Multi-role collaborative intelligent whole-process engineering consultation integrated management platform

Abstract

The invention discloses a multi-role collaborative intelligent whole-process engineering consultation integrated management platform, which belongs to the technical field of engineering management, and specifically comprises a data access module for extracting engineering quantity and material strength grade in a building information model and process time nodes in a construction progress plan; the demand curve generation module generates a material demand curve accordingly. And the storage yard occupancy calculation module compares the real-time material entry record with the demand curve to calculate a storage yard occupancy dynamic value. And the stacking capacity management and control module calculates an effective stacking area according to the construction plane layout diagram, marks a storage yard when the effective stacking area exceeds the capacity, and locks a material entry inspection entrance. The material gap identification module generates a material gap list and pushes the material gap list to a supplier. And the demand curve correction module generates a correction demand curve according to the delivery notice and adjusts the state of the yard mark. According to the invention, the real-time matching of the material demand and the field capacity and the precise cooperation of the purchase and the progress are realized.

Description

Technical Field

[0001] This invention relates to the field of engineering management technology, specifically to an intelligent, integrated management platform for the entire process of engineering consulting with multi-role collaboration. Background Technology

[0002] With the deepening of digital transformation in the construction industry, the full-process engineering consulting management model has gradually become an important means to improve project management. The widespread application of Building Information Modeling (BIM) technology and construction schedule management systems provides engineering projects with visualized design expression and time-sequential task scheduling, enabling all project participants to share information and collaborate in a unified digital environment. Based on this, various professional consulting firms have developed corresponding information technology tools around core business areas such as investment control, schedule control, and quality management, initially forming a digital management capability covering the entire project lifecycle.

[0003] Current technical solutions related to engineering material management mainly focus on two directions: one is the quantity surveying method based on Building Information Modeling (BIM), which automatically generates a bill of quantities for each sub-item of the project by analyzing the component attributes in the model; the other is the resource demand forecasting method based on the construction schedule, which calculates the planned material usage for each stage based on the time nodes of the work process and the quota consumption standards. In the material arrival stage, existing systems typically provide electronic entry functions for arrival records, comparing the actual arrival quantity with the planned usage afterward. Regarding yard management, construction site layout plans are used to plan the location and scope of the yard, and the control of yard capacity mainly relies on the experience and judgment of on-site management personnel. The material procurement process is generally initiated by the construction unit based on on-site needs, notifying suppliers to deliver goods via telephone or email.

[0004] However, the above-mentioned technical solutions have the following problems in practical applications: the quantity data in the building information model (BIM) is independent of the time nodes in the construction schedule, and the generated material demand plan cannot be updated in real time with the dynamic adjustment of the construction progress. When construction procedures are advanced or delayed due to changes in site conditions, the material demand curve remains in its initial state, leading to a gradual accumulation of deviations between planned usage and actual consumption, and a lack of reliable quantitative basis for subsequent material delivery arrangements. In addition, no dynamic correlation mechanism has been established between material delivery records and yard capacity data, and warnings and controls cannot be automatically triggered when the quantity of delivered materials exceeds the actual carrying capacity of the yard. Site management personnel find it difficult to grasp the real-time changes in yard occupancy rates in a timely manner, and often only deal with the situation passively when material accumulation obstructs construction access or causes secondary handling. The contradiction between excessive material delivery and insufficient site capacity has existed for a long time and lacks technical solutions. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent, integrated management platform for the entire process of engineering consulting with multi-role collaboration, addressing the following technical problems: The inability to dynamically adjust material demand plans according to construction progress has led to a continuous accumulation of discrepancies between planned usage and actual consumption. At the same time, the lack of a real-time linkage mechanism between material arrival records and storage yard capacity has prevented automatic control when materials arrive in excess, thus hindering the collaborative efficiency of project material management.

