A primary and secondary fusion ring network box global state perception method and system

Abstract

The application provides a primary-secondary fusion ring network box global state perception method and system, relates to the technical field of data processing, and the method comprises the following steps: extracting the change starting point, change ending point and change continuation process of each state data, combining state data with continuous change relationship into a state event, identifying the correlation strength inside each state transmission process to obtain a state coupling value, identifying a first evidence cluster and a second evidence cluster and comparing the common state events and the difference state events between the first evidence cluster and the second evidence cluster, identifying the degree of deviation from the continuous propagation rule in the fault evolution process to obtain an evolution deviation value, constructing a main evidence chain and an auxiliary evidence chain, and counting the participation order, repeated participation times and cross-region transmission relationship of each detection object in the fault evidence chain to obtain a state perception result. The application can perform correlation analysis on multiple detection objects and avoid ignoring early fault weak features.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for full-domain status perception of a primary and secondary integrated ring network box. Background Technology

[0002] Existing ring main unit (RMU) status sensing technology typically deploys current, voltage, temperature, humidity, and partial discharge detection units in the primary switchgear, busbars, cable joints, and secondary measurement and control units within the RMU. The data acquisition terminal samples, converts, and uploads the electrical and status quantities output by each detection unit. The status monitoring platform then performs threshold judgment or trend analysis on the collected data to achieve online monitoring of the RMU's operating status, insulation status, and environmental status.

[0003] However, existing technologies typically assess different detection quantities independently against their corresponding thresholds during condition monitoring, potentially lacking the ability to identify the correlations between multiple detection quantities during the evolution of the same fault. For example, in the case of early insulation degradation of cable joints, when increased humidity inside the cabinet leads to a decline in joint insulation performance, only intermittent partial discharge and slight temperature rise may occur. Since individual detection values ​​may not exceed alarm thresholds, the system may classify each data point as normal, potentially failing to promptly identify the early stages of cable joint insulation degradation. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for full-domain state perception of primary and secondary integrated ring network boxes, aiming to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] Firstly, a method for full-domain state perception of a ring network enclosure using a primary and secondary fusion method, the method comprising:

[0007] Obtain the status dataset of multiple detection objects in the fusion ring network box during a continuous acquisition period;

[0008] Based on the state dataset, extract the starting point, ending point and continuation process of each state data, and combine state data with continuous change relationship into state events to obtain state transmission process data.

[0009] Based on the state transition process data, the correlation strength within each state transition process is identified to obtain the state coupling value;

[0010] The first evidence cluster and the second evidence cluster are identified based on the state coupling value, and the common state events and different state events between the first evidence cluster and the second evidence cluster are compared to obtain fault evolution evidence data.

[0011] Based on fault evolution evidence data, the degree to which the fault deviates from the continuous propagation law during the fault evolution process is identified, and the evolution offset value is obtained.

[0012] Based on the evolution offset value, the fault evolution evidence data are continuously linked and cross-connected to construct the main evidence chain and the auxiliary evidence chain, thus obtaining the fault evidence chain.

[0013] The state perception results are obtained by statistically analyzing the participation order, repeated participation times, and cross-regional transmission relationships of each detected object in the fault evidence chain.

[0014] Furthermore, based on the state dataset, the starting point, ending point, and continuation process of each state data change are extracted. State data with continuous change relationships are combined into state events to obtain state transmission process data, including:

[0015] Based on the state dataset, determine the start position, end position, and duration of change for each state data to obtain the change description dataset;

[0016] Based on the change description dataset, compare the continuous change relationship of the change duration interval between each change description dataset and the continuity relationship between the start position and the end position of the change. Associate the change description datasets that satisfy the continuous continuity relationship to obtain the change association set.

[0017] By combining multiple change description data into state events based on the continuous change relationships of each change association set, and maintaining the succession relationship between each state event, a state event set is obtained.

[0018] Based on the set of state events, state events with succession relationships are connected according to the connection relationship between the detected objects to obtain state transmission process data.

[0019] Furthermore, based on the state transition process data, the correlation strength within each state transition process is identified to obtain the state coupling value, including:

[0020] Based on the state transmission process data, the continuity and completeness of state events over time are identified to obtain the continuity span term; the tightness of the sequential relationship between state events is identified to obtain the continuity density term; and the degree of correlation of the state transmission process across multiple detection objects is identified to obtain the object coverage term.

[0021] By fusing the continuous span term, the continuity density term, and the object coverage term, the degree of correlation between the state transmission process in terms of continuity, continuity, and coverage is identified, and the state coupling value is obtained.

[0022] Furthermore, based on the state coupling values, a first evidence cluster and a second evidence cluster are identified, and the common state events and different state events between the first and second evidence clusters are compared to obtain fault evolution evidence data, including:

[0023] Based on the state coupling value, state transmission processes with continuous change trends are grouped into the first state set, and state transmission processes with discontinuous change trends are grouped into the second state set.

[0024] Based on the first state set, state transmission processes with the same state events and continuous connections between detected objects are combined into a first evidence cluster; based on the second state set, state transmission processes with different state events and continuous connections between detected objects are combined into a second evidence cluster.

[0025] Based on the first evidence cluster and the second evidence cluster, state events that exist simultaneously in both the first and second evidence clusters are identified as common state events, and state events that exist only in one of the evidence clusters are identified as differential state events, thus obtaining a set of differential events.

[0026] Based on the set of differential events, continuous correlations are established between evidence based on common state events, and branch correlations are established between evidence based on differential state events. The evolutionary relationships between state events are then reconstructed according to the continuous and branch correlations to obtain fault evolution evidence data.

[0027] Furthermore, based on fault evolution evidence data, the degree to which the fault deviates from the continuous propagation pattern during the fault evolution process is identified, and the evolutionary offset value is obtained, including:

[0028] Based on the fault evolution evidence data, the degree of continuity of common state events in the first and second evidence clusters is identified to obtain the continuity maintenance term; the degree of branch expansion of differential state events in the first and second evidence clusters is identified to obtain the branch deviation term; and the degree of propagation deviation of branch associations relative to continuous associations is identified to obtain the association offset term.

[0029] By fusing the continuous maintenance term, branch deviation term, and associated offset term, the degree of deviation from the continuous propagation law during the fault evolution process is identified, and the evolution offset value is obtained.

[0030] Furthermore, based on the evolutionary offset values, the fault evolution evidence data are continuously concatenated and cross-linked to construct the main evidence chain and auxiliary evidence chain, resulting in the fault evidence chain, including:

[0031] By arranging the fault evolution evidence data in ascending order of evolution offset values ​​and maintaining the evolutionary order among the fault evolution evidence data, an evolution sequence set is obtained.

[0032] Based on the evolution sequence set, fault evolution evidence data with continuously changing evolution offset values ​​are continuously chained together, and adjacent fault evolution evidence data with common state events are connected to form the main evidence chain.

[0033] Based on the evolution sequence set, the fault evolution evidence data where the evolution offset value jumps are cross-linked, and the fault evolution evidence data with different state events are linked to the position of the common state event in the main evidence chain to obtain the auxiliary evidence chain.

[0034] By fusing and connecting the auxiliary evidence chain with the main evidence chain according to the corresponding common state events, the fault evolution evidence data are ensured to maintain the main propagation relationship and branch propagation relationship, thus obtaining the fault evidence chain.

[0035] Furthermore, by statistically analyzing the participation order, repeated participation frequency, and cross-regional transmission relationships of each detected object in the fault evidence chain, the state perception results are obtained, including:

[0036] Based on the fault evidence chain, each state event is mapped to the detection object that participated in that state event, and the participation mapping relationship between the detection objects is established according to the order of each state event in the fault evidence chain, thus obtaining the object mapping set;

[0037] Based on the object mapping set, state events that correspond to the same detection object multiple times in the fault evidence chain are aggregated, and repeated participation relationships are established according to the order of the state events in the fault evidence chain to obtain the object participation set.

[0038] Based on the object participation set, a correspondence is established between the detection objects in different power supply areas through common state events, and a cross-regional transmission relationship is established based on the main propagation relationship and branch propagation relationship in the fault evidence chain, thus obtaining the regional transmission set;

[0039] Based on the participation mapping relationship, repeated participation relationship and cross-regional transmission relationship of each detected object in the regional transfer set, the global state association relationship between each detected object is constructed to obtain the state perception result.

[0040] Secondly, a primary and secondary integrated ring network box global status perception system, the system comprising:

[0041] The acquisition module is used to acquire the status dataset of multiple detection objects in the fusion ring network box within a continuous acquisition period;

[0042] The transmission module is used to extract the starting point, ending point and continuation process of each state data from the state dataset, and combine state data with continuous change relationship into state events to obtain state transmission process data.

[0043] The coupling module is used to identify the correlation strength within each state transmission process based on the state transmission process data, and obtain the state coupling value.

[0044] The fault module is used to identify the first evidence cluster and the second evidence cluster based on the state coupling value, and to compare the common state events and different state events between the first evidence cluster and the second evidence cluster to obtain fault evolution evidence data.

[0045] The evolution module is used to identify the degree to which the fault deviates from the continuous propagation law during the fault evolution process based on fault evolution evidence data, and to obtain the evolution offset value;

[0046] The evidence chain module is used to continuously connect and cross-connect fault evolution evidence data according to the evolution offset value, construct the main evidence chain and the auxiliary evidence chain, and obtain the fault evidence chain.

[0047] The perception module is used to obtain state perception results by statistically analyzing the participation order, repeated participation times, and cross-regional transmission relationships of each detected object in the fault evidence chain.

