A multi-source state perception-based electrical equipment cooperative control system and method

Abstract

The application provides a kind of electrical equipment collaborative control system and method based on multi-source state perception, it is related to collaborative control technical field, the multi-source state information of electrical equipment in the running process is collected;Multi-source state information is handled in layers, the mechanical operation characteristics and electrical control rules of electrical equipment are obtained, the operation control mode when the abnormal operation state of electrical equipment is determined;The feedback control amount when the running state of electrical equipment exists sudden disturbance is obtained, the operation control mode of electrical equipment is actively selected according to the operation regulation and control request under the state of sudden disturbance, and collaborative control instruction is generated;According to the global synchronous control of abnormal operation electrical equipment of dynamically updated collaborative control instruction.This application can carry out collaborative control to electrical equipment under the condition that electrical equipment operation exists sudden disturbance, to improve the adaptability of control mode when electrical equipment abnormally operates.

Description

Technical Field

[0001] This application relates to the field of collaborative control technology, and more specifically, to a collaborative control system and method for electrical equipment based on multi-source state perception. Background Technology

[0002] Collaborative control is widely used in the operation and control of complex electrical equipment such as high-voltage motors, transformers, and frequency converters in industrial production, energy supply, and intelligent manufacturing. These electrical devices are often composed of multiple interconnected modules, including drive, braking, protection, and cooling systems. Their operating conditions are complex and susceptible to sudden disturbances from multiple sources, such as power grid fluctuations and load abrupt changes. Independent control of a single module can easily lead to problems such as conflicting action timings, fragmented information perception, and delayed abnormal responses, potentially causing equipment imbalances or secondary failures. Collaborative control integrates multi-source state perception data, coordinates the control timing, execution parameters, and linkage logic of each module, and adapts to the complex control requirements of electrical equipment. This is the core technological support for ensuring accurate regulation and safe, stable operation of electrical equipment under abnormal conditions.

[0003] However, in existing collaborative control systems for electrical equipment, mechanical operating characteristics and electrical control rules are intertwined. This results in a lack of precise characteristic and rule-based criteria for matching control methods under abnormal operating conditions. Furthermore, the selection of control methods is detached from the dual constraints of disturbance intensity and regulation requirements. Moreover, once collaborative control commands are generated, they cannot be dynamically updated based on abnormal operating parameters; the commands remain static. This leads to delayed equipment response to sudden disturbances, mismatches between control commands and dynamic disturbance changes, and timing conflicts between execution modules. Consequently, it easily causes miscontrol and overcontrol problems in abnormal control of electrical equipment, and even secondary equipment failures, significantly reducing the accuracy, timeliness, and reliability of collaborative control. Therefore, how to achieve collaborative control of electrical equipment under conditions of sudden disturbances to improve the adaptability of control methods during abnormal operation is a problem facing the industry. Summary of the Invention

[0004] This application provides a collaborative control system and method for electrical equipment based on multi-source state perception, which can perform collaborative control of electrical equipment under operating conditions where there are sudden disturbances, thereby improving the adaptability of control methods when electrical equipment is operating abnormally.

[0005] In a first aspect, this application provides a method for coordinated control of electrical equipment based on multi-source state perception, the method comprising the following steps:

[0006] Collect multi-source status information of electrical equipment during operation;

[0007] The multi-source state information is processed in layers to obtain the mechanical operating characteristics and electrical control rules of the electrical equipment. Based on the mechanical operating characteristics and electrical control rules, the operation control mode when the electrical equipment generates an abnormal operating state is determined.

[0008] The system acquires feedback control quantities when there is a sudden disturbance in the operating status of electrical equipment. Based on the feedback control quantities and the operation control request under the sudden disturbance, the system actively selects the operating control mode of the electrical equipment and generates a cooperative control command corresponding to the operating control mode. The cooperative control command is then dynamically updated by the abnormal operating parameters under the sudden disturbance.

[0009] Global synchronous control is performed on abnormally operating electrical equipment based on dynamically updated collaborative control instructions.

[0010] In this embodiment, the hierarchical processing of the multi-source state information to obtain the mechanical operating characteristics and electrical control rules of the electrical equipment specifically includes:

[0011] The multi-source state information is synchronized in time to obtain a multi-source state data stream;

[0012] Extract mechanical operation feature vectors and electrical control feature vectors from the multi-source state data stream;

[0013] Based on the mechanical operation feature vector, the mechanical operation features of the electrical equipment are output through a state classification model;

[0014] Based on the electrical control feature vector, the electrical control rules for electrical equipment are output through association rule analysis.

[0015] In this embodiment, the feedback control quantity for obtaining the operating status of electrical equipment when there is a sudden disturbance specifically includes:

[0016] Real-time monitoring of the operating status data of electrical equipment; when a sudden change in the operating status data is detected, time-series data before and after the change are extracted to construct a disturbance feature sequence.

[0017] The intensity and trend of sudden disturbances in the operating state of electrical equipment are determined based on the disturbance characteristic sequence.

[0018] The feedback control quantity for the operation of electrical equipment when there is a sudden disturbance is determined by the intensity of the sudden disturbance and the trend index.

[0019] In this embodiment, the sudden disturbance refers to the operating state of electrical equipment during normal operation, which is caused by sudden changes in the external environment and sudden internal failures, without prior warning, and causes irregular and drastic fluctuations in the equipment's operating status data.

[0020] In this embodiment, actively selecting the operation control mode of the electrical equipment based on the feedback control quantity and the operation control request under sudden disturbance conditions, and generating the corresponding cooperative control command for the operation control mode specifically includes:

[0021] Determine the operational control requests under sudden disturbance conditions;

[0022] Based on the feedback control quantity and the operation control request, determine the type of control strategy and the target control priority required at present;

[0023] Map the control strategy type and the target control priority to the operation control mode of the electrical equipment;

[0024] Generate the corresponding collaborative control command based on the operation control mode and the feedback control quantity.