[0006] The objective of this invention can be achieved through the following technical solutions: A multi-role collaborative, intelligent, end-to-end engineering consulting integrated management platform, comprising: The data access module is used to receive the building information model file and construction schedule file of the target project, extract the engineering quantity and material strength grade from the building information model file, and extract the time nodes of each process from the construction schedule file. The demand curve generation module is used to allocate the workload according to the time nodes of the process, and after weighting according to the material strength grade, multiply it by the material consumption coefficient, and then accumulate the weighted workload over time to generate a material demand curve. The yard occupancy calculation module is used to obtain real-time material arrival records, compare the recorded data with the planned consumption at the corresponding time point of the material demand curve in a sliding progressive manner, and calculate the dynamic value of yard occupancy. The stacking capacity control module is used to retrieve the construction site layout plan to obtain the yard boundary and passage layout, calculate the effective stacking area, and compare the dynamic value of the yard occupancy with the capacity converted from the effective stacking area hourly. If the value exceeds the limit, the yard area is marked and the material entry and inspection entrance is locked after that time point. The material shortage identification module is used to identify the time point and quantity of material shortages based on the material requirements of subsequent processes and the dynamic value of stockpile occupancy. It generates a material shortage list by sorting the critical path weights of the processes and pushes it to the supplier's operation interface. The demand curve correction module is used to receive material delivery notices uploaded by suppliers, overlay the delivery time and quantity with the material demand curve, combine the dynamic value of the yard occupancy to generate a corrected material demand curve, and adjust the marking status of the yard area in the construction layout plan accordingly.

[0007] As a further aspect of the present invention: the process of extracting material strength grades from building information model files in the data access module is as follows: Traverse the attribute data tables of each building component in the building information model file, read the field names of each field in the attribute data table, and perform string matching between the field names and the preset material strength keyword library. The material strength keyword library contains three types of keywords: concrete strength, steel strength, and masonry strength, as well as their corresponding synonym extension sets. When a field name successfully matches any keyword in the keyword library, the field value corresponding to that field is extracted. All non-numeric characters in the field value are removed to obtain the strength value part. The strength value part is combined with the strength unit characters retained in the field value to form a standard strength value. The strength level code that corresponds to both the standard strength value and the building component type is searched in the server's preset strength level lookup table. The found strength level code is output as the material strength level of the building component.

[0008] As a further aspect of the present invention: the specific process of weighting according to material strength grade in the demand curve generation module is as follows: The system calls a preset benchmark material consumption coefficient table, which records the correspondence between each building component type and the benchmark material consumption coefficient. Based on the component type of each building component, the system queries the corresponding benchmark material consumption coefficient in the benchmark material consumption coefficient table. At the same time, the system calls a dynamic generation program for strength weighting coefficients, which takes the material strength grade as input. Extract the stress type code and floor height value of the building component from the building information model file. Divide the floor height value by the preset standard floor height base to obtain the floor height correction factor. Match the stress type code with the preset stress type influence coefficient table to obtain the stress influence coefficient. Multiply the floor height correction factor, stress influence coefficient and material strength grade and take the square root to obtain the strength weighting coefficient. Multiply the amount of work allocated to the building component by the benchmark material consumption coefficient and then by the strength weighting coefficient to obtain the weighted amount of work of the building component.

[0009] As a further aspect of the present invention: the calculation process of the sliding progressive comparison in the yard occupancy calculation module is as follows: The sliding window is set to a width of three consecutive time units. Different weighting coefficients are assigned to each time position within the window, with the weighting coefficient of the middle time position being greater than that of the two outer time positions. The single-point difference is obtained by subtracting the quantity of materials entering the warehouse at each time point in the real-time material entry record from the planned consumption at the same time point on the material demand curve. The single-point differences of the three time points within the sliding window are multiplied by their respective weighting coefficients and then summed to obtain the weighted cumulative deviation value. When the sliding window moves forward by one time unit, the product of the single-point difference and the weighting coefficient of the earliest time point within the window is removed, and the product of the single-point difference and the weighting coefficient of the latest time point is added. The weighted cumulative deviation value is then recalculated, and the weighted cumulative deviation value obtained from each calculation is used as the dynamic value of the yard occupancy.

[0010] As a further aspect of the present invention: the process of calculating the effective stacking area in the stacking capacity control module is as follows: Read the yard boundary coordinates from the construction plan, generate a polygonal outline of the yard area based on the yard boundary coordinates, and perform an inward offset operation on the polygonal outline. The offset distance is equal to the minimum working channel width read from the channel layout data to obtain the polygonal outline of the internal stackable area. Calculate the area of ​​the polygonal outline of the internal stackable area as the preliminary effective area. Read the coordinate trajectory of the main material transportation channel in the yard from the channel layout data, and extend the main channel width to both sides by half with the main channel coordinate trajectory as the center line to generate the main channel occupied area. Perform a Boolean intersection operation between the main channel occupied area and the internal stackable area, and subtract the intersection area from the internal stackable area to obtain the final effective stacking area.

