Intelligent control method and system for electrical equipment monitoring

Abstract

The application relates to the technical field of electric appliance control, in particular to an intelligent control method and system for electric appliance equipment monitoring, which comprises the following steps: obtaining a time sequence of operating parameters and environmental data and calculating a change rate, generating a comprehensive change rate index based on equipment type weight and environmental adaptation weight, determining a dynamic threshold interval in combination with historical operating data, further determining a probability distribution interval and performing operating condition calibration and consistency verification, obtaining a state judgment result, and outputting an early warning signal or performing control according to the state judgment result. The application can improve the state judgment accuracy, reduce false positives and false negatives, and enhance the timeliness of control response.

Description

Technical Field

[0001] This application relates to the field of electrical control technology, specifically to an intelligent control method and system for monitoring electrical equipment. Background Technology

[0002] The operational stability of electrical equipment directly affects production continuity, energy efficiency, and operational safety. As equipment operating conditions become increasingly complex, the industry places higher demands on the real-time performance, accuracy, and interoperability of data acquisition and control, as well as process control. Current technologies often use a single operating parameter combined with a fixed threshold for status judgment, which struggles to reflect the interconnected changes between multiple parameters and is ill-suited to variations in equipment type, environment, and operational phase. This can easily lead to false alarms, missed alarms, or control lags, affecting the timeliness of equipment protection and the reliability of control decisions. Summary of the Invention

[0003] This application provides an intelligent control method and system for monitoring electrical equipment, which at least solves the problem of how to accurately determine the operating status of electrical equipment and implement timely control responses under changing equipment type and operating environment conditions.

[0004] In a first aspect, this application provides an intelligent control method for monitoring electrical equipment, the method comprising:

[0005] Obtain the time series of operating parameters and environmental data of the target electrical equipment, and calculate the rate of change of each operating parameter in the time series of operating parameters;

[0006] The change rate is weighted and fused based on the equipment type weight and the environmental adaptation weight determined according to environmental data to obtain a comprehensive change rate index.

[0007] Based on historical operating data that matches the equipment type and operating environment, and combined with the comprehensive rate of change index, the dynamic threshold range is determined.

[0008] The probability distribution range is determined based on the dynamic threshold range and historical operating data, and the current operating condition is identified based on environmental data and operating parameter time series. The comprehensive change rate index is calibrated and consistency verified according to the current operating condition to obtain the state judgment result.

[0009] In response to a status determination result indicating an early warning state, an early warning signal is output; in response to a status determination result indicating a protection state, pre-set control commands are retrieved to control the target electrical equipment to perform load limiting, operating parameter adjustment, or shutdown control.

[0010] In one possible implementation, the operating parameter time series includes at least two of the following: current time series, temperature time series, voltage time series, and power time series; calculating the rate of change of each operating parameter in the operating parameter time series includes: performing time alignment and smoothing on the operating parameter time series, and calculating the rate of change of the operating parameter within a preset sliding time window.

[0011] In one possible implementation, the rate of change is weighted and fused, including: standardizing the rate of change and weighting and fusing it according to the weight of equipment type and the weight of environmental adaptability to obtain a comprehensive rate of change index.

[0012] In one possible implementation, environmental adaptation weights are determined based on environmental data and obtained by comparing at least one of the environmental data (ambient temperature, ambient humidity, load status, start-stop status, and power supply fluctuation status) with a corresponding reference interval to obtain a corresponding environmental correction coefficient; the obtained environmental correction coefficients are combined to generate environmental adaptation weights; wherein the reference interval is preset according to the rated operating conditions of the target electrical equipment.

[0013] In one possible implementation, the dynamic threshold range is determined by combining the comprehensive rate of change index, including: extracting the mean, fluctuation range and trend of historical operating data, and correcting the mean, fluctuation range and trend according to the comprehensive rate of change index to obtain the dynamic threshold range.

[0014] In one possible implementation, the mean, fluctuation range, and trend are corrected based on the comprehensive rate of change index, including: determining the threshold correction direction based on the direction of change of the comprehensive rate of change index, and determining the threshold correction magnitude based on the absolute value of the comprehensive rate of change index; increasing the upper limit of the dynamic threshold interval when the comprehensive rate of change index is greater than zero; and decreasing the lower limit of the dynamic threshold interval when the comprehensive rate of change index is less than zero.

[0015] In one possible implementation, the probability distribution interval is determined, the current operating condition is identified, and the comprehensive rate of change index is calibrated based on the current operating condition. This includes: determining the normal probability interval, abnormal probability interval, and transitional probability interval based on the dynamic threshold interval and historical operating data; identifying the current operating condition based on environmental data and the time series of operating parameters; correcting the comprehensive rate of change index according to the calibration rules corresponding to the current operating condition to obtain the calibrated comprehensive rate of change index; and determining the state determination result based on the positional relationship between the calibrated comprehensive rate of change index and the normal probability interval, abnormal probability interval, and transitional probability interval.

[0016] In one possible implementation, consistency verification includes time consistency verification and multi-parameter correlation consistency verification. Time consistency verification is used to verify whether the comprehensive rate of change index after calibration deviates from the dynamic threshold range within multiple consecutive judgment periods. Multi-parameter correlation consistency verification is used to verify whether the rate of change corresponding to at least two operating parameters changes synchronously in the abnormal direction. If either time consistency verification or multi-parameter correlation consistency verification fails, the state judgment result is determined to be a normal state.

[0017] In one possible implementation, in response to a state determination result indicating a protection state, the target electrical equipment is controlled to perform load limiting, operating parameter adjustment, or shutdown control, including: determining the control level based on the magnitude and duration of the deviation of the comprehensive rate of change index from the dynamic threshold range; performing load limiting when the control level is the first level; performing operating parameter adjustment when the control level is the second level; and performing shutdown control when the control level is the third level.

[0018] Secondly, this application provides an intelligent control system for monitoring electrical equipment, and an intelligent control method for implementing electrical equipment monitoring. The system includes:

[0019] The data acquisition and calculation module is used to acquire the time series of operating parameters and environmental data of the target electrical equipment, and to calculate the rate of change of each operating parameter in the time series of operating parameters;

[0020] The fusion generation module is used to weight and fuse the rate of change based on the equipment type weight and the environmental adaptation weight determined according to the environmental data to obtain a comprehensive rate of change index.

