An intelligent processing method for medical institution logistics operation log

Abstract

The application discloses an intelligent processing method for medical institution logistics operation log, and relates to the technical field of operation log processing. The method comprises the following steps: collecting multi-source log data for time sequence and semantic coding processing to generate a log semantic vector; establishing a time and task semantic flow matrix, calculating a semantic change rate and a correlation intensity, and generating a multi-layer fractal logic graph; identifying a logical similar task pair, constructing a task symmetry layer, and establishing a semantic coupling channel; based on the semantic flow matrix and the task symmetry layer, generating a time sequence reflection rule, comparing a predicted path with an actual execution path, and calculating a path deviation residual value; triggering a self-evolution residual correction mechanism to obtain a corrected path result and performing a semantic feedback resonance operation. The application realizes intelligent analysis and self-learning optimization of the medical institution logistics operation log, and improves task execution consistency, semantic understanding depth and operation management efficiency.

Description

Technical Field

[0001] This application relates to the field of operational log processing technology, and in particular to an intelligent processing method for logistics operational logs of medical institutions. Background Technology

[0002] Logistics management in medical institutions involves multiple aspects, including equipment maintenance, material allocation, environmental disinfection, energy management, and handling of abnormal events, generating a large amount of multi-source, multi-dimensional operational logs. These logs contain rich information on time, task behavior, operation objects, status descriptions, and logical relationships between events. Therefore, efficiently processing this massive amount of logistics operation log data is particularly important.

[0003] In related technologies, traditional medical institution logistics operation mechanisms often rely on keyword matching, rule queries, or statistical analysis. These methods lack in-depth semantic analysis of task behaviors, operational objects, and state descriptions, failing to understand the logical dependencies and evolutionary trends between tasks, resulting in a lack of intelligent support for logistics management. Secondly, existing technologies typically analyze single events or tasks, lacking methods for modeling semantic relationships across multiple tasks and time segments. This makes it difficult to identify logically similar task pairs and potential synergies, hindering process optimization and resource scheduling. Furthermore, existing technologies largely rely on fixed rules or empirical models, lacking the ability to adaptively adjust to execution deviations. They cannot dynamically optimize paths and rule systems based on historical data and task logic, lacking self-learning and closed-loop optimization capabilities, thus requiring improvement. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent processing method for logistics operation logs of medical institutions, so as to solve the problems mentioned in the background art.

[0005] This application provides an intelligent processing method for logistics operation logs in medical institutions, which adopts the following technical solution:

[0006] Collect multi-source log data generated during the logistics operations of medical institutions, perform time-series processing and semantic encoding on it, and generate log semantic vectors;

[0007] Based on the log semantic vector, a semantic flow matrix of time and task is established, the semantic change rate and correlation strength between each task node are calculated, and a multi-layer fractal logic graph is generated according to the semantic change trend.

[0008] Based on the semantic node correlation in the multi-layer fractal logic graph, logically similar task pairs are identified, a task symmetry layer is constructed, and a semantic coupling channel is established for logically similar task pairs in the task symmetry layer.

[0009] Based on the semantic rheometry matrix and the task symmetry layer, a temporal reflection rule is generated. The predicted path of the temporal reflection rule is compared with the actual execution path, and the path deviation residual value is calculated.

[0010] Based on the path deviation residual value, a self-evolutionary residual correction mechanism is triggered to obtain the corrected path result, and a semantic feedback resonance operation is performed to realize the self-learning closed loop of the logistics operation log.

[0011] Preferably, the steps of collecting multi-source log data generated during the logistics operations of medical institutions, performing time-series processing and semantic encoding on it, and generating log semantic vectors are as follows:

[0012] Collect multi-source log data generated during the logistics operation of medical institutions. The multi-source log data includes equipment operation and maintenance logs, material allocation and supply logs, environmental cleaning and disinfection logs, energy consumption scheduling logs, and abnormal alarm handling logs.

[0013] The multi-source log data is processed to unify the data format and standardize the timestamps to form a preliminary time-series log;

[0014] The time series log is segmented by a sliding window, and the time interval and duration of each log event are calculated to generate a time vector matrix;

[0015] Perform content semantic recognition on the time series logs to extract key semantic units containing task behavior, operation objects and status descriptions;

[0016] The key semantic units are semantically labeled, and the semantic labels are converted into semantic vectors through a semantic vectorization algorithm to generate a semantic encoding set;

[0017] The time vector matrix is ​​associated with the semantic encoding set, the time weight and semantic weight of each task node are calculated, and the log semantic vector is generated based on the task time continuity and semantic relevance.

[0018] Preferably, the steps of associating the time vector matrix with the semantic encoding set, calculating the time weight and semantic weight of each task node, and generating log semantic vectors based on task temporal continuity and semantic relevance are as follows:

[0019] Extract time segment information of each log event from the time vector matrix, and extract the set of semantic tags corresponding to the time segments from the semantic encoding set to establish a preliminary mapping relationship between time segments and semantic tags;

[0020] Based on the event persistence of each task in the time series and its time dependency with adjacent events, assign time dimension weights to each task node;

[0021] Semantic relevance analysis is performed on the task behaviors, operation objects and state descriptions in the semantic encoding set. Based on the semantic similarity between tasks in terms of behavior or object and their functional importance in the logistics process, semantic dimension weights are assigned to each task node.

[0022] The time dimension weights and semantic dimension weights are correlated and mapped to generate log semantic vectors.

[0023] Preferably, the steps of establishing a semantic flow matrix of time and task based on the log semantic vector, calculating the semantic change rate and correlation strength between each task node, and generating a multi-layer fractal logic graph according to the semantic change trend are as follows:

[0024] Based on the log semantic vectors, each log semantic vector is regarded as a task node, and a semantic flow matrix of time and task is established.

[0025] Based on the semantic rheometry matrix, the semantic change of task nodes within adjacent time segments is calculated, and the semantic change rate of task nodes is determined according to the ratio of the semantic change to the time interval.

[0026] Based on the semantic rheometry matrix, the semantic correlation strength between task nodes is calculated according to the semantic labels, semantic similarity and contextual dependencies of each task node.