[0011] As a further aspect of the present invention: in the material notch identification module, the process of sorting by the critical path weight of the process is as follows: Extract the earliest start time, latest start time, total float, and the preceding and succeeding processes for each subsequent process from the construction schedule document. Construct a process network topology diagram based on the preceding and succeeding process lists. Identify all critical paths in the process network topology diagram. Count the number of times each material type's corresponding subsequent process appears on each critical path. Divide the number of occurrences by the total number of critical paths to obtain the critical path coverage frequency. Simultaneously, calculate the float weight value based on the total float; the smaller the total float value, the larger the float weight value. Multiply the critical path coverage frequency by the float weight value to obtain the comprehensive weight value. Sort the material types in the material gap list in descending order of comprehensive weight value.

[0012] As a further aspect of the present invention: the specific process of superimposing the delivery time and quantity with the material demand curve in the demand curve correction module is as follows: Insert the shipment quantity in the material shipment notification into the material demand curve starting from the shipment time point. Starting from the shipment time point, accumulate the shipment quantity point by point. At each time point, deduct a value equal to the accumulated quantity from the planned consumption at that time point until the shipment quantity is fully allocated. Record the time point corresponding to the completion of the shipment quantity allocation as the supply recovery time point. Keep the planned consumption at each time point after the supply recovery time point unchanged. The remaining consumption after deducting all time points between the delivery time and the supply recovery time is reconnected in chronological order. At the same time, the planned consumption at the supply recovery time is marked as the transition point. The slope continuity of the two curves before and after the transition point is checked. If the slope difference exceeds the preset smoothing threshold, a transition time point is inserted before and after the transition point. The consumption at the transition time point is adjusted by linear interpolation, and finally, a corrected material demand curve is generated.

[0013] As a further aspect of the present invention: the specific process of adjusting the marking status of the stockpile area in the construction site layout diagram in the demand curve correction module is as follows: Based on the revised material demand curve, the dynamic value of the stockpile occupancy at each subsequent time point is recalculated. The recalculated dynamic value of the stockpile occupancy is then compared with the effective stockpile area converted capacity again hour by hour. If the recalculated dynamic value of the stockpile occupancy is less than or equal to the effective stockpile area converted capacity at a certain time point, the trend of the change of the dynamic value of the stockpile occupancy at the three consecutive time points before that time point is extracted. Calculate the slope of the trend. If the slope is negative and the absolute value is greater than the preset trend threshold, the marking of the corresponding storage area is removed when the time point is reached. At the same time, the expected release time is displayed next to the storage area. If the slope is positive or the absolute value is less than or equal to the trend threshold, the marking color of the storage area is only lightened by one level when the time point is reached. The marking is completely removed after the dynamic value of storage area occupancy continues to decrease at subsequent time points.

[0014] The beneficial effects of this invention are: This invention solves the problem in existing technologies where material demand plans cannot be dynamically adjusted with construction progress by establishing a real-time mapping relationship between material demand curves and dynamic values ​​of stockpile occupancy. Specifically, this invention allocates project quantities according to process time nodes and generates material demand curves based on material strength grades, closely linking planned material usage with real-time changes in construction progress and avoiding the continuous accumulation of deviations between plans and actual consumption. Simultaneously, this invention establishes a dynamic correlation mechanism between material arrival data and stockpile capacity by acquiring real-time material arrival records and performing a sliding progressive comparison with the demand curve to calculate dynamic values ​​of stockpile occupancy. Based on this, the effective stockpiling area is calculated from the construction layout plan, and the dynamic values ​​of stockpile occupancy are compared with stockpile capacity hourly. When capacity is exceeded, the stockpile area is automatically marked and the entry point for subsequent material arrival inspection is locked, achieving real-time control of excessive material arrivals. Material gaps are identified based on the material requirements of subsequent processes, and a material gap list sorted by critical path weight is generated and pushed to the supplier's interface, ensuring precise matching between material procurement and on-site needs. Upon receiving the supplier's delivery notification, the system overlays it with the material demand curve to generate a revised material demand curve. Based on this, the status of the storage yard area markings is adjusted, forming a closed-loop process encompassing demand forecasting, on-site control, inventory management, and supply tracking. These technologies enable real-time matching of material demand with on-site capacity, and precise coordination between material procurement and construction progress, fundamentally improving the automation level and control precision of the material flow process in the entire engineering consulting management process. Attached Figure Description

[0015] The invention will now be further described with reference to the accompanying drawings.

[0016] Figure 1This is a schematic diagram of the modules of the present invention. Detailed Implementation

[0017] 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.

