A method, device, and medium for monitoring the lease status of operating assets.

Abstract

This invention discloses a method, device, and medium for monitoring the lease status of operating assets, relating to the field of lease management technology. The method includes: establishing support edges for evidence nodes with consistent event categories, sequential timing, and complementary data sources within adjacent time windows; establishing conflict edges for evidence nodes with overlapping time intervals and opposing event attributes within the same continuous time window; and constructing an operational authenticity evidence graph. The operational authenticity evidence graph is then processed through support accumulation, conflict elimination, and cross-window merging, and support segments maintaining complementary data sources are organized into candidate status fragments. The matching dimensions of the candidate status fragments are mapped and matched with the business format structure, operating time structure, and operational load structure in the lease registration data to obtain the lease status. This invention improves the coherence, relevance, and business adaptability of lease status identification.

Description

Technical Field

[0001] This invention relates to the field of lease management technology, and in particular to a method, device and medium for monitoring the lease status of operating assets. Background Technology

[0002] Monitoring the status of leased operating assets typically revolves around identifying surveillance footage, recording entry and exit records, collecting energy consumption load data, and verifying lease registration. By periodically collecting data on the external appearance of the business premises, internal activities, and equipment operation traces, and combining this with time series analysis and status rule comparison, the system identifies the leased, idle, and abnormal use status of operating assets such as shops, stalls, and booths.

[0003] The above methods tend to focus on the direct judgment of single-source observation results in application. On the one hand, there is a lack of a unified evidence organization method among cross-source monitoring information, making it difficult to form a continuous chain of authenticity judgment between signs of operation and signs of rejection in different time periods. On the other hand, the information on business type, business hours and operating load in lease registration is mostly statically checked with the monitoring results, making it difficult to establish a structured mapping relationship around the continuous operation status, thus affecting the coherence and pertinence of lease status identification. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for monitoring the lease status of operating assets to solve the problems of difficulty in forming a continuous chain of authenticity judgments from cross-source monitoring information and difficulty in establishing a mapping relationship between lease filing structure and monitoring results around the continuous operating status.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for monitoring the lease status of operating assets, comprising:

[0008] Collect external monitoring data, internal monitoring data and operational monitoring data of the same operating asset, establish corresponding relationships according to asset identifiers and align them according to timestamps to generate asset time-series data;

[0009] The asset time-series data is segmented into continuous time windows, and operational events and exclusionary events within each time window are extracted to generate evidence nodes.

[0010] In adjacent time windows, support edges are established for evidence nodes with consistent event categories, sequential timelines, and complementary data sources. In the same continuous time window, conflict edges are established for evidence nodes with overlapping time intervals and opposing event attributes. A business authenticity evidence graph is then constructed.

[0011] The evidence graph of business authenticity is supported by accumulation, conflict elimination and cross-window merging, and the supporting chain segments that maintain complementary data sources are organized into candidate state segments;

[0012] The matching dimensions of the candidate status fragments are mapped and matched with the business format structure, business hours structure, and operating load structure in the lease registration data to obtain the lease status.

[0013] As a preferred embodiment of the method for monitoring the lease status of operating assets according to the present invention, the generation of asset time-series data is specifically as follows:

[0014] Using asset identification as the sole criterion, external monitoring data, internal monitoring data, and operational monitoring data with consistent asset identification are grouped into the same asset identification set.

[0015] Convert the timestamps of external monitoring data, internal monitoring data and operational monitoring data in the same asset identifier set to a unified timing benchmark, and rearrange them into consecutive time positions according to the timestamps;

[0016] External monitoring data, internal monitoring data, and operational monitoring data falling at the same continuous time position are written side by side to the same time position, and multiple continuous time positions are combined to form asset time series data.

[0017] As a preferred embodiment of the method for monitoring the leasing status of operating assets according to the present invention, the extraction of operating events and operating exclusion events within each time window is specifically as follows:

[0018] Starting with the first timestamp of the asset time series data, the asset time series data is segmented by connecting the beginning and end of the data along the timestamp sequence to obtain a continuous time window.

[0019] Within a continuous time window, each monitoring item in the asset time series data is compared to its adjacent time position in time stamp order, and operational events and operational exclusion events are identified.

[0020] In a preferred embodiment of the method for monitoring the lease status of operating assets according to the present invention, the generation of evidence nodes is specifically as follows:

[0021] For each consecutive time window, business events that are adjacent in time location and change in the same direction are spliced ​​together to form consecutive business events.

[0022] For each consecutive time window, adjacent business exclusion events with the same direction of change are spliced ​​together to form consecutive business exclusion events.

[0023] Continuing operations events and continuing operations exclusion events are written into node records according to asset identifier, continuous time window position, start timestamp, end timestamp, data source, and event category, and arranged as evidence nodes in timestamp order.

[0024] As a preferred embodiment of the method for monitoring the lease status of operating assets according to the present invention, the construction of the business authenticity evidence diagram is specifically as follows:

[0025] Within adjacent time windows, identify evidence node pairs that are consistent in event category, sequential in time, and complementary in data source, and generate mutually corroborating evidence node pairs.

[0026] Within the same continuous time window, identify evidence node pairs where time intervals overlap and one corresponds to an operational event while the other corresponds to an operational exclusion event, and generate contradictory evidence node pairs.

