Digital management method and system for whole process of engineering construction

Abstract

The present application relates to the technical field of engineering management, in particular to a whole-process digital management method and system for engineering construction, wherein the method comprises: generating corresponding decision input description results for each piece of engineering management data through a decision input evaluation function, constructing an input description result set, correcting the decision input evaluation value based on a stage adjustment coefficient to generate an access score, and constructing a qualified data set by screening engineering management data that meets a threshold based on the access score; grouping the qualified data set, calculating the reference level and dispersion degree after grouping based on business value, calculating the abnormal score corresponding to each qualified data, and constructing an engineering anomaly set based on the comparison result of the abnormal score and the early warning threshold; calculating the disposal priority of each anomaly object in the engineering anomaly set, sorting the anomaly objects in the engineering anomaly set based on the disposal priority to form an anomaly disposal queue, and triggering the corresponding engineering management disposal process based on the anomaly disposal team.

Description

Technical Field

[0001] This invention relates to the field of engineering management technology, and in particular to a digital management method and system for the entire process of engineering construction. Background Technology

[0002] Engineering construction projects are typically characterized by long construction periods, numerous participating entities, complex management elements, and continuously changing project status throughout the construction process. Throughout the entire construction process, management elements such as progress, quality, safety, and resources rely on a large amount of engineering management data for reflection and judgment. This data may originate from on-site reporting, equipment data collection, or automatic generation by system processes. With the increasing informatization of engineering management, the scale of data accumulated in engineering management systems continues to expand. However, existing digital management methods generally focus on the display, statistics, or simple early warning of engineering data itself, often assuming that the data inherently possesses consistent management significance, lacking systematic constraints on data formation conditions, timeliness characteristics, and conflicts between similar data. In actual engineering scenarios, the reference value of the same type of engineering management data varies significantly across different project stages. Some data, while existing, may not be suitable for directly triggering anomaly management processes. Furthermore, engineering anomalies are concurrent and stage-sensitive; when multiple anomalies occur simultaneously, the lack of a unified anomaly generation and handling mechanism can easily lead to misallocation of management resources or even amplify project risks. Existing technologies typically separate data screening, anomaly identification, and management and handling, or rely on human experience for judgment. This makes it difficult to form a stable, calculable, and executable digital management chain throughout the entire engineering construction process, thus limiting the actual effectiveness of engineering digital management systems in complex engineering scenarios. Summary of the Invention

[0003] To address the aforementioned issues, this invention provides a digital management method and system for the entire engineering construction process.

[0004] To achieve the above objectives, this invention proposes a method for digital management of the entire engineering construction process, comprising: A set of engineering management data is obtained, and a corresponding decision input description result is generated for each piece of engineering management data in the set of engineering management data through a decision input evaluation function. An input description result set is constructed based on the decision input description result, and the input description result set includes decision input evaluation values. The entire process of engineering construction is divided into several management stages, and a stage adjustment coefficient is configured for each stage. The decision input evaluation value is corrected based on the stage adjustment coefficient to generate an admission score. Based on the admission score, engineering management data that meets the threshold is selected to construct a qualified dataset. Extract the business value corresponding to each qualified data in the qualified dataset, and group the qualified dataset. Calculate the reference level and dispersion of the grouped data based on the business value. Calculate the anomaly score corresponding to each qualified data based on the business value, reference level, and dispersion using an anomaly scoring function. Construct an engineering anomaly set based on the comparison between the anomaly score and the warning threshold. Based on the anomaly score and the preset anomaly handling weight, the handling priority of each anomaly object in the engineering anomaly set is calculated. The anomaly objects in the engineering anomaly set are sorted according to the handling priority to form an anomaly handling queue. The corresponding engineering management handling process is triggered based on the anomaly handling queue.

[0005] Preferably, the process of triggering the corresponding engineering management handling procedure based on the anomaly handling team specifically includes: Based on the anomaly type identifier and the project stage identifier carried by the project anomaly set, and based on the pre-configured mapping relationship between the anomaly type identifier and the handling process, the corresponding handling process is matched. Based on the aforementioned handling process, a corresponding process instance or management task is automatically created, and the business identifier of the engineering exception set is passed into the handling process as a context parameter.

[0006] Preferably, the parameters of the evaluation function include a generation method evaluation factor, a time validity evaluation factor, and a consistency evaluation factor.