[0048] The above-described solution of the present invention has at least the following beneficial effects:

[0049] This invention identifies the internal correlation strength and outputs state coupling values ​​through the state transit process, enabling the continuity, succession, and correlation between state events to be quantified into data indicators required for subsequent evidence identification. In a ring network enclosure, the same fault evolution process may simultaneously affect multiple detection objects, but the correlation between different state changes is not the same. The state coupling value transforms the internal correlations of the state transit process into comparable data, allowing the system to distinguish between closely related and loosely related state transit processes. This provides intermediate data results for judging the correlation between multi-source detection data, ensuring that the identification of subsequent first and second evidence clusters does not directly rely on individual detection thresholds, but rather on data merging and classification based on the internal coupling relationships of the state transit process, forming a data discrimination basis for the state evolution of power distribution equipment.

[0050] This invention categorizes different state transmission processes into different evidence clusters based on state coupling values. By comparing common and differing state events between the first and second evidence clusters, the data analysis object in ring network enclosure state sensing is transformed from a single state event into a comparative relationship between evidence clusters. Within the same fault evolution chain, there may be state events common to multiple detected objects, as well as differing state events occurring only in local objects or regions. Common state events can represent the continuous basis of fault evolution at the data level, while differing state events can represent branches, disturbances, or local anomalies that occur during the evolution process. By comparing common and differing events, the system can simultaneously record consistent and inconsistent state changes, enabling subsequent processing to obtain data reflecting the fault evolution structure, rather than just isolated anomaly records of a single detected object.

[0051] This invention converts fault evolution evidence data into evolutionary offset values ​​to describe the degree to which the fault evolution process deviates from the continuous propagation pattern. Normal fault propagation or state changes typically exhibit a certain continuity along electrical connection relationships, power supply area relationships, equipment layout relationships, or the correlation of detected objects. However, when certain state events suddenly appear in discontinuous objects, non-adjacent areas, or unexpected stages, it indicates that the fault evolution evidence data contains branch propagation, jump propagation, or abnormal connection relationships. By generating evolutionary offset values, the system can transform this deviation from continuous propagation into a numerical indicator, providing an offset metric for different fault evolution evidence data, thus providing a data basis for the construction of subsequent evidence chains.

[0052] This invention organizes fault evolution evidence data in a chain based on evolutionary offset values. Evidence data with strong continuity is linked together to form a main evidence chain, while evidence data exhibiting offset, branching, or jump relationships are cross-linked to form auxiliary evidence chains. Fault data often contains both main propagation information and branching changes caused by environmental factors, load fluctuations, local contact states, or insulation states. Simply arranging the data chronologically may not be sufficient to express the master-slave and branching relationships between different state events. Through continuous chaining and cross-linking, fault evolution evidence data is organized into a data structure with main and auxiliary chains, allowing the main propagation and branching relationships of fault states to be preserved simultaneously. This provides a chained input for subsequent statistical analysis of the participation order, repeated participation counts, and cross-regional transmission relationships of detection objects, giving the ring network enclosure's overall state perception results a data source and structured basis.

[0053] This invention extracts statistical information at the level of the detected object from the fault evidence chain, transforming state events, evidence data, and evidence chain structure into state perception results oriented towards the detected object. By statistically analyzing the participation order, the sequential position of each detected object in the fault evolution chain is obtained; by statistically analyzing the number of repeated participations, the occurrence of a certain detected object repeatedly in multiple state events or multiple evidence chain nodes is obtained; by statistically analyzing cross-regional transmission relationships, the correlation path of state changes between different power supply areas or different groups of detected objects is obtained. The aforementioned data processing results are ultimately mapped to the object layer, region layer, and transmission layer of the distribution ring network box, forming a data-driven state perception result that can reflect the state correlation relationships across the entire domain. Attached Figure Description

[0054] Figure 1 This is a flowchart of a method for full-domain state perception of a primary and secondary fusion ring network box provided by an embodiment of the present invention.

[0055] Figure 2 This is a flowchart illustrating the process of obtaining state coupling values ​​provided in an embodiment of the present invention.

[0056] Figure 3 This is a flowchart illustrating the process of obtaining evolutionary offset values ​​provided in an embodiment of the present invention. Detailed Implementation

[0057] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0058] like Figure 1 As shown, an embodiment of the present invention proposes a method for full-domain state perception of a primary and secondary fusion ring network box, the method comprising:

[0059] Obtain the status dataset of multiple detection objects in the fusion ring network box during a continuous acquisition period;

[0060] Based on the state dataset, extract the starting point, ending point and continuation process of each state data, and combine state data with continuous change relationship into state events to obtain state transmission process data.

[0061] Based on the state transition process data, the correlation strength within each state transition process is identified to obtain the state coupling value;

[0062] The first evidence cluster and the second evidence cluster are identified based on the state coupling value, and the common state events and different state events between the first evidence cluster and the second evidence cluster are compared to obtain fault evolution evidence data.

[0063] Based on fault evolution evidence data, the degree to which the fault deviates from the continuous propagation law during the fault evolution process is identified, and the evolution offset value is obtained.

[0064] Based on the evolution offset value, the fault evolution evidence data are continuously linked and cross-connected to construct the main evidence chain and the auxiliary evidence chain, thus obtaining the fault evidence chain.

[0065] The state perception results are obtained by statistically analyzing the participation order, repeated participation times, and cross-regional transmission relationships of each detected object in the fault evidence chain.

[0066] In this embodiment of the invention, a state dataset of multiple detected objects in a fusion ring network box is acquired within a continuous acquisition cycle. This ensures that the state information of multiple detected objects in the ring network box has a unified time reference, avoiding the inability to establish relationships due to different data sources and acquisition times during subsequent processing. Based on the state dataset, the starting point, ending point, and continuation process of each state data are extracted. State data with continuous change relationships are combined into state events to obtain state transmission process data. This allows the continuous changes between different detected objects and different detection quantities in the ring network box to be expressed in a structured manner, providing input for subsequent identification of the correlation strength within the state transmission process. Based on the state transmission process data, the correlation strength within each state transmission process is identified to obtain a state coupling value. This enables the differentiation of multiple state transmission processes at the data processing level, so that subsequent evidence cluster identification no longer directly relies on the threshold of a single detection quantity.

[0067] The first and second evidence clusters are identified based on state coupling values. Common and differing state events between the two clusters are compared to obtain fault evolution evidence data. Simultaneously, continuous and differing information in the fault evolution process is preserved, providing a data foundation for subsequent identification of evolutionary deviations. Based on this evidence data, the degree to which the fault deviates from the continuous propagation pattern is identified, yielding evolutionary deviation values. Discontinuities, jumps, or branching phenomena in the fault evolution process are transformed into data indicators, facilitating the subsequent construction of the main and auxiliary evidence chains. Finally, the fault evolution is analyzed based on these evolutionary deviation values. Evidence data is continuously linked and cross-connected to construct a main evidence chain and an auxiliary evidence chain, resulting in a fault evidence chain. This chain can preserve the evolution path of the fault state within the ring network box and related power supply areas, ensuring that subsequent state perception results are fault evidence chains with sequential order, master-slave relationships, and connection relationships. By statistically analyzing the participation order, repeated participation frequency, and cross-regional transmission relationships of each detected object in the fault evidence chain, state perception results are obtained. The state events in the fault evidence chain are mapped back to specific detected objects and power supply areas, expressing the occurrence order, participation frequency, and regional transmission relationships of each detected object during the fault evolution process.

[0068] Specifically, acquiring the status dataset of multiple detection objects in the fusion ring network enclosure within a continuous acquisition period includes:

[0069] Before implementing state-sensing processing in the integrated primary and secondary ring main unit (RMU), multiple detection objects participating in the state-sensing process are first identified, and an object identifier is established for each detection object. These detection objects include, but are not limited to, primary switchgear, incoming busbars, outgoing busbars, cable joints, circuit breaker or load switch connections, grounding switches, secondary measurement and control units, communication units, environmental monitoring areas within the cabinet, and detection nodes associated with the power supply circuit. Each detection object corresponds to one or more detection units, which output state data characterizing the operating, electrical, environmental, or insulation status of the detection object. The acquisition time of each detection unit is then uniformly configured so that detection units corresponding to different detection objects output data according to the same or calculable continuous acquisition cycle. The continuous acquisition cycle can be preset according to the RMU operation monitoring requirements, or a basic sampling cycle can be set separately according to the type of detection data, and data from different sampling cycles are aligned to a unified cycle sequence during data aggregation. For electrical quantity data such as current and voltage, data can be collected at shorter intervals; for data such as temperature and humidity that change relatively slowly, data can be collected at relatively longer intervals; for state data such as partial discharge, switching action, and communication status that are sudden or event-driven, a combination of periodic collection and event-triggered collection can be used.

[0070] Within each continuous acquisition cycle, the acquisition terminal reads the detection values ​​or status quantities output by each detection unit and converts them into a unified data format. For analog quantity detection data, the acquisition terminal performs analog-to-digital conversion, range conversion, and engineering quantity conversion to convert it into current, voltage, temperature, humidity, or partial discharge characterization values ​​that can be directly used in subsequent data processing. For digital quantity or status quantity detection data, the acquisition terminal reads its open / closed status, communication status, alarm status, operating status, or self-test status and converts it into the corresponding status code. For data uploaded by the secondary measurement and control unit, the acquisition terminal extracts valid fields based on the device number, data type, acquisition time, and measured value in the communication message and incorporates it into the same status data acquisition system. During the acquisition process, data attribute information is added to each status data. The data attribute information includes at least the detection object identifier, detection unit identifier, status data type, acquisition cycle number, acquisition timestamp, acquisition value, data unit, and data source. For data involving power supply circuits or cabinet areas, circuit identifiers, area identifiers, phase identifiers, device connection relationship identifiers, and data quality identifiers can also be added. The system continuously receives and caches the status data of each detection object in chronological order. For continuous sampling results of the same detection object and the same state data type, a single object state data sequence is formed according to the collection period number; for sampling results of multiple detection objects within the same collection period, a periodic state data set is formed according to the detection object identifier and detection type.

