Method and system for collaborative processing of multi-source heterogeneous data of distribution box

Abstract

The application provides a power distribution box multi-source heterogeneous data collaborative processing method and system, relates to the technical field of data processing, and comprises the following steps: setting the same data of adjacent collection time points as a longitudinal connection relationship, setting different source data as a horizontal reference relationship, taking a collection time point at which state data changes as a window reference point, identifying the time sequence alignment degree of different source data in the same state traction window, obtaining a time sequence fitting value, dividing the state traction window into a complete fitting window and a broken fitting window, determining the change starting point, change continuation position and change termination position in adjacent reconstructed fitting windows, identifying the change correlation degree of different source data between adjacent reconstructed fitting windows, obtaining a cross-source connection value, determining the guide source, follow-up source and confirmation source of a basic data set, and obtaining a multi-source data collaborative result. The application enables multi-source heterogeneous data to form a collaborative analysis result around the same state change process.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for collaborative processing of multi-source heterogeneous data from distribution boxes. Background Technology

[0002] With the development of intelligent power distribution equipment and digital operation and maintenance technologies, distribution boxes are gradually integrating various data acquisition units such as current sensors, voltage sensors, temperature and humidity sensors, smart meters, and image acquisition devices to obtain equipment operating status information. Existing technologies typically receive data uploaded from different data sources through edge gateways or data processing terminals, parse it according to the corresponding data protocols, and then uniformly store the parsed data on a backend platform. This data is then used for status monitoring, anomaly identification, and result display, achieving centralized management and digital analysis of multi-source heterogeneous data from the distribution box.

[0003] However, in fault monitoring scenarios involving poor contact at distribution box terminals, existing technologies may lack a collaborative processing mechanism for multi-source heterogeneous data due to differences in sampling periods and data structures from different data sources. This makes it difficult to establish a correspondence between temperature data, current data, and image feature data within a unified time dimension. When the terminal is in the early stages of a fault, with only localized temperature rise and minimal current change, the system may not be able to fully utilize the correlation features between multi-source data for judgment, easily leading to a decrease in the accuracy of abnormal state identification. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for collaborative processing of multi-source heterogeneous data in distribution 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 collaborative processing of multi-source heterogeneous data from a distribution box, the method comprising:

[0007] Acquire current, temperature, and status data from the distribution box to obtain the basic dataset;

[0008] Based on the basic dataset, the same data at adjacent acquisition times are set as a vertical continuation relationship, and adjacent data from different sources at adjacent acquisition times are set as a horizontal reference relationship, resulting in three-source parallel chain data;

[0009] Based on the three-source parallel chain data, the acquisition time when the state data changes is used as the window reference point, and the current data and temperature data are selected forward and backward as the preceding reference data and the following response data to obtain the state traction window set.

[0010] Based on the state-guided window set, the temporal alignment degree of data from different sources within the same state-guided window is identified to obtain the temporal splicing value.

[0011] Based on the temporal splicing value, the state traction window is divided into a complete splicing window and a broken splicing window. The missing data sources in the broken splicing window are identified and used as borrowing sources to obtain the reconstructed splicing window.

[0012] Based on the reconstructed interlocking window, the starting point, continuation position, and termination position of the changes in current data, temperature data, and state data in adjacent reconstructed interlocking windows are determined to obtain the cross-source change chain.

[0013] Based on the cross-source change chain, the degree of change correlation between data from different sources in adjacent reconstruction splicing windows is identified, and the cross-source inheritance value is obtained.

[0014] Based on the cross-source acceptance value, the guiding source, following source, and confirmation source of the basic dataset are determined, and the data changes of the guiding source, following source, and confirmation source are determined to obtain the multi-source data collaboration results.

[0015] Furthermore, based on the three-source parallel chain data, using the acquisition time when the state data changes as the window reference point, and selecting current data and temperature data forward and backward as the preceding reference data and the following response data, a state-driven window set is obtained, including:

[0016] Based on the three-source parallel chain data, the data location where the state changes is determined as the state change location, and the acquisition time of the state change location is determined as the window reference time, thus obtaining the reference time data;

[0017] Based on the reference time data, the current data and temperature data adjacent to the window reference time in the current data column and temperature data column are read before the window reference time, and arranged in order of the acquisition time closest to the window reference time to obtain the preceding reference data.

[0018] Based on the reference time data, the current data and temperature data adjacent to the window reference time are read from the current data column and temperature data column after the window reference time, and arranged in the order of the acquisition time being far away from the window reference time to obtain the post-response data.

[0019] By writing the preceding reference data to the window position before the window reference time, writing the state change content to the window position at the window reference time, and writing the subsequent response data to the window position after the window reference time, the state-driven window data is obtained.

[0020] Furthermore, based on the state-driven window set, the temporal alignment degree of data from different sources within the same state-driven window is identified to obtain a temporal splicing value, including:

[0021] Based on the proportion of current and temperature data before the window reference time, the degree of forward correspondence between different sources of data before the window reference time is identified to obtain forward placeholders; based on the proportion of current and temperature data after the window reference time, the degree of backward correspondence between different sources of data after the window reference time is identified to obtain backward placeholders.

[0022] Based on the coverage ratio of current and temperature data, the overall coverage completeness of data from different sources around the moment of state change is identified, and the front and back coverage terms are obtained; by identifying the degree of positional offset of data from different sources within the window, the time series constraint terms are obtained.

[0023] By fusing forward placeholders, backward placeholders, front and back overlays, and temporal constraints, the temporal alignment degree of data from different sources within the same state traction window is identified, resulting in a temporal splicing value.

[0024] Furthermore, based on the temporal splicing values, the state traction window is divided into a complete splicing window and a broken splicing window. The missing data sources in the broken splicing window are identified and used as borrowing sources to obtain the reconstructed splicing window, including:

[0025] By defining the state-driven window whose temporal splicing value meets the preset splicing threshold as a complete splicing window, and vice versa, the state-driven window is defined as a broken splicing window, thus obtaining window classification data;

[0026] Based on the window classification data, the missing data positions in the fractured interlocking window are read, and the data source of the missing data positions is identified to obtain the source data of the borrowing position.

[0027] Based on the source data of the candidate borrowing, the same data as the source data of the candidate borrowing is read in the complete interlocking window before and after the fracture interlocking window, and sorted according to the interval relationship between the acquisition time and the reference time of the fracture interlocking window to obtain candidate borrowing data.

[0028] The supplementary data with the smallest interval to the reference time of the fractured interlocking window among the candidate borrowed data is written into the missing data position, and the interval between the supplementary data and the reference time of the window is identified to obtain the reconstructed interlocking window.

[0029] Furthermore, based on the reconstructed interlocking window, the starting point, continuation position, and termination position of the changes in current data, temperature data, and state data within adjacent reconstructed interlocking windows are determined, resulting in a cross-source change chain, including:

[0030] Based on the reconstructed interlocking window, read the changes in current data, temperature data, and status data in adjacent reconstructed interlocking windows, and determine the window position where the first change occurs as the starting point of the change to obtain the starting point data;

[0031] Based on the starting data, identify the data positions in adjacent reconstructed splicing windows that have the same continuous trend of change, and determine the data positions with the continuous trend of change as the change continuation positions to obtain the continuation data;

[0032] Based on the continuous data, the window position where the data change ends is determined as the change termination position, and the termination data is obtained;

[0033] The starting point data, continuing data, and ending data are connected according to the order of change between different data, and the window correspondence between different data sources is identified to obtain the cross-source change chain.

[0034] Furthermore, based on the cross-source change chain, the degree of change correlation between data from different sources in adjacent reconstruction splicing windows is identified to obtain the cross-source inheritance value, including:

[0035] Based on the proportion of changes in current data, temperature data, and state data, the degree of synchronous continuity of data from different sources in the cross-source change chain is identified, and continuity correlation terms are obtained; by identifying the degree of continuous acceptance of changes in data from different sources in the cross-window propagation process, acceptance correlation terms are obtained.

[0036] Based on the chronological order of different data change starting points, the degree of consistency in the triggering order of different data changes is identified to obtain sequential correlation terms; based on the proportion of change break points in the cross-source change chain, the degree of positional offset and directional deviation of data from different sources in the cross-source change chain is identified to obtain constraint correlation terms.

[0037] By integrating continuation associations, succession associations, sequential associations, and constraint associations, the degree of association between different data changes between adjacent reconstruction and fusion windows, and cross-source succession values ​​are obtained.

[0038] Furthermore, based on the cross-source acceptance value, the guiding source, following source, and confirmation source of the basic dataset are determined, and the data changes in the guiding source, following source, and confirmation source are identified to obtain the multi-source data collaboration results, including:

[0039] By identifying cross-source change chains whose cross-source acceptance values ​​meet a preset acceptance threshold as associated data, the data source that first changes in the associated data is identified and determined as the guiding source, guiding data is obtained;

[0040] Based on the guidance data, read the data sources that change after the guidance source and maintain a window correspondence with the guidance source, and determine the data sources that have a continuous change succession relationship with the guidance source as the follow-up sources to obtain the follow-up data;

[0041] Based on the follow-up data, read the data source that forms the state change at the end of the cross-source change chain in the associated data and mark it as the confirmation source to obtain the confirmation data;

[0042] The changes in the guiding data, follow-up data, and confirmation data are correlated according to the order of change, and the window correspondence between different data sources is identified to obtain multi-source data collaboration results.

[0043] Secondly, a multi-source heterogeneous data collaborative processing system for distribution boxes, the system comprising:

[0044] The data acquisition module is used to acquire current data, temperature data, and status data from the distribution box to obtain the basic dataset;

[0045] The parallel chain module is used to set the same data at adjacent acquisition times as a vertical continuation relationship and set adjacent data from different sources at adjacent acquisition times as a horizontal reference relationship, so as to obtain three-source parallel chain data.

[0046] The window module is used to select current data and temperature data as the preceding reference data and the following response data based on the data from the three-source parallel chain, using the acquisition time when the state data changes as the window reference point, to obtain the state-driven window set.

[0047] The timing module is used to identify the timing alignment degree of data from different sources within the same state traction window based on the state traction window set, and to obtain the timing splicing value.