[0007] Preferably, the generation method evaluation factor maps the entry identifier to the score by maintaining a project-level configuration mapping table, the time validity evaluation factor is obtained by calculating the timeliness score based on the current time and data timestamp, and the consistency evaluation factor is obtained by pulling a comparison set within the same project range based on the same type of business key, calculating the degree of deviation between the record and the comparison set, and mapping the score.

[0008] Preferably, the reference level is the median or mean of the business values ​​within the group, and the dispersion is the mean absolute deviation or standard deviation of the business values ​​within the group.

[0009] Preferably, the parameters of the anomaly scoring function include business values, reference level, dispersion, stage adjustment coefficient, group stability smoothing parameter, and anomaly cluster count.

[0010] Preferably, the engineering anomaly set includes original data location information, group identifier, engineering stage identifier, anomaly score, and anomaly type field.

[0011] Preferably, the project management data set includes progress reporting records, equipment status records, and automatically generated process records.

[0012] Preferably, the progress reporting record is submitted by the on-site management terminal and written into the business database, the device status record is reported by the device gateway to the access service through a standard interface and stored in the database, and the process automatically generated record is written into the event table through the process engine when the node flows.

[0013] To achieve the above objectives, another aspect of the present invention proposes a digital management system for the entire engineering construction process, comprising: The decision input description construction module is used to acquire an engineering management data set, generate a corresponding decision input description result for each piece of engineering management data in the engineering management data set through a decision input evaluation function, and construct an input description result set based on the decision input description result, the input description result set including decision input evaluation values; The qualified data screening module is used to divide the entire process of engineering construction into several management stages, and configure a stage adjustment coefficient for each stage. Based on the stage adjustment coefficient, the decision input evaluation value is corrected to generate an admission score. Based on the admission score, engineering management data that meets the threshold is screened to construct a qualified dataset. The engineering anomaly set construction module is used to extract the business value corresponding to each qualified data in the qualified dataset, group the qualified dataset, calculate the reference level and dispersion of the grouped data based on the business value, calculate the anomaly score corresponding to each qualified data based on the business value, reference level and dispersion, and construct the engineering anomaly set based on the comparison result of the anomaly score and the warning threshold. The project management execution module is used to calculate the handling priority of each abnormal object in the project abnormality set based on the abnormality score and preset abnormality handling weight, sort the abnormal objects in the project abnormality set according to the handling priority to form an abnormality handling queue, and trigger the corresponding project management handling process based on the abnormality handling queue. The beneficial effects of this invention are as follows: This invention provides a clear and configurable basis for determining whether data should enter the anomaly management process by uniformly characterizing the formation conditions, time characteristics, and consistency of engineering management data. Based on this, it generates a set of engineering anomalies that matches the actual situation on-site, combining the characteristics of engineering stages and the relative deviation patterns of engineering status. This eliminates the reliance on static thresholds or isolated judgments for anomaly identification. Furthermore, by introducing a quantitative calculation mechanism for anomaly handling priorities, it comprehensively considers the severity of anomalies, the sensitivity of engineering stages, and the impact of anomaly types, forming a sortable and executable anomaly handling sequence that automatically triggers the corresponding engineering management handling process.

[0014] Through the above-mentioned technical means, the present invention achieves close integration between engineering management data, engineering anomaly identification and engineering management handling throughout the entire engineering construction process, enabling the digital management system to stably output executable management behaviors in complex engineering scenarios, thereby effectively improving the reliability and practicality of digital management throughout the entire engineering construction process. Attached Figure Description

[0015] Figure 1 This is a flowchart of the digital management method for the entire construction process in a specific embodiment of the present invention; Figure 2 This is a block diagram of the digital management system for the entire engineering construction process in a specific embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] refer to Figure 1 As shown, Embodiment 1 of this application proposes a digital management method for the entire engineering construction process, including: S101: Obtain an engineering management data set, and generate a corresponding decision input description result for each piece of engineering management data in the engineering management data set using a decision input evaluation function. Construct an input description result set based on the decision input description results. The input description result set includes decision input evaluation values, specifically including: Throughout the entire construction process, the same type of project management data may originate from different sources (e.g., on-site reporting, equipment interfaces, and automated process generation), and its timeliness varies significantly across different project stages. Furthermore, conflicts may arise between similar records (e.g., two inconsistent completion statuses for the same process occurring around the same time). This step does not involve generating exceptions or executing handling procedures, but rather processing the existing project management data set within the project management system. Generate a set of decision input description results one by one This allows subsequent steps to be conducted solely based on data that is more suitable for management decisions.

