Intelligent monitoring system for high-voltage distribution control cabinet based on multi-source data fusion

Abstract

This invention discloses an intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion, comprising: a multi-source monitoring module for collecting and preprocessing multi-source monitoring data; a branch coding module for performing branch coding and timing expansion according to preset state categories; a stage anchoring module for retrieving abnormal responses and calculating their duration to generate a stage anchoring sequence; a hold-and-write module for performing enhanced and basic writes according to stage markers to generate a stage hold-and-write sequence; a cross-state hold-and-write module for performing interactive hold-and-writes to generate a coupled hold-and-write sequence; a backoff suppression module for limiting the hold-and-write decay update of short-term fallback segments; and a result output module for outputting monitoring results. This invention achieves accurate monitoring of high-voltage power distribution control cabinets while improving anomaly identification capabilities and early warning effectiveness.

Description

Technical Field

[0001] This invention relates to the field of power equipment monitoring technology, and in particular to an intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion. Background Technology

[0002] High-voltage power distribution control cabinets are responsible for receiving, distributing, controlling, and protecting electrical energy. Their operating status is directly related to power supply continuity and equipment safety. With the increasing complexity of power distribution equipment operating environments, intensified load fluctuations, and rising demands for online operation and maintenance, condition monitoring of high-voltage power distribution control cabinets has become a crucial aspect of power distribution automation. Existing monitoring solutions often employ single-item monitoring methods, such as temperature acquisition, electrical quantity acquisition, partial discharge detection, mechanical motion detection, and environmental quantity detection. Some solutions use multi-sensor access to acquire multiple types of monitoring data, and then determine the status through threshold comparison, independent alarms, or itemized displays.

[0003] Existing technologies have significant shortcomings. Various types of monitoring data differ considerably in sampling frequency, dimensional form, and state semantics. Current solutions largely rely on parallel acquisition and itemized analysis, lacking classification, merging, and decoupling encoding processes for unified state semantics. This makes it difficult to form a continuous and consistent state representation from monitoring data from different sources. Regarding anomaly identification, existing solutions largely depend on single-moment exceedance judgments, lacking a sustained write mechanism based on anomaly response intensity, duration, and stage markers, making it difficult to continuously model accumulated anomaly segments. For the correlation between different state categories, existing solutions lack an interactive sustained write process, failing to reflect the coupling and propagation relationships between thermal anomalies, insulation anomalies, mechanical anomalies, and environmental disturbances. For short-term decline phenomena, existing solutions lack backslide suppression processing, easily misjudging continuous degradation as anomaly weakening, resulting in insufficient accuracy of monitoring results and inadequate early warning effectiveness.

[0004] Therefore, how to provide an intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] One objective of this invention is to propose an intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion. This invention combines multi-source monitoring, stage anchoring, write-through, cross-state interactive hold, and backoff suppression processing to achieve continuous monitoring and hierarchical early warning of the control cabinet's operating status. It has the advantages of high monitoring accuracy, strong anomaly identification, and timely early warning.

[0006] The intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion according to an embodiment of the present invention includes:

[0007] The data acquisition module collects multi-source monitoring data from the high-voltage power distribution control cabinet and preprocesses it to generate a standardized monitoring data set. The branch coding module inputs the standardized monitoring data set into an improved RetNet network, performs branch coding and temporal expansion according to preset state categories, and generates an initial state feature sequence. The stage anchoring module performs anomaly response retrieval and duration statistics, writes stage markers at corresponding time-series positions based on the anomaly response intensity and duration, and generates a stage anchoring sequence. The hold-write module adjusts the hold-write weights at corresponding time-series positions based on the stage markers, performs enhanced writes on anomaly accumulation segments, and performs enhanced writes on stable segments. The system performs basic write operations to generate a phase-based hold sequence. A cross-state hold module performs interactive hold writes on associated segments between adjacent state categories, writing the interactive hold results back to the corresponding time-series positions to generate a coupled hold sequence. A backsliding suppression module performs short-term fallback detection; when the phase marker corresponding to a short-term fallback segment is a continuous degradation marker or a warning marker, it restricts the hold decay update at the corresponding time-series position to generate a comprehensive characterization sequence. A result output module inputs the comprehensive characterization sequence into a cascaded decision structure, sequentially performing operational status identification, abnormal trend determination, risk level classification, and warning type output to generate monitoring results for the high-voltage power distribution control cabinet.

[0008] Optionally, the multi-source monitoring data in the data acquisition module includes electrical operation data, thermal status data, insulation status data, mechanical motion data, and environmental status data. The preprocessing includes performing timestamp unification, missing data completion, abnormal data removal, and normalization on the acquired multi-source monitoring data in sequence to generate a standardized monitoring data set.

[0009] Optionally, the improved RetNet network includes a basic Retention backbone and a stage anchoring constraint structure, a cross-state interactive retention structure, and a backslide suppression decay structure set in the basic Retention backbone. The stage anchoring constraint structure is used to introduce a stage anchoring sequence at the retention write position of the basic Retention backbone, increasing the retention write weight for abnormal accumulation segments and maintaining the basic retention write weight for stable segments. The cross-state interactive retention structure is used to establish an interactive retention write path between adjacent retention paths of the basic Retention backbone, writing the accumulated retention result in the previous state category into the retention path corresponding to the next state category. The backslide suppression decay structure is used to introduce stage label constraints at the retention decay position of the basic Retention backbone, limiting the decay update magnitude of the corresponding retention path when the stage label corresponding to a short-term fallback segment is a continuous degradation label or a warning label.

[0010] Optionally, the process of generating the initial state feature sequence in the split coding module includes:

[0011] Each monitoring item in the standardized monitoring dataset is labeled according to a preset state category, which includes electrical load state, heat accumulation state, insulation degradation state, mechanical response state, and environmental disturbance state. Based on the combination relationship of monitoring items corresponding to each preset state category, the standardized monitoring dataset is classified and merged. Monitoring items belonging to the same preset state category are arranged in timestamp order to form corresponding category monitoring sequences. Adjacent monitoring items in each category monitoring sequence are read by sliding, and continuous monitoring segments are extracted according to a fixed time window. Each continuous monitoring segment is written into the corresponding branch coding channel. Within each branch coding channel, independent feature mapping is performed on each continuous monitoring segment. The magnitude and direction of numerical change in the continuous monitoring segment and the difference between adjacent time points are written into the same coding position to form the corresponding state coding segment.

[0012] The state coding segments in each branch coding channel are recursively expanded in chronological order. The state coding segment at the current moment is sequentially spliced ​​with the state coding segment of the same channel at the previous moment to form a temporal expanded segment. The temporal expanded segments in each branch coding channel are aligned so that the expanded results of each channel at the same time position are arranged accordingly. A preset state category identifier is written at the corresponding position. The expanded results of each channel after the position alignment and the writing of the preset state category identifier are collected to generate an initial state feature sequence.

[0013] Optionally, the generation process of the stage anchoring sequence in the stage anchoring module includes:

[0014] The initial state feature sequence is read position by position in chronological order, and the state response value corresponding to each time position is extracted; the time position where the state response value exceeds the preset abnormal response threshold is determined as the abnormal response position; the consecutive abnormal response positions are merged to form an abnormal response segment; the abnormal response intensity and duration of each abnormal response segment are calculated, and the stage level of the corresponding abnormal response segment is determined based on the combination relationship between the abnormal response intensity and duration.