[0027] By jointly analyzing the semantic change rate and semantic relevance strength, the direction and trend of semantic evolution are identified, semantic change trend data is obtained, and semantic change trajectory is generated.

[0028] The semantic change trend data is input into the fractal logic construction module to generate a multi-layer fractal logic graph, which includes three levels: task layer, event layer, and semantic fine-grained layer.

[0029] Preferably, the step of identifying logically similar task pairs based on the semantic node correlation in the multi-layer fractal logic graph, constructing a task symmetry layer, and establishing a semantic coupling channel for logically similar task pairs in the task symmetry layer specifically includes:

[0030] The semantic similarity and semantic correlation strength are extracted from the multi-layer fractal logic graph, and the semantic correlation between task nodes is calculated by weighted combination to generate a semantic correlation matrix.

[0031] Set a dynamic threshold for relevance, and identify semantically stable regions and semantically abrupt regions in the semantic relevance matrix based on the semantic change rate and the dynamic threshold for relevance.

[0032] The semantically stable region is a region with a low semantic change rate and semantic relevance exceeding the dynamic threshold of relevance, and the semantically abrupt region is a region with a high semantic change rate and semantic relevance not exceeding the dynamic threshold of relevance, used to represent sudden task events.

[0033] Based on the semantic relevance matrix, logically similar task pairs are identified, a task symmetry layer is constructed, and a semantic coupling channel is established for logically similar task pairs in the task symmetry layer.

[0034] Preferably, the step of identifying logically similar task pairs based on the semantic relevance matrix, constructing a task symmetry layer, and establishing a semantic coupling channel for logically similar task pairs in the task symmetry layer specifically includes:

[0035] Based on the semantic relevance matrix, the logical similarity index of task nodes is calculated, logically similar task pairs are selected and clustered to generate semantically symmetric identifiers;

[0036] A task symmetry layer is established above the task layer of the multi-layer fractal logic graph. The task symmetry layer is an extended logic layer of the multi-layer fractal logic graph, with the logically similar task pairs as basic node units.

[0037] In the task symmetry layer, a spatial mapping relationship for logically similar task pairs is constructed based on the distribution of the logical similarity index.

[0038] In the task symmetry layer, an independent semantic coupling channel is assigned to each logically similar task pair for dynamic sharing of semantic information between the logically similar task pairs.

[0039] Preferably, the step of generating temporal reflection rules based on the semantic rheological matrix and the task symmetry layer, comparing the predicted path with the actual execution path, and calculating the path deviation residual value specifically includes:

[0040] Extract the time series information and semantic change trajectory of each task node from the semantic rheometry matrix, identify the logical dependencies between consecutive tasks, and form a time series chain of task execution.

[0041] Based on the semantic coupling channel of logically similar task pairs in the task symmetry layer, the time delay relationship and semantic mapping between different task nodes are calculated inversely to generate time-series reflection rules.

[0042] The predicted path based on the temporal reflection rule is compared node by node with the actual execution path in the logistics operation of the medical institution. By calculating the time interval difference and semantic deviation between task nodes, a path deviation index set is obtained.

[0043] The residual distribution values ​​at the task node level are calculated based on the path deviation index set to generate path deviation residual values.

[0044] Preferably, the step of calculating the residual distribution value at the task node level based on the path deviation index set and generating the path deviation residual value specifically includes:

[0045] Extract the time interval difference and semantic deviation of each task node from the path deviation index set, match the corresponding position of the task node in the semantic rheological matrix with the time weight, and determine the time deviation component and semantic deviation component of each task node.

[0046] The time deviation component and semantic deviation component of each task node are weighted and integrated to form a comprehensive deviation value of each task node. The comprehensive deviation value is then mapped on the time axis of the semantic rheological matrix to generate a residual distribution curve at the task node level.

[0047] The residual distribution curves are aggregated to calculate the overall weighted residual value, generating a path deviation residual value, which is used to characterize the degree of deviation between the predicted path and the actual executed path.

[0048] Preferably, based on the path deviation residual value, a self-evolutionary residual correction mechanism is triggered to obtain the corrected path result, and a semantic feedback resonance operation is performed to realize the self-learning closed loop of the logistics operation log. Specifically, the steps are as follows:

[0049] The path deviation residual value is fed back into the semantic rheological matrix, a residual threshold is set, and the path deviation residual value is compared with the residual threshold.

[0050] When the path deviation residual value exceeds the residual threshold, the self-evolutionary residual correction mechanism is triggered, and the semantic rheological region is marked as a high residual region to determine the target path segment;

[0051] The time weights and semantic weights of each task node in the target path segment are adaptively adjusted, and the semantic association strength between logically similar task pairs is updated based on the semantic coupling channel to generate a corrected path result.

[0052] The corrected path result is written to the semantic rheological matrix and the task symmetry layer. The difference in semantic changes before and after the correction is compared, the semantic feedback intensity value is calculated, and a semantic feedback resonance layer is established.

[0053] In the semantic resonance layer, the semantic resonance center node is identified, cross-task common logical patterns are extracted, and updated temporal reflection rules are generated and replaced to form a semantic self-learning closed loop of the medical institution's logistics operation log.