[0048] The reconstruction module is used to divide the state traction window into a complete splicing window and a broken splicing window based on the temporal splicing value, identify the missing data sources in the broken splicing window and use them as borrowing sources to obtain the reconstructed splicing window;

[0049] The cross-source chain module is used to determine the starting point, continuation position, and termination position of the changes in current data, temperature data, and state data in adjacent reconstruction interlocking windows based on the reconstruction interlocking window, and to obtain the cross-source change chain;

[0050] The inheriting module is used to identify the degree of change correlation between data from different sources in adjacent reconstruction splicing windows based on the cross-source change chain, and obtain the cross-source inheriting value;

[0051] The collaboration module is used to determine the guiding source, following source, and confirmation source of the basic dataset based on the cross-source acceptance value, and to determine the data changes of the guiding source, following source, and confirmation source to obtain the multi-source data collaboration results.

[0052] The above-described solution of the present invention has at least the following beneficial effects:

[0053] This invention uses the acquisition moment when the state data changes as a window reference point, and selects current and temperature data as preceding reference data and subsequent response data, respectively. This transforms the state change from a single data record into a windowed data organization center. By forming local data segments centered on the state change moment, the change positions of the state data, the preceding and following segments of the current data, and the preceding and following segments of the temperature data are organized into the same window structure. This rearranges the data, originally distributed at different positions in the three-source parallel chain, into window data surrounding the same reference moment. The current and temperature data are no longer processed solely according to their respective acquisition order, but rather categorized into preceding reference data and subsequent response data according to their relationship with the state change moment. This window data provides input for subsequent identification of the temporal embedding relationship of multi-source data within the same window.

[0054] This invention obtains temporal embedding values ​​by identifying the degree of temporal alignment of data from different sources within the same state traction window, thus transforming the temporal correspondence between multi-source data within the window into a calculable data indicator. The distribution, positional integrity, and temporal proximity of data from different sources within the window may vary. By quantifying these differences, the correspondence between data from different sources within the same state traction window is no longer merely an implicit positional state within the window structure, but is instead used as a temporal embedding value as a criterion for subsequent classification and reconstruction. This allows the temporal distribution, source coverage, and data embedding relationships within the window to enter the computational process, providing data for distinguishing between complete and fragmented embedding windows.

[0055] This invention divides the state-driven window into complete and broken interlocking windows using temporal interlocking values. It identifies the missing data sources in broken interlocking windows and designates them as borrowing sources, ensuring that window data undergoes structural state identification and missing source labeling before entering cross-window change analysis. For broken interlocking windows, the system determines the missing data source and marks it as a borrowing source, pinpointing the missing data to a specific source rather than simply representing null values ​​or abnormal windows. This transforms windows that would otherwise be difficult to use directly for cross-source change analysis due to missing sources or temporal breaks into data objects with filler identifiers and reconstructed structures, providing a continuous window basis for identifying the start, continuation, and termination points of changes.

[0056] This invention transforms the change process of multi-source data from single-point data differences into a chain-like change record across windows by determining the starting point, continuation position, and termination position of changes in current, temperature, and state data within adjacent reconstruction interlocking windows. By extracting the change segments within adjacent reconstruction interlocking windows and connecting these segments from different sources into a cross-source change chain according to window correspondence, a structured change trajectory containing the starting point, continuation position, and termination position is formed. This allows subsequent processing to directly read the sequential, continuous, and termination relationships between changes from different sources. The state-driven window is converted into a cross-source change chain data structure, providing a chain-like input for subsequent cross-source carryover value calculations.

[0057] This invention obtains cross-source continuity values ​​by identifying the degree of correlation between changes in data from different sources within adjacent reconstructed embedded windows. This transforms the cross-window change relationships between current, temperature, and state data into data indicators that can participate in subsequent classification. By converting the relationships such as change connections, window correspondences, source connections, and change continuations in the cross-source change chain into cross-source continuity values, the dynamic correlations between data from different sources are quantified or indexed. These cross-source continuity values ​​serve as the basis for subsequently determining the guiding source, the following source, and the confirming source. This allows the system to no longer rely solely on data values ​​at a single moment or independent changes from a single source during data processing. Instead, it organizes results based on cross-window and cross-source change continuity relationships, transforming the chain-like structure of change correlations between multi-source data into comparable results. Attached Figure Description

[0058] Figure 1 This is a flowchart of a method for collaborative processing of multi-source heterogeneous data in a distribution box, provided in an embodiment of the present invention. Detailed Implementation

[0059] 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.

[0060] like Figure 1 As shown, embodiments of the present invention propose a method for collaborative processing of multi-source heterogeneous data in a distribution box, the method comprising:

[0061] Acquire current, temperature, and status data from the distribution box to obtain the basic dataset;

[0062] Based on the basic dataset, the same data at adjacent acquisition times are set as a vertical continuation relationship, and adjacent data from different sources at adjacent acquisition times are set as a horizontal reference relationship, resulting in three-source parallel chain data;

[0063] Based on the three-source parallel chain data, the acquisition time when the state data changes is used as the window reference point, and the current data and temperature data are selected forward and backward as the preceding reference data and the following response data to obtain the state traction window set.

[0064] Based on the state-guided window set, the temporal alignment degree of data from different sources within the same state-guided window is identified to obtain the temporal splicing value.

[0065] Based on the temporal splicing value, the state traction window is divided into a complete splicing window and a broken splicing window. The missing data sources in the broken splicing window are identified and used as borrowing sources to obtain the reconstructed splicing window.

[0066] Based on the reconstructed interlocking window, the starting point, continuation position, and termination position of the changes in current data, temperature data, and state data in adjacent reconstructed interlocking windows are determined to obtain the cross-source change chain.

[0067] Based on the cross-source change chain, the degree of change correlation between data from different sources in adjacent reconstruction splicing windows is identified, and the cross-source inheritance value is obtained.

[0068] Based on the cross-source acceptance value, the guiding source, following source, and confirmation source of the basic dataset are determined, and the data changes of the guiding source, following source, and confirmation source are determined to obtain the multi-source data collaboration results.

[0069] In this embodiment of the invention, current data, temperature data, and status data from a distribution box are acquired to obtain a basic dataset, providing a unified input for subsequent chain organization, window construction, temporal embedding, and cross-source change analysis. Based on the basic dataset, the same data at adjacent acquisition times are set as a vertical continuation relationship, and adjacent data from different sources at adjacent acquisition times are set as a horizontal reference relationship, resulting in three-source parallel chain data. The vertical continuation relationship preserves the change order of data from the same source, while the horizontal reference relationship preserves the corresponding positions of data from different sources between adjacent acquisition times. Based on the three-source parallel chain data, the acquisition time when the status data changes is used as the window reference point, and current data and temperature data are selected forward and backward as the preceding reference data and the following response data, respectively, to obtain a status-driven window set, providing structured input for subsequent judgment of the temporal correspondence of data from different sources within the same window. Based on the status-driven window set, the temporal alignment degree of data from different sources within the same status-driven window is identified to obtain a temporal embedding value, representing the embedding status of multi-source data in the time dimension within the same status-driven window.

[0070] Based on the temporal splicing values, the state-driven window is divided into complete splicing windows and broken splicing windows. The missing data sources in broken splicing windows are identified and used as borrowing sources to obtain reconstructed splicing windows. This allows the breakage problem to be located at a specific data source, providing structural conditions for windows with missing data or temporal breaks to continue participating in subsequent processing. Based on the reconstructed splicing windows, the starting point, continuation position, and termination position of changes in current, temperature, and state data in adjacent reconstructed splicing windows are determined, resulting in cross-source change chains. These chains represent the occurrence, continuation, and termination positions of changes in data from different sources, enabling subsequent processing to be based on changes in these chains. The transformation process is calculated; based on the cross-source change chain, the degree of change correlation between different source data in adjacent reconstruction splicing windows is identified, and the cross-source acceptance value is obtained, which represents the connection status of changes in different source data between adjacent reconstruction splicing windows, so that the role of each data source in the collaborative change process can be determined based on this indicator; based on the cross-source acceptance value, the guiding source, following source, and confirming source of the basic dataset are determined, and the data change content of the guiding source, following source, and confirming source is determined, so as to obtain the multi-source data collaboration result, which represents the data relationship of three different data in the same collaborative change process, so that the backend system can directly call the multi-source collaboration result.

[0071] This involves acquiring current, temperature, and status data from the distribution box to obtain a basic dataset, which specifically includes:

[0072] The data processing terminal establishes data communication relationships with the current acquisition unit, temperature acquisition unit, and status acquisition unit within the distribution box. The current acquisition unit collects the current values ​​of the target circuit, the corresponding branch of the terminal block, or the incoming / outgoing circuit within the distribution box. The temperature acquisition unit collects the temperature values ​​of the terminal block, busbar connection, the environment inside the box, or preset temperature measurement locations. The status acquisition unit collects the operating status of the distribution box, the status of the terminal block, the status of switches, the status of alarms, the status of image recognition, or the working status of equipment. Each acquisition unit generates raw data records according to its own sampling period and sends these raw data records to the data processing terminal. After receiving the raw data records, the data processing terminal configures a data source identifier, a data type identifier, a collection time identifier, and a device location identifier for each data record. The data source identifier distinguishes whether the data comes from the current acquisition unit, the temperature acquisition unit, or the status acquisition unit; the data type identifier indicates whether the data is current data, temperature data, or status data; the collection time identifier records the collection time corresponding to the data; and the device location identifier indicates the location of the distribution box, circuit, terminal, or measurement point corresponding to the data. For data from different acquisition units but belonging to the same distribution box or the same target wiring area, the data processing terminal classifies them into the same processing object according to the device location identifier.

[0073] The data processing terminal formats the received data. For current data, it formats it into data items including current value, acquisition time, loop number, and source identifier; for temperature data, it formats it into data items including temperature value, acquisition time, temperature measurement location, and source identifier; for status data, it formats it into data items including status value, status category, acquisition time, and source identifier. If data uploaded by different acquisition units uses different field names, units, or encoding methods, it is converted into a unified field structure before being written into the basic dataset, ensuring that each data entry includes at least the data source, data type, acquisition time, and data content. The data processing terminal timestamps the current, temperature, and status data according to the acquisition time, retaining the original acquisition time for each data entry and not forcing data from different sources to be rewritten to the same sampling time. For multiple data entries received at the same or similar times, their source identifiers and data type identifiers are retained separately, allowing them to be distinguished and read in subsequent processing. For data with upload delays, the acquisition time is used as the sorting criterion, not just the receiving time, ensuring that the basic dataset reflects the time order of on-site acquisition. The data processing terminal writes current data, temperature data, and status data into the same dataset to form a basic dataset.

