A power distribution engineering construction progress deviation automatic early warning method

Abstract

The application relates to a power distribution engineering construction progress deviation automatic early warning method, construction event sequences are constructed through process logs, BIM change records and scheduling instruction data, semantic correlations among key construction entities are extracted, and three-dimensional semantic fingerprint is generated to represent a construction path. In combination with a historical case library, a state machine model is used to complete path state identification and predictor parameter mapping. The system compares the current semantic fingerprint with the center fingerprint in real time, jointly triggers the prediction model switching through the scheduling change signal, and realizes the adaptive response to the working condition change. The sliding window and residual compensation are used to improve the progress prediction accuracy, risk early warning information is generated according to the deviation amplitude, and the result is fed back to the management platform to support the dynamic data closed loop iteration. The method improves the real-time performance, accuracy and robustness of the progress monitoring.

Description

Technical Field

[0001] This invention relates to the field of intelligent prediction and early warning technology for the progress of power distribution projects, and in particular to an automatic early warning method for deviations in the construction progress of power distribution projects. Background Technology

[0002] Intelligent prediction and deviation early warning systems for power distribution engineering construction progress are a crucial component of the digital construction of power infrastructure. Addressing the challenges of complex construction processes, frequent resource allocation adjustments, and sudden scheduling changes at construction sites, the industry has gradually adopted progress prediction solutions based on big data, graph models, and machine learning technologies. Current mainstream technologies include constructing a construction process topology using multi-source information such as process flow logs, BIM models, and on-site scheduling instructions. Traditional graph analysis algorithms, topology sorting, critical path method (CPM / PDM), and graph neural networks are then used to extrapolate construction progress trends and provide risk warnings. These solutions can achieve a certain level of accuracy in progress analysis under static or weakly dynamic scenarios. Furthermore, by combining statistical regression or neural network models, historical data can be fitted to optimize prediction results. In recent years, with the enrichment of digital data streams at engineering sites, some research has introduced dynamic topology learning, online updating of sequence model parameters, deep learning model retraining, and multi-model fusion to enhance adaptability in dynamic environments.

[0003] Currently, representative technical approaches include: First, using process network graphs, which reflect resource and time-series dependencies through node weights and edge constraints. This is suitable for engineering scenarios with low change frequency, but under conditions of frequent construction path adjustments, resource conflicts, and temporary scheduling, the graph structure needs to be reconstructed in real time, significantly increasing computational and storage costs, and prediction results are prone to time lag. Second, applying neural network-based progress prediction schemes, such as using LSTM or Transformer to predict process time series, or combining graph neural networks to extract features from construction topology changes. These schemes have strong generalization ability on large-scale, long-term construction datasets and can adapt to certain path disturbances. However, when actual working conditions change drastically, multiple rounds of deep model retraining are required to maintain accuracy, leading to increased inference latency and resource consumption, and limited real-time compensation capability for on-site feedback. Third, some systems attempt incremental parameter updates or multi-model weighted decision-making, that is, adjusting key parameters or switching sub-models based on the latest data to improve prediction adaptability. However, this still relies on frequent calibration of the underlying model parameters and fails to effectively avoid problems such as model oscillation, prediction drift, and data overfitting.

[0004] From the perspective of the application scope of existing technologies, traditional critical path analysis and static graph models are suitable for power distribution projects with stable schedules and infrequent changes. While neural networks and deep learning methods improve the expressive power and dynamic adjustment capabilities of models, they have high requirements for data quality, model training resources and real-time performance. They are easily affected by noisy data and multi-source data fusion errors. In actual deployment, it is difficult to accurately capture local features in complex process changes, and the adaptive adjustment of model parameters lacks a unified and robust process control mechanism.

[0005] In the context of real-world scenarios during power distribution engineering construction, such as frequent adjustments to path structures, sudden changes in scheduling events, and the interplay of data heterogeneity and resource conflicts, existing technologies still have the following prominent issues in terms of adaptive adjustment of model parameters: First, relying on real-time reconstruction of the complete topology map presents challenges such as high computational burden, weak topology stability, and difficulty in on-site implementation. Predictive performance is prone to drastic fluctuations when topology changes frequently. Second, the online parameter learning process of neural networks and deep learning methods is limited by real-time feedback from on-site data, lacking intrinsic stability and automatic switching mechanisms. Frequent model retraining can easily lead to decreased prediction consistency and resource waste. Third, multi-model fusion or incremental parameter adjustment faces risks such as overfitting, prediction delays, and even inaccurate model selection, making it difficult to continuously output highly reliable progress prediction results under conditions of changing construction paths and frequent on-site instructions. Fourth, existing solutions lack a mechanism to organically combine construction semantics with the dynamic state of work processes, making it difficult to achieve accurate state identification and parameter switching under different changing working conditions, easily introducing redundant decisions and stray errors. Fifth, the real-time compensation capability for feedback errors during the prediction process is limited, making it unable to flexibly respond to abnormal situations such as drastic fluctuations in construction efficiency and sudden resource shortages, thus affecting the timeliness and accuracy of the progress early warning system.

[0006] In summary, existing technologies for predicting the progress of power distribution engineering projects and providing early warning of deviations have not fully addressed the uncertainties of adaptive adjustment of model parameters in dynamic construction environments. Particularly in scenarios involving changes in path semantics, temporary scheduling instructions, and heterogeneous concurrent multi-source data, prediction systems are prone to problems such as unstable switching, error propagation, and delayed response. To address these technical shortcomings, there is an urgent need to develop a new progress prediction solution that combines semantic abstraction and state control capabilities, enabling low-overhead adaptation to drastic path changes and rapid model switching and error compensation. This solution should avoid traditional topology reconstruction and deep model retraining practices, fundamentally improving the system's adaptability and stability, and ensuring highly reliable and consistent progress prediction and timely risk warnings throughout the entire dynamic evolution of power distribution engineering construction. Summary of the Invention

[0007] This application provides an automatic early warning method for deviations in the construction progress of power distribution projects, which aims to solve one of the problems or issues of the existing technology mentioned in the background.

[0008] This application provides an automatic early warning method for construction progress deviations in power distribution engineering, specifically including: S1: Acquire multi-source data during the construction of the power distribution project and preprocess it to generate a construction event sequence.

[0009] S2: Based on the construction event sequence, extract three types of semantic associations: temporal constraints, resource coupling, and preconditions. Map these three types of semantic associations to three dimensions: logical rigidity, resource sensitivity, and environmental dependence to generate semantic fingerprints.

[0010] S3: Based on the semantic fingerprints of different working conditions in the historical change case library, construct a state machine model and label multiple state clusters, bind a set of offline-calibrated regressor parameter groups to each state cluster, and generate a mapping table.

[0011] S4: Calculate the cosine distance between the current semantic fingerprint and the center fingerprint of the currently active state in the state machine model, and perform a dual-condition judgment in combination with the process change mark to generate a migration trigger signal.

[0012] S5: If the migration trigger signal meets the preset jump threshold and is accompanied by a process change mark, then the regressor parameter group corresponding to the target state cluster is loaded according to the mapping table, and the state switch is completed after the dwell time constraint is met, and a prediction model instance is generated.

[0013] S6: Use the prediction model instance to perform trend extrapolation on subsequent construction events, and call the progress feedback data of the most recent time windows in the cache to construct a deviation compensation vector. Perform residual correction on the output result of the trend extrapolation to generate a compensated progress prediction value.

[0014] S7: Compare the compensated progress forecast with the planned schedule baseline, calculate the progress deviation, and if it exceeds the warning threshold, generate warning information including the deviation direction and the expected lag time.

[0015] S8: Upload the warning information and the state identifier of the current state machine model to the power engineering management platform, and update the local fingerprint cache to enhance the statistical distribution characteristics of the current state cluster, thus completing the data iteration of the closed-loop monitoring process.

[0016] The automatic early warning method for construction progress deviation in power distribution engineering provided in this application has the following beneficial effects: (1) In view of the problem that the traditional topology modeling method is difficult to adapt in real time, has high computational load and poor prediction stability due to frequent changes in the construction path of power distribution projects, this application constructs a lightweight trend prediction mechanism based on semantic fingerprint and finite state machine, which significantly improves the response speed and engineering applicability of the prediction system. Traditional methods rely on the dynamic reconstruction of complete construction drawings and online updates of neural network weights. These methods are prone to lag effects and model oscillations when faced with frequent changes. This solution, however, extracts process entities and their three core semantic relationships (temporal constraints, resource coupling, and preconditions) from unstructured text, generating a low-dimensional, compact path semantic fingerprint. This fingerprint only characterizes the path's distribution features across three orthogonal dimensions: "logical rigidity," "resource sensitivity," and "environmental dependence." This achieves a highly abstract expression of complex construction logic, avoiding strong dependence on specific node connections, thus possessing strong generalization ability and cross-project transfer potential. Furthermore, the path evolution process is modeled as a finite state machine, with each state corresponding to a typical construction mode (such as "mainline-first" and "emergency detour"). State transitions are judged through a dual triggering mechanism of semantic fingerprint similarity jumps and scheduling events, effectively identifying substantial path adjustments rather than local fluctuations. This effectively overcomes the technical shortcomings of existing technologies where minor perturbations lead to frequent retraining or misjudgments.

[0017] (2) At the prediction execution level, this application abandons complex online learning strategies such as multi-model fusion and error iteration correction, and adopts an architecture design with pre-calibrated lightweight regressor binding state, combined with a sliding window bias compensation mechanism, which greatly reduces computational overhead and system parameter tuning difficulty while ensuring prediction accuracy. Each state is associated with a set of piecewise linear models and residual correction trees that have been calibrated for multiple operating conditions offline. After state transition, the corresponding predictor combination is directly loaded without any parameter optimization or gradient update, achieving a "ready-to-use" rapid response capability, suitable for resource-constrained field edge environments. At the same time, a cache-based actual progress feedback mechanism is introduced, using measured data from multiple recent time windows to dynamically offset the output results. This retains the ability to correct short-term deviations while avoiding repeated adjustments to the internal structure of the model, significantly improving the robustness and long-term operational stability of the system. In addition, a hard constraint is set for the state dwell time of no less than 24 hours to forcibly suppress state jitter caused by temporary interference in a short period of time, prevent high-frequency oscillations in the prediction results, and ensure sufficient continuity and operability of management decisions, which is significantly better than the prediction drift and control instability problems commonly found in existing technologies.

[0018] (3) By sinking the key processing logic to the edge gateway side and only uploading the status ID and compensation offset to the central platform, this application greatly reduces the communication bandwidth occupation and the centralized computing pressure in the cloud, and constructs an efficient, stable and scalable distributed prediction architecture. The core operations such as semantic fingerprint generation, status matching and cache update are all completed on the local edge device. The transmitted data is compressed from the megabyte volume of the original log to the status identifier and numerical offset of hundreds of bytes. The communication frequency and data throughput are reduced by more than two orders of magnitude compared with the traditional full upload scheme, which fully adapts to the real conditions such as network instability and bandwidth limitation that are common in power construction sites. This design not only improves the deployment flexibility and operating efficiency of the system, but also provides a replicable technical path for the unified monitoring of large-scale engineering project groups. In summary, this scheme replaces structural fitting with semantic abstraction, parameter updates with state switching and online learning with cache compensation, forming a closed-loop adaptive prediction system for dynamic construction scenarios. While maintaining high prediction adaptability, it achieves system-level lightweighting, low latency and strong robustness, and provides a new technical support with both practicality and reliability for power distribution project progress management. Attached Figure Description

[0019] Figure 1 This is the main flowchart of an automatic early warning method for deviations in the construction progress of power distribution projects.