[0021] The threshold determination module is used to determine the dynamic threshold range based on historical operating data that matches the equipment type and operating environment, combined with the comprehensive rate of change index.

[0022] The judgment and verification module is used to determine the probability distribution range based on the dynamic threshold range and historical operating data, identify the current operating condition based on environmental data and operating parameter time series, calibrate and verify the consistency of the comprehensive change rate index according to the current operating condition, and obtain the state judgment result.

[0023] The control output module is used to output a warning signal in response to a state determination result of a warning state; and to retrieve a pre-set control command in response to a state determination result of a protection state, and to control the target electrical equipment to perform load limiting, operating parameter adjustment or shutdown control.

[0024] Compared with existing technologies, the advantages and beneficial effects of this application are as follows:

[0025] By using a weighted fusion technique to analyze the rate of change of operating parameters, a unified representation of the collaborative trend of multiple parameters is achieved, enhancing the ability to identify the status. By combining historical operating data to generate dynamic threshold ranges, the judgment boundary is adaptively adjusted according to equipment type and operating environment. Through probability distribution range, operating condition calibration, and consistency verification techniques, the distinction between short-term disturbances and continuous anomalies is achieved. By linking the status judgment results with early warning and control output, a closed-loop connection between anomaly identification and control is achieved. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating the method described in this application;

[0027] Figure 2 This is a block diagram of the module composition of the system in this application. Detailed Implementation

[0028] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0029] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0030] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0031] Intelligent control refers to a control method that integrates data acquisition and control, status analysis, decision generation, and execution adjustment during equipment operation. Its core lies not in simply collecting data or in single control outputs, but in forming a continuous closed loop of perception, judgment, and adjustment around the equipment's operating status. In process control scenarios, intelligent control typically requires real-time acquisition of multi-source operating information, followed by dynamic judgment of the current state based on the equipment's own characteristics, changes in the external environment, and differences in operating stages. The judgment results are then translated into control actions such as early warning, load limiting, parameter adjustment, or shutdown. Based on this approach, this application addresses the problem of the difficulty in uniformly representing the changing trends of multiple parameters during electrical equipment operation and the difficulty in dynamically adjusting the judgment boundaries according to changes in operating conditions. It proposes an intelligent control scheme oriented towards the linkage of operating status identification and control.

[0032] like Figure 1 As shown, an intelligent control method for monitoring electrical equipment includes:

[0033] Obtain the time series of operating parameters and environmental data of the target electrical equipment, and calculate the rate of change of each operating parameter in the time series of operating parameters;

[0034] The controller continuously receives time series data of operating parameters and environmental data from the target electrical equipment. The operating parameter time series characterizes the state changes of the equipment during continuous operation, while the environmental data characterizes the external conditions corresponding to the equipment's operation. The controller first organizes the data according to a unified time base, then calculates the rate of change for each item in the organized operating parameter time series, obtaining a set of rates of change corresponding to the current operating moment. The rate of change characterizes how quickly each operating parameter changes between adjacent sampling periods, thus transforming static numerical changes into dynamic trend information. Through this processing, state analysis can be performed in subsequent stages based on the changing trends of multiple operating parameters rather than the absolute values ​​at a single moment, providing a consistent data foundation for generating a comprehensive rate of change index.

[0035] The operating parameter time series includes at least two of the following: current time series, temperature time series, voltage time series, and power time series; the calculation of the rate of change of each operating parameter in the operating parameter time series includes: performing time alignment and smoothing on the operating parameter time series, and calculating the rate of change of the operating parameter within a preset sliding time window.

[0036] In one embodiment, the operating parameter time series may include at least two of the following: current time series, temperature time series, voltage time series, and power time series. The specific selection of operating parameters can be determined based on the structural type, rated operating mode, and common abnormal behaviors of the target electrical equipment. For equipment where load surges are the primary source of risk, current and power time series can be prioritized; for equipment where heat accumulation is the primary source of risk, temperature and current time series can be prioritized; and for equipment that is highly sensitive to power supply stability, voltage time series can be added.

[0037] Environmental data can include one or more of the following: ambient temperature, ambient humidity, load status, start / stop status, and power supply fluctuation status, reflecting the external conditions corresponding to the operation of the target electrical equipment. Since the sampling frequencies and sampling start points of different sensors may not be consistent, the controller first performs time alignment processing on the operating parameter time series. Time alignment can be achieved using a unified time-scale mapping method, that is, using a pre-set sampling period as a benchmark, merging data falling within the same sampling interval to the same time; for missing sampling points, nearest neighbor supplementation or linear interpolation can be used to fill in the gaps. After time alignment, smoothing processing is then performed on the operating parameter time series to reduce the impact of single-point noise, instantaneous jitter, and sampling spikes on subsequent rate of change calculations.

[0038] Smoothing can be achieved using any of the following methods: moving average, median filtering, or amplitude limiting filtering. The method with lower computational complexity and easier real-time deployment is preferred. The preset sliding time window is set based on the response speed, sampling period, and operational fluctuation characteristics of the target electrical equipment. When the target electrical equipment's state changes rapidly, a shorter preset sliding time window is used; when the target electrical equipment's state changes slowly, a longer preset sliding time window is used.

[0039] Generally, the preset sliding time window can be set to three to ten consecutive sampling periods. The controller then calculates the rate of change for each operating parameter within the preset sliding time window. The rate of change can be obtained by dividing the difference between the parameter value at the end of the window and the parameter value at the beginning of the window by the window duration. When an operating parameter exhibits frequent reverse fluctuations within the window, the rate of change can also be obtained by dividing the difference between the mean values ​​at the beginning and end of the window by the window duration to improve the stability of the results. After collection, environmental data is correlated with the time-aligned operating parameter time series. This can be achieved using unified timestamp matching or mapping between adjacent sampling intervals, ensuring that each set of rate of change results corresponds to the environmental state at the same time or within the same time period. After the above processing, a set of operating parameter rate of change and environmental data with time correspondence is obtained, which can be directly used for subsequent determination of environmental adaptation weights and identification of the current operating condition.