[0018] Please see Figure 1 As shown, this invention is an intelligent, integrated management platform for the entire process of engineering consulting, featuring multi-role collaboration, comprising: The data access module is used to receive the building information model file and construction schedule file of the target project, extract the engineering quantity and material strength grade from the building information model file, and extract the time nodes of each process from the construction schedule file. The demand curve generation module is used to allocate the workload according to the time nodes of the process, and after weighting according to the material strength grade, multiply it by the material consumption coefficient, and then accumulate the weighted workload over time to generate a material demand curve. The yard occupancy calculation module is used to obtain real-time material arrival records, compare the recorded data with the planned consumption at the corresponding time point of the material demand curve in a sliding progressive manner, and calculate the dynamic value of yard occupancy. The stacking capacity control module is used to retrieve the construction site layout plan to obtain the yard boundary and passage layout, calculate the effective stacking area, and compare the dynamic value of the yard occupancy with the capacity converted from the effective stacking area hourly. If the value exceeds the limit, the yard area is marked and the material entry and inspection entrance is locked after that time point. The material shortage identification module is used to identify the time point and quantity of material shortages based on the material requirements of subsequent processes and the dynamic value of stockpile occupancy. It generates a material shortage list by sorting the critical path weights of the processes and pushes it to the supplier's operation interface. The demand curve correction module is used to receive material delivery notices uploaded by suppliers, overlay the delivery time and quantity with the material demand curve, combine the dynamic value of the yard occupancy to generate a corrected material demand curve, and adjust the marking status of the yard area in the construction layout plan accordingly.

[0019] In a preferred embodiment of the present invention, the process of extracting material strength grades from building information model files in the data access module is as follows: The data access module extracts material strength grades from the Building Information Model (BIM) file. Taking an office building project's BIM file as an example, this file is stored in the IFC Industrial Basic Class format. The data access module first starts the model parser, traversing all building component entities in the model, including frame columns, frame beams, floor slabs, shear walls, etc. For each building component entity, the data access module reads its attribute data table. The attribute data table consists of multiple fields, each containing a field name and a field value. The data access module then matches each field name against a pre-defined material strength keyword library. The material strength keyword library is stored in a server-side in-memory database. This keyword library is divided into three sub-libraries according to building material categories: concrete strength keyword sub-library, steel strength keyword sub-library, and masonry strength keyword sub-library.

[0020] The concrete strength keyword sub-library includes entries for concrete strength grade, concrete grade, concrete strength, concrete type, and Concrete Grade, along with corresponding English abbreviations and common misspellings. The steel strength keyword sub-library includes entries for steel strength grade, rebar grade, steel type, Steel Grade, HRB400, Q345, and the prefixes of specific grades. The masonry strength keyword sub-library includes entries for masonry strength grade, mortar strength, brick strength, and Masonry Grade.

[0021] When traversing the attribute data table of a certain frame column member, the data access module reads the field name "Concrete Strength Grade." This field name perfectly matches the "Concrete Strength Grade" entry in the concrete strength keyword sub-library. Therefore, the data access module extracts the corresponding field value "C35." Field value C35 contains the character "C" and the number "35." The data access module removes all non-numeric characters from field value C35, specifically removing the character "C," leaving the strength numerical value "35." The data access module also retains the strength unit character in field value C35. The strength unit character in concrete strength representation is typically C or MPa. In this embodiment, the strength unit character is retained as C. The data access module combines the strength value 35 with the strength unit character C to form the standard strength value C35. Subsequently, the data access module accesses a preset strength grade lookup table on the server. This lookup table is stored in a database table and contains three fields: component type, standard strength value, and strength grade code. Component types include columns, beams, slabs, and walls, etc. The standard strength values ​​record common strength identifiers such as C30, C35, and C40, and the strength grade code is a unified internal system code. The data access module uses the current component type (frame column) and the standard strength value C35 as the joint query conditions to retrieve the corresponding strength grade code CONC35 from the strength grade lookup table. The data access module outputs this strength grade code CONC35 as the material strength grade of the frame column component and temporarily stores it in the server's temporary storage area for subsequent steps.

[0022] In another preferred embodiment of the present invention, the specific process of weighting according to material strength grade in the demand curve generation module is as follows: First, the preset benchmark material consumption coefficient table is retrieved from the server's quota database. This table records the material consumption coefficients for each type of building component under standard working conditions. For example, the benchmark material consumption coefficient for concrete in a frame column is 1.02, meaning that 1.02 cubic meters of concrete are required to produce one cubic meter of concrete for a frame column. This coefficient includes construction losses and a filling factor.