[0020] Figure 2 This is a sub-flowchart of an automatic early warning method for deviations in the construction progress of power distribution projects.

[0021] Figure 3 This is another sub-flowchart of an automatic early warning method for deviations in the construction progress of power distribution projects. Detailed Implementation

[0022] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0023] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0024] like Figure 1As shown, this application provides an automatic early warning method for deviations in the construction progress of power distribution projects, specifically including: S1: Acquire multi-source data during the construction of the power distribution project and preprocess it to generate a construction event sequence.

[0025] S2: Based on the construction event sequence, extract three types of semantic associations: temporal constraints, resource coupling, and preconditions. Map these three types of semantic associations to three dimensions: logical rigidity, resource sensitivity, and environmental dependence to generate semantic fingerprints.

[0026] S3: Based on the semantic fingerprints of different working conditions in the historical change case library, construct a state machine model and label multiple state clusters, bind a set of offline-calibrated regressor parameter groups to each state cluster, and generate a mapping table.

[0027] S4: Calculate the cosine distance between the current semantic fingerprint and the center fingerprint of the currently active state in the state machine model, and perform a dual-condition judgment in combination with the process change mark to generate a migration trigger signal.

[0028] S5: If the migration trigger signal meets the preset jump threshold and is accompanied by a process change mark, then the regressor parameter group corresponding to the target state cluster is loaded according to the mapping table, and the state switch is completed after the dwell time constraint is met, and a prediction model instance is generated.

[0029] S6: Use the prediction model instance to perform trend extrapolation on subsequent construction events, and call the progress feedback data of the most recent time windows in the cache to construct a deviation compensation vector. Perform residual correction on the output result of the trend extrapolation to generate a compensated progress prediction value.

[0030] S7: Compare the compensated progress forecast with the planned schedule baseline, calculate the progress deviation, and if it exceeds the warning threshold, generate warning information including the deviation direction and the expected lag time.

[0031] S8: Upload the warning information and the state identifier of the current state machine model to the power engineering management platform, and update the local fingerprint cache to enhance the statistical distribution characteristics of the current state cluster, thus completing the data iteration of the closed-loop monitoring process.

[0032] Step S1: Acquire multi-source data during the power distribution project construction process and preprocess it to generate a construction event sequence. Specifically, this includes: S1.1: Perform multi-source data acquisition and processing on the process log text stream, BIM change record data packet, and on-site dispatch instruction message queue to generate a set of multi-source data to be processed containing the original text content and metadata tags.

[0033] It should be noted that the preprocessing includes five sub-steps: multi-source data acquisition, named entity extraction, timestamp time zone normalization and format standardization, sliding time window time sequence alignment, redundancy filtering and logical consistency verification, which correspond to S1.1 to S1.5 below, respectively.

[0034] Perform connection initialization on the raw data channels of the process log text stream, BIM change record data packet, and site dispatch instruction message queue from the construction site, and establish a set of session control parameters for multi-source data access to ensure that each data source starts transmitting synchronously.

[0035] The character encoding format of the process log text stream is standardized by adopting the UTF-8 standard and eliminating illegal character sequences to ensure unbiased input conditions in subsequent natural language processing.

[0036] Perform structural decompression on the BIM change record data package, extract metadata fields such as version number, component identifier, and change time, and store them separately from the original model file for independent subsequent use.

[0037] The system performs protocol parsing and header field validation on the on-site dispatch instruction message queue, extracts the operation type, sender identity, and priority level, and establishes a message index table in the local cache to sort messages according to arrival time.

[0038] For the three types of data sources mentioned above, master data fields containing raw text or binary content are generated respectively, and metadata tags such as source type, collection time, and collection location are attached to achieve data source traceability and context integrity.

[0039] Through a multi-source data synchronization buffer mechanism, the master data fields and metadata tags processed from all sources are combined into a unified format set of multi-source data to be processed, thereby achieving consistent management and a unified entry point for cross-source data.

[0040] By using a multi-source data acquisition and processing method, the results of the previous step are transformed into a complete set of multi-source data to be processed, enabling high-precision input conditions for subsequent entity extraction and time-series alignment.

[0041] For example, at a power distribution construction site, the process log text stream is uploaded in real time by three construction tablet terminals via a TCP long connection. Each log entry contains information such as the construction process description, executor, and operation time, encoded in GB2312. In this step, it is converted to UTF-8 and unparseable byte sequences are removed. BIM change record data packets are automatically pushed by the on-site BIM server every two hours. The file format is a ZIP compressed package, containing IFC model fragments and change instructions. In this step, the decompression parameters are configured to a maximum file size of 500MB and the change time field is extracted. The on-site dispatch instruction message queue comes from the dispatch center's MQTT message channel, with the message subject being "Construction / Dispatch Instruction". In this step, the operation type field value is parsed as "Temporary Addition" and the priority level is "3". In this scenario, unified metadata tags were established for the three types of data sources, such as "Source Type = Log", "Collection Time = 2024-06-13 08:45:00", and "Collection Location = Line Segment A". The final multi-source data set to be processed contains 96 log records, 4 BIM change record packages, and 12 scheduling instruction messages. Each data in the set has complete content and metadata tags. Verification showed that the input integrity and time synchronization indicators were significantly improved, providing stable input for named entity extraction in S1.2.

[0042] S1.2: Based on the multi-source data set to be processed, a key construction entity extraction operation is performed using a pre-trained named entity recognition model in the field of power engineering to generate an original entity recognition result list containing specific work objects such as cable laying and transformer installation and their attribute information.

[0043] Based on the process log text stream, BIM change record data package and on-site dispatch instruction message queue contained in the multi-source data set to be processed, a pre-trained named entity recognition model in the power engineering field is invoked, its weight parameter file and domain-specific dictionary are loaded, and a context encoder and sequence labeling inference chain for power construction scenarios are established.

[0044] Sentences in the process log text stream are serialized and mapped according to character-level word segmentation rules to generate an embedding matrix for the model input, and a domain label vector representing the process type is appended to the embedding matrix.

[0045] The attribute fields in the BIM change record data package are subjected to structured expansion processing. The component name, change type and quantity information in the nested table are converted into a continuous coding sequence, and the coding dimension is unified to ensure compatibility with the process log input format.

[0046] For each message in the on-site dispatch instruction message queue, keyword matching and context window extraction are performed. Within the context window, entity words related to the construction task are captured to form a candidate sequence of annotations that can be input into the named entity recognition model.

[0047] The sequences from the three sources mentioned above are input into the multi-channel inference engine of the named entity recognition model. The conditional random field decoding layer performs optimal path search on the label probability distribution of each sequence to generate a label sequence output that indicates the entity category, location range, and attribute features.

[0048] Perform entity instantiation on the tag sequence output, record the name, specifications, quantity, location and other attribute information of specific work objects such as cable laying, transformer installation, and grounding resistance testing in a preset format, and generate a list of original entity recognition results.

[0049] This processing method transforms the multi-source data set to be processed in the previous step into raw entity data with clear work objects and attribute characteristics, ensuring the consistency and accuracy of subsequent timestamp standardization and time sequence alignment inputs, and achieving high-precision conversion of multi-source text data into structured construction entity information.

[0050] For example, named entity recognition (NAME) processing is performed on a multi-source dataset containing 100 process log records, 50 BIM change records, and 30 on-site dispatch instructions. The NAME recognition model employs a bidirectional LSTM encoder and a CRF decoding layer, with an embedding dimension of 256, a dictionary size of 5000, and a power construction-specific thesaurus. After character-level word segmentation of the process log input sequence, an embedding matrix of length 120 is generated. For the component names and change types extracted from the BIM change records, an attribute embedding sequence of length 80 is generated. For the context windows extracted from the on-site dispatch instructions, a candidate embedding sequence of length 60 is generated. In the label probability distribution output by the model decoding, the label probability of the cable laying entity is 0.98, the label probability of the transformer installation entity is 0.95, and the label probability of the grounding resistance test entity is 0.93, all exceeding the preset confidence threshold of 0.9. Record the name, specifications and quantity information according to the preset entity attribute format. For example, the entity attribute record for transformer installation is "Name: Transformer, Specification: 10kV, Quantity: 2 units". Finally, a list of original entity recognition results with a total of 180 items is generated. Subsequent timestamp normalization and time sequence alignment are performed on the basis of this result, which significantly improves the recognition accuracy and greatly improves the completeness of attribute fields.

[0051] S1.3: Perform time zone normalization and format standardization processing on the timestamp field of each entity in the original entity recognition result list to eliminate the time base differences of multi-source data and generate a standardized timestamp sequence with a unified time dimension.

[0052] Establish a unified parsing entry point for the timestamp field of each entity in the original entity identification result list, load the timestamp data from the process log, BIM change record and on-site scheduling instructions, and confirm its time zone identifier and data source format specification.

[0053] All timestamps are mapped to the corresponding time zone offset table based on the data source identifier, and the time value offset processing is performed using the Coordinated Universal Time (UTC) conversion algorithm to generate an intermediate timestamp sequence with a unified reference.

[0054] The intermediate timestamp sequence is processed by calling the format standardization module to convert string formats, epoch time values ​​or second formats from different sources into the "YYYY-MM-DDTHH:MM:SS" format that conforms to the ISO8601 standard, and ensure that the full accuracy reaches the second level.

[0055] The formatted time value is calibrated to the fractional second place using a timestamp precision detector. Values ​​in millisecond or microsecond format are adjusted to the second granularity through rounding or truncation rules to ensure the consistency of timing calculations.

[0056] A unified time dimension index table is established for time values ​​that have completed time zone normalization and format standardization, so that they can be quickly called up and the time comparison can be guaranteed to be unbiased in the subsequent sliding time window matching and event sequence generation process.

[0057] By using time zone normalization and format standardization, the original entity recognition result list from the previous step is transformed into a standardized timestamp sequence with a unified time dimension, thereby eliminating the time reference of multi-source data and ensuring consistency in time series calculation.

[0058] For example, in the data acquisition system of a power distribution engineering construction site, the timestamp format of the work log is "2024 / 03 / 15 14:25:36" and the time zone is UTC+8; the timestamp format of the BIM change record is the Unix epoch second value "1710486336" and the time zone is UTC; and the timestamp of the on-site dispatch instruction is "15-Mar-2024 06:25:36" and the time zone is Eastern North American Time. During processing, the work log time is first subtracted by an offset of 8 × 3600 seconds to convert it to UTC; the BIM change record is directly parsed into UTC time; and the dispatch instruction time is added to an offset of 5 × 3600 seconds to convert it to UTC. The unified UTC time values ​​are "2024-03-15T06:25:36", "2024-03-15T06:25:36", and "2024-03-15T06:25:36". These are converted to ISO8601 format and calibrated for second precision using a format standardization module, forming a standardized timestamp sequence. This sequence ensures complete consistency of the same event time across the three types of source data during subsequent time alignment, significantly improving the accuracy of time sequence matching and the stability of process logic derivation.

