A water conservancy project management system

Abstract

The present invention relates to the field of project management technology, specifically a water conservancy project management system, the system includes: a terrain identification module, an operation cluster division module, a construction arrangement module, a loss verification module, and an abnormality archiving module. In the present invention, by extracting the elevation value sequence of each grid in the terrain model, combining the slope, surface hardness and vegetation index, the landform boundary is accurately located, and the key difference characteristics of the construction boundary can be effectively identified in areas with complex geographical environments, thereby improving the scientific nature of terrain division. After demarcating the operation area, the degree of time overlap and spatial adjacency are integrated, and the node clusters are screened by the density index, so that the operation node division is more in line with the actual construction process and terrain layout, avoiding resource allocation conflicts. In the construction arrangement stage, the overlapping frequency of resource calls is used to judge the resource conflict section, and the node pairs are sorted accordingly in combination with the process priority, which can form a more reasonable construction rhythm.

Description

Technical Field

[0001] The present invention relates to the technical field of project management, and in particular to a water conservancy project management system. Background Art

[0002] The technical field of project management encompasses the organization, coordination, supervision, and control of the entire engineering project implementation process, with the goal of achieving project objectives and improving execution efficiency. The core of this technical field primarily encompasses the management of project costs, schedules, quality, and safety, encompassing systematic management approaches at every stage, from project initiation, design, procurement, construction, to final acceptance.

[0003] The water conservancy project management system refers to an information management system used to implement planning, resource allocation, construction scheduling, progress control, and data collection throughout the entire water conservancy project construction process. This system addresses the complex geographical environment, strict construction deadlines, and volatile resource allocation characteristics of water conservancy projects. It uses data collection to capture construction progress, personnel and equipment dynamics, and material usage. It also employs planning to develop construction calendars and resource schedules. It utilizes document collection to achieve unified management of construction records, approval processes, design changes, and other data. Furthermore, it generates various monitoring and statistical data through report generation for management decision-making.

[0004] In existing technologies, terrain information is typically determined based on a single elevation value, lacking comprehensive considerations such as slope, surface hardness, and vegetation. This leads to ambiguous and overlapping work boundary demarcations in areas with dense elevation differences or complex surfaces, increasing the risk of conflicts later in construction. Work divisions are often roughly arranged based on construction schedules, ignoring the spatial adjacency between nodes. This can lead to duplicate resource allocation for adjacent tasks and equipment idling, resulting in wasted resources and reduced efficiency. Process scheduling is often manually established based on empirical rules, making it difficult to dynamically identify periods of resource conflicts. This can lead to multiple tasks being concentrated during resource bottlenecks, impacting overall construction progress. Material verification often relies on a comparison of incoming and outgoing materials, ignoring actual usage differences between node processes. This makes it difficult to accurately identify abnormal usage and losses, making data deviations difficult to track to their source, ultimately impacting cost control accuracy. Abnormal data is often archived chronologically, lacking a structural connection to terrain classification and unable to support regionalized anomaly analysis and construction optimization decisions. For example, in areas where mountains and plains meet, if material anomalies are not classified in combination with the terrain background and are only archived by time sorting, it will be difficult to determine whether the abnormal consumption is caused by geological changes, which will weaken the interpretability and guiding value of the abnormal data. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a water conservancy project management system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: A water conservancy project management system includes:

[0007] The terrain recognition module obtains the elevation value sequence of each unit grid in the digital terrain model of the project area, extracts the elevation value of each unit grid, combines the slope, surface hardness and vegetation index to detect the difference between grids, and generates the terrain distribution results of the interface area;

[0008] The operation cluster division module obtains the corresponding block in the boundary area terrain distribution result, calculates the density index of the nodes with overlapping time intervals in the block, and selects the operation node cluster according to the density index to generate the selected boundary operation cluster;

[0009] The construction arrangement module compares the priorities and sequentially numbers the node pairs in the cluster based on the filtered boundary operation cluster, and generates a construction arrangement sequence for the boundary section;

[0010] The loss verification module calculates the difference ratio between the material purchase quantity, on-site quantity and actual quantity used at each node and the standard loss ratio based on the numbers of all operation nodes in the construction arrangement sequence of the junction section, screens the nodes with abnormal ratios, and generates abnormal records of materials in the junction section;

[0011] The abnormality archiving module groups the abnormal operation node numbers listed in the abnormal material records of the junction section according to the terrain classification labels to generate a project partition management data set under the classified terrain.

[0012] As a further solution of the present invention, the terrain distribution results of the junction area include terrain continuity identification labels, water surface edge superimposed segment indexes, and spatial distribution coordinates of junction points. The screened junction operation cluster specifically refers to the construction node spatial adjacency matrix, time overlapping node group, and density exceeding threshold mark set. The construction arrangement sequence of the junction section includes an operation node sorting table, resource conflict segment identification, and a set of priority node pairs. The material anomaly records of the junction section specifically include data offset anomaly numbers, usage discrepancy anomaly numbers, and abnormal node resource configuration relationships. The project partition management data set under the classified terrain includes a terrain label clustering table, a work type distribution structure set, and an abnormal node partition index.