[0054] In summary, this application includes at least one of the following beneficial technical effects:

[0055] 1. A unified collection and time-series processing method is implemented for multi-source log data generated during the logistics operations of medical institutions. Through a semantic encoding mechanism, the multi-source log data is converted into computable semantic vectors, providing standardized input for subsequent semantic association analysis. The time dimension is fused with task semantic features to construct a semantic rheological matrix, which characterizes the evolution of semantic states between different task nodes over time. By calculating the rate of semantic change and correlation strength, potential logical associations and semantic fluctuation trends between tasks are identified. Based on the change patterns, a multi-layer fractal logic graph is generated, enabling hierarchical visualization of task logic and semantic topology modeling. By analyzing the semantic node correlations in the multi-layer fractal logic graph, logically similar or functionally complementary task pairs are identified, and a task symmetry layer is constructed based on them. By establishing semantic coupling channels in the task symmetry layer, semantic information sharing and association reasoning between logically similar task pairs are realized. This automatically identifies repetitive logical structures and redundant behaviors in logistics operations, thereby improving the intelligence level of task scheduling optimization, energy efficiency analysis, and anomaly linkage handling. By integrating the temporal evolution information of the semantic rheology matrix with the logical correspondence of the task symmetry layer, temporal reflection rules are generated to predict the expected execution path and semantic state of a task at different time nodes. The predicted path is compared with the actual execution path to calculate the path deviation residual value, quantifying the semantic degree and temporal difference of task execution deviation. This enables semantic deviation detection and task path self-diagnosis, identifying potential process anomalies, scheduling delays, or resource allocation imbalances. Based on the path deviation residual value, a self-evolutionary correction mechanism is automatically triggered, dynamically adjusting the semantic and temporal weights of high residual regions to correct the task execution path and obtain an optimized path result. Simultaneously, through semantic feedback resonance operations, the corrected semantic state is written back to the semantic rheology matrix and the task symmetry layer, and common logical patterns across tasks are identified to update the temporal reflection rules, achieving adaptive evolution of the semantic structure. This constructs a semantic self-learning closed loop for the logistics operation logs of medical institutions, realizing an intelligent evolutionary mechanism from passive recording to proactive optimization, enabling the logistics system to possess long-term learning, logical self-stabilization, and continuous improvement capabilities.

[0056] 2. Discrete log semantic vectors are mapped to a time-seriesd task node system, enabling each task to be quantified and expressed in both time and semantic dimensions. By constructing a semantic rheology matrix, the dynamic flow patterns of task semantics in logistics operations are revealed. The semantic drift of task nodes over time is quantified, and the rhythm and fluctuation characteristics of semantic evolution are identified by calculating the semantic change rate. This rate reflects the stability and abrupt changes of task semantics on the time axis, helping to identify semantically stable phases and sudden change points in the process, providing time-sensitive indicators for subsequent trend prediction. By integrating semantic label consistency, task behavior similarity, and contextual logical dependencies, the degree of semantic connection between different tasks is determined, revealing the semantic logical coupling characteristics within the logistics process, enabling the system to identify which tasks form logical links at the semantic level. The dynamic characteristics of semantic change rate and correlation strength are fused and analyzed to extract the evolution direction and diffusion trend of semantics between time and tasks. By generating semantic change trajectories, the migration path and evolutionary form of logistics task semantics are clearly described. By structuring semantic change patterns into a hierarchical logical graph, the system can simultaneously observe the evolutionary relationships between task logic, event semantics, and detailed semantics at different granularities. Through a multi-layered fractal structure, a panoramic expression from macro-scheduling logic to micro-semantic fluctuations can be achieved, forming a central structure for medical logistics semantic cognition. This provides a unified semantic framework to support subsequent task symmetry identification, path deviation diagnosis, and self-learning mechanisms.

[0057] 3. The semantic change trajectories of tasks in the logistics operation logs are structured into continuous time series chains, revealing the logical dependencies and execution order between tasks. By analyzing the semantic flow matrix, the temporal patterns and semantic continuity of task flow are accurately identified, providing a traceable task sequence model for subsequent path prediction and logical verification, thus realizing the transition from static semantic analysis to dynamic logical reasoning in logistics tasks. Utilizing the semantic coupling characteristics of the task symmetry layer, a temporal reflection mechanism between different tasks is established. By calculating the time delay and semantic back-mapping relationship between task nodes, predictive and self-comparative temporal reflection rules can be generated, enabling the prediction of potential path evolution based on the known logical symmetric behavior of tasks, enhancing the intelligence and foresight of logistics operation path analysis. By comparing the theoretically predicted path with the actual execution path, the degree of deviation between the two at the temporal and semantic levels is calculated. Through node-by-node difference calculation, the deviation of task execution is quantified, generating a path deviation index set reflecting process offset and semantic drift. This allows the system to identify logical node decoupling, delayed links, and semantic mismatch phenomena in the logistics operation process, providing quantifiable evidence for operational quality assessment and anomaly diagnosis. By performing hierarchical weighted calculations on the path deviation index set, residual statistical results at the task node level and the global level are generated. These residual values ​​reflect the semantic consistency and temporal stability between the prediction model and the actual execution process, providing a feedback basis for the subsequent self-evolutionary residual correction mechanism. This enables the system to achieve logical optimization and path correction through residual self-learning, thereby continuously improving the intelligent management level and execution consistency of medical logistics operations. Attached Figure Description

[0058] Figure 1 This is a schematic diagram illustrating the specific steps of an embodiment of the intelligent processing method for logistics operation logs of medical institutions according to the present invention. Detailed Implementation

[0059] The following examples and... Figure 1 The present invention will be described in further detail, but the embodiments of the present invention are not limited thereto.

[0060] This invention discloses an intelligent processing method for logistics operation logs in medical institutions, specifically including the following steps:

[0061] Collect multi-source log data generated during the logistics operations of medical institutions, perform time-series processing and semantic encoding on it, and generate log semantic vectors;

[0062] Based on the log semantic vector, a semantic flow matrix of time and task is established, the semantic change rate and correlation strength between each task node are calculated, and a multi-layer fractal logic graph is generated according to the semantic change trend.

[0063] Based on the semantic node correlation in the multi-layer fractal logic graph, logically similar task pairs are identified, a task symmetry layer is constructed, and a semantic coupling channel is established for logically similar task pairs in the task symmetry layer.

[0064] Based on the semantic rheometry matrix and the task symmetry layer, a temporal reflection rule is generated. The predicted path of the temporal reflection rule is compared with the actual execution path, and the path deviation residual value is calculated.

[0065] Based on the path deviation residual value, a self-evolutionary residual correction mechanism is triggered to obtain the corrected path result, and a semantic feedback resonance operation is performed to realize the self-learning closed loop of the logistics operation log.