[0025] In this embodiment, the coordinated control command refers to a standardized operation command that can coordinate the synchronous actions of various control modules of the device.

[0026] In this embodiment, the dynamically updated collaborative control command refers to the control command that adapts to the dynamic changes of sudden disturbances in real time, ensuring that the control command always matches the actual operating state of the equipment.

[0027] In this embodiment, the global synchronous control of abnormally operating electrical equipment based on dynamically updated collaborative control instructions specifically includes:

[0028] Extract the control target parameters and synchronization control requirements of electrical equipment from the dynamically updated collaborative control instructions;

[0029] The synchronous execution command is sent to the electrical equipment based on the control target parameters and the synchronous control requirements.

[0030] Monitor the response status of each electrical device to synchronous execution commands, adjust command output in real time based on feedback data, and complete coordinated control actions.

[0031] In this embodiment, global synchronous control refers to a unified control method in which, when electrical equipment experiences sudden disturbances or abnormal operation, each execution module of the electrical equipment executes control actions according to preset synchronous control requirements and unified timing sequence through a unified clock reference and low-latency instruction transmission, based on dynamically updated collaborative control instructions. At the same time, the system monitors the instruction response status of each execution module in real time and dynamically adjusts the instruction output, thereby realizing an integrated control method in which each execution module works in coordination.

[0032] Secondly, this application provides a multi-source state perception-based electrical equipment cooperative control system for executing a multi-source state perception-based electrical equipment cooperative control method, the cooperative control system comprising:

[0033] The information acquisition module is used to collect multi-source status information of electrical equipment during operation;

[0034] The control mode decision module is used to perform hierarchical processing on the multi-source state information to obtain the mechanical operation characteristics and electrical control rules of the electrical equipment, and to determine the operation control mode when the electrical equipment generates an abnormal operating state based on the mechanical operation characteristics and the electrical control rules.

[0035] The control update module is used to obtain the feedback control quantity when the operating status of electrical equipment is subject to sudden disturbance, actively select the operating control mode of electrical equipment according to the feedback control quantity and the operation control request under the sudden disturbance state, and generate the corresponding collaborative control command for the operating control mode. Then, the collaborative control command is dynamically updated by the abnormal operating parameters under the sudden disturbance state.

[0036] The global synchronization control module is used to perform global synchronization control on abnormally operating electrical equipment based on dynamically updated collaborative control instructions.

[0037] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0038] The system collects multi-source state information of electrical equipment during operation; performs hierarchical processing on the multi-source state information to obtain the mechanical operating characteristics and electrical control rules of the electrical equipment; determines the operation control mode when the electrical equipment experiences abnormal operating conditions based on the mechanical operating characteristics and the electrical control rules; obtains the feedback control quantity when the operating state of the electrical equipment experiences sudden disturbances; actively selects the operation control mode of the electrical equipment based on the feedback control quantity and the operation control request under the sudden disturbance state, and generates a corresponding collaborative control command for the operation control mode; then dynamically updates the collaborative control command based on the abnormal operating parameters under the sudden disturbance state; and performs global synchronous control of the abnormally operating electrical equipment based on the dynamically updated collaborative control command.

[0039] Therefore, this application demonstrates that when the synchronization of multi-source state perception-based collaborative control of electrical equipment is insufficient, it can achieve collaborative control of abnormal operation of electrical equipment. Specifically, by collecting multi-source state information of electrical equipment during operation, accurate and time-series synchronized acquisition of state data across all dimensions of mechanical operation and electrical control is achieved, ensuring the comprehensiveness of the data foundation. By performing hierarchical processing of multi-source state information to obtain mechanical operation characteristics and electrical control rules, the classification and analysis of mechanical and electrical data and the accurate extraction of feature rules can be achieved, improving the accuracy of control mode matching under normal abnormal conditions. By acquiring feedback control quantities from sudden disturbances, actively selecting control modes and generating collaborative control commands based on operation control requests, and then dynamically updating the commands with abnormal operation parameters, the control commands are matched with the dynamic changes of disturbances in real time, improving the dynamic adaptability of control decisions under sudden disturbances. Through globally synchronized control of abnormally operating electrical equipment using dynamically updated collaborative control commands, all execution modules can coordinate actions according to a unified timing sequence, and the command output can be adjusted in real time, solving the defects of easy deviation in command execution in existing technologies and improving the synchronization of abnormal control execution of electrical equipment.

[0040] In summary, the technical solution adopted in this application can perform coordinated control of electrical equipment under operating conditions where there are sudden disturbances, thereby improving the adaptability of the control method when the electrical equipment is operating abnormally. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is an exemplary flowchart of a collaborative control method for electrical equipment based on multi-source state perception provided in this application;

[0043] Figure 2 This is a flowchart illustrating the determination of the operation control method provided in this application;

[0044] Figure 3 This is a flowchart illustrating the process of updating collaborative control instructions according to the present application;

[0045] Figure 4 This is a module structure diagram of an electrical equipment collaborative control system based on multi-source state perception provided in this application. Detailed Implementation

[0046] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0047] This application provides a collaborative control system and method for electrical equipment based on multi-source state perception. The core of this system is to collect multi-source state information of electrical equipment during operation; perform hierarchical processing on the multi-source state information to obtain the mechanical operating characteristics and electrical control rules of the electrical equipment; determine the operation control mode when the electrical equipment experiences abnormal operating states based on the mechanical operating characteristics and the electrical control rules; obtain feedback control quantities when the operating state of the electrical equipment experiences sudden disturbances; actively select the operation control mode of the electrical equipment based on the feedback control quantities and the operation control request under the sudden disturbance state; generate a collaborative control command corresponding to the operation control mode; dynamically update the collaborative control command based on the abnormal operating parameters under the sudden disturbance state; and perform global synchronous control of the abnormally operating electrical equipment based on the dynamically updated collaborative control command.