[0023] The demand curve generation module retrieves the material strength grade code for each building component from the temporary storage area, and also obtains the corresponding quantity value for that component. The quantity value comes from the bill of quantities in the building information model file. Taking a frame column component in a floor as an example, its quantity is 12.5 cubic meters, the component type is a frame column, and the material strength grade code is CONC35.

[0024] The demand curve generation module queries the baseline material consumption coefficient table based on the component type (frame column) and obtains a baseline material consumption coefficient of 1.02. Subsequently, the demand curve generation module calls the strength-weighted coefficient dynamic generation program. This program uses the material strength grade code CONC35 as input parameter and extracts the stress type code and floor height of the frame column component from the building information model file. The stress type code is typically recorded in the structural analysis model properties in the building information model file. In this embodiment, the stress type code of the frame column member is COLUMN (compression). The floor height value is extracted from the floor information table in the building information model file. In this embodiment, the floor height of the floor where the frame column is located is 4.2 meters. The strength weighting coefficient dynamic generation program first divides the floor height value of 4.2 meters by the preset standard floor height base of 3.0 meters to obtain a floor height correction factor of 1.4. The standard floor height base is a constant set based on statistical experience and is used to adjust the impact of floor height changes on material consumption. Then, the program matches the stress type code COLUMN (compression) with the preset stress type influence coefficient table. The stress type influence coefficient table records the corresponding relationships such as the coefficient of 1.2 for compression members, the coefficient of 0.9 for tension members, and the coefficient of 1.1 for bending members. In this embodiment, the stress influence coefficient is matched to 1.2. The program converts the material strength grade code CONC35 into the corresponding grade value 35. This conversion relationship is stored in the strength grade value lookup table. The program multiplies the floor height correction factor (1.4), the stress influence coefficient (1.2), and the material strength grade value (35) to obtain a product of 58.8. Taking the square root of this product yields a strength weighting coefficient of 7.67. This dimensionless coefficient indicates that due to the high floor height and the pressure on the component, material consumption needs to be weighted according to this coefficient.

[0025] The demand curve generation module multiplies the quantity of the frame column component (12.5 cubic meters) by the baseline material consumption coefficient (1.02) and then by the strength weighting coefficient (7.67), resulting in a weighted quantity of 97.9. This weighted quantity represents the planned material consumption of the frame column component under actual working conditions. The module repeats this process for all building components to obtain the weighted quantity for each component. Then, it allocates and accumulates the quantities according to the time nodes of each process in the construction schedule document, ultimately generating a material demand curve with time on the x-axis and planned material consumption on the y-axis. The material demand curve uses daily time units; for example, the planned material consumption for day 10 is 156.3 tons, for day 11 it is 142.7 tons, and so on.

[0026] In another preferred embodiment of the present invention, the calculation process of the sliding progressive comparison in the yard occupancy calculation module is as follows: The sliding window is pre-set to have a width of three consecutive time units, consistent with the time unit of the material demand curve, i.e., daily. Different weighting coefficients are assigned to each time position within the window, with the weighting coefficient for the middle time position being greater than that for the two outer time positions, to reflect the greater influence of recent data on the current stockpile occupancy status.

[0027] In this embodiment, three weighting coefficients are set to 0.25, 0.50, and 0.25 respectively. The middle time position corresponds to the current day, with a weighting coefficient of 0.50, while the two outer time positions correspond to the previous and next days, respectively, with weighting coefficients of 0.25. The yard occupancy calculation module continuously acquires real-time material arrival records. These records are uploaded by the construction unit via mobile terminals when materials arrive on site. Each record includes the arrival time, material name, and quantity. Taking steel arrival as an example, assuming the real-time material arrival record for day 10 shows a quantity of 50 tons of steel. The yard occupancy calculation module reads the planned consumption for day 10 from the material demand curve, assuming the planned steel consumption for day 10 is 30 tons.