[0040] The change rate is weighted and fused based on the equipment type weight and the environmental adaptation weight determined according to environmental data to obtain a comprehensive change rate index.

[0041] After obtaining the rate of change for each operating parameter, the controller performs uniform scaling on the rate of change and then weights and fuses it with the equipment type characteristics and environmental adaptability characteristics of the target electrical equipment to generate a comprehensive rate of change index. The comprehensive rate of change index characterizes the overall trend of multiple operating parameters at the same moment. This index reflects both the consistency of the direction of change of each operating parameter and the difference in the importance of different operating parameters to the state judgment. By simultaneously introducing equipment type weights and environmental adaptability weights during the fusion process, the comprehensive rate of change index can be kept consistent with the structural characteristics, rated operating conditions, and current environmental state of the target electrical equipment, providing a unified and stable input basis for subsequent determination of dynamic threshold intervals.

[0042] The weighted fusion of the change rate includes: standardizing the change rate and weighting it according to the weight of equipment type and environmental adaptability to obtain a comprehensive change rate index.

[0043] In one embodiment, the weighted fusion process of the rate of change adopts a method of standardization followed by weighted fusion. The reason for setting this method is that different operating parameters have different physical dimensions, normal fluctuation ranges, and anomaly sensitivities. If the rates of change are directly superimposed, the operating parameters with larger fluctuation amplitudes may occupy too high a proportion in the fusion result, affecting the ability of the comprehensive rate of change index to represent the actual operating state.

[0044] The controller first reads the equipment type configuration table corresponding to the target electrical equipment. This table records the level of concern and basic weight of different operating parameters for each type of equipment. For equipment with thermal anomalies as the primary risk characteristic, the equipment type weight corresponding to temperature can be set higher; for equipment with load surges as the primary risk characteristic, the equipment type weights corresponding to current and power can be set higher; for equipment significantly affected by power supply quality, the equipment type weight corresponding to voltage can be appropriately increased. The equipment type weights can be set based on the rated operating conditions in the equipment manual, historical fault records from the field, and trial operation data. After completing the equipment type weight reading, the controller standardizes the rate of change for each operating parameter.

[0045] The purpose of standardization is to convert the rates of change of different operating parameters to a comparable scale. In practice, the current rate of change can be compared with the reference range of the corresponding operating parameter under normal operating conditions to obtain the standardization result; alternatively, the standardization coefficient can be determined based on the upper and lower limits of normal fluctuations in historical operating data. If an operating parameter is not a critical parameter in the current equipment type, the controller retains the corresponding rate of change, but assigns it a lower equipment type weight after standardization.

[0046] After standardization, the controller integrates the standardized change rates according to equipment type weight and environmental adaptability weight to obtain a comprehensive change rate index, the calculation expression of which is:

[0047]

[0048] in, It is a comprehensive rate of change indicator; The number of operating parameters participating in the fusion; For the first The device type weight corresponding to each operating parameter; For the first The environmental adaptation weights corresponding to each operating parameter; For the first The standardized rate of change corresponding to each operating parameter.

[0049] At the end of each judgment cycle, the controller outputs a comprehensive rate of change index and stores it in conjunction with the current timestamp, the target electrical equipment identifier, and the environmental state. Through this process, the dispersed trends of multiple operating parameters can be converged into a single index, facilitating subsequent determination of dynamic threshold ranges, comparison of probability distributions, and state judgment based on the same data.

[0050] The environmental adaptation weights are determined based on environmental data and obtained by comparing at least one of the environmental data (ambient temperature, ambient humidity, load status, start-stop status, and power supply fluctuation status) with the corresponding reference interval to obtain the corresponding environmental correction coefficients; the obtained environmental correction coefficients are combined to generate the environmental adaptation weights; wherein, the reference interval is preset according to the rated operating conditions of the target electrical equipment.

[0051] In one embodiment, the environmental adaptation weight is obtained by comparing the relationship between environmental data and a reference interval, reflecting the degree of influence of the current environmental state on the normal fluctuation range of the target electrical equipment. The reason for introducing the environmental adaptation weight is that the normal variation range of operating parameters of the same target electrical equipment is not entirely consistent under different ambient temperatures, humidity levels, load levels, and start-stop states. If a fixed weight is still used for fusion, normal environmental disturbances are easily misjudged as abnormal trends. The controller first establishes an environmental reference table, which records the reference intervals corresponding to ambient temperature, humidity, load state, start-stop state, and power supply fluctuation state. The reference intervals are set based on the rated operating conditions of the target electrical equipment. Ambient temperature can be divided into normal temperature range, slightly higher range, and slightly lower range according to the rated operating temperature range; ambient humidity can be divided into normal range and high humidity range according to the allowable humidity range; load state can be divided into light load range, normal load range, and high load range according to the rated load ratio; start-stop state can be divided into stable operating range and transitional operating range according to the duration after the start-stop command is issued; power supply fluctuation state can be divided into stable range and fluctuating range according to the fluctuation amplitude of the input voltage within a preset observation time.

[0052] The controller compares the currently collected environmental data with the corresponding reference interval item by item to obtain the environmental correction coefficient for each environmental item. The environmental correction coefficient can be represented by a level value, a proportional value, or a lookup table value. The normal range corresponds to the baseline correction coefficient, while deviations from the reference range correspond to increased or decreased correction coefficients. For cases where multiple environmental items are collected simultaneously, the controller combines the environmental correction coefficients to generate environmental adaptation weights. Combination methods can include weighted average, priority stacking, or preset combination lookup table methods. If the target electrical equipment is more sensitive to ambient temperature and load status, the environmental correction coefficients corresponding to ambient temperature and load status will have a higher weighting in the combination; if the target electrical equipment is more sensitive to power supply fluctuations, the environmental correction coefficient corresponding to power supply fluctuations will have a higher priority in the combination. For environmental items not collected, the controller can use default correction coefficients to supplement them. The environmental adaptation weights obtained through the above methods can transform the difference between rated operating conditions and the current environmental state into a correction basis during the fusion process, ensuring that the comprehensive rate of change index remains consistent with the actual operating state under conditions of high temperature, high humidity, high load, start-stop switching, or power supply fluctuations.