[0015] The stage level is written as a stage marker to each time position covered by the corresponding abnormal response segment to generate a stage anchoring sequence.

[0016] Optionally, the process of generating the stage hold sequence in the hold write module includes:

[0017] The initial state feature sequence and the stage anchor sequence are read one-to-one according to time position, and the state feature representation and stage label of each time position are extracted. Based on the stage label, each time position is classified into writing categories. Time positions labeled as initial abnormal stage, continuous abnormal stage, continuous degradation stage, and warning stage are determined as enhanced writing positions, and time positions labeled as normal stage are determined as basic writing positions. For each enhanced writing position, the hold state of the previous time position is read, and the state feature representation of the current time position is read. The corresponding hold writing weight is selected from the preset weight level table according to the current stage label. The state feature representation of the current time position is amplified according to the hold writing weight and written to the hold state of the previous time position. The state corresponding to the current time position in the hold state of the previous time position is then classified. The quantity is accumulated in the same direction. For state components in the previous time position that do not correspond to the current time position, the original value is retained to form the enhanced write result of the current time position. For each basic write position, the state of the previous time position is read, the state feature representation of the current time position is read, and the state feature representation of the current time position is written according to the preset basic write weight. The state of the previous time position is smoothly continued to form the basic write result of the current time position. When the stage markers of two adjacent time positions are both abnormal stage markers, the state feature representation of the next time position is written to the state of the previous time position according to the current hold write weight. The position-by-position recursive accumulation is performed on consecutive time positions within the same abnormal accumulation segment to form the continuous enhanced write result corresponding to the abnormal accumulation segment.

[0018] When the current time position changes from the basic write position corresponding to the normal stage to the enhanced write position corresponding to the abnormal stage, the basic write result of the previous time position is used as the initial hold state of the enhanced write at the current time position. The state feature representation of the current time position is used to perform the first enhanced write according to the current hold write weight. The enhanced write results and basic write results corresponding to each time position are arranged in chronological order to generate the stage hold sequence.

[0019] Optionally, the process of generating the coupling preservation sequence in the cross-state preservation module includes:

[0020] The system reads the hold segments corresponding to adjacent state categories in the stage hold sequence according to the preset state category order, extracts the hold segments of the previous state category and the hold segments of the next state category within the same time position range, performs time position alignment on the hold segments of the previous state category and the hold segments of the next state category, filters overlapping segments that have abnormal stage markers at the same time position range, and determines the associated segments; for each associated segment, it reads the hold components in the hold segments of the previous state category and the hold components in the hold segments of the next state category, compares the change direction and change magnitude of the corresponding hold components, and determines the interactive hold write position; at each interactive hold write position, it writes the corresponding hold components in the hold segments of the previous state category to the corresponding time position of the hold segments of the next state category according to the preset interactive write weight, performs same-direction enhancement write on hold components with the same change direction, performs difference adjustment write on hold components with different change directions, and generates interactive hold results;

[0021] Write the interaction holding result back to the corresponding time position of the next state category holding segment, and update the corresponding holding component in the next state category holding segment;

[0022] In the same manner, continue to perform associated fragment extraction, interactive persistence writing, and result write-back on the updated next state category persistence fragment and the next state category persistence fragment, until the interactive persistence processing between all adjacent state categories is completed.

[0023] After completing the interactive write and result write-back, the state category holding segments are rearranged according to the preset state category order and time order to generate a coupled holding sequence.

[0024] Optionally, the process of generating the comprehensive characterization sequence in the backoff suppression module includes:

[0025] The coupled holding sequence is read position by position in chronological order. The holding components and stage markers at each time position are extracted. The holding components at the current time position are compared with the holding components at the previous time position. Time position segments in which the holding components continuously decrease and the duration of the decrease does not exceed the length of a preset short window are selected to determine short-term fallback segments. For each time position covered by each short-term fallback segment, the corresponding stage markers are read. Short-term fallback segments with stage markers of continuous degradation or warning are determined as fallback suppression segments. For each fallback suppression segment, the holding state at the previous time position of the fallback suppression segment's starting time position is read as the reference holding state. Then, the current holding components at each time position within the fallback suppression segment are read. For each time position within the fallback suppression segment, the attenuation amplitude is determined based on the component difference between the reference holding state and the current holding component. The attenuation amplitude is compared with a preset attenuation limit threshold. When the attenuation amplitude is greater than the preset attenuation limit threshold, the current holding component is reverted to the limit-after holding component according to the preset attenuation limit threshold. When the attenuation amplitude is less than or equal to the preset attenuation limit threshold, the current holding component is retained as the limit-after holding component.

[0026] The constrained and preserved components at each time position are written back to the corresponding time position to obtain the update and preserve results corresponding to the backsliding and suppression segments. The update and preserve results at each time position after the backsliding and suppression processing are arranged in chronological order to generate a comprehensive characterization sequence.

[0027] Optionally, the process of generating the monitoring results of the high-voltage power distribution control cabinet in the result output module includes:

[0028] The comprehensive representation sequence is read position by position in chronological order, and the comprehensive representation components at each time position are extracted. The comprehensive representation components at each time position are compared with the preset state judgment rules to determine the operation state recognition result at each time position.

[0029] A continuity retrieval is performed on the operation status identification results and the direction of change of the comprehensive characterization components at adjacent time locations to determine the continuation direction and magnitude of abnormal changes, and to generate abnormal trend judgment results.

[0030] Based on the operational status identification results and abnormal trend judgment results, a level mapping is performed to determine the risk level classification results for the corresponding time location;

[0031] Based on the risk level classification results and the anomaly source categories corresponding to the comprehensive characterization components, the warning type is matched to determine the warning type output result for the corresponding time location;

[0032] The results of operation status identification, abnormal trend judgment, risk level classification, and early warning type output are correlated and aggregated in chronological order to generate monitoring results for high-voltage power distribution control cabinets.

[0033] The beneficial effects of this invention are:

[0034] (1) This invention performs classification and merging, branch coding and time-series expansion processing on the multi-source monitoring data of high-voltage power distribution control cabinet, so that monitoring information from different sources and with different dimensions can enter the subsequent analysis process according to a unified state semantics. This improves the problem that multiple types of monitoring data are only collected in parallel and displayed separately in the prior art, and enhances the consistency of state representation and the completeness of monitoring results.

[0035] (2) In this invention, a stage anchoring constraint structure and a hold-write structure are introduced into the improved multi-source state-preserving constraint RetNet network to jointly process the intensity and duration of abnormal response, perform enhanced writing on abnormal accumulation segments and basic writing on stable segments, so that continuous abnormal changes can form a continuous accumulation representation along the time direction, thereby improving the ability to identify continuous degradation and the accuracy of anomaly judgment.

[0036] (3) In this invention, a cross-state interactive retention structure and a backoff suppression decay structure are introduced in the improved multi-source state retention constraint RetNet network. Interactive retention writing is performed on the associated segments between adjacent state categories. When the short-term fallback segment corresponds to the continuous degradation mark or warning mark, the retention decay update is restricted. This reflects the coupling effect between thermal anomalies, insulation anomalies, mechanical anomalies and environmental disturbances, reduces the interference of short-term fluctuations on monitoring results, and enhances the effectiveness of risk level classification and warning type output. Attached Figure Description

[0037] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0038] Figure 1 This is a module connection diagram of the intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion proposed in this invention.