[0018] The input is a set of engineering management data. Each All data originates from existing links in the engineering management system, without adding new data categories: for example, progress reports are submitted by the site management terminal (Web / mobile) and written to the business database; equipment status records are reported by the equipment gateway to the access service and stored in the database via standard interfaces (such as OPC-UA / MQTT / HTTP); and automatically generated process records are written to the event table by the engineering management system's process engine during node transitions. Each record inherently carries at least three types of directly readable system attributes: entry identifier (used to locate the generation method), record timestamp (used for timeliness judgment), and same-type business key (used to limit the scope of comparison within the same category, such as the key-value combination of the same project - the same section - the same process / component).

[0019] The comprehensive evaluation value used in this step belongs to the classic multi-indicator comprehensive scoring concept. Its basic formula comes from the linear weighted summation model widely used in mathematics and engineering management, and is common in multi-indicator decision-making and engineering evaluation systems: normalized multiple indicators are linearly combined with weights to form a single score. The premise of this basic formula is that all indicators must be on the same comparable scale (usually a dimensionless score), and the weights reflect relative importance. The key modifications and derivations made by this application are: solidifying the three most critical and commonly obtained attributes in the engineering construction scenario regarding "whether it can enter the subsequent anomaly management link" into three evaluation factors and normalizing them, so that the decision input evaluation value can be obtained without introducing additional data categories. The three factors are the generation method evaluation factor. Time-effectiveness evaluation factors Consistency evaluation factors All three are constructed as After scoring the dimensionless values ​​within the interval, a linear combination is then performed.

[0020] in The method of obtaining the entry identifier is through entry identifier mapping: the project management system maintains a project-level configuration mapping table (which can be fixed during project initialization) and maps the entry identifier to the score. For example, "automatic generation by process engine" is mapped to a higher score, and "manual on-site entry" is mapped to a lower score. The method for obtaining this is through a time validity evaluation factor: the system configures a valid time window parameter for each type of project management data during project initialization. (This parameter remains fixed throughout the project cycle), and the timeliness score is calculated based on the current time and data timestamp during runtime. To ensure operability and uniformity of scale, this application provides a directly implementable time factor normalization method: when data exceeds the effective window, the score is... The decay rate is proportional within the window. The specific calculation formula for the time-effectiveness evaluation factor is as follows: ; in This is a time-efficiency evaluation factor, calculated by the system during runtime. The time difference between the data recording timestamp and the current project stage determination time is obtained by subtracting the recording timestamp directly from the system time service; This parameter defines the effective time window for this type of project management data within the current project phase. It is configured and fixed by the project management system during the project initialization phase to unify the timeliness judgment of different data. The same scale.

[0021] Consistency evaluation factor The method for obtaining this information is through a project-wide consistency check of records of the same type: the system pulls a comparison set (e.g., the most recent records of the same process / component) within the same project scope based on the same type of business key, calculates the degree of deviation between the record and the comparison set, and maps it to... Scoring. This can be implemented using either a "conflict counting method" or a "deviation ratio method." For example, for status fields: if the status of a record matches that of most records in the comparison set, a high score is given; if there is a clear conflict, a low score is given. This process does not require an external data source, using only data of the same type within the system.

[0022] Once all three factors have been obtained and are dimensionless scores, the decision input evaluation value is calculated using the decision input evaluation function, which employs a linear weighted summation model to form the decision input evaluation value. The specific calculation method is as follows: ; in For project management data The decision input evaluation value is calculated by the system; The evaluation factors for the generation method are obtained from the entry identifier through the project configuration mapping table; This is the time-effectiveness evaluation factor, calculated using the previous formula; This is a consistency evaluation factor, obtained by comparing similar records within the project; These are weight parameters, configured and fixed during the project initialization phase, and are typically non-negative and satisfy the following conditions: To keep the scores within a stable range; The data record entity for engineering management originates from the existing data collection and recording links of the engineering management system.

[0023] Both formulas in this step are linear and proportional operations on dimensionless scores. Designed as The interval rating does not carry physical dimensions; weight. It is also a dimensionless parameter; therefore It is also a dimensionless score, with consistency on both sides. The time factor formula is... and Both belong to time difference and time window; their ratio is dimensionless, and the overall output falls into... Interval.