[0048] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is an exemplary flowchart of a collaborative control method for electrical equipment based on multi-source state perception according to this embodiment of the present application. The collaborative control method includes the following steps:

[0049] In step S1, multi-source status information of electrical equipment during operation is collected.

[0050] In practical implementation, piezoelectric vibration sensors are magnetically mounted on the bearing end caps, stator cores, and the middle of the housing of the electrical equipment. PT100 platinum resistance temperature sensors are inserted into the bearing oil cavity, stator winding slots, and housing heat sinks. High-precision current transformers are connected in series with the main circuit, and high-precision voltage transformers are connected in parallel with the main circuit. Data from the internal registers of the programmable logic controller and frequency converter is directly read via an industrial Ethernet bus interface. All detection devices are connected to a GPS time synchronization module to achieve clock synchronization and control the synchronization error within 1ms. Subsequently, a tiered acquisition frequency is set, and the electrical equipment operates according to... Periodic acquisition is performed at a frequency of 1Hz. When any parameter acquired for three consecutive times exceeds the normal operating range of the equipment, the acquisition automatically switches to a high-frequency acquisition of 100Hz. During the acquisition process, the mean filtering method is used for vibration and temperature data to take the average value of five consecutive acquisition points as valid data and remove outliers that deviate from the mean ±3σ. For control signal data, a deduplication algorithm is used to remove duplicate data to complete data preprocessing. The preprocessed data is used as the multi-source status information of the electrical equipment during operation. In other embodiments, other methods can also be used to acquire the multi-source status information of the electrical equipment during operation, which is not limited here.

[0051] It should be noted that, in this application, multi-source status information refers to the collection of all heterogeneous data on the real-time operating status of electrical equipment.

[0052] In step S2, the multi-source state information is processed in layers to obtain the mechanical operation characteristics and electrical control rules of the electrical equipment. Based on the mechanical operation characteristics and the electrical control rules, the operation control mode when the electrical equipment generates an abnormal operating state is determined.

[0053] In this embodiment, the hierarchical processing of the multi-source state information to obtain the mechanical operating characteristics and electrical control rules of the electrical equipment can be achieved through the following steps:

[0054] The multi-source state information is synchronized in time to obtain a multi-source state data stream;

[0055] Extract mechanical operation feature vectors and electrical control feature vectors from the multi-source state data stream;

[0056] Based on the mechanical operation feature vector, the mechanical operation features of the electrical equipment are output through a state classification model;

[0057] Based on the electrical control feature vector, the electrical control rules for electrical equipment are output through association rule analysis.

[0058] In practical implementation, firstly, the IEEE 1588 precision clock synchronization protocol can be used to calibrate the local clocks of all acquisition devices, using the control center clock as the reference time source. A synchronization signal is sent to each acquisition device every 0.001 seconds to correct clock deviations. A unique timestamp is added to each acquired multi-source status information data, accurate to the microsecond level. The timestamps of all data are iterated, and the time period with the most complete data coverage is selected as the synchronization time window. Missing data within the window is filled in using linear interpolation, and redundant data exceeding the time window is removed. Finally, the data is arranged in ascending order of timestamps to form a continuous multi-source status data stream. Then, for the mechanical data in the multi-source status data stream, time-domain analysis is used for vibration frequency and amplitude to calculate the mean, variance, and peak factor of 100 consecutive data points. For rotational speed and displacement data, a sliding window method is used, extracting the maximum, minimum, and rate of change within a 1-second window. For temperature data, exponential smoothing is used to weaken the impact of instantaneous fluctuations. For electrical data, the effective values ​​of three-phase voltage and current within the calculation cycle, the average values ​​of active and reactive power per unit time, and the on / off states of switches and relay action signals are quantized using 0-1 encoding. All the mechanical quantization results processed above are combined in a preset order to form a mechanical operation feature vector; the electrical quantization results are combined in a preset order to form an electrical control feature vector. Next, historical normal operating data and known abnormal operating condition data are selected as training samples, with normal samples accounting for 70% and abnormal samples accounting for 30%. The proportion of training samples can be adjusted according to specific circumstances. The mechanical operation feature vectors corresponding to the samples and their labels (normal / abnormal and abnormal type) are input into a random forest model. The initial number of decision trees in the model is 100, and the Gini coefficient is used as the feature importance evaluation index. The depth and split threshold of the decision trees are adjusted through iterative training, and redundant features with importance below 0.1 are removed. The remaining 30% of historical data is used as a test set to validate the model, ensuring that the model's classification accuracy is not less than 95%. The mechanical operation feature vectors to be processed are input into the trained model, and the model outputs the corresponding state categories and core representation parameters. All core representation parameters are used as the mechanical operation features of the electrical equipment. Finally, electrical control feature vectors and corresponding mechanical operation feature data from the equipment's historical operation are collected to construct an association analysis dataset. The Apriori algorithm is used, with a minimum support of 5% (determined based on the statistical proportion of effective associations in historical data) and a minimum confidence of 85% (to ensure the reliability of the rules), to traverse the dataset and mine frequent itemsets. Using mechanical operation features as antecedents and adjustment parameters in the electrical control feature vectors as consequents, a candidate set of association rules is constructed, and rules with confidence below the set threshold are eliminated.The effectiveness of candidate rules is verified by historical anomaly handling cases, the range of adjustment parameters is corrected, and finally an electrical control rule is formed that "if the mechanical operating characteristics meet a certain condition, the electrical parameters are adjusted in a specific way".