[0013] As a further solution of the present invention, the terrain recognition module includes:

[0014] The grid feature extraction submodule obtains the elevation value sequence of each unit grid in the digital terrain model of the project area, detects the three parameters of slope value, surface hardness value and vegetation index value corresponding to the continuous elevation value interval, and generates a grid multi-parameter combination set;

[0015] The continuity node judgment submodule extracts the difference between the slope and surface hardness of adjacent grids in the grid multi-parameter combination set, judges whether the difference exceeds the terrain continuity judgment threshold, and selects the corresponding node group according to the judgment result to generate a terrain discontinuous node set;

[0016] The boundary edge positioning submodule calls the boundary node coordinates of the node group in the terrain discontinuous node set, compares the spatial position with the water surface edge node coordinate set, determines whether the relative position meets the edge matching criteria, and numbers the coordinate pairs that meet the matching criteria to generate the boundary area terrain distribution result.

[0017] As a further solution of the present invention, the job cluster division module includes:

[0018] The node information extraction submodule obtains the construction nodes, construction start and end times, and work types corresponding to each block in the boundary area terrain distribution result, compares the construction start and end times, identifies node pairs with overlapping time intervals, counts the number of all node pairs with time overlap, and generates the number of time-overlapping node pairs;

[0019] The adjacency relationship quantification submodule uses the formula based on the number of time-overlapping node pairs and the plane space coordinates of each node:

[0020] ;

[0021] Compute the first node in a node pair With the second node The spatial adjacency coefficient between , establish a set of spatial adjacency coefficients;

[0022] in, is the Euclidean distance between two nodes, 、 are the horizontal coordinates of the first and second nodes, represents the adjacent reference distance;

[0023] The density index generation submodule adopts the formula according to the spatial adjacency coefficient set and the number of time overlapping node pairs:

[0024] ;

[0025] Operate to obtain the density index of each first node , after sorting, we get a list of density indices;

[0026] in, The first node The density index, 、 They represent the adjustment ratios of the construction time influencing factor and the spatial adjacency influencing factor, The first node With the second node The number of days of construction time overlap, is the maximum construction time overlap value among all node pairs, is the spatial adjacency coefficient between node pairs, is the maximum value among all adjacency coefficients;

[0027] The dense node screening submodule compares the density index value of each first node in the density index list with the set job cluster density threshold, selects the node numbers and coordinates of all nodes whose density index values are greater than the job cluster density threshold, and obtains the screened boundary job cluster.

[0028] As a further solution of the present invention, the construction arrangement module includes:

[0029] Overlapping frequency extraction submodule: Based on the filtered boundary job cluster, the job types and corresponding resource demand types of the job nodes in the boundary job cluster are extracted, the time period information of each node is obtained, the start and end time points of the call of each type of resource within the time period are calculated, the overlap of the same resource demand type in the time period is determined, the frequency of overlap of each type of resource call is counted, and the resource overlapping interval identification result is obtained;

[0030] The node pair sorting submodule screens the pairs of operation nodes in the same resource call overlapping interval based on the resource overlapping interval identification results, extracts the process information corresponding to each pair of operation nodes, calls the process priority reference table, compares the priorities and sequentially numbers the node pairs, sorts the node pairs according to the priority size, and generates the construction arrangement sequence of the junction section.

[0031] As a further solution of the present invention, the loss verification module includes:

[0032] The material data extraction submodule obtains the material purchase quantity, material delivery quantity and current material usage data corresponding to each node based on the numbers of all operation nodes in the construction arrangement sequence of the junction section, and obtains the node material usage data set;

[0033] The offset ratio judgment submodule calls the node material usage data set, calculates the deviation between the purchase quantity and the incoming quantity of each node, determines the proportion of the deviation in the purchase quantity, compares the proportion result with the material data offset judgment threshold, obtains the current material usage value and the standard loss value of the corresponding node process, and obtains the node material offset comparison result;

[0034] The error ratio screening submodule calculates the ratio of each node usage to the standard usage based on the node material offset comparison results, compares the ratio with the material usage error judgment ratio, screens any operation node number whose ratio exceeds the threshold, and generates a material abnormality record for the boundary section.

[0035] As a further solution of the present invention, the exception filing module includes:

[0036] The abnormal node integration submodule obtains the boundary terrain classification label, node set ownership status, arrangement order and material abnormality type corresponding to each node based on the abnormal operation node number listed in the boundary section material abnormality record, integrates the above data content according to the node number, and generates an abnormal node ownership information set;

[0037] The terrain label grouping submodule calls the abnormal node attribution information set, groups and organizes the data according to the boundary terrain classification label, displays the node subset and attribute field set corresponding to each type of terrain, and establishes a project partition management data set under the classified terrain.

[0038] Compared with the prior art, the advantages and positive effects of the present invention are:

[0039] This method extracts elevation sequences from each grid cell in the terrain model and combines them with slope, surface hardness, and vegetation index to accurately locate landform boundaries. This effectively identifies key differential features of construction boundaries in geographically complex areas, enhancing the scientific nature of terrain demarcation. After demarcating the work area, the degree of temporal overlap and spatial adjacency are integrated, and node clusters are screened using a density index. This ensures that the work node division is more aligned with the actual construction process and terrain layout, avoiding resource allocation conflicts. During the construction scheduling phase, the frequency of resource call overlap is used to identify resource conflict zones. This information is then combined with process priority to prioritize nodes, enabling a more balanced construction rhythm, improving resource utilization and process integration. During material verification, three types of data—purchased quantity, on-site quantity, and actual quantity—are compared for deviation ratios. Error screening is performed using the standard loss ratio, enabling precise tracing of material anomalies and achieving refined cost control. During the archiving of anomaly data, anomalous nodes are grouped and organized based on terrain classification labels, helping to develop a regional profile of material usage behavior and providing a data foundation for subsequent correlation analysis between terrain categories and construction anomalies. The entire processing flow is supported by the cross-mapping of spatial data and temporal logic, and runs through the entire process of data identification, job allocation, process scheduling, material verification and exception archiving. It provides refined management capabilities in response to typical challenging scenarios such as complex terrain, intensive processes, and easy loss of hydraulic materials, enhancing the scientific nature of construction scheduling, the accuracy of resource management and control, and the timeliness of exception feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a system flow chart of the present invention;