[0059] S1.4: Based on the standardized timestamp sequence and the original entity identification result list, a sliding time window matching algorithm is used to perform multi-source data time sequence alignment operation to solve the reporting delay problem of different data sources and generate a preliminary aligned construction event sequence strictly arranged in time order.

[0060] S1.5: Perform redundancy filtering and logical consistency verification on the preliminary aligned construction event sequence to remove duplicate reported events and abnormal records that violate the process logic, and finally generate a construction event sequence containing key construction entities and their precise timestamps.

[0061] Step S2: Based on the construction event sequence, extract three types of semantic relationships: temporal constraints, resource coupling, and preconditions. Map these three types of semantic relationships to three dimensions: logical rigidity, resource sensitivity, and environmental dependence, generating a semantic fingerprint. Specifically, this includes: S2.1: Perform sliding window traversal processing on the construction event sequence, identify the temporal constraints between key construction entities based on the order of timestamps, and generate a temporal constraint set containing pairing information of preceding and subsequent processes.

[0062] It should be noted that the three dimensions represent different semantic features of the construction path: the logical rigidity dimension quantifies the stability of temporal constraints, reflecting the certainty of the time dependency between preceding and subsequent processes; the resource sensitivity dimension quantifies the intensity of resource coupling, reflecting the sensitivity of the construction path to schedule fluctuations due to resource competition or sharing; and the environmental dependence dimension quantifies the complexity of the precondition logic chain, reflecting the strength of the construction path's dependence on external environmental constraints. These three dimensions are orthogonal to each other and are independently calculated by an orthogonal encoding algorithm from the temporal constraint set, resource coupling matrix, and precondition logic chain, respectively, together constituting the three-dimensional feature space of the semantic fingerprint.

[0063] S2.2: Based on the time-series constraint set and resource allocation records in the multi-source data, execute the resource conflict detection algorithm to extract the resource coupling between key construction entities and generate a resource coupling matrix that describes the competition or sharing status of the same resource between different processes.

[0064] Based on the time-series constraint set and resource allocation records in multi-source data, a structured resource record table containing construction entity identifiers, resource type codes, and scheduling allocation details is read to form an initial resource entity mapping set that can be analyzed.

[0065] The initial resource entity mapping set is grouped and statistically analyzed according to the construction entity identifier, generating a unique resource code set and an index table of the time period for each type of construction entity to establish the temporal reference relationship of resource occupation.

[0066] Cross-reference the pairing information of the preceding and subsequent processes in the time-series constraint set with the index table of occupied time periods to construct an association list of shared resources between process nodes, and initialize the resource coupling candidate set.

[0067] A conflict detection algorithm is applied to the resource coupling candidate set. The overlap determination function of time periods is used to calculate the overlap degree of any two processes occupying the same resource. The overlap degree formula is defined as follows:

[0068] in This represents the set of time periods occupied by the first process. This represents the set of time periods occupied by the second process, with the numerator being the overlap time length and the denominator being the total coverage time length.

[0069] The system compares the crossover value with a preset conflict threshold to determine whether the resource is in a competitive state (crossover greater than the threshold) or a shared state (crossover less than or equal to the threshold) between different processes, and writes the determination result into the corresponding position of the resource coupling matrix.

[0070] The resource coupling matrix is ​​subjected to symmetry verification and sparsity compression processing. Zero-valued elements with no coupling relationship are removed, while entries with significant competition and sharing relationships are retained. The final resource coupling matrix data describing the resource coupling between key construction entities is output.

[0071] Through the above processing method, the time-series constraint set and resource allocation record generated in the previous step are transformed into a quantifiable resource coupling matrix, realizing the accurate extraction and structured expression of the resource association relationship of the construction path, and providing a stable data foundation for subsequent precondition analysis and feature dimension mapping.

[0072] For example, in a power distribution engineering construction scenario, the input time-series constraint set includes the pairing information of the sequence of cable laying and cable joint fabrication. In the resource allocation record table of multi-source data, the cable laying process occupies resource R1 (construction team A) for a time period of 08:00-12:00, and the cable joint fabrication process occupies the same resource R1 for a time period of 10:00-14:00. The time periods occupied by the two processes on resource R1 are represented as T={8,9,10,11,12} and T'={10,11,12,13,14}, respectively. The calculated value of the resource occupation crossover degree is 0.428. If the conflict threshold is set to 0.4, it is determined that the resource is in a competing state between the two processes, and the corresponding entry in the resource coupling matrix is ​​assigned a value of 1 (competition identifier). In another pair of processes, the transformer installation process occupies resource R2 (crane equipment) from 09:00 to 11:00, and the grounding resistance testing process occupies the same resource from 15:00 to 17:00. The cross-degree calculation result is 0, indicating a fully shared state, and the corresponding entry is assigned a value of -1 (sharing identifier). After full calculation and symmetry verification, the matrix compression retains only entries with significant competition and sharing relationships. The output matrix is ​​used for subsequent precondition analysis and orthogonal feature mapping. Verification results show that the accuracy of coupling relationship extraction is significantly improved, and the storage overhead after matrix sparsification is significantly reduced compared to the original matrix.

[0073] S2.3: Using the resource coupling matrix and the environmental constraints in the on-site scheduling instructions, analyze the preconditions between key construction entities and generate a logical chain of preconditions representing the necessary environmental dependencies for starting a process.

[0074] S2.4: Execute an orthogonal encoding algorithm on the time-series constraint set, resource coupling matrix and precondition logic chain to calculate the values ​​of the three feature dimensions: logic rigidity coefficient, resource sensitivity index and environment dependence weight, and generate a three-dimensional orthogonal feature vector.

[0075] Based on the generated set of temporal constraints, resource coupling matrix and precondition logic chain, feature numerical calculation operations are performed according to the fixed three-dimensional orthogonal coding principle to ensure that the three types of semantic associations do not interfere with each other and are comparable in the feature space.

[0076] The set of timing constraints is input into the logic rigidity calculation module. For each pair of preceding and subsequent processes, constraint strength coefficients are constructed based on the minimum time interval between processes and the frequency of process sequence changes. These coefficients are then stored in the coefficient matrix as corresponding entity pairs in rows and columns.

[0077] The constraint strength coefficient matrix is ​​subjected to feature normalization processing, all coefficients are mapped to the closed interval [0,1] and weighted by time span to form the logic rigidity coefficients that characterize the stability of the path logic.

[0078] Input the resource coupling matrix into the resource sensitivity calculation module, calculate the probability of use conflict and the proportion of occupation duration for shared resources and competing resources respectively, and sum and average them according to resource category to form the resource sensitivity index.

[0079] A dependency weight extraction module based on environmental constraints is constructed, which maps the trigger frequency of factors such as climate, site, and regulation involved in the precondition logic chain to numerical weights, and considers the product effect of multiple conditions to form environmental dependency weights.

[0080] An orthogonal coding algorithm is used to map the above-mentioned logical rigidity coefficient, resource sensitivity index and environmental dependence weight to three mutually perpendicular feature dimensions. Each dimension is then processed into a unit vector and numerical concatenation is performed.

[0081] The spliced ​​3D vectors are corrected to ensure that the Euclidean distance calculation between different dimensions is not affected by the difference in dimensions, and then solidified into a 3D orthogonal feature vector format.

[0082] By using orthogonal coding algorithms and numerical processing methods, the semantic association results from the previous step are transformed into quantifiable three-dimensional vector data, thereby achieving accurate numerical representation of the semantic association of construction paths.

[0083] For example, in a power distribution project construction dataset, the timing constraint set contains 15 pairs of process relationships, where the minimum time interval for "cable laying → grounding resistance testing" is 2 hours, the variation frequency is 0.05, and the logic rigidity coefficient is calculated using the formula:

[0084] in The time interval is in hours. For the frequency of change, The logical rigidity coefficient is calculated to be approximately 0.904. In the resource coupling matrix, transformer installation and busbar connection share the same hoisting equipment, with a conflict probability of 0.2 and an occupancy duration ratio of 0.4. The sensitivity index is calculated using the formula:

[0085] in Let's say it's the probability of conflict. For the percentage of occupancy, The sensitivity index is calculated to be 0.3. In the precondition logic chain, cable laying depends on ground humidity being below 15% and a construction permit being required. The trigger frequencies for these two conditions are 0.25 and 0.4 respectively, and the dependency weights are calculated using the formula:

[0086] in , These are the conditional trigger frequencies, The dependency weight was calculated to be 0.1. After processing with the orthogonal coding algorithm, the three-dimensional feature vector is (0.904, 0.3, 0.1), and its length has been fixed by unit vectorization. The verification results show that this vector can significantly improve the matching accuracy and stability of subsequent semantic fingerprints.

[0087] S2.5: Based on the three-dimensional orthogonal feature vectors, perform normalization concatenation and fixed-length hash mapping to eliminate dimensional differences and solidify the vector length, thereby generating the semantic fingerprint.

[0088] Based on the calculated three-dimensional orthogonal feature vector, the input object is a vector set containing three numerical components: logical rigidity coefficient, resource sensitivity index, and environmental dependence weight. Each component has different dimensions and value ranges. Normalization is performed on this three-dimensional orthogonal feature vector using a max-min normalization method to map each component to the [0,1] interval, ensuring comparability of different feature dimensions in subsequent concatenation. The normalization formula is:

[0089] in These are the original component values. For the normalized result, and These are the minimum and maximum values, respectively.

[0090] The three normalized components are concatenated in a fixed order to form a consistent and clearly dimensional three-dimensional feature vector structure. A fixed-length hash mapping is then performed on the concatenated vector, selecting a hash function with low collision probability and preserving numerical distribution characteristics to map the concatenated vector to encoded values ​​of a preset length. During the hash mapping process, the normalized concatenated vector is converted into a byte sequence and input into a hash compression function. Cyclic shifts and bitwise XOR operations are used to generate a highly unique fixed-length fingerprint vector. The output fixed-length hash value undergoes sign equalization and distribution smoothing to ensure the uniformity and stability of the feature values ​​across all dimensions.

[0091] By using normalized concatenation and fixed-length hash mapping, the three-dimensional orthogonal feature vector from the previous step is transformed into a semantic fingerprint with fixed length, consistent dimensions, and stable distribution, thereby achieving performance consistency and improved judgment accuracy in subsequent state machine matching and path dynamic recognition stages.

[0092] For example, the input three-dimensional orthogonal feature vector has a logical rigidity coefficient of 8.5, a resource sensitivity index of 15.2, and an environmental dependence weight of 4.3. Using the max-min normalization method, the known global minimum values ​​of the three-dimensional components are 2.0, 10.0, and 1.0, and the known global maximum values ​​are 10.0, 20.0, and 5.0, respectively. The normalization results are then 0.8125, 0.52, and 0.825. The normalization results are concatenated in a predetermined order to form [0.8125, 0.52, 0.825], and processed by a fixed-length hash mapping function to obtain a 32-bit fixed-length semantic fingerprint code, such as "A9F34C7B126D85E0...". In comparative verification of different construction paths, this fingerprint code can significantly improve the stability of state cluster matching, avoid similarity calculation deviations caused by inconsistent dimensional scales, and greatly improve the judgment accuracy and response stability of the prediction model in dynamic path recognition scenarios.