[0053] Based on historical operating data that matches the equipment type and operating environment, and combined with the comprehensive rate of change index, the dynamic threshold range is determined.

[0054] After obtaining the comprehensive rate of change index, the controller filters data segments from historical operating data that match the equipment type and current operating environment of the target electrical device, and uses these data segments as the basis for threshold generation. Subsequently, the controller extracts the center level, fluctuation amplitude, and trend of change corresponding to the historical operating data, and then dynamically corrects the threshold boundary by combining it with the comprehensive rate of change index at the current moment, obtaining a dynamic threshold range corresponding to the current operating state. The dynamic threshold range is not a fixed threshold, but a judgment boundary that is adjusted in real time according to changes in equipment type, environmental conditions, and trends. It is used to reflect the normal fluctuation range and allowable deviation range of the target electrical device in the current scenario, providing a basis for subsequent probability distribution range determination and state judgment.

[0055] The dynamic threshold range is determined by combining the comprehensive rate of change index, including: extracting the mean, fluctuation range and trend of historical operating data, and correcting the mean, fluctuation range and trend according to the comprehensive rate of change index to obtain the dynamic threshold range.

[0056] In one embodiment, the generation process of the dynamic threshold range is further limited to a correction based on statistical results of historical operating data. The controller first establishes historical operating data filtering conditions based on the device type of the target electrical equipment, and then establishes environmental matching conditions based on the current operating environment. The device type filtering conditions ensure that the data involved in the calculation comes from the same or similar devices, avoiding the mixing of devices with different structures, rated power, or load methods into the same calculation set. The environmental matching conditions ensure that the data involved in the calculation are under similar external conditions, such as ambient temperature within the same temperature range, load status within a similar load range, and start / stop status within the same operating stage. After filtering, the controller extracts historical operating data within a preset backtracking time.

[0057] The preset backtracking duration can be determined based on the equipment's operating cycle and data storage capacity, typically set to the most recent hours, days, or several complete operating cycles. Subsequently, the controller performs statistical processing on the filtered historical operating data. The mean value characterizes the normal operating center of the target electrical equipment under matching conditions, the fluctuation range characterizes the normal fluctuation amplitude of the target electrical equipment under matching conditions, and the trend characterizes the overall direction of change of the target electrical equipment over a continuous period. The mean value can be determined by the average value of historical operating data within the backtracking period; the fluctuation range can be determined by the difference between the maximum and minimum values ​​of historical operating data or by upper and lower quantile intervals; and the trend can be obtained by comparing the mean difference between two data points before and after the backtracking period. After obtaining the mean value, fluctuation range, and trend, the controller reads the comprehensive rate of change index corresponding to the current moment and uses the comprehensive rate of change index as a correction basis. If the overall rate of change index is close to the historical normal level, the controller maintains the original threshold width; if the overall rate of change index continues to increase, the controller appropriately raises the upper boundary and simultaneously evaluates the adjustment range of the lower boundary to avoid premature triggering during the continuous rise of parameters; if the overall rate of change index continues to decrease, the controller appropriately lowers the lower boundary and simultaneously evaluates the adjustment range of the upper boundary to reflect the actual trend of the overall decrease of parameters.

[0058] The principle for setting the correction range is that it must not exceed the rated operating range of the equipment, nor exceed the reliable boundaries of historical operating data. To this end, the controller can preset a correction upper limit, which is determined based on the equipment's rated parameters, on-site trial operation results, and historical false alarms. When the correction result corresponding to the comprehensive rate of change index exceeds the correction upper limit, the controller truncates the data according to the upper limit. Through this process, the dynamic threshold range can both inherit the statistical basis of historical operating data and respond to current trend changes, thus avoiding the problem of fixed thresholds being unable to adapt to fluctuations in operating conditions.

[0059] The mean, fluctuation range, and trend are corrected based on the comprehensive rate of change index, including: determining the threshold correction direction based on the direction of change of the comprehensive rate of change index, and determining the threshold correction magnitude based on the absolute value of the comprehensive rate of change index; increasing the upper limit of the dynamic threshold range when the comprehensive rate of change index is greater than zero; and decreasing the lower limit of the dynamic threshold range when the comprehensive rate of change index is less than zero.

[0060] In one embodiment, the dynamic threshold interval correction process is performed after the historical operational data statistics have been obtained. The controller first extracts the mean, fluctuation range, and trend from the historical operational data as the basic parameters for generating the current threshold, and then makes directional corrections to these basic parameters based on the comprehensive rate of change index at the current moment. The direction of change of the comprehensive rate of change index is used to determine the direction of threshold correction, and the absolute value of the comprehensive rate of change index is used to determine the magnitude of threshold correction. The purpose of this setup is to ensure that the dynamic threshold interval not only inherits historical statistical characteristics but also responds to current trend changes.

[0061] In practice, when the comprehensive rate of change index is greater than zero, it indicates that the overall trend of the target electrical equipment is trending upward. The controller raises the upper limit of the dynamic threshold range to maintain a reasonable allowable range when the parameters are rising overall but still within an acceptable fluctuation range. Raising the upper limit is not an unlimited relaxation, but rather an adjustment based on the absolute value of the comprehensive rate of change index in a tiered manner. The closer the comprehensive rate of change index is to the normal level, the smaller the upper limit correction; the more significantly the comprehensive rate of change index deviates from the normal level, the larger the upper limit correction, but it still needs to be limited within the preset maximum correction range. The preset maximum correction range can be determined based on the equipment's rated safety boundary, historical extreme value distribution, and on-site protection requirements.