[0066] In practical applications, multi-source log data generated during the logistics operations of medical institutions is uniformly collected and processed using time-series methods. A semantic encoding mechanism transforms this multi-source log data into computable semantic vectors, providing standardized input for subsequent semantic association analysis. The temporal dimension is fused with task semantic features to construct a semantic flow matrix, which characterizes the evolution of semantic states between different task nodes over time. By calculating the rate of semantic change and correlation strength, potential logical connections and semantic fluctuation trends between tasks are identified. Based on these change patterns, a multi-layer fractal logic graph is generated, enabling hierarchical visualization of task logic and semantic topology modeling, thus giving log data logical fluidity and structural decomposability. By analyzing the semantic node correlations in the multi-layer fractal logic graph, logically similar or functionally complementary task pairs are identified, and a task symmetry layer is constructed based on these pairs. By establishing semantic coupling channels within the task symmetry layer, semantic information sharing and association reasoning between logically similar task pairs are achieved. This automatically identifies repetitive logical structures and redundant behaviors in logistics operations, thereby improving the intelligence level of task scheduling optimization, energy efficiency analysis, and anomaly linkage handling. By integrating the temporal evolution information of the semantic rheology matrix with the logical correspondence of the task symmetry layer, temporal reflection rules are generated to predict the expected execution path and semantic state of a task at different time nodes. The predicted path is compared with the actual execution path to calculate the path deviation residual value, quantifying the semantic degree and temporal difference of task execution deviation. This enables semantic deviation detection and task path self-diagnosis, identifying potential process anomalies, scheduling delays, or resource allocation imbalances. Based on the path deviation residual value, a self-evolutionary correction mechanism is automatically triggered, dynamically adjusting the semantic and temporal weights of high residual regions to correct the task execution path and obtain an optimized path result. Simultaneously, through semantic feedback resonance operations, the corrected semantic state is written back to the semantic rheology matrix and the task symmetry layer, and common logical patterns across tasks are identified to update the temporal reflection rules, achieving adaptive evolution of the semantic structure. This constructs a semantic self-learning closed loop for the logistics operation logs of medical institutions, realizing an intelligent evolutionary mechanism from passive recording to proactive optimization, enabling the logistics system to possess long-term learning, logical self-stabilization, and continuous improvement capabilities.

[0067] The steps for collecting multi-source log data generated during the logistics operations of medical institutions, performing time-series processing and semantic encoding on it, and generating log semantic vectors are as follows:

[0068] Collect multi-source log data generated during the logistics operation of medical institutions. The multi-source log data includes equipment operation and maintenance logs, material allocation and supply logs, environmental cleaning and disinfection logs, energy consumption scheduling logs, and abnormal alarm handling logs.

[0069] The multi-source log data is processed to unify the data format and standardize the timestamps to form a preliminary time-series log;

[0070] The time series log is segmented by a sliding window, and the time interval and duration of each log event are calculated to generate a time vector matrix;

[0071] Perform content semantic recognition on the time series logs to extract key semantic units containing task behavior, operation objects and status descriptions;

[0072] The key semantic units are semantically labeled, and the semantic labels are converted into semantic vectors through a semantic vectorization algorithm to generate a semantic encoding set;

[0073] The time vector matrix is ​​associated with the semantic encoding set, the time weight and semantic weight of each task node are calculated, and the log semantic vector is generated based on the task time continuity and semantic relevance.

[0074] In practical applications, by collecting multi-type log data from different systems and sources in logistics operations, a complete logistics operation information foundation is constructed, providing comprehensive and reliable data support for subsequent time-series analysis and semantic modeling, and avoiding operational cognitive biases caused by single-source logs. Through format standardization and timestamp alignment, raw discrete data is transformed into a comparable and traceable unified time series, achieving integrated management of cross-system data and ensuring the temporal continuity and logical comparability of subsequent semantic calculations. A sliding window algorithm is used to capture time change characteristics, transforming dynamic behaviors in the logs into time vector forms, providing calculable time parameters for subsequent time semantic association, thereby revealing the implicit time logic of operational behaviors. Natural language understanding is applied to the log content to identify the core task semantic structure (such as maintenance-equipment-completion or allocation-materials-delay), enabling the differentiation of task behaviors and object relationships. A semantic vectorization model quantifies textual semantic information into vector representations, achieving semantic-level calculations and similarity analysis, establishing a unified semantic measurement system for logistics tasks, and enabling the accurate calculation of semantic differences and relationships between different log events. By integrating temporal and semantic features, a two-dimensional log semantic vector is constructed, which not only preserves the temporal dynamics of the task but also reflects the semantic logical structure, realizing the semantic-temporal integrated representation of logistics tasks and providing core input for subsequent semantic rheological matrix, fractal logic graph and path deviation analysis.

[0075] The steps of associating the time vector matrix with the semantic encoding set, calculating the time weight and semantic weight of each task node, and generating log semantic vectors based on task temporal continuity and semantic relevance are as follows:

[0076] Extract time segment information of each log event from the time vector matrix, and extract the set of semantic tags corresponding to the time segments from the semantic encoding set to establish a preliminary mapping relationship between time segments and semantic tags;

[0077] Based on the event persistence of each task in the time series and its time dependency with adjacent events, assign time dimension weights to each task node;

[0078] Semantic relevance analysis is performed on the task behaviors, operation objects and state descriptions in the semantic encoding set. Based on the semantic similarity between tasks in terms of behavior or object and their functional importance in the logistics process, semantic dimension weights are assigned to each task node.

[0079] The time dimension weights and semantic dimension weights are correlated and mapped to generate log semantic vectors.