[0074] Based on the basic dataset, data from adjacent acquisition times are set as vertical continuation relationships, and data from different sources adjacent to adjacent acquisition times are set as horizontal reference relationships, resulting in three-source parallel chain data, specifically including:

[0075] The data processing terminal reads current data, temperature data, and status data from the basic dataset according to data type, and sorts each type of data based on the acquisition time. For current data, a current data sequence is formed in chronological order of acquisition time; for temperature data, a temperature data sequence is formed in chronological order of acquisition time; and for status data, a status data sequence is formed in chronological order of acquisition time. Within the same data type, the data processing terminal establishes a vertical continuation relationship. For two current data points at adjacent acquisition times in the current data sequence, the preceding and following current data points are set as consecutive; for two temperature data points at adjacent acquisition times in the temperature data sequence, the preceding and following temperature data points are set as consecutive; and for two status data points at adjacent acquisition times in the status data sequence, the preceding and following status data points are set as consecutive. This vertical continuation relationship is represented by a preceding pointer, a following pointer, an adjacent index, a linked list relationship field, or a time sequence number field, enabling subsequent processing to read data from the same data source at both the preceding and following acquisition times. Simultaneously, the data processing terminal establishes horizontal reference relationships between different data types. Taking the acquisition time of a certain data record as a reference, it searches for data records adjacent to that acquisition time in other data type sequences and sets them as horizontal reference objects. For example, for current data at a certain acquisition time, it reads the temperature data and status data adjacent to that acquisition time and establishes a reference relationship between the current data and the temperature and status data; for temperature data at a certain acquisition time, it reads the current data and status data adjacent to that acquisition time and establishes a reference relationship between the temperature data and the current and status data; for status data at a certain acquisition time, it reads the current data and temperature data adjacent to that acquisition time and establishes a reference relationship between the status data and the current and temperature data.

[0076] When establishing lateral reference relationships, if data from different sources do not have exactly the same acquisition time, the reference object is determined according to the proximity of the acquisition times. The data processing terminal uses the data record with the closest acquisition time as the lateral reference data, or it can use data records adjacent to the current acquisition time as lateral reference data. In cases where multiple adjacent data exist, their relative positional relationship with the current data is preserved, ensuring that the lateral reference relationship records not only the reference object but also the time direction of the reference object relative to the current data. The data processing terminal organizes the current data sequence, temperature data sequence, and state data sequence in chronological order, forming a three-source parallel chain data. This three-source parallel chain data includes the vertical links within the three types of data themselves and the lateral reference links between different data types.

[0077] In a preferred embodiment of the present invention, based on the three-source parallel chain data, using the acquisition time when the state data changes as the window reference point, current data and temperature data are selected forward and backward as the preceding reference data and the following response data to obtain a state-driven window set, including:

[0078] Based on the three-source parallel chain data, the data location where the state changes is determined as the state change location, and the acquisition time of the state change location is determined as the window reference time, thus obtaining the reference time data;

[0079] Based on the reference time data, the current data and temperature data adjacent to the window reference time in the current data column and temperature data column are read before the window reference time, and arranged in order of the acquisition time closest to the window reference time to obtain the preceding reference data.

[0080] Based on the reference time data, the current data and temperature data adjacent to the window reference time are read from the current data column and temperature data column after the window reference time, and arranged in the order of the acquisition time being far away from the window reference time to obtain the post-response data.

[0081] By writing the preceding reference data to the window position before the window reference time, writing the state change content to the window position at the window reference time, and writing the subsequent response data to the window position after the window reference time, the state-driven window data is obtained.

[0082] In this embodiment of the invention, based on the three-source parallel chain data, the data location where the state changes is determined as the state change location, and the acquisition time of the state change location is determined as the window reference time, thus obtaining reference time data. The state change location is identified and reference time data is generated, clarifying the window reference point. Based on the reference time data, current and temperature data adjacent to the window reference time are read from the current and temperature data columns before the window reference time, and arranged in order of acquisition time closest to the window reference time to obtain preceding reference data. This preceding reference data represents the most recent data distribution of current and temperature before the state change, providing input for identifying the data state before the state change. Based on the reference time data, at the window reference time... After a certain moment, the current and temperature data adjacent to the window reference time are read from the current and temperature data columns and arranged in order of the acquisition time being furthest from the window reference time to obtain the post-response data, which represents the continuous occurrence position of data from different sources after the state change, so that the direction of change, duration or response process of current and temperature after the state change can be read in subsequent readings. By writing the preceding reference data into the window position before the window reference time, writing the state change content into the window position at the window reference time, and writing the post-response data into the window position after the window reference time, the state-pulling window data is obtained, so that subsequent calculations can read the positions of data from different sources relative to the state change time within the same window.

[0083] Specifically, based on the three-source parallel chain data, the data location where the state changes is determined as the state change location, and the acquisition time of the state change location is determined as the window reference time, thus obtaining the reference time data, which specifically includes:

[0084] The data processing terminal reads the data links corresponding to the state data from the three-source parallel chain data and compares the state data at adjacent acquisition times sequentially according to the vertical continuity of the state data. During the comparison, the data processing terminal reads the data content of the current state data and the data content of the state data at the previous acquisition time, and determines whether there are changes in the state value, state category, state identifier, or state code. When the current state data changes relative to the previous state data, the position of the current state data in the three-source parallel chain data is determined as the state change position. This state change position includes not only the data content of the state data itself, but also its sequence number in the state data link, acquisition time, source identifier, and horizontal reference relationship with current data and temperature data. The data processing terminal reads the acquisition time corresponding to the state change position and determines this acquisition time as the window reference time. The state change position, the window reference time, the state content before and after the state change, and the link relationship of the state change position in the three-source parallel chain data are all written into the reference time data.

[0085] Specifically, based on the reference time data, current and temperature data adjacent to the window reference time are read from the current and temperature data columns before the window reference time, and arranged in order of acquisition time closest to the window reference time to obtain the preceding reference data, which specifically includes:

[0086] The data processing terminal reads the window reference time from the reference time data and locates the corresponding state change position in the three-source parallel chain data. The data processing terminal then enters the current data column and the temperature data column respectively, searching for data records along their respective vertical continuation relationships before the window reference time. For the current data column, it reads the current data whose acquisition time is earlier than and adjacent to the window reference time; for the temperature data column, it reads the temperature data whose acquisition time is earlier than and adjacent to the window reference time. All read data retains the original acquisition time, data content, source identifier, and time interval relative to the window reference time. If the sampling periods of the current data and temperature data are different, the preceding data is determined according to the temporal proximity of each data column to the window reference time. That is, the data in the current data column that is closest to and earlier than the window reference time is used as the current preceding data close to the reference time, and the data in the temperature data column that is closest to and earlier than the window reference time is used as the temperature preceding data close to the reference time. If multiple preceding data need to be read, the reading continues forward along the vertical continuation relationship, and the sequential distance of each data relative to the window reference time is recorded. The data processing terminal arranges the preceding current and temperature data in order of their acquisition time closest to the window reference time. Specifically, data acquired closer to the window reference time is placed closer to the window reference position in the preceding reference data; conversely, data acquired further from the window reference time is placed further away from the window reference position. The arranged current and temperature data together constitute the preceding reference data and are correlated with the reference time data.

[0087] Specifically, based on the reference time data, the current and temperature data adjacent to the window reference time are read from the current and temperature data columns after the window reference time, and arranged in order of their acquisition time being furthest from the window reference time to obtain the subsequent response data, which specifically includes:

[0088] The data processing terminal reads the window reference time from the reference time data and locates the state change position in the three-source parallel chain data. It then enters the current data column and temperature data column respectively, searching for data records along their respective vertical continuation relationships after the window reference time. For the current data column, it reads current data whose acquisition time is later than the window reference time but adjacent to it; for the temperature data column, it reads temperature data whose acquisition time is later than the window reference time but adjacent to it. Each read data record retains its acquisition time, data content, source identifier, and the time interval between it and the window reference time. If the current and temperature data have different sampling periods, they are read according to the order of data later than the window reference time in their respective data columns. The subsequent current and temperature data closest to the window reference time are first identified as the subsequent data close to the reference time. If further reading is needed, the search continues along their respective vertical continuation relationships until the data range required for window construction is obtained. For multiple subsequent data records, the data processing terminal records their subsequent order relative to the window reference time, ensuring that the subsequent data reflects the data development process after the state change occurs. The data processing terminal arranges the subsequent current and temperature data in order of their acquisition time being furthest from the window reference time. In other words, the subsequent data acquired closest to the window reference time is placed in the region of the subsequent response data closest to the window reference time, and subsequent data acquired further away from the window reference time are arranged in regions further away from the window reference time. Through this arrangement, the subsequent response data forms a temporal order from closest to furthest within the window structure, establishing a correspondence with the reference time data.

[0089] In a preferred embodiment of the present invention, based on a set of state-guided windows, the temporal alignment degree of data from different sources within the same state-guided window is identified to obtain a temporal splicing value, including:

[0090] Based on the proportion of current and temperature data before the window reference time, the degree of forward correspondence between different sources of data before the window reference time is identified to obtain forward placeholders; based on the proportion of current and temperature data after the window reference time, the degree of backward correspondence between different sources of data after the window reference time is identified to obtain backward placeholders.

[0091] Based on the coverage ratio of current and temperature data, the overall coverage completeness of data from different sources around the moment of state change is identified, and the front and back coverage terms are obtained; by identifying the degree of positional offset of data from different sources within the window, the time series constraint terms are obtained.

[0092] By fusing forward placeholders, backward placeholders, front and back overlays, and temporal constraints, the temporal alignment degree of data from different sources within the same state traction window is identified, resulting in a temporal splicing value.

[0093] In this embodiment of the invention, based on the proportion of current and temperature data before the window reference time, the forward correspondence of different source data before the window reference time is identified, resulting in a forward occupancy term, which indicates the occupancy ratio and correspondence of different source data in the window before the state change; based on the proportion of current and temperature data after the window reference time, the backward correspondence of different source data after the window reference time is identified, resulting in a backward occupancy term, which indicates whether current and temperature data form a backward response relationship within the same window after the state change; based on the front-to-back coverage ratio of current and temperature data, the overall coverage integrity of different source data around the state change time is identified, resulting in a front-to-back coverage term, which describes whether the same source data forms a front-to-back coverage across the state change time; by identifying the front-to-back position offset of different source data within the window, a timing constraint term is obtained, which describes whether the data exists but is misaligned within the window; by fusing the forward occupancy term, backward occupancy term, front-to-back coverage term, and timing constraint term, the timing alignment degree of different source data within the same state traction window is identified, resulting in a timing splicing value, so that the timing alignment state of different source data within the same state traction window is quantitatively expressed.