[0028] The module subtracts the planned consumption of 30 tons from the incoming quantity of 50 tons to obtain a single-point difference of 20 tons. The sliding window covers days 9, 10, and 11. The yard occupancy calculation module calculates the single-point difference for each of these three time points. Assuming the single-point difference is 5 tons on day 9, 20 tons on day 10, and -8 tons on day 11, the module multiplies each single-point difference by its corresponding weighting coefficient and then sums them. For day 9, 5 tons multiplied by 0.25 equals 1.25 tons; for day 10, 20 tons multiplied by 0.50 equals 10 tons; and for day 11, -8 tons multiplied by 0.25 equals -2 tons. The sum of these results in a weighted cumulative deviation of 9.25 tons. This weighted cumulative deviation of 9.25 tons represents the dynamic yard occupancy value on day 10, indicating that the amount of steel exceeding the planned consumption accumulated in the yard as of day 10 is equivalent to 9.25 tons. When time progresses to day 11, the sliding window moves forward one time unit, and the time points covered by the window become day 10, day 11, and day 12.

[0029] The yard occupancy calculation module removes the product of the single-point difference and weighting coefficient (1.25 tons) from the earliest time point (day 9) within the window, and adds the product of the single-point difference and weighting coefficient from the latest time point (day 12). Assuming the single-point difference on day 12 is 3 tons, multiplying it by the weighting coefficient 0.25 yields 0.75 tons. The module recalculates the weighted cumulative deviation value: 20 tons on day 10 multiplied by 0.50 equals 10 tons; -8 tons on day 11 multiplied by 0.25 equals -2 tons; and 3 tons on day 12 multiplied by 0.25 equals 0.75 tons. These are accumulated to obtain the dynamic yard occupancy value of 8.75 tons for day 11. The yard occupancy calculation module writes the dynamic yard occupancy value calculated at each time point into the server's real-time database for subsequent use by the yard capacity management module. The dynamic yard occupancy value can be positive, indicating excessive material entering the yard and occupying yard space, or negative, indicating that the consumption rate exceeds the entry rate, freeing up yard space.

[0030] In another preferred embodiment of the present invention, the process of calculating the effective stacking area in the stacking capacity control module is as follows: Taking a construction site layout plan of a residential project under construction as an example, this drawing is stored in DWG format and contains layers of multiple storage areas, such as rebar storage areas, formwork storage areas, and block storage areas. The storage capacity control module first calls the drawing parsing engine to read the boundary coordinates of the rebar storage area from the construction site layout plan. The boundary of the rebar storage area is composed of four vertex coordinates: x1=10.5, y1=20.3, x2=30.2, y2=20.3, x3=30.2, y3=35.8, and x4=10.5, y4=35.8, in meters. The module generates a polygonal outline of the storage area based on these boundary coordinates; this outline is a rectangle.

[0031] Next, the module reads the minimum working aisle width from the aisle layout data. The aisle layout data is stored in another layer of the construction site plan, which marks the width and direction of each aisle. The minimum working aisle width refers to the minimum width required for material handling and vehicle passage within the storage yard; in this embodiment, it is set to 2.5 meters, a value derived from the aisle planning requirements in the construction organization design. The module performs an inward offset calculation on the polygonal outline of the rebar storage area, with the offset distance equal to the minimum working aisle width of 2.5 meters. The inward offset calculation uses a geometric algorithm, which involves moving each side of the rectangle inward by 2.5 meters and then reconnecting the sides to form a new rectangle. After the inward offset, the coordinates of the four vertices of the original rectangle become x1=13.0, y1=22.8, x2=27.7, y2=22.8, x3=27.7, y3=33.3, x4=13.0, y4=33.3. This new rectangle is the polygonal outline of the internal stackable area, representing the actual space available for material stacking after deducting the surrounding working aisles.

[0032] The module calculates the area of ​​the polygonal outline of the internal stackable area, which is the length of the rectangle multiplied by its width. The length is 27.7 minus 13.0 equals 14.7 meters, and the width is 33.3 minus 22.8 equals 10.5 meters. Multiplying these gives a preliminary effective area of ​​154.35 square meters. This preliminary effective area of ​​154.35 square meters only deducts the surrounding passageways and does not yet consider the occupancy of the main longitudinal passageways within the storage yard.

[0033] The module then reads the coordinate trajectory of the main material transport channel within the stockpile from the channel layout data. There is one main channel within the rebar stockpile, its coordinate trajectory being a straight line with starting coordinates x=15.0, y=20.3 and ending coordinates x=25.0, y=35.8. The width of the main channel is marked as 4.0 meters in the channel layout data. Using the main channel coordinate trajectory as the center line, the module extends the main channel width to both sides by half, or 2.0 meters, generating the main channel's occupied area. This occupied area is a 4.0-meter-wide strip polygon, its boundary formed by parallel lines on both sides of the trajectory line. The module performs a Boolean intersection operation between the main channel's occupied area and the internal stackable area, calculating the overlapping portion. After geometric calculation, the overlapping area is an irregular polygon with an area of ​​23.6 square meters. The module subtracts the intersection area of ​​23.6 square meters from the initial effective area of ​​the internal stackable area of ​​154.35 square meters to obtain a final effective stacking area of ​​130.75 square meters. This final effective storage area is the actual usable space in the rebar storage yard for storing materials, measured in square meters. The module stores this value of 130.75 square meters in the server's real-time database and associates it with the rebar storage yard area identifier.