[0041] Figure 2 This is a flow chart of the terrain recognition module of the present invention;

[0042] Figure 3 This is a flow chart of the job cluster partitioning module of the present invention;

[0043] Figure 4 This is a flow chart of the construction arrangement module of the present invention;

[0044] Figure 5 This is a flow chart of the loss verification module of the present invention;

[0045] Figure 6 This is a flow chart of the exception filing module of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0048] See also Figure 1 , a water conservancy project management system includes:

[0049] The terrain recognition module obtains the elevation value sequence of each unit grid in the digital terrain model of the project area, extracts the elevation value of each unit grid, combines the slope, surface hardness and vegetation index to detect the difference between grids, and generates the terrain distribution results of the interface area;

[0050] The job cluster division module obtains the corresponding blocks in the boundary area terrain distribution results, calculates the density index of the nodes with overlapping time intervals in the blocks, and filters the job node clusters based on the density index to generate the filtered boundary job clusters;

[0051] The construction arrangement module compares the priorities and sequentially numbers the node pairs in the filtered boundary operation clusters to generate a construction arrangement sequence for the boundary section.

[0052] The loss verification module calculates the difference ratio between the material purchase quantity, on-site quantity and actual quantity used at each node and the standard loss ratio based on the numbers of all operation nodes in the construction arrangement sequence of the junction section. It then screens nodes with abnormal ratios and generates abnormal records of materials in the junction section.

[0053] The abnormality archiving module groups abnormal operation node numbers listed in the abnormal material records of the junction section according to the terrain classification labels to generate a project partition management data set under the classified terrain;

[0054] The terrain distribution results of the junction area include terrain continuity identification labels, water surface edge overlay segment indexes, and spatial distribution coordinates of junction points. The filtered junction operation clusters specifically refer to the construction node spatial adjacency matrix, time overlapping node groups, and density exceeding threshold marker sets. The construction arrangement sequence of the junction section includes the operation node sorting table, resource conflict section identification, and priority node pair set. The material anomaly records of the junction section specifically include data offset anomaly numbers, usage discrepancy anomaly numbers, and abnormal node resource allocation relationships. The project partition management dataset under classified terrain includes the terrain label clustering table, work type distribution structure set, and abnormal node partition index.

[0055] See also Figure 2 , the terrain recognition module includes:

[0056] The grid feature extraction submodule obtains the elevation value sequence of each unit grid in the digital terrain model of the project area, detects the three parameters of slope value, surface hardness value and vegetation index value corresponding to the continuous elevation value interval, and generates a grid multi-parameter combination set;

[0057] First, the study area is divided into regular grid units with equal sides of 1 meter × 1 meter. For each grid, the vertical height component of the three-dimensional coordinates of its center point is obtained (recorded as elevation, set as ) as the elevation value of the unit. If the original data is in point cloud format, all points in the grid are extracted. The average of the values is calculated as the elevation value of the grid. Then, the slope of each grid is calculated based on the elevation difference with its adjacent grids and the horizontal distance between the centers, using the following formula: ,in, is the current grid slope angle value, Indicates the elevation difference between the current grid and its adjacent grid. is the horizontal distance between the two grid center points (taken as 1 meter at standard resolution); Its right adjacent grid point For example, if the former elevation is 132.4 meters and the latter elevation is 130.9 meters, then , , substituting into the formula we get Then collect the surface hardness value, use the rebound hammer to evenly distribute three measuring points on each grid to record its rebound force, which is recorded as , such as grid The three-point rebound forces are 22N, 24N and 21N, and the calculated hardness values are: ; Then, obtain the vegetation index value NDVI, which is based on the reflectivity of the near infrared band in the remote sensing image ( ) and red light band reflectivity ( ) is calculated, and the calculation formula is: , with grid For example, if 、 ,but: Finally, the system will calculate the slope value of each grid , hardness value , the vegetation index value NDVI constitutes the parameter vector , for example , collected into a complete grid multi-parameter combination set according to the grid arrangement order.

[0058] The continuity node judgment submodule extracts the difference between the slope and surface hardness of adjacent grids in the grid multi-parameter combination set, determines whether the difference exceeds the terrain continuity judgment threshold, and selects the corresponding node group based on the judgment result to generate a terrain discontinuous node set;

[0059] Extract the difference between the slope and hardness of adjacent grids in the grid multi-parameter combination set. In the operation, the slope difference and hardness difference are calculated for each pair of adjacent grids. Suppose the two adjacent grids are numbered as and , then the slope difference is expressed as: , the hardness difference is expressed as: ,in, 、 Respectively represent the numbers 、 The slope values of the two rasters, 、 are their surface hardness values, The value of represents the order in which the adjacent grid pairs appear in the traversal; for example, Number , slope of 45.0°, hardness of 26.5N, grid Number , the slope is 48.2°, the hardness is 23.1N, then the slope difference is , the hardness difference is ; Determine whether these differences exceed the set threshold: the slope judgment threshold is , the hardness judgment threshold is , if any one of the items exceeds the corresponding threshold, the grid pair is determined to be a discontinuous node pair; in the above example, due to 、 , so it is judged as a discontinuous node pair. All node pairs that meet this condition are stored in the terrain discontinuous node set with their grid coordinates. The structure is as follows: , where the subscript Respectively represent the position numbers of two rasters that are judged to be discontinuous in the region.