[0039] Figure 2 This is a diagram of the improved RetNet network structure of the intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion proposed in this invention.

[0040] Figure 3 This is a graph showing the monitoring results of a high-voltage power distribution control cabinet generated by the intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion proposed in this invention. Detailed Implementation

[0041] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0042] refer to Figures 1-3 A high-voltage power distribution control cabinet intelligent monitoring system based on multi-source data fusion includes:

[0043] The data acquisition module collects multi-source monitoring data from the high-voltage power distribution control cabinet and preprocesses it to generate a standardized monitoring data set. The branch coding module inputs the standardized monitoring data set into the improved RetNet network, performs branch coding and temporal expansion according to preset state categories, and generates an initial state feature sequence. The stage anchoring module performs anomaly response retrieval and duration statistics on the initial state feature sequence, writes stage markers at corresponding time positions based on the anomaly response intensity and duration, and generates a stage anchoring sequence. The hold-write module inputs the initial state feature sequence and the stage anchoring sequence into the hold-write structure of the improved RetNet network, adjusts the hold-write weights at corresponding time positions based on the stage markers, performs enhanced writes on anomaly accumulation segments, and performs basic writes on stable segments. The system generates a phase-based hold sequence; a cross-state hold module, which inputs the phase-based hold sequence into the cross-state hold structure of the improved RetNet network, performs interactive hold writing on associated segments between adjacent state categories, and writes the interactive hold results back to the corresponding time-series positions to generate a coupled hold sequence; a backslide suppression module, which inputs the coupled hold sequence into the backslide suppression structure of the improved RetNet network, performs short-term fallback detection, and restricts the hold decay update at the corresponding time-series position when the phase marker corresponding to the short-term fallback segment is a continuous degradation marker or a warning marker, generating a comprehensive characterization sequence; and a result output module, which inputs the comprehensive characterization sequence into the cascaded decision structure, sequentially performs operation status identification, abnormal trend determination, risk level classification, and warning type output to generate the monitoring results of the high-voltage distribution control cabinet.

[0044] In this embodiment, the multi-source monitoring data in the data acquisition module includes electrical operation data, thermal status data, insulation status data, mechanical motion data, and environmental status data. The preprocessing includes performing timestamp unification, missing data completion, abnormal data removal, and normalization on the acquired multi-source monitoring data in sequence to generate a standardized monitoring data set.

[0045] In this embodiment, the improved RetNet network includes a basic Retention backbone and a stage anchoring constraint structure, a cross-state interactive retention structure, and a backslide suppression decay structure set in the basic Retention backbone. The stage anchoring constraint structure is used to introduce stage anchoring sequences at the retention write positions of the basic Retention backbone, increasing the retention write weight for abnormal accumulation segments and maintaining the basic retention write weight for stable segments. The cross-state interactive retention structure is used to establish interactive retention write paths between adjacent retention paths of the basic Retention backbone, writing the accumulated retention result in the previous state category into the retention path corresponding to the next state category. The backslide suppression decay structure is used to introduce stage label constraints at the retention decay positions of the basic Retention backbone, limiting the decay update magnitude of the corresponding retention path when the stage label corresponding to a short-term fallback segment is a continuous degradation label or a warning label.

[0046] In this embodiment, the process of generating the initial state feature sequence in the split coding module includes:

[0047] Each monitoring item in the standardized monitoring data set is labeled according to a preset state category, which includes electrical load state, heat accumulation state, insulation degradation state, mechanical response state, and environmental disturbance state.

[0048] In this embodiment, preset state categories are used to perform state semantic division on the standardized monitoring data set. Specifically, the electrical load state corresponds to monitoring items characterizing the current carrying capacity, load fluctuation amplitude, and electrical operating pressure of the power distribution control cabinet; the heat accumulation state corresponds to monitoring items characterizing the temperature rise level, temperature rise rate, and hot spot duration of the busbar, contacts, cable joints, and local areas within the cabinet; the insulation degradation state corresponds to monitoring items characterizing the activity level of partial discharge, the degree of leakage current change, and the duration of insulation abnormalities; and the mechanical response state corresponds to monitoring items characterizing the opening and closing action process, the energy storage mechanism response process, the degree of action delay, and vibration. The monitoring items represent the degree of dynamic change; the environmental disturbance states correspond to the monitoring items characterizing the impact of humidity, temperature difference, condensation, water immersion, smoke, and cabinet door opening and closing changes on the cabinet's operating conditions; through preset state category division, multi-source monitoring data from different sources enter the corresponding branch coding channel according to the consistent state meaning, for use in time-series unfolding, stage anchoring, hold-write, and cross-state hold-processing; based on the combination relationship of monitoring items corresponding to each preset state category, the standardized monitoring data set is classified and merged, and the monitoring items belonging to the same preset state category are arranged in the order of timestamps to form the corresponding category monitoring sequence;

[0049] In this embodiment, classifying and merging the standardized monitoring data set refers to classifying, aggregating, and reordering the monitoring items in the standardized monitoring data set according to the pre-established correspondence between preset state categories and each monitoring item, so that monitoring items with the same state semantics enter the same branch coding channel; specifically, reading the identifier, corresponding value, and timestamp information of each monitoring item in the standardized monitoring data set; determining the preset state category to which each monitoring item belongs based on the pre-established state category correspondence table, classifying monitoring items representing current carrying capacity and load fluctuations into electrical load state, classifying monitoring items representing temperature rise level and hotspot persistence into heat accumulation state, and classifying monitoring items representing partial discharge activity into other states. Monitoring items indicating changes in insulation temperature and leakage are categorized into insulation degradation state; those representing changes in action delay, mechanism response, and vibration are categorized into mechanical response state; and those representing changes in humidity, temperature difference, condensation, water immersion, smoke, and cabinet door opening / closing are categorized into environmental disturbance state. After categorization, monitoring items belonging to the same preset state category are arranged according to a unified timestamp order. Multiple similar monitoring values ​​at the same time are aggregated to form a category monitoring sequence corresponding to the preset state category. Through classification and merging, the standardized monitoring data set is transformed from the original mixed arrangement into a category monitoring sequence organized according to state semantics, providing a consistent data input basis for split coding and time-series unfolding. Sliding reading is performed on adjacent monitoring items in each category monitoring sequence, and continuous monitoring segments are extracted according to a fixed time window. Each continuous monitoring segment is written into the corresponding split coding channel. Within each split coding channel, independent feature mapping is performed on each continuous monitoring segment, and the magnitude and direction of numerical changes in the continuous monitoring segment are written into the same coding position along with the differences between adjacent times to form the corresponding state coding segment.

[0050] Performing independent feature mapping on each continuous monitoring segment means reading the monitoring values ​​of each monitoring item point by point in the time sequence of each continuous monitoring segment within each branch coding channel, extracting the magnitude, direction, and difference of the numerical change of each monitoring item between adjacent sampling times, and writing the extraction results into the corresponding state coding segment according to a unified position rule to form a state coding segment that corresponds one-to-one with each continuous monitoring segment; through independent feature mapping processing, the continuous monitoring segments corresponding to different preset state categories are converted into a unified coding representation that can be processed by time series expansion.