[0027] To establish connections between mutually corroborating evidence node pairs, support edges are generated; and to establish connections between contradictory evidence node pairs, conflict edges are generated.

[0028] Using evidence nodes as the node set and supporting edges and conflict edges as the edge set, a business authenticity evidence graph is constructed.

[0029] As a preferred embodiment of the method for monitoring the lease status of operating assets according to the present invention, the generation of candidate status fragments is specifically as follows:

[0030] Extract a sequence of evidence nodes connected continuously by supporting edges from the evidence diagram of business authenticity to form a supporting chain segment;

[0031] Support accumulation, conflict elimination, and cross-window merging are performed on the support chain segments, and support chain segments with complementary data sources are organized into candidate state segments.

[0032] In a preferred embodiment of the method for monitoring the lease status of operating assets according to the present invention, the process of obtaining the lease status is as follows:

[0033] Retrieve the business format structure, operating hours structure, and operational load structure corresponding to the same operating asset from the lease filing data;

[0034] Extract the distribution of business activity categories, time coverage, and load change from the candidate state fragments, and form matching dimensions corresponding to the business format structure, business hours structure, and operating load structure, respectively;

[0035] The matching dimensions are matched with the business format structure, business hours structure, and operating load structure in the lease registration data, and the matching results are combined into the lease status.

[0036] As a preferred embodiment of the leasing status monitoring method for operating assets described in this invention, the support accumulation is to perform time continuity verification and data source mutual verification on the evidence nodes in the support chain segment, and retain the evidence nodes that meet the requirements of consistent event category, time connection and complementary data source in the same support chain segment;

[0037] The conflict removal process involves extracting evidence nodes from the business authenticity evidence graph that have conflicting edge connections with the supporting chain segment, performing conflict checks on evidence nodes with overlapping time intervals and opposing event attributes, and removing the evidence nodes corresponding to the business exclusion event from the supporting chain segment.

[0038] The cross-window merging process involves splicing together the support chain segments that have completed conflict elimination according to the sequential connection relationship between continuous time windows, merging adjacent support chain segments that extend continuously along the support edge.

[0039] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the method for monitoring the lease status of operating assets as described in the first aspect of the present invention.

[0040] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein, when the computer program is executed by a processor, it implements any step of the method for monitoring the lease status of operating assets as described in the first aspect of the present invention.

[0041] The beneficial effects of this invention are as follows: By constructing a business authenticity evidence graph, it achieves the synchronous organization of mutually corroborative and contradictory relationships, which is used to depict the authenticity structure of business activities in the time and source dimensions; by performing support accumulation, conflict elimination, and cross-window merging on the business authenticity evidence graph, and organizing the support chain segments that maintain complementary data sources into candidate state fragments, and then mapping and matching them with the business format structure, business time structure, and operating load structure in the lease filing data, it can support the generation of lease status, thereby achieving the beneficial effects of improving the coherence, relevance, and business adaptability of lease status identification. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart of a method for monitoring the lease status of operating assets.

[0044] Figure 2 A comparison chart of the accuracy of comprehensive lease status under different data source conflict rates.

[0045] Figure 3 This is a comparison chart of the temporal distribution of candidate state segments under different methods.

[0046] Figure 4 This is a comparison chart of the accuracy of multidimensional matching results and comprehensive rental status under different methods. Detailed Implementation

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0050] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a method for monitoring the lease status of operating assets, comprising the following steps:

[0051] S1: Collect external monitoring data, internal monitoring data and operational monitoring data of the same operating asset, establish corresponding relationships according to asset identifiers and align them according to timestamps to generate asset time-series data;

[0052] S1.1: Collect external monitoring data, internal monitoring data, and operational monitoring data for the same operating asset;

[0053] Furthermore, synchronous data collection is performed around the external area, internal area, and operational phases corresponding to the same operating asset. External monitoring data includes entrance / exit access monitoring, facade change monitoring, and surrounding proximity monitoring. Internal monitoring data includes personnel activity monitoring, object interaction monitoring, and area occupancy monitoring. Operational monitoring data includes equipment start / stop monitoring, load change monitoring, and access control trigger monitoring. When each piece of external, internal, and operational monitoring data is generated, an asset identifier, timestamp, monitoring item, monitoring value, and data source marker are simultaneously written, allowing the external, internal, and operational monitoring data to directly enter the subsequent collection process.

[0054] S1.2: Using asset identification as the sole basis for aggregation, external monitoring data, internal monitoring data, and operational monitoring data with consistent asset identification are grouped into the same asset identification set;

[0055] Furthermore, asset identifiers are extracted from external monitoring data, internal monitoring data, and operational monitoring data, and classified and merged using asset identifiers as the sole basis for aggregation. External monitoring data, internal monitoring data, and operational monitoring data with consistent asset identifiers are written into the same aggregation unit. Extraction and merging are repeated for all asset identifiers until all external monitoring data, internal monitoring data, and operational monitoring data have been aggregated. Finally, external monitoring data, internal monitoring data, and operational monitoring data with consistent asset identifiers are aggregated into the same asset identifier set.