[0060] The boundary edge positioning submodule calls the boundary node coordinates of the node group in the terrain discontinuous node set, compares them with the water surface edge node coordinate set, determines whether the relative position meets the edge matching criteria, and numbers the coordinate pairs that meet the matching criteria to generate the boundary area terrain distribution results;

[0061] First, identify which grids in the discontinuous node set are located at the edge of the region by counting the frequency of each grid appearing adjacent to the discontinuous node set. If a grid is adjacent to only one or two other discontinuous grids, it is determined to be a boundary grid node, and its coordinates are marked as , where the subscript Indicates that the node is the first in a discontinuous set. boundary nodes; for example, the node pair Medium, if raster A node is a boundary node if it is connected to only one other node. The system then calls the known water surface edge node coordinate set, which is a structure of several points. , where the subscript Represents the edge of the water surface The coordinate points are numbered, and then the boundary nodes and the water surface edge points are spatially matched using the Euclidean distance formula. The formula is: ,in are the coordinates of the discontinuous boundary nodes, is the coordinate of the water surface edge node; if the distance Less than the matching tolerance threshold , that is, it is judged to be a match, for example 、 , then , which is less than the set matching tolerance , so the match is successful; finally, all matching pairs are numbered as the intersection point matching set, with the following structure: , complete the generation of terrain distribution results in the junction area.

[0062] See also Figure 3 ,The job cluster partitioning module includes:

[0063] The node information extraction submodule obtains the construction nodes, construction start and end times, and work types corresponding to each block in the boundary area terrain distribution results, compares the construction start and end times, identifies node pairs with overlapping time intervals, counts all node pairs with time overlap, and generates the number of time-overlapping node pairs.

[0064] Obtain the construction nodes, construction start and end times, and work types corresponding to each block in the boundary area terrain distribution results. First, index the node numbers of all covered construction areas according to the block boundary coordinates, and record them as node set numbers in the form of a numbered list. Further read the construction start and end dates corresponding to each node, convert them into a standard timestamp format, and perform time interval analysis. Perform intersection judgment on the start and end times between any two nodes. If there is a non-empty overlapping interval between the two time periods, it is judged as a time overlapping node pair, and the node pair code is recorded. To perform this operation, all node combinations must be traversed. For example, the start and end time of construction node 1 is from June 1 to June 10, and the start and end time of node 2 is from June 5 to June 15. Since June 5 to June 10 is an overlapping time period, the two constitute a time overlapping node pair. Then, the number of node pairs that meet all the conditions is accumulated to obtain the total number of nodes. A total of 89 groups of time-overlapping node pairs are formed in the area. Then, the types of work to which the node pairs belong are identified and duplicated, and node pair combinations that cannot share work resources due to inconsistent work types are eliminated. Finally, valid node pairs are retained as the basis for subsequent density and adjacency analysis, and the number of time-overlapping node pairs is generated.

[0065] The adjacency quantification submodule is based on the number of time-overlapping node pairs and the plane space coordinates of each node, using the formula:

[0066] ;

[0067] Compute the first node in a node pair With the second node The spatial adjacency coefficient between , establish a set of spatial adjacency coefficients;

[0068] in, is the Euclidean distance between two nodes, 、 are the horizontal coordinates of the first and second nodes, Indicates the adjacent reference distance, which is set to 2 meters.

[0069] For the first node in each pair of nodes With the second node The two-dimensional coordinate points are extracted and recorded as and , and calculate its spatial distance by constructing the Euclidean distance function. The formula is:

[0070] ;

[0071] The coordinate units are meters. For example, the first node is located at , the second node is located at , the distance is , then set the adjacent reference distance to , normalized with this value, the normalization operation adopts the inverse transformation form to form the spatial adjacency coefficient value, which is defined as:

[0072] ;when The times , the square is 3.26, and the adjacency value is , repeatedly calculate the adjacency values of all 89 sets of time-overlapping node pairs to obtain a dimensionless set of spatial adjacency coefficient values.

[0073] The density index generation submodule uses the formula based on the spatial adjacency coefficient set and the number of time overlapping node pairs:

[0074] ;

[0075] Operate to obtain the density index of each first node , after sorting, we get a list of density indices;

[0076] in, The first node The density index, 、 Respectively represent the adjustment ratio of the construction time influencing factor and the spatial adjacency influencing factor, satisfying , set to 、 , The first node With the second node The number of days of construction time overlap, is the maximum construction time overlap value among all node pairs, is the spatial adjacency coefficient between node pairs, is the maximum value among all adjacency coefficients, Indicates the first node All associated second nodes with time overlap The set sum of .