[0059] It should be noted that, in this application, layered processing refers to a processing method that divides multi-source state information according to mechanical operation and electrical control attributes to avoid interference with the coordinated control of electrical equipment; multi-source state data stream refers to a continuous data sequence organized in a structured manner according to "timestamp-data type-value"; mechanical operation feature vector refers to a multi-dimensional numerical combination of the mechanical structure operation state of electrical equipment; electrical control feature vector refers to a multi-dimensional numerical combination of the electrical system control state of electrical equipment; state classification model refers to an algorithm model used to classify mechanical operation feature vectors; mechanical operation features refer to the key quantitative parameters that define the essence of the mechanical structure operation of electrical equipment; association rule analysis refers to a data analysis method that mines the logical associations within electrical control feature vectors and between them and mechanical operation features; electrical control rules refer to the electrical parameter adjustment logic that uses mechanical operation features as constraints and aims at the normal operation of electrical equipment.

[0060] Preferably, in this embodiment, the operation control mode when the electrical equipment experiences an abnormal operating state is determined based on the mechanical operating characteristics and the electrical control rules, with reference to... Figure 2 As shown in the figure, this is a flowchart illustrating the process of determining the operation control mode in some embodiments of this application. In this embodiment, the determination of the operation control mode can be achieved by the following steps:

[0061] In step S21, the real-time acquired mechanical operating status data is matched with the mechanical operating characteristics to identify and determine the type of abnormal operating status of the electrical equipment;

[0062] In step S22, the electrical control rules are queried according to the abnormal operating state type, and the control constraints corresponding to the abnormal operating state type are obtained;

[0063] In step S23, a sequence of device control instructions for the abnormal operating state type is determined based on the control constraints.

[0064] In step S24, the operation control mode when the electrical equipment experiences an abnormal operating state is determined according to the equipment control command sequence.

[0065] In practice, firstly, the real-time acquired mechanical operating status data is converted into a numerical vector with the same dimension as the mechanical operating features. The Euclidean distance algorithm is used to calculate the similarity between this real-time vector and the mechanical operating feature vectors corresponding to each preset anomaly type. The similarity threshold is determined based on statistical analysis of historical equipment anomaly data, and the threshold recognition accuracy is verified to be no less than 98% through historical sample verification. If the similarity between the real-time vector and a certain type of feature vector is higher than the threshold, the equipment is determined to match that type of anomaly; if both are lower than the threshold, it is considered a normal state, and a clear abnormal operating status type is output. Then, electrical control rules are categorized and indexed according to the abnormal operating status type, forming a structured electrical control rule library, with each index corresponding to anomaly type identifier. The identified abnormal operating status type is used as a search keyword, and the corresponding electrical control rule entries are matched using precise indexing within the library. The core content, such as preset electrical parameter adjustment ranges, execution action priorities, and adjustment timing limits, is extracted from these entries and integrated to form control constraints for that anomaly type. Next, using control constraints as the core criterion, all electrical regulation commands that satisfy the constraints are first identified. Then, based on the operating logic of the equipment's electrical system, they are prioritized according to the execution priority of "protection first, regulation second, monitoring third." Simultaneously, the execution timing interval is set based on the linkage relationship of each command, forming a preliminary equipment control command sequence. This sequence is then substituted into the equipment's digital simulation model for feasibility verification, correcting commands that conflict with the equipment's operating logic, and finally determining the directly executable equipment control command sequence. Finally, the determined equipment control command sequence is standardized, assigning a unique anomaly type association code to each sequence. Simultaneously, four core elements are extracted from the sequence: core regulation object, core execution action, key regulation boundary, and overall execution timing. All elements are then bound and integrated with the equipment control command sequence. This is solidified according to the structure of "anomaly type - core element - command sequence," forming an operation control method for this anomaly operating state type. This method is then stored in the equipment control method matching library, completing the determination of the operation control method.

[0066] It should be noted that, in this application, mechanical operating status data refers to real-time mechanical monitoring data continuously collected during the operation of electrical equipment; abnormal operating status type refers to the specific categories of mechanical abnormalities of equipment classified according to their causes and manifestations; control constraints refer to the limitations of electrical parameter adjustment boundaries and execution priorities set in electrical control rules for specific abnormality types; equipment control instruction sequence refers to the set of electrical adjustment instructions arranged according to execution timing and linkage relationship; and operation control mode refers to the standardized electrical adjustment strategy set for specific abnormalities in electrical equipment.

[0067] In step S3, the feedback control quantity when the operating state of the electrical equipment is subject to a sudden disturbance is obtained. Based on the feedback control quantity and the operation control request under the sudden disturbance state, the operating control mode of the electrical equipment is actively selected, and the corresponding collaborative control command is generated. Then, the collaborative control command is dynamically updated by the abnormal operating parameters under the sudden disturbance state.

[0068] In this embodiment, obtaining the feedback control quantity when the operating state of the electrical equipment experiences a sudden disturbance can be achieved through the following steps:

[0069] Real-time monitoring of the operating status data of electrical equipment; when a sudden change in the operating status data is detected, time-series data before and after the change are extracted to construct a disturbance feature sequence.

[0070] The intensity and trend of sudden disturbances in the operating state of electrical equipment are determined based on the disturbance characteristic sequence.

[0071] The feedback control quantity for the operation of electrical equipment when there is a sudden disturbance is determined by the intensity of the sudden disturbance and the trend index.