[0056] S1.3: Convert the timestamps of external monitoring data, internal monitoring data and operational monitoring data in the same asset identifier set to a unified timing benchmark, and rearrange them into consecutive time positions according to the timestamps;

[0057] Furthermore, all timestamps are extracted from the same set of asset identifiers and converted to the same time starting point and the same time unit to form a unified timing benchmark. The non-zero time intervals between adjacent timestamps under the unified timing benchmark are counted, and the smallest non-zero time interval is used as the continuous time position step. The position corresponding to the first timestamp under the unified timing benchmark is used as the starting continuous time position, and the positions are numbered sequentially along the time direction according to the continuous time position step. External monitoring data, internal monitoring data, and operational monitoring data in the same set of asset identifiers are mapped to their corresponding numbers one by one, and the data is rearranged to continuous time positions according to timestamps.

[0058] S1.4: External monitoring data, internal monitoring data, and operational monitoring data falling at the same continuous time position are written to the same time position in parallel, and multiple continuous time positions are combined to form asset time series data;

[0059] Furthermore, using the continuous time location number as an index, external monitoring data, internal monitoring data, and operational monitoring data falling within the same continuous time location are read one by one, and the external monitoring data, internal monitoring data, and operational monitoring data, along with asset identifiers, timestamps, monitoring items, monitoring values, and data source markers, are written side by side into the same time location record; then, all time location records are connected in ascending order according to the continuous time location number to form asset time-series data from multiple continuous time locations; the asset time-series data retains the monitoring items, monitoring values, and data source markers.

[0060] S2: Divide the asset time series data into continuous time windows, extract the operational events and operational exclusion events within each time window, and generate evidence nodes;

[0061] S2.1: Starting from the first timestamp of the asset time series data, the asset time series data is segmented by connecting the first and last timestamps in sequence to obtain a continuous time window;

[0062] Furthermore, all continuous time positions are extracted from the asset time series data, and a fixed number of continuous time positions corresponding to the continuous time position step size under a unified timing benchmark (for example, when the continuous time position step size under a unified timing benchmark is 1 minute, 15 continuous time positions can be used as the continuous time window length, and adjacent 15 continuous time positions are grouped into the same continuous time window) are used as the continuous time window length. The first continuous time position corresponding to the first timestamp of the asset time series data is used as the starting segmentation position, and continuous time position records are read in the order of continuous time positions, and continuous time position records within the continuous time window length are grouped into the same continuous time window. After the current continuous time window is completed, the next continuous time position after the end position of the current continuous time window is used as the new starting segmentation position, and the first and last segments are continued to be performed until all asset time series data is segmented, resulting in continuous time windows numbered in chronological order.

[0063] S2.2: Within a continuous time window, perform adjacent time position comparisons on each monitoring item in the asset time series data in timestamp order, and identify operational events and operational exclusion events;

[0064] Furthermore, within each consecutive time window, the monitoring items, monitoring values, and data source markers in the records of each consecutive time position are read in the order of timestamps, and the state changes and value changes of the same monitoring item between adjacent consecutive time positions are compared; changes from inactivity to activity, from closed to open, from decrease to increase, and from suspension to continuation are written into the business occurrence event, and changes from activity to inactivity, from open to closed, from increase to decrease, and from continuation to suspension are written into the business exclusion event; when writing the business occurrence event and the business exclusion event, the asset identifier, consecutive time window position, corresponding timestamp, monitoring item, monitoring value, and data source marker are written simultaneously.

[0065] It should be noted that when a monitored item changes from no record to recorded, from off to on, or from low value to high value between adjacent consecutive time positions, it is recorded as an operational event; when a monitored item changes from recorded to no record, from on to off, or from high value to low value between adjacent consecutive time positions, it is recorded as an operational exclusion event; when a monitored item continuously changes in the same direction between adjacent consecutive time positions, it is recorded as a continuation status; the event attribute is recorded as an event category according to the correspondence between the monitored item category and the operational event and operational exclusion events.

[0066] S2.3: For each consecutive time window, business events that are adjacent in time location and change in the same direction are spliced ​​together to form consecutive business events;

[0067] Furthermore, within the same continuous time window, all operational events are read in timestamp order, and the first and last events are spliced ​​together according to the merging conditions of consistent monitoring items, consistent data source markers, adjacent time positions, and consistent change directions. Adjacent operational events that meet the merging conditions are connected into the same continuous interval, and the start timestamp of the first operational event and the end timestamp of the last operational event are written into the boundary of the continuous interval to form continuous operational events. Operational events that do not meet the merging conditions are kept in separate intervals until all operational events within the current continuous time window are spliced ​​together.

[0068] S2.4: For each consecutive time window, adjacent business exclusion events with the same direction of change are spliced ​​together to form consecutive business exclusion events;

[0069] Furthermore, within the same continuous time window, all business exclusion events are read in timestamp order, and the first and last events are spliced ​​together according to the merging conditions of consistent monitoring items, consistent data source markings, adjacent time positions, and consistent change directions. Adjacent business exclusion events that meet the merging conditions are connected into the same continuous interval, and the start timestamp of the first business exclusion event and the end timestamp of the last business exclusion event are written into the boundary of the continuous interval to form continuous business exclusion events. Business exclusion events that do not meet the merging conditions are kept in separate intervals until all business exclusion events within the current continuous time window are spliced ​​together.