[0077] Extract each first node All associated second nodes , respectively call the number of days of overlapping construction time and adjacency coefficient , perform normalization operations uniformly and take the maximum construction overlap time value in the entire domain , maximum adjacency coefficient As the normalized denominator, 、 Normalized to a ratio, Indicates the adjustment ratio of the time impact factor, which is used to adjust the contribution of construction time overlap to the density index; Indicates the adjustment ratio of the spatial adjacency influence factor, which is used to adjust the impact of node spatial proximity on the density index; these two adjustment ratios must meet the normalization condition , ensuring that the two factors act together on the overall density result within a unified weight range. In the construction scenario, time overlap more directly reflects the degree of resource scheduling and job intersection of construction activities in the same period, and therefore has a stronger driving effect on density; for example, when the construction times of two construction nodes completely overlap, but the spatial distance is slightly far, there may still be conflicts in construction equipment scheduling or parallel resource allocation requirements between the two; on the contrary, if the two nodes are close in space but the construction times do not overlap, the possibility of actual conflicts or overlapping operations is significantly reduced. Therefore, in order to reflect the dominant role of the construction scheduling process in density assessment, the adjustment ratio of the time overlap item in this scheme is set to , the relative weight is higher, and the spatial adjacency term is set to , but retains appropriate consideration of spatial clustering characteristics, allowing the density index to comprehensively consider the synchronization and spatial overlap of construction activities. This ratio is based on statistical derivation of job scheduling requirements in typical construction areas. Analysis of construction conflict cases shows that in more than 70% of scheduling conflict cases, there is a clear trend of temporal overlap, making the time-dominant adjustment setting reasonable. The superposition calculation expression is constructed as follows:

[0078] ;

[0079] First Node For example, assume that there are three overlapping node pairs, , where the corresponding values are: , ; , ; , .but:

[0080] ;

[0081] The result is the density index value of node A001. The density of all first nodes is repeatedly calculated to form a density index list.

[0082] The dense node screening submodule compares the density index value of each first node in the density index list with the set job cluster density threshold, selects the node numbers and coordinates of all nodes whose density index values are greater than the job cluster density threshold, and obtains the filtered boundary job clusters;

[0083] According to the density index value of each first node in the density index list, the job cluster density threshold is set to 1.3, and the relationship between the density values of all nodes and the threshold is compared one by one. All nodes that meet the threshold are selected using the greater than judgment strategy. The node number is obtained and the corresponding coordinates are extracted from the original node space information to construct a combination list consisting of node numbers and coordinates. For example, the density of nodes A001, A005, and A013 are 1.488, 1.42, and 1.51 respectively, which all meet the screening conditions. The number and location points After sequential combination, they are aggregated to form a node set data structure, which is used as the final output result to obtain the filtered boundary operation cluster.

[0084] See also Figure 4 , the construction arrangement module includes:

[0085] The overlapping frequency extraction submodule extracts the job types and corresponding resource demand types of the job nodes in the boundary job cluster based on the filtered boundary job clusters, obtains the time period information of each node, calculates the start and end time points of each type of resource call within the time period, determines the overlap of the same resource demand type in the time period, and counts the frequency of overlap of each type of resource call to obtain the resource overlapping interval identification result;

[0086] First, a fine extraction operation is performed on each of the operation nodes. This operation can be divided into three parts. The first step is to obtain the operation type corresponding to each operation node and record it as structured data, such as A101, A102, etc. The second step is to extract the resource demand type, such as excavator, crane, concrete pump, etc., and number them to form a standardized number sequence such as R1, R2, R3, etc. The third step is to extract the operation time period corresponding to the operation node, such as node From May 1st to May 3rd, the node From May 2 to May 5, here you can use specific date values as the time start and end points, such as node The time period is [121, 123], and the node =[122, 125], where 121 represents the 121st day since the project base date. Subsequently, for each resource requirement type, a call schedule on the time axis is constructed. By analyzing the call time period of the resource in all job nodes, the start and end time points are aggregated to form a time series set. Taking resource R1 as an example, if node and another node both call resource R1 and their time periods are [121, 123] and [124, 126] respectively, then the time series of R1 resource is two independent intervals; next, the overlap between time periods is judged, that is, for all pairs of nodes that call the same resource, whether their time periods have an intersection is judged. The intersection judgment is based on: if the node pair and middle, and , then it is considered that there is an overlapping interval; on this basis, all node pairs with intersections are accumulated, and the number of overlaps of the resource in the overall operation is recorded, which is the resource call overlapping frequency. For example, resource R2 has a total of 5 groups of node time overlaps during the project, so its overlapping frequency is 5; then, combined with the job node time period corresponding to each type of resource, the overlapping interval is compared again to filter out the node set that meets the "node time period and resource call overlapping interval completely or partially overlap". Through the above steps, the job node set that actually participates in scheduling within each resource call overlapping interval can be finally determined. Here, taking resource R3 as an example, if its time overlapping interval is [130, 134], the job node If the time period of [132, 135] is [132, 135], it is determined that it intersects with the overlapping interval and is recorded in the participating node set. Repeat the above operation for all resources to finally obtain the resource overlapping interval identification result.

[0087] The node pair sorting submodule, based on the resource overlap interval identification results, screens the pairs of operation nodes within the same resource call overlap interval, extracts the process information corresponding to each pair of operation nodes, calls the process priority reference table, compares the priority of the node pairs and numbers them in sequence, sorts the node pairs according to their priority, and generates a construction arrangement sequence for the junction section;

[0088] For each overlapping interval corresponding to each type of resource, perform node pairing screening operations to build a set of job node pairs in the same overlapping interval. and nodes There is an intersection in time and the same type of resources are called; then the process code of each operation node is extracted, and according to the arrangement of the node in the process sequence, the process priority reference table is called. The table is pre-set according to the operation type and engineering logic. For example, the priority of concrete pouring is 5, steel bar binding is 3, and formwork installation is 4. The specific codes can be seen in the table as shown below; by and nodes The process priority values of the nodes are compared. If the priority value is smaller, it should be sorted first. Then the node pair arrangement order is generated by sorting by priority size. It should be noted that in the sorting process, multiple factors should be considered for coordinated adjustment. If the same priority appears, the secondary parameters such as the operation intensity of the resources called by the node and the node start time can be used as the sorting reference value. By comparing all the node pairs that meet the resource call overlapping interval one by one, a complete sorting list is generated, and the result is output as a node pair sequence to form a construction arrangement sequence of the junction section.

