, and to determine the key electrical parameters corresponding to the target equipment based on the comprehensive deviation index and the current electrical parameters of the target equipment.

[0088] In implementation, the electrical operating deviation MP and the environmental operating deviation HP are weighted and summed to determine the comprehensive deviation index P. For example, P = a × MP + b × HP, where a + b = 1, preferably a = 0.6 and b = 0.4.

[0089] Understandably, the key electrical parameters of the target equipment are determined by multiplying the comprehensive deviation index by the current electrical parameters of the target equipment. For example, if the current operating current of the target equipment is PS, then the key operating current of the target equipment is GS = P × PS.

[0090] An adjustment analysis submodule is connected to the data analysis module and the parameter analysis submodule, respectively, to determine the adjustment electrical parameters based on the key electrical parameters and the predicted electrical parameters;

[0091] Specifically, the adjustment analysis submodule determines the adjustment deviation based on the key electrical parameters and the predicted electrical parameters, and determines the adjustment electrical parameters based on the comparison result between the adjustment deviation and the preset deviation.

[0092] In implementation, the adjustment deviation TZ = (∑ m j= (abs((TA j -TB j ) / TA j ))) / m, where TA j For the j-th critical electrical parameter, TB j Let j be the j-th predicted electrical parameter.

[0093] Understandably, if the adjustment deviation is greater than the preset deviation, the average of the key electrical parameter and the predicted electrical parameter is determined as the adjusted electrical parameter; if the adjustment deviation is less than or equal to the preset deviation, the key electrical parameter is determined as the adjusted electrical parameter. Implementers can set the preset deviation based on actual conditions; preferably, the preset deviation range is set to 0.1–0.3.

[0094] A parameter adjustment submodule, which is connected to the adjustment analysis submodule, is used to adjust the electrical parameters of the target equipment during operation based on the adjusted electrical parameters;

[0095] The model adjustment submodule is connected to both the adjustment analysis submodule and the model generation module, and is used to adjust the electrical twin model based on the adjusted electrical parameters.

[0096] In implementation, the electrical parameters of the target device in the electrical twin model are adjusted to adjust the electrical parameters.

[0097] This invention's control adjustment module, through a parameter analysis submodule, determines a comprehensive deviation index based on electrical and environmental operational deviations. This allows for a comprehensive assessment of the target equipment's operational status. Based on the comprehensive deviation index and the target equipment's current electrical parameters, it accurately identifies key electrical parameters, ensuring more precise and effective adjustment strategies and avoiding instability caused by blind adjustments. The adjustment analysis submodule, by determining adjustment electrical parameters based on key and predicted electrical parameters, considers not only the impact of historical electrical and environmental operational deviations but also the simulation prediction results of the electrical twin model. This improves the control accuracy of the target equipment and ensures its operational stability. The parameter adjustment submodule, by adjusting electrical parameters in real-time during the target equipment's operation, enables rapid response to changes in the equipment's operational status, ensuring the equipment always operates in a highly efficient and stable state, improving operational efficiency and reliability. Finally, the model adjustment submodule, by adjusting the electrical twin model based on the adjusted electrical parameters, ensures the digital twin model always reflects the actual operational status of the equipment, improving model accuracy and reliability and providing stronger support for equipment operation monitoring, fault prediction, and optimized control.