[0071] For missing data, duplicate data, data with obvious format errors, or data from abnormal sources, the system marks them according to data quality indicators. For data whose arrival order differs from the collection order due to communication delays, the system reorders it based on the collection timestamp and collection cycle number. For data that is temporarily lost, the system can retain the missing marker so that subsequent data gaps can be identified and not mistakenly considered as stable. For data from different detection units but representing the same detection object and the same state type, the system merges them according to preset data merging rules to avoid duplicate records within the same collection cycle that could cause errors in subsequent state event identification. The system generates a state dataset according to a preset storage structure. The state dataset includes state data records of multiple detection objects in multiple consecutive collection cycles, with each state data record corresponding to the detection object, detection type, and collection cycle.

[0072] In a preferred embodiment of the present invention, based on the state dataset, the starting point, ending point, and continuation process of each state data are extracted. State data with continuous change relationships are combined into state events to obtain state transmission process data, including:

[0073] Based on the state dataset, determine the start position, end position, and duration of change for each state data to obtain the change description dataset;

[0074] Based on the change description dataset, compare the continuous change relationship of the change duration interval between each change description dataset and the continuity relationship between the start position and the end position of the change. Associate the change description datasets that satisfy the continuous continuity relationship to obtain the change association set.

[0075] By combining multiple change description data into state events based on the continuous change relationships of each change association set, and maintaining the succession relationship between each state event, a state event set is obtained.

[0076] Based on the set of state events, state events with successive relationships are connected according to the connection relationship between the detected objects to obtain state transmission process data.

[0077] In this embodiment of the invention, based on the state dataset, the start position, end position, and duration interval of each state data change are determined to obtain a change description dataset. This transforms the data object from a single sampling point into a state change segment, preserving the temporal change boundaries of electrical quantities, environmental quantities, insulation state quantities, and equipment operating state quantities in the ring network box. This provides a data foundation for subsequent identification of the continuous relationship between different state changes. Based on the change description dataset, the continuous change relationship of the duration intervals of each change description data and the relationship between the start and end positions of the change are compared. Change description data that satisfy the continuous relationship are associated to obtain a change association set, enabling the identification of states between different detection objects and different detection types. Changes are no longer independent fragments, but rather form data sets with sequential and successive relationships, providing a basis for subsequent combination of state events. By combining multiple change description data into state events based on the continuous change relationships of each change association set, and maintaining the successive relationships between each state event, a state event set is obtained, representing the state change process composed of one or more detection objects and one or more state data types within a continuous acquisition cycle. Based on the state event set, state events with successive relationships are connected according to the connection relationships between detection objects to obtain state transmission process data, which can reflect the actual correlation structure within the power distribution equipment and related areas, providing a data foundation that conforms to the equipment connection logic for subsequent operations.

[0078] Specifically, based on the state dataset, the start position, end position, and duration of change for each state data are determined to obtain the change description dataset, which includes:

[0079] The system categorizes and organizes status data according to the detection object identifier, status data type, and acquisition cycle number. For continuously acquired data of the same detection object and the same status data type, the system arranges them into status data sequences according to the acquisition time. For example, temperature data for a cable joint is formed into a temperature time series, partial discharge data for the same cable joint is formed into a partial discharge time series, and humidity data for the environmental detection area inside the cabinet is formed into a humidity time series. Each status data sequence retains the detection object, data type, acquisition cycle, acquired value, and data quality identifier. The system identifies changes in each status data sequence. The system determines whether the status data has changed based on the numerical difference between adjacent acquisition cycles, the change amplitude per unit time, continuous upward or downward trends, fluctuation amplitude changes, changes in status code, or transitions from a stable state to an unstable state. When the data of a certain acquisition cycle deviates continuously from the previous stable interval, or exhibits unidirectional changes, intermittent fluctuations, step changes, or state transitions within several consecutive acquisition cycles, the system determines the acquisition cycle position corresponding to the start of the change as the change start position. For digital status variables, such as switch status, communication status, or secondary measurement and control unit operating status, the system can determine the location where the status code switches as the start of the change. The system continues to track the changes of this status data in subsequent continuous acquisition cycles. When the status data recovers to the original stable range, enters a new stable range, the change direction stops continuing, the change amplitude is lower than the preset change identification condition, or the status code remains stable for a preset number of cycles, the system determines the corresponding acquisition cycle position as the end of the change. For status data such as intermittent partial discharge, short-term temperature rise, or slow humidity changes, the system can retain the intervals in the continuous change process as part of the change duration interval to avoid splitting short pauses in the same fault evolution stage into multiple unrelated changes. The system determines the range of continuous acquisition cycles between the change start position and the change end position as the change duration interval and records the sequence of acquired values, change direction, change amplitude, change duration, detection object identifier, and status data type within this change duration interval. Each segment of status data change is organized into a change description data, and multiple detection objects, multiple status data types, and change description data formed in multiple continuous acquisition cycles together constitute the change description dataset.

[0080] Specifically, based on the change description dataset, the continuous change relationships within the duration of changes and the continuity relationships between the start and end positions of changes are compared among the change description data. Change description data that satisfy the continuous continuity relationship are associated to obtain a change association set, which specifically includes:

[0081] The system compares the duration intervals of two change description data to determine if there is temporal overlap, temporal adjacency, or sequential continuity. If the time interval between the end position of the first change description data and the start position of the second change description data is within a preset continuous range, or if their duration intervals overlap within a portion of the acquisition period, the system considers them to have a basis for temporal continuity. For data with different acquisition periods but convertible to a unified time axis, the system aligns them according to a unified acquisition period number or timestamp before comparison. The system further compares the continuity between the start and end positions of the change. If the state change of one detected object ends, and the data of another detected object or another state type begins to change in an adjacent period, and there is a sequential continuity between the two, the system considers these two change description data as candidate related objects. For example, after the duration interval of humidity change in the cabinet, partial discharge change in the cable joint begins; after the duration interval of partial discharge change, the joint temperature changes; or the load current change and temperature change show a continuity relationship within a continuous acquisition period, all of these can be identified as continuity relationships between change description data.

[0082] The system combines the attributes and status data types of the detection objects corresponding to the change description data. For different status data on the same detection object, the system determines whether their change duration intervals have a sequential or overlapping relationship in time. For status data on different detection objects, the system determines whether their corresponding detection objects have electrical connection relationships, power supply path relationships, cabinet adjacency relationships, or regional association relationships. Only when the change duration intervals have a continuous relationship, and the change start position and change end position meet the continuity condition, will the system associate the relevant change description data. The system merges multiple change description data that meet the continuity relationship into a change association set. Each change association set records the change description data involved in the association, the sequential order of each change description data, the continuous relationship between change duration intervals, the continuity relationship between the change end position and change start position, and the relationship between the corresponding detection objects. If a change description data can form a continuity relationship with multiple subsequent change description data, the system can retain a one-to-many branch association in the change association set; if multiple change description data point to the same subsequent change description data, the system can retain a many-to-one convergence association.

[0083] Specifically, by combining multiple change description data into state events based on the continuous change relationships of each change association set, and maintaining the successive relationships between each state event, a state event set is obtained, which specifically includes:

[0084] The system processes change association sets, reading multiple change description data and their continuous change relationships. For change description data with common evolutionary characteristics within the same change association set, the system combines them into a single state event. These common evolutionary characteristics can manifest as changes occurring within a continuous acquisition period, continuity between change duration intervals, corresponding change directions or stages, correlations between detected objects, or different state data collectively reflecting the same device state change process. The state event records the detected object, state data type, change start position, change end position, change duration interval, and continuous change relationships between each change description data. The system does not merge all change description data within the same time period but determines the combination boundary based on the continuity relationships recorded in the change association set. If multiple change description data collectively describe a state change process within the same continuous change relationship, they are combined into a single state event; if there is a clear break between change description data, a switch in associated objects, or a loss of continuity in change direction, the system divides them into different state events. The system continues to maintain the continuity relationships between each state event. If there is a continuous succession relationship between the end change description data in the first state event and the beginning change description data in the second state event, the system records the succession relationship from the first state event to the second state event in the state event set. If multiple state events occur after a certain state event, the system can record the branch succession between the state events; if multiple state events converge into the same state event, the system can record the convergence succession between the state events.

[0085] In a preferred embodiment of the present invention, based on state transition process data, the correlation strength within each state transition process is identified to obtain a state coupling value, including:

[0086] Based on the state transmission process data, the continuity and completeness of state events over time are identified to obtain the continuity span term; the tightness of the sequential relationship between state events is identified to obtain the continuity density term; and the degree of correlation of the state transmission process across multiple detection objects is identified to obtain the object coverage term.

[0087] By fusing the continuous span term, the continuity density term, and the object coverage term, the degree of correlation between the state transmission process in terms of continuity, continuity, and coverage is identified, and the state coupling value is obtained.