[0093] like Figure 2 As shown, step S3 involves: constructing a state machine model and labeling multiple state clusters based on the semantic fingerprints of different operating conditions in the historical change case library; binding a set of offline-calibrated regressor parameter groups to each state cluster; and generating a mapping table. Specifically, this includes: S3.1: Perform cluster analysis on the multi-condition semantic fingerprint data stored in the historical change case database, and use the orthogonal feature spatial density peak detection algorithm to identify high-confidence fingerprint clustering regions to generate multiple state cluster center fingerprint sets with clear boundary features.

[0094] It should be noted that the state machine model is a finite state automaton used to characterize the dynamic evolution logic of the power distribution engineering construction path under different working conditions. The specific architecture of this state machine model includes: a set of state nodes, where each state node corresponds to a typical path state cluster identified by cluster analysis, and is uniquely identified by the central semantic fingerprint of the state cluster; a set of state transition edges, where each directed edge represents a migration relationship from one state node to another, and the edge weights are derived based on the temporal transition frequency statistics of adjacent states in a historical change case library; and a currently active state register, used to store the state node identifier of the construction path at the current moment. This state machine model can adopt a deterministic finite state machine structure, meaning that for any combination of current state and input conditions, at most one target state can be transitioned to, where the input conditions include the cosine distance jump signal between the current semantic fingerprint and the central fingerprint, and a process change marker. The size of the state space of the state machine model is determined by the number of typical path state clusters generated by cluster analysis, and it supports offline pre-construction and online static operation. That is, after the topology of the state nodes and migration edges is constructed in the offline stage, the state identifier is only updated according to the migration trigger signal in the online stage without changing the structure of the state machine model itself.

[0095] The initial conditions for performing clustering analysis on the multi-condition semantic fingerprint data stored in the historical change case database are as follows: the input data contains fixed-length semantic fingerprint vectors generated by step S2, along with condition labels and path change timestamps. The dataset covers various path characteristic scenarios, such as trunk-first, parallel-interlacing, and emergency detour. Based on this input, the orthogonal feature space builder is invoked to remap each fingerprint vector to a unified feature space according to the three-dimensional feature dimensions of logical rigidity, resource sensitivity, and environmental dependence, and normalization is performed to eliminate the dimensional differences between the dimensions. The feature space density function is calculated for the normalized fingerprint vector set, and the density peak detection algorithm is used to scan the density distribution surface of the entire three-dimensional space, extracting the coordinates of local extreme points as the initial cluster centers. For each initial center point, a neighborhood search operation is performed, and a neighborhood set containing high-confidence samples is selected according to the set density threshold. The centroid coordinates of the fingerprint vectors in the set are calculated as the center fingerprint of the state cluster. The following formula is used to calculate the centroid coordinates:

[0096] in, As the center fingerprint vector, For a single fingerprint vector in the neighborhood set, The total number of samples within the neighborhood set is represented. All centroid coordinates are isolated and tested based on boundary features, eliminating clusters with frequent boundary intersections to improve clustering stability, and outputting a set of state cluster center fingerprints with clear boundary features. Through this processing, the semantic fingerprint data from the previous step is transformed into a set of center fingerprints with high-confidence clustering characteristics, thereby improving the initialization accuracy of state nodes during the finite state machine construction phase.

[0097] For example, in a historical case database of a power distribution construction site, there are 1200 semantic fingerprint vectors for different path change scenarios. Each vector is 64-dimensional and is composed of orthogonally encoded features of three categories: logical rigidity, resource sensitivity, and environmental dependence. After normalization, the density threshold of the orthogonal feature space density peak detection algorithm is set to 0.15, and 38 local density extrema points are obtained as initial cluster centers. A search is performed on the neighborhood radius of each center point with a radius of 0.35 to extract a high-confidence sample set and calculate the centroid. For example, for a certain parallel interleaved path state cluster, there are 52 sample vectors in its neighborhood set. The centroid is calculated using the above formula, and the coordinates of the center fingerprint in the three-dimensional feature space are (0.62, 0.48, 0.31). After boundary isolation testing, this cluster does not intersect with other clusters and is retained as the center fingerprint of the high-confidence state cluster. After the entire clustering process is executed, the output central fingerprint set has a size of 27, covering all major working condition types. This set can be used in the subsequent state machine definition stage to effectively improve the accuracy of state transition determination.

[0098] S3.2: Define the state nodes of the state machine model based on the central fingerprint set of the state clusters, and construct state transition edges according to the statistical results of the transition frequency of each state cluster in the time-series evolution process, so as to generate a finite state machine topology that can completely represent the dynamic evolution logic of the construction path.

[0099] S3.3: For the historical progress deviation sequence data associated with each state cluster in the finite state machine topology, a hybrid modeling strategy combining piecewise linear regression and residual correction tree is used for offline training to generate a set of regressor parameters that are adapted to the operating characteristics of the state cluster.

[0100] For the historical schedule deviation sequence data associated with each state cluster in the finite state machine topology, an archived historical schedule deviation vector arranged by time slices is loaded to ensure the consistency of the data samples within the corresponding state cluster. A segmented indexing operation is performed on the loaded historical schedule deviation vector, dividing the overall time series into several segments according to defined process node boundaries, ensuring that the data in each segment maintains local stability in three dimensions: logical rigidity, resource sensitivity, and environmental dependence. Least square fitting is performed on each segment, and a piecewise linear regression model is used to extract the baseline trend coefficient and intercept value for that segment. The calculation process uses the following formula:

[0101] in, Represents a time slice sequence. This indicates the corresponding schedule deviation value. The regression slope coefficient is used. The obtained piecewise linear regression residual sequence is trained using a residual correction tree. The CART decision tree algorithm is used to map the interpretable portion of the residuals into adjustment factors based on semantic fingerprint features. Standardization is performed on the piecewise regression coefficients and residual correction tree node weights to form a structured regressor parameter set, ensuring that parameters from different state clusters can be directly loaded and used. Through a hybrid modeling strategy of piecewise linear regression and residual correction trees, historical deviation sequence data is transformed into lightweight regressor parameters that can be quickly invoked and are adaptable to different operating conditions, enabling the prediction module to accurately extrapolate without online optimization.

[0102] For example, in a backbone-led state cluster, the historical progress deviation sequence data contains 120 time slices, with a slice interval of 2 hours, divided into 4 segments according to the process boundary, each segment containing 30 samples. Least square fitting is performed on the time series x and deviation value y of the first segment, resulting in a regression slope β = 0.45, an intercept of -0.12, and a corresponding piecewise fitting residual mean of 0.03. The residual sequence of each segment is input into a CART residual correction tree, with a tree depth of 5 and 12 leaf nodes. The semantic fingerprint value in the logical rigidity dimension is used as one of the input features, and the output node weighted residual correction value ranges from -0.05 to 0.06. Z-score standardization is performed on the fitting coefficients and tree node weights to form a regressor parameter set containing 4 sets of piecewise linear model coefficients and corresponding weights of the 4 residual correction trees. During the prediction invocation phase, this state cluster can directly load this parameter set, significantly improving the prediction accuracy of construction trends, and eliminating the need to retrain the model during state transitions.

[0103] S3.4: Perform key-value pair mapping and binding operations on the state node identifiers in the finite state machine topology and the corresponding regressor parameter groups, and use the hash index mechanism to establish a fast retrieval channel to generate a state predictor mapping table containing the correspondence between all states and predictors.

[0104] Using the state node identifiers in the finite state machine topology as retrieval keys, the input data structure of the mapping table construction process is normalized to ensure that the data type and length of the identifiers remain consistent across all state nodes. Based on the regressor parameter set calibrated offline under multiple operating conditions, index serialization operations are performed on the piecewise linear model coefficients and residual correction tree node weights within it, mapping the multidimensional features of the parameter set into fast-retrieval fixed address references. A hash function suitable for the state node identifiers is selected using a hash index mechanism, and hash buckets are initialized according to a preset number of buckets. The identifier is input into the hash function to calculate the hash value, and the corresponding storage bucket is located through the hash value to perform key-value pair binding operations. For each state node identifier, the corresponding regressor parameter set reference is stored in the hash bucket using a hash collision resolution strategy (such as chaining or open addressing), ensuring that the target parameter set can be accessed with constant time complexity during the retrieval phase. A full traversal verification is performed on the bound key-value pair set to verify the consistency between the state node identifiers and parameter set references, and a state predictor mapping table containing the correspondence between all state nodes and predictors is generated. By establishing a retrieval channel with hash index acceleration capabilities, the finite state machine topology result from the previous step is transformed into state-to-predictor mapping data that can be quickly invoked by the predictive logic, thereby achieving low-latency parameter loading during state transitions.

[0105] For example, in the dynamic evolution management scenario of power distribution engineering construction paths, there are three state clusters: backbone-first, parallel-interleaving, and emergency detour. Each cluster corresponds to the offline-calibrated piecewise linear model coefficient matrix and residual correction tree weight table. State node identifiers use 32-bit unsigned integer encoding, and the regressor parameter group reference storage address is represented by a 64-bit pointer. The hash function uses multiplicative hashing, with the multiplier parameter set to 2654435761 and the number of hash buckets set to 256. The hash value is calculated using the following formula:

[0106] in, The integer value of the state node identifier. The modulo operator is used. When k is 1024, the calculated hash value is 64, and the corresponding hash bucket contains parameter group references to the backbone-lookahead state cluster. Collision resolution uses chaining, maintaining a linked list of parameter group references within the same hash bucket. During retrieval, the hash bucket address is directly located using the above formula, and the linked list within the bucket is traversed sequentially. After matching the state node identifier, the corresponding predictor parameter group reference is returned, keeping the retrieval latency on the order of constants. This method achieves fast parameter loading in 1000 state transition queries, significantly improving the response speed of prediction logic switching and effectively reducing online computational overhead.

[0107] It should be noted that the mapping table is the state predictor mapping table, which uses the state node identifier as the key and the corresponding regressor parameter group storage reference as the value, and is organized using a hash index mechanism.

[0108] S3.5: Perform consistency verification and redundancy compression on the generated state predictor mapping table, remove low-frequency abnormal state clusters and optimize the parameter group storage format to output a final state predictor mapping table with high stability and low storage overhead for the real-time prediction module to call.

[0109] The state predictor mapping table dataset generated by the preceding sub-step S3.4 is subjected to structural consistency verification. Input conditions include the established set of finite state machine state node identifiers, the corresponding regressor parameter group storage units, and the key-value pair mappings output by the hash index mechanism. A multi-pass scan verification algorithm is employed. First, it checks all state entries in the mapping table for anomalies such as unbound parameter groups or mismatched bound parameter group formats. Structural integrity is ensured by comparing the mapping table index with the metadata of the parameter group storage area. A state call frequency vector is generated using redundancy detection logic, and abnormal state clusters with call frequencies below a set lower limit are removed based on statistical thresholds, thereby reducing the number of invalid predictor loads. Parameter group storage compression is performed on the retained high-frequency state clusters. Fixed-length encoding and differential storage strategies are used to compress the piecewise linear model coefficients and residual correction tree structure data. Before compression, binary sequence consistency hash verification is used to ensure that parameter accuracy is not lost during compression. The storage overhead of the compressed parameter group is evaluated using the following formula to ensure that it does not exceed a preset upper limit:

[0110] in Let be the number of bytes in the compressed parameter group of the i-th state cluster. This represents the total number of state clusters. The compressed mapping table, which has passed consistency verification, is output to the real-time prediction module's call interface. Through the aforementioned multiple logical verifications, redundancy removal, and storage optimization processes, the result of the previous step is transformed into a final state predictor mapping table with high stability and low storage overhead, enabling fast retrieval and steady-state operation for real-time calls.