[0024] To demonstrate the computational implementation method, a computational example is given. Assume a certain piece of project management data. For the "Process Completion Status Record", the entry identifier indicates that it is automatically generated by the process engine node, and the project configuration mapping table sets its generation method rating to [value missing]. The effective time window for configuring this type of record in the current project phase is... The system calculates that the time difference between this record and the current judgment time is... Substituting this into the time factor formula yields... Regarding consistency checks, the system retrieves the most recent result from the dropdown menu for the same project and the same work process. 10 records of the same type, among which The status of the record is consistent with the record. If there is any inconsistency, the consistency score will be set according to the conflict counting method. The weights are configured during the project initialization phase. Substituting into the linear combination formula, we get .

[0025] The system uses this to manage each piece of project data. Generate corresponding decision input description results, each corresponding one-to-one with engineering management data, and record whether the data meets the input status for entering the subsequent exception management process under the current engineering management scenario. The output is a set of decision input description results. Its elements and the input set The records in the dataset correspond one-to-one and serve as direct input for the next step of forming a qualified dataset.

[0026] S102: Divide the entire construction process into several management stages, and assign a stage adjustment coefficient to each stage. Based on the stage adjustment coefficient, correct the decision input evaluation value to generate an admission score. Based on the admission score, select engineering management data that meet the threshold to construct a qualified dataset, specifically including: Throughout the entire construction process, step one has already addressed the engineering management data set generated within the engineering management system. For each piece of data The corresponding decision input description results were constructed, and the decision input evaluation values ​​were used. The format describes the applicability of the data in subsequent management judgments within the current engineering management scenario. This step builds upon this foundation, aiming not to re-evaluate the data itself, but to further organize the evaluated data into a qualified dataset that can directly support subsequent anomaly generation and management, thereby forming a clear, stable, and operable data access layer within the engineering management process.

[0027] The input for this step is a set of decision input description results. ,gather Each item in the dataset is related to the engineering management database. One piece of data One-to-one correspondence, and at least includes the decision input evaluation value of this data. This includes the business key and system identification information used to locate the data. In this step, the system no longer accesses the content fields of the original project management data, but instead relies entirely on... The description results already formed in the first step are processed to ensure a strict connection between the steps: only the evaluation results formed in the first step are eligible to enter the judgment process of this step.

[0028] In engineering management practice, anomaly management does not treat all data equally. Instead, it requires a clear definition of "which data deserves further management attention," taking into account the project stage, management objectives, and management resources. This step revolves around this actual engineering need by introducing an access judgment mechanism tailored to the engineering scenario, transforming decision input evaluation values ​​into direct control signals for the anomaly generation process. To this end, during the project initialization phase, the engineering management system configures a basic access parameter for different types of engineering management data to reflect the overall sensitivity of that type of data to triggering anomaly management in the current engineering project.

[0029] In practice, the system does not simply input the decision value into the evaluation value. Instead of comparing to a single threshold, this step considers a common but easily overlooked characteristic throughout the entire construction process: the level of "priority attention" for the same type of project management data structurally changes as the project progresses. For example, in the early stages of construction, slight fluctuations in schedule data may not immediately trigger anomaly management, while fluctuations of the same magnitude have higher management value as critical milestones approach. Based on this engineering characteristic, this step introduces a stage adjustment factor into the admission judgment to modify the decision input evaluation value according to specific scenarios.

[0030] Specifically, during the project initialization phase, the system divides the entire construction process into several management phases and assigns a phase adjustment coefficient to each phase, denoted as . This coefficient remains fixed within the phase and reflects the overall requirements for anomaly management sensitivity at the current phase. During system operation, the corresponding coefficient is obtained based on the current project phase. The decision input evaluation value is then corrected based on this coefficient to obtain the stage-corrected admission score. The calculation relationship is as follows: ; in, The decision input evaluation value has been calculated in step one; This is the stage adjustment factor corresponding to the current project stage, configured by the project management system during project initialization and remaining unchanged throughout the stage; The admission score, taking into account factors at different project stages, is used directly in subsequent qualification assessments. This multiplicative adjustment method originates from the stage weight adjustment concept commonly used in project management, allowing the same evaluation value to have different management implications at different project stages.

[0031] After obtaining the revised admission score, the system further incorporates practical experience from project management, specifically the principle that "abnormal triggers should avoid critical fluctuations," by introducing a smoothing constraint to suppress frequent changes in the admission status due to minor fluctuations in the score near the threshold. To this end, a smoothing interval parameter is configured during the project initialization phase. This is used to define the buffer zone near the admission boundary. The system uses this to... A qualification assessment is conducted to obtain the final admission decision. The determination relationship can be expressed as: ; in, Basic access parameters configured for the engineering management system during the project initialization phase are used to represent the baseline requirements for entering the exception management process; This is a smoothing interval parameter used to limit the stable interval for admission determination; The admission determination result is calculated directly by the system during runtime. When... When the data falls within the smoothing range, the system can maintain the data in the admission status of the previous period, thereby avoiding frequent entry and exit of abnormal management objects at the engineering management level.