[0062] Correspondingly, when the comprehensive rate of change index is less than zero, it indicates that the overall trend of the target electrical equipment is downward. The controller lowers the lower limit of the dynamic threshold range so that the threshold boundary can reflect the actual fluctuations during the overall parameter decline. Lowering the lower limit is also implemented in stages based on the absolute value of the comprehensive rate of change index and is constrained by the minimum correction boundary of the lower limit to prevent the threshold boundary from deviating from the actual operating capacity of the equipment. If the comprehensive rate of change index is close to zero, it indicates that the overall trend of the target electrical equipment is weak. The controller keeps the basic boundaries corresponding to the mean, fluctuation range, and trend unchanged, or only performs minor adjustments.

[0063] To avoid drastic fluctuations in the threshold boundary caused by occasional fluctuations within a single judgment period, the controller can also set a threshold correction buffer rule. This means that the correction magnitude is only amplified when the comprehensive rate of change index maintains the same direction of change for multiple consecutive judgment periods; if the comprehensive rate of change index frequently changes direction in adjacent judgment periods, a smaller correction magnitude is applied. Through this process, the dynamic threshold range can adaptively change with the operating trend. An upward offset is mainly reflected in the upper limit adjustment, and a downward offset is mainly reflected in the lower limit adjustment, thus establishing subsequent probability distribution ranges and state judgment results based on boundaries that better conform to the current trend.

[0064] The dynamic threshold range includes the normal upper limit, normal lower limit, upper buffer zone, and lower buffer zone; the upper buffer zone is outside the normal upper limit, and the lower buffer zone is outside the normal lower limit; the upper buffer zone and lower buffer zone are used to characterize the transition range of the target electrical equipment from normal state to abnormal state.

[0065] In one embodiment, the dynamic threshold interval is further refined into a normal upper limit, a normal lower limit, an upper buffer zone, and a lower buffer zone to distinguish between normal, transitional, and abnormal states. The normal upper limit and normal lower limit together constitute the normal operating boundary of the target electrical equipment under the current equipment type and operating environment. When the comprehensive rate of change index falls between the normal upper limit and the normal lower limit, the controller considers the current trend to be within the normal fluctuation range. The upper buffer zone is set outside the normal upper limit, and the lower buffer zone is set outside the normal lower limit; both are used to characterize the transition range of the target electrical equipment from a normal state to an abnormal state. The reason for setting buffer zones is that when the equipment switches under high load, experiences short-term start-stop, or experiences slight power fluctuations, the comprehensive rate of change index may briefly cross the normal boundary, but this crossing does not necessarily mean a real anomaly. If only a single normal boundary is set, frequent false alarms are likely to occur during short-term fluctuations. Therefore, the controller reserves an upper buffer zone of a certain width outside the normal upper limit and a lower buffer zone of a certain width outside the normal lower limit. The width of the buffer zone is determined based on the rated fluctuation capability of the target electrical equipment, common instantaneous offset amplitudes in historical operating data, and field fault tolerance requirements. For devices with rapid fluctuations, the buffer width can be appropriately increased; for devices with stable operation but high abnormal costs, the buffer width can be appropriately decreased.

[0066] In practice, the controller first calculates the normal fluctuation boundary based on historical operating data, and then expands outwards according to a preset ratio to obtain the upper and lower buffer zones. The preset ratio can be set based on historical false alarm records and maintenance experience, for example, set to a certain percentage of the normal fluctuation range, or set to the larger of a fixed amplitude and a proportional amplitude, to ensure that the buffer zones are operable on different devices. When the comprehensive rate of change index enters the upper or lower buffer zone, the controller does not immediately determine it as an anomaly, but marks the current state as a transitional state, and continues to judge in subsequent stages by combining probability distribution intervals and consistency verification. Only when the comprehensive rate of change index continuously crosses the buffer zone boundary, or stays in the buffer zone for more than a preset duration, does the controller further increase the anomaly judgment level. The preset duration is set according to the sampling period and the device response speed, and can generally be multiple consecutive judgment periods. By dividing the dynamic threshold interval into normal and transitional boundaries, the state judgment process can be made more closely resemble the actual operating process, retaining sensitivity to abnormal trends while reducing false triggers caused by short-term disturbances.

[0067] The probability distribution range is determined based on the dynamic threshold range and historical operating data, and the current operating condition is identified based on environmental data and operating parameter time series. The comprehensive change rate index is calibrated and consistency verified according to the current operating condition to obtain the state judgment result.

[0068] After obtaining the dynamic threshold range, the controller further retrieves historical operating data matching the current device type and operating environment to determine the probability distribution range corresponding to the current monitoring scenario. Subsequently, the controller combines environmental data and operating parameter time series to identify the current operating condition and calibrates the comprehensive rate of change index based on the current operating condition, enabling the comprehensive rate of change index to reflect the true change trend under different stages such as startup, shutdown, load switching, and stable operation. After calibration, the controller performs consistency verification on the calibration results and forms a status judgment result based on the position of the comprehensive rate of change index in the probability distribution range and the consistency verification result. The status judgment result is used to distinguish between normal state, warning state, and protection state.

[0069] The process involves determining the probability distribution range, identifying the current operating condition, and calibrating the comprehensive rate of change index based on the current operating condition. This includes: determining the normal probability range, abnormal probability range, and transitional probability range based on dynamic threshold ranges and historical operating data; identifying the current operating condition based on environmental data and operating parameter time series; correcting the comprehensive rate of change index according to the calibration rules corresponding to the current operating condition to obtain the calibrated comprehensive rate of change index; and determining the state judgment result based on the positional relationship between the calibrated comprehensive rate of change index and the normal probability range, abnormal probability range, and transitional probability range.

[0070] In one embodiment, the determination of the probability distribution interval, the identification of the current operating condition, and the calibration of the comprehensive rate of change index are performed in a fixed order. The controller first establishes a normal probability interval, an abnormal probability interval, and a transitional probability interval based on the dynamic threshold interval and historical operating data. The normal probability interval corresponds to the distribution of the comprehensive rate of change index during the stable operating phase in the historical operating data, the abnormal probability interval corresponds to the data distribution that deviates significantly from the normal boundary, and the transitional probability interval corresponds to the data distribution that falls between the two.