[0088] In this embodiment of the invention, based on the state transmission process data, the continuity and completeness of state events during the time duration are identified to obtain a continuity span term. At the data processing level, the occurrence time, duration, and interruption status of state events are retained, providing a data source for the continuity dimension in the state coupling value. The tightness of the sequential relationship between state events is identified to obtain a connection density term, reflecting whether there is continuous connection, breakage, multi-branch connection, or sparse connection between state events. The degree of correlation of the state transmission process across multiple detection objects is identified to obtain an object coverage term, describing whether the state transmission process crosses multiple detection objects and whether these detection objects have a device connection basis. The continuity span term, connection density term, and object coverage term are fused to identify the degree of correlation of the state transmission process in terms of continuity, connection, and coverage, obtaining a state coupling value. This value can serve as the data basis for subsequent evidence cluster division, comparison of common state events, and construction of fault evolution evidence, enabling state perception processing to move from the state event layer to the correlation strength calculation layer.

[0089] The formula for calculating the state coupling value is as follows:

[0090] ,

[0091] in, For state coupling values, For the index of the state event, For the first The starting position of the state event change. For the first The end position of the change in each state event. For the first The length of the duration interval of a state event change. The total number of state events. This represents the number of adjacent state events that satisfy a consecutive succession relationship. The number of detection objects participating in this state transmission process. To integrate the total number of objects being detected in the ring network box.

[0092] Specifically, based on the state transition process data, the correlation strength within each state transition process is identified to obtain the state coupling value, which includes:

[0093] The system is based on the formula Calculate the continuous span term. This represents the cumulative span between the start and end positions of each state event. This represents the cumulative length of the actual duration of each state event's change. The ratio of these two values ​​describes the correspondence between the span of a state event over time and its actual duration. When the boundary of a state event's change can relatively completely cover its actual duration, this ratio reflects that the state transmission process has a relatively complete temporal duration. The formula is further multiplied by... ,in This represents the number of adjacent state events that satisfy a consecutive succession relationship. for The product represents the total number of adjacent succession relationships that can theoretically be formed by a given state event. This product ensures that the continuous span term considers not only the continuity of a single state event itself, but also whether there is a continuous succession relationship between adjacent state events.

[0094] The system is based on the formula Calculate the bearing density term. This represents the percentage of adjacent state events that satisfy a consecutive succession relationship relative to the total number of state events, used to describe the density of succession relationships between state events within the state transition process. If multiple state events can form a sequential succession, then... The value increases accordingly; if there are many breaks, jumps, or no continuity between state events, then... The value of is relatively small. In the formula... This represents the ratio between the sum of the actual duration intervals of all state events and the total span of the entire state transition process, where... This represents the total time span from the start of the first state event to the end of the last state event. This proportion reflects the time density of valid state events throughout the entire state transition process. By multiplying the two factors mentioned above, the continuity density term can simultaneously express whether adjacent events are consecutively inherited and whether the entire transition process is fully occupied by valid state events, transforming the sequential relationship between state events into a computable data item.

[0095] The system is based on the formula When calculating object overriding items, This describes the coverage area of ​​the detection objects involved in the current state transition process. If a state transition process involves only a single detection object, the object coverage area is small; if it involves multiple detection objects such as primary switchgear, busbars, cable joints, secondary measurement and control units, and the environment area inside the cabinet, the object coverage area will increase accordingly. Further settings are made in the formula. This ensures that the object coverage item not only reflects the number of detected objects, but also incorporates the number of consecutive relationships. Total number of status events and the number of subjects participating in the testing The correction is made when the state transfer process across multiple detection objects has many consecutive relationships, so that the object coverage item can reflect the synergistic relationship between detection object coverage and event succession; when multiple detection objects are involved but the consecutive relationships are insufficient, the object coverage item will not be unilaterally amplified simply because the number of objects increases.

[0096] The system multiplies the continuous span term, the continuity density term, and the object coverage term according to the formula to obtain the state coupling value. This product structure makes the state coupling value subject to the joint constraints of temporal continuity, event continuity, and detection object coverage. If a state transmission process is only continuous in time, but there is no continuity relationship between adjacent state events, or it is concentrated on a single detection object, the result of the state coupling value will be limited by the corresponding term. If a state transmission process has a relatively complete state event duration interval, a relatively dense continuity relationship, and a relatively clear multi-detection object coverage relationship, then all three calculation terms will support the state coupling value.

[0097] In a preferred embodiment of the present invention, a first evidence cluster and a second evidence cluster are identified based on state coupling values, and common state events and different state events between the first evidence cluster and the second evidence cluster are compared to obtain fault evolution evidence data, including:

[0098] Based on the state coupling value, state transmission processes with continuous change trends are grouped into the first state set, and state transmission processes with discontinuous change trends are grouped into the second state set.

[0099] Based on the first state set, state transmission processes with the same state events and continuous connections between detected objects are combined into a first evidence cluster; based on the second state set, state transmission processes with different state events and continuous connections between detected objects are combined into a second evidence cluster.

[0100] Based on the first evidence cluster and the second evidence cluster, state events that exist simultaneously in both the first and second evidence clusters are identified as common state events, and state events that exist only in one of the evidence clusters are identified as differential state events, thus obtaining a set of differential events.

[0101] Based on the set of differential events, continuous correlations are established between evidence based on common state events, and branch correlations are established between evidence based on differential state events. The evolutionary relationships between state events are then reconstructed according to the continuous and branch correlations to obtain fault evolution evidence data.

[0102] In this embodiment of the invention, based on the state coupling value, state transmission processes with continuous changing trends are grouped into a first state set, and state transmission processes with discontinuous changing trends are grouped into a second state set. The evolution process of multi-source state data is divided into two comparable data sets. The first state set is used to store state transmission processes with a continuous propagation basis, and the second state set is used to store state transmission processes with jump, branching, or local perturbation characteristics. Based on the first state set, state transmission processes with the same state events and continuous connections between detected objects are combined into a first evidence cluster. Based on the second state set, state transmission processes with different state events and continuous connections between detected objects are combined into a second evidence cluster. The first evidence cluster stores the same state events and continuous connection relationships between detected objects in a continuous changing trend, and the second evidence cluster stores... The system identifies the connection relationships between different state events and continuously detected objects within discontinuous change trends. Based on the first and second evidence clusters, state events existing simultaneously in both clusters are identified as common state events, while those existing only in one cluster are identified as differential state events, resulting in a differential event set. Simultaneously, it records the main common data and branch differential data in the fault evolution process, providing input for subsequent reconstruction of the evolutionary relationships between state events. Based on the differential event set, it establishes continuous correlations between evidence using common state events and branch correlations, reconstructing the evolutionary relationships between state events according to these continuous and branch correlations to obtain fault evolution evidence data. It incorporates both continuous and discontinuous change trends into the fault evolution evidence data, providing input for subsequent identification of evolutionary offset values.

[0103] Specifically, based on the state coupling value, state transmission processes with continuous changing trends are grouped into a first state set, and state transmission processes with discontinuous changing trends are grouped into a second state set, including:

[0104] The system establishes a state transfer process index table. This index table records the process identifier of each state transfer process, the included state events, the start and end positions of the state event changes, the duration of the change, the participating detection objects, the connection relationships between the detection objects, and the corresponding state coupling value. The system sorts each state transfer process according to its state coupling value and reads the distribution of state coupling values ​​for all state transfer processes within the current continuous acquisition window for the same fusion ring network box. Since the state coupling value is obtained from the continuous span term, the continuity density term, and the object coverage term, this value reflects the comprehensive data relationship formed by the state transfer process in terms of temporal continuity, event continuity, and detection object coverage. The system assigns state transfer processes whose state coupling values ​​meet the continuous change trend judgment condition to the first state set. The continuous change trend judgment condition can be that the state coupling value is not lower than a preset coupling benchmark value, or that the state coupling value is within the continuous distribution range of the coupling value sequence within the current acquisition window, or that the difference between the state coupling value and the coupling value of an adjacent state transfer process is lower than a preset fluctuation range. When the state coupling value of a state transfer process meets the above conditions, it indicates that the state events in the process have a continuous expression basis in terms of time span, sequential continuity, and detection object coverage. The system writes its process identifier, state event set, participating detection object set, and state coupling value into the first state set. For state transfer processes whose state coupling values ​​do not meet the continuous change trend determination conditions, the system determines whether they still contain valid state events and detection object connection information. If the state transfer process has state events, but its state coupling value is discontinuous, jumps, significantly lower than the continuous distribution range, or the difference in state coupling value between it and adjacent state transfer processes exceeds a preset fluctuation range, the system classifies it into the second state set. The second state set is used to store data processes with discontinuous change trends, which may include state transfer processes where there are time breaks between state events, insufficient sequential continuity, incomplete detection object coverage, state event jumps, or independent changes in local areas.

[0105] Specifically, based on the first state set, state transmission processes with the same state events and continuous connections between detected objects are combined to form a first evidence cluster; based on the second state set, state transmission processes with different state events and continuous connections between detected objects are combined to form a second evidence cluster, specifically including:

[0106] The system reads the state events contained in each state transmission process in the first state set and establishes an event feature identifier for each state event. The event feature identifier can be composed of the detection object identifier, state data type, change start position, change end position, change duration interval, and change direction. The system compares the state transmission processes in the first state set pairwise to determine whether different state transmission processes contain the same state events. If the state events in two state transmission processes meet the same conditions in terms of detection object, state data type, and change duration interval, or if their change start position and change end position are within the allowable time alignment range, the system confirms the state event as the same state event. The system reads the connection relationships between the detection objects corresponding to the same state events. The continuous connection relationships include the electrical connection relationships between detection objects, the connection relationships between preceding and following objects on the same power supply path, the connection relationships between busbars and switchgear, the connection relationships between cable joints and outgoing circuits, the correspondence relationships between primary equipment and secondary measurement and control units, and the regional association relationships between the cabinet environment detection area and adjacent equipment parts. If there is a continuous connection between the detection objects corresponding to multiple state transmission processes containing the same state event, the system combines these state transmission processes into a first evidence cluster, and records the same state event, the continuously connected detection objects, the state transmission process identifier, and the corresponding state coupling value in the first evidence cluster.