[0051] The state coding segments in each branch coding channel are recursively expanded in chronological order. The state coding segment at the current moment is sequentially spliced ​​with the state coding segment of the same channel at the previous moment to form a temporal expanded segment. The temporal expanded segments in each branch coding channel are aligned so that the expanded results of each channel at the same time position are arranged accordingly. A preset state category identifier is written at the corresponding position. The expanded results of each channel after the position alignment and the writing of the preset state category identifier are collected to generate an initial state feature sequence.

[0052] In this embodiment, the generation process of the stage anchoring sequence in the stage anchoring module includes:

[0053] The initial state feature sequence is read position by position in chronological order, and the state response value corresponding to each time position is extracted. The state response value is a numerical quantity that characterizes the degree of state change at a specific time position in the initial state feature sequence, reflecting whether an abnormal response deviating from normal operation has occurred at that time position. The state response value is not the original monitoring value itself, but rather the state feature representation result formed at the corresponding time position after the de-coding module performs de-coding and time-series expansion on the standardized monitoring data set. The state response value is used to uniformly represent the abnormal activity level, offset degree, and change intensity of different preset state categories at the corresponding time position. Time positions where the state response value exceeds a preset abnormal response threshold are identified as abnormal response positions. Consecutive abnormal response positions are merged into segments to form abnormal response segments. The abnormal response intensity and duration are calculated for each abnormal response segment, and the stage level of the corresponding abnormal response segment is determined based on the combination relationship between the abnormal response intensity and duration.

[0054] When calculating the intensity and duration of anomaly responses, the state response values ​​corresponding to all time-series locations covered by the anomaly response segment are first read. The intensity of the anomaly response is characterized by the degree of deviation of the maximum state response value from the preset anomaly response threshold and the overall degree of exceedance of the state response values ​​at each time-series location by the preset anomaly response threshold. Then, the start and end time positions of the anomaly response segment are located, and the duration is obtained based on the corresponding time span. When determining the stage level of the corresponding anomaly response segment, the anomaly response intensity is divided into low-intensity response, medium-intensity response, and high-intensity response, and the duration is divided into short-duration duration, medium-duration duration, and long-duration duration. Based on the combination relationship between the anomaly response intensity level and the duration level, the corresponding anomaly response segment is sequentially labeled as the initial anomaly stage, the continuous anomaly stage, the continuous deterioration stage, or the warning stage.

[0055] The stage level is written as a stage marker to each time position covered by the corresponding abnormal response segment to generate a stage anchoring sequence.

[0056] In this embodiment, the process of maintaining the sequence during the generation phase in the write module includes:

[0057] The initial state feature sequence and the stage anchor sequence are read one-to-one according to time position, and the state feature representation and stage mark of each time position are extracted. Based on the stage mark, the writing category of each time position is divided. The time positions marked as initial abnormal stage, continuous abnormal stage, continuous deterioration stage and warning stage are determined as enhanced writing positions, and the time positions marked as normal stage are determined as basic writing positions. For each enhanced writing position, the hold state of the previous time position is read, the state feature representation of the current time position is read, and the corresponding hold writing weight is selected from the preset weight level table according to the current stage mark.

[0058] In this invention, the preset weight level table refers to a pre-established correspondence table between stage markers and hold-and-write weights. It is used to select the corresponding hold-and-write weight based on the stage marker at the current time position during the hold-and-write process. The preset weight level table includes at least the base write weight corresponding to the normal stage, the first enhanced write weight corresponding to the initial abnormal stage, the second enhanced write weight corresponding to the continuous abnormal stage, the third enhanced write weight corresponding to the continuous degradation stage, and the fourth enhanced write weight corresponding to the warning stage. Each enhanced write weight increases sequentially in ascending order of stage level, with the base write weight less than the first enhanced write weight, and the first enhanced write weight greater than the base write weight but less than... The second enhanced write weight is greater than the first enhanced write weight and less than the third enhanced write weight, and the third enhanced write weight is greater than the second enhanced write weight and less than the fourth enhanced write weight. When calling the preset weight level table, the stage marker corresponding to the current time position is read first, and then the hold write weight corresponding to that stage marker is retrieved from the preset weight level table. The retrieved hold write weight is used in the enhanced write process or basic write process at the current time position. Through the preset weight level table, the hold write weight can be adjusted hierarchically according to the stage marker, so that the state feature representation corresponding to different abnormal development stages obtains different strengths of hold write control; the current time position... The state feature representation of the previous time position is amplified according to the hold-write weight and written to the hold state of the previous time position. The state components in the hold state of the previous time position that correspond to the current time position are accumulated in the same direction, while the state components in the hold state of the previous time position that do not correspond to the current time position are retained in their original values, forming the enhanced write result for the current time position. For each basic write position, the hold state of the previous time position is read, the state feature representation of the current time position is read, and the preset basic write weight is called to perform basic proportional writing on the state feature representation of the current time position. The hold state of the previous time position is smoothly continued, forming the basic write result for the current time position. The previous time position... Maintaining a smooth continuation of state execution specifically means that at the base write position, the state feature representation of the current time position is written to the previous time position's hold state at a low proportion according to the preset base write weight, while retaining the main state components and original change trends in the previous time position's hold state, so that the hold state of the current time position has a continuous transition relative to the previous time position's hold state; when the stage markers of two adjacent time positions are both abnormal stage markers, the state feature representation of the later time position is continued to be written to the previous time position's hold state according to the current hold write weight, and position-by-position recursive accumulation is performed on consecutive time positions within the same abnormal accumulation segment to form a continuous enhanced write result corresponding to the abnormal accumulation segment;Specifically, after determining the start and end time positions of the abnormal accumulation segment, the process proceeds chronologically, starting from the start time position of the abnormal accumulation segment. The held state of the previous time position is used as the basis for the held state write of the current time position. The state feature representation of the current time position is written to the basis according to the corresponding held state write weight to obtain the updated held state of the current time position. The updated held state is then used as the held state of the previous time position for the next time position to continue the held state write until the recursive write of the last time position in the abnormal accumulation segment is completed. Through position-by-position recursive accumulation processing, the held state of subsequent time positions in the abnormal accumulation segment continuously retains the accumulated results of the previous time positions, forming a continuous accumulation representation of continuous abnormal changes.

[0059] When the current time position changes from the basic write position corresponding to the normal stage to the enhanced write position corresponding to the abnormal stage, the basic write result of the previous time position is used as the initial hold state of the enhanced write at the current time position. The state feature representation of the current time position is used to perform the first enhanced write according to the current hold write weight. The enhanced write results and basic write results corresponding to each time position are arranged in chronological order to generate the stage hold sequence.