[0111] For example, when executing this sub-step in a power distribution engineering management platform, the input state predictor mapping table contains 120 state entries, each bound to piecewise linear model coefficients (32 bytes long) and residual correction tree data (128 bytes long). During the consistency check phase, 12 missing parameter groups for state entries were detected. After repair, a call frequency vector was generated, and 18 low-frequency state clusters with a call frequency less than 5 were removed. In the compression phase, fixed-length encoding was used for the piecewise linear model coefficients, compressing the 32 bytes to 24 bytes; differential storage was used for the residual correction tree data, compressing the 128 bytes to 80 bytes. Binary hash verification was performed on the compressed data to confirm no precision loss. During storage overhead evaluation, based on the above calculation formula, the total number of state clusters was 102. The evaluation result was lower than the preset upper limit, and the compressed mapping table was finally exported to the platform's real-time prediction module. After execution, the predictor loading response time was significantly shortened, and the system maintained prediction stability even in scenarios with frequent changes in construction paths.

[0112] like Figure 3 As shown, step S4 involves calculating the cosine distance between the current semantic fingerprint and the center fingerprint of the currently active state in the state machine model, and combining this distance with a process change marker to perform a two-condition judgment, thereby generating a migration trigger signal. Specifically, this includes: S4.1: Obtain the semantic fingerprint generated at the current moment and the center fingerprint of the currently active state in the state machine model, and perform normalization preprocessing on the two fixed-length feature vectors to eliminate the difference in dimensions and generate a standardized semantic fingerprint vector and a standardized center fingerprint vector.

[0113] S4.2: Based on the standardized semantic fingerprint vector and the standardized central fingerprint vector, the vector space mapping algorithm is used to perform dot product operation and modulus ratio calculation to quantify the distribution differences between the two in the three orthogonal dimensions of logical rigidity, resource sensitivity and environmental dependence, thereby generating a cosine distance value that represents the degree of deviation of path evolution.

[0114] For the normalized preprocessed semantic fingerprint vector and the center fingerprint vector, the computation module of the vector space mapping algorithm is called to perform the dot product operation, multiplying and summing the corresponding components of the three orthogonal feature dimensions to form the dot product value that quantifies the overall similarity of the two vectors.

[0115] The two vectors are input into the modulus calculation unit, and their respective modulus values ​​are obtained by taking the square root of the sum of squares. This ensures that the modulus acquisition process maintains equal weighting in the three dimensions of logical rigidity, resource sensitivity, and environmental dependence.

[0116] The dot product value and the magnitude values ​​of the two vectors are input into the ratio calculation unit, and the cosine distance is calculated using fractional ratio logic. To avoid zero magnitude anomalies, a protection term with a very small positive constant needs to be added to the denominator.

[0117] The cosine distance values ​​are truncated and rounded using a high-precision floating-point format to remove low-significant bit noise generated during floating-point operations, ensuring the stability of subsequent threshold comparisons.

[0118] The processed cosine distance value is stored in the path evolution deviation cache area and used as one of the inputs for subsequent dual-condition logic judgment to quantify the degree of deviation of the current construction path evolution.

[0119] By using a vector space mapping algorithm and a modulus ratio calculation method, the normalized feature vector from the previous step is transformed into a cosine distance value that represents the degree of deviation in path evolution, thus achieving precise quantification of the state changes of the construction path.

[0120] S4.3: Obtain the on-site dispatch instruction data stream, perform keyword matching and semantic parsing on the data stream to identify whether it contains operation identifiers for process cancellation or temporary addition, and then generate a Boolean value for process change marker that represents a substantial change in the construction plan.

[0121] The system obtains the field dispatch instruction data stream as input and calls the power engineering dedicated communication interface to receive dispatch instruction message packets containing text content and metadata tags in real time.

[0122] The scheduling instruction message packet is processed by protocol decoding and content separation to extract plain text paragraphs and associated timestamps and executor identification information, forming a structured scheduling instruction data unit that can be semantically parsed.

[0123] Based on the structured scheduling instruction data unit, the keyword matching logic is executed. A special keyword dictionary for the power construction field is used to search for relevant identifier words for process adjustment in the text content in both exact matching and fuzzy matching modes, and a list of keyword hit records containing the matching hit position index and corresponding words is generated.

[0124] The semantic parsing module is invoked to perform contextual dependency analysis on the keyword hit record list. Syntactic dependency tree and semantic role labeling technology are used to confirm the semantic category and operation intent of the keyword in the instruction text, and to determine whether it belongs to the operation type that causes substantial changes to the construction path plan, such as process cancellation or temporary addition.

[0125] Based on the semantic category determination result, construct a Boolean value generation logic. If any process cancellation or temporary addition operation type is detected, assign the value "true"; otherwise, assign the value "false". Output a Boolean value representing a process change flag indicating a substantial change in the construction plan.

[0126] Through the semantic parsing and logical judgment processing methods described above, the scheduling instruction data is transformed into Boolean decision variables that can directly participate in the generation of migration trigger signals, thereby achieving accurate identification of major changes in the construction plan.

[0127] For example, in a power distribution engineering construction management platform, the data stream of on-site dispatch instructions is received in real time by the gateway device. Each instruction message packet is 5KB in size and includes fields such as timestamp, executor, and text content. The keyword matching dictionary is configured to include 150 entries, including "cancel," "delete," "add," "temporary," and combined phrases. The matching mode threshold is set to consider a fuzzy match hit if the character edit distance is ≤2. The semantic parsing module uses a role labeling algorithm based on dependency syntax, with a syntax tree depth of 5 levels. Role categories include 10 types, such as actions, objects, and conditions. For example, an instruction text is "Temporarily add a grounding resistance test procedure." Keyword matching hits "temporary" and "add." Semantic parsing confirms that it acts on the procedure object category and labels it as "temporary addition," assigning a true Boolean value. In a test processing 100 instructions at once, the proportion of true Boolean values ​​for output procedure change markers significantly increased, and the accuracy of triggering path state transitions was greatly improved when combined with cosine distance jump judgments.

[0128] It should be noted that the process change flag is a Boolean value generated by keyword matching and semantic parsing of the on-site scheduling instruction data stream. It is used to characterize whether a substantial change has occurred in the construction plan. A true flag indicates that a process cancellation or temporary addition has been detected; a false flag indicates that no such operation has been detected.

[0129] S4.4: Perform a size comparison operation based on the cosine distance value and the preset jump threshold, and input the comparison result and the Boolean value of the process change mark into a dual-condition logic AND gate circuit for joint judgment to verify whether the two constraints of significant fingerprint distribution jump and explicit change of scheduling instruction are satisfied at the same time, thereby generating a preliminary state transition judgment result.

[0130] S4.5: Based on the preliminary state transition determination result, if the determination is true, a high-level active pulse signal is instantiated; if the determination is false, a low-level hold signal is instantiated to output the final transition trigger signal, driving subsequent modules to perform the loading and switching operations of the regressor parameter group.

[0131] The input conditions include the Boolean value of the preliminary state transition judgment result generated from sub-step S4.4, and the corresponding cosine distance value and process change mark dual-condition judgment result. The Boolean value is input as a digital signal to the state judgment logic unit, and two control parameters, high-level threshold and low-level threshold, are set for signal instantiation processing. A judgment value matching operation is performed. If the Boolean value is true, the high-level pulse template data is called in the signal generation module, and the waveform amplitude, duration, and trigger delay parameters in the template are loaded into the output buffer. If the Boolean value is false, the low-level hold template data is called, its amplitude parameter is set to zero, and the spectral components are calibrated to an idle state to ensure that subsequent modules are not triggered. A timestamp binding operation is performed on the generated pulse or hold waveform, appending the current system clock count value to the signal header to ensure that subsequent regressor parameter group loading and switching operations can be executed according to the accurate time of triggering. The signal with the appended timestamp is then subjected to amplitude normalization processing to adapt to the calibration standard of the state machine model input characteristics. By using the above signal instantiation and calibration processing methods, the judgment result of the previous step is transformed into a standardized migration trigger signal that can drive the predictor switching module, thereby achieving accurate start-up and false trigger suppression of the state switching process.

[0132] For example, in a dynamic progress monitoring scenario for power distribution engineering construction, the high-level amplitude is set to 5V with a duration of 200ms, and the low-level amplitude is set to 0V with a constant duration of 500ms. If the on-site dispatch instruction identifies a process cancellation and the cosine distance calculation result satisfies the threshold condition D≥θ, where D is the cosine distance between the real-time fingerprint and the central fingerprint, and θ is a preset jump threshold, a high-level valid pulse signal is generated and bound to the current clock count value, for example, 102345678ms. The signal amplitude is normalized to 15 times the output, driving the regressor loading process corresponding to the target state in the state predictor mapping table. If the cosine distance is 0.15 and lower than the threshold of 0.25, a low-level hold signal is generated and the idle state waveform record is maintained to avoid unnecessary predictor switching. In this embodiment, the predictor switching delay after the high-level pulse trigger is less than 1ms, which significantly improves the prediction logic response speed under dynamic path identification and maintains stable triggering characteristics in multiple repeated experiments.

[0133] Step S5: If the migration trigger signal meets the preset jump threshold and is accompanied by a process change marker, then the regressor parameter group corresponding to the target state cluster is loaded according to the mapping table, and the state switch is completed after the dwell time constraint is met, generating a prediction model instance. Specifically, this includes: S5.1: Perform logical parsing processing on the migration trigger signal to extract the target state cluster identifier contained therein, so as to locate the corresponding regressor parameter group index in the state predictor mapping table.

[0134] It should be noted that the prediction model instance is an independently executable adaptive progress prediction unit used to extrapolate the trend of subsequent construction event sequences. The specific architecture of this prediction model instance includes: a regressor parameter group storage area, used to store the structured weight data of the piecewise linear regression model coefficient vector and residual correction tree calibrated offline under multiple working conditions; a deviation compensation vector cache area, used to store the sliding window deviation compensation vector generated by residual correction calculation from the actual progress feedback data of the most recent multiple time windows; a prediction logic execution unit, with built-in piecewise linear mapping algorithm and exponentially weighted moving average algorithm, used to receive the construction event sequence to be predicted and output the original progress trend extrapolation result; and a residual correction interface, used to receive the deviation compensation vector and perform algebraic superposition correction on the original progress trend extrapolation result to generate the compensated progress prediction value. The prediction model instance is bound one-to-one with the state clusters of the state machine model. That is, each state cluster is uniquely associated with a set of regressor parameters. When the state machine model switches states, the prediction model instance atomically replaces the regressor parameter set by redirecting the internal register pointer, and at the same time resets the historical cache count of the bias compensation vector, so as to achieve the adaptive prediction capability that can be used immediately without the need for online parameter optimization or model retraining.

[0135] The input migration trigger signal is parsed at the protocol layer and structured decoded, separating the high-level active pulse flag and the attached status flag field in the signal into independently processable data units.

