Whole-process digital collaborative management platform for electric power engineering construction

Abstract

The invention relates to the technical field of electric power engineering digital management, and discloses a whole-process digital collaborative management platform for electric power engineering construction. The method comprises a dynamic management framework, a data acquisition and injection module, a data analysis and vector generation module, a vector space management module and a collaborative operation module. The platform performs hierarchical analysis on a field real-time data stream according to a dynamic management framework, extracts data fragments corresponding to each management hierarchy and generates a multi-dimensional management vector so as to construct a global management vector space. And defining a cooperative operation track in the space, and generating a specific management instruction according to the cooperative operation track. According to the method, mixed field data are converted into structured and computable management state vectors, and accurate association of data and management is realized; and through trajectory operation of a vector space, a cross-level and multi-target management decision is converted into a continuous and adaptive collaborative optimization process, and the integrity and automation level of management are improved.

Description

Technical Field

[0001] This invention relates to the field of digital management technology for power engineering, specifically to a digital collaborative management platform for the entire process of power engineering construction. Background Technology

[0002] Currently, digital management platforms are widely used in power engineering construction. These platforms typically rely on deploying various sensors on-site to collect real-time data. Most existing technical solutions centrally store and flatten the massive amounts of real-time data collected, lacking deep correlation between the data and the actual levels of engineering management. The data is chaotic, making it difficult for management to quickly and accurately extract structured information directly related to a specific management task from the complex data stream, thus failing to effectively realize the value of the data.

[0003] At the management decision-making level, existing technologies largely rely on rule-based alerts based on fixed thresholds or on analysis and judgment based on the experience of management personnel. This approach cannot perform holistic, mathematical modeling and correlation analysis of the status of multiple management dimensions throughout the entire engineering construction process. When different management objectives need to be coordinated, the system lacks a mechanism that can dynamically integrate multi-dimensional statuses and automatically generate collaborative optimization instructions accordingly. The generation of management instructions is often delayed and isolated, making it difficult to achieve cross-level adaptive collaboration and precise control. Summary of the Invention

[0004] The purpose of this invention is to provide a digital collaborative management platform for the entire process of power engineering construction, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this invention provides a full-process digital collaborative management platform for power engineering construction, the method comprising: The dynamic management framework module is used to establish a dynamic management framework for power engineering construction scenarios. The dynamic management framework consists of multiple interrelated management levels. The data acquisition and injection module is used to acquire real-time data streams from multiple sensing devices deployed at the power engineering construction site and inject the real-time data streams into the dynamic management framework. The data parsing and vector generation module is used to perform hierarchical parsing of the real-time data stream through the dynamic management framework, extract hierarchical data fragments corresponding to each management level, and generate multi-dimensional management vectors for each management level based on its hierarchical data fragments. The vector space management module is used to construct a management vector space that reflects the construction status of the entire power project based on multi-dimensional management vectors from all management levels. The collaborative operation module is used to define operation trajectories for collaborative management in the management vector space, and to generate specific management instructions for the entire process of power engineering construction based on the operation trajectories.

[0006] Preferably, establishing a dynamic management framework for power engineering construction scenarios includes: The entire life cycle of power engineering construction is analyzed, and the entire life cycle is decomposed into multiple construction stages; For each construction phase, identify all management elements involved in that construction phase, including personnel, equipment, materials, and methods; Based on the strength of the correlation between management elements, management elements are clustered into management element sets; Based on the functional positioning of the management element set in power engineering construction, the management element set is mapped to the corresponding management level; Define data exchange protocols between different management levels to complete the construction of a dynamic management framework.

[0007] Preferably, the hierarchical parsing of the real-time data stream through the dynamic management framework includes: Configure dedicated data filtering rules for each management level. These data filtering rules are pre-set based on the management element characteristics of the management level, including personnel attendance rate thresholds, equipment operating parameter ranges, and material consumption limits. The real-time data stream is input into the dynamic management framework, so that the real-time data stream passes through each management level in sequence; In each management level, the data filtering rules of the management level are applied to filter the real-time data streams that flow through it, and data segments that conform to the rules are retained. The filtered data segments are marked as hierarchical data segments corresponding to the management level.