[0080] In practical applications, time-series features are initially aligned with semantic content to establish a correspondence between the time and semantic dimensions. This identifies the semantic activities of tasks within each time period, creating a cross-analyzable structural foundation for time changes and semantic behaviors, thus transforming the process from independent log event recording to a semantic time chain. By quantifying the time characteristics of tasks, such as duration, interval patterns, and triggering order, their time dimension weights within the entire logistics sequence are calculated. These weights reflect the task's rhythmic control role and time sensitivity within the operational chain. By calculating the semantic similarity and functional coupling between different tasks, the logical connections and semantic strength of tasks in the semantic space are identified. Semantic dimension weights characterize the core nature and influence of tasks within the logistics semantic network, establishing a semantic importance ranking of logistics tasks. This enables the system to distinguish between critical task semantics and auxiliary semantics, providing accurate input for the subsequent construction of the semantic flow matrix. By fusing temporal and semantic features to form a unified log semantic vector, this vector is used to represent the comprehensive features of task nodes in the temporal-semantic dual-dimensional space. This enables the structuring and computability of multi-source logistics logs, constructs the core representation unit for intelligent medical logistics analysis, and provides basic feature support for subsequent semantic evolution, path deviation analysis, and self-learning optimization.

[0081] Based on the log semantic vector, the steps of establishing a semantic flow matrix of time and task, calculating the semantic change rate and correlation strength between each task node, and generating a multi-layer fractal logic graph according to the semantic change trend are as follows:

[0082] Based on the log semantic vectors, each log semantic vector is regarded as a task node, and a semantic flow matrix of time and task is established.

[0083] Based on the semantic rheometry matrix, the semantic change of task nodes within adjacent time segments is calculated, and the semantic change rate of task nodes is determined according to the ratio of the semantic change to the time interval.

[0084] Based on the semantic rheometry matrix, the semantic correlation strength between task nodes is calculated according to the semantic labels, semantic similarity and contextual dependencies of each task node.

[0085] By jointly analyzing the semantic change rate and semantic relevance strength, the direction and trend of semantic evolution are identified, semantic change trend data is obtained, and semantic change trajectory is generated.

[0086] The semantic change trend data is input into the fractal logic construction module to generate a multi-layer fractal logic graph, which includes three levels: task layer, event layer, and semantic fine-grained layer.

[0087] In practical applications, discrete log semantic vectors are mapped to a time-seriesd task node system, enabling each task to be quantified and expressed in both time and semantic dimensions. By constructing a semantic flow matrix, the dynamic flow patterns of task semantics in logistics operations are revealed. The degree of semantic drift of task nodes over time is quantified, and the rhythm and fluctuation characteristics of semantic evolution are identified by calculating the semantic change rate. This rate reflects the stability and abrupt changes of task semantics on the time axis, helping to identify semantically stable phases and sudden change points in the process, providing time-sensitive indicators for subsequent trend prediction. By comprehensively considering semantic label consistency, task behavior similarity, and contextual logical dependencies, the degree of semantic connection between different tasks is determined, revealing the semantic logical coupling characteristics within the logistics process, enabling the system to identify which tasks form logical links at the semantic level. The dynamic characteristics of semantic change rate and correlation strength are fused and analyzed to extract the evolution direction and diffusion trend of semantics between time and tasks. By generating semantic change trajectories, the migration path and evolutionary form of logistics task semantics are clearly described. By structuring semantic change patterns into a hierarchical logical graph, the system can simultaneously observe the evolutionary relationships between task logic, event semantics, and detailed semantics at different granularities. Through a multi-layered fractal structure, a panoramic expression from macro-scheduling logic to micro-semantic fluctuations can be achieved, forming a central structure for medical logistics semantic cognition. This provides a unified semantic framework to support subsequent task symmetry identification, path deviation diagnosis, and self-learning mechanisms.

[0088] Based on the semantic node correlations in the multi-layer fractal logic graph, the steps of identifying logically similar task pairs, constructing a task symmetry layer, and establishing semantic coupling channels for logically similar task pairs in the task symmetry layer are as follows:

[0089] The semantic similarity and semantic correlation strength are extracted from the multi-layer fractal logic graph, and the semantic correlation between task nodes is calculated by weighted combination to generate a semantic correlation matrix.

[0090] Set a dynamic threshold for relevance, and identify semantically stable regions and semantically abrupt regions in the semantic relevance matrix based on the semantic change rate and the dynamic threshold for relevance.

[0091] The semantically stable region is a region with a low semantic change rate and semantic relevance exceeding the dynamic threshold of relevance, and the semantically abrupt region is a region with a high semantic change rate and semantic relevance not exceeding the dynamic threshold of relevance, used to represent sudden task events.

[0092] Based on the semantic relevance matrix, logically similar task pairs are identified, a task symmetry layer is constructed, and a semantic coupling channel is established for logically similar task pairs in the task symmetry layer.

[0093] In practical applications, by weighting the combination of semantic similarity and semantic relevance strength, and comprehensively considering the semantic consistency and logical dependence between tasks, the semantic features of each layer in the fractal logic graph are quantified into a unified relevance matrix. This achieves the fusion and expression of multi-dimensional semantic relationships, providing a quantitative basis for subsequent identification of logically similar task pairs, enabling the system to accurately capture the deep logical connections between task semantics. A dynamic threshold mechanism is introduced, dynamically dividing the semantic stable region and the semantic mutation region, thereby accurately distinguishing the stability and volatility of task semantics at different stages, strengthening the system's self-awareness of semantic evolution, and avoiding classification errors caused by fixed thresholds. The semantic stable region corresponds to the stable mode in system operation, reflecting the long-term consistent logical cooperation relationship between tasks; the semantic mutation region reveals dynamic events such as sudden tasks, process disturbances, or semantic deviations, enabling the system to distinguish between stable semantic links and sudden semantic events, providing a basic structure for intelligent anomaly diagnosis and process optimization. By using a semantic relevance matrix to select task pairs with similar logical features, a task symmetry layer is formed in the high-level logical structure. By establishing a semantic coupling channel in this layer, information synchronization and semantic resonance between logically similar tasks can be achieved, improving the coordination and consistency between logistics tasks. This enables the system to reuse semantic experience in different scenarios and achieve semantic-level adaptive optimization and intelligent process balance.

[0094] Based on the semantic relevance matrix, the steps of identifying logically similar task pairs, constructing a task symmetry layer, and establishing semantic coupling channels for logically similar task pairs within the task symmetry layer are as follows:

[0095] Based on the semantic relevance matrix, the logical similarity index of task nodes is calculated, logically similar task pairs are selected and clustered to generate semantically symmetric identifiers;

[0096] A task symmetry layer is established above the task layer of the multi-layer fractal logic graph. The task symmetry layer is an extended logic layer of the multi-layer fractal logic graph, with the logically similar task pairs as basic node units.