[0107] The system reads the state events contained in each state transmission process in the second state set and identifies whether different state transmission processes have different state events. Different state events refer to state events that are not completely consistent in terms of state data type, detection object, change start position, change end position, change duration interval, or change direction. The system does not directly exclude the correlation between state events simply because they are different; instead, it continues to determine whether there is a continuous connection relationship between the detection objects corresponding to these different state events. If different state events occur in detection objects on continuous power supply paths, adjacent equipment locations, the same cabinet area, or primary and secondary corresponding links, the system combines these state transmission processes into a second evidence cluster. In the second evidence cluster, the system records the differences between different state events, including the different state data type, different detection object, different occurrence time, different duration interval, and the state coupling value corresponding to the different events. Simultaneously, the system records the continuous connection relationship of the detection objects to which these different state events depend. Therefore, the second evidence cluster is not a collection of disordered abnormal data, but rather consists of state transmission processes with discontinuous change trends, different state events, but where the detection objects still have a connection basis.

[0108] Specifically, based on the set of differing events, continuous correlations are established between evidence based on common state events, and branching correlations are established between evidence based on differing state events. The evolutionary relationships between state events are then reconstructed according to the continuous and branching correlations to obtain fault evolution evidence data, which specifically includes:

[0109] The system reads common state events and differential state events recorded in the differential event set. Common state events are state events that exist simultaneously in both the first and second evidence clusters, while differential state events are state events that exist only in either the first or second evidence cluster. The system maps common state events onto a unified timeline according to the start position, end position, and duration of the change, and establishes continuous association nodes according to the order of occurrence of the common state events. If the end position of a previous common state event and the start position of a subsequent common state event satisfy the continuity condition, and there is a continuous connection between their corresponding detection objects, the system establishes a continuous association between them. The system uses common state events as the common connection basis between evidence; that is, state events appearing in both the first and second evidence clusters are used as the backbone nodes in the fault evolution relationship. The system records the preceding common state event, the following common state event, the corresponding detection object, the event interval, the direction of continuity, and the source of the evidence cluster for each continuous association. When multiple common state events are established sequentially according to the time order and the connection relationship of the detected objects, the system forms a continuous correlation sequence. This continuous correlation sequence is used to represent the stable and continuous propagation part in the fault evolution evidence.

[0110] The system reads the differential state events from the differential event set and determines the corresponding position between each differential state event and a common state event. If the starting position of a change in a differential state event is after a common state event, and there is a connection between the detection object corresponding to the differential state event and the detection object corresponding to the common state event, the system attaches the differential state event after the common state event, establishing a branch association. If a differential state event occurs between two common state events, the system determines whether it has a temporal succession or detection object connection relationship with both the preceding and following common state events; if so, it is treated as a branch segment between continuous associations. If a differential state event has a succession relationship with only one common state event, it is recorded as a unidirectional branch node. When establishing a branch association, the system records the source evidence cluster, corresponding detection object, change duration interval, temporal position relationship with the common state event, detection object connection relationship, and branch direction of the differential state event. For multiple differential state events extending from the same common state event, the system establishes a one-to-many branch association; for multiple differential state events ultimately pointing to the same common state event, the system establishes a many-to-one converging association.

[0111] The system reconstructs the evolutionary relationships between state events based on continuous and branching relationships. Specifically, common state events form a continuous evolutionary main line, while differing state events form branching structures connected to this main line. Fault evolution evidence data is generated based on the temporal order of state events, the connection relationships between detected objects, and the source relationships of evidence clusters. This fault evolution evidence data includes a set of common state events, a set of differing state events, a set of continuous relationships, a set of branching relationships, the detected objects corresponding to each state event, the duration of change for each state event, the direction of connection between state events, and the source identifier of the evidence cluster.

[0112] In a preferred embodiment of the present invention, based on fault evolution evidence data, the degree to which the fault deviates from the continuous propagation pattern during the fault evolution process is identified to obtain an evolution offset value, including:

[0113] Based on the fault evolution evidence data, the degree of continuity of common state events in the first and second evidence clusters is identified to obtain the continuity maintenance term; the degree of branch expansion of differential state events in the first and second evidence clusters is identified to obtain the branch deviation term; and the degree of propagation deviation of branch associations relative to continuous associations is identified to obtain the association offset term.

[0114] By fusing the continuous maintenance term, branch deviation term, and associated offset term, the degree of deviation from the continuous propagation law during the fault evolution process is identified, and the evolution offset value is obtained.

[0115] In this embodiment of the invention, based on fault evolution evidence data, the degree of continuity of common state events in the first and second evidence clusters is identified to obtain a continuity maintenance term. This term expresses whether the common changing state is stable, whether it maintains continuity, and whether it maintains the same evolutionary position in different evidence clusters as data results, providing data basis for the continuous propagation dimension of subsequent evolutionary offset values. The degree of branch expansion of differential state events in the first and second evidence clusters is identified to obtain a branch deviation term. This term retains the expansion position, expansion range, and expansion level of differential state events relative to common state events. The degree of propagation deviation of branch associations relative to continuous associations is identified to obtain an association offset term. This term saves event relationships that deviate from the normal continuous transmission path in data form, providing data basis for the propagation path dimension of fault evolution offset value generation. The continuity maintenance term, branch deviation term, and association offset term are fused to identify the degree of deviation from the continuous propagation law during fault evolution, obtaining an evolutionary offset value. This value expresses the degree of deviation of the fault evolution process from the continuous propagation law, providing data processing basis for the formation of the main evidence chain and auxiliary evidence chain.

[0116] The formula for calculating the evolutionary offset value is as follows:

[0117] ,

[0118] in, This is the evolutionary offset value. The number of common state events, The number of differential state events. The total number of state events in the first and second evidence clusters. The number of consecutive associations. This represents the number of branch relationships.

[0119] Specifically, based on fault evolution evidence data, the degree to which the fault deviates from the continuous propagation pattern during the fault evolution process is identified, and the evolutionary offset value is obtained, including:

[0120] in the formula This parameter represents the proportion of differential state events within the total number of common state events and differential state events. A lower proportion indicates fewer differential state events, suggesting that the fault evolution evidence data primarily consists of common state events. Conversely, an increased proportion indicates more events existing only within a single evidence cluster appearing in the evolution evidence. This parameter converts inconsistent state changes, local branching changes, or jump events occurring in locally detected objects into numerical proportions, providing a calculable data representation of the degree of branch expansion.

[0121] in the formula This is used to represent the proportion of branch relationships among all relationships. If most relationships in the fault evolution evidence data are formed by continuous associations between common state events, then... The proportion is relatively low; if a large number of differing state events are linked as branches, or multiple branch relationships extend from a continuous main line, then The proportion of this increases. This factor allows branch propagation relationships to go beyond mere event description and participate in the calculation of evolutionary offset values ​​as a percentage of the number of branch associations, reflecting the degree of deviation of branch propagation from continuous propagation.

[0122] in the formula This indicates the proportion of common state events maintained across all state events in both the first and second evidence clusters. A high proportion of common state events indicates a significant number of shared events between the first and second evidence clusters, providing a strong foundation for continuous propagation. Conversely, a low proportion indicates fewer shared events between the two clusters, suggesting that discrepancies or branching elements constitute a larger share of the fault evolution evidence data. The product of the aforementioned proportion of discrepancy events and the proportion of branching events is then divided by [the formula is missing here]. This makes the retention ratio of common state events the benchmark for suppressing shifts. When common state events are sufficiently retained, even if there are a few branch events or branch associations, the evolutionary shift value will not be amplified individually. However, when common state events are insufficiently retained, the impact of differential events and branch associations on the evolutionary shift value is amplified.

[0123] in the formula Used for secondary correction of the proportion of events with different states. This represents the square of the proportion of differential state events. When the proportion of differential state events is low, the squared term is small, and its correction to the evolutionary shift value is limited. When the proportion of differential state events is high, the squared term increases significantly, reflecting the large number of differential events in the evolutionary shift value. The denominator contains... Simultaneously, the proportion of common state events maintained and the proportion of branch relationships are introduced to balance the two factors: the continued existence of common events and the expansion of branch relationships. The correction term avoids judging the offset solely based on the number of difference events, and processes the number of difference events, the maintenance status of common events, and the proportion of branch relationships within the same calculation framework. The system calculates according to preset fusion rules, using the continuous maintenance term as the basic term to offset the offset, and fusing the branch deviation term and the associated offset term as incremental terms to form the offset. The fusion is performed using weighted, product, or hierarchical rules to obtain the evolutionary offset value.

[0124] In a preferred embodiment of the present invention, based on the evolution offset value, the fault evolution evidence data is continuously concatenated and cross-linked to construct a main evidence chain and an auxiliary evidence chain, resulting in a fault evidence chain, including:

[0125] By arranging the fault evolution evidence data in ascending order of evolution offset values ​​and maintaining the evolutionary order among the fault evolution evidence data, an evolution sequence set is obtained.

[0126] Based on the evolution sequence set, fault evolution evidence data with continuously changing evolution offset values ​​are continuously chained together, and adjacent fault evolution evidence data with common state events are connected to form the main evidence chain.

[0127] Based on the evolution sequence set, the fault evolution evidence data where the evolution offset value jumps are cross-linked, and the fault evolution evidence data with different state events are linked to the position of the common state event in the main evidence chain to obtain the auxiliary evidence chain.