[0072] In practice, firstly, real-time data on the electrical equipment's operating status, including voltage, current, speed, and vibration, are collected at a high frequency of 100Hz. Based on historical normal operating data, statistical analysis is used to determine the normal rate of change threshold for each data point. The rate of change per unit time for each data point is calculated in real-time. When the rate of change of any data point exceeds the threshold, it is considered a data mutation. The time-series data of this type and related operating statuses are immediately extracted for the 5 seconds before and 10 seconds after the mutation, sorted in ascending order by timestamp, and invalid noise points are removed, ultimately forming a disturbance feature sequence with unified dimensions and continuous time sequence. Then, normal baseline values ​​for each dimension of data are determined using the rated operating parameters of the electrical equipment and the average of historical normal operating data. Based on the disturbance feature sequence, the deviation value between each time-series data point and the corresponding normal baseline value is calculated. The deviation accumulation method is used to sum all deviation values ​​and then normalize them to obtain the sudden disturbance intensity in the 0-1 interval; a larger value indicates a higher degree of disturbance impact. Next, a linear fit is performed on the disturbance characteristic sequence, and the slope of the fitted line is calculated as an indicator of the trend. A positive slope indicates that the disturbance is continuously increasing, a negative slope indicates that the disturbance is gradually weakening, and a slope of 0 indicates that the disturbance is in a stable state. Finally, a weighted joint calculation model of the sudden disturbance intensity and the trend indicator is constructed. The weights of this weighted joint calculation model are determined based on historical sudden disturbance case data through multiple regression analysis. The calculated sudden disturbance intensity is denoted as X, and the trend indicator is denoted as Y. The feedback control quantity is calculated according to the formula K=αX+βY, where α is the weight of the sudden disturbance intensity and β is the weight of the trend indicator, α+β=1. The weights can be determined based on historical case data, through multiple regression analysis combined with engineering experimental verification and disturbance type subdivision calibration, and are not limited here. After the calculation is completed, the feedback control quantity is mapped to a preset 0-10 quantization range. The upper and lower limits of the range are set based on the equipment's safe operation boundary to ensure that the feedback control quantity effectively represents the disturbance state while conforming to the equipment's operating rules.

[0073] It should be noted that, in this application, a sudden disturbance refers to an operating state of electrical equipment caused by sudden changes in the external environment or sudden internal faults during normal operation, without prior warning, and causing irregular and drastic fluctuations in the equipment's operating status data; a sudden change in operating status data refers to the phenomenon where the operating status data of electrical equipment deviates from the normal change pattern and exhibits irregular and drastic fluctuations within a short period of time; a disturbance characteristic sequence refers to an ordered data set formed by regularizing the time-series data of the operating status before and after the sudden disturbance along a time axis; a sudden disturbance intensity refers to a quantitative indicator of the degree of impact of the sudden disturbance on the operation of the electrical equipment; a change trend indicator refers to a quantitative indicator of the direction and rate of change of the sudden disturbance over time; and a feedback control quantity refers to a quantitative parameter of the overall state of the electrical equipment during a sudden disturbance.

[0074] In this embodiment, the process of actively selecting the operating control mode of the electrical equipment based on the feedback control quantity and the operation control request under sudden disturbance conditions, and generating the corresponding cooperative control command for the operating control mode, can be achieved through the following steps:

[0075] Determine the operational control requests under sudden disturbance conditions;

[0076] Based on the feedback control quantity and the operation control request, determine the type of control strategy and the target control priority required at present;

[0077] Map the control strategy type and the target control priority to the operation control mode of the electrical equipment;

[0078] Generate the corresponding collaborative control command based on the operation control mode and the feedback control quantity.

[0079] In practical implementation, firstly, three types of standardized operation control requests are preset, corresponding to the core objectives of ensuring equipment safety, maintaining operational continuity, and rapidly restoring normal operation. The equipment control system has a built-in disturbance intensity threshold judgment logic, which is determined based on statistical analysis of historical disturbance case data. After a sudden disturbance occurs, the system automatically reads the feedback control quantity. If the feedback control quantity is higher than the threshold, a request to ensure equipment safety is triggered by default; if it is lower than the threshold, the operator can manually select a control request within 3 seconds via an industrial host computer. If no selection is made within the time limit, a request to rapidly restore normal operation is automatically triggered, ultimately outputting a uniquely determined operation control request. Next, the quantification range of the feedback control quantity is divided into three levels: mild, moderate, and severe, based on the correlation analysis between historical disturbance data and the equipment's safe operation boundary. A three-dimensional mapping logic of "feedback control quantity level - operation control request - control strategy type" is established: mild disturbance + maintaining operational continuity corresponds to parameter fine-tuning strategy, moderate disturbance + rapidly restoring normal operation corresponds to combined adjustment strategy, and severe disturbance + ensuring equipment safety corresponds to emergency protection strategy. Simultaneously, a priority ranking rule is established: ensuring equipment safety has the highest priority, followed by rapid recovery to normal operation, and ensuring operational continuity has the lowest priority. The target control priority is output according to this rule. Then, a structured control mode matching library is constructed, storing standardized operation control modes corresponding to various control strategy types and target control priority combinations. The data sources are historical equipment disturbance handling cases and physical simulation experiment results. Using the determined control strategy type and target control priority as search keywords, candidate control modes are quickly located through the library index. The core parameters of the candidate control modes are extracted and their consistency is verified with the current equipment operating status data. If parameter conflicts exist, the conflicting parameters are corrected based on the target control priority, ultimately determining the uniquely suitable electrical equipment operation control mode. Finally, a collaborative control instruction framework is designed according to a fixed structure of "module identifier - execution action - parameter value - execution sequence". Based on the determined operation control mode, the core actions and basic parameters of each execution module are clarified, and the specific parameter values ​​are adjusted in conjunction with the feedback control quantity: the larger the feedback control quantity, the closer the parameter adjustment is to the limit threshold of the control mode. The adjusted parameters and actions are sorted according to the target control priority, converted into a standardized protocol format that can be recognized by each module, and command conflicts are eliminated through logical verification. Finally, they are integrated to form a collaborative control command that includes the operational requirements of all execution modules.