[0070] S2.5: Write the continuing operations occurrence event and continuing operations exclusion event into the node record according to the asset identifier, continuous time window position, start timestamp, end timestamp, data source and event category, and arrange them as evidence nodes in the order of timestamp;

[0071] Furthermore, continuous operation occurrence events and continuous operation exclusion events are written into node records according to asset identifier, continuous time window position, start timestamp, end timestamp, data source, and event category, respectively. The event category is written according to the combination relationship between the monitoring item category and the continuous operation occurrence event and the continuous operation exclusion event. Then, all node records are arranged in order of start timestamp to generate evidence nodes. The evidence nodes retain the continuous time window position, start timestamp, end timestamp, data source, and event category.

[0072] S3: In adjacent time windows, establish supporting edges for evidence nodes with consistent event categories, sequential timelines, and complementary data sources. In the same continuous time window, establish conflict edges for evidence nodes with overlapping time intervals and opposing event attributes, and construct a business authenticity evidence graph.

[0073] S3.1: In adjacent time windows, identify evidence node pairs that are consistent in event category, sequential in time, and complementary in data source, and generate evidence node pairs that can corroborate each other;

[0074] Furthermore, evidence nodes corresponding to adjacent time windows are extracted from the evidence nodes in sequential order of continuous time window positions. Evidence nodes corresponding to the business events are then filtered from the evidence nodes corresponding to the previous continuous time window. Starting from the evidence nodes corresponding to the previous continuous time window, evidence nodes with consistent event categories, continuous time intervals, and mutually corroborating data sources are retrieved from the evidence nodes corresponding to the next continuous time window. The continuity of time intervals is determined by the start timestamp of the evidence node corresponding to the next continuous time window being after the end timestamp of the evidence node corresponding to the previous continuous time window, while maintaining a continuous time position. Mutual corroboration of data sources is determined by data source markers originating from different source categories and corroborating the same business activity (e.g., evidence nodes from different source categories that are continuous in continuous time window position, continuous in time interval, and consistent in event category are considered to corroborate the same business activity). Evidence nodes corresponding to the previous and next continuous time windows that meet the judgment conditions are paired and written into the node pair record, generating mutually corroborating evidence node pairs.

[0075] S3.2: Within the same continuous time window, identify evidence node pairs where time intervals overlap and one corresponds to an operational event while the other corresponds to an operational exclusion event, and generate contradictory evidence node pairs;

[0076] Furthermore, from the evidence nodes corresponding to the operational occurrence event and the operational exclusion event, evidence nodes are simultaneously extracted from the evidence nodes corresponding to the same continuous time window, and pairwise comparisons are performed in order of start timestamp. For each group of evidence nodes, the start timestamp and end timestamp are extracted, and the overlap length of the time interval is calculated, expressed as: ;

[0077] in, For the first The evidence node and the first The length of overlap in the time intervals of each evidence node For the first The end timestamp of each evidence node For the first The end timestamp of each evidence node For the first The start timestamp of each evidence node For the first The start timestamp of each evidence node;

[0078] When the overlap length of the time interval is greater than zero and the event attributes correspond to the business occurrence event and the business exclusion event respectively, the corresponding evidence nodes are written into the node pair record to generate contradictory evidence node pairs. The evidence node corresponding to the business occurrence event is written into the preceding position, and the evidence node corresponding to the business exclusion event is written into the following position to generate contradictory evidence node pairs.

[0079] S3.3: Establish connection relationships for mutually corroborating evidence node pairs and generate supporting edges; establish connection relationships for contradictory evidence node pairs and generate conflict edges.

[0080] Furthermore, for each pair of mutually corroborating evidence nodes, the starting evidence node identifier, ending evidence node identifier, starting continuous time window position, ending continuous time window position, connection type, and connection time interval are written, and the connection type is written as a supporting edge to generate a supporting edge; for each pair of contradictory evidence nodes, the corresponding evidence node identifier for the business occurrence event, the corresponding evidence node identifier for the business exclusion event, the continuous time window position, the conflict type, and the overlapping range of the time interval are written, and the conflict type is written as a conflict edge to generate a conflict edge; the supporting edge maintains the directed connection from the previous continuous time window to the next continuous time window, and the conflict edge maintains the directed connection from the evidence node corresponding to the business occurrence event to the evidence node corresponding to the business exclusion event.

[0081] S3.4: Construct a business authenticity evidence graph using evidence nodes as the node set and supporting edges and conflicting edges as the edge set;

[0082] Furthermore, using all evidence nodes as the node set and all supporting edges and all conflicting edges as the edge set, node connection relationships are established according to the evidence node identifier and edge relationship identifier. The asset identifier, continuous time window position, start timestamp, end timestamp, data source, and event category in the node set are written into the same graph structure record, corresponding to the starting evidence node identifier, ending evidence node identifier, connection type, and connection time interval in the edge set. All graph structure records are organized according to the continuous time window position and timestamp order to construct a business authenticity evidence graph. The business authenticity evidence graph retains the continuous connection relationship of supporting edges and the opposing connection relationship of conflicting edges.