[0128] By fusing and connecting the auxiliary evidence chain with the main evidence chain according to the corresponding common state events, the fault evolution evidence data are ensured to maintain the main propagation relationship and branch propagation relationship, thus obtaining the fault evidence chain.

[0129] In this embodiment of the invention, by arranging the fault evolution evidence data in ascending order of evolution offset values ​​while maintaining the evolutionary sequence relationship between the fault evolution evidence data, an evolution sequence set is obtained. Independent fault evolution evidence data are uniformly arranged into the same evolution sequence, providing a data entry point for subsequent continuous concatenation. Based on the evolution sequence set, fault evolution evidence data with continuously changing evolution offset values ​​are continuously concatenated, connecting adjacent fault evolution evidence data with common state events into the main evidence chain, ensuring that the stable main propagation relationship during fault evolution maintains a complete data structure. Based on the evolution sequence set, the... Fault evolution evidence data with abrupt changes in evolutionary offset values ​​are cross-linked, and fault evolution evidence data with different state events are linked to the position of common state events in the main evidence chain to obtain an auxiliary evidence chain. This allows local changes, abnormal propagation, and branch propagation relationships in fault evolution to be preserved independently. By merging and connecting the auxiliary evidence chain with the main evidence chain according to the corresponding common state events, it is ensured that each fault evolution evidence data maintains the main propagation relationship and branch propagation relationship, resulting in a fault evidence chain that can completely preserve the evolutionary relationship between state events, the propagation relationship between detected objects, and the continuous propagation and local expansion relationship in fault evolution.

[0130] Specifically, based on the evolutionary sequence set, fault evolution evidence data with continuously changing evolutionary offset values ​​are continuously concatenated, and adjacent fault evolution evidence data with common state events are connected to form the main evidence chain, which includes:

[0131] The system reads the sorting position, evolutionary offset value, common state event set, continuous correlation, and corresponding detection objects of each fault evolution evidence data in the evolutionary sequence set. The evolutionary sequence set is arranged in ascending order of evolutionary offset value, maintaining the evolutionary sequence relationship between each fault evolution evidence data. Therefore, the system can sequentially compare adjacent fault evolution evidence data along this sequence. For any adjacent first and second fault evolution evidence data, the system calculates the difference in their evolutionary offset values ​​and determines whether this difference is within a preset continuous change range. If the difference in their evolutionary offset values ​​is within the preset continuous change range, it indicates that the two sets of fault evolution evidence data maintain a continuous change basis in terms of the degree of deviation from the continuous propagation law, and can be used as candidates for continuous concatenation. The system compares the common state events in the two sets of fault evolution evidence data. If the final common state event of the first fault evolution evidence data is the same as the initial common state event of the second fault evolution evidence data, or if they are not the same state event but have a continuous correlation relationship, and there is a continuous connection relationship between the corresponding detection objects, then the system continuously concatenates the two sets of fault evolution evidence data. During continuous concatenation, the system uses common state events as connection nodes to link the end-of-line continuous association of the preceding fault evolution evidence data with the beginning-of-line continuous association of the following fault evolution evidence data, while preserving the original state event sequence, state event change duration interval, detection object identifier, and evidence cluster source identifier of both. When multiple adjacent fault evolution evidence data all meet the conditions for continuous change in evolution offset value and common state event continuation, the system performs continuous concatenation sequentially according to the arrangement order in the evolution sequence set. During concatenation, the system merges and marks already connected common state events to avoid generating multiple nodes for the same common state event in the main evidence chain; for common state events occurring in the same detection object, of the same state data type, and with continuous change duration intervals, the system records them as continuations of the same main chain node; for common state events between different detection objects, the system establishes adjacent main chain nodes based on the detection object connection relationship. The main evidence chain is composed of multiple common state events and their continuous association relationships.

[0132] Specifically, based on the evolutionary sequence set, fault evolution evidence data where evolutionary offset values ​​jump are cross-linked, and fault evolution evidence data with differing state events are linked to the positions of common state events in the main evidence chain to obtain an auxiliary evidence chain, which specifically includes:

[0133] The system reads fault evolution evidence data from the evolution sequence set that is not continuously linked to the main evidence chain, or fault evolution evidence data that, although located adjacent to the main evidence chain, has an evolution offset value difference exceeding a preset continuous change range. This type of fault evolution evidence data is identified as jump evidence data. Jump evidence data typically contains many differential state events or branch relationships, and its evolution offset value shows a significant change relative to the preceding and following fault evolution evidence data. Therefore, it is not directly used as a continuous node in the main evidence chain but instead undergoes cross-linking processing. The system identifies the common state event corresponding to each differential state event. This correspondence is determined based on branch relationships, or it can be determined based on the temporal sequence between the differential state event and the common state event, the connection relationship of the detected objects, and the event continuation direction. If a differential state event occurs after a common state event, the system identifies that common state event as the attachment reference node. If a differential state event is located between two common state events, the system determines whether to attach it to the preceding or following common state event based on its connection relationship with the preceding and following common state events, or uses the continuous association between the two as the attachment position. If a differential state event has connections with multiple common state events, the system prioritizes the common state event with the smaller time interval and the direct connection relationship between the detected objects as the attachment reference node.

[0134] The system searches for the main chain node corresponding to the common state event in the main evidence chain. If the common state event exists in the main evidence chain, the system attaches the transition evidence data containing the difference state event to that main chain node, forming an auxiliary evidence chain. During attachment, the system does not change the original order of the common state events in the main evidence chain, but instead generates an auxiliary chain node on the side of the main chain node, and writes the difference state event, branch association, corresponding detection object, evolution offset value, and evidence source into the auxiliary evidence chain. If a transition evidence data contains multiple difference state events, the system establishes the internal order of the auxiliary chain according to the start and end positions of the difference state events; if there is a branch connection relationship between multiple difference state events, the branch connection relationship is retained within the auxiliary evidence chain. When multiple transition evidence data are attached to the same main chain node, the system establishes multiple auxiliary evidence chains or multiple branch segments within the same auxiliary evidence chain based on the evolution offset value, number of difference state events, number of branch associations, and occurrence time order of each transition evidence data. For transition evidence data for which the corresponding common state event cannot be directly found in the main evidence chain, the system can find the closest common state event in the main evidence chain as the attachment position based on the connection relationship and temporal proximity relationship of the detected objects, and mark the attachment relationship as an indirect attachment relationship, forming an auxiliary evidence chain that includes differential state events and branch propagation relationships.

[0135] Specifically, by fusing and connecting the auxiliary evidence chain with the main evidence chain according to the corresponding common state events, the fault evolution evidence data are ensured to maintain the main propagation relationship and branch propagation relationship, thus obtaining the fault evidence chain, which specifically includes:

[0136] The system establishes a common state event mapping table. This table records the main chain node number, corresponding detection object, change duration interval, link position, and available auxiliary evidence chain identifiers for each common state event in the main evidence chain. The system reads the connection benchmark common state event from each auxiliary evidence chain and, according to the common state event mapping table, connects the auxiliary evidence chain to the corresponding common state event position in the main evidence chain. This connection does not insert the auxiliary evidence chain into the continuous sequence of the main evidence chain, but rather adds a branch connection relationship between the main chain node and the auxiliary chain node without changing the continuous propagation order of the main evidence chain. During the fusion connection process, the system verifies each auxiliary evidence chain. If the connection benchmark common state event of the auxiliary evidence chain is consistent with the main evidence chain node, and there is a detection object connection relationship between the detection object corresponding to the difference state event in the auxiliary evidence chain and the detection object corresponding to the main chain node, then the system confirms that the auxiliary evidence chain can be directly fused. If the connection benchmark common state event of the auxiliary evidence chain is not completely consistent with the main evidence chain node, but both belong to a continuous segment of the same state event, the system fuses it to the main chain node corresponding to that continuous segment. If an auxiliary evidence chain is linked to multiple main chain nodes, the system determines the primary linked node based on the order of state events and the connection direction of the detected objects, and saves other linked relationships as association identifiers to avoid circular connections or duplicate links. When multiple auxiliary evidence chains are linked to the same common state event, the system arranges them according to the evolutionary offset value, jump magnitude, and occurrence time of each auxiliary evidence chain. Auxiliary evidence chains with smaller evolutionary offset values ​​and closer temporal relationships with main chain nodes are arranged closer to the main evidence chain; auxiliary evidence chains with larger evolutionary offset values ​​or wider ranges across detected objects are arranged at outer branch positions. In this way, the faulty evidence chain can simultaneously express the main chain propagation relationship and the auxiliary chain branch propagation hierarchy. The system can also merge cases where the same differential state event appears repeatedly in multiple auxiliary evidence chains, retaining its earliest occurrence position, corresponding detected object, and all evidence sources, avoiding duplicate nodes affecting the subsequent detection object participation count. After fusion, a faulty evidence chain is generated.

[0137] In a preferred embodiment of the present invention, the state perception result is obtained by statistically analyzing the participation order, repeated participation frequency, and cross-regional transmission relationship of each detected object in the fault evidence chain, including:

[0138] Based on the fault evidence chain, each state event is mapped to the detection object that participated in that state event, and the participation mapping relationship between the detection objects is established according to the order of each state event in the fault evidence chain, thus obtaining the object mapping set;

[0139] Based on the object mapping set, state events that correspond to the same detection object multiple times in the fault evidence chain are aggregated, and repeated participation relationships are established according to the order of the state events in the fault evidence chain to obtain the object participation set.

[0140] Based on the object participation set, a correspondence is established between the detection objects in different power supply areas through common state events, and a cross-regional transmission relationship is established based on the main propagation relationship and branch propagation relationship in the fault evidence chain, thus obtaining the regional transmission set;

[0141] Based on the participation mapping relationship, repeated participation relationship and cross-regional transmission relationship of each detected object in the regional transfer set, the global state association relationship between each detected object is constructed to obtain the state perception result.