[0008] Preferably, the multi-dimensional management vectors for generating management levels include: Identify the key management indicators for the management level, which are extracted from the data fragments at that level; Each key management indicator is assigned a weight, which is determined based on the importance of the key management indicator in the power engineering construction. The key management indicators with weights are vectorized to form the initial management vector of the management level. The initial management vector is normalized to obtain the multidimensional management vector of the management level.

[0009] Preferably, constructing a management vector space that reflects the overall construction status of the power project includes: The multidimensional management vector of each management level is used as the spatial basis vector; Calculate the cosine value of the angle between each multidimensional management vector, and determine the spatial relationship between the basis vectors based on the cosine value of the angle. Based on the aforementioned spatial relationships, all multidimensional management vectors are embedded into the same mathematical space to form the management vector space. A coordinate system is established in the management vector space, and each dimension of the coordinate system corresponds to a management level.

[0010] Preferably, the operation trajectory defined in the management vector space for collaborative management includes: In the management vector space, a reference point representing the ideal state of power engineering construction is set; Starting from the actual point representing the current state of the power engineering construction, calculate multiple paths to the reference point; Evaluate the management complexity of the management vector space regions traversed by each path; The path with the lowest management complexity is selected as the operation trajectory.

[0011] Preferably, generating specific management instructions for the entire power engineering construction process based on the operation trajectory includes: The operation trajectory is discretized into multiple trajectory points, each trajectory point corresponding to a coordinate position in the management vector space; Analyze the coordinates of each trajectory point to determine the target state required by each management level for the coordinates. Compare the current state with the target state at each management level to generate state adjustment requirements; The state adjustment requirements are converted into specific, executable management instructions.

[0012] Preferably, the allocation of indicator weights to each key management indicator includes: Collect historical project data for power engineering construction and extract the actual performance values ​​of each key management indicator in historical projects; Organizational experts assess the importance of each key management indicator and obtain expert scores; Perform statistical analysis on the expert scores and calculate the average score for each key management indicator; The average score is normalized and mapped to a weight range of zero to one. Based on the project types and stage characteristics of current power engineering construction, the normalized weights are fine-tuned to determine the final indicator weights.

[0013] Preferably, calculating the cosine of the angle between the various multidimensional management vectors includes: Extract the multidimensional management vector for each management level and represent each vector as a direction vector in mathematical space; Calculate the dot product of any two multidimensional management vectors, and calculate the magnitude of each multidimensional management vector. Divide the dot product by the product of the magnitudes of the two vectors to obtain the cosine of the included angle. Based on the magnitude of the cosine of the included angle, we can determine the similarity or difference between vectors. A cosine value close to one indicates that the vectors are similar in direction, close to zero indicates that the vectors are orthogonal, and close to negative one indicates that the vectors are opposite in direction. Based on the results of the cosine value of the included angle, a spatial relationship matrix between multidimensional management vectors is established.

[0014] Preferably, setting reference points in the management vector space to represent the ideal state of power engineering construction includes: Based on the standards and specifications for power engineering construction and historical best data, we define ideal indicator values ​​for each management level. The ideal indicator values ​​for each management level are combined into a multi-dimensional vector, which serves as the coordinates of the reference point in the management vector space. The multiple paths calculated from the actual point representing the current state of the power engineering construction to the reference point include: Obtain the actual coordinates of the current point and calculate the Euclidean distance between the actual point and the reference point; Analyze feasible directions in the management vector space and generate straight-line paths connecting actual points and reference points; Considering management constraints, generate a curved path that bypasses the obstacle area; Evaluate the length and management complexity of each path, and select the optimal path as the operation trajectory.

[0015] Compared with the prior art, the beneficial effects of the present invention are: By establishing a dynamic management framework consisting of multiple interconnected management levels, the technology automatically performs hierarchical parsing of continuous real-time data streams acquired from field sensors. Based on specific rules for each management level, the technology extracts precisely corresponding hierarchical data segments from the raw data stream, and then transforms each segment into a structured multidimensional management vector. This achieves a precise and structured mapping between raw field data and different management dimensions, converting unstructured data streams into computable vectors that represent the status of key indicators at each level. This changes the disconnect between data and management work, providing directly computable, clearly defined mathematical state inputs for high-level analysis and decision-making.