[0094] The formula for calculating the timing splicing value is as follows:

[0095] ,

[0096] in, Indicates the first The timing splicing value corresponding to each state traction window Indicates the first The proportion of current data occupancy before the window reference time in each state traction window. Indicates the first The proportion of temperature data occupancy before the window reference time in each state traction window. Indicates the first The proportion of current data occupancy after the window reference time in each state traction window. Indicates the first The proportion of temperature data occupancy after the window reference time in each state traction window. Indicates the first The coverage ratio of current data in each state traction window relative to the window reference time. Indicates the first The coverage ratio of temperature data within a state traction window relative to the window reference time. Indicates the first The proportion of missing items in each state traction window. This represents the normalized position concentration of the current data prior to the window reference time. This represents the normalized positional concentration of temperature data prior to the window reference time. This represents the normalized position concentration of the current data after the window reference time. This represents the normalized positional concentration of temperature data after the window reference time. Indicates the current data in the first... Fluctuation in the acquisition interval within each state traction window Indicates the temperature data in the first... Fluctuation in the acquisition interval within each state traction window This represents the minimum coefficient.

[0097] Specifically, based on the state-driven window set, the temporal alignment degree of data from different sources within the same state-driven window is identified to obtain the temporal splicing value, which includes:

[0098] The formula for calculating the temporal chimerism value is used to calculate the first... The formula comprehensively calculates the temporal alignment of current and temperature data within a state-driven window around the window's reference time. The formula consists of a numerator and a denominator. The numerator represents the effective embedding degree of data from different sources within the window in terms of forward and backward occupancy and front-to-back coverage. The denominator represents the constraint effect of missing data, positional offsets, and acquisition interval fluctuations on the temporal embedding state. By calculating the ratio of the numerator to the denominator, the more complete the data occupancy, the more sufficient the front-to-back coverage, the smaller the positional offset, and the more stable the acquisition interval within the state-driven window, the more accurately the corresponding temporal embedding value reflects the complete temporal correspondence of the window. Conversely, when there are missing data, uneven front-to-back distribution, or acquisition interval disturbances within the window, the formula uses the denominator to constrain the embedding state of the window.

[0099] Specifically, the forward occupancy portion of the formula is composed of the occupancy ratios of current data and temperature data prior to the window reference time. This is achieved by multiplying the current data occupancy ratio and temperature data occupancy ratio prior to the window reference time, then dividing by their sum and the minimum value. This can represent the shared occupancy relationship of current and temperature data in the front region of the window. This structure does not examine the existence of a single source of data, but rather requires that both current and temperature data have valid occupancy before the reference time; when either source's occupancy is insufficient, the product term decreases, thus reducing the contribution corresponding to the forward occupancy. The denominator is added... This is to avoid calculation anomalies when the occupancy ratio is zero or close to zero, ensuring the formula is applicable to windows with missing data. The backward occupancy part is used to calculate the corresponding occupancy relationship of current and temperature data after the window's reference time. The formula calculates the occupancy ratio by multiplying the current data occupancy ratio and the temperature data occupancy ratio after the window's reference time, and then normalizes the sum of the two, so that the coexistence of the two types of data in the backward region is expressed as a unified calculation term. The response data after the state change needs to be supported by data from both current and temperature sources. If only one source appears in the backward region, the backward correspondence of the window is incomplete. The formula distinguishes this one-sided occupancy situation through the product structure.

[0100] The front-to-back coverage component is composed of the front-to-back coverage ratios of current data and temperature data relative to the window reference time. The formula multiplies the current coverage ratio by the temperature coverage ratio and normalizes the sum of the two. This indicates whether both types of data can form front-to-back coverage across the window reference time. If the current or temperature data exists only before or only after the window reference time, it cannot fully reflect the front-to-back data distribution around the state change time. Only when both types of data form coverage before and after the reference time does this term constitute a relatively complete cohesive contribution. The normalized position concentration of current data and temperature data before the window reference time is used to represent the degree to which the two types of data are close to the reference time in the preceding region. The normalized position concentration of current data and temperature data after the window reference time is used to represent the degree of concentration of the two types of data in the backward window position in the following region. The formula takes the absolute value of the difference between the position concentration of current data and temperature data and forms a structure of 1 minus the average difference, so that the closer the positions of data from different sources are within the window, the more complete their temporal constraint contribution. If both types of data exist, but one is concentrated near the reference time and the other is concentrated far from the reference time, the positional difference between the two will be identified by the formula and reflected in the temporal splicing value.

[0101] The denominator of the formula mainly consists of the missing ratio, the position offset, and the fluctuation of the acquisition interval. The missing ratio is used to represent the missing data. The overall degree of data gaps within a state-driven window. When current data, temperature data, or related positions are missing within a window, the gap ratio increases, increasing the denominator and thus reflecting the structural breakage of the window in the calculation results. The position offset is jointly represented by the difference in the normalized position concentration of the current and temperature data before and after, used to constrain the misaligned distribution of data from different sources within the window. The acquisition interval fluctuation corresponds to the acquisition interval disturbance of the current and temperature data within the window, respectively, used to represent the temporal fluctuations caused by unstable sampling periods or uneven data arrival intervals from different sources.

[0102] In a preferred embodiment of the present invention, the state traction window is divided into a complete splicing window and a broken splicing window based on the temporal splicing value. The missing data sources in the broken splicing window are identified and used as borrowing sources to obtain the reconstructed splicing window, including:

[0103] By defining the state-driven window whose temporal splicing value meets the preset splicing threshold as a complete splicing window, and vice versa, the state-driven window is defined as a broken splicing window, thus obtaining window classification data;

[0104] Based on the window classification data, the missing data positions in the fractured interlocking window are read, and the data source of the missing data positions is identified to obtain the source data of the borrowing position.

[0105] Based on the source data of the candidate borrowing, the same data as the source data of the candidate borrowing is read in the complete interlocking window before and after the fracture interlocking window, and sorted according to the interval relationship between the acquisition time and the reference time of the fracture interlocking window to obtain candidate borrowing data.

[0106] The supplementary data with the smallest interval to the reference time of the fractured interlocking window among the candidate borrowed data is written into the missing data position, and the interval between the supplementary data and the reference time of the window is identified to obtain the reconstructed interlocking window.

[0107] In this embodiment of the invention, a state-guided window whose temporal splicing value meets a preset splicing threshold is determined as a complete splicing window, and vice versa, the state-guided window is determined as a broken splicing window. Window classification data is obtained, which can prioritize the identification of data windows that need to be reconstructed, providing a data foundation for subsequent identification of missing sources and execution of fill-in operations. Based on the window classification data, the missing data positions in the broken splicing window are read, and the data sources of the missing data positions are identified to obtain the source data to be borrowed, transforming the window breakage state from abstract splicing insufficiency to explicit missing data source information. Based on the source data to be borrowed, data with the same source as the source to be borrowed is read in the complete splicing windows before and after the broken splicing window, and sorted according to the interval relationship between the acquisition time and the reference time of the broken splicing window to obtain candidate borrowing data, providing multiple optional fill-in objects for the reconstruction operation. The fill-in data with the smallest interval between the candidate borrowing data and the reference time of the broken splicing window is written into the missing data position, and the interval between the fill-in data and the window reference time is identified to obtain the reconstructed splicing window, enabling the broken splicing window with missing sources to regain a complete data structure, providing a window foundation for subsequent cross-source change chain construction.

[0108] Specifically, based on the source data of the candidate graft, data identical to the source data are read within the complete interlocking windows before and after the fracture interlocking window, and sorted according to the interval between the acquisition time and the reference time of the fracture interlocking window to obtain candidate graft data, which specifically includes:

[0109] The data processing terminal reads the broken-fit window identifier, the reference time of the broken-fit window, the location of the missing data, and the data source type of the missing data location from the source data to be borrowed. It uses window data already identified as complete fit windows as the candidate data reading range, ensuring that the candidate data originates from window structures that have already undergone temporal fit judgment. Centered on the broken-fit window, the data processing terminal searches for complete fit windows in the window classification data sequentially forward and backward. When searching forward, the data processing terminal reads window records before the broken-fit window and determines whether their window category is a complete fit window according to the window reference time from nearest to farthest. When searching backward, the data processing terminal reads window records after the broken-fit window and similarly determines whether their window category is a complete fit window according to the window reference time from nearest to farthest. Once a complete fit window is read, the data processing terminal continues to check whether there is a data source identical to the source to be borrowed within that complete fit window. If the source to be borrowed is current data, then the current data is read in the complete nested window; if the source to be borrowed is temperature data, then the temperature data is read in the complete nested window; if the source to be borrowed is status data, then the corresponding status data is read in the complete nested window.

[0110] The data processing terminal reads data from the same source as the data to be borrowed, retaining the original data content, acquisition time, source identifier, complete splicing window identifier, and window position within the complete splicing window. If multiple data points from the same source exist within a complete splicing window, the data processing terminal can read each data point separately and record them all as candidate data. For complete splicing windows preceding a broken splicing window, the acquisition time of the candidate data is usually earlier than the reference time of the broken splicing window; for complete splicing windows following a broken splicing window, the acquisition time of the candidate data is usually later than the reference time of the broken splicing window. When recording candidate data, the data processing terminal simultaneously indicates its source direction, i.e., whether the candidate data comes from a complete splicing window preceding or following a broken splicing window. The data processing terminal calculates the time interval between the acquisition time of each candidate data point and the reference time of the broken splicing window. This time interval can be determined by the absolute time difference between the candidate data acquisition time and the reference time of the broken splicing window, or by the difference in window sequence number, sampling sequence number, or link distance between the candidate data's window and the broken splicing window. If an absolute time difference is used, the data processing terminal converts data later than the reference time and data earlier than the reference time into a non-negative time interval. If a window sequence difference is used, the distance between the preceding and following windows relative to the fracture-and-fit window is converted into a window interval. The data processing terminal sorts the candidate data in ascending order of interval. The smaller the interval, the closer the acquisition time of the candidate data is to the reference time of the fracture-and-fit window, and the higher its ranking in the candidate borrowing data; the larger the interval, the farther the time distance between the candidate data and the reference time of the fracture-and-fit window, and the lower its ranking. If multiple candidate data have the same interval with the reference time of the fracture-and-fit window, the data processing terminal performs secondary sorting based on the front-to-back direction of the candidate data window relative to the fracture-and-fit window, the position of the candidate data within its window, the consistency of the candidate data source identifier, or the temporal fit value of its complete fit window. The data processing terminal writes the candidate data content, acquisition time, time interval, source direction, complete fit window identifier, window position, and sorting sequence number into the candidate borrowing data.