[0071] To ensure consistency between the three probability intervals and the current scenario, the controller prioritizes historical operational data with consistent device type, environmental level, and similar operating phase as construction samples. When the number of samples meeting all matching conditions is insufficient, the controller maintains consistency in device type and makes limited relaxations to the environmental level or operating phase. After completing the probability interval division, the controller uses environmental data and time series of operating parameters to identify the current operating condition. The current operating condition can include stable operation, startup, shutdown, load increase, and load decrease.

[0072] During identification, the controller simultaneously considers changes in control commands, the direction of load state changes, and the pattern of operating parameter changes. For example, if the current and power both rise rapidly within a preset time after receiving a start control command, the controller can identify the current operating condition as a start-up condition; if the load state continues to rise and temperature, current, and power all increase in tandem, the controller can identify the current operating condition as a load increase condition; and if the control command is stable, the load fluctuation is small, and the rate of change of each operating parameter is close to historical normal, the controller can identify the current operating condition as a stable operating condition.

[0073] After identifying the current operating condition, the controller corrects the comprehensive rate of change index according to the calibration rules corresponding to the current operating condition, obtaining the calibrated comprehensive rate of change index. The purpose of the calibration rules is to correct reasonable deviations that should occur during operating condition transitions. For example, under startup conditions, a significant increase in current and power is allowed in a short period of time, and the controller can appropriately reduce the abnormal contribution of the comprehensive rate of change index; under shutdown conditions, a rapid drop in operating parameters is allowed, and the controller can appropriately weaken the judgment weight of the downward direction deviation; under load increase conditions, a more tolerant comparison benchmark can be set for trend deviations caused by temperature and power increases; under stable operating conditions, the original comparison benchmark remains unchanged. After obtaining the calibrated comprehensive rate of change index, the controller compares its positional relationship with the normal probability interval, abnormal probability interval, and transition probability interval.

[0074] When the calibrated comprehensive rate of change falls within the normal probability range, the state is determined to be in a normal state; when it falls within the transitional probability range, it is determined to be a candidate state for early warning; and when it falls within the abnormal probability range, it is determined to be a candidate state for protection. Subsequently, the controller combines the consistency verification results to make a final confirmation of the candidate states. The advantage of this setup is that the state determination is not given directly by a single comparison, but rather by first establishing historical distribution boundaries, then identifying operating conditions, then performing calibration, and then comparing positional relationships, thus providing the determination process with a continuous data source and a clear execution path.

[0075] Consistency verification includes time consistency verification and multi-parameter correlation consistency verification. Time consistency verification is used to verify whether the comprehensive rate of change index after calibration deviates from the dynamic threshold range in multiple consecutive judgment periods. Multi-parameter correlation consistency verification is used to verify whether the rate of change corresponding to at least two operating parameters changes synchronously in the abnormal direction. If either time consistency verification or multi-parameter correlation consistency verification fails, the state judgment result is determined to be a normal state.

[0076] In one embodiment, consistency verification includes time consistency verification and multi-parameter correlation consistency verification, further defining the confirmation conditions for state determination results and focusing on solving the problem of misjudgment caused by instantaneous disturbances and occasional anomalies of single parameters. With this limitation, even if the calibrated comprehensive rate of change index has entered the transition probability range or the anomaly probability range, the controller still needs to perform continuous and correlational confirmation of the offset behavior. Only offsets that meet the consistency requirements are retained as valid state determination results.

[0077] In practice, the controller first performs a time consistency verification. This verification checks whether the calibrated overall rate of change deviates from the dynamic threshold range over multiple consecutive decision periods. The decision period can be set based on the sampling period and device response speed, typically three to five consecutive sampling periods. If the calibrated overall rate of change only enters the transitional or abnormal probability range within a single decision period, and then quickly returns to the normal probability range in subsequent decision periods, the controller treats this deviation as a transient disturbance and does not use it as a basis for steady-state changes. If the calibrated overall rate of change remains within the transitional or abnormal probability range over multiple consecutive decision periods, or if it briefly falls back but still maintains an overall trend of deviating from the dynamic threshold range, the controller considers the time consistency verification successful.

[0078] After completing the time consistency verification, the controller further performs multi-parameter correlation consistency verification. Multi-parameter correlation consistency verification verifies whether the rates of change corresponding to at least two operating parameters synchronously change in the abnormal direction. Synchronous change in the abnormal direction here means that multiple operating parameters exhibit mutually supportive offset relationships within the same observation period. For example, when the load of the target electrical equipment abnormally increases, the rates of change corresponding to the current time series and the power time series often increase simultaneously; when the heat dissipation capacity decreases, the rate of change corresponding to the temperature time series continues to increase, while the rate of change corresponding to the current time series may remain high or experience amplified fluctuations.

[0079] The controller can pre-establish a parameter association rule table to record the parameter combinations that indicate faults under different equipment types. When the rate of change of at least two operating parameters meets the preset association rules, the multi-parameter association consistency verification passes. If only a single operating parameter deviates abnormally while other key operating parameters remain stable, the controller considers the current deviation lacking association support, and the multi-parameter association consistency verification fails. If either the time consistency verification or the multi-parameter association consistency verification fails, the controller determines the state as normal. The purpose of determining it as normal is not to ignore the current data, but to prevent isolated disturbances from directly escalating into warning or protection results. For ease of subsequent tracking, the controller can still record this deviation as an event to be observed and continue to participate in the calculation in the next judgment cycle. Through the above dual consistency verification, truly abnormal changes with a continuous development trend and multi-parameter support can be filtered out, while false triggers caused by short-term sensor fluctuations, instantaneous power supply jitter, or single parameter sampling anomalies are excluded, making the final state judgment result more stable and more consistent with the actual operating state of the equipment.

[0080] In response to a status determination result indicating an early warning state, an early warning signal is output; in response to a status determination result indicating a protection state, pre-set control commands are retrieved to control the target electrical equipment to perform load limiting, operating parameter adjustment, or shutdown control.