[0083] S4: Perform support accumulation, conflict elimination, and cross-window merging on the evidence graph of business authenticity, and organize the support chain segments that maintain complementary data sources into candidate state segments;

[0084] S4.1: Extract the sequence of evidence nodes continuously connected by supporting edges from the evidence diagram of business authenticity to form a supporting chain segment;

[0085] Furthermore, all supporting edges and their associated evidence nodes are extracted from the business authenticity evidence graph, and supporting edge connection paths are established according to the continuous time window position and timestamp order. Among all evidence nodes, evidence nodes corresponding to business events without preceding supporting edges are selected as starting nodes. Subsequent evidence nodes are traced one by one along the directed connection direction of the supporting edges. Evidence nodes with consistent event categories, consecutive time intervals, and continuously extending connection directions are sequentially written into the same path record. When the supporting edge connection is interrupted, the event category changes, the consecutive time interval connection relationship is terminated, or the end evidence node has been reached, the current path record is terminated. The sequence of evidence nodes continuously connected by supporting edges corresponding to each path record is written into the chain segment record, forming a supporting chain segment. Each supporting chain segment synchronously retains the starting continuous time window position, ending continuous time window position, starting timestamp, ending timestamp, data source, and event category.

[0086] The process of filtering out evidence nodes corresponding to business events that do not have preceding supporting edges is as follows: First, extract all evidence nodes and all supporting edges from the business authenticity evidence graph. Then, establish a corresponding set using the endpoint evidence node identifier of each supporting edge as the pointed-to identifier. Subsequently, read the evidence node identifiers one by one from all evidence nodes corresponding to business events and compare the evidence node identifiers with all pointed-to identifiers in the corresponding set. When the evidence node identifier of a certain business event does not appear in any pointed-to identifier, it indicates that the evidence node corresponding to the business event exists only as the starting point of a supporting edge and is not connected by a previous supporting edge. Therefore, the evidence node corresponding to the business event is filtered out as the evidence node corresponding to the business event that does not have a preceding supporting edge and is written into the path record as the starting node of the supporting edge connection path.

[0087] S4.2: Perform support accumulation, conflict elimination, and cross-window merging on the support chain segments, and organize the support chain segments that maintain complementary data sources into candidate state segments;

[0088] Furthermore, all support chain segments are processed sequentially according to their continuous time window positions. First, support accumulation is performed within each support chain segment. Then, conflict elimination is performed on the support chain segments that have completed support accumulation. Subsequently, cross-window merging is performed on the support chain segments that have completed conflict elimination. The support chain segments that have completed cross-window merging and maintain mutual verification of data sources are fragmented according to the starting continuous time window position, ending continuous time window position, starting timestamp, and ending timestamp to form candidate state segments. The candidate state segments simultaneously retain the event category distribution, time coverage, and data source composition.

[0089] S4.2.1: Support accumulation is the process of verifying the continuity of the execution time and the mutual verification of the data sources of the evidence nodes in the support chain segment, and retaining the evidence nodes that meet the requirements of consistent event categories, time continuity and complementary data sources in the same support chain segment;

[0090] Furthermore, the evidence nodes in the support chain are read one by one in the order of the support edges, and the event category, start timestamp, end timestamp, continuous time window position, and data source are extracted sequentially. A time continuity check is performed on adjacent evidence nodes to determine whether the start timestamp of the next evidence node is after the end timestamp of the previous evidence node and whether the time intervals are connected. A data source mutual verification check is performed on adjacent evidence nodes to determine whether the data sources of adjacent evidence nodes belong to different source categories and can mutually corroborate each other around the same business activity. Evidence nodes that meet the requirements of consistent event category, continuous time intervals, and mutual data source verification are continuously retained in the same support chain segment, while evidence nodes that do not meet the verification conditions are separated from the current continuously retained sequence, completing the support accumulation.

[0091] S4.2.2: Conflict removal is to extract evidence nodes that have conflicting edge connections with the supporting chain segment from the business authenticity evidence diagram, perform conflict verification on evidence nodes with overlapping time intervals and opposing event attributes, and remove the evidence nodes corresponding to the business exclusion event from the supporting chain segment;

[0092] Furthermore, evidence nodes with conflicting connections to supporting segments are extracted from the business authenticity evidence diagram. The evidence nodes corresponding to business events associated with the conflicting edges and the evidence nodes corresponding to business exclusion events are then written into conflict verification records group by group. For each group of conflict verification records, the time interval and event attributes are extracted to determine whether the time intervals overlap and whether the event attributes form an opposing relationship. For conflict verification records that satisfy the conditions of overlapping time intervals and opposing event attributes, the evidence nodes corresponding to business exclusion events are removed from the supporting segments, while the evidence nodes corresponding to business events that do not conflict with the evidence nodes corresponding to business exclusion events in the supporting segments are retained. The removed supporting segments are then reconnected to retain the evidence nodes, thus completing the conflict removal process.