[0111] Specifically, the supplementary data with the smallest interval to the reference time of the fractured interlocking window from the candidate borrowed data is written into the missing data position, and the interval between the supplementary data and the reference time of the window is identified to obtain the reconstructed interlocking window, which includes:

[0112] The data processing terminal reads the sorted candidate data sequence from the candidate borrowing data and selects the data with the highest sorting position as the filler data. This filler data is the data with the smallest interval between itself and the reference time of the fracture embedding window, and its data source type is consistent with the source of the data to be borrowed. Before selecting the filler data, the data processing terminal can also check whether the source identifier of the filler data is consistent with the source identifier corresponding to the missing data position, and check whether the filler data has valid data content, acquisition time, and window information. After verification, the filler data is determined as the data object to be written to the missing data position of the fracture embedding window. The data processing terminal reads the missing data position in the fracture embedding window and writes the data content of the filler data into the missing data position. The data processing terminal retains the original acquisition time and original source identifier of the filler data, and adds a filler identifier to the position in the fracture embedding window. After writing, the data processing terminal calculates the interval between the acquisition time of the filler data and the reference time of the fracture embedding window, and writes this interval as the filler interval information into the current window record. This filler interval information is used to represent the time relationship between the filler data and the reference time of the current window. If the supplementary data comes from a complete interlocking window before the fracture interlocking window, the supplementary interval information can be recorded as a forward interval; if the supplementary data comes from a complete interlocking window after the fracture interlocking window, the supplementary interval information can be recorded as a backward interval. In the case of multiple missing data positions within a fracture interlocking window, the data processing terminal reads the corresponding source data for each missing data position and selects the supplementary data from the candidate borrowing data that matches the missing position and has the smallest interval with the fracture interlocking window's reference time.

[0113] After each missing data position is filled, the data processing terminal records the interval between the filled data and the window reference time, the source window of the filled data, and the direction of the source of the filled data. If multiple missing data positions correspond to the same source of data to be borrowed, the data processing terminal can select candidate borrow data sequentially according to the order of the missing data positions in the window. Alternatively, it can write the same filled data to multiple corresponding missing data positions while ensuring consistency of source and minimizing the interval, and record the writing position for each position. After the missing data positions in the fractured interlocking window are filled, the data processing terminal reorganizes the source distribution, window position, acquisition time, and filled interval information of the current data, temperature data, and status data in the window. The system updates the window category of the original fractured interlocking window to a reconstructed interlocking window and configures a reconstruction identifier for the window. The reconstruction identifier indicates that the window has been formed by the fractured interlocking window after candidate borrowing and filled data writing. The reconstructed interlocking window includes not only the data already existing in the original window, but also the filled data written to the missing data positions, and the filled interval information used to explain the source and time relationship of the filled data.

[0114] In a preferred embodiment of the present invention, based on the reconstructed interlocking window, the starting point, continuation position, and termination position of the changes in current data, temperature data, and state data within adjacent reconstructed interlocking windows are determined to obtain a cross-source change chain, including:

[0115] Based on the reconstructed interlocking window, read the changes in current data, temperature data, and status data in adjacent reconstructed interlocking windows, and determine the window position where the first change occurs as the starting point of the change to obtain the starting point data;

[0116] Based on the starting data, identify the data positions in adjacent reconstructed splicing windows that have the same continuous trend of change, and determine the data positions with the continuous trend of change as the change continuation positions to obtain the continuation data;

[0117] Based on the continuous data, the window position where the data change ends is determined as the change termination position, and the termination data is obtained;

[0118] The starting point data, continuing data, and ending data are connected according to the order of change between different data, and the window correspondence between different data sources is identified to obtain the cross-source change chain.

[0119] In this embodiment of the invention, based on the reconstructed interlocking window, the data changes of current data, temperature data, and state data in adjacent reconstructed interlocking windows are read, and the window position where the first change occurs is determined as the starting point of the change, thus obtaining the starting point data. This allows for the identification of the starting position of a data source change in a multi-window data sequence, providing a starting reference for the identification of the change continuation position and the change termination position. Based on the starting point data, the data positions in adjacent reconstructed interlocking windows that continuously exhibit the same change trend are identified, and these positions are determined as the change continuation positions, thus obtaining the continuation data. This indicates that the change of a data source is not an isolated change at a single moment, but rather continues to exist in adjacent reconstructed interlocking windows. Based on the continuation data, the window position where the data change ends is determined as the change termination position, thus obtaining the termination data. This allows for the determination of the complete boundary of a change from start to end, avoiding the situation where the change chain only contains start and continuation information but lacks termination conditions. The starting point data, continuation data, and termination data are connected according to the order of change between different data, and the window correspondence between different data sources is identified, resulting in a cross-source change chain. This allows subsequent steps to directly read the change sequence and window correspondence between data from different sources, providing input for calculating the cross-source carrying capacity value.

[0120] Specifically, based on the reconstructed interlocking window, the changes in current data, temperature data, and status data within adjacent reconstructed interlocking windows are read, and the window position where the first change occurs is determined as the starting point of the change, obtaining the starting point data, which specifically includes:

[0121] The data processing terminal reads multiple reconstructed embedded windows in chronological order according to the window reference time, and uses adjacent reconstructed embedded windows as window comparison objects. Each reconstructed embedded window includes current data, temperature data, and status data, and retains the source identifier, acquisition time, window position, padding identifier, and padding interval information for each data. Before performing change recognition, the data processing terminal first matches the corresponding positions of similar data in adjacent reconstructed embedded windows according to the data source, so that the current data in the current window corresponds to the current data in the previous window, the temperature data in the current window corresponds to the temperature data in the previous window, and the status data in the current window corresponds to the status data in the previous window. The data processing terminal reads the data content of current data, temperature data, and status data in adjacent reconstructed embedded windows respectively, and determines whether there is a data change in the current window relative to the previous window according to preset change recognition rules. For current data, it can determine whether there is a change based on the difference in current value, the direction of change, the magnitude of change, or the change in the numerical range; for temperature data, it can determine whether there is a change based on the difference in temperature value, the rising or falling trend, the magnitude of change, or the change in the temperature range; for status data, it can determine whether there is a change based on the status identifier, status category, status code, or whether the status content has switched. When a source data condition is met for the first time in the current reconstructed nested window relative to the previous reconstructed nested window, the data processing terminal determines the window position of the source data in the current reconstructed nested window as the starting point of the change. The data processing terminal determines whether this change is the first change in the current change chain. If the same change content or the same change trend is not identified in the consecutive windows before the source data, and the change occurs for the first time in the current window, the current window position is marked as the starting point of the change. If the same change trend already exists in the previous window, the current window is no longer used as a new starting point of the change, but enters the subsequent continuation position judgment, recording the window number, window reference time, data source type, data change content, data before the change, data after the change, window position, and whether it contains supplementary data, etc., to form the starting point data.

[0122] Specifically, based on the starting point data, data locations in adjacent reconstructed splicing windows that exhibit the same continuous trend of change are identified, and these locations are determined as the continuity of change locations, thus obtaining the continuity data, which includes:

[0123] The data processing terminal uses the reconstruction and embedding window corresponding to the starting data as the starting window and continues to read subsequent adjacent reconstruction and embedding windows in the window time sequence. For the data source corresponding to the starting data, the data processing terminal reads the data content of the same source in subsequent windows and determines whether it maintains the same trend or state of change as the change content in the starting data. For example, when the starting data is an increasing current data, if the current data in subsequent windows continues to rise, remains in the maintenance range after the rise, or still meets the upward trend condition, it is determined that the same trend of change continues; when the starting data is an increasing temperature data, if the temperature data in subsequent windows continues to rise, remains in the range after the rise, or continues to meet the temperature rise trend condition, it is determined that the temperature change continues; when the starting data is a change in state data from normal to abnormal, if the state data in subsequent windows remains in the abnormal state or the relevant state indicator has not recovered, it is determined that the state change continues. The data processing terminal not only determines whether data values ​​are completely identical, but also judges whether they belong to the same change process based on the change trend, change direction, change range, or state persistence. For windows involving supplementary data, the data processing terminal simultaneously reads the supplementary identifier and supplementary interval information, and uses them as supplementary records in the continuation judgment, so that the data at that position in the continuation data retains whether it was generated by supplementation and its time relationship with the window's reference time. If the data from the same source in a subsequent window still meets the same change trend as the starting data, the corresponding data position in that window is marked as the change continuation position. The data processing terminal continuously reads multiple adjacent reconstructed nested windows and judges whether the change is continuous for each one. As long as the data from the same source in the subsequent window continues to meet the same change trend, that window position is added to the continuation data. When the data from the same source in a window no longer meets the same change trend, the system stops recording the continuation position of that change segment and moves to the change termination position identification.

[0124] Specifically, based on the continuous data, the window position where the data change ends is determined as the change termination position, and the termination data is obtained, including:

[0125] The data processing terminal continues to read subsequent windows in the time sequence of the reconstructed embedded window and compares the data sources corresponding to the continuing data with similar data. When the data from the same source in a subsequent window no longer meets the change trend, change direction, change range, or state continuity conditions corresponding to the continuing data, the data processing terminal determines that the change has disappeared or ended at that window position. For current data, if the current value no longer maintains its original change direction, the change amplitude falls back to outside the change conditions, or enters a stable range, the window position can be identified as the current change termination position; for temperature data, if the temperature value no longer maintains a temperature rise or fall trend, the change amplitude falls back, or the temperature change conditions are no longer met, the window position can be identified as the temperature change termination position; for state data, if the state identifier recovers, switches to another state, or no longer maintains the state content corresponding to the starting data, the window position can be identified as the state change termination position. The data processing terminal reads the window number, window reference time, data source type, data content before termination, data content after termination, and the judgment result of the change disappearance at the change termination position. If multiple continuing positions exist before the termination position of a change, the system establishes an association between the termination position and the corresponding starting and continuing data, making the same change process a complete change segment consisting of the starting point, continuing positions, and termination positions. If no continuing position is formed after the starting point of the change, and the next window no longer meets the change conditions, the data processing terminal can still determine the next window or the position where the change disappears as the termination position and form a short change segment record. For windows containing padding data, the data processing terminal retains the padding source and padding interval information and writes it into the auxiliary field of the termination data to generate termination data.