[0016] Based on the multi-dimensional management vectors generated across all management levels, a global management vector space is constructed, mathematically representing the comprehensive state of the entire power engineering construction. Within this space, operational trajectories are defined through predefined collaborative rules and algorithms; these trajectories are continuous change paths of a series of state points in the vector space. The system automatically generates specific management instructions for the entire construction process based on the trajectory calculation from the current state vector to the target state vector. This transforms collaborative management across multiple objectives such as schedule, quality, and safety from a discrete decision-making process relying on experience and fixed rules into a continuous trajectory optimization and dynamic response process within a unified mathematical space, achieving adaptive, automated generation, and precise collaboration of management instructions. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the working principle of the full-process digital collaborative management platform for power engineering construction described in this invention. Figure 2 Flowchart for building a dynamic management framework; Figure 3 Flowchart for generating multidimensional management vectors; Figure 4 A heatmap showing the similarity of the cosine values ​​of the angles between the multidimensional management vectors at various management levels in power engineering. Figure 5 A bar chart showing the distribution of status adjustment demand at various management levels in power engineering. Detailed Implementation

[0018] 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.

[0019] Please see Figure 1This invention provides a digital collaborative management platform for the entire process of power engineering construction. The method includes: achieving digital collaborative management of the entire power engineering construction process by integrating multiple modules; a dynamic management framework module first establishing a dynamic management framework for the power engineering construction scenario, which consists of multiple interrelated management levels; a data acquisition and injection module acquiring real-time data streams from multiple sensing devices deployed at the power engineering construction site and injecting the real-time data streams into the dynamic management framework; a data parsing and vector generation module performing layered parsing of the real-time data streams through the dynamic management framework, extracting hierarchical data fragments corresponding to each management level, and generating multi-dimensional management vectors for each management level based on its hierarchical data fragments; a vector space management module constructing a management vector space reflecting the entire power engineering construction status based on the multi-dimensional management vectors of all management levels; and a collaborative operation module defining operation trajectories for collaborative management in the management vector space and generating specific management instructions for the entire power engineering construction process based on the operation trajectories.

[0020] Example 1: See Figure 2 In practical implementation, establishing a dynamic management framework for power engineering construction scenarios includes analyzing the entire lifecycle of power engineering construction. This lifecycle is decomposed into multiple construction phases. For each construction phase, all management elements involved are identified, including personnel, equipment, materials, and methods. In some embodiments, management elements are clustered into management element sets based on the strength of their correlation. The strength of the correlation between management elements can be calculated by analyzing the co-occurrence frequency and state coupling degree of different management elements in historical project data over time. Optionally, a quantified correlation coefficient is introduced when calculating the correlation strength; management elements with higher correlation coefficient values ​​are grouped into the same management element set. In practical implementation, the strength of the correlation between management elements is quantified by analyzing the co-occurrence frequency and state coupling degree of different management elements in historical power engineering construction data over time. Co-occurrence frequency refers to the number of times management elements appear together at the same time point or within a time period, while state coupling degree reflects the mutual influence relationship between the state values ​​of management elements. For example, when frequent changes in the state of one management element cause synchronous changes in the state of another management element, the state coupling degree is considered high. Based on the functional positioning of the management element set in power engineering construction, the management element set is mapped to the corresponding management level. This functional positioning is understood to be based on the main management responsibilities undertaken by the management element set, such as resource scheduling, quality monitoring, or safety control. A data exchange protocol is defined between different management levels to complete the construction of the dynamic management framework. The data exchange protocol specifies the data transmission format, triggering conditions, and verification mechanisms.