[0081] After receiving the status determination result, the controller executes output processing according to the corresponding response relationship. When the status determination result is a warning state, the controller outputs a warning signal to indicate that the target electrical equipment is showing a trend of deviating from the normal operating range, but has not yet reached the mandatory protection condition. The warning signal can be sent to the audible and visual alarm unit, display unit, or remote monitoring terminal. When the status determination result is a protection state, the controller retrieves the pre-set control command corresponding to that state and sends the control command to the execution unit to control the target electrical equipment to perform load limiting, operating parameter adjustment, or shutdown control, thereby providing timely intervention for continuous or high-risk anomalies.

[0082] In response to a status determination result indicating a protection state, the target electrical equipment is controlled to perform load limiting, operating parameter adjustment, or shutdown control, including: determining the control level based on the magnitude and duration of the deviation of the comprehensive rate of change index from the dynamic threshold range; performing load limiting when the control level is level one; performing operating parameter adjustment when the control level is level two; and performing shutdown control when the control level is level three.

[0083] In one embodiment, when the state determination result finally confirms a protection state, the controller does not directly adopt a single control action. Instead, it determines the control level based on the magnitude and duration of the deviation of the comprehensive rate of change index from the dynamic threshold range, and then executes the corresponding control measures according to the control level. Here, the deviation magnitude reflects the degree to which the current state exceeds the dynamic threshold range, and the duration reflects the length of time the abnormal state is maintained. Both are used together to distinguish between mild, moderate, and severe anomalies. The controller can first calculate the difference between the comprehensive rate of change index and the nearest boundary of the dynamic threshold range, and then combine this difference with the duration of its maintenance within a continuous determination period to determine the control level.

[0084] If the deviation is small and short-lived, the control level is set to Level 1; if the deviation increases or the duration lengthens, the control level is set to Level 2; if the deviation increases significantly and the duration consistently exceeds the preset limit, the control level is set to Level 3. The preset limit can be set based on the equipment's capacity, protection response requirements, and historical fault evolution speed. At Level 1, the controller performs load limiting. Load limiting can manifest as reducing output power, limiting power supply intensity, reducing drive frequency, or suspending non-critical load access. Its purpose is to first alleviate the equipment's burden and observe whether the abnormal trend subsides. At Level 2, the controller adjusts operating parameters.

[0085] Adjusting operating parameters can manifest as lowering the target temperature, adjusting the speed, adjusting the upper limit of current, or lengthening the working cycle. The aim is to bring the equipment back to a safer operating range without interrupting overall operation. At control level three, the controller executes shutdown control. Shutdown control is suitable for scenarios where the anomaly has continued to escalate or the deviation is approaching the safety boundary. The controller can complete the shutdown by disconnecting the operating circuit, sending a shutdown command, or triggering a protective relay. To prevent further deterioration of the equipment after a control action fails, the controller can also read the execution feedback signal after issuing the control command. If the feedback signal indicates that the control action has taken effect, the control level, control time, and control result are recorded; if the feedback signal indicates that the control action has not taken effect, the controller can repeat the control command or directly escalate to a higher control level. By implementing load limiting, operating parameter adjustment, and shutdown control in stages according to the deviation magnitude and duration, protection actions can be matched to the severity of the anomaly. This avoids unnecessary interruptions caused by direct shutdown for minor anomalies and also prevents major anomalies from remaining in the minor adjustment stage and missing the protection opportunity.

[0086] like Figure 2 As shown, an intelligent control system for monitoring electrical equipment, and an intelligent control method for implementing monitoring of electrical equipment, the system includes:

[0087] The data acquisition and calculation module is used to acquire the time series of operating parameters and environmental data of the target electrical equipment, and calculate the rate of change of each operating parameter in the time series. The module mainly consists of parameter sensors, environmental sensors, signal conditioning circuits, analog-to-digital converters, a clock synchronization unit, and an edge processor. Parameter sensors collect operating parameters such as current, voltage, power, and temperature, while environmental sensors collect environmental data such as ambient temperature and humidity. The signal conditioning circuit performs filtering, amplification, and anti-interference processing, and the analog-to-digital converter converts analog signals into digital signals. The edge processor performs time alignment of the sampled data based on a unified clock and calculates the rate of change.

[0088] The fusion generation module is used to weight and fuse the change rate based on device type weights and environmental adaptation weights determined from environmental data to obtain a comprehensive change rate index. The fusion generation module mainly consists of a processor, a weight parameter storage unit, and a computation acceleration unit. The weight parameter storage unit stores the device type weights corresponding to different device types, as well as the environmental adaptation weights under different environmental conditions. The processor calls the change rate data output by the acquisition and calculation module and reads the corresponding weights from the storage unit to complete the standardization and weighted fusion calculations, generating the comprehensive change rate index. For scenarios with high real-time requirements, a digital signal processor or an embedded parallel computing unit can be used to improve computational efficiency.

[0089] The threshold determination module is used to determine a dynamic threshold range based on historical operating data matched with the equipment type and operating environment, combined with a comprehensive rate of change index. The threshold determination module mainly consists of a historical data storage unit, a parameter indexing unit, and a threshold calculation unit. The historical data storage unit stores operating data related to equipment type, operating environment, and historical status. The parameter indexing unit retrieves and matches data according to equipment type and operating environment. The threshold calculation unit extracts the mean, fluctuation range, and trend from the historical operating data and combines them with the comprehensive rate of change index to generate a dynamic threshold range. This module is typically deployed inside the main controller, but can also be implemented in conjunction with external industrial storage devices.

[0090] The judgment and verification module is used to determine the probability distribution range based on dynamic threshold ranges and historical operating data, and to identify the current operating condition based on environmental data and operating parameter time series. It then calibrates and verifies the consistency of the comprehensive change rate index based on the current operating condition to obtain the state judgment result. The judgment and verification module mainly consists of a judgment processor, a state cache unit, and a logic verification unit. The judgment processor determines the probability distribution range based on dynamic threshold ranges and historical operating data, and performs operating condition calibration in conjunction with current operating data. The state cache unit stores the comprehensive change rate index and intermediate judgment results over multiple consecutive judgment periods for use in time consistency verification. The logic verification unit performs time consistency verification and multi-parameter correlation consistency verification, and ultimately outputs the normal state, warning state, or protection state.