[0126] Specifically, the starting point data, continuing data, and ending data are connected according to the order of change between different data, and the window correspondence between different data sources is identified to obtain a cross-source change chain, which includes:

[0127] The data processing terminal sorts the change segments from each source according to the window reference time and window number. Each change segment includes a change start point, one or more change continuation positions, and a change end point. Within the same data source, the data processing terminal connects the start point data, continuation data, and end point data in chronological order to form a single-source change segment. Then, it connects change segments from different sources across sources according to the order of their change start points, the overlap of their continuation positions, and the correspondence of their end points. The data processing terminal reads the window correspondence between change segments from different sources. If the start point, continuation position, or end point of a current change segment is in the same or adjacent reconstruction nested window as a temperature change segment or state change segment, a window correspondence is established between these change segments. If the change start point of one source is before the change start point of another source, the chronological relationship between them is recorded. If the continuation position of one source and the continuation position of another source appear together in multiple adjacent windows, the continuous correspondence between them is recorded. If the end point of one source is adjacent to the state change position of another source, the end correspondence between them is recorded. The data processing terminal writes the aforementioned same-source connection relationships and cross-source correspondence relationships into a cross-source change chain. This cross-source change chain includes chain nodes and chain edges. The chain nodes represent the start point, continuation position, and termination position of changes from different sources. The chain edges represent the sequential continuation relationships within the same source and the window correspondence relationships between different sources. Each chain node retains the data source type, window number, window reference time, change content, and location identifier; each chain edge retains the connection source, connection direction, window interval, and change sequence.

[0128] In a preferred embodiment of the present invention, based on the cross-source change chain, the degree of change correlation between data from different sources in adjacent reconstruction splicing windows is identified to obtain the cross-source inheritance value, including:

[0129] Based on the proportion of changes in current data, temperature data, and state data, the degree of synchronous continuity of data from different sources in the cross-source change chain is identified, and continuity correlation terms are obtained; by identifying the degree of continuous acceptance of changes in data from different sources in the cross-window propagation process, acceptance correlation terms are obtained.

[0130] Based on the chronological order of different data change starting points, the degree of consistency in the triggering order of different data changes is identified to obtain sequential correlation terms; based on the proportion of change break points in the cross-source change chain, the degree of positional offset and directional deviation of data from different sources in the cross-source change chain is identified to obtain constraint correlation terms.

[0131] By integrating continuation associations, succession associations, sequential associations, and constraint associations, the degree of association between different data changes between adjacent reconstruction and fusion windows, and cross-source succession values ​​are obtained.

[0132] In this embodiment of the invention, based on the proportion of changes in current data, temperature data, and state data, the degree of synchronous continuity of data from different sources in the cross-source change chain is identified, resulting in a continuity correlation term indicating whether current data, temperature data, and state data jointly maintain the change process within adjacent reconstructed nested windows; by identifying the degree of continuous acceptance of changes in data from different sources during cross-window propagation, an acceptance correlation term is obtained, recording whether changes can be transmitted from one source to another along adjacent windows and continue to form subsequent source changes; based on the chronological order between the starting points of different data changes, the degree of consistency in the triggering order of different data changes is identified, resulting in a sequence correlation term, which records the occurrence of changes in the cross-source change chain. The time sequence and source sequence are recorded in a structured manner so that subsequent cross-source inheritance values ​​can include the triggering sequence information between the starting points of change; based on the proportion of change disconnection positions in the cross-source change chain, the degree of positional offset and directional deviation of data from different sources in the cross-source change chain are identified to obtain constraint association terms, which can record whether there are connection interruptions, position delays or directional inconsistencies in data from different sources during cross-window changes; the continuation association terms, inheritance association terms, sequence association terms and constraint association terms are fused to identify the degree of change association between different data in adjacent reconstruction nested windows, and cross-source inheritance values ​​are obtained so that the degree of change association between different data in adjacent reconstruction nested windows forms the data results.

[0133] The formula for calculating the cross-source acceptance value is as follows:

[0134] ,

[0135] in, Indicates the first The cross-source inheritance value corresponding to each cross-source change chain. Indicates the first The percentage of continuous change in current data within a cross-source change chain. Indicates the first The percentage of temperature data variation continuity in a cross-source change chain. Indicates the first The percentage of state data change continuity in a cross-source change chain. This indicates the window coverage ratio between changes in current data and changes in temperature data. This indicates the window coverage ratio between changes in temperature data and changes in state data. This indicates the window coverage ratio between changes in current data and changes in state data. This indicates the sequential overlap between the starting point of the current data change and the starting point of the temperature data change. This indicates the sequential alignment between the starting point of temperature data change and the starting point of state data change. Indicates the first The percentage of breakpoints in a cross-source change chain. This indicates the normalized position of the starting point of the current data change in the cross-source change chain. This indicates the normalized position of the starting point of the temperature data change in the cross-source change chain. This indicates the normalized position of the starting point of the state data change in the cross-source change chain. Indicates the first The number of data locations in a cross-source change chain where the direction of change is inconsistent.

[0136] Specifically, based on the cross-source change chain, the degree of change correlation between data from different sources in adjacent reconstruction splicing windows is identified to obtain the cross-source inheritance value, which includes:

[0137] The formula for calculating the cross-source acceptance value is used to determine the first... The formula comprehensively calculates the continuity of changes, window continuity, order of change start points, and link constraints among current, temperature, and state data in a cross-source change chain. The formula consists of a numerator and a denominator. The numerator represents the effective continuity factors formed by data from different sources in the cross-source change chain, while the denominator represents the constraints imposed on the cross-source continuity relationship by factors such as change disconnection, positional offset, and inconsistent change directions.

[0138] Specifically, the first term in the formula consists of the percentage of continuity of current data changes, the percentage of continuity of temperature data changes, and the percentage of continuity of state data changes. This part uses the product of the three percentages of continuity and the division by the sum of the three to represent the common continuity of current data, temperature data, and state data in the same cross-source change chain. If all three types of data have relatively continuous continuity in the cross-source change chain, the product term makes a valid contribution; if any of the data sources lacks continuity, the product result will decrease accordingly, reflecting the degree of synchronous continuity of data from different sources in the cross-source change chain.

[0139] The second term in the formula consists of the window coverage ratios between current and temperature data changes, temperature and state data changes, and current and state data changes. This part also uses the method of multiplying the three window coverage ratios and normalizing the sum of the three to indicate whether a continuous cross-window coverage relationship can be formed between data changes from different sources. Cross-source coverage does not only determine whether there is a single correspondence between two sources, but also considers the coverage paths between current and temperature, temperature and state, and current and state simultaneously. When window coverage relationships exist between multiple sources, this term forms a relatively complete coverage contribution; when a certain path is missing or the coverage ratio is low, this term decreases accordingly.

[0140] The third term in the formula consists of the sequential alignment between the starting point of current data change and the starting point of temperature data change, and the sequential alignment between the starting point of temperature data change and the starting point of state data change. This part is normalized by multiplying the two sequential alignments and combining their sum, indicating whether there is a continuous connection between the starting points of data changes from different sources. Changes from different data sources in a cross-source change chain usually have different starting point sequences; for example, one source changes first, another source changes later, and then the state data confirms the change. The sequential alignment quantifies this sequential connection between the starting points of change. When the starting points of current, temperature, and state data changes can form a mutually aligned sequential relationship according to the window sequence in the cross-source change chain, this term constitutes a valid sequential contribution.

[0141] The numerator of the formula, 1 minus the average positional offset difference, is used to express the positional consistency of the starting points of data changes from different sources in the cross-source change chain. By reading the normalized positions of the starting points of current data changes, temperature data changes, and state data changes in the cross-source change chain, the positional differences between the current starting point and the temperature starting point, the temperature starting point and the state starting point, and the current starting point and the state starting point are calculated respectively. These positional differences are averaged, and the degree of positional proximity is obtained by subtracting the average positional difference from 1. If the starting points of the three types of data changes are close to each other in the cross-source change chain, the positional difference is small, and this part makes a more complete contribution to the inherited value. If the starting point of a certain source is significantly lagging or advancing, the positional difference increases, and the contribution of this part decreases accordingly.

[0142] The denominator of the formula includes constraints such as the percentage of changing disconnection locations, the difference in the starting point location, and the number of inconsistent directions. The percentage of changing disconnection locations is used to represent the... The proportion of locations in a cross-source change chain where data from different sources fail to form a continuous connection. When there are many disconnections in the cross-source change chain, the denominator increases, constraining the cross-source continuity value. The source start point position difference is composed of the normalized position differences of the start points of current data changes, temperature data changes, and state data changes, used to represent the degree of offset between the start points of different sources. The number of inconsistencies in direction indicates the number of data locations in the cross-source change chain where the change direction is inconsistent, such as the change direction of one source being inconsistent with the change direction of other sources, or the connection direction in the cross-source change chain being inconsistent with the expected continuity direction.

[0143] Data processing terminal from the first The number of change continuation nodes for three types of data is extracted from the cross-source change chain, and the change continuation ratio of current data, temperature data, and state data is calculated respectively. Then, the window connection relationship between change nodes from different sources is read, and the window coverage ratio between current and temperature, temperature and state, and current and state is calculated respectively. The order and normalized position of the change starting points of the three types of data in the cross-source change chain are read, and the sequential alignment and position offset are calculated. Finally, the proportion of change break points and the number of data positions with inconsistent change directions are counted. All the above data are then fused using a formula to obtain the first... The cross-source continuity value corresponding to each cross-source change chain. This cross-source continuity value is used to characterize whether a continuous, well-ordered, and offset-restricted change continuity relationship is formed between different data sources in the cross-source change chain.