[0021] In implementation, dedicated data filtering rules are configured for each management level. These rules are pre-defined based on the management element characteristics of each level, including personnel attendance rate thresholds, equipment operating parameter ranges, and material consumption limits. The personnel attendance rate threshold corresponds to personnel management elements and is used to filter data related to the attendance of personnel in the required positions at each management level. The equipment operating parameter range corresponds to equipment management elements, focusing on the operational status data of construction equipment, monitoring equipment, etc., involved at each management level. The material consumption limit corresponds to material management elements, filtering data related to the consumption of building materials and consumables required at each management level. This ensures that the filtered data segments are directly related to the management elements of each management level, providing data support for the subsequent generation of accurate multi-dimensional management vectors. Real-time data streams are input into the dynamic management framework, allowing them to pass through each management level sequentially. At each management level, the data filtering rules of that level are applied to filter the flowing real-time data stream. The filtering operation retains data segments that conform to the rules. This filtering can be understood as matching the tag information contained in the data packet with the preset conditions in the data filtering rules. The filtered data segments are then marked as hierarchical data segments corresponding to the management level. In some embodiments, the data filtering rule is embodied as a logical judgment function, used to extract data segments from the real-time data stream D(t) that conform to the characteristics of the current management level s. The relationship can be expressed as follows:

[0022] Wherein: F s (t) represents the hierarchical data segment selected at time t for management level s, the function Let represent the i-th data filtering rule configured for management level s, and D(t) be the real-time data stream at time t. It is a rule Threshold or pattern matching parameter.

[0023] Example 2: See Figure 3In practical implementation, key management indicators (KPIs) for each management level are determined. These KPIs are extracted from hierarchical data segments, and weights are assigned to each KPI based on its importance in the power engineering construction. The extraction process involves structured analysis and feature identification of hierarchical data segments. For example, from hierarchical data segments related to construction safety, "daily number of violations" and "safety protection equipment integrity rate" are extracted as KPIs. The weighted KPIs are then vectorized to form initial management vectors for each management level. These initial management vectors are multidimensional arrays, where each element corresponds to a combination of a KPI value and its weight. Normalization of these initial management vectors yields multidimensional management vectors for each management level. Normalization aims to eliminate differences in the dimensions and numerical ranges of different KPIs, enabling spatial operations and comparisons on the same scale. The multidimensional management vectors are structured as one-dimensional arrays, where each element corresponds one-to-one with a key management indicator for each management level, and the element value is the quantified value of the key management indicator after weight allocation and normalization. Its storage method adopts structured storage classified by management level. The multi-dimensional management vector corresponding to each management level is stored in association with the level identifier and the list of key management indicators, which supports fast access when searching by level and performing vector space operations.

[0024] In some embodiments, assigning weights to each key management indicator (KMI) involves collecting historical project data from power engineering construction projects and extracting the actual performance values ​​of each KMI in those projects. Domain experts assess the importance of each KMI to obtain expert scores, and statistical analysis is performed on these expert scores to calculate the average score for each KMI. It is understood that expert scoring is conducted anonymously and back-to-back, with domain experts independently scoring the contribution of each KMI to the project's schedule, cost, quality, and safety based on their professional knowledge. The average score is normalized and mapped to a weight range of zero to one. The normalized weights are then fine-tuned based on the project type and stage characteristics of the current power engineering construction to determine the final KMI weights. In some embodiments, the fine-tuning process is based on the management focus specific to the project type. For example, the final KMI weight for the key management indicator "tower foundation construction accuracy" in an ultra-high voltage transmission line construction project would be adjusted upwards compared to a distribution network renovation project. Optionally, the calculation of the final KMI weights can be expressed as follows:

[0025] Where: ω j This represents the final indicator weight of the j-th key management indicator. Let represent the average expert score for the j-th key management indicator, n be the total number of key management indicators, α be a balancing factor between 0 and 1 used to adjust the weighting between historical statistics and project-specific adjustments, and β be the average score for the j-th key management indicator. j It is a special adjustment coefficient set for the j-th key management indicator based on the current project type and stage characteristics.