[0032] Based on the above judgment results, the system will include all those that meet the criteria. The corresponding items of the project management data are organized into a qualified dataset, denoted as... At the system implementation level, Instead of copying the original engineering management data, an index set is built using business keys and record identifiers, enabling subsequent steps to directly locate the corresponding engineering management data and its decision input description results. The system will not delete or modify the data, but will simply exclude it from the anomaly generation process at the current stage of the project, thus ensuring the integrity and traceability of the project management data. Through this step, the project management system, based on the decision input description results formed in step one, further introduces a qualification judgment mechanism that combines the characteristics of the project stage. This ensures that whether data enters the anomaly management process no longer depends solely on static evaluation values, but is closely coupled with the progress status of the entire project construction process. This approach allows subsequent anomaly generation steps to operate directly based on a controllable and scenario-appropriate data set, thus playing a crucial role in the overall invention.

[0033] S103: Extract the business value corresponding to each qualified data point in the qualified dataset, group the qualified dataset, calculate the reference level and dispersion of the grouped data based on the business value, calculate the anomaly score corresponding to each qualified data point using the anomaly scoring function based on the business value, reference level, and dispersion, and construct an engineering anomaly set based on the comparison result of the anomaly score and the warning threshold, specifically including: This step follows the output of the qualified dataset from step two in the digital management chain of the entire engineering construction process. And convert it into a set of exception objects that the project management system can directly process. In engineering sites, anomaly management requires objects to possess the engineering attributes of being "locatable, explainable, and triggerable for handling," which simple data recording does not meet. Therefore, this step revolves around two core characteristics of engineering construction scenarios: First, engineering status has obvious phases and milestone constraints, and the tolerance for fluctuations in the same type of data varies at different stages; second, engineering anomalies often exhibit chain propagation characteristics, and once certain anomalies occur, they can quickly spread to multiple processes or management elements, requiring explicit characterization of their potential impact at the anomaly object level. Based on these two points, this step focuses on... Using a single input range, a calculable reference baseline is established within the stage, and constraints on stage sensitivity and chain propagation risk are introduced into the anomaly scoring, thereby obtaining a set of engineering anomalies that can be directly used to trigger subsequent handling processes.

[0034] Input is a qualified dataset .gather Each item can be located to the original project management data via a business key. This corresponds to the decision input description result formed in step one; this input already implicitly includes the admission determination result from step two. This means that the data range for this step, in an engineering sense, is a set of data "suitable for triggering exception management candidates". Based on this, this step extracts the business value for each qualified data entry, denoted as... And compare them according to comparable dimensions such as the same project, the same bid section, the same process, or the same component. Grouping is performed. The business keys required for grouping are derived from the existing master data and business coding system of the project management system, and can be directly read from the records without additional data collection.

[0035] The reference baseline within each phase is constructed using statistical reference principles commonly found in engineering management. Its source can be viewed as a formalized expression of engineering experience that uses historically similar conditions as the current benchmark. Within each group, the system calculates the reference level for that group based on the business values ​​of qualified data. And calculate the degree of dispersion of the group. Reference level The median or mean of the values ​​within the group can be used, and the degree of dispersion can be considered. The mean absolute deviation or standard deviation of values ​​within the group can be used, with the specific selection configured by the engineering management system for the data type during the project initialization phase. The engineering meaning of this reference baseline is to define the "normal state" as the typical level of similar data in this phase, and to bind the tolerance range to the actual fluctuation level in this phase, avoiding misjudgments caused by using fixed thresholds in scenarios with large and small fluctuations.