[0144] In a preferred embodiment of the present invention, the guiding source, following source, and confirmation source of the basic dataset are determined based on the cross-source acceptance value, and the data changes of the guiding source, following source, and confirmation source are determined to obtain a multi-source data collaboration result, including:

[0145] By identifying cross-source change chains whose cross-source acceptance values ​​meet a preset acceptance threshold as associated data, the data source that first changes in the associated data is identified and determined as the guiding source, guiding data is obtained;

[0146] Based on the guidance data, read the data sources that change after the guidance source and maintain a window correspondence with the guidance source, and determine the data sources that have a continuous change succession relationship with the guidance source as the follow-up sources to obtain the follow-up data;

[0147] Based on the follow-up data, read the data source that forms the state change at the end of the cross-source change chain in the associated data and mark it as the confirmation source to obtain the confirmation data;

[0148] The changes in the guiding data, follow-up data, and confirmation data are correlated according to the order of change, and the window correspondence between different data sources is identified to obtain multi-source data collaboration results.

[0149] In this embodiment of the invention, cross-source change chains whose cross-source acceptance values ​​meet a preset acceptance threshold are identified as associated data. The data source that first changes in the associated data is identified and designated as the guiding source, thus obtaining guiding data. This allows subsequent processing to analyze the response process of other source data around this source, providing a starting point for establishing collaborative relationships between sources. Based on the guiding data, data sources that change after the guiding source and maintain a window correspondence with the guiding source are read, and data sources with a continuous change acceptance relationship with the guiding source are identified as follower sources, thus obtaining follower data. This clarifies which source data changes are subsequent responses to changes in the guiding source, enabling changes to propagate in the cross-source change chain. The process is transformed into a traceable source-response relationship; based on the follow-up data, the data source that forms the state change at the end of the cross-source change chain in the associated data is read and marked as the confirmation source, thus obtaining confirmation data. This assigns a clear source role to the end node in the cross-source change chain, providing a termination node for the organization of the final collaborative result; the data change content of the guiding data, follow-up data, and confirmation data is associated according to the order of change, and the window correspondence between different data sources is identified to obtain the multi-source data collaborative result. The collaborative change process between current data, temperature data, and state data is uniformly expressed, so that the association, response, and confirmation relationships between different source data form a complete data organization result.

[0150] Specifically, by identifying cross-source change chains whose cross-source acceptance values ​​meet a preset acceptance threshold as associated data, and identifying the data source that first changes in the associated data as the guiding source, guiding data is obtained, which specifically includes:

[0151] When the cross-source acceptance value of a cross-source change chain reaches or exceeds a preset acceptance threshold, the data processing terminal identifies the cross-source change chain as associated data and writes it into the associated data set. The associated data represents the relationships of change continuation, window acceptance, sequential correspondence, and constraint satisfaction formed between data from different sources within adjacent reconstruction nested windows. For cross-source change chains that do not reach the preset acceptance threshold, the data processing terminal can retain their original link records but will not use them as objects for subsequent guiding, following, and confirming source identification. The data processing terminal reads all change start nodes in the associated data. Each change start node corresponds to a data source type, window number, window reference time, change content, and data values ​​or status content before and after the change. The data processing terminal sorts the change start points of current data, temperature data, and status data according to the window reference time from earliest to latest. If the change start points of different sources are located in different windows, the change start point with the earlier window reference time is taken as the node that changes first; if the change start points of different sources are located in the same window, the order can be further determined based on their acquisition time within the window, window position, or connection order in the cross-source change chain. The data processing terminal identifies the earliest point of change in the sorting results and reads the corresponding data source type. If the earliest point of change is a current data change, the current data source is identified as the guiding source; if the earliest point of change is a temperature data change, the temperature data source is identified as the guiding source; if the earliest point of change is a state data change, the state data source is identified as the guiding source. The data processing terminal writes the guiding source type, guiding change point, guiding change content, guiding window position, guiding change time, corresponding cross-source change chain identifier, and subsequent succession relationship into the guiding data. The guiding data represents the earliest data source and its change content among the associated data that meets the succession conditions.

[0152] Specifically, based on the guidance data, data sources that change after the guidance source and maintain a window correspondence with the guidance source are read, and data sources with a continuous change relationship with the guidance source are identified as follow-up sources, thus obtaining follow-up data, which specifically includes:

[0153] The data processing terminal reads other source change nodes located after the guiding source along the cross-source change chain connection direction corresponding to the associated data. "Located after the guiding source" means that the window reference time of the other source change node is later than the window reference time of the guiding change starting point, or that it is located in the same window as the guiding change node, following the link position. The data processing terminal determines whether the change node maintains a window correspondence with the guiding source. This window correspondence can be expressed as the change node and the guiding change node being located in the same reconstruction nested window, or in a reconstruction nested window adjacent to the guiding change node, or within the window range covered by the guiding source change continuation interval. If a subsequent source change node has a link edge connection in the cross-source change chain with the guiding change node, or if they have an adjacent window succession relationship, the data processing terminal records this subsequent source change node as a candidate follower node. The data processing terminal further determines whether the candidate follower node has a continuous change succession relationship with the guiding source. The system reads the change continuation position and change termination position of the guiding source, and determines whether the change start point of the candidate follower node appears after the change start point of the guiding source, before the change termination position, or in its adjacent subsequent window; at the same time, it reads the change continuation position of the candidate follower node and determines whether it can form a continuous window connection with the change continuation position of the guiding source along the cross-source change chain. If, after the guiding source changes, the candidate follower node changes in subsequent adjacent windows, and this change can continuously inherit the change process of the guiding source in one or more windows, then the data source corresponding to the candidate follower node is determined as the follower source.

[0154] In cases with multiple candidate follower nodes, the data processing terminal determines the window correspondence and continuous connection relationship between each node and the guiding source, and identifies one or more data sources that meet the conditions as follower sources. If a source changes after the guiding source, but there is no adjacent window relationship between it and the guiding source, or there is a break in the change between them, then the source is not identified as a follower source, or is recorded as a discontinuous response source. The data processing terminal reads the change start point, change continuation position, change termination position, change content, change direction, change window range, and connection path between the follower source and the guiding source. The connection path includes the window connection order, window interval, link direction, and continuous connection length between the changing nodes of the guiding source and the changing nodes of the follower source. The data processing terminal writes the follower source type, follower change content, follower change start point, follower continuation position, follower termination position, window correspondence with the guiding source, and connection path into the follower data. The follower data is used to represent the data source and its change content that changes after the guiding source and forms a continuous change connection relationship with the guiding source.

[0155] Specifically, based on the follow-up data, the data sources that form state changes at the end of the cross-source change chain in the associated data are read and marked as confirmed sources to obtain confirmed data, which specifically includes:

[0156] The data processing terminal reads the associated data identifier, the type of the associated source, the associated change node, and the connection path recorded in the associated data. It then continues reading the end node of the link along the cross-source change chain corresponding to the associated data. The end node is the termination node in the cross-source change chain that no longer extends to other source change nodes, or it can be a state data node that has undergone a state change after being connected to the guiding source and the associated source. The data processing terminal determines whether the end node has formed a state change. If the data source of the end node is a state data source, and its state value, state category, state code, or state identifier has changed relative to the previous window, then the end node is identified as the end node that has formed a state change. If the end node is not a state data source, but it is subsequently connected to a state data change node, the data processing terminal continues reading along the cross-source change chain until it reaches the state change node, and uses this state change node as the state confirmation node. If there are multiple state change nodes in a single piece of associated data, the data processing terminal can select the state change node located at the end of the cross-source change chain and having a continuous connection with the associated source as the confirmation node. Once the data source that causes a state change at the end of the cross-source change chain is determined, the data processing terminal marks this data source as a confirmation source. The confirmation source can be a state data source, used to indicate the data source that forms a state change result at the end of the chain after changes from the guiding source and the following source. The data processing terminal reads the state change content corresponding to the confirmation source, including the state change window, state change time, state before change, state after change, state change category, the position of the state change node in the cross-source change chain, and its connection relationship with the following source. The data processing terminal writes the confirmation source type, confirmation change node, confirmation state change content, confirmation window position, confirmation change time, window correspondence with the following source, and corresponding connection path into the confirmation data. The confirmation data is used to represent the data source and its state change content that causes a state change at the end of the cross-source change chain in the associated data.

[0157] Specifically, the changes in guiding data, follow-up data, and confirmation data are correlated according to the order of change, and the window correspondence between different data sources is identified to obtain multi-source data collaboration results, including:

[0158] The data processing terminal reads the guiding data, following data, and confirmation data corresponding to the same associated data. It sorts the changes in the guiding data, following data, and confirmation data according to the order in which they occurred, using the window reference time as the primary basis and the acquisition time within the window, link connection order, or change node position as secondary basis, to form a data sequence of changes from each source in chronological order. The data processing terminal identifies the window correspondence between the guiding source, following source, and confirmation source. For guiding and following sources, the system reads their change start window, change continuation window, and change termination window, and determines whether there are identical windows, adjacent windows, or consecutive window succession relationships. For following and confirmation sources, the system similarly reads the change node positions between them and determines whether the status change of the confirmation source is located in an adjacent window or at the end of a link after the change of the following source. For nodes with corresponding relationships, the data processing terminal establishes an inter-source association field, recording the associated source, associated window, window interval, succession direction, and connection order. The data processing terminal combines the data changes from the guiding source, following source, and confirmation source according to the source role. The data changes from the guiding source serve as the initial data for the collaborative change process, the data changes from the following source serve as the subsequent data, and the data changes from the confirming source serve as the final confirmation data. During the combination process, the system retains the data before the change, the data after the change, the direction of change, the duration of the change, and the termination of the change for each source. The system also writes the window correspondence between different sources into the same collaborative result structure. This multi-source data collaborative result includes associated data identifiers, cross-source change chain identifiers, the guiding source and its change content, the following source and its change content, the confirming source and its status change content, the order of changes, the window correspondence, the succession path, and the window intervals between each source. Current data, temperature data, and status data are no longer output as isolated change records, but rather as a collaborative structure with source roles, change order, and window succession relationships. This multi-source data collaborative result can serve as input data for subsequent status identification, anomaly analysis, data display, or operation and maintenance processing modules.

[0159] Embodiments of the present invention also provide a multi-source heterogeneous data collaborative processing system for distribution boxes, the system comprising:

[0160] The data acquisition module is used to acquire current data, temperature data, and status data from the distribution box to obtain a basic dataset.