[0136] The separated state identifier field is semantically mapped to convert the original state identifier into a hash-encoded value that can be directly indexed into the state predictor mapping table.

[0137] Perform conflict detection and correction operations on the generated hash code values ​​to ensure that parameter group positioning is not incorrect due to encoding conflicts during the mapping table retrieval process.

[0138] After conflict correction, the mapping table retrieval algorithm is called on the hash code value to locate the corresponding target state cluster entry and extract the bound regressor parameter group index.

[0139] The extracted index value is validated to determine whether it exists in the full index set of the current version's state predictor mapping table, and recorded as the unique location basis for subsequent loading operations.

[0140] Through the above processing method, the migration trigger signal of the previous step is transformed into index data that can be used to retrieve the target state prediction parameter group, so as to achieve accurate pointing of the loading of prediction logic parameters during the state switching process.

[0141] For example, in the operating environment of the power distribution engineering construction management platform, the local migration trigger signal contains two fields: "State Cluster ID: SC_07" and "Trigger Type: High Level Active". After decoding by the protocol layer, the original state identifier SC_07 is obtained. SC_07 is CRC32 hash-encoded to generate the encoded value 25146312. After comparison with the hash collision table, no collision is found, and the value is directly retrieved from the mapping table. In the state predictor mapping table, the regressor parameter group index corresponding to the encoded value 25146312 is IDX_145. The piecewise linear model coefficient set and residual correction tree structure data associated with this index are verified to be in a usable state. After loading IDX_145, the adaptive prediction model can complete the logical switching while maintaining the current state dwell constraint. It achieves high stability and high accuracy in trend prediction output under the condition of frequent dynamic adjustments of on-site construction nodes. The verification results show that the deviation between the model's progress prediction value and the actual value is significantly reduced in the following three days, and the prediction consistency is significantly improved.

[0142] S5.2: Based on the regressor parameter group index, read the piecewise linear model coefficients and residual correction tree structure data that have been calibrated offline under multiple operating conditions from the local parameter storage area to construct candidate prediction model instances to be activated.

[0143] S5.3: Use the system clock counter to obtain the duration of the current state machine model in the previous state, and compare the duration with the preset state dwell time threshold to generate a state switching permission determination result.

[0144] The input consists of the candidate prediction model instance constructed in step S5.2 and the state operation record of the current system state machine model. For this state operation record, the precise time sampling function of the system clock counter is invoked to generate high-precision time data representing the cumulative dwell time of the current state. This high-precision time data is input to the state dwell time calculation module, and a difference operation is performed in conjunction with the state start timestamp to form the continuous running time value of the current state. The continuous running time value and a preset state dwell time threshold are loaded into the comparison operation unit, and a difference judgment signal is output using an absolute size comparison strategy. The numerical calculation of the difference judgment signal uses the following formula:

[0145] in, For continuous runtime, This is the current system clock count value. This is the count value at the state initiation time. The... With threshold The following logic is used to perform size comparison operations:

[0146] in This is a boolean value for state transition permission. This is a preset threshold for the duration of state dwell. If... If the result is true, the state transition permission determination result is "transition allowed"; otherwise, the result is "transition denied". Through this differential calculation and threshold comparison processing method, the candidate model instance and system state information from the previous step are transformed into an executable state transition permission determination result, thereby achieving accurate verification of the predicted logical transition conditions.

[0147] For example, in a power distribution project construction management platform, a preset state dwell time threshold T is set. r Set to 86400 seconds. The current state machine model records a state start timestamp t0 of 1672451200 seconds, and the current system clock count t is 1672537600 seconds. Differential calculation yields Δt = 86400 seconds. Compare Δt with T... r A size comparison is performed. If the Permit result is true, the state switching permission result is output as allowed. In another scenario, if the current state only runs for 72,000 seconds, then Δt = 72,000 seconds. The comparison result Permit is false, and the switching result is denied. This effectively avoids replacing the prediction logic when the state resides for an insufficient period, maintaining the stability of the prediction system under dynamic path changes.

[0148] S5.4: If the state switching permission determination result is that switching is allowed, then the internal register pointer in the candidate prediction model instance is set to the newly loaded piecewise linear model coefficients and residual correction tree structure data to complete the atomic replacement operation of the prediction logic execution unit.

[0149] The system receives the internal register mapping configuration table from the candidate prediction model instance, along with the newly loaded piecewise linear model coefficient vector and residual correction tree structure data, and establishes the correspondence between register pointers and parameter storage units. The current reference address of the register pointer is written to the starting position of the storage unit pointing to the newly loaded piecewise linear coefficient and residual tree node structure through pointer redirection. After register pointer redirection, a data consistency check is performed to verify that the floating-point bit width of the piecewise linear coefficients and the residual correction tree node index are within the numerical range that the model execution unit can accommodate. An atomic replacement operation is performed on the memory mapping table of the model execution unit, that is, the old parameter group reference is completely replaced with the new parameter group reference within an uninterrupted execution cycle to prevent read-write conflicts under multi-threaded asynchronous access. A register-level barrier instruction is used to refresh the high-speed cache of the model execution unit, allowing the new parameter group to be directly accessed from the cache during subsequent prediction calculations.

[0150] By redirecting register pointers and atomically replacing execution units, the candidate prediction model instance output in the previous step is transformed into a data state whose internal parameters completely point to the newly loaded structure, thereby achieving instant switching and highly stable updates of the prediction logic execution unit.

[0151] For example, when the construction path of a power distribution project transitions from a "parallel interleaving" to an "emergency detour" state, the regressor parameter set corresponding to the target state cluster contains a piecewise linear model coefficient vector with a length of 40 and a residual correction tree node count of 128. During register pointer redirection, the address offset is set to 512-byte alignment to ensure a significant improvement in cache hit rate. Data consistency checks verify that the floating-point width of the piecewise linear coefficients is 32 bits, matching the specifications of the execution unit's arithmetic logic operation module; the correction tree node index range is 0 to 127, conforming to storage area configuration limitations. The atomic replacement process is completed within a 20-nanosecond uninterrupted execution cycle, allowing the execution unit to call the new parameter set for trend extrapolation in the next clock cycle. After the register-level barrier refreshes the cache, the newly loaded piecewise linear coefficients and correction tree nodes show significantly reduced call latency, improving the stability of the prediction output after the construction path state switch, and significantly reducing the deviation of the prediction results from the actual construction period estimation.

[0152] S5.5: Perform initialization and reset processing on the prediction logic execution unit after the replacement is completed, reset the historical cache count of the deviation compensation vector, and output the prediction model instance.

[0153] Step S6: Using the prediction model instance, trend extrapolation is performed on subsequent construction events. A deviation compensation vector is constructed by calling the progress feedback data from multiple recent time windows in the cache. Residual correction is then performed on the output of the trend extrapolation to generate a compensated progress prediction value. Specifically, this includes: S6.1: Obtain the regressor parameter set and the subsequent construction node sequence to be predicted in the prediction model instance, and perform schedule trend extrapolation processing on the subsequent construction node sequence based on the piecewise linear mapping algorithm to generate the original schedule trend extrapolation result containing the basic time estimate.

[0154] S6.2: Read the actual progress feedback data set of the most recent multiple time windows in the local fingerprint cache, and perform time-series alignment and noise filtering on the actual progress feedback data set based on the sliding window statistical algorithm to generate a standardized progress feedback vector that characterizes the recent construction efficiency fluctuations.

[0155] It receives the local fingerprint cache data index passed in from the prediction model instance call chain, locates the actual progress feedback data set stored in the most recent multiple time windows, and extracts the original progress value sequence and its corresponding timestamp identifier according to the time window parameters.

[0156] The extracted timestamp identifiers are compared with a unified time benchmark, and the timestamps of each feedback data are standardized using a time axis normalization mapping algorithm, thereby eliminating time offsets between different windows and constructing a unified time series index.

[0157] Based on a unified time series index, a sliding window statistical algorithm is executed to map the progress feedback numerical sequence in each window to the corresponding time slice position, and the data coverage range is controlled by the window length parameter to achieve time alignment of progress feedback.

[0158] Noise filtering is performed on the time-aligned progress feedback numerical sequence. An exponential smoothing filtering method is used, and the smoothing coefficient is adaptively adjusted according to the noise variance estimate to eliminate the interference of short-term spikes or outliers on the determination of construction efficiency characteristics.

[0159] To achieve quantitative verification of the filtering effect, a smoothing filter formula is constructed:

[0160] in, This represents the progress feedback value for the current time slice. This is the smoothed output value of the previous time slice. This is a smoothing coefficient, and its value is dynamically adjusted based on the noise intensity.

[0161] All time-slice progress feedback values ​​that have undergone noise filtering are stored sequentially in each dimension of a three-dimensional orthogonal feature space vector. Normalization transformation is then performed to eliminate dimensional differences, forming a standardized progress feedback vector that characterizes recent construction efficiency fluctuations.

[0162] By using the aforementioned sliding window timing alignment and noise filtering methods, the original set of actual progress feedback from the previous step is transformed into a standardized progress feedback vector with time consistency, noise suppression capability, and feature normalization properties, thereby providing high-stability input data support for trend extrapolation residual correction.

[0163] For example, in a power distribution project construction monitoring scenario, the system sets the sliding window length to 4 hours. The time ranges covered by the three most recent windows are T0-4h~T0, T0-8h~T0-4h, and T0-12h~T0-8h, respectively, where T0 is the current time. The captured actual progress feedback values ​​within the three windows are [5.2, 5.5, 5.4, 5.3], [4.9, 5.0, 5.1, 5.0], and [4.8, 4.9, 5.0, 4.9], respectively, with timestamp precision at the second level. After time axis normalization processing, the data in each window are arranged according to a unified time index. Assuming the noise variance estimate σ² is 0.002, the smoothing coefficient α = 0.7 is dynamically calculated using the algorithm. Substituting this into the smoothing formula yields the smoothed sequences for the first window: [5.2, 5.41, 5.41, 5.36], the second window: [4.9, 4.97, 5.03, 5.01], and the third window: [4.8, 4.87, 4.93, 4.91]. The filtering results of the three windows are normalized to [0.98, 1.02, 1.02, 1.01], [0.92, 0.93, 0.94, 0.94], and [0.90, 0.91, 0.92, 0.91], forming a 12-dimensional standardized progress feedback vector. This vector is input into the subsequent residual difference calculation logic, ensuring the stability and reliability of the input signal during the trend extrapolation and correction stage, and significantly improving the consistency and availability of the predicted output under dynamic path conditions.

[0164] S6.3: Receive the original progress trend projection result and the standardized progress feedback vector, and perform a comparison operation on the numerical differences between the two in the corresponding time slices based on the residual difference calculation logic to generate an initial residual error sequence that reflects the magnitude of the model prediction deviation.

[0165] S6.4: Obtain the initial residual error sequence and the preset deviation smoothing coefficient, and perform dynamic weight allocation and smoothing fitting on the initial residual error sequence based on the exponential weighted moving average algorithm to generate a deviation compensation vector with time-series memory characteristics.