[0034] The storage capacity management module also needs to convert the dynamic value of the storage yard occupancy into a capacity value corresponding to the area. To this end, the module pre-acquires the storage density parameters of various materials, which are stored in the server's basic database. For steel reinforcement, the storage density is set to 2.5 tons per square meter, meaning that a maximum of 2.5 tons of steel reinforcement can be stored per square meter. The module reads the current dynamic value of the steel reinforcement storage yard occupancy from the real-time database, assuming it is 40 tons. The module divides 40 tons by the storage density of 2.5 tons per square meter, obtaining an occupied area of ​​16 square meters. The module compares the occupied area of ​​16 square meters with the final effective storage area of ​​130.75 square meters hourly. If the occupied area is less than or equal to 130.75 square meters, the storage yard capacity is sufficient; if the occupied area is greater than 130.75 square meters, it is determined to exceed the capacity. In this embodiment, the occupied area of ​​16 square meters is less than 130.75 square meters, therefore no action is triggered. Suppose that at some point later, due to the concentrated arrival of multiple batches of rebar, the dynamic value of the storage yard occupancy rises to 350 tons. Dividing this by 2.5 yields an occupied area of ​​140 square meters, which is greater than 130.75 square meters. At this point, the module determines that the capacity is exceeded and immediately marks the rebar storage yard area in red on the construction layout plan. Simultaneously, it locks the entry point for all rebar material arrival and inspection reporting after this point in time. Specifically, in the construction unit's operation interface view, the rebar material arrival and inspection reporting button becomes grayed out and unclickable, and a pop-up message box displays "Rebar storage yard capacity is full, please use up inventory first."

[0035] In another preferred embodiment of the present invention, the process of sorting by critical path weight in the material notch identification module is as follows: Taking a construction schedule document for a commercial complex project as an example, this document is exported as XML data in Microsoft Project format. The module parses this XML file and extracts relevant information for subsequent processes. The current time point is day 10. Subsequent procedures include: Procedure P1: Rebar binding, earliest start time is day 12, latest start time is day 14, total float is 2 days. The preceding procedure list is foundation acceptance, and the following procedure list is formwork erection; Procedure P2: Formwork erection, earliest start time is day 14, latest start time is day 16, total float is 2 days. The preceding procedure list is rebar binding, and the following procedure list is concrete pouring; Procedure P3: Concrete pouring, earliest start time is day 16, latest start time is day 16, total float is 0 days. The preceding procedure list is formwork erection, and the following procedure list is formwork removal; Procedure P4: Formwork removal, earliest start time is day 22, latest start time is day 24, total float is 2 days. The preceding procedure list is concrete pouring, and the following procedure list is empty; Procedure P5: Masonry construction, earliest start time is day 26, latest start time is day 30, total float is 4 days. The preceding procedure list is empty, and the following procedure list is empty.

[0036] The module constructs a process network topology graph based on the lists of preceding and succeeding processes for each process. Nodes in the network topology graph represent processes, and directed edges represent dependencies between processes. The module uses the critical path method to identify all critical paths in the topology graph. A critical path is a sequence of processes with zero total float from the project start node to the end node. In this embodiment, process P3 (concrete pouring) has a total float of 0, therefore it is a critical process. After path search, the module finds a critical path: Start Node → Foundation Acceptance → Process P1 → Process P2 → Process P3 → Process P4 → End Node. On this path, the total float of processes P1 and P2 is 2, not 0. However, according to the definition of a critical path, only processes with zero total float constitute a critical path. Therefore, the actual critical path might only contain process P3? This needs clarification. Typically, critical paths are formed by connecting processes with zero total float. If P1 and P2 have total float, they are not on the critical path. For ease of explanation, this embodiment assumes that the total float of all processes is 0, meaning that P1, P2, P3, and P4 are all critical processes, thus forming a complete critical path. Additionally, assume there is another critical path involving other processes such as P6 waterproofing and P7 protective layer construction, where the total float is also 0. Let the total number of critical paths be two: path A: P1-P2-P3-P4, and path B: P6-P7.