[0089] See also Figure 5 , the loss verification module includes:

[0090] The material data extraction submodule obtains the material purchase quantity, material delivery quantity and actual material usage data corresponding to each node based on the numbers of all operation nodes in the construction arrangement sequence of the junction section, and obtains the node material usage data set;

[0091] First, query the material purchase quantity corresponding to the node number from the construction material management system. The purchase quantity is the material purchase order quantity issued by the project department to the supplier according to the plan list before construction. For example, node A corresponds to the purchase of 120 tons of cement, and its purchase record shows that the supply order quantity that has been paid is 120 tons. Then collect the corresponding material entry quantity. The entry quantity is provided by the material entry registration system, which records the actual weighing record and inspection registration quantity of the transport vehicle entering the construction site. For example, the cement entry quantity record of node A is 115 tons, indicating that there are 5 tons of materials that have not been brought in. Further analyze the material usage record filled in after the construction of the node. The record shows that the actual cement usage of the node is 109 tons, forming the actual usage data. Then, the three values of the node are unified and aggregated. The purchased quantity is 120 tons, the delivered quantity is 115 tons, and the actual used quantity is 109 tons. The operation process is repeated until all node number information is extracted. For example, node B purchases 200 tons of steel bars, delivers 197 tons, and uses 195 tons. Node C purchases 300 tons of sand and gravel, delivers 295 tons, and uses 282 tons. The data of each node are aggregated to form a unified data set. During the execution process, it is necessary to establish consistency verification rules between node numbers and corresponding material categories and unit quantity standards to ensure the comparability of extracted data types. For example, the material category unit is unified as tons, and special records measured in packages or cubic meters are eliminated to avoid calculation errors. A node material information mapping list is established one by one according to the node construction sequence to support subsequent proportion judgment and deviation analysis to obtain the node material usage data set.

[0092] The offset ratio judgment submodule calls the node material usage data set, calculates the deviation between the purchase quantity and the incoming quantity at each node, determines the proportion of the deviation in the purchase quantity, compares the proportion result with the material data offset judgment threshold, obtains the actual material usage value and the standard loss value of the corresponding node process, and obtains the node material offset comparison result;

[0093] The node material usage data set is called, and the difference between the purchase and delivery quantity is calculated node by node. The formula is the purchase quantity minus the delivery quantity. For example, the difference of node A is 120-115=5 tons. The difference is divided by the purchase quantity to calculate the offset ratio, which is 5 / 120=0.0417, indicating that the offset ratio is 4.17%. Then the material data offset judgment threshold is introduced. For example, the upper limit of the allowable offset of cement materials is set to 5%, which is 0.05. According to this threshold, if the ratio value is less than or equal to 0.05, it is considered to be within the allowable offset range and the node passes the first step of judgment. Otherwise, it is considered to be an offset abnormality. Similarly, the difference of node B is 3 tons, and the offset ratio is 3 / 200=0.015, which is 1.5%, which is less than 5%. The offset ratio of node C is 5 / 300=0.0167, which is 1.67%, both within the threshold. After judgment through this step, the actual usage value and the corresponding node are called. The standard loss of each process is extracted from the enterprise standard manual. For example, the standard for cement pouring process is 0.35 tons of cement per cubic meter of concrete. Node A constructs 310 cubic meters of concrete, with a standard consumption of 0.35×310=108.5 tons and an actual consumption of 109 tons. The standard comparison value can be obtained from this. Node B is steel bar binding, with 7.5 kilograms of steel bar consumed per square meter of reinforced concrete slab and an area of 2,600 square meters. The corresponding standard consumption is 7.5×2,600 / 1,000=19.5 tons, and the actual consumption is 19.5 tons. If there is a difference in unit conversion, it needs to be converted and unified before comparison. Node C constructs the foundation cushion layer, with a sand and gravel standard of 0.2 tons per square meter and an area of 1,100 square meters. The standard consumption is 0.2×1,100=220 tons, and the actual consumption is 282 tons. Further classification is performed to obtain the node material offset comparison results.

[0094] The error ratio screening submodule calculates the ratio of each node's usage to the standard usage based on the node material offset comparison results, compares the ratio with the material usage error judgment ratio, and screens any operation node number whose ratio exceeds the threshold, generating a record of material anomalies at the boundary section.

[0095] First, the actual usage of each node is divided by the standard usage, and the ratio is calculated as the basis for error judgment. The ratio of node A is 109 / 108.5=1.0046, which is a 0.46% error. The ratio of node B is 19.5 / 19.5=1, which is a 0 error. The ratio of node C is 282 / 220=1.2818, which is a 28.18% error. The material usage error judgment ratio is further introduced and set to 1.1, indicating that the standard usage error tolerance range is within 10%. Nodes A and B are both less than this threshold, and node C exceeds the threshold. According to the set rules, the node numbers exceeding the threshold are filtered out and added to the abnormal list. The list is further organized into a structured information format. For example, the fields are node number, material type, standard usage, actual usage, error ratio, and error judgment result. The output abnormal node is number C, corresponding to the error ratio of 1.2818 and the status is abnormal, generating a material abnormality record for the junction section.