[0060] In this embodiment, the process of generating the coupling preservation sequence in the cross-state preservation module includes:

[0061] The system reads the hold segments corresponding to adjacent state categories in the stage hold sequence according to the preset state category order, extracts the hold segments of the previous and next state categories within the same time position range, performs time position alignment on the previous and next state category hold segments, filters overlapping segments that simultaneously show abnormal stage markers within the same time position range, and determines associated segments; for each associated segment, it reads the hold components in the previous and next state category hold segments, compares the change direction and change magnitude of the corresponding hold components, and determines the interactive hold write position; at each interactive hold write position, it writes the corresponding hold component in the previous state category hold segment to the corresponding time position of the next state category hold segment according to the preset interactive write weight, and performs the following steps: For components with consistent change directions, a same-direction enhanced write is performed; for components with inconsistent change directions, a difference adjustment write is performed to generate an interactive hold result. The difference adjustment write specifically involves first calculating the component difference between the corresponding hold component in the previous state category hold segment and the corresponding hold component in the next state category hold segment, then scaling the component difference according to a preset interactive write weight and writing it to the corresponding time position in the next state category hold segment, so that the corresponding hold component in the next state category hold segment is closer to the corresponding hold component in the previous state category hold segment. When the component difference is small, a small adjustment write is performed; when the component difference is large, a limiting adjustment write is performed, thereby introducing the interactive influence of the previous state category hold segment while preserving the original hold trend of the next state category hold segment.

[0062] Write the interaction retention result back to the corresponding time position of the next state category retention segment, and update the corresponding retention component in the next state category retention segment; update means writing the interaction retention result back to the next state category retention segment according to the corresponding time position, performing fusion replacement on the corresponding retention component that participated in the interaction writing, and retaining the original value of the remaining retention components that did not participate in the interaction writing, so that the next state category retention segment forms a new retention result containing the interaction effect at the corresponding time position.

[0063] In the same manner, continue to perform associated fragment extraction, interactive persistence writing, and result write-back on the updated next state category persistence fragment and the next state category persistence fragment, until the interactive persistence processing between all adjacent state categories is completed.

[0064] After completing the interactive write and result write-back, the state category holding segments are rearranged according to the preset state category order and time order to generate a coupled holding sequence.

[0065] In this embodiment, the process of generating the comprehensive characterization sequence in the backoff suppression module includes:

[0066] The coupled holding sequence is read position by position in chronological order. The holding components and stage markers at each time position are extracted. The holding components at the current time position are compared with the holding components at the previous time position. Time position segments in which the holding components continuously decrease and the duration of the decrease does not exceed the length of a preset short window are selected to determine short-term fallback segments. For each time position covered by each short-term fallback segment, the corresponding stage markers are read. Short-term fallback segments with stage markers of continuous degradation or warning are determined as fallback suppression segments. For each fallback suppression segment, the holding state at the previous time position of the fallback suppression segment's starting time position is read as the reference holding state. Then, the current holding components at each time position within the fallback suppression segment are read. For each time position within the fallback suppression segment, the attenuation amplitude is determined based on the component difference between the reference holding state and the current holding component. The attenuation amplitude is compared with a preset attenuation limit threshold. When the attenuation amplitude is greater than the preset attenuation limit threshold, the current holding component is reverted to the limit-after holding component according to the preset attenuation limit threshold. When the attenuation amplitude is less than or equal to the preset attenuation limit threshold, the current holding component is retained as the limit-after holding component.

[0067] Specifically, when determining the attenuation amplitude based on the component difference between the reference hold state and the current hold component, the corresponding hold component in the reference hold state and the corresponding component in the current hold component are read, and the numerical difference between the two is calculated; when the current hold component is less than the corresponding hold component in the reference hold state, the numerical difference is used as the attenuation amplitude; when the current hold component is greater than or equal to the corresponding hold component in the reference hold state, the attenuation amplitude is determined to be zero.

[0068] The constrained and preserved components at each time position are written back to the corresponding time position to obtain the update and preserve results corresponding to the backsliding and suppression segments. The update and preserve results at each time position after the backsliding and suppression processing are arranged in chronological order to generate a comprehensive characterization sequence.

[0069] In this embodiment, the process of generating the monitoring results of the high-voltage power distribution control cabinet in the result output module includes:

[0070] The comprehensive representation sequence is read position by position in chronological order, and the comprehensive representation components at each time position are extracted. The comprehensive representation components at each time position are compared with the preset state judgment rules to determine the operation state recognition result at each time position.

[0071] The preset state determination rule refers to the pre-established correspondence rules between the comprehensive characterization components and the operation state identification results, which are used to determine the state of the comprehensive characterization components at each time position. Specifically, the comprehensive characterization components at the current time position are first read and compared with the corresponding state determination threshold intervals. When the comprehensive characterization components are in the normal interval, the operation state identification result is determined to be in a normal state. When the comprehensive characterization components exceed the normal interval but do not reach the continuous deterioration interval, the operation state identification result is determined to be in an abnormal state. When the comprehensive characterization components are in the continuously rising interval and the corresponding time position has abnormal continuation characteristics, the operation state identification result is determined to be in a continuously deteriorating state. When the comprehensive characterization components reach the warning interval, the operation state identification result is determined to be in a warning state. Through the preset state determination rule, the comprehensive characterization sequence forms a unified operation state identification result at each time position.

[0072] A continuity retrieval is performed on the operation status identification results and the direction of change of the comprehensive characterization components at adjacent time locations to determine the continuation direction and magnitude of abnormal changes, and to generate abnormal trend judgment results.

[0073] Performing continuous retrieval refers to sequentially comparing the operational status identification results and changes in the comprehensive characterization components at adjacent time points to identify whether an anomaly continues within a continuous time range. Specifically, the operational status identification results at the current and previous time points are read in chronological order, and then the direction and magnitude of change in the comprehensive characterization components at the current and previous time points are compared. When the operational status identification results at adjacent time points remain continuously in an abnormal state, a continuously deteriorating state, or a warning state, and the comprehensive characterization components change continuously in the same direction, it is determined that there is a continuous abnormal change. When the operational status identification results at adjacent time points are interrupted, or the direction of change in the comprehensive characterization components is reversed, it is determined that the continuous abnormal change has terminated. Through continuous retrieval, the direction and length of the abnormal change can be determined.

[0074] The anomaly trend determination result is a judgment made on the anomaly change trend based on the continuous retrieval results, used to characterize the evolution state of the anomaly in the time direction. Specifically, when the comprehensive characterization component continuously increases within the continuous anomaly change segment, the anomaly trend determination result is determined to be an enhancing trend; when the comprehensive characterization component maintains small fluctuations and remains generally unchanged within the continuous anomaly change segment, the anomaly trend determination result is determined to be a stable trend; when the comprehensive characterization component continuously decreases within the continuous anomaly change segment, the anomaly trend determination result is determined to be a declining trend; when the comprehensive characterization component only shows anomalies at short time positions and does not form a continuous anomaly change segment, the anomaly trend determination result is determined to be a short-term fluctuation trend. Through the anomaly trend determination result, continuously enhancing anomalies, continuously maintained anomalies, gradually weakening anomalies, and short-term fluctuation anomalies can be distinguished.

[0075] Based on the operational status identification results and abnormal trend judgment results, a level mapping is performed to determine the risk level classification results for the corresponding time location;

[0076] Level mapping refers to converting the operational status identification results and abnormal trend judgment results into risk level classification results according to a pre-defined correspondence. Specifically, when the operational status identification result is a normal state, the risk level classification result is determined to be low risk; when the operational status identification result is an abnormal state and the abnormal trend judgment result is a short-term fluctuation trend or a stable trend, the risk level classification result is determined to be medium risk; when the operational status identification result is a continuously deteriorating state and the abnormal trend judgment result is an enhancing trend, the risk level classification result is determined to be high risk; when the operational status identification result is a warning state, the risk level classification result is determined to be warning risk. Through level mapping, the operational status identification results and abnormal trend judgment results can jointly participate in the risk level classification.