[0161] The parallel chain module is used to set the same data at adjacent acquisition times as a vertical continuation relationship and set adjacent data from different sources at adjacent acquisition times as a horizontal reference relationship, so as to obtain three-source parallel chain data.

[0162] The window module is used to select current data and temperature data as the preceding reference data and the following response data based on the data from the three-source parallel chain, using the acquisition time when the state data changes as the window reference point, to obtain the state-driven window set.

[0163] The timing module is used to identify the timing alignment of data from different sources within the same state traction window based on the state traction window set, and to obtain the timing splicing value.

[0164] The reconstruction module is used to divide the state traction window into a complete splicing window and a broken splicing window based on the temporal splicing value, identify the missing data sources in the broken splicing window and use them as borrowing sources to obtain the reconstructed splicing window;

[0165] The cross-source chain module is used to determine the starting point, continuation position, and termination position of the changes in current data, temperature data, and state data in adjacent reconstruction interlocking windows based on the reconstruction interlocking window, and to obtain the cross-source change chain;

[0166] The inheriting module is used to identify the degree of change correlation between data from different sources in adjacent reconstruction splicing windows based on the cross-source change chain, and to obtain the cross-source inheriting value;

[0167] The collaboration module is used to determine the guiding source, following source, and confirmation source of the basic dataset based on the cross-source acceptance value, and to determine the data changes of the guiding source, following source, and confirmation source to obtain the multi-source data collaboration results.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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 collaborative processing of multi-source heterogeneous data in a distribution box, characterized in that, The method includes: Acquire current, temperature, and status data from the distribution box to obtain the basic dataset; Based on the basic dataset, the same data at adjacent acquisition times are set as a vertical continuation relationship, and adjacent data from different sources at adjacent acquisition times are set as a horizontal reference relationship, resulting in three-source parallel chain data; Based on the three-source parallel chain data, the acquisition time when the state data changes is used as the window reference point, and the current data and temperature data are selected forward and backward as the preceding reference data and the following response data to obtain the state traction window set. Based on the state-guided window set, the temporal alignment degree of data from different sources within the same state-guided window is identified to obtain the temporal splicing value. Based on the temporal splicing value, the state traction window is divided into a complete splicing window and a broken splicing window. The missing data sources in the broken splicing window are identified and used as borrowing sources to obtain the reconstructed splicing window. Based on the reconstructed interlocking window, the starting point, continuation position, and termination position of the changes in current data, temperature data, and state data in adjacent reconstructed interlocking windows are determined to obtain the cross-source change chain. Based on the cross-source change chain, the degree of change correlation between data from different sources in adjacent reconstruction splicing windows is identified, and the cross-source inheritance value is obtained. Based on the cross-source acceptance value, the guiding source, following source, and confirmation source of the basic dataset are determined, and the data changes of the guiding source, following source, and confirmation source are determined to obtain the multi-source data collaboration results.

2. The method for collaborative processing of multi-source heterogeneous data in a distribution box according to claim 1, characterized in that, Based on the three-source parallel chain data, using the acquisition time when the state data changes as the window reference point, and selecting current data and temperature data as the preceding reference data and the following response data, a state-driven window set is obtained, including: Based on the three-source parallel chain data, the data location where the state changes is determined as the state change location, and the acquisition time of the state change location is determined as the window reference time, thus obtaining the reference time data; Based on the reference time data, the current data and temperature data adjacent to the window reference time in the current data column and temperature data column are read before the window reference time, and arranged in order of the acquisition time closest to the window reference time to obtain the preceding reference data. Based on the reference time data, the current data and temperature data adjacent to the window reference time in the current data column and temperature data column are read after the window reference time, and arranged in the order of the acquisition time being far away from the window reference time to obtain the post-response data. By writing the preceding reference data to the window position before the window reference time, writing the state change content to the window position at the window reference time, and writing the subsequent response data to the window position after the window reference time, the state-driven window data is obtained.

3. The method for collaborative processing of multi-source heterogeneous data in a distribution box according to claim 2, characterized in that, Based on the state-driven window set, the temporal alignment degree of data from different sources within the same state-driven window is identified, yielding a temporal splicing value, including: Based on the proportion of current and temperature data before the window reference time, the degree of forward correspondence between different sources of data before the window reference time is identified to obtain forward placeholders; based on the proportion of current and temperature data after the window reference time, the degree of backward correspondence between different sources of data after the window reference time is identified to obtain backward placeholders. Based on the coverage ratio of current and temperature data, the overall coverage completeness of data from different sources around the moment of state change is identified, and the front and back coverage terms are obtained; by identifying the degree of positional offset of data from different sources within the window, the time series constraint terms are obtained. By fusing forward placeholders, backward placeholders, front and back overlays, and temporal constraints, the temporal alignment degree of data from different sources within the same state traction window is identified, resulting in a temporal splicing value.

4. The method for collaborative processing of multi-source heterogeneous data in a distribution box according to claim 3, characterized in that, Based on the temporal splicing values, the state traction window is divided into a complete splicing window and a broken splicing window. The missing data sources in the broken splicing window are identified and used as borrowing sources to obtain the reconstructed splicing window, including: By defining the state-driven window whose temporal splicing value meets the preset splicing threshold as a complete splicing window, and otherwise defining the state-driven window as a broken splicing window, window classification data is obtained. Based on the window classification data, the missing data positions in the fractured interlocking window are read, and the data source of the missing data positions is identified to obtain the source data of the borrowing position. Based on the source data of the candidate borrowing, the same data as the source data of the candidate borrowing is read in the complete interlocking window before and after the fracture interlocking window, and sorted according to the interval relationship between the acquisition time and the reference time of the fracture interlocking window to obtain candidate borrowing data. The supplementary data with the smallest interval to the reference time of the fractured interlocking window among the candidate borrowed data is written into the missing data position, and the interval between the supplementary data and the reference time of the window is identified to obtain the reconstructed interlocking window.

5. The method for collaborative processing of multi-source heterogeneous data in a distribution box according to claim 4, characterized in that, Based on the reconstructed interlocking window, the starting point, continuation position, and termination position of the changes in current data, temperature data, and state data within adjacent reconstructed interlocking windows are determined, resulting in a cross-source change chain, including: Based on the reconstructed interlocking window, read the changes in current data, temperature data, and status data in adjacent reconstructed interlocking windows, and determine the window position where the first change occurs as the starting point of the change to obtain the starting point data; Based on the starting data, identify the data positions in adjacent reconstructed splicing windows that have the same continuous trend of change, and determine the data positions with the continuous trend of change as the change continuation positions to obtain the continuation data; Based on the continuous data, the window position where the data change ends is determined as the change termination position, and the termination data is obtained; The starting point data, continuing data, and ending data are connected according to the order of change between different data, and the window correspondence between different data sources is identified to obtain the cross-source change chain.

6. The method for collaborative processing of multi-source heterogeneous data in a distribution box according to claim 5, characterized in that, Based on the cross-source change chain, the degree of change correlation between data from different sources in adjacent reconstruction splicing windows is identified, and the cross-source inheritance value is obtained, including: Based on the proportion of changes in current data, temperature data, and state data, the degree of synchronous continuity of data from different sources in the cross-source change chain is identified, and continuity correlation terms are obtained; by identifying the degree of continuous acceptance of changes in data from different sources in the cross-window propagation process, acceptance correlation terms are obtained. Based on the chronological order of different data change starting points, the degree of consistency in the triggering order of different data changes is identified to obtain sequential correlation terms; based on the proportion of change break points in the cross-source change chain, the degree of positional offset and directional deviation of data from different sources in the cross-source change chain is identified to obtain constraint correlation terms. By integrating continuation associations, succession associations, sequential associations, and constraint associations, the degree of association between different data changes between adjacent reconstruction and fusion windows, and cross-source succession values ​​are obtained.

7. The method for collaborative processing of multi-source heterogeneous data in a distribution box according to claim 6, characterized in that, Based on the cross-source acceptance values, the guiding source, following source, and confirmation source of the basic dataset are determined, and the data changes in the guiding source, following source, and confirmation source are identified to obtain the multi-source data collaboration results, including: By identifying cross-source change chains whose cross-source acceptance values ​​meet a preset acceptance threshold as associated data, the data source that first changes in the associated data is identified and determined as the guiding source, guiding data is obtained; Based on the guidance data, read the data sources that change after the guidance source and maintain a window correspondence with the guidance source, and determine the data sources that have a continuous change succession relationship with the guidance source as the follow-up sources to obtain the follow-up data; Based on the follow-up data, read the data source that forms the state change at the end of the cross-source change chain in the associated data and mark it as the confirmation source to obtain the confirmation data; The changes in the guiding data, follow-up data, and confirmation data are correlated according to the order of change, and the window correspondence between different data sources is identified to obtain multi-source data collaboration results.

8. A multi-source heterogeneous data collaborative processing system for distribution boxes, 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 data acquisition module is used to acquire current data, temperature data, and status data from the distribution box to obtain a basic dataset. The parallel chain module is used to set the same data at adjacent acquisition times as a vertical continuation relationship and set adjacent data from different sources at adjacent acquisition times as a horizontal reference relationship, so as to obtain three-source parallel chain data. The window module is used to select current data and temperature data as the preceding reference data and the following response data based on the data from the three-source parallel chain, using the acquisition time when the state data changes as the window reference point, to obtain the state-driven window set. The timing module is used to identify the timing alignment of data from different sources within the same state traction window based on the state traction window set, and to obtain the timing splicing value. The reconstruction module is used to divide the state traction window into a complete splicing window and a broken splicing window based on the temporal splicing value, identify the missing data sources in the broken splicing window and use them as borrowing sources to obtain the reconstructed splicing window; The cross-source chain module is used to determine the starting point, continuation position, and termination position of the changes in current data, temperature data, and state data in adjacent reconstruction interlocking windows based on the reconstruction interlocking window, and to obtain the cross-source change chain; The inheriting module is used to identify the degree of change correlation between data from different sources in adjacent reconstruction splicing windows based on the cross-source change chain, and to obtain the cross-source inheriting value; The collaboration module is used to determine the guiding source, following source, and confirmation source of the basic dataset based on the cross-source acceptance value, and to determine the data changes of the guiding source, following source, and confirmation source to obtain the multi-source data collaboration results.

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.

Images