[0166] The system receives the initial residual error sequence and the stored deviation smoothing coefficients as input conditions. It then maps the indices of each time slice of the residual sequence to the sliding window indices to form a matrix to be weighted. Exponential weights are generated on this matrix in ascending time order, and a weight factor sequence is constructed using an exponentially weighted moving average algorithm. The weight factor formula is as follows:

[0167] in This is the deviation smoothing coefficient. This represents the time window offset from the current time. The weighted factor sequence is multiplied term-by-term with the initial residual error sequence to generate a weighted residual sequence. The weighted residual sequence is then summed and divided by the sum of the weighted factor sequences to obtain the smoothed fitting residual value, as shown in the formula:

[0168] in This is the initial residual error value. For the first The weights of each time slice are assigned. The resulting smoothed fitting residual values ​​are filled into the sliding window index space to generate a bias compensation vector with temporal memory characteristics. Through the above weighting and smoothing fitting process, the initial residual error sequence calculated in the previous step is transformed into temporal compensation data that can be used for subsequent residual correction, thereby improving the dynamic stability of the prediction output.

[0169] S6.5: Load the original progress trend projection result and the deviation compensation vector, and perform residual correction operation on the original progress trend projection result based on the algebraic superposition correction strategy to generate a compensated progress prediction value that eliminates systematic deviations and adapts to the current path state.

[0170] It receives the original progress trend projection results containing the basic time estimate and the deviation compensation vector with time memory characteristics, and uses both as input objects for residual correction calculation.

[0171] The estimated value of each time slice in the original progress trend projection result is subjected to algebraic matching processing with the corresponding deviation compensation vector element to ensure that the mapping relationship is one-to-one.

[0172] Once the matching is complete, the algebraic superposition correction strategy is invoked to add the elements of the deviation compensation vector to the estimated values, thereby forming a corrected sequence of instantaneous predicted values.

[0173] Logical consistency checks are performed on the corrected instantaneous prediction sequence to remove abnormal predictions caused by compensation amplitudes exceeding reasonable limits, and boundary constraints are applied using a limiting function.

[0174] Perform a path state adaptive mapping operation on the predicted value sequence after boundary constraints, mapping the numerical results to the state feature domain corresponding to the current state machine model, so as to generate a compensated progress prediction value that conforms to the current path state.

[0175] By using algebraic superposition correction and state adaptive mapping, the original trend deduction results from the previous step are transformed into highly reliable prediction data that eliminates systematic biases and closely matches the current path state characteristics, thereby optimizing the accuracy and stability of progress prediction under dynamic paths.

[0176] For example, in a power distribution project construction progress prediction scenario, the original progress trend projection results are the basic estimated construction periods for the subsequent five construction nodes, which are 10.4, 9.8, 8.5, 7.9, and 6.8 hours, respectively, with deviation compensation vectors of 0.6, 0.4, -0.2, -0.1, and 0.3 hours. An algebraic superposition operation is performed on each node; for example, the formula for calculating the predicted value after compensation for the first node is:

[0177] in, The predicted value after compensation, These are the original trend projection values. The calculated value is the deviation compensation value. Substituting the specific parameters, the compensated predicted value for the first node is 11.0 hours. The subsequent compensated predicted value sequences are 11.0, 10.2, 8.3, 7.8, and 7.1 hours. Amplitude limiting is applied to this sequence to ensure the predicted values ​​remain within a preset tolerance range (upper and lower limits are 6.0 and 12.0 hours, respectively). All values ​​are within this range, completing the consistency check. The amplitude-limited predicted values ​​are mapped to the feature domain of the current state. For example, if the current state is "parallel interleaving," the mapping coefficient matrix is ​​an identity matrix, and the predicted values ​​remain unchanged before being directly entered into the state domain. Verification results show that the compensated predicted value sequence remains stable under the past three path changes, and the average deviation amplitude is significantly reduced in the subsequent comparative analysis, achieving a substantial improvement in prediction accuracy.

[0178] Step S7: Compare the compensated progress forecast with the planned schedule baseline, calculate the progress deviation, and if it exceeds the warning threshold, generate warning information including the deviation direction and expected lag time. Specifically, this includes: S7.1: Obtain the compensated progress prediction value sequence and the planned duration baseline data set, and perform time axis alignment and interpolation resampling processing on the two time series data to eliminate sampling frequency differences and generate a standardized progress comparison dataset with uniform time granularity.

[0179] The first input dataset is the sequence of compensated projected progress values, and the second input dataset is the baseline dataset of the planned project duration. Both datasets undergo unified time analysis to eliminate sampling frequency differences. Based on the system's global time index table, all timestamp fields in the first input dataset are extracted and standardized to generate a time-zone-specific time-label vector. This time-zone-specific time-label vector is then matched with the original timestamp set of the second input dataset, and the intersection and difference of their timelines are calculated to determine the location of missing sampling points. For each missing sampling point, a polynomial interpolation function is used to generate interpolation estimation results based on the time intervals between adjacent known values.

[0180] The interpolation results are merged back into the original dataset to generate a complete time series. Resampling is then performed on the merged time series, unifying the sampling interval to a preset granularity Δt. During this process, a mean aggregation method is used to merge multiple data points within the oversampling window, calculated using the following formula:

[0181] in For a single data point value within the window, Calculate a uniform window value to represent the number of data points in the window. Perform the above resampling process on the first and second input datasets respectively to generate standardized progress comparison datasets with the same time granularity. Achieve complete comparability by matching timestamps point by point.

[0182] By using a unified time axis normalization and interpolation resampling processing method, the prediction results and planning baseline of the previous step are transformed into standardized progress comparison data with synchronous sampling characteristics, thereby improving the accuracy and stability of the subsequent deviation magnitude calculation.

[0183] For example, in a power distribution project construction progress monitoring scenario, the sampling period for the compensated progress prediction value sequence is 30 minutes, the sampling period for the planned construction period baseline is 60 minutes, and the system presets a unified time granularity Δt of 15 minutes. The first input dataset, after timestamp format conversion, yields a time label vector with unified time zone attributes. The second input dataset, after matching, reveals 8 missing points. Quadratic polynomial interpolation is performed on the missing points, with the interpolation coefficient matrix Pi calculated based on adjacent sampled values ​​and time intervals. For example, for the missing time point t=15:45, the interpolation results are aggregated using the above formula to obtain the window mean. The merged time series is resampled to a unified 15-minute granularity, and the mean calculated when the number of data points within the window is 2 is used to replace the window value. After this processing, the two sequences completely correspond on the time axis, and the standardized progress comparison dataset effectively eliminates sampling frequency differences, demonstrating significantly improved deviation resolution stability and calculation accuracy in subsequent point-by-point difference calculations.

[0184] S7.2: Based on the standardized schedule comparison dataset, the numerical difference between the compensated schedule prediction value and the planned schedule baseline under the same time slice is calculated using the point-by-point difference algorithm to generate the original schedule deviation vector characterizing the degree of instantaneous schedule deviation.

[0185] S7.3: Perform sign discrimination and absolute value mapping operation on the original schedule deviation vector, extract the positive and negative polarity of the deviation value as the deviation direction identifier, and calculate the magnitude of the deviation amplitude to generate a structured schedule deviation feature tuple containing positive lead or negative lag attributes and specific deviation magnitude.

[0186] The input conditions for performing sign discrimination and absolute value mapping operations on the original schedule deviation vector include: the original schedule deviation numerical sequence output from step S7.2, arranged according to a uniform time granularity, and the deviation direction discrimination rules and amplitude calculation modes stored in the system configuration file. Based on the discrimination rules, for each data point in the original schedule deviation numerical sequence, the sign discrimination function is first called to extract its numerical sign. A positive sign is marked as "positive lead," and a negative sign is marked as "negative lag." After sign discrimination, the original deviation numerical sequence is input into the absolute value mapping operator to convert all deviation values ​​into non-negative amplitudes, thus forming an amplitude sequence. For the amplitude sequence, precise modulus calculation is performed to obtain the quantitative index of the deviation amplitude. For cases where the amplitude is a composite difference result, the modulus is calculated using the sum of squares and square root formula. Specifically:

[0187] in, For the magnitude of the deviation, This represents the original deviation value for the corresponding time slice. This represents the index sequence of the i-th time slice. After the magnitude calculation is completed, the sign determination result and the magnitude value are bound into structured data units in tuple format to form a structured schedule deviation feature tuple containing the deviation direction attribute (positive or negative) and the specific deviation magnitude. Through sign discrimination and magnitude calculation processing, the original schedule deviation vector from the previous step is transformed into structured deviation feature data that can be directly used for early warning threshold comparison, realizing the quantitative identification of deviation direction and magnitude.

[0188] For example, in a power distribution project construction scenario, the system obtains the original progress deviation numerical sequence of {-1.8, 2.3, -0.5} days within a certain monitoring period. The sign determination function outputs "negative lag," "positive lead," and "negative lag," respectively, while the absolute value mapping operator outputs the amplitude sequence {1.8, 2.3, 0.5}. During the amplitude magnitude calculation, the squares of each element in the amplitude sequence are summed, and the square root is taken, resulting in a magnitude of 3.02 days. Finally, the system binds the sign determination result with the amplitude magnitude to generate three tuples: [negative lag, 1.8], [positive lead, 2.3], and [negative lag, 0.5], and records the magnitude as 3.02 days in the overall deviation amplitude assessment. This processing result is directly applied in the subsequent warning threshold comparison in S7.4, significantly improving the consistency of deviation direction determination and the accuracy of amplitude quantification.

[0189] S7.4: Receive the structured schedule deviation feature tuple, compare the magnitude of the deviation with a preset dynamic warning threshold, and if the magnitude of the deviation exceeds the dynamic warning threshold, trigger the warning generation mechanism to output a schedule overrun determination result containing a high-confidence risk marker.

[0190] The system receives a structured schedule deviation feature tuple containing positive lead or negative lag attributes and specific deviation magnitudes, and extracts the deviation amplitude magnitude data as the processing object for this sub-step. It performs a dynamic early warning threshold retrieval and loading operation on the deviation amplitude magnitude, reading the dynamic early warning threshold value generated in real-time based on historical operating condition fluctuations and current path status characteristics. The deviation amplitude magnitude and the dynamic early warning threshold are input to the numerical comparison unit, and an absolute value amplitude comparison strategy is used to determine their magnitude relationship. The comparison determination formula is expressed as:

[0191] in, The residual value is used to determine if the deviation exceeds the limit. For the magnitude of the deviation, The dynamic early warning threshold is used. Sign detection processing is performed on the judgment residual value, mapping values ​​greater than zero to a high-level logic signal for a risk-triggered state, and values ​​less than zero to a low-level logic signal for a risk-non-triggered state. The judgment signal is combined and encapsulated with the risk marker information generated by the deviation direction identifier mapping. A coupled logic module is used to generate a high-confidence risk marker and output it to the trigger input of the early warning generation mechanism. Through the above numerical extraction, threshold loading, amplitude comparison, residual sign parsing, and risk marker combination processing, the structured progress deviation feature tuple from the previous step is transformed into a progress over-limit judgment result with risk intensity judgment attributes, achieving accurate screening of early warning trigger conditions and high-reliability marker output.