[0036] After obtaining the reference baseline, each qualified data point is evaluated using an anomaly scoring function. Calculate anomaly scores This score quantifies the degree of relative deviation and structurally introduces two types of engineering scenario enhancements: a stage sensitivity term and a chain reaction risk constraint term. The stage sensitivity term uses the engineering stage adjustment coefficient introduced in step two. This ensures consistency in sensitivity between anomaly generation and data acceptance at each stage. The cascading propagation risk constraint is represented as an "anomaly cluster within the same group," meaning that if multiple high-deviation records have already appeared within the same process or component dimension, the judgment of subsequent deviations becomes more sensitive, allowing anomaly objects to more closely reflect the cascading risk characteristics of the engineering site. Considering the above, the anomaly scoring function is defined as: ; in, For project management data The business values ​​are obtained from the engineering management system. The business fields can be read directly; for The reference level for a given group at the current stage is calculated by the system from the qualified data values ​​within that group. The degree of dispersion of this group is calculated by the system based on the values ​​taken within the group; This is a group stability smoothing parameter, configured during the project initialization phase, used to maintain computational stability when fluctuations within the group are minimal. This is the adjustment coefficient for the engineering stage, derived from the stage configuration in step two; For anomaly cluster counting, the system calculates "anomaly scores" within the same group. Reaching the warning threshold The number of records obtained is used to characterize the degree of clustering of chain transmission risk. The warning threshold parameters configured during the project initialization phase, and which meet the following requirements. ; The chain propagation risk weight parameter is configured during the project initialization phase to control the intensity of the impact of abnormal clusters on the abnormal score. This is used for anomaly scoring and subsequent anomaly trigger determination. The above formula uses the "relative deviation term" as the main term to match anomaly identification with the baseline within the stage. At the same time, it introduces a logarithmic constraint term on anomaly clusters to make the risk of chain propagation numerically controllable in affecting the generation of anomaly objects, avoiding excessive amplification of the number of anomalies.

[0037] After obtaining the anomaly score, the system uses the anomaly triggering parameters configured for different data types during the project initialization phase. Make a judgment and generate an abnormal project object; warning threshold. This is used only for calculating the anomaly cluster count and does not directly trigger the generation of anomaly objects. In engineering terms, this determination process corresponds to "when the deviation reaches a manageable level of concern, a resolvable object is formed," and its formal expression is as follows: ; in, This is an exception trigger flag, determined by the system based on... With exception triggering parameters The comparison yielded the following results; These are exception triggering parameters, configured for data types during project initialization, to reflect the management's settings regarding exception sensitivity. When... When this happens, the system generates a corresponding project exception object and writes the exception object into the project exception collection. Each exception object contains at least: original data location information (used to locate the location in the system). ), group identifier (e.g., process or component key), project stage identifier, and anomaly score. And an exception type field (derived from a data type mapping to a grouping dimension). In project implementation, this exception object can be used as an exception event record in the database or as an exception event message in a message queue. The output is a collection of project exceptions. .gather By all satisfying The composition of abnormal objects, and the abnormal objects and qualified datasets The business keys are kept traceable, so that subsequent handling processes can use the abnormal object as the entry point to directly locate the corresponding engineering management data and its context information.

[0038] S104: Based on the anomaly score and the preset anomaly handling weight, calculate the handling priority of each anomaly object in the engineering anomaly set; sort the anomaly objects in the engineering anomaly set according to the handling priority to form an anomaly handling queue; and trigger the corresponding engineering management handling process based on the anomaly handling queue, specifically including: In a digital management scenario covering the entire construction process, this step involves the collection of engineering anomalies generated in the previous stage. As input. Set Each anomalous object has already undergone anomaly identification and quantitative description in the previous steps, including at least anomaly scoring. The system includes an anomaly type identifier and a business identifier that can uniquely locate the original project management data; the project stage identifier to which the anomaly belongs is used to look up the corresponding stage adjustment coefficient according to the project configuration table during the handling stage. The steps involve identifying the exception type and the business identifier that uniquely locates the original engineering management data. The task of this step is not to re-determine whether the exception is valid, but rather to address a core issue more relevant to engineering management practice: when multiple exceptions exist simultaneously, how to determine the order of exception handling in a deterministic and computable manner within the engineering management system, and how to transform this ordering result into engineering management actions that the system can directly execute.

[0039] In engineering management theory, prioritizing the handling of multiple events typically employs a multi-factor comprehensive evaluation method, based on scheduling theory and risk assessment models. This involves combining the severity, urgency, and impact weights of events to obtain comparable priority indicators. This step follows this linear weighting approach and provides a constrained extrapolation for engineering construction scenarios: it does not introduce new evaluation dimensions but fully reuses the anomaly scores and stage factors obtained in previous steps. Furthermore, considering the objective fact that different anomaly types in engineering management consume different levels of management resources, it introduces anomaly handling weights, thus forming a priority calculation method suitable for engineering management scheduling. Specifically, during the project initialization phase, the engineering management system configures handling weight parameters for different anomaly types according to engineering management specifications, denoted as... This parameter reflects the relative importance of different anomaly types at the management and handling level when they exhibit the same degree of deviation. For example, quality anomalies typically require a higher priority response than schedule anomalies. This handling weight remains constant throughout the project lifecycle to ensure consistency in management decisions. During system operation, the set of engineering anomalies is... For each abnormal object, the system calculates its handling priority value based on the abnormal score and the abnormal handling weight. The calculation relationship is as follows: ; in, This represents the anomaly score calculated in the preceding steps, used to characterize the degree of deviation of the anomaly from the reference state of the engineering stage; The parameter representing the handling weight corresponding to the anomaly type is used to reflect the importance of the anomaly at the management and handling level. This is the priority value for handling exception objects, used for subsequent exception sorting and scheduling execution.