[0142] In this embodiment of the invention, based on the fault evidence chain, each state event is mapped to a detection object participating in that state event. A participation mapping relationship is established between detection objects according to the order of each state event in the fault evidence chain, resulting in an object mapping set. Common and differing state events in the fault evidence chain are mapped to specific detection objects such as switchgear, busbars, cable joints, secondary measurement and control units, and cabinet interior environments, enabling subsequent analysis to be statistically analyzed and expressed on a per-detection-object basis. Based on the object mapping set, state events that correspond multiple times to the same detection object in the fault evidence chain are grouped together, and a repeated participation relationship is established according to the sequential position of the state events in the fault evidence chain, resulting in an object participation set. This set can save the number of times, order, and link position of detection objects participating in fault evolution, providing a basis for subsequent construction of a complete fault evidence chain. The domain state correlation provides statistical basis; based on the object participation set, the corresponding relationship between the detection objects in different power supply areas is established through common state events, and the cross-regional transmission relationship is established based on the main line propagation relationship and branch propagation relationship in the fault evidence chain, resulting in a regional transmission set, which can save the main line propagation relationship and branch propagation relationship of state events between different power supply areas; based on the participation mapping relationship, repeated participation relationship and cross-regional transmission relationship of each detection object in the regional transmission set, the global state correlation relationship between each detection object is constructed to obtain the state perception result, which can indicate which detection object a certain state change corresponds to, which detection object it is transmitted to, which detection objects appear multiple times, and which power supply areas have continuous or branch transmission relationships, providing data expression for the operation status analysis of distribution ring network boxes.

[0143] Specifically, based on the fault evidence chain, each state event is mapped to a detection object participating in that state event, and a participation mapping relationship is established between detection objects according to the order of each state event in the fault evidence chain, resulting in an object mapping set, which specifically includes:

[0144] The system reads all nodes in the fault evidence chain and distinguishes between main evidence chain nodes, auxiliary evidence chain nodes, and the connection nodes between the main and auxiliary evidence chains. Each node corresponds to a state event, which can be a common state event or a different state event. The system reads the event identifier, the type of the chain to which it belongs, the start position of the change, the end position of the change, the duration of the change, the data type of the state event, the source of evidence, the preceding state event, the following state event, and the state data records contained in the state event. Since a state event is formed by combining one or more state data, the system further reads the detection object identifier corresponding to each state data, binding the state event with the detection object participating in the state event.

[0145] The system uses the detection object identifier as the mapping primary key, and supplements the records with the equipment location to which the detection object belongs, the detection object type, the power supply circuit identifier, the area identifier, and the primary and secondary correspondences. If a state event consists of state data from only one detection object, the system establishes a one-to-one mapping relationship between the state event and the detection object. If a state event consists of state data from multiple detection objects, such as humidity state data from an internal environment detection object and partial discharge state data from a cable joint detection object forming a single state event, the system establishes a one-to-many mapping relationship between the state event and multiple detection objects. If a detection object contains multiple types of state data in the same state event, such as a cable joint containing both partial discharge state data and temperature state data, the system establishes a composite participation mapping relationship between the detection object and the state event, and records the data types involved. The system establishes participation mapping relationships between detection objects according to the order of arrangement in the fault evidence chain. For state events in the main evidence chain, the system sequentially reads the detection objects corresponding to the state events according to the continuous propagation direction of the main evidence chain, and establishes a sequential mapping relationship between the detection objects corresponding to the previous state event and the detection objects corresponding to the next state event. For state events in an auxiliary evidence chain, the system starts with the common state event attached to the chain and sequentially reads the detection objects corresponding to the differing state events according to the branch propagation direction of the chain, establishing a participation mapping relationship from the attached detection object to the branch detection object. For cases where the same state event corresponds to multiple detection objects, the system writes all detection objects into the participation object set of the state event and establishes an internal participation mapping based on the temporal order or data source relationship of the state data of these detection objects within the state event. The system then summarizes the above mapping results into an object mapping set.

[0146] Specifically, based on the object mapping set, state events that correspond to the same detection object multiple times in the fault evidence chain are aggregated, and repeated participation relationships are established according to the order of the state events in the fault evidence chain to obtain the object participation set, which specifically includes:

[0147] The system groups all records in the object mapping set according to the detected object identifier. For each detected object, the system retrieves all state events corresponding to that detected object in the fault evidence chain and reads the link node number, link type, order, change start position, change end position, change duration, and state data type of these state events. If the same detected object corresponds to only one state event, the system records it as a single-participation object; if the same detected object corresponds to two or more state events, the system aggregates these state events into a set of repeated participation events for the same detected object. The system sorts the state events according to their sequential position in the fault evidence chain. When the same object appears multiple times in the main evidence chain, the system establishes a main chain repeat participation relationship for that object according to the main chain node number. When the same object appears in both the main evidence chain and the auxiliary evidence chain, the system records its main and auxiliary chain repeat participation relationship and indicates its position in the main evidence chain, its position in the auxiliary evidence chain, and which common or different state event connects them. When the same object appears in multiple auxiliary evidence chains, the system establishes a branch repeat participation relationship according to the attachment nodes and branch order of the auxiliary evidence chains.

[0148] The system associates the state data types in which the same detection object participates multiple times. For example, if a cable joint detection object first appears in a partial discharge state event and then in a temperature state event, the system records the two participation relationships of this detection object as a repeated participation sequence from the partial discharge state event to the temperature state event. Similarly, if a secondary measurement and control unit first appears in an acquisition anomaly state event and then in a communication state event, the system records it as a repeated participation sequence of measurement and control states. When the same detection object participates multiple times in the same type of state event, the system records the number of repetitions and the repetition interval; when the same detection object participates multiple times in different types of state events, the system records the state type conversion relationship, ultimately forming an object participation set.

[0149] Specifically, based on the object participation set, a correspondence is established between detected objects in different power supply areas through common state events, and a cross-regional transmission relationship is established based on the main propagation relationship and branch propagation relationship in the fault evidence chain, resulting in a regional transmission set, which specifically includes:

[0150] The system reads the area identifier corresponding to each detection object. The area identifier can be an incoming line area, busbar area, switchgear area, outgoing line area, cable joint area, secondary monitoring and control area, cabinet environment area, or a pre-configured area number based on power supply circuits, feeder sections, cabinet intervals, or power distribution zones. The system categorizes the detection objects participating in the cluster according to their area identifiers and establishes a correspondence between the detection objects and power supply areas. For detection objects spanning multiple areas, the system records their primary area and associated areas; for detection objects with primary and secondary correspondence, the system records the correspondence between the primary equipment area and the secondary monitoring and control area. The system reads common state events in the fault evidence chain and determines whether the detection objects corresponding to the common state events belong to different power supply areas. If the same common state event is associated with detection objects in different power supply areas, or if adjacent common state events correspond to detection objects in different power supply areas, the system establishes a correspondence between these power supply areas. This correspondence includes the common state event identifier, starting area, target area, starting detection object, target detection object, and the position of the common state event in the fault evidence chain.

[0151] The system constructs cross-regional transmission relationships based on the main propagation and branch propagation relationships in the fault evidence chain. For continuous associations between adjacent state events in the main evidence chain, if the detection object corresponding to the previous state event and the detection object corresponding to the next state event are located in different power supply areas, the system establishes a continuous cross-regional transmission relationship and records the transmission direction, transmission start point, transmission end point, continuous association identifier, and the corresponding main chain node position. For differential state events derived from common state events in the auxiliary evidence chain, if the detection object corresponding to the differential state event is located in a power supply area different from the power supply area attached to the common state event, the system establishes a branch cross-regional transmission relationship and records the attached common state event, differential state event, main chain area, branch area, auxiliary chain node position, and branch direction. If a power supply area establishes corresponding relationships with other power supply areas through multiple common state events, the system retains multiple regional transmission records and arranges them according to the chronological order in the fault evidence chain. If multiple auxiliary evidence chains extend from the same common state event to different areas, a one-to-many cross-regional branch transmission structure is established; if the branches of multiple areas eventually converge to the same main chain common state event, a many-to-one cross-regional convergence transmission structure is established, forming a regional transmission set.

[0152] Specifically, based on the participation mapping relationship, repeated participation relationship, and cross-regional transfer relationship of each detected object in the regional transfer set, a global state association relationship between each detected object is constructed to obtain the state perception result, which includes:

[0153] The system establishes a global state association model using detected objects as basic nodes. Each detected object node corresponds to a detected object identifier, and is further associated with the detected object type, its power supply region, the corresponding state data type, the number of state events it participates in, and the number of times it participates repeatedly. The system establishes sequential association edges between detected objects based on the preceding and subsequent detected objects recorded in the object mapping set; establishes repeated participation identifiers within the same detected object based on the repeated participation relationships recorded in the object participation set; and establishes cross-regional association edges between detected objects in different power supply regions based on the continuous cross-regional transmission relationships and branch cross-regional transmission relationships recorded in the region transmission set. When constructing the global state association, the system maintains the main chain and auxiliary chain structure in the fault evidence chain. For detected object associations originating from the main evidence chain, the system marks them as continuous propagation associations; for detected object associations originating from the auxiliary evidence chain, the system marks them as branch propagation associations; and for the same detected object appearing in both the main and auxiliary evidence chains, the system marks it as a cross-participation association. If two detected objects have both a sequential mapping of state events and a cross-regional transmission relationship, the system merges them into a composite association and records its corresponding state events and evidence chain source. If a detected object appears in multiple regional propagation paths, the system records it as a multi-path participating node and generates a state perception result. The state perception result includes detected object participation order data, detected object repeated participation data, cross-regional propagation data, and global state association topology data. Specifically, detected object participation order data indicates the sequential position of each detected object in the fault evidence chain; detected object repeated participation data indicates the repeated appearance of the same detected object in different state events and different link positions; cross-regional propagation data indicates the state propagation path formed between detected objects in different power supply areas through common state events, mainline propagation relationships, and branch propagation relationships; and global state association topology data indicates the overall relationship between detected object nodes, state event nodes, regional nodes, and various associated edges.