[0077] Based on the risk level classification results and the anomaly source categories corresponding to the comprehensive characterization components, the warning type is matched to determine the warning type output result for the corresponding time location;

[0078] Warning type matching refers to determining the warning type to be output at the current time position based on the risk level classification results and the anomaly source categories corresponding to the comprehensive characterization components. Specifically, the risk level classification results for the current time position are read first, and then the dominant anomaly source categories that form the comprehensive characterization components for the current time position are read. When the dominant anomaly source category corresponds to the thermal accumulation state, the warning type output result is determined to be a thermal anomaly warning; when the dominant anomaly source category corresponds to the insulation degradation state, the warning type output result is determined to be an insulation anomaly warning; when the dominant anomaly source category corresponds to the mechanical response state, the warning type output result is determined to be a mechanical anomaly warning; when the dominant anomaly source category corresponds to the environmental disturbance state, the warning type output result is determined to be an environmental anomaly warning; when the dominant anomaly source category involves two or more preset state categories, the warning type output result is determined to be a composite anomaly warning. Through warning type matching, the risk level classification results and the anomaly source categories together form a warning type output result that can be directly output.

[0079] The results of operation status identification, abnormal trend judgment, risk level classification, and early warning type output are correlated and aggregated in chronological order to generate monitoring results for high-voltage power distribution control cabinets.

[0080] Example 1: To verify the feasibility of this invention in practice, it was applied to the monitoring of a high-voltage distribution control cabinet in a high-load continuous power supply scenario. In this scenario, the distribution control cabinet is subjected to high current-carrying pressure for a long time. Temperature rise, insulation aging, sluggish mechanical movement, and environmental disturbances all combine to cause problems. Traditional monitoring methods mainly rely on single-item temperature threshold alarms, electrical quantity over-limit alarms, and decentralized partial discharge alerts. These methods are prone to two prominent problems in actual operation. First, monitoring data from different sources are displayed independently. Maintenance personnel can only view temperature, current, partial discharge, and mechanical movement records separately, making it difficult to determine whether there is a causal relationship between anomalies, leading to insufficient identification of the continuous deterioration process. Second, some anomalies manifest as slight fluctuations in the early stages, before a single threshold triggers an alarm. After a short period of decline, they are mistakenly considered to have returned to normal, easily missing true early signs of faults, resulting in monitoring lag and delayed early warnings.

[0081] In Example 1, electrical operation acquisition units, thermal status acquisition units, insulation status acquisition units, mechanical response acquisition units, and environmental status acquisition units are deployed on the target high-voltage power distribution control cabinet to continuously acquire multi-source monitoring data. The multi-source monitoring module first performs time alignment, missing data completion, anomaly removal, and normalization on the acquired data to form a standardized monitoring data set. The branch coding module classifies and merges the data according to electrical load status, thermal accumulation status, insulation degradation status, mechanical response status, and environmental disturbance status, converting the originally mixed data into a category monitoring sequence with unified state semantics. Then, independent feature mapping and temporal unfolding are performed on continuous monitoring segments to form an initial state feature sequence. After this processing, the originally scattered current fluctuations, local hot spot rises, partial discharge enhancements, action hysteresis, and humidity disturbances are no longer isolated records, but are organized into a continuously analyzable state evolution trajectory.

[0082] The stage anchoring module performs anomaly response retrieval and duration statistics on the initial state feature sequence. A typical phenomenon observed in the scenario is that the temperature rises slowly at several consecutive locations, the partial discharge amplitude increases synchronously, and the mechanism's action delay slightly increases, but none of these factors individually reach the traditional strong alarm threshold. Using this invention, the stage anchoring module identifies this continuous abnormal segment as a sustained abnormal stage and writes the stage marker to the corresponding time sequence position. The retention writing module then adjusts the retention writing weight based on the stage marker, performing enhanced writing on the abnormal accumulation segment and basic writing on the stable segment, ensuring that the abnormal information at multiple consecutive positions is preserved and not diluted by a short-term drop at a particular position. The cross-state retention module further performs interactive retention writing on the associated segments between adjacent state categories, writing the continuous rising trend in the thermal accumulation state to the insulation degradation state retention segment, and writing the action hysteresis change in the mechanical response state to the thermal accumulation state retention segment, thus reflecting the coupling relationship between multiple types of anomalies. The backoff suppression module performs short-term fallback detection on the coupled hold sequence. When the stage marker corresponding to the fallback segment is still a continuous degradation marker or a warning marker, it limits the hold attenuation update amplitude to avoid misjudging a real continuous anomaly as a recovery. The result output module completes the operation status identification, anomaly trend determination, risk level classification, and warning type output based on the comprehensive characterization sequence, ultimately forming the monitoring results of the high-voltage power distribution control cabinet.

[0083] In practical applications, this invention can identify heat accumulation trends caused by load fluctuations at an earlier stage, providing a medium-risk warning before the linkage between heat accumulation and insulation degradation intensifies. When local discharge activity continues to rise and the response becomes sluggish, this invention will upgrade the operating status from an abnormal state to a continuously deteriorating state, further outputting a high-risk level and a compound anomaly warning. Maintenance personnel can then conduct targeted inspections of connection points, insulation surfaces, and mechanical transmission parts, enabling them to address issues before serious faults develop. Compared to traditional segmented threshold monitoring methods, this invention does not simply look at whether a single value exceeds the limit, but continuously analyzes whether multiple sources of conditions are evolving in a detrimental direction. Therefore, it is more sensitive to early fault precursors and provides a more complete identification of abnormal propagation chains.

[0084] To demonstrate the beneficial effects of this invention, a comparison was made between traditional segmented threshold monitoring methods and this invention under similar operating conditions. Statistical results show that with the traditional method, the average lead time for identifying persistent anomalies as clear risks is short, the number of short-term fallback misjudgments is high, and the recognition rate of composite anomalies is low. With this invention, the accuracy of identifying continuous anomaly segments is significantly improved, the number of short-term fallback misjudgments is significantly reduced, and the recognition rate and early warning effectiveness of composite anomalies are significantly enhanced. Especially in scenarios involving the superposition of thermal and insulation anomalies, and the superposition of mechanical hysteresis and thermal accumulation, this invention can maintain preceding anomaly information and incorporate related influences into subsequent state categories, transforming previously scattered, fragmented, and easily overlooked anomaly changes into continuous, traceable, and graded comprehensive characterization results. Observational data shows that this invention achieves a high overall accuracy rate in identifying anomalies within a continuous observation period, and the consistency between risk level classification and actual handling results is significantly better than traditional methods, indicating that this invention possesses strong engineering adaptability, monitoring stability, and early warning practicality.

[0085] As can be seen from this embodiment, the present invention solves the problems in the prior art such as fragmented multi-source monitoring data, difficulty in identifying continuous degradation processes, susceptibility to misjudgment due to short-term declines, and difficulty in characterizing complex anomaly coupling relationships. By adopting an improved multi-source state-keeping constraint RetNet network, the monitoring process changes from single-point judgment to continuous state evolution judgment; anomaly identification changes from single-limit exceedance to multi-state linkage judgment; and early warning output changes from passive alarm to hierarchical and categorized early warning, which can meet the application requirements of refined online monitoring and risk-proactive control of high-voltage power distribution control cabinets.