[0093] S4.2.3: Cross-window merging is to splice the support chain segments that have completed conflict elimination according to the connection relationship between the consecutive time windows, and merge the adjacent support chain segments that extend continuously on the support edge;

[0094] Furthermore, the support chain segments that have completed conflict elimination are arranged in order of the starting and ending continuous time window positions, and the support edge extension relationships between adjacent support chain segments are extracted, including the end timestamp and start timestamp, the ending continuous time window position and the starting continuous time window position, and the boundary evidence nodes. For adjacent support chain segments that satisfy the conditions of seamless connection between time intervals, continuous progression of continuous time window positions, and continuous extension of support edges, the beginning and end are spliced ​​together, connecting the ending boundary of the previous support chain segment with the starting boundary of the next support chain segment to form the same continuous segment. The splicing process is repeated for all support chain segments until there are no more adjacent support chain segments that can be spliced, thus completing the cross-window merging.

[0095] S5: Map and match the matching dimensions of the candidate state fragments with the business format structure, business hours structure and operating load structure in the lease filing data to obtain the lease status;

[0096] S5.1: Read the business format structure, operating hours structure, and operating load structure corresponding to the same operating asset from the lease filing data;

[0097] Furthermore, the asset identifier corresponding to the same operating asset is extracted from the lease filing data, and the business format structure, business hours structure and operating load structure corresponding to the same operating asset are retrieved based on the asset identifier; the business format structure, business hours structure and operating load structure are written into the same filing record according to the asset identifier, and the same filing record is established to correspond to the asset identifier in the candidate status segment.

[0098] S5.2: Extract the distribution of business activity categories, time coverage, and load change from the candidate state fragments, and form matching dimensions corresponding to the business format structure, business hours structure, and operating load structure, respectively;

[0099] Furthermore, event categories, start timestamps, end timestamps, continuous time window positions, and load change records are extracted segment by segment from the candidate state segments and merged into the same candidate state segment set according to asset identifiers. Statistical aggregation is performed on the event categories in the candidate state segment set to form a distribution of business activity categories, and the distribution of business activity categories is mapped to the matching dimension corresponding to the business format structure. Coverage statistics are performed on the start timestamps, end timestamps, and continuous time window positions in the candidate state segment set to form a time coverage distribution, and the time coverage distribution is mapped to the matching dimension corresponding to the business period structure. Interval aggregation and trend merging are performed on the load change records in the candidate state segment set to form a load change distribution, and the load change distribution is mapped to the matching dimension corresponding to the operating load structure.

[0100] S5.3: Match the matching dimensions with the business format structure, business hours structure, and operating load structure in the lease filing data respectively, and combine the matching results into the lease status;

[0101] Furthermore, the matching dimensions corresponding to the business format structure are mapped and matched with the business format structure in the lease registration data to obtain the business format structure matching result; the matching dimensions corresponding to the business hours structure are mapped and matched with the business hours structure in the lease registration data to obtain the business hours structure matching result; the matching dimensions corresponding to the operating load structure are mapped and matched with the operating load structure in the lease registration data to obtain the operating load structure matching result; then, consistency relationship merging and deviation relationship merging are performed on the business format structure matching result, business hours structure matching result, and operating load structure matching result, and the merged comprehensive result is written into the lease status, thus completing the mapping and matching of the matching dimensions of the candidate status fragments with the business format structure, business hours structure, and operating load structure in the lease registration data to obtain the lease status.

[0102] For example, the distribution of business activity categories extracted from candidate state segments is represented by people entering the store, shelf interaction, cashier stay, and continuous equipment operation, forming a matching dimension corresponding to the business format structure; the distribution of time coverage extracted from candidate state segments is represented by continuous daytime coverage extending into the evening, forming a matching dimension corresponding to the business hours structure; the distribution of load changes extracted from candidate state segments is represented by the continuous existence of lighting load, refrigeration load, and settlement equipment load during business hours, forming a matching dimension corresponding to the operating load structure; then, the above matching dimensions are mapped and matched with the business format structure, business hours structure, and operating load structure in the lease filing data, respectively. If the matching results of the business format structure, business hours structure, and operating load structure all show a consistent relationship, the comprehensive result after merging the consistent relationships is written into the lease status to obtain a lease status that matches the lease filing data.

[0103] Consistency and deviation relationship merging can be performed item by item according to the three types of matching results for the same operating asset under the same candidate status segment: the business format structure matching result, business time structure matching result, and operating load structure matching result are compared with the corresponding filing structure. All content with consistent matching direction, consistent coverage, and no conflicting change trends is uniformly written into the consistency relationship record and merged into a consistency relationship merging result according to asset identifier and candidate status segment position; all content with inconsistent matching direction, misaligned coverage, opposite change trends, or missing content is uniformly written into the deviation relationship record and merged into a deviation relationship merging result according to deviation source, deviation position, and deviation duration interval; then, the consistency relationship merging result and the deviation relationship merging result are placed in the same comprehensive judgment record, with the consistency relationship merging result representing the part that matches the lease filing data, and the deviation relationship merging result representing the part that does not match the lease filing data, and then the lease status is written according to the combination of the two types of merging results.

[0104] In a set of simulation embodiments, external monitoring data, internal monitoring data, and operational monitoring data are constructed around the same operating asset, and lease filing data corresponding to the operating asset are constructed simultaneously. The time-series data of the same batch of assets are respectively input into single-source direct judgment, evidence node judgment, evidence graph judgment, and the complete method of the present invention, and the differences between the different methods in terms of comprehensive lease status accuracy, candidate status segment continuity, business format structure matching rate, business hour structure matching rate, and operational load structure matching rate are compared.