[0091] The control output module is used to output a warning signal in response to a status determination result of an early warning state; and to retrieve pre-set control commands in response to a status determination result of a protection state, controlling the target electrical equipment to perform load limiting, operating parameter adjustment, or shutdown control. The control output module mainly consists of an alarm drive circuit, an execution interface circuit, and a power control unit. The alarm drive circuit is used to drive audible and visual alarms, display units, or remote communication terminals to output warning signals. The execution interface circuit is used to send control commands to relays, contactors, frequency converters, voltage regulators, or upper-level controllers. The power control unit is used to perform load limiting, operating parameter adjustment, or shutdown control. This module typically also includes isolation and protection circuits to ensure the electrical safety and stability of the control signal output process.

[0092] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0093] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. An intelligent control method for monitoring electrical equipment, characterized in that, The method includes: Obtain the time series of operating parameters and environmental data of the target electrical equipment, and calculate the rate of change of each operating parameter in the time series of operating parameters; The change rate is weighted and fused based on the equipment type weight and the environmental adaptation weight determined according to the environmental data to obtain a comprehensive change rate index. Based on historical operating data that matches the equipment type and operating environment, and combined with the comprehensive rate of change index, the dynamic threshold range is determined. The probability distribution interval is determined based on the dynamic threshold interval and the historical operating data, and the current operating condition is identified based on the environmental data and the time series of operating parameters. The comprehensive rate of change index is calibrated and consistency verified according to the current operating condition to obtain the state determination result. In response to the state determination result being an early warning state, an early warning signal is output; in response to the state determination result being a protection state, a pre-set control command is retrieved to control the target electrical equipment to perform load limiting, operating parameter adjustment, or shutdown control.

2. The method according to claim 1, characterized in that, The operating parameter time series includes at least two of the following: current time series, temperature time series, voltage time series, and power time series. The calculation of the rate of change of each operating parameter in the time series of the operating parameters includes: The time series of the operating parameters is time-aligned and smoothed, and the rate of change of the operating parameters is calculated within a preset sliding time window.

3. The method according to claim 1, characterized in that, The weighted fusion of the rate of change includes: The rate of change is standardized and then weighted and fused according to the weight of the equipment type and the weight of the environmental adaptation to obtain the comprehensive rate of change index.

4. The method according to claim 3, characterized in that, The environmental adaptation weight is determined based on the environmental data, and is obtained through the following method: At least one of the environmental data, namely environmental temperature, environmental humidity, load status, start-stop status, and power supply fluctuation status, is compared with the corresponding reference interval to obtain the corresponding environmental correction coefficient. The obtained environmental correction coefficients are combined to generate the environmental adaptation weights; The reference range is preset according to the rated operating conditions of the target electrical equipment.

5. The method according to claim 1, characterized in that, The determination of the dynamic threshold range by combining the comprehensive rate of change index includes: The mean, fluctuation range, and trend of the historical operating data are extracted, and the mean, fluctuation range, and trend are corrected according to the comprehensive rate of change index to obtain the dynamic threshold range.

6. The method according to claim 5, characterized in that, The step of correcting the mean, the fluctuation range, and the trend based on the comprehensive rate of change index includes: The threshold correction direction is determined based on the direction of change of the comprehensive rate of change index, and the threshold correction magnitude is determined based on the absolute value of the comprehensive rate of change index. If the overall rate of change index is greater than zero, increase the upper limit of the dynamic threshold range; If the overall rate of change index is less than zero, the lower limit of the dynamic threshold range is lowered.

7. The method according to claim 1, characterized in that, The process of determining the probability distribution interval, identifying the current operating condition, and calibrating the comprehensive rate of change index based on the current operating condition includes: The normal probability range, abnormal probability range, and transition probability range are determined based on the dynamic threshold range and the historical operating data. The current operating condition is identified based on the environmental data and the time series of the operating parameters. The comprehensive rate of change index is corrected according to the calibration rules corresponding to the current operating conditions to obtain the calibrated comprehensive rate of change index. The state determination result is determined based on the positional relationship between the calibrated comprehensive rate of change index and the normal probability interval, the abnormal probability interval, and the transition probability interval.

8. The method according to claim 7, characterized in that, The consistency verification includes time consistency verification and multi-parameter correlation consistency verification; The time consistency verification is used to verify whether the calibrated comprehensive rate of change index deviates from the dynamic threshold range within multiple consecutive judgment periods, and the multi-parameter correlation consistency verification is used to verify whether the rate of change corresponding to at least two of the operating parameters changes synchronously in the abnormal direction. If either the time consistency verification or the multi-parameter association consistency verification fails, the state determination result will be determined as a normal state.

9. The method according to claim 1, characterized in that, In response to the status determination result being a protection state, controlling the target electrical equipment to perform the load limiting, the operating parameter adjustment, or the shutdown control includes: The control level is determined based on the magnitude and duration of the deviation of the comprehensive rate of change index from the dynamic threshold range; When the control level is at level one, the load limit is executed; When the control level is level two, the operating parameter adjustment is performed; When the control level is level three, the shutdown control is executed.

10. An intelligent control system for monitoring electrical equipment, used to implement the intelligent control method for monitoring electrical equipment according to any one of claims 1-9, characterized in that, The system includes: The data acquisition and calculation module is used to acquire the time series of operating parameters and environmental data of the target electrical equipment, and to calculate the rate of change of each operating parameter in the time series of operating parameters; The fusion generation module is used to perform weighted fusion of the change rate based on the device type weight and the environmental adaptation weight determined according to the environmental data to obtain a comprehensive change rate index. The threshold determination module is used to determine a dynamic threshold range based on historical operating data that matches the device type and operating environment, combined with the comprehensive rate of change index. The determination and verification module is used to determine the probability distribution range based on the dynamic threshold range and the historical operating data, identify the current operating condition based on the environmental data and the operating parameter time series, and calibrate and verify the comprehensive rate of change index according to the current operating condition to obtain the state determination result. The control output module is used to output a warning signal in response to the state determination result being a warning state; and to retrieve a pre-set control command in response to the state determination result being a protection state, and to control the target electrical equipment to perform load limiting, operating parameter adjustment or shutdown control.

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