[0097] In the task symmetry layer, a spatial mapping relationship for logically similar task pairs is constructed based on the distribution of the logical similarity index.

[0098] In the task symmetry layer, an independent semantic coupling channel is assigned to each logically similar task pair for dynamic sharing of semantic information between the logically similar task pairs.

[0099] In practical applications, quantitative analysis of the semantic relevance matrix is ​​performed to calculate the logical similarity index between task nodes, thereby identifying task pairs that are highly consistent in semantic patterns, functional purposes, or execution logic. By clustering similar task pairs and assigning them semantically symmetric labels, a logically identifiable semantic grouping structure is formed, enabling the system to extract stable logically symmetric units from the complex semantic network of logistics logs. This semantically symmetric structure is explicitly introduced into the fractal logic graph system, constructing an extended logic layer above the task layer. Semantic symmetry becomes a structural component of the system's logical evolution. This task symmetry layer uses logically similar task pairs as the smallest logical unit, forming a structural network reflecting semantic mirror relationships. This elevates the system's logical abstraction level, ensuring that logistics task relationships are not limited to operational logic but also reflect the symmetry and complementarity of semantic logic. Based on the numerical distribution of logical similarity indices, the spatial location and topological relationship of each logically similar task pair in the symmetry layer are determined. By establishing spatial mapping relationships, the distance and hierarchical structure of semantic similarity between tasks can be intuitively reflected, enabling the system to have spatial representation capabilities of semantic structure. This facilitates task mapping, semantic reasoning, and symmetry interference detection in subsequent analysis, realizing the visualization and operationalization of complex semantic networks. By establishing dedicated semantic coupling channels between logically similar task pairs, the system can transfer semantic experience and state information between different but logically similar tasks, thereby achieving semantic-level linkage updates and significantly improving the adaptability and task collaboration of semantic processing.

[0100] Based on the semantic rheological matrix and the task symmetry layer, the steps for generating temporal reflection rules, comparing the predicted path with the actual execution path, and calculating the path deviation residual value are as follows:

[0101] Extract the time series information and semantic change trajectory of each task node from the semantic rheometry matrix, identify the logical dependencies between consecutive tasks, and form a time series chain of task execution.

[0102] Based on the semantic coupling channel of logically similar task pairs in the task symmetry layer, the time delay relationship and semantic mapping between different task nodes are calculated inversely to generate time-series reflection rules.

[0103] The predicted path based on the temporal reflection rule is compared node by node with the actual execution path in the logistics operation of the medical institution. By calculating the time interval difference and semantic deviation between task nodes, a path deviation index set is obtained.

[0104] The residual distribution values ​​at the task node level are calculated based on the path deviation index set to generate path deviation residual values.

[0105] In practical applications, the semantic change trajectories of tasks in logistics operation logs are structured into continuous time-series chains, revealing the logical dependencies and execution order between tasks. By analyzing the semantic flow matrix, the temporal patterns and semantic continuity of task flow are accurately identified, providing a traceable task sequence model for subsequent path prediction and logical verification. This facilitates the transition from static semantic analysis to dynamic logical reasoning in logistics tasks. Utilizing the semantic coupling characteristics of the task symmetry layer, a temporal reflection mechanism between different tasks is established. By calculating the time delay and semantic back-mapping relationship between task nodes, predictive and self-comparative temporal reflection rules can be generated. This allows the system to predict potential path evolution based on the known logical symmetry behavior of tasks, enhancing the intelligence and foresight of logistics operation path analysis. By comparing theoretically predicted paths with actual execution paths, the degree of deviation between them in terms of time and semantics is calculated. Through node-by-node difference calculation, the deviation of task execution is quantified, generating a path deviation index set reflecting process offset and semantic drift. This enables the system to identify logical node decoupling, delayed links, and semantic mismatch phenomena in the logistics operation process, providing quantifiable evidence for operational quality assessment and anomaly diagnosis. By performing hierarchical weighted calculations on the path deviation index set, residual statistical results at the task node level and the global level are generated. These residual values ​​reflect the semantic consistency and temporal stability between the prediction model and the actual execution process, providing a feedback basis for the subsequent self-evolutionary residual correction mechanism. This enables the system to achieve logical optimization and path correction through residual self-learning, thereby continuously improving the intelligent management level and execution consistency of medical logistics operations.

[0106] The steps for calculating the residual distribution values ​​at the task node level based on the path deviation index set and generating path deviation residual values ​​are as follows:

[0107] Extract the time interval difference and semantic deviation of each task node from the path deviation index set, match the corresponding position of the task node in the semantic rheological matrix with the time weight, and determine the time deviation component and semantic deviation component of each task node.

[0108] The time deviation component and semantic deviation component of each task node are weighted and integrated to form a comprehensive deviation value of each task node. The comprehensive deviation value is then mapped on the time axis of the semantic rheological matrix to generate a residual distribution curve at the task node level.

[0109] The residual distribution curves are aggregated to calculate the overall weighted residual value, generating a path deviation residual value, which is used to characterize the degree of deviation between the predicted path and the actual executed path.

[0110] In practical applications, the temporal and semantic differences in the path deviation index are precisely mapped to the semantic rheology matrix, achieving dynamic alignment between deviation data and task nodes. Through time-weighted matching, key offset sources in the execution process of task nodes can be identified, enabling fine-grained localization of deviation sources. This allows the system to distinguish between execution errors caused by time lag or semantic offset, providing a structured foundation for subsequent residual calculation. Different types of deviation factors are fused through a weighting mechanism to form a unified comprehensive deviation index. By mapping the comprehensive deviation value to the time axis of the semantic rheology matrix, a visualized residual distribution curve can be generated, showing the dynamic deviation trends of task nodes in the temporal and semantic dimensions, transforming abstract deviation information into analyzable residual trajectories. By aggregating and analyzing the residual curves of task nodes, a quantitative deviation index at the system-wide level is obtained. The generated weighted residual value can comprehensively reflect the degree of deviation of the entire execution path in terms of temporal progress and semantic logic. The dispersed node deviations are summarized into a global deviation index, providing core input for the self-evolving residual correction mechanism. This enables the system to achieve self-learning and path correction based on the overall residuals, promoting intelligent closed-loop optimization of medical logistics operations.