[0026] Example 3: In specific implementation, the multi-dimensional management vector of each management level is used as the spatial basis vector. The cosine value of the angle between each multi-dimensional management vector is calculated, and the spatial relationship between the basis vectors is determined based on the cosine value. Based on the spatial relationship, all multi-dimensional management vectors are embedded into the same mathematical space to form a management vector space. In the management vector space, each dimension of the coordinate system corresponds to a management level. It can be understood that the multi-dimensional management vector of each management level mathematically represents a direction in a high-dimensional space, and the management vector space is the subspace spanned by these vectors. The establishment of the coordinate system allows the overall state of the power engineering construction at different points in time to be mapped to a specific coordinate point in the management vector space.

[0027] In some embodiments, calculating the cosine of the angle between various multidimensional management vectors includes extracting the multidimensional management vector for each management level and representing each vector as a direction vector in mathematical space. The dot product of any two multidimensional management vectors is calculated, and the magnitude of each multidimensional management vector is calculated. It can be understood that the dot product reflects the projection relationship between the two vectors in direction, while the magnitude represents the magnitude of the vector. The cosine of the angle is obtained by dividing the dot product by the product of the magnitudes of the two vectors. The similarity or difference between the vectors is judged based on the magnitude of the cosine. A cosine value close to one indicates that the vector directions are similar; close to zero indicates that the vector directions are orthogonal; and close to negative one indicates that the vector directions are opposite. A spatial relationship matrix between multidimensional management vectors is established based on the results of the cosine values. Optionally, the spatial relationship matrix is ​​a symmetric matrix, where each element represents the degree of directional association between its corresponding two management level multidimensional management vectors. This matrix is ​​used to guide path planning and state deduction during subsequent collaborative operations in the management vector space. In some embodiments, for any two management level multidimensional management vectors u and v, their degree of directional association, i.e., the cosine of the angle, is calculated through the following relationship:

[0028] Where: sim(u,v) represents the cosine of the angle between the multidimensional management vectors u and v, and u·v represents the result of the dot product operation between vectors u and v. This represents the magnitude of vector u. Let v represent the magnitude of vector v. By systematically calculating the cosine of the angle between all vector pairs, the cooperative, independent, or antagonistic relationships in the state behavior of different management levels can be quantitatively characterized, providing a geometric basis for constructing a structured management vector space. Optionally, in actual calculations, all multidimensional management vectors must be normalized before participating in the operation.

[0029] See Figure 4 This diagram is a visualization of the multidimensional management vector space relationship matrix. The horizontal and vertical axes represent the core management levels of the power engineering project. The color intensity corresponds to the cosine of the angle between the color and the color scale on the right. The closer the value is to 1, the more similar the state vector directions of the two management levels are, and the higher their correlation. The value of this diagram lies in transforming abstract vector space relationships into an intuitive distribution of hierarchical correlations, providing a geometric basis for subsequent embedding of the management vector space and establishment of the coordinate system. It also helps identify the collaborative / independent relationships between levels and is a key reference for selecting low-complexity paths in subsequent operational trajectory planning.

[0030] Example 4: In specific implementation, a reference point representing the ideal state of power engineering construction is set in the management vector space. Multiple paths to the reference point are calculated starting from the actual point representing the current state of the power engineering construction. The management complexity of the management vector space region traversed by each path is evaluated, and the path with the lowest management complexity is selected as the operation trajectory. The management vector space is specifically implemented using a high-dimensional array to store multi-dimensional management vectors for all management levels. A matrix operation library supports spatial operations such as vector dot product and modulus calculation. The mapping relationship between each dimension of the coordinate system and the management level is stored in a configuration file and can be dynamically adjusted according to the engineering construction stage. The operation trajectory is defined through a path planning algorithm. Based on the spatial relationship matrix of the management vector space and the coordinates of the obstacle area, feasible paths are traversed and management complexity is calculated. Finally, trajectory data consisting of a series of coordinate points is output. The trajectory data includes the timestamp corresponding to each coordinate point and the target state parameters of each management level, supporting subsequent discretization processing and instruction generation. It is understandable that the coordinates of the reference point are composed of the ideal indicator values ​​of each management level, representing the optimal state of the project when all preset standards are fully met. The coordinates of the actual point are composed of the actual values ​​of the key management indicators parsed from each management level at the current moment. Path calculation is to find the effective mathematical trajectory connecting the actual point and the reference point.