[0086] Table 1: Comparison of Intelligent Monitoring Effects of High-Voltage Power Distribution Control Cabinets

[0087] Continuous anomaly identification accuracy 82.4% 95.8% Accuracy of composite anomaly identification 74.6% 93.1% Number of short-term pullback misjudgments 18 times 5 times Anomaly warning effectiveness 76.9% 94.3% High-risk event early identification rate 68.5% 91.7% thermal anomaly identification accuracy 84.1% 96.2% Insulation anomaly identification accuracy 79.3% 94.8% Accuracy of mechanical anomaly identification 77.8% 92.6% Environmental disturbance correlation identification rate 71.5% 90.4% Consistency rate of risk level classification 73.9% 93.6% False alarm rate 11.8% 4.2% Missed alarm rate 9.6% 3.5% Targeted evaluation of abnormal handling generally higher Evaluation of the continuity of monitoring results Weak Strong Comprehensive operation and maintenance support effect lower higher

[0088] As shown in Table 1, traditional segmented threshold monitoring methods have a certain foundation in identifying single anomalies, but they are significantly insufficient in identifying continuous anomalies, compound anomalies, and risk classification. The accuracy rate for identifying continuous anomalies is 82.4%, and the accuracy rate for identifying compound anomalies is 74.6%, indicating that existing methods are more suitable for handling single-point, single-type, and short-term anomaly signals, and do not fully grasp the continuous degradation process formed by the superposition of multiple sources. The environmental disturbance correlation identification rate is only 71.5%, further indicating that traditional methods are unable to establish stable correlations between external disturbances such as humidity, temperature difference, condensation, and water immersion and thermal anomalies, insulation anomalies, and mechanical anomalies, thus lacking a complete perception capability for fault precursors in complex operating scenarios.

[0089] After adopting the monitoring method of this invention, all key indicators showed significant improvement. The accuracy rate for identifying continuous anomalies increased to 95.8%, and the accuracy rate for identifying complex anomalies increased to 93.1%. This indicates that by classifying and merging multi-source monitoring data, decoupling and encoding, stage anchoring, persistent writing, and cross-state interactive persistent processing, this invention can organize originally scattered anomaly changes into a continuous state evolution process, thereby more accurately identifying continuous degradation and complex anomalies. The accuracy rate for identifying thermal anomalies reached 96.2%, insulation anomalies reached 94.8%, and mechanical anomalies reached 92.6%, reflecting that this invention not only has a high ability to identify single anomalies but also has a stronger ability to characterize the linkage relationship between different anomalies.

[0090] From the perspective of misjudgment control, the advantages of this invention are also obvious. The number of short-term decline misjudgments decreased from 18 to 5, the false alarm rate decreased from 11.8% to 4.2%, and the missed alarm rate decreased from 9.6% to 3.5%. This indicates that the backoff suppression processing set in this invention can effectively overcome the interference of short-term fluctuations on the judgment results and avoid misjudging the continuous deterioration process as abnormal weakening or recovery to normal. The early identification rate of high-risk events increased from 68.5% to 91.7%, and the anomaly warning effectiveness increased from 76.9% to 94.3%. This shows that this invention no longer stops at the level of post-event alarm, but can complete trend identification and risk advance warning before the anomaly develops into a serious failure.

[0091] From the perspective of operational and maintenance application effectiveness, the consistency rate of risk level classification increased from 73.9% to 93.6%, the continuity evaluation of monitoring results improved from weak to strong, and the overall operational and maintenance support effect improved from low to high, indicating that the monitoring results output by this invention better meet on-site handling needs. This invention not only improves data identification accuracy but also enhances the interpretability and executability of the results, enabling operational and maintenance personnel to locate the source of anomalies earlier, more accurately assess the degree of risk, and take more targeted handling measures. Therefore, this invention has strong practical application value in continuous monitoring, anomaly identification, and graded early warning of high-voltage power distribution control cabinets.

[0092] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A high-voltage power distribution control cabinet intelligent monitoring system based on multi-source data fusion, characterized in that, include: The data acquisition module is used to collect multi-source monitoring data from the high-voltage power distribution control cabinet and preprocess it to generate a standardized monitoring data set. The split coding module is used to input the standardized monitoring data set into the improved RetNet network, perform split coding and temporal expansion according to the preset state categories, and generate an initial state feature sequence. The phase anchoring module is used to perform anomaly response retrieval and duration statistics. Based on the anomaly response intensity and duration, it writes phase markers at the corresponding time positions to generate a phase anchoring sequence. The hold-write module is used to adjust the hold-write weight at the corresponding time positions based on the phase markers. It performs enhanced writes on anomaly accumulation segments and basic writes on stable segments to generate a phase hold-write sequence. The cross-state persistence module is used to perform interactive persistence writing on associated segments between adjacent state categories, write back the interactive persistence results to the corresponding time position, and generate a coupled persistence sequence; The rollback suppression module is used to perform short-term fallback detection. When the stage marker corresponding to the short-term fallback segment is a continuous degradation marker or a warning marker, it restricts the maintenance decay update of the corresponding time position and generates a comprehensive characterization sequence. The result output module is used to input the comprehensive characterization sequence into the cascaded judgment structure, and sequentially perform operation status identification, abnormal trend judgment, risk level classification and warning type output to generate the monitoring results of the high-voltage power distribution control cabinet.

2. The intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion according to claim 1, characterized in that, The data acquisition module contains multi-source monitoring data including electrical operation data, thermal status data, insulation status data, mechanical motion data, and environmental status data. Preprocessing includes performing timestamp unification, missing data completion, abnormal data removal, and normalization on the acquired multi-source monitoring data in sequence to generate a standardized monitoring data set.

3. The intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion according to claim 1, characterized in that, The improved RetNet network includes a basic Retention backbone and a stage anchoring constraint structure, a cross-state interaction preservation structure, and a backoff suppression decay structure set in the basic Retention backbone. The stage anchoring constraint structure is used to introduce stage anchoring sequences at the hold-write positions of the basic Retention backbone. For abnormal accumulation segments, the hold-write weight is increased, and for stable segments, the basic hold-write weight is maintained. Cross-state interactive retention structure is used to establish interactive retention write paths between adjacent retention paths in the basic Retention backbone, writing the accumulated retention results in the previous state category into the retention path corresponding to the next state category. The fallback suppression decay structure is used to introduce stage marker constraints at the retention decay position of the basic Retention backbone. When the stage marker corresponding to the short fallback segment is a continuous degradation marker or a warning marker, the decay update magnitude of the corresponding retention path is limited.