[0098] This invention integrates and manages data through a database, providing data support for evaluating the operation of electrical equipment. By setting up a model generation module, based on historical environmental parameters of the electrical equipment, it can accurately identify associated electrical equipment within a target area, facilitating a deeper understanding of the interrelationships between electrical devices. This provides crucial support for building more accurate electrical twin models, enabling comprehensive and precise simulation of the target equipment's operation. By utilizing historical electrical parameters of the target equipment and its associated electrical equipment, an electrical twin model corresponding to the target equipment is generated, improving the efficiency and quality of model building. The generation of electrical twin models enables digital mapping and virtual simulation of electrical equipment, laying the foundation for intelligent management of electrical equipment and improving the intelligence level and efficiency of electrical equipment management. By setting up a data monitoring module, the operation of each electrical device within the target area is monitored in real time, acquiring its current electrical and environmental parameters, ensuring the timeliness and accuracy of the data, and providing data support for refined and intelligent management of electrical equipment. By setting up a data analysis module, based on the current electrical parameters and environmental parameters of the target device and its associated electrical equipment, electrical and environmental operational deviations can be accurately determined. This allows for the timely detection of anomalies and potential problems during equipment operation, facilitating proactive adjustments and optimizations to prevent equipment failures and performance degradation. Combining the electrical twin model with the target device's current electrical parameters, changes in the target device's electrical parameters over a predetermined time period can be accurately predicted. By setting up a control adjustment module, based on electrical and environmental operational deviations, key electrical parameters of the target device can be accurately determined. By comprehensively considering the predicted electrical parameters and key electrical parameters, adjustment electrical parameters are determined. Based on these adjustment parameters, the electrical parameters during the target device's operation, as well as the electrical twin model, are adjusted. This enables precise adjustment and optimization of the target device's electrical parameters during operation, ensuring the stability and reliability of the target device's operation and reducing operational risks.

[0099] Example 1

[0100] This embodiment provides a production workshop control process using the electrical automation intelligent control system of the present invention. A mold production equipment A in the production workshop is selected as the target equipment. The electrical parameters (current, output power, operating frequency) and environmental parameters (equipment temperature, ambient temperature vibration frequency, vibration amplitude) of each mold production equipment (A, B, C, D, E, F, G, H, I, J) in the production workshop are monitored and analyzed over a 24-hour period. Based on the changes in the environmental parameters of each mold production equipment over a 24-hour period, mold production equipment D, E, and F are identified as associated electrical equipment of mold production equipment A. The electrical operating deviation of the target equipment is 0.6, and the environmental operating deviation of the target equipment is 0.7. Therefore, the comprehensive deviation index is 0.64, the preset deviation is 0.2, and the performance is shown in Table 1.

[0101] Table 1 Performance Comparison Table

[0102] ;

[0103] Table 1 shows that the overall performance of the electrical automation intelligent control system of the present invention in adjusting the electrical parameters of the target equipment during operation is excellent. The present invention can improve the control efficiency and accuracy of electrical equipment and effectively ensure the stability of electrical equipment operation.

[0104] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An intelligent control system for electrical automation, characterized in that, include: The database is used to store historical electrical parameters of several electrical devices in the target area during operation, as well as historical environmental parameters corresponding to each electrical device. A model generation module, connected to the database, is used to determine several associated electrical devices of the target device within the target area based on the historical environmental parameters corresponding to each of the electrical devices, and to generate an electrical twin model of the target device based on the historical electrical parameters corresponding to the target device and each of the associated electrical devices, wherein the target device is any electrical device within the target area; The data monitoring module is used to monitor the operation of each electrical device in the target area in real time, so as to obtain the current electrical parameters and current environmental parameters of each electrical device; The data analysis module, which is connected to the model generation module and the data monitoring module respectively, is used to determine the electrical operation deviation based on the current electrical parameters of the target device and each of the associated electrical devices, determine the environmental operation deviation based on the current environmental parameters of the target device and each of the associated electrical devices, and determine the predicted electrical parameters corresponding to the target device based on the electrical twin model and the current electrical parameters of the target device. A control adjustment module, which is connected to the model generation module and the data analysis module respectively, is used to determine the key electrical parameters corresponding to the target equipment based on the electrical operation deviation and the environmental operation deviation, and to determine the adjustment electrical parameters based on the key electrical parameters and the predicted electrical parameters, so as to adjust the electrical parameters and the electrical twin model during the operation of the target equipment; The model generation module includes: The correlation analysis submodule is connected to the database and is used to construct the environmental parameter change curves corresponding to each electrical device based on the historical environmental parameters corresponding to each electrical device, and to determine several associated electrical devices of the target device in the target area based on the environmental parameter change curves. The correlation analysis submodule includes: A curve construction unit, which is connected to the database, is used to construct environmental parameter change curves for each electrical device based on the historical environmental parameters corresponding to each electrical device. A change analysis unit, connected to the curve construction unit, is used to determine key change areas based on the environmental parameter change curves of the target device. Candidate change areas are determined according to the slope of any environmental parameter change curve. The absolute value of the slope in the candidate change area is greater than a preset slope. For other environmental parameter change curves of the target device, if the absolute value of the slope in the corresponding area of ​​the candidate change area is greater than the preset slope, then the candidate change area is determined as a key change area. The correlation analysis unit, which is connected to the change analysis unit, is used to determine several associated electrical devices of the target device within the target area based on the key change area.