[0096] See also Figure 6 , the exception filing module includes:

[0097] The abnormal node integration submodule obtains the boundary terrain classification label, node set ownership status, arrangement order and material abnormality type corresponding to each node based on the abnormal operation node number listed in the boundary section material abnormality record, and integrates the above data content according to the node number to generate the abnormal node ownership information set;

[0098] First, extract the data attributes corresponding to each abnormal node, including the boundary terrain classification label, node set ownership status, arrangement order and material abnormality type. During the execution process, it is necessary to call the structured database or the preset data form, and use the node number as the primary index key to retrieve the value of each field in sequence. In the specific execution, if the node number is N101, N102, N103, the corresponding boundary terrain labels in the data table are "hilly area", "plain area", and "mountain edge", the node set ownership status is "centralized", "dispersed", and "centralized", the arrangement order is 12, 7, and 20, and the material abnormality type is "super Consumption", "short entry", "missing count", here involves the precise association judgment of the "node number", and its execution process is to match and retrieve one by one and generate the associated data structure. For example, N101 corresponds to a hilly area, and its position in the arrangement queue is 12, belonging to the centralized set, and the abnormal mark is over-consumption. It is necessary to establish a set of structure arrays {"N101", "hilly area", "centralized", 12, "over-consumption"}, which constitute a record form as a row; for the acquisition of the field "attribution status", it is usually necessary to read the node aggregation density of the area where the node is located in the arrangement path diagram. If the density threshold is set to 0.6, when the proportion of nodes in a certain area to the total number of arranged nodes exceeds When this value is exceeded, the nodes in the area are judged to be a concentrated set, otherwise they are judged to be a dispersed set; the proportion of nodes in the area where node number N102 is located is 0.42, which is less than the threshold value of 0.6, so the belonging state is defined as "dispersed"; the setting basis of the threshold of 0.6 here is based on the fact that in the node distribution pattern, it is found through manual analysis that when the density is greater than 0.6, the nodes are visually dense, and when it is lower than 0.6, the overall distribution is discrete, which meets the on-site management division standard, so this judgment basis threshold is set; and the field "arrangement order position" comes from the arrangement position number of the nodes in the queue from left to right or from front to back in the arrangement result list, and is assigned in ascending order; for example, in the queue, N0 is 99, N100, N101, N102, N103, then N101 is ranked 3 and N103 is ranked 5; the last field "Material Abnormal Type" is filtered and output by the previous module, and contains type label content "overconsumption", "shortage", "omission" and other enumeration items, among which "overconsumption" means that the material usage exceeds the standard loss, "shortage" means that the number of incoming materials is less than the purchased quantity, and "omission" means that the material usage record is missing. In actual operation, if the purchased material of node N103 is 5.0 tons and the actual usage record is 0 tons, it is classified as "omission"; after the above items are integrated through node numbering, a clearly structured node record entry is formed, and an abnormal node attribution information set is generated.

[0099] The terrain label grouping submodule calls the abnormal node attribution information set, groups and organizes the data according to the boundary terrain classification label, displays the node subset and attribute field set corresponding to each type of terrain, and establishes a project partition management data set under the classified terrain;

[0100] First, identify and call the boundary terrain classification label field in each record, and perform clustering according to the terrain label value during execution, and classify the records with the same label value into the same terrain subset. In the actual processing process, if there are a total of 10 records of abnormal nodes, the frequency of their terrain labels is as follows: 4 in plain areas, 3 in hilly areas, and 3 on the edge of mountains, then the system needs to establish 3 subsets: plain group, hilly group, and mountain group. Each group contains the complete record content of the corresponding node. For example, the plain group contains {"N100", "plain area", "concentrated type", 8, "short entry"}, {"N104", "plain area", "concentrated type", 11, "missing"}, etc.; then sort the records in each subset according to the arrangement order, and the sorting is based on the value of the field "arrangement order position". After sorting, the nodes in the terrain are obtained in the operation. The actual layout in the sequence; then the nodes in each subset are classified and summarized according to the material anomaly type field. For example, if there are 2 "overconsumption" and 1 "missing" nodes in the hilly group, the distribution results of this type are counted in the summary information of the terrain subset to form the material anomaly composition structure within the project partition, which is convenient for subsequent reporting or management record registration; at the same time, the attribution status field in each record can also be used as a statistical dimension to count the number ratios of abnormal nodes in different aggregation forms in each terrain subset according to centralized and decentralized types. For example, in the mountainous group, 2 of the 3 records are centralized and 1 is decentralized, so the distribution ratio is 2:1. This distribution information is combined with the terrain category as an important reference for regional division management; finally, according to the above terrain clustering, information collection and structure statistics process, a project zoning management data set under classified terrain is established.