[0080] It should be noted that, in this application, the operation control request refers to the preset instruction to be executed on the control target in the event of a sudden disturbance; the control strategy type refers to the standardized control logic category divided according to the disturbance state and control requirements; the target control priority refers to the execution criteria ranked according to the importance of the control target; and the coordinated control instruction refers to the standardized operation instruction that can coordinate the synchronous action of each control module of the equipment.

[0081] Preferably, in this embodiment, the coordinated control command is dynamically updated based on abnormal operating parameters under sudden disturbance conditions, with reference to... Figure 3 As shown in the figure, this is a flowchart illustrating the process of updating cooperative control instructions in some embodiments of this application. In this embodiment, updating cooperative control instructions can be achieved using the following steps:

[0082] In step S31, the operating state deviation corresponding to the current coordinated control command is determined based on the abnormal operating parameters under the sudden disturbance state.

[0083] In step S32, the dynamic adjustment amount of the coordinated control command is determined based on the deviation of the operating state;

[0084] In step S33, the incremental update parameters of the collaborative control command are determined based on the dynamic adjustment amount;

[0085] In step S34, the incremental update parameters are superimposed on the original collaborative control instructions in real time to obtain dynamically updated collaborative control instructions.

[0086] In practical implementation, firstly, the target parameters (such as voltage target value, speed target value, etc.) of each execution module in the coordinated control command are extracted, and the reference range of the target parameters is determined based on the equipment's rated operating standards and the preset requirements of the control command. Abnormal operating parameters under sudden disturbances are collected in real time. For each target parameter, the difference between the abnormal operating parameter and the midpoint value of the target parameter reference range is calculated using the absolute deviation method. If the abnormal parameter is within the reference range, the deviation is 0; otherwise, the degree of deviation is quantified by the absolute value of the difference, obtaining the operating state deviation corresponding to the current coordinated control command. Next, a proportional-integral-derivative composite algorithm can be used to calculate the dynamic adjustment amount. The proportional component outputs the basic adjustment value proportional to the magnitude of the operating state deviation, the integral component accumulates historical deviations to eliminate static errors, and the derivative component predicts the adjustment trend based on the rate of change of the deviation. The algorithm parameters are optimized and determined based on historical disturbance case data using the gradient descent method. Adjustment coefficients are set for different types of deviations, with the adjustment coefficients for key parameters (such as current and speed) being greater than those for secondary parameters, ensuring that core deviations are corrected first. Finally, the dynamic adjustment amount corresponding to each parameter is output. Then, the safe operating parameter boundaries of each execution module of the equipment (such as the frequency limit of the inverter, the upper limit of the cooling fan speed, etc.) are retrieved. These safe operating parameter boundaries are determined based on the equipment design standards and long-term operation test data to ensure the safe operation of the equipment. The dynamic adjustment amount is superimposed with the original cooperative control command parameters, and it is predicted whether the superimposed parameters exceed the safety boundaries. If they do not exceed the boundaries, the dynamic adjustment amount is directly used as the incremental update parameter; if they do exceed the boundaries, the adjustment amount is corrected according to the safety boundary difference to ensure that the superimposed parameters are within the safe range. The corrected incremental update parameters are logically verified to eliminate adjustment conflicts between parameters of different modules, and finally, the executable incremental update parameters of each module are determined. Finally, a one-to-one correspondence between the original cooperative control command and the incremental update parameters is established, and the update operation is executed according to the process of "parameter identifier matching - real-time numerical superposition - command format reconstruction". The incremental update parameters are transmitted using a real-time data bus to ensure that the superposition operation delay is controlled at the millisecond level to meet the real-time adjustment requirements under sudden disturbances. After the superposition is completed, the integrity of the new instruction is checked, and the parameter format and execution timing are checked to see if they meet the device module recognition requirements. If there is a format error, the instruction format is automatically reconstructed. Finally, a dynamically updated collaborative control instruction with complete structure, accurate parameters and direct execution is output, which is the dynamically updated collaborative control instruction.

[0087] It should be noted that, in this application, abnormal operating parameters refer to electrical and mechanical operating data that are collected in real time during sudden disturbances and deviate from the normal operating range of the equipment; operating state deviation refers to the numerical value that quantifies the difference between abnormal operating parameters and target parameters of the coordinated control command; dynamic adjustment refers to the parameter change value used to correct the coordinated control command; incremental update parameters refer to standardized adjustment parameters that prevent the adjustment from exceeding the safe operating range of the equipment and ensure command updates; and dynamically updated coordinated control commands refer to control commands that adapt to the dynamic changes of sudden disturbances in real time and ensure that the control commands always match the actual operating state of the equipment.

[0088] In step S4, global synchronous control is performed on the abnormally operating electrical equipment based on the dynamically updated collaborative control instructions.

[0089] In this embodiment, global synchronous control of abnormally operating electrical equipment based on dynamically updated cooperative control instructions can be achieved through the following steps:

[0090] Extract the control target parameters and synchronization control requirements of electrical equipment from the dynamically updated collaborative control instructions;

[0091] The synchronous execution command is sent to the electrical equipment based on the control target parameters and the synchronous control requirements.

[0092] Monitor the response status of each electrical device to synchronous execution commands, adjust command output in real time based on feedback data, and complete coordinated control actions.