2. The electrical automation intelligent control system according to claim 1, characterized in that, The model generation module includes: The model generation submodule is connected to the database and the association analysis submodule, respectively, and is used to generate a target twin sub-model based on the historical electrical parameters corresponding to the target device, generate a number of associated twin sub-models based on the historical electrical parameters corresponding to each associated electrical device, and generate an electrical twin model corresponding to the target device based on the target twin sub-model and each of the associated twin sub-models.

3. The electrical automation intelligent control system according to claim 2, characterized in that, The model generation submodule includes: The target equipment analysis unit, which is connected to the database, is used to determine the target important electrical parameters based on the historical electrical parameters corresponding to the target equipment, and to generate a target twin model based on the target important electrical parameters; The associated equipment analysis unit is connected to the database and is used to determine the associated important electrical parameters of each associated electrical device based on the historical electrical parameters of each associated electrical device, and to generate associated twin models of each associated electrical device based on the associated important electrical parameters. A model generation unit, which is connected to the target device analysis unit and the associated device analysis unit respectively, is used to fuse the target twin sub-model and each of the associated twin sub-models to generate an electrical twin model corresponding to the target device.

4. The electrical automation intelligent control system according to claim 3, characterized in that, The data analysis module determines the electrical deviation characterization value between the target device and each associated electrical device based on the current electrical parameters of the target device and each associated electrical device, and determines the electrical operating deviation corresponding to the target device based on each electrical deviation characterization value.

5. The electrical automation intelligent control system according to claim 4, characterized in that, The data analysis module determines the environmental deviation characterization value between the target device and each associated electrical device based on the current environmental parameters of the target device and each associated electrical device, and determines the environmental operation deviation corresponding to the target device based on each environmental deviation characterization value.

6. The electrical automation intelligent control system according to claim 5, characterized in that, The data analysis module inputs the current electrical parameters of the target device into the electrical twin model to simulate the operation process, and obtains the simulated electrical parameters of each electrical device within a preset time period during the simulation process, so as to determine the predicted electrical parameters corresponding to the target device.

7. The electrical automation intelligent control system according to claim 6, characterized in that, The control adjustment module includes: The parameter analysis submodule, which is connected to the data analysis module, is used to determine a comprehensive deviation index based on the electrical operating deviation and the environmental operating deviation, and to determine the key electrical parameters corresponding to the target equipment based on the comprehensive deviation index and the current electrical parameters of the target equipment. An adjustment analysis submodule is connected to the data analysis module and the parameter analysis submodule, respectively, to determine the adjustment electrical parameters based on the key electrical parameters and the predicted electrical parameters; A parameter adjustment submodule, which is connected to the adjustment analysis submodule, is used to adjust the electrical parameters of the target equipment during operation based on the adjusted electrical parameters; The model adjustment submodule is connected to both the adjustment analysis submodule and the model generation module, and is used to adjust the electrical twin model based on the adjusted electrical parameters.

8. The electrical automation intelligent control system according to claim 7, characterized in that, The adjustment analysis submodule determines the adjustment deviation based on the key electrical parameters and the predicted electrical parameters, and determines the adjustment electrical parameters based on the comparison result between the adjustment deviation and the preset deviation.

9. The electrical automation intelligent control system according to claim 8, characterized in that, The data analysis module determines the predicted electrical parameters of the target device based on the simulated electrical parameters of each associated electrical device within a preset time period during the simulation process and the simulated electrical parameters of the target device.

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