[0040] To illustrate the feasibility of this calculation process in an engineering management system, a specific implementation example is given below. Assume that three abnormal objects exist in the current project phase, denoted as Abnormal A, Abnormal B, and Abnormal C. The abnormality score for Abnormal A is... The stage adjustment coefficient of the project to which it belongs is The handling weight corresponding to the anomaly type is The abnormality score for abnormality B is: The stage adjustment coefficient is the same. The weight of disposal is The abnormality score for abnormality C is: The stage adjustment coefficient is The weight of disposal is Substituting the above parameters into the priority calculation formula, we can obtain the priority of exception A as follows: The priority for handling exception B is The priority for handling exception C is Therefore, the order of handling the three anomalies can be determined as: anomaly C, anomaly A, and anomaly B. This result reflects the management logic that high-risk anomalies should be handled first, even if their deviation is slightly lower.

[0041] After obtaining the processing priority value, the system proceeds according to... Size of the set of engineering anomalies The system sorts the abnormal objects in the queue to form an exception handling queue. This sorting process uses a standard sorting algorithm found in engineering management systems, and its input is entirely derived from the handling priority calculation results mentioned above. After sorting, the system triggers the corresponding engineering management handling procedures for each abnormal object, starting from the first object in the handling queue.

[0042] During the triggering phase of the handling process, the system uses the exception type identifier and project stage identifier carried in the exception object to find the pre-configured mapping relationship between exception types and handling processes in the project management system. This mapping relationship originates from established process definitions in project management practice; for example, schedule exceptions are mapped to plan adjustment or resource coordination processes, quality exceptions to re-inspection or rectification processes, and safety exceptions to work stoppage inspections or special approval processes. After matching the corresponding handling process, the system automatically creates the corresponding process instance or management task and passes the business identifier of the exception object as a context parameter to the handling process, enabling the management handling to accurately apply to the corresponding project management data and project objects.

[0043] refer to Figure 2 As shown, to achieve the above objectives, another aspect of this application embodiment proposes a digital management system for the entire engineering construction process, including: The decision input description construction module is used to acquire an engineering management data set, generate a corresponding decision input description result for each piece of engineering management data in the engineering management data set through a decision input evaluation function, and construct an input description result set based on the decision input description result, the input description result set including decision input evaluation values; The qualified data screening module is used to divide the entire process of engineering construction into several management stages, and configure a stage adjustment coefficient for each stage. Based on the stage adjustment coefficient, the decision input evaluation value is corrected to generate an admission score. Based on the admission score, engineering management data that meets the threshold is screened to construct a qualified dataset. The engineering anomaly set construction module is used to extract the business value corresponding to each qualified data in the qualified dataset, group the qualified dataset, calculate the reference level and dispersion of the grouped data based on the business value, calculate the anomaly score corresponding to each qualified data based on the business value, reference level and dispersion, and construct the engineering anomaly set based on the comparison result of the anomaly score and the warning threshold. The project management execution module is used to calculate the handling priority of each abnormal object in the project abnormality set based on the abnormality score and the preset abnormality handling weight, sort the abnormal objects in the project abnormality set according to the handling priority to form an abnormality handling queue, and trigger the corresponding project management handling process based on the abnormality handling queue.