[0093] In practical implementation, firstly, the dynamically updated collaborative control instruction structure is parsed. Following the logic of "module identifier - parameter type - numerical range," control target parameters such as voltage, current, speed, and action duration corresponding to each execution module are extracted, clarifying the allowable fluctuation range for each parameter. Simultaneously, synchronization control requirements are extracted from the instructions, including clock synchronization accuracy requirements, the upper limit of the execution start time deviation for each module, and inter-module linkage triggering conditions (e.g., triggering module B after module A completes its action). Extraction rules are formulated based on the communication protocols and operating logic of each module in the equipment, thus obtaining the control target parameters and synchronization control requirements of the electrical equipment. Then, the EtherCAT industrial real-time bus can be used as the instruction transmission carrier. The bus transmission rate has been tested and meets the low latency requirements of this application. With the control center as the master station and each execution module as a slave station, the clocks of all slave stations are calibrated using the IEEE 1588 precision clock synchronization protocol, ensuring that the time deviation between the master and slave stations is controlled within a preset range. According to the execution start time in the synchronization control requirements, the synchronization execution instructions containing the control target parameters are encapsulated into standardized data frames. The master station synchronously sends instructions to all slave stations at a unified time node. During instruction transmission, CRC check is used to ensure data integrity, the instruction sending delay is less than 1ms, and the timing deviation of each slave station receiving instructions does not exceed a preset threshold. Finally, response status data of each module is collected at a high frequency of 100Hz, including instruction reception confirmation signals, action start feedback, real-time operating parameters, etc., to form a feedback dataset. The feedback data is compared with the control target parameters to determine whether the module executes the action as required: if the real-time parameters are within the allowable fluctuation range of the target parameters and the action timing meets the synchronization control requirements, the original instruction is maintained; if the parameter deviation exceeds the threshold or the action is delayed, a proportional-integral-derivative adjustment algorithm is used to calculate the fine adjustment amount, generate a supplementary adjustment instruction, and send it in real time. The adjustment mechanism is verified through the equipment physical simulation platform to ensure that the time for the adjusted parameters to return to the target range meets the disturbance handling requirements, and finally, the equipment operating parameters tend to stabilize after all modules complete the coordinated control action.

[0094] It should be noted that, in this application, global synchronous control refers to a holistic control method in which, during the operation of electrical equipment under sudden disturbances, each execution module of the electrical equipment executes control actions according to preset synchronous control requirements and a unified timing sequence through a unified clock reference and low-latency instruction transmission, based on dynamically updated collaborative control instructions. Simultaneously, the system monitors the instruction response status of each execution module in real time and dynamically adjusts the instruction output, achieving coordinated linkage among the execution modules. Control target parameters refer to the quantified operating standards that each execution module of the electrical equipment needs to achieve. Synchronous control requirements refer to the constraints that ensure consistent timing and parameter coordination among the actions of each module of the electrical equipment. Synchronous execution instructions refer to standardized control signals that are transformed from abstract control requirements into recognizable and executable signals for the electrical equipment modules. Response status refers to the real-time status information of each module of the electrical equipment after receiving the synchronous execution instructions, including the action initiation status, parameter execution progress, and whether any abnormalities exist. Feedback data refers to the operating parameters and status signals collected in real time during the execution of instructions by the modules.

[0095] Therefore, this application demonstrates that when the synchronization of multi-source state perception-based collaborative control of electrical equipment is insufficient, it can achieve collaborative control of abnormal operation of electrical equipment. Specifically, by collecting multi-source state information of electrical equipment during operation, accurate and time-series synchronized acquisition of state data across all dimensions of mechanical operation and electrical control is achieved, ensuring the comprehensiveness of the data foundation. By performing hierarchical processing of multi-source state information to obtain mechanical operation characteristics and electrical control rules, the classification and analysis of mechanical and electrical data and the accurate extraction of feature rules can be achieved, improving the accuracy of control mode matching under normal abnormal conditions. By acquiring feedback control quantities from sudden disturbances, actively selecting control modes and generating collaborative control commands based on operation control requests, and then dynamically updating the commands with abnormal operation parameters, the control commands are matched with the dynamic changes of disturbances in real time, improving the dynamic adaptability of control decisions under sudden disturbances. Through globally synchronized control of abnormally operating electrical equipment using dynamically updated collaborative control commands, all execution modules can coordinate actions according to a unified timing sequence, and the command output can be adjusted in real time, solving the defects of easy deviation in command execution in existing technologies and improving the synchronization of abnormal control execution of electrical equipment.

[0096] In summary, the technical solution adopted in this application can perform coordinated control of electrical equipment under operating conditions where there are sudden disturbances, thereby improving the adaptability of the control method when the electrical equipment is operating abnormally.

[0097] Example 2: This application provides a collaborative control system for electrical equipment based on multi-source state perception, referring to... Figure 4 As shown in the figure, this is a module structure diagram of an electrical equipment cooperative control system based on multi-source state perception according to this embodiment of the present application. The cooperative control system includes:

[0098] Information acquisition module 100 is used to collect multi-source status information of electrical equipment during operation;

[0099] The control mode decision module 200 is used to perform hierarchical processing on the multi-source state information to obtain the mechanical operation characteristics and electrical control rules of the electrical equipment, and to determine the operation control mode when the electrical equipment generates an abnormal operating state based on the mechanical operation characteristics and the electrical control rules.

[0100] The control update module 300 is used to obtain the feedback control quantity when the operating status of the electrical equipment is subject to a sudden disturbance, actively select the operating control mode of the electrical equipment according to the feedback control quantity and the operation control request under the sudden disturbance state, and generate the cooperative control instruction corresponding to the operating control mode, and then dynamically update the cooperative control instruction based on the abnormal operating parameters under the sudden disturbance state.