[0105] Figure 2 The results show that under the condition of gradually increasing data source conflict rate, the accuracy of comprehensive lease status for single-source direct determination, evidence node determination, evidence graph determination, and the complete method of this invention all change. Among them, the complete method of this invention maintains a high comprehensive lease status accuracy across all conflict rate intervals. This result indicates that by establishing support edges for evidence nodes with consistent event categories, sequential timing, and complementary data sources in adjacent time windows, and by establishing conflict edges for evidence nodes with overlapping time intervals and opposing event attributes in the same continuous time window, the business authenticity evidence graph can simultaneously retain mutually corroborative and contradictory relationships, thus preserving the authenticity structure of business activities in both the time and source dimensions. Further execution of support accumulation, conflict elimination, and cross-window merging can reduce the interference of conflicting data on lease status generation, enabling the complete method of this invention to maintain a relatively stable lease status identification effect even when data source conflicts exist. This figure illustrates the supporting role of the business authenticity evidence graph and its subsequent processing in lease status generation.

[0106] Figure 3This illustrates the discrepancy between the actual business activity and the time distribution results obtained by different methods within the same observation period for the same operating asset. The continuous segments obtained by single-source direct determination are relatively discrete. While evidence node determination and evidence graph determination can retain some continuous intervals, segment breaks still exist. The candidate state segments generated by the complete method of this invention have a coverage range on the time axis that more closely resembles the distribution of actual business activity, with fewer breaks between segments and longer continuous intervals. The reason for this result is that this invention extracts a sequence of evidence nodes continuously connected by supporting edges from the business authenticity evidence graph to form supporting chains. These supporting chains are then subjected to support accumulation, conflict removal, and cross-window merging. This ensures that evidence nodes meeting the conditions of consistent event categories, temporal continuity, and complementary data sources are retained within the same continuous segment. Simultaneously, evidence nodes conflicting with business exclusion events are removed from the supporting chains, and adjacent supporting chains are spliced ​​together, resulting in more continuous candidate state segments. This figure illustrates how this invention improves the coherence of lease status identification during the candidate state segment generation stage.

[0107] Figure 4 The comparison of single-source direct determination, evidence node determination, evidence graph determination, and the complete method of this invention in terms of comprehensive lease status accuracy, business format structure matching rate, business hours structure matching rate, and operational load structure matching rate shows that the complete method of this invention maintains superior results in all four dimensions. This result indicates that after obtaining candidate status fragments, this invention does not directly write the candidate status fragments into the lease status. Instead, it extracts the distribution of business activity categories, time coverage distribution, and load change distribution from the candidate status fragments, and forms matching dimensions corresponding to the business format structure, business hours structure, and operational load structure, respectively. Then, each matching dimension is mapped and matched with the business format structure, business hours structure, and operational load structure in the lease registration data, and finally, the matching results are combined into the lease status. Using this processing method, the lease status not only reflects whether business activities continue to exist, but also reflects the degree of consistency between business activities and the registered business format, business hours, and operational load, thus improving the targeting and business adaptability of lease status identification.

[0108] It should be noted that in the simulation experiment, single-source direct judgment refers to selecting only the monitoring results from a single source as the basis for judging the lease status. For example, it can directly give a judgment on whether business activities exist based solely on the results of personnel activities or object interactions in the internal monitoring data. This method can reflect the basic identification capability of a single monitoring source in the absence of cross-confirmation. Evidence node judgment refers to extracting external monitoring data, internal monitoring data, and operational monitoring data into business occurrence events or business exclusion events, generating evidence nodes according to continuous time windows, and then directly giving a judgment on the lease status based on the occurrence of evidence nodes in each time window. This method has utilized multi-source information compared to single-source direct judgment, but it has not yet established the supporting and conflicting relationships between evidence nodes. Evidence graph determination refers to establishing support edges for evidence nodes with consistent event categories, sequential timing, and complementary data sources in adjacent time windows, and establishing conflict edges for evidence nodes with overlapping time intervals and opposing event attributes in the same continuous time window, forming an evidence graph of business authenticity. Based on the node connections in this graph, a judgment related to the leasing status is given. This method already has the ability to organize mutually corroborative and contradictory relationships, but it has not yet fully implemented support accumulation, conflict elimination, cross-window merging, and mapping and matching with business format structure, business time structure, and operational load structure. Therefore, the three correspond to three progressive levels of single-source judgment, multi-source node judgment, and graph structure judgment, which can be used to compare and highlight the technical effects of the complete method of this invention.

[0109] This embodiment also provides a computer device applicable to the method for monitoring the lease status of operating assets, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for monitoring the lease status of operating assets as proposed in the above embodiment.

[0110] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0111] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for monitoring the lease status of operating assets as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0112] In summary, this invention constructs a business authenticity evidence graph to simultaneously organize mutually corroborating and contradictory relationships, thus depicting the authenticity structure of business activities in terms of time and source dimensions. By performing support accumulation, conflict elimination, and cross-window merging on the business authenticity evidence graph, and organizing support segments that maintain complementary data sources into candidate state fragments, and then mapping and matching them with the business format structure, business time structure, and operational load structure in the lease filing data, it can support the generation of lease status, thereby achieving the beneficial effects of improving the coherence, relevance, and business adaptability of lease status identification.