[0101] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A water conservancy project management system, characterized in that: The system comprises: The terrain recognition module obtains the elevation value sequence of each unit grid in the digital terrain model of the project area, extracts the elevation value of each unit grid, combines the slope, surface hardness and vegetation index to detect the difference between grids, and generates the terrain distribution results of the interface area; The terrain recognition module includes: The grid feature extraction submodule obtains the elevation value sequence of each unit grid in the digital terrain model of the project area, detects the three parameters of slope value, surface hardness value and vegetation index value corresponding to the continuous elevation value interval, and generates a grid multi-parameter combination set; The continuity node judgment submodule extracts the difference between the slope and surface hardness of adjacent grids in the grid multi-parameter combination set, judges whether the difference exceeds the terrain continuity judgment threshold, and selects the corresponding node group according to the judgment result to generate a terrain discontinuous node set; The boundary edge positioning submodule calls the boundary node coordinates of the node group in the terrain discontinuous node set, compares the spatial position with the water surface edge node coordinate set, determines whether the relative position meets the edge matching standard, and numbers the coordinate pairs that meet the matching standard to generate the boundary area terrain distribution result; The operation cluster division module obtains the corresponding block in the boundary area terrain distribution result, calculates the density index of the nodes with overlapping time intervals in the block, and selects the operation node cluster according to the density index to generate the selected boundary operation cluster; The construction arrangement module compares the priorities and sequentially numbers the node pairs in the cluster based on the filtered boundary operation cluster, and generates a construction arrangement sequence for the boundary section; The loss verification module calculates the difference ratio between the material purchase quantity, on-site quantity and actual quantity used at each node and the standard loss ratio based on the numbers of all operation nodes in the construction arrangement sequence of the junction section, screens the nodes with abnormal ratios, and generates abnormal records of materials in the junction section; The abnormality archiving module groups abnormal operation node numbers listed in the abnormal material records of the junction section according to terrain classification labels to generate a project partition management data set under the classified terrain; The terrain distribution results of the junction area include terrain continuity identification labels, water surface edge superimposed segment indexes, and spatial distribution coordinates of junction points. The filtered junction operation cluster specifically refers to the construction node spatial adjacency matrix, time overlapping node group, and density exceeding threshold mark set. The construction arrangement sequence of the junction section includes an operation node sorting table, resource conflict segment identification, and a set of priority node pairs. The material anomaly records of the junction section specifically include data offset anomaly numbers, usage discrepancy anomaly numbers, and abnormal node resource configuration relationships. The project partition management data set under the classified terrain includes a terrain label clustering table, a work type distribution structure set, and an abnormal node partition index.

2. The water conservancy project management system according to claim 1, characterized in that: The job cluster division module includes: The node information extraction submodule obtains the construction nodes, construction start and end times, and work types corresponding to each block in the boundary area terrain distribution result, compares the construction start and end times, identifies node pairs with overlapping time intervals, counts the number of all node pairs with time overlap, and generates the number of time-overlapping node pairs; The adjacency relationship quantification submodule uses the formula based on the number of time-overlapping node pairs and the plane space coordinates of each node: Calculate the spatial adjacency coefficient between the first node i1 and the second node j1 in the node pair Establish a set of spatial adjacency coefficients; in, is the Euclidean distance between two nodes, are the horizontal coordinates of the first and second nodes respectively, and d0 represents the adjacent reference distance; The density index generation submodule adopts the formula according to the spatial adjacency coefficient set and the number of time overlapping node pairs: Operate to obtain the density index of each first node After sorting, we get a list of density indices; in, is the density index of the first node i1, λ and μ represent the adjustment ratios of the construction time influencing factor and the spatial adjacency influencing factor, respectively. is the number of days that the construction time of the first node i1 and the second node j1 overlaps, T max is the maximum construction time overlap value among all node pairs, is the spatial adjacency coefficient between node pairs, C max is the maximum value among all adjacency coefficients; The dense node screening submodule compares the density index value of each first node in the density index list with the set job cluster density threshold, selects the node numbers and coordinates of all nodes whose density index values are greater than the job cluster density threshold, and obtains the screened boundary job cluster.

3. The water conservancy project management system according to claim 2, characterized in that: The construction arrangement module includes: The overlapping frequency extraction submodule extracts the job types and corresponding resource demand types of the job nodes in the boundary job cluster based on the filtered boundary job cluster, obtains the time period information of each node, calculates the start and end time points of the call of each type of resource within the time period, determines the overlap of the same resource demand type in the time period, counts the frequency of overlap of each type of resource call, and obtains the resource overlapping interval identification result; The node pair sorting submodule screens the pairs of operation nodes in the same resource call overlapping interval based on the resource overlapping interval identification results, extracts the process information corresponding to each pair of operation nodes, calls the process priority reference table, compares the priorities and sequentially numbers the node pairs, sorts the node pairs according to the priority size, and generates the construction arrangement sequence of the junction section.

4. The water conservancy project management system according to claim 3, characterized in that: The loss verification module includes: The material data extraction submodule obtains the material purchase quantity, material delivery quantity and current material usage data corresponding to each node based on the numbers of all operation nodes in the construction arrangement sequence of the junction section, and obtains the node material usage data set; The offset ratio judgment submodule calls the node material usage data set, calculates the deviation between the purchase quantity and the incoming quantity of each node, determines the proportion of the deviation in the purchase quantity, compares the proportion result with the material data offset judgment threshold, obtains the current material usage value and the standard loss value of the corresponding node process, and obtains the node material offset comparison result; The error ratio screening submodule calculates the ratio of each node usage to the standard usage based on the node material offset comparison results, compares the ratio with the material usage error judgment ratio, screens any operation node number whose ratio exceeds the threshold, and generates a material abnormality record for the boundary section.

5. The water conservancy project management system according to claim 4, characterized in that: The exception filing module includes: The abnormal node integration submodule obtains the boundary terrain classification label, node set ownership status, arrangement order and material abnormality type corresponding to each node based on the abnormal operation node number listed in the boundary section material abnormality record, integrates the above data content according to the node number, and generates an abnormal node ownership information set; The terrain label grouping submodule calls the abnormal node attribution information set, groups and organizes the data according to the boundary terrain classification label, displays the node subset and attribute field set corresponding to each type of terrain, and establishes a project partition management data set under the classified terrain.

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