[0101] The global synchronization control module 400 is used to perform global synchronization control on abnormally operating electrical equipment based on dynamically updated collaborative control instructions.

[0102] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0103] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compactdisc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0104] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A method for coordinated control of electrical equipment based on multi-source state perception, characterized in that, The collaborative control method includes the following steps: Collect multi-source status information of electrical equipment during operation; The multi-source state information is processed in layers to obtain the mechanical operating characteristics and electrical control rules of the electrical equipment. Based on the mechanical operating characteristics and electrical control rules, the operation control mode when the electrical equipment generates an abnormal operating state is determined. The system acquires feedback control quantities when there is a sudden disturbance in the operating status of electrical equipment. Based on the feedback control quantities and the operation control request under the sudden disturbance, the system actively selects the operating control mode of the electrical equipment and generates a cooperative control command corresponding to the operating control mode. The cooperative control command is then dynamically updated by the abnormal operating parameters under the sudden disturbance. Global synchronous control is performed on abnormally operating electrical equipment based on dynamically updated collaborative control instructions.

2. The method for coordinated control of electrical equipment based on multi-source state perception as described in claim 1, characterized in that, The hierarchical processing of the multi-source state information to obtain the mechanical operating characteristics and electrical control rules of the electrical equipment specifically includes: The multi-source state information is synchronized in time to obtain a multi-source state data stream; Extract mechanical operation feature vectors and electrical control feature vectors from the multi-source state data stream; Based on the mechanical operation feature vector, the mechanical operation features of the electrical equipment are output through a state classification model; Based on the electrical control feature vector, the electrical control rules for electrical equipment are output through association rule analysis.

3. The method for coordinated control of electrical equipment based on multi-source state perception as described in claim 1, characterized in that, The feedback control quantities for obtaining the operating status of electrical equipment when there is a sudden disturbance specifically include: Real-time monitoring of the operating status data of electrical equipment; when a sudden change in the operating status data is detected, time-series data before and after the change are extracted to construct a disturbance feature sequence. The intensity and trend of sudden disturbances in the operating state of electrical equipment are determined based on the disturbance characteristic sequence. The feedback control quantity for the operation of electrical equipment when there is a sudden disturbance is determined by the intensity of the sudden disturbance and the trend index.

4. The method for coordinated control of electrical equipment based on multi-source state perception as described in claim 1, characterized in that, The aforementioned sudden disturbance refers to an operating state caused by sudden changes in the external environment and sudden internal failures during the normal operation of electrical equipment, which occurs without prior warning and causes irregular and drastic fluctuations in the equipment's operating status data.

5. The method for coordinated control of electrical equipment based on multi-source state perception as described in claim 1, characterized in that, Based on the feedback control quantity and the operation control request under sudden disturbance conditions, the system actively selects the operation control mode of the electrical equipment and generates the corresponding collaborative control command, which specifically includes: Determine the operational control requests under sudden disturbance conditions; Based on the feedback control quantity and the operation control request, determine the type of control strategy and the target control priority required at present; Map the control strategy type and the target control priority to the operation control mode of the electrical equipment; Generate the corresponding collaborative control command based on the operation control mode and the feedback control quantity.

6. The method for coordinated control of electrical equipment based on multi-source state perception as described in claim 1, characterized in that, The aforementioned coordinated control commands refer to standardized operating commands that can coordinate the synchronous actions of various control modules of the equipment.

7. The method for coordinated control of electrical equipment based on multi-source state perception as described in claim 1, characterized in that, The dynamically updated collaborative control commands refer to control commands that adapt to the dynamic changes of sudden disturbances in real time, ensuring that the control commands always match the actual operating state of the equipment.

8. The method for coordinated control of electrical equipment based on multi-source state perception as described in claim 1, characterized in that, Global synchronous control of abnormally operating electrical equipment based on dynamically updated collaborative control instructions specifically includes: Extract the control target parameters and synchronization control requirements of electrical equipment from the dynamically updated collaborative control instructions; The synchronous execution command is sent to the electrical equipment based on the control target parameters and the synchronous control requirements. Monitor the response status of each electrical device to synchronous execution commands, adjust command output in real time based on feedback data, and complete coordinated control actions.

9. The method for coordinated control of electrical equipment based on multi-source state perception as described in claim 1, characterized in that, Global synchronous control refers to a holistic control method that, when electrical equipment experiences sudden disturbances or abnormal operation, uses dynamically updated collaborative control commands, a unified clock reference, and low-latency command transmission to enable each execution module of the electrical equipment to perform control actions according to preset synchronous control requirements and a unified timing sequence. At the same time, it monitors the command response status of each execution module in real time and dynamically adjusts the command output, thereby achieving coordinated linkage among the execution modules.

10. A multi-source state perception-based collaborative control system for electrical equipment, used to execute a multi-source state perception-based collaborative control method for electrical equipment as described in any one of claims 1 to 9, characterized in that, The collaborative control system includes: The information acquisition module is used to collect multi-source status information of electrical equipment during operation; The control mode decision module is used to perform hierarchical processing on the multi-source state information to obtain the mechanical operation characteristics and electrical control rules of the electrical equipment, and to determine the operation control mode when the electrical equipment generates an abnormal operating state based on the mechanical operation characteristics and the electrical control rules. The control update module is used to obtain the feedback control quantity when the operating status of electrical equipment is subject to sudden disturbance, actively select the operating control mode of electrical equipment according to the feedback control quantity and the operation control request under the sudden disturbance state, and generate the corresponding collaborative control command for the operating control mode. Then, the collaborative control command is dynamically updated by the abnormal operating parameters under the sudden disturbance state. The global synchronization control module is used to perform global synchronization control on abnormally operating electrical equipment based on dynamically updated collaborative control instructions.

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