[0111] Based on the path deviation residual value, a self-evolutionary residual correction mechanism is triggered to obtain the corrected path result. A semantic feedback resonance operation is then performed to achieve the self-learning closed loop of the logistics operation log. Specifically, the steps are as follows:

[0112] The path deviation residual value is fed back into the semantic rheological matrix, a residual threshold is set, and the path deviation residual value is compared with the residual threshold.

[0113] When the path deviation residual value exceeds the residual threshold, the self-evolutionary residual correction mechanism is triggered, and the semantic rheological region is marked as a high residual region to determine the target path segment;

[0114] The time weights and semantic weights of each task node in the target path segment are adaptively adjusted, and the semantic association strength between logically similar task pairs is updated based on the semantic coupling channel to generate a corrected path result.

[0115] The corrected path result is written to the semantic rheological matrix and the task symmetry layer. The difference in semantic changes before and after the correction is compared, the semantic feedback intensity value is calculated, and a semantic feedback resonance layer is established.

[0116] In the semantic resonance layer, the semantic resonance center node is identified, cross-task common logical patterns are extracted, and updated temporal reflection rules are generated and replaced to form a semantic self-learning closed loop of the medical institution's logistics operation log.

[0117] In practical applications, a residual monitoring triggering mechanism is established. By feeding back path deviation residual values ​​to the semantic rheological matrix, real-time binding of deviation states and semantic structures is achieved. Setting residual thresholds enables quantitative identification and dynamic triggering control of abnormal paths, giving the system the ability to consciously identify execution deviations and providing precise start signals for subsequent self-evolutionary correction. Through high residual identification and path segment localization, automatic labeling of deviation areas and determination of target task range are achieved. Through local labeling of semantic rheological regions, the source of anomalies is locked in the global model, focusing on problem concentration areas, reducing global interference, and improving the model's adaptive correction accuracy and execution stability. By adjusting time and semantic dual weights, the path evolution trend is made to conform to logical consistency again. The association strength of logically similar task pairs is updated based on semantic coupling channels, enabling semantic relationships to synchronously self-balance and constructing a task chain structure with self-optimization capabilities. This ensures that the logistics process maintains semantic consistency and execution efficiency even under dynamic changes. By correcting the path results and writing them back, the semantic rheological matrix and the task symmetry layer are updated synchronously. Calculating the semantic feedback intensity value quantifies the scope of the correction's impact. The establishment of the semantic feedback resonance layer is used to capture the collaborative changes between multiple logical layers, enabling the system to achieve feedback linkage at multiple task and semantic levels, laying a dynamic coupling foundation for subsequent self-learning. Global patterns are extracted from the semantic resonance layer. By identifying the resonance center node, cross-task logical commonalities are extracted to form new temporal reflection rules, achieving self-updating of the system's knowledge structure. This transforms the logistics operation log from an experience record into a semantic evolution, realizing a self-learning closed loop at the semantic level and promoting intelligent, adaptive, and continuous optimization of logistics scheduling decisions.

[0118] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. An intelligent processing method for logistics operation logs of medical institutions, characterized in that, Includes the following steps: Collect multi-source log data generated during the logistics operations of medical institutions, perform time-series processing and semantic encoding on it, and generate log semantic vectors; Based on the log semantic vector, a semantic flow matrix of time and task is established, the semantic change rate and correlation strength between each task node are calculated, and a multi-layer fractal logic graph is generated according to the semantic change trend. The specific steps are as follows: Based on the log semantic vectors, each log semantic vector is regarded as a task node, and a semantic flow matrix of time and task is established. Based on the semantic rheometry matrix, the semantic change of task nodes within adjacent time segments is calculated, and the semantic change rate of task nodes is determined according to the ratio of the semantic change to the time interval. Based on the semantic rheometry matrix, the semantic correlation strength between task nodes is calculated according to the semantic labels, semantic similarity and contextual dependencies of each task node. By jointly analyzing the semantic change rate and semantic relevance strength, the direction and trend of semantic evolution are identified, semantic change trend data is obtained, and semantic change trajectory is generated. The semantic change trend data is input into the fractal logic construction module to generate a multi-layer fractal logic graph, which includes three levels: task layer, event layer, and semantic fine-grained layer. Based on the semantic node correlation in the multi-layer fractal logic graph, logically similar task pairs are identified, a task symmetry layer is constructed, and a semantic coupling channel is established for logically similar task pairs in the task symmetry layer. Based on the semantic rheometry matrix and the task symmetry layer, a temporal reflection rule is generated. The predicted path of the temporal reflection rule is compared with the actual execution path, and the path deviation residual value is calculated. Based on the path deviation residual value, a self-evolutionary residual correction mechanism is triggered to obtain the corrected path result, and a semantic feedback resonance operation is performed to realize the self-learning closed loop of the logistics operation log.

2. The intelligent processing method for logistics operation logs of medical institutions according to claim 1, characterized in that, The steps of collecting multi-source log data generated during the logistics operations of medical institutions, performing time-series processing and semantic encoding on it, and generating log semantic vectors are as follows: Collect multi-source log data generated during the logistics operation of medical institutions. The multi-source log data includes equipment operation and maintenance logs, material allocation and supply logs, environmental cleaning and disinfection logs, energy consumption scheduling logs, and abnormal alarm handling logs. The multi-source log data is processed to unify the data format and standardize the timestamps to form a preliminary time-series log; The time series log is segmented by a sliding window, and the time interval and duration of each log event are calculated to generate a time vector matrix; Perform content semantic recognition on the time series logs to extract key semantic units containing task behavior, operation objects and status descriptions; The key semantic units are semantically labeled, and the semantic labels are converted into semantic vectors through a semantic vectorization algorithm to generate a semantic encoding set; The time vector matrix is ​​associated with the semantic encoding set, the time weight and semantic weight of each task node are calculated, and the log semantic vector is generated based on the task time continuity and semantic relevance.