[0031] In some embodiments, setting a reference point representing the ideal state of power engineering construction in the management vector space includes defining ideal indicator values ​​for each management level based on the specifications and standards of power engineering construction and historical best data, and combining the ideal indicator values ​​of each management level into a multi-dimensional vector as the coordinates of the reference point in the management vector space. Calculating multiple paths to the reference point from the actual point representing the current state of power engineering construction includes obtaining the coordinates of the actual point in the current state, calculating the Euclidean distance between the actual point and the reference point, analyzing feasible directions in the management vector space, generating a straight path connecting the actual point and the reference point, and generating a curved path that bypasses obstacle areas, considering management constraints. It can be understood that management constraints are mapped to areas that cannot be directly traversed in the management vector space; for example, when the indicator combination of a certain management level is below the safety red line or in a state of severe resource conflict, this coordinate area is considered an obstacle area. The length and management complexity of each path are evaluated, and the optimal path is selected as the operational trajectory. In some embodiments, the evaluation of management complexity involves quantitatively analyzing the changes in the management level state associated with each small segment traversed by the path; optionally, the management complexity C... path A computational relationship can be expressed as:

[0032] Where: C path Let |ΔV| represent the total management complexity of a candidate path, where m represents the number of segments after discretizing the path. i | represents the combined change magnitude of the state vectors of all management levels on the i-th path segment, K i Let C represent the coordination cost coefficient between management levels involved in traversing the i-th path segment, where λ and γ are the weighting factors for the magnitude of change and the coordination cost, respectively. By comparing the C values ​​of different candidate paths... path The value, combined with the path length, allows us to select the most feasible path in terms of engineering and the least execution resistance as the final operation trajectory. Optionally, see Table 1, which shows one dimension for evaluating the management complexity of two candidate paths.

[0033] Table 1: Path Management Complexity Evaluation Dimensions Evaluation Dimensions Path A evaluation value Path B evaluation value Total range of state adjustment Larger smaller Number of cross-level coordination less More Resource reconfiguration frequency high Low Crossing the obstacle course yes no In some embodiments, when generating a curved path connecting the actual point and the reference point, it is necessary to identify the direction with weak correlation in the management vector space based on the spatial relationship matrix. The direction with weak correlation means that adjusting the state of one management level has a smaller linkage effect on other management levels, and can therefore be used as the preferred detour direction of the curved path.

[0034] Example 5: In specific implementation, the operation trajectory is discretized into multiple trajectory points, each corresponding to a coordinate position in the management vector space. The coordinate position of each trajectory point is analyzed to determine the target state required for each management level. The current state of each management level is compared with the target state to generate state adjustment requirements. It can be understood that the current state is the management vector space coordinates obtained in real-time through the data acquisition and injection module and processed by the data parsing and vector generation module, corresponding to the target state at the same moment. The state adjustment requirements are then converted into executable specific management instructions, which are a set of digital instructions directly issued to personnel, equipment, or control systems at the power engineering construction site.

[0035] In some embodiments, the coordinate position of each trajectory point is analyzed to determine the target state of each management level required by that coordinate position. This process involves decomposing the multidimensional coordinates of the trajectory point into sub-vectors constituting each management level of that coordinate. Each sub-vector represents a set of specific indicators that the corresponding management level should achieve at the trajectory point position. The current state and target state of each management level are compared to generate state adjustment requirements. State adjustment requirements are a quantified description of the differences, clearly indicating the direction and magnitude of adjustment needed for each management level. Optionally, the state adjustment requirement ΔS... i The calculation for the i-th management level can be expressed as: ΔS i =W i ×(G i -C i ) Where: ΔS i W represents the state adjustment demand vector for the i-th management level. i It is a diagonal matrix whose diagonal elements consist of the indicator weights of key management indicators under this management level, used to reflect the priority of adjustments to different indicators. G i C is the target state vector of the i-th management level obtained from the analysis. i This is the current state vector of the i-th management level. Based on the above calculations, the state adjustment requirements for each management level can be systematically generated.