[0192] For example, in a power distribution engineering construction scheduling system, the received deviation amplitude magnitude is 7.5 hours, and the dynamic early warning threshold is set to 5.0 hours after real-time correction based on historical operating conditions and the current path status. After loading this threshold, an amplitude comparison calculation is performed to determine a residual value of 2.5 hours. Since the residual value is greater than zero, the symbol detection module outputs a high-level valid signal. This signal is combined with the deviation direction identifier "negative hysteresis" to generate a high-confidence risk marker "high hysteresis risk," which triggers an adjustment to the management-side scheduling strategy through the early warning generation mechanism. Under various operating conditions, this logic can significantly improve the accuracy of early warnings when the deviation amplitude magnitude exceeds the threshold, avoiding false triggers. The output results demonstrate high stability and adaptability in actual deployment.

[0193] S7.5: Based on the progress over-limit judgment result and the corresponding structured progress deviation feature tuple, and combined with the historical construction efficiency decay coefficient, calculate the expected lag time under the current deviation trend, so as to encapsulate and generate early warning information containing clear deviation direction identification and quantified expected lag time data.

[0194] It should be noted that the expected lag time is calculated by multiplying the magnitude of the deviation amplitude by the historical construction efficiency attenuation coefficient. Specifically, when the deviation direction is negative lag, the efficiency attenuation coefficient corresponding to the current state cluster is obtained from the historical database, and the magnitude of the deviation amplitude is multiplied by the attenuation coefficient to obtain the expected lag time value, which is output as the expected lag time; when the deviation direction is positive lead, the expected lag time is set to zero.

[0195] The system receives the progress overrun determination result and the structured progress deviation feature tuple, and uses the deviation direction identifier and deviation magnitude as input parameters to calculate the expected lag time.

[0196] The system calls upon the historical construction efficiency attenuation coefficient database, matches the corresponding attenuation coefficient curve based on the deviation direction, and obtains the attenuation coefficient value that matches the current path state cluster characteristics.

[0197] The formula for calculating execution time loss uses the product of deviation amplitude and attenuation coefficient to obtain the calculated value of expected lag time. The value of attenuation coefficient has state cluster specificity to ensure the accuracy of calculation.

[0198] The formula is as follows:

[0199] in, To estimate the lag time, The magnitude of the current deviation. This represents the historical construction efficiency attenuation coefficient.

[0200] The expected lag duration and deviation direction identifier are encapsulated in a unified early warning information data structure to ensure that the fields include directional attributes, quantified lag duration, and high-confidence risk markers.

[0201] During the encapsulation process, the current path status identifier and timestamp are appended to form a warning message with complete semantic context.

[0202] This processing method transforms the results of progress overrun judgments into time loss data that can be directly used for management decisions, thereby achieving high-precision quantification of progress warning duration.

[0203] For example, when the construction path of a power distribution project is in a "parallel and interspersed" state cluster, the magnitude of the deviation amplitude is 2.5 days, and the efficiency attenuation coefficient corresponding to this state cluster in the historical database is 0.8. Using the formula... The calculated value is L = 2.5 × 0.8, and the expected lag time is 2.0 days. For the same state cluster but with the deviation direction marked as positive lead, the system will invert or set the attenuation coefficient to zero according to the configuration, and calculate and output a warning message with no lag time loss. After this step is applied, the management platform can display a risk warning of "lag of 2.0 days" on the warning interface, which significantly improves the scheduling department's response speed to process delay trends.

[0204] Step S8: Upload the early warning information and the state identifier of the current state machine model to the power engineering management platform, and simultaneously update the local fingerprint cache to enhance the statistical distribution characteristics of the current state cluster, completing the data iteration of the closed-loop monitoring process. Specifically, this includes: S8.1: Obtain early warning information containing deviation direction identifier and quantified expected lag time data, as well as state machine model state identifier representing the dynamic evolution logic of the construction path. Perform protocol encapsulation and encryption compression processing on the early warning information and state machine model state identifier to generate a platform interaction data packet with complete context semantics and conforming to transmission security standards.

[0205] S8.2: Receive the platform interaction data packet, perform uplink transmission operation using the power engineering dedicated communication interface, and send the platform interaction data packet to the power engineering management platform to trigger the management end's scheduling strategy adjustment mechanism and generate a construction progress monitoring view containing real-time risk status.

[0206] S8.3: Extract the fixed-length semantic fingerprint generated at the current time and the set of current state cluster center fingerprints representing the high-confidence fingerprint clustering region. Perform a weighted fusion operation on the fixed-length semantic fingerprint generated at the current time and the set of current state cluster center fingerprints based on the incremental learning algorithm to generate an updated state cluster statistical distribution model that reflects the latest path evolution characteristics.

[0207] S8.4: Read the historical semantic fingerprint sequence data stored in the local fingerprint cache, and perform time-series splicing and redundancy removal processing on the updated state cluster statistical distribution model and the historical semantic fingerprint sequence data to generate a new generation of local fingerprint cache dataset containing enhanced statistical features.

[0208] S8.5: Based on the new generation local fingerprint cache dataset, the weights of the state transition edges in the state machine model are dynamically corrected, and the corrected state machine model is used to replace the original model instance to complete the data iteration of the closed-loop monitoring process and improve the judgment accuracy of subsequent transition trigger signals.

[0209] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this invention.

[0210] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains. The terms “first,” “second,” “third,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising” or “including” and similar terms mean that the element or object preceding “comprising” or “including” encompasses the element or object listed following “comprising” or “including” and its equivalents, and do not exclude other elements or objects. The “multiple” mentioned in the embodiments of this application refers to two or more. A and / or B indicate three possibilities: A; B; and A and B.

[0211] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An automatic early warning method for construction progress deviations in power distribution projects, specifically including: S1: Acquire multi-source data during the construction process of the power distribution project and preprocess it to generate a construction event sequence; S2: Based on the construction event sequence, extract three types of semantic associations: temporal constraints, resource coupling, and preconditions. Map these three types of semantic associations to three dimensions: logical rigidity, resource sensitivity, and environmental dependence to generate semantic fingerprints. S3: Based on the semantic fingerprints of different working conditions in the historical change case library, construct a state machine model and label multiple state clusters, bind a set of offline-calibrated regressor parameter groups to each state cluster, and generate a mapping table; S4: Calculate the cosine distance between the current semantic fingerprint and the center fingerprint of the currently active state in the state machine model, and combine it with the process change mark to perform a two-condition judgment to generate a migration trigger signal; S5: If the migration trigger signal meets the preset jump threshold and is accompanied by a process change mark, then load the regressor parameter group corresponding to the target state cluster according to the mapping table, and complete the state switch after meeting the dwell time constraint to generate a prediction model instance. S6: Use the prediction model instance to perform trend extrapolation on subsequent construction events, and call the progress feedback data of the most recent time windows in the cache to construct a deviation compensation vector. Perform residual correction on the output result of the trend extrapolation to generate a compensated progress prediction value. S7: Compare the compensated progress forecast with the planned schedule baseline, calculate the progress deviation, and if it exceeds the warning threshold, generate warning information including the deviation direction and the expected lag time.

2. The automatic early warning method for construction progress deviation in power distribution engineering according to claim 1, characterized in that, Step S7 is followed by step S8, which specifically includes: S8: Upload the warning information and the state identifier of the current state machine model to the power engineering management platform, and update the local fingerprint cache to enhance the statistical distribution characteristics of the current state cluster, thus completing the data iteration of the closed-loop monitoring process.

3. The automatic early warning method for construction progress deviation in power distribution engineering according to claim 1, characterized in that, Step S2 specifically includes: The construction event sequence is processed by sliding window traversal, and the temporal constraints between key construction entities are identified based on the order of timestamps, generating a set of temporal constraints containing pairing information of preceding and subsequent processes. Based on the time-series constraint set and resource allocation records in the multi-source data, a resource conflict detection algorithm is executed to extract the resource coupling between key construction entities and generate a resource coupling matrix describing the competition or sharing status of the same resource between different processes. By using the resource coupling matrix and the environmental constraints in the on-site scheduling instructions, the preconditions between key construction entities are analyzed, and a logical chain of preconditions representing the necessary environmental dependencies for the start of a process is generated. An orthogonal encoding algorithm is executed on the time-series constraint set, resource coupling matrix, and precondition logic chain to calculate the values ​​of the three feature dimensions: logic rigidity coefficient, resource sensitivity index, and environment dependence weight, respectively, and generate a three-dimensional orthogonal feature vector. Based on the three-dimensional orthogonal feature vectors, normalization concatenation and fixed-length hash mapping are performed to eliminate the difference in dimensionality and solidify the vector length, thereby generating the semantic fingerprint.

4. The automatic early warning method for construction progress deviation in power distribution engineering according to claim 1, characterized in that, Step S5 specifically includes: The migration trigger signal is logically parsed to extract the target state cluster identifier contained therein, so as to locate the corresponding regressor parameter group index in the state predictor mapping table; Based on the regressor parameter group index, the piecewise linear model coefficients and residual correction tree structure data calibrated offline under multiple operating conditions are read from the local parameter storage area to construct candidate prediction model instances to be activated. The system clock counter is used to obtain the duration of the current state machine model in the previous state, and the duration of the current state is compared with a preset state dwell time threshold to generate a state transition permission determination result. If the state switching permission determination result is that switching is allowed, the internal register pointer in the candidate prediction model instance is set to the newly loaded piecewise linear model coefficients and residual correction tree structure data to complete the atomic replacement operation of the prediction logic execution unit. The prediction logic execution unit after replacement is initialized and reset, the historical cache count of the deviation compensation vector is reset, and the prediction model instance is output.

5. The automatic early warning method for construction progress deviation in power distribution engineering according to claim 1, characterized in that, The extraction of resource coupling is based on time constraints and resource allocation records. A resource conflict detection algorithm is used to generate a resource coupling matrix that describes the competition or sharing status of the same resource in different processes. The matrix is ​​then subjected to symmetry verification and sparsification compression.

6. The automatic early warning method for construction progress deviation in power distribution engineering according to claim 1, characterized in that, Step S2 further includes using the resource coupling matrix in conjunction with the environmental constraints in the on-site scheduling instructions to analyze the preconditions between construction entities and generate a logical chain of preconditions that characterizes the necessary environmental dependencies for starting a process.

7. The automatic early warning method for construction progress deviation in power distribution engineering according to claim 1, characterized in that, The state nodes of the state machine model are aggregated and extracted from the historical construction case library by a clustering analysis mechanism. The state transition edges are constructed by statistical state time series transition frequencies. Each state cluster is bound to the parameter group obtained by offline piecewise linear regression and residual correction tree hybrid modeling, and stored as a mapping table through hash index.

8. The automatic early warning method for construction progress deviation in power distribution engineering according to claim 1, characterized in that, The regressor parameter group corresponding to the target state cluster is obtained according to the migration trigger signal, and the current state dwell time is combined to determine whether switching is allowed. If it is allowed, the parameter register of the prediction model instance is redirected to the target parameter group to achieve smooth model switching, and the deviation compensation vector is reset.

9. The automatic early warning method for construction progress deviation in power distribution engineering according to claim 1, characterized in that, The dwell time constraint is achieved by setting a state dwell time threshold, which is the minimum dwell time allowed for state switching. The state switching is only allowed when the dwell time of the current state has reached the threshold.

10. The automatic early warning method for construction progress deviation in power distribution engineering according to claim 1, characterized in that, In step S7, the compensated progress prediction value is compared with the planned schedule baseline using a point-by-point differential comparison under a unified time axis to generate progress deviation characteristics, and whether it is a risk event is determined according to the current warning threshold.

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