[0113] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for monitoring the lease status of operating assets, characterized in that: include: Collect external monitoring data, internal monitoring data and operational monitoring data of the same operating asset, establish corresponding relationships according to asset identifiers and align them according to timestamps to generate asset time-series data; The asset time-series data is segmented into continuous time windows, and operational events and exclusionary events within each time window are extracted to generate evidence nodes. In adjacent time windows, support edges are established for evidence nodes with consistent event categories, sequential timelines, and complementary data sources. In the same continuous time window, conflict edges are established for evidence nodes with overlapping time intervals and opposing event attributes. A business authenticity evidence graph is then constructed. The evidence graph of business authenticity is supported by accumulation, conflict elimination and cross-window merging, and the supporting chain segments that maintain complementary data sources are organized into candidate state segments; The matching dimensions of the candidate status fragments are mapped and matched with the business format structure, business hours structure, and operating load structure in the lease registration data to obtain the lease status.

2. The method for monitoring the lease status of operating assets as described in claim 1, characterized in that, The specific details of generating asset time-series data are as follows: Using asset identification as the sole criterion, external monitoring data, internal monitoring data, and operational monitoring data with consistent asset identification are grouped into the same asset identification set. Convert the timestamps of external monitoring data, internal monitoring data and operational monitoring data in the same asset identifier set to a unified timing benchmark, and rearrange them into consecutive time positions according to the timestamps; External monitoring data, internal monitoring data, and operational monitoring data falling at the same continuous time position are written side by side to the same time position, and multiple continuous time positions are combined to form asset time series data.

3. The method for monitoring the lease status of operating assets as described in claim 1, characterized in that, The extraction of operational events and operational exclusion events within each time window is as follows: Starting with the first timestamp of the asset time series data, the asset time series data is segmented by connecting the beginning and end of the data along the timestamp sequence to obtain a continuous time window. Within a continuous time window, each monitoring item in the asset time series data is compared to its adjacent time position in time stamp order, and operational events and operational exclusion events are identified.

4. The method for monitoring the lease status of operating assets as described in claim 1 or 3, characterized in that, The generated evidence nodes are as follows: For each consecutive time window, business events that are adjacent in time location and change in the same direction are spliced ​​together to form consecutive business events. For each consecutive time window, adjacent business exclusion events with the same direction of change are spliced ​​together to form consecutive business exclusion events. Continuing operations events and continuing operations exclusion events are written into node records according to asset identifier, continuous time window position, start timestamp, end timestamp, data source, and event category, and arranged as evidence nodes in timestamp order.

5. The method for monitoring the lease status of operating assets as described in claim 4, characterized in that, The specific steps for constructing the evidence diagram of business authenticity are as follows: Within adjacent time windows, identify evidence node pairs that are consistent in event category, sequential in time, and complementary in data source, and generate mutually corroborating evidence node pairs. Within the same continuous time window, identify evidence node pairs where time intervals overlap and one corresponds to an operational event while the other corresponds to an operational exclusion event, and generate contradictory evidence node pairs. To establish connections between mutually corroborating evidence node pairs, support edges are generated; and to establish connections between contradictory evidence node pairs, conflict edges are generated. Using evidence nodes as the node set and supporting edges and conflict edges as the edge set, a business authenticity evidence graph is constructed.

6. The method for monitoring the lease status of operating assets as described in claim 5, characterized in that, The generation of candidate state fragments is specifically as follows: Extract a sequence of evidence nodes connected continuously by supporting edges from the evidence diagram of business authenticity to form a supporting chain segment; Support accumulation, conflict elimination, and cross-window merging are performed on the support chain segments, and support chain segments with complementary data sources are organized into candidate state segments.

7. The method for monitoring the lease status of operating assets as described in claim 1, characterized in that, The rental status is obtained as follows: Retrieve the business format structure, operating hours structure, and operational load structure corresponding to the same operating asset from the lease filing data; Extract the distribution of business activity categories, time coverage, and load change from the candidate state fragments, and form matching dimensions corresponding to the business format structure, business hours structure, and operating load structure, respectively; The matching dimensions are matched with the business format structure, business hours structure, and operating load structure in the lease registration data, and the matching results are combined into the lease status.

8. The method for monitoring the lease status of operating assets as described in claim 6, characterized in that, The support accumulation is to perform time continuity verification and data source mutual verification on the evidence nodes in the support chain segment, and retain evidence nodes that meet the requirements of event category consistency, time connection and data source complementarity in the same support chain segment; The conflict removal process involves extracting evidence nodes from the business authenticity evidence graph that have conflicting edge connections with the supporting chain segment, performing conflict checks on evidence nodes with overlapping time intervals and opposing event attributes, and removing the evidence nodes corresponding to the business exclusion event from the supporting chain segment. The cross-window merging process involves splicing together the support chain segments that have completed conflict elimination according to the sequential connection relationship between continuous time windows, merging adjacent support chain segments that extend continuously along the support edge.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for monitoring the lease status of operating assets as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for monitoring the lease status of operating assets as described in any one of claims 1 to 8.

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