[0036] In some embodiments, converting state adjustment requirements into executable management instructions requires translating the meaning of each component in the state adjustment requirement vector and its mapping relationship with the on-site execution units. For example, a state adjustment requirement vector related to the construction progress management level might contain components such as "daily pouring volume needs to be increased by X cubic meters" and "critical path workers need to be supplemented by Y people." These components will be converted into production scheduling instructions issued to the concrete mixing plant and work assignment instructions issued to the human resources system. In specific implementation, the conversion process relies on a pre-set instruction template library. The instruction template library defines mapping rules between state adjustment requirement components of different categories and management levels and specific, operable instruction statements. Through matching and filling, the state adjustment requirement is instantiated into a series of specific management instructions with execution parameters, execution objects, and execution time limits.

[0037] See Figure 5 This diagram visually represents the process of generating adjustment requirements by comparing the current state with the target state. With management levels on the vertical axis and the magnitude of adjustment requirements on the horizontal axis, it intuitively shows the degree of optimization needed at each level. The value of this diagram lies in its transformation of quantified state adjustment requirements into intuitive hierarchical differences. It clarifies high-priority optimization directions such as safety control and resource scheduling, and provides a precise hierarchical focus for subsequently converting adjustment requirements into executable management instructions, thus contributing to the targeted and efficient collaborative management of power engineering projects.

[0038] See Figure 5 This diagram visually represents the process of generating adjustment requirements by comparing the current state with the target state. With management levels on the vertical axis and the magnitude of adjustment requirements on the horizontal axis, it intuitively shows the degree of optimization needed at each level. The value of this diagram lies in its transformation of quantified state adjustment requirements into intuitive hierarchical differences. It clarifies high-priority optimization directions such as safety control and resource scheduling, and provides a precise hierarchical focus for subsequently converting adjustment requirements into executable management instructions, thus contributing to the targeted and efficient collaborative management of power engineering projects.

Claims

1. A whole-process digital collaborative management platform for power engineering construction, characterized in that, The platform comprises: a dynamic management framework module for establishing a dynamic management framework for a power engineering construction scene, the dynamic management framework being composed of a plurality of interrelated management levels; a data acquisition and injection module for acquiring real-time data streams from a plurality of sensing devices deployed at the power engineering construction site and injecting the real-time data streams into the dynamic management framework; a data analysis and vector generation module for analyzing the real-time data streams in layers through the dynamic management framework, extracting level data segments corresponding to each management level, and generating a multi-dimensional management vector for each management level based on its level data segment; a vector space management module for constructing a management vector space reflecting the overall power engineering construction state based on the multi-dimensional management vectors of all management levels; a collaborative operation module for defining an operation trajectory for collaborative management in the management vector space and generating specific management instructions for the entire power engineering construction process according to the operation trajectory.

2. The whole-process digital collaborative management platform for power engineering construction according to claim 1, characterized in that, Establishing a dynamic management framework for a power engineering construction scene includes: analyzing the entire life cycle process of power engineering construction and decomposing the entire life cycle process into a plurality of construction stages; for each construction stage, identifying all management elements involved in the construction stage, the management elements including personnel, equipment, materials and methods; clustering management elements according to the correlation strength between management elements to form a management element set; mapping the management element set to the corresponding management level according to the functional positioning of the management element set in the power engineering construction; defining data exchange protocols between different management levels to complete the construction of the dynamic management framework.

3. The whole-process digital collaborative management platform for power engineering construction according to claim 2, characterized in that, Analyzing the real-time data streams in layers through the dynamic management framework includes: configuring a dedicated data filtering rule for each management level, the data filtering rule being pre-set according to the management element characteristics of the management level, including personnel on-duty rate threshold, equipment operating parameter range and material consumption limit; inputting the real-time data streams into the dynamic management framework, so that the real-time data streams pass through each management level in turn; in each management level, applying the data filtering rule of the management level to filter the passing real-time data streams, and retaining the data segments that meet the rule; marking the filtered data segments as level data segments of the corresponding management level.