[0154] Embodiments of the present invention also provide a primary and secondary fusion ring network box global state perception system, the system comprising:

[0155] The acquisition module is used to acquire the status dataset of multiple detection objects in the fusion ring network box within a continuous acquisition period;

[0156] The transmission module is used to extract the starting point, ending point and continuation process of each state data from the state dataset, and combine state data with continuous change relationship into state events to obtain state transmission process data.

[0157] The coupling module is used to identify the correlation strength within each state transmission process based on the state transmission process data, and obtain the state coupling value.

[0158] The fault module is used to identify the first evidence cluster and the second evidence cluster based on the state coupling value, and to compare the common state events and different state events between the first evidence cluster and the second evidence cluster to obtain fault evolution evidence data.

[0159] The evolution module is used to identify the degree to which the fault deviates from the continuous propagation law during the fault evolution process based on fault evolution evidence data, and to obtain the evolution offset value;

[0160] The evidence chain module is used to continuously connect and cross-connect fault evolution evidence data according to the evolution offset value, construct the main evidence chain and the auxiliary evidence chain, and obtain the fault evidence chain.

[0161] The perception module is used to obtain state perception results by statistically analyzing the participation order, repeated participation times, and cross-regional transmission relationships of each detected object in the fault evidence chain.

[0162] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0163] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0164] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0165] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for full-domain state perception of a ring network box using a primary and secondary fusion method, characterized in that, The method includes: Obtain the status dataset of multiple detection objects in the fusion ring network box during a continuous acquisition period; Based on the state dataset, extract the starting point, ending point and continuation process of each state data, and combine state data with continuous change relationship into state events to obtain state transmission process data. Based on the state transition process data, the correlation strength within each state transition process is identified to obtain the state coupling value; The first evidence cluster and the second evidence cluster are identified based on the state coupling value, and the common state events and different state events between the first evidence cluster and the second evidence cluster are compared to obtain fault evolution evidence data. Based on fault evolution evidence data, the degree to which the fault deviates from the continuous propagation law during the fault evolution process is identified, and the evolution offset value is obtained. Based on the evolution offset value, the fault evolution evidence data are continuously linked and cross-connected to construct the main evidence chain and the auxiliary evidence chain, thus obtaining the fault evidence chain. The state perception results are obtained by statistically analyzing the participation order, repeated participation times, and cross-regional transmission relationships of each detected object in the fault evidence chain.

2. The method for full-domain state perception of a primary and secondary fusion ring network box according to claim 1, characterized in that, Based on the state dataset, the starting point, ending point, and duration of change for each state data point are extracted. State data with continuous change relationships are combined into state events to obtain state transmission process data, including: Based on the state dataset, determine the start position, end position, and duration of change for each state data to obtain the change description dataset; Based on the change description dataset, compare the continuous change relationship of the change duration interval between each change description dataset and the continuity relationship between the start position and the end position of the change. Associate the change description datasets that satisfy the continuous continuity relationship to obtain the change association set. By combining multiple change description data into state events based on the continuous change relationships of each change association set, and maintaining the succession relationship between each state event, a state event set is obtained. Based on the set of state events, state events with succession relationships are connected according to the connection relationship between the detected objects to obtain state transmission process data.

3. The method for full-domain state perception of a primary and secondary fusion ring network box according to claim 2, characterized in that, Based on the state transition process data, the correlation strength within each state transition process is identified to obtain the state coupling value, including: Based on the state transmission process data, the continuity and completeness of state events over time are identified to obtain the continuity span term; the tightness of the sequential relationship between state events is identified to obtain the continuity density term; and the degree of correlation of the state transmission process across multiple detection objects is identified to obtain the object coverage term. By fusing the continuous span term, the continuity density term, and the object coverage term, the degree of correlation between the state transmission process in terms of continuity, continuity, and coverage is identified, and the state coupling value is obtained.

4. The method for full-domain state perception of a primary and secondary fusion ring network box according to claim 3, characterized in that, Based on the state coupling value, a first evidence cluster and a second evidence cluster are identified. By comparing the common and differing state events between the first and second evidence clusters, fault evolution evidence data is obtained, including: Based on the state coupling value, state transmission processes with continuous change trends are grouped into the first state set, and state transmission processes with discontinuous change trends are grouped into the second state set. Based on the first state set, state transmission processes with the same state events and continuous connections between detected objects are combined into a first evidence cluster; based on the second state set, state transmission processes with different state events and continuous connections between detected objects are combined into a second evidence cluster. Based on the first evidence cluster and the second evidence cluster, state events that exist simultaneously in both the first and second evidence clusters are identified as common state events, and state events that exist only in one of the evidence clusters are identified as differential state events, thus obtaining a set of differential events. Based on the set of differential events, continuous correlations are established between evidence based on common state events, and branch correlations are established between evidence based on differential state events. The evolutionary relationships between state events are then reconstructed according to the continuous and branch correlations to obtain fault evolution evidence data.

5. The method for full-domain state perception of a primary and secondary fusion ring network box according to claim 4, characterized in that, Based on fault evolution evidence data, the degree to which the fault deviates from the continuous propagation pattern during the fault evolution process is identified, and the evolutionary offset value is obtained, including: Based on the fault evolution evidence data, the degree of continuity of common state events in the first and second evidence clusters is identified to obtain the continuity maintenance term; the degree of branch expansion of differential state events in the first and second evidence clusters is identified to obtain the branch deviation term; and the degree of propagation deviation of branch associations relative to continuous associations is identified to obtain the association offset term. By fusing the continuous maintenance term, branch deviation term, and associated offset term, the degree of deviation from the continuous propagation law during the fault evolution process is identified, and the evolution offset value is obtained.

6. The method for full-domain state perception of a primary and secondary fusion ring network box according to claim 5, characterized in that, Based on the evolutionary offset values, the fault evolution evidence data are continuously concatenated and cross-linked to construct the main evidence chain and auxiliary evidence chain, resulting in the fault evidence chain, which includes: By arranging the fault evolution evidence data in ascending order of evolution offset values ​​and maintaining the evolutionary order among the fault evolution evidence data, an evolution sequence set is obtained. Based on the evolution sequence set, fault evolution evidence data with continuously changing evolution offset values ​​are continuously chained together, and adjacent fault evolution evidence data with common state events are connected to form the main evidence chain. Based on the evolution sequence set, the fault evolution evidence data where the evolution offset value jumps are cross-linked, and the fault evolution evidence data with different state events are linked to the position of the common state event in the main evidence chain to obtain the auxiliary evidence chain. By fusing and connecting the auxiliary evidence chain with the main evidence chain according to the corresponding common state events, the fault evolution evidence data are ensured to maintain the main propagation relationship and branch propagation relationship, thus obtaining the fault evidence chain.

7. The method for full-domain state perception of a primary and secondary fusion ring network box according to claim 6, characterized in that, By statistically analyzing the participation order, repeated participation frequency, and cross-regional transmission relationships of each detected object in the fault evidence chain, the state perception results are obtained, including: Based on the fault evidence chain, each state event is mapped to the detection object that participated in that state event, and the participation mapping relationship between the detection objects is established according to the order of each state event in the fault evidence chain, thus obtaining the object mapping set; Based on the object mapping set, state events that correspond to the same detection object multiple times in the fault evidence chain are aggregated, and repeated participation relationships are established according to the order of the state events in the fault evidence chain to obtain the object participation set. Based on the object participation set, a correspondence is established between the detection objects in different power supply areas through common state events, and a cross-regional transmission relationship is established based on the main propagation relationship and branch propagation relationship in the fault evidence chain, thus obtaining the regional transmission set; Based on the participation mapping relationship, repeated participation relationship and cross-regional transmission relationship of each detected object in the regional transfer set, the global state association relationship between each detected object is constructed to obtain the state perception result.

8. A primary and secondary integrated ring network box global status perception system, characterized in that, The system is used to perform the method as described in any one of claims 1 to 7, the system comprising: The acquisition module is used to acquire the status dataset of multiple detection objects in the fusion ring network box within a continuous acquisition period; The transmission module is used to extract the starting point, ending point and continuation process of each state data from the state dataset, and combine state data with continuous change relationship into state events to obtain state transmission process data. The coupling module is used to identify the correlation strength within each state transmission process based on the state transmission process data, and obtain the state coupling value. The fault module is used to identify the first evidence cluster and the second evidence cluster based on the state coupling value, and to compare the common state events and different state events between the first evidence cluster and the second evidence cluster to obtain fault evolution evidence data. The evolution module is used to identify the degree to which the fault deviates from the continuous propagation law during the fault evolution process based on fault evolution evidence data, and to obtain the evolution offset value; The evidence chain module is used to continuously connect and cross-connect fault evolution evidence data according to the evolution offset value, construct the main evidence chain and the auxiliary evidence chain, and obtain the fault evidence chain. The perception module is used to obtain state perception results by statistically analyzing the participation order, repeated participation times, and cross-regional transmission relationships of each detected object in the fault evidence chain.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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