3. The intelligent processing method for logistics operation logs of medical institutions according to claim 2, characterized in that, The steps of associating and mapping the time vector matrix with the semantic encoding set, calculating the time weight and semantic weight of each task node, and generating log semantic vectors based on task time continuity and semantic relevance are as follows: Extract time segment information of each log event from the time vector matrix, and extract the set of semantic tags corresponding to the time segments from the semantic encoding set to establish a preliminary mapping relationship between time segments and semantic tags; Based on the event persistence of each task in the time series and its time dependency with adjacent events, assign time dimension weights to each task node; Semantic relevance analysis is performed on the task behaviors, operation objects and state descriptions in the semantic encoding set. Based on the semantic similarity between tasks in terms of behavior or object and their functional importance in the logistics process, semantic dimension weights are assigned to each task node. The time dimension weights and semantic dimension weights are correlated and mapped to generate log semantic vectors.

4. The intelligent processing method for logistics operation logs of medical institutions according to claim 3, characterized in that, The steps of identifying logically similar task pairs based on the semantic node correlation in the multi-layer fractal logic graph, constructing a task symmetry layer, and establishing semantic coupling channels for logically similar task pairs in the task symmetry layer are as follows: The semantic similarity and semantic correlation strength are extracted from the multi-layer fractal logic graph, and the semantic correlation between task nodes is calculated by weighted combination to generate a semantic correlation matrix. Set a dynamic threshold for relevance, and identify semantically stable regions and semantically abrupt regions in the semantic relevance matrix based on the semantic change rate and the dynamic threshold for relevance. The semantically stable region is a region with a low semantic change rate and semantic relevance exceeding the dynamic threshold of relevance, and the semantically abrupt region is a region with a high semantic change rate and semantic relevance not exceeding the dynamic threshold of relevance, used to represent sudden task events. Based on the semantic relevance matrix, logically similar task pairs are identified, a task symmetry layer is constructed, and a semantic coupling channel is established for logically similar task pairs in the task symmetry layer.

5. The intelligent processing method for logistics operation logs of medical institutions according to claim 4, characterized in that, The steps of identifying logically similar task pairs based on the semantic relevance matrix, constructing a task symmetry layer, and establishing semantic coupling channels for logically similar task pairs within the task symmetry layer are as follows: Based on the semantic relevance matrix, the logical similarity index of task nodes is calculated, logically similar task pairs are selected and clustered to generate semantically symmetric identifiers; A task symmetry layer is established above the task layer of the multi-layer fractal logic graph. The task symmetry layer is an extended logic layer of the multi-layer fractal logic graph, with the logically similar task pairs as basic node units. In the task symmetry layer, a spatial mapping relationship for logically similar task pairs is constructed based on the distribution of the logical similarity index. In the task symmetry layer, an independent semantic coupling channel is assigned to each logically similar task pair for dynamic sharing of semantic information between the logically similar task pairs.

6. The intelligent processing method for logistics operation logs of medical institutions according to claim 1, characterized in that, The step of generating temporal reflection rules based on the semantic rheological matrix and the task symmetry layer, comparing the predicted path with the actual execution path, and calculating the path deviation residual value is as follows: Extract the time series information and semantic change trajectory of each task node from the semantic rheometry matrix, identify the logical dependencies between consecutive tasks, and form a time series chain of task execution. Based on the semantic coupling channel of logically similar task pairs in the task symmetry layer, the time delay relationship and semantic mapping between different task nodes are calculated inversely to generate time-series reflection rules. The predicted path based on the temporal reflection rule is compared node by node with the actual execution path in the logistics operation of the medical institution. By calculating the time interval difference and semantic deviation between task nodes, a path deviation index set is obtained. The residual distribution values ​​at the task node level are calculated based on the path deviation index set to generate path deviation residual values.

7. The intelligent processing method for logistics operation logs of medical institutions according to claim 6, characterized in that, The step of calculating the residual distribution value at the task node level based on the path deviation index set and generating the path deviation residual value is as follows: Extract the time interval difference and semantic deviation of each task node from the path deviation index set, match the corresponding position of the task node in the semantic rheological matrix with the time weight, and determine the time deviation component and semantic deviation component of each task node. The time deviation component and semantic deviation component of each task node are weighted and integrated to form a comprehensive deviation value of each task node. The comprehensive deviation value is then mapped on the time axis of the semantic rheological matrix to generate a residual distribution curve at the task node level. The residual distribution curves are aggregated to calculate the overall weighted residual value, generating a path deviation residual value, which is used to characterize the degree of deviation between the predicted path and the actual executed path.

8. The intelligent processing method for logistics operation logs of medical institutions according to claim 7, characterized in that, The steps of triggering a self-evolutionary residual correction mechanism based on the path deviation residual value to obtain the corrected path result, executing a semantic feedback resonance operation, and realizing a self-learning closed loop for the logistics operation log are as follows: The path deviation residual value is fed back into the semantic rheological matrix, a residual threshold is set, and the path deviation residual value is compared with the residual threshold. When the path deviation residual value exceeds the residual threshold, the self-evolutionary residual correction mechanism is triggered, and the semantic rheological region is marked as a high residual region to determine the target path segment; The time weights and semantic weights of each task node in the target path segment are adaptively adjusted, and the semantic association strength between logically similar task pairs is updated based on the semantic coupling channel to generate a corrected path result. The corrected path result is written to the semantic rheological matrix and the task symmetry layer. The difference in semantic changes before and after the correction is compared, the semantic feedback intensity value is calculated, and a semantic feedback resonance layer is established. In the semantic resonance layer, the semantic resonance center node is identified, cross-task common logical patterns are extracted, and updated temporal reflection rules are generated and replaced to form a semantic self-learning closed loop of the medical institution's logistics operation log.

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