4. The intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion according to claim 1, characterized in that, The process of generating the initial state feature sequence in the split coding module includes: Each monitoring item in the standardized monitoring dataset is labeled according to a preset state category, which includes electrical load state, heat accumulation state, insulation degradation state, mechanical response state, and environmental disturbance state. Based on the combination relationship of monitoring items corresponding to each preset state category, the standardized monitoring dataset is classified and merged. Monitoring items belonging to the same preset state category are arranged in timestamp order to form corresponding category monitoring sequences. Adjacent monitoring items in each category monitoring sequence are read by sliding, and continuous monitoring segments are extracted according to a fixed time window. Each continuous monitoring segment is written into the corresponding branch coding channel. Within each branch coding channel, independent feature mapping is performed on each continuous monitoring segment. The magnitude and direction of numerical change in the continuous monitoring segment and the difference between adjacent time points are written into the same coding position to form the corresponding state coding segment. The state coding segments in each branch coding channel are recursively expanded in chronological order. The state coding segment at the current moment is sequentially spliced ​​with the state coding segment of the same channel at the previous moment to form a temporal expanded segment. The temporal expanded segments in each branch coding channel are aligned so that the expanded results of each channel at the same time position are arranged accordingly. A preset state category identifier is written at the corresponding position. The expanded results of each channel after the position alignment and the writing of the preset state category identifier are collected to generate an initial state feature sequence.

5. The intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion according to claim 1, characterized in that, The generation process of the stage anchoring sequence in the stage anchoring module includes: The initial state feature sequence is read position by position in chronological order, and the state response value corresponding to each time position is extracted; the time position where the state response value exceeds the preset abnormal response threshold is determined as the abnormal response position; the consecutive abnormal response positions are merged to form an abnormal response segment; the abnormal response intensity and duration of each abnormal response segment are calculated, and the stage level of the corresponding abnormal response segment is determined based on the combination relationship between the abnormal response intensity and duration. The stage level is written as a stage marker to each time position covered by the corresponding abnormal response segment to generate a stage anchoring sequence.

6. The intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion according to claim 1, characterized in that, The process of generating the stage hold sequence in the hold write module includes: The initial state feature sequence and the stage anchor sequence are read one-to-one according to time position, and the state feature representation and stage label of each time position are extracted. Based on the stage label, each time position is classified into writing categories. Time positions labeled as initial abnormal stage, continuous abnormal stage, continuous degradation stage, and warning stage are determined as enhanced writing positions, and time positions labeled as normal stage are determined as basic writing positions. For each enhanced writing position, the hold state of the previous time position is read, and the state feature representation of the current time position is read. The corresponding hold writing weight is selected from the preset weight level table according to the current stage label. The state feature representation of the current time position is amplified according to the hold writing weight and written to the hold state of the previous time position. The state corresponding to the current time position in the hold state of the previous time position is then classified. The quantity is accumulated in the same direction. For state components in the previous time position that do not correspond to the current time position, the original value is retained to form the enhanced write result of the current time position. For each basic write position, the state of the previous time position is read, the state feature representation of the current time position is read, and the state feature representation of the current time position is written according to the preset basic write weight. The state of the previous time position is smoothly continued to form the basic write result of the current time position. When the stage markers of two adjacent time positions are both abnormal stage markers, the state feature representation of the next time position is written to the state of the previous time position according to the current hold write weight. The position-by-position recursive accumulation is performed on consecutive time positions within the same abnormal accumulation segment to form the continuous enhanced write result corresponding to the abnormal accumulation segment. When the current time position changes from the basic write position corresponding to the normal stage to the enhanced write position corresponding to the abnormal stage, the basic write result of the previous time position is used as the initial hold state of the enhanced write at the current time position. The state feature representation of the current time position is used to perform the first enhanced write according to the current hold write weight. The enhanced write results and basic write results corresponding to each time position are arranged in chronological order to generate the stage hold sequence.

7. The intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion according to claim 1, characterized in that, The process of generating the coupling preservation sequence in the cross-state preservation module includes: The system reads the hold segments corresponding to adjacent state categories in the stage hold sequence according to the preset state category order, extracts the hold segments of the previous state category and the hold segments of the next state category within the same time position range, performs time position alignment on the hold segments of the previous state category and the hold segments of the next state category, filters overlapping segments that have abnormal stage markers at the same time position range, and determines the associated segments; for each associated segment, it reads the hold components in the hold segments of the previous state category and the hold components in the hold segments of the next state category, compares the change direction and change magnitude of the corresponding hold components, and determines the interactive hold write position; at each interactive hold write position, it writes the corresponding hold components in the hold segments of the previous state category to the corresponding time position of the hold segments of the next state category according to the preset interactive write weight, performs same-direction enhancement write on hold components with the same change direction, performs difference adjustment write on hold components with different change directions, and generates interactive hold results; Write the interaction holding result back to the corresponding time position of the next state category holding segment, and update the corresponding holding component in the next state category holding segment; In the same manner, continue to perform associated fragment extraction, interactive persistence writing, and result write-back on the updated next state category persistence fragment and the next state category persistence fragment, until the interactive persistence processing between all adjacent state categories is completed. After completing the interactive write and result write-back, the state category holding segments are rearranged according to the preset state category order and time order to generate a coupled holding sequence.

8. The intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion according to claim 1, characterized in that, The process of generating the comprehensive characterization sequence in the backoff suppression module includes: The coupled holding sequence is read position by position in chronological order. The holding components and stage markers at each time position are extracted. The holding components at the current time position are compared with the holding components at the previous time position. Time position segments in which the holding components continuously decrease and the duration of the decrease does not exceed the length of a preset short window are selected to determine short-term fallback segments. For each time position covered by each short-term fallback segment, the corresponding stage markers are read. Short-term fallback segments with stage markers of continuous degradation or warning are determined as fallback suppression segments. For each fallback suppression segment, the holding state at the previous time position of the fallback suppression segment's starting time position is read as the reference holding state. Then, the current holding components at each time position within the fallback suppression segment are read. For each time position within the fallback suppression segment, the attenuation amplitude is determined based on the component difference between the reference holding state and the current holding component. The attenuation amplitude is compared with a preset attenuation limit threshold. When the attenuation amplitude is greater than the preset attenuation limit threshold, the current holding component is reverted to the limit-after holding component according to the preset attenuation limit threshold. When the attenuation amplitude is less than or equal to the preset attenuation limit threshold, the current holding component is retained as the limit-after holding component. The constrained and preserved components at each time position are written back to the corresponding time position to obtain the update and preserve results corresponding to the backsliding and suppression segments. The update and preserve results at each time position after the backsliding and suppression processing are arranged in chronological order to generate a comprehensive characterization sequence.

9. The intelligent monitoring system for high-voltage power distribution control cabinets based on multi-source data fusion according to claim 1, characterized in that, The process of generating monitoring results for the high-voltage power distribution control cabinet in the result output module includes: The comprehensive representation sequence is read position by position in chronological order, and the comprehensive representation components at each time position are extracted. The comprehensive representation components at each time position are compared with the preset state judgment rules to determine the operation state recognition result at each time position. A continuity retrieval is performed on the operation status identification results and the direction of change of the comprehensive characterization components at adjacent time locations to determine the continuation direction and magnitude of abnormal changes, and to generate abnormal trend judgment results. Based on the operational status identification results and abnormal trend judgment results, a level mapping is performed to determine the risk level classification results for the corresponding time location; Based on the risk level classification results and the anomaly source categories corresponding to the comprehensive characterization components, the warning type is matched to determine the warning type output result for the corresponding time location; The results of operation status identification, abnormal trend judgment, risk level classification, and early warning type output are correlated and aggregated in chronological order to generate monitoring results for high-voltage power distribution control cabinets.

Images