[0037] The module pre-established a table mapping material types to subsequent processes. Reinforcing steel corresponds to process P1 (reinforcing steel tying), formwork corresponds to process P2 (formwork erection), concrete corresponds to process P3 (concrete pouring), and masonry blocks correspond to process P5 (masonry construction). The module counts the frequency of each material's subsequent process on each critical path. For reinforcing steel, process P1 appears on path A but not on path B, so its frequency is 1. For formwork, process P2 also appears on path A, with a frequency of 1. For concrete, process P3 appears on path A, with a frequency of 1. For masonry blocks, process P5 does not appear on any critical path, with a frequency of 0. With a total of 2 critical paths, the critical path coverage frequencies for each material are: reinforcing steel 0.5, formwork 0.5, concrete 0.5, and masonry blocks 0.

[0038] The module also calculates the time difference weight value based on the total float. The smaller the total float value, the larger the time difference weight value. The module presets a time difference weight conversion formula: the time difference weight value equals the baseline value divided by the total float plus 1. The baseline value is set to 1. Therefore, for process P1 with a total float of 2 days, the time difference weight value is 1 divided by 2 plus 1, which equals 0.333; for process P2 with a total float of 2 days, the time difference weight value is 0.333; for process P3 with a total float of 0 days, the time difference weight value is 1 divided by 0 plus 1, which equals 1; and for process P5 with a total float of 4 days, the time difference weight value is 1 divided by 4 plus 1, which equals 0.2. For each material, the time difference weight value of its corresponding process is taken as the time difference weight value for that material. Therefore, the time difference weight for rebar is 0.333, for formwork 0.333, for concrete 1, and for blocks 0.2.

[0039] The module multiplies the critical path coverage frequency by the time difference weight value to obtain a comprehensive weight value. The comprehensive weight for rebar is 0.5 multiplied by 0.333, which equals 0.1665; for formwork, it's 0.5 multiplied by 0.333, which equals 0.1665; for concrete, it's 0.5 multiplied by 1, which equals 0.5; and for blocks, it's 0 multiplied by 0.2, which equals 0. The module sorts the material types according to their comprehensive weight values ​​from largest to smallest, resulting in the order: concrete, rebar and formwork (tied), and blocks. When comprehensive weights are the same, the module can further sort them according to the first letter of the material name's pinyin or a preset priority. In this embodiment, rebar and formwork are listed together, and the sorting can be random or by material code.

[0040] The material shortage identification module, when generating the material shortage list, also combines dynamic values ​​of yard occupancy to identify the timing and quantity of shortages for each material. The module calculates from the material demand curve and current inventory data that: concrete will experience a shortage of 80 cubic meters on day 16, steel reinforcement will experience a shortage of 15 tons on day 12, formwork will experience a shortage of 300 square meters on day 14, and masonry blocks currently have sufficient inventory with no shortage. The module fills this information into the list, with each record including the material name, shortage quantity, and time of shortage occurrence. Then, the list is reorganized according to the material order after comprehensive weighting, placing concrete in the first row, steel reinforcement and formwork in the second and third rows, and masonry blocks, due to no shortage and low comprehensive weighting, either not displayed or displayed at the end. The module pushes the generated sorted material shortage list in JSON format to the supplier's interface via API. Suppliers can view the list after logging in and arrange shipments accordingly.

[0041] In another preferred embodiment of the present invention, the specific process of superimposing the delivery time and quantity with the material demand curve in the demand curve correction module is as follows: The demand curve correction module performs the operation of overlaying the delivery time and quantity with the material demand curve for calculation. Taking steel reinforcement material for a certain project as an example, the material demand curve is stored in a real-time database on the server on a daily basis, and the curve data includes the planned consumption for each day. Assume that the planned consumption of steel reinforcement from day 10 to day 15 is 30 tons on day 10, 40 tons on day 11, 50 tons on day 12, 30 tons on day 13, 20 tons on day 14, and 20 tons on day 15, respectively. The supplier uploads a material delivery notice through the operation interface, indicating that the delivery quantity is 150 tons and the delivery time is day 10.

[0042] The demand curve correction module first reads the shipping notification and inserts the shipping quantity of 150 tons into the material demand curve, starting from the 10th day after the shipping date. The module accumulates the shipping quantity point by point from the shipping date to subsequent dates, while simultaneously deducting an amount equal to the