4. The whole-process digital collaborative management platform for power engineering construction according to claim 1, characterized in that, Generating a multi-dimensional management vector for a management level includes: determining the key management indicators of the management level, the key management indicators being extracted from the level data segments; assigning an index weight to each key management indicator, the index weight being determined according to the importance of the key management indicator in the power engineering construction; vectorizing the key management indicators with weights to form an initial management vector of the management level; normalizing the initial management vector to obtain a multi-dimensional management vector of the management level.

5. The whole-process digital collaborative management platform for power engineering construction according to claim 4, characterized in that, Constructing a management vector space reflecting the overall power engineering construction state includes: using the multi-dimensional management vector of each management level as a space base vector; calculating the cosine of the angle between each multi-dimensional management vector to determine the spatial relationship between the base vectors according to the cosine of the angle; embedding all the multi-dimensional management vectors into the same mathematical space based on the spatial relationship to form the management vector space; establishing a coordinate system in the management vector space, each dimension of the coordinate system corresponding to a management level.

6. The whole-process digital collaborative management platform for power engineering construction according to claim 5, characterized in that, defining an operation trajectory for collaborative management in the management vector space includes: setting a reference point representing an ideal state of power engineering construction in the management vector space; starting from an actual point representing the current state of power engineering construction, calculating multiple paths to reach the reference point; evaluating the management complexity of the region of the management vector space passed by each path; selecting the path with the lowest management complexity as the operation trajectory.

7. The whole-process digital collaborative management platform for power engineering construction according to claim 6, characterized in that, generating specific management instructions for the entire process of power engineering construction according to the operation trajectory includes: discretizing the operation trajectory into multiple trajectory points, each trajectory point corresponding to a coordinate position in the management vector space; analyzing the coordinate position of each trajectory point to determine the target state of each management level required by the coordinate position; comparing the current state and the target state of each management level to generate state adjustment requirements; converting the state adjustment requirements into executable specific management instructions.

8. The whole-process digital collaborative management platform for power engineering construction of claim 4, wherein, allocating index weights to each key management indicator includes: collecting historical project data of power engineering construction to extract the actual performance value of each key management indicator in the historical project; organizing field experts to evaluate the importance of each key management indicator to obtain expert scores; statistically analyzing the expert scores to calculate the average score of each key management indicator; normalizing the average score to map to a weight range of zero to one; combining the project type and stage characteristics of the current power engineering construction to fine-tune the normalized weight and determine the final index weight.

9. The whole-process digital collaborative management platform for power engineering construction of claim 5, wherein, calculating the cosine of the angle between each multi-dimensional management vector includes: extracting the multi-dimensional management vector of each management level and representing each vector as a directional vector in the mathematical space; calculating the dot product of any two multi-dimensional management vectors and the length of each multi-dimensional management vector; dividing the dot product by the product of the lengths of the two vectors to obtain the cosine of the angle; based on the size of the cosine of the angle, judging the similarity or difference between the vectors, the cosine of the angle close to one indicates that the vector direction is similar, close to zero indicates that the vector direction is orthogonal, and close to negative one indicates that the vector direction is opposite; based on the results of the cosine of the angle, establishing a spatial relationship matrix between the multi-dimensional management vectors.

10. The whole-process digital collaborative management platform for power engineering construction of claim 6, wherein, setting a reference point representing an ideal state of power engineering construction in the management vector space includes: defining ideal indicator values for each management level based on the specification standards and historical optimal data of power engineering construction; combining the ideal indicator values of each management level into a multi-dimensional vector as the coordinate of the reference point in the management vector space; starting from an actual point representing the current state of power engineering construction, calculating multiple paths to reach the reference point includes: obtaining the actual point coordinates of the current state to calculate the Euclidean distance between the actual point and the reference point; analyzing the feasible directions in the management vector space to generate a straight line path connecting the actual point and the reference point; considering the management constraints to generate a curved path bypassing the obstacle region; Evaluate the length and management complexity of each path, and select the optimal path as the operation trajectory.

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