[0044] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A digital management method for the entire construction process, characterized in that: include: A set of engineering management data is obtained, and a corresponding decision input description result is generated for each piece of engineering management data in the set of engineering management data through a decision input evaluation function. An input description result set is constructed based on the decision input description result, and the input description result set includes decision input evaluation values. The expression for the decision input evaluation function is: ； in Provide evaluation values ​​for decision-making based on engineering management data; Evaluation factors for the generation method; As a factor for evaluating time effectiveness; As a consistency evaluation factor; These are weight parameters; The entire process of engineering construction is divided into several management stages, and a stage adjustment coefficient is configured for each stage. The decision input evaluation value is corrected based on the stage adjustment coefficient to generate an admission score. Based on the admission score, engineering management data that meets the threshold is selected to construct a qualified dataset. Extract the business value corresponding to each qualified data in the qualified dataset, and group the qualified dataset. Calculate the reference level and dispersion of the grouped data based on the business value. Calculate the anomaly score corresponding to each qualified data based on the business value, reference level, and dispersion using an anomaly scoring function. Construct an engineering anomaly set based on the comparison between the anomaly score and the warning threshold. Based on the anomaly score and the preset anomaly handling weight, the handling priority of each anomaly object in the engineering anomaly set is calculated. The anomaly objects in the engineering anomaly set are sorted according to the handling priority to form an anomaly handling queue. The corresponding engineering management handling process is triggered based on the anomaly handling queue.

2. The digital management method for the entire construction process according to claim 1, characterized in that, The process of triggering the corresponding engineering management handling procedure based on the exception handling queue specifically includes: Based on the anomaly type identifier and the project stage identifier carried by the project anomaly set, and based on the pre-configured mapping relationship between the anomaly type identifier and the handling process, the corresponding handling process is matched. Based on the aforementioned handling process, a corresponding process instance or management task is automatically created, and the business identifier of the engineering exception set is passed into the handling process as a context parameter.

3. The digital management method for the entire construction process according to claim 1, characterized in that, The parameters of the evaluation function include the generation method evaluation factor, the time validity evaluation factor, and the consistency evaluation factor.

4. The digital management method for the entire construction process according to claim 3, characterized in that, The generation method evaluation factor maps the entry identifier to the score by maintaining a project-level configuration mapping table. The time validity evaluation factor is obtained by calculating the timeliness score based on the current time and the data timestamp. The consistency evaluation factor pulls a comparison set within the same project range based on the same type of business key, calculates the degree of deviation between the record and the comparison set, and maps the score.

5. The digital management method for the entire construction process according to claim 1, characterized in that, The reference level is the median or mean of the business values ​​within the group, and the dispersion is the average absolute deviation or standard deviation of the business values ​​within the group.

6. The digital management method for the entire construction process according to claim 1, characterized in that, The parameters of the anomaly scoring function include business values, reference level, dispersion, stage adjustment coefficient, group stability smoothing parameter, and anomaly cluster count.

7. The digital management method for the entire construction process according to claim 1, characterized in that, The engineering anomaly set includes original data location information, group identifier, engineering stage identifier, anomaly score, and anomaly type field.

8. The digital management method for the entire construction process according to claim 1, characterized in that, The project management data set includes progress reporting records, equipment status records, and automatically generated process records.

9. The digital management method for the entire construction process according to claim 8, characterized in that, The progress reporting record is submitted by the on-site management terminal and written into the business database. The device status record is reported by the device gateway to the access service through the standard interface and stored in the database. The process automatically generated record is written into the event table through the process engine when the node flows.

10. A digital management system for the entire construction process, characterized in that: include: The decision input description construction module is used to acquire an engineering management data set, generate a corresponding decision input description result for each piece of engineering management data in the engineering management data set through a decision input evaluation function, and construct an input description result set based on the decision input description result, the input description result set including decision input evaluation values; The qualified data screening module is used to divide the entire process of engineering construction into several management stages, and configure a stage adjustment coefficient for each stage. Based on the stage adjustment coefficient, the decision input evaluation value is corrected to generate an admission score. Based on the admission score, engineering management data that meets the threshold is screened to construct a qualified dataset. The expression for the decision input evaluation function is: ； in Provide evaluation values ​​for decision-making based on engineering management data; Evaluation factors for the generation method; As a factor for evaluating time effectiveness; As a consistency evaluation factor; These are weight parameters; The engineering anomaly set construction module is used to extract the business value corresponding to each qualified data in the qualified dataset, group the qualified dataset, calculate the reference level and dispersion of the grouped data based on the business value, calculate the anomaly score corresponding to each qualified data based on the business value, reference level and dispersion, and construct the engineering anomaly set based on the comparison result of the anomaly score and the warning threshold. The project management execution module is used to calculate the handling priority of each abnormal object in the project abnormal set based on the abnormal score and the preset abnormal handling weight, sort the abnormal objects in the project abnormal set according to the handling priority to form an abnormal handling queue, and trigger the corresponding project management handling process based on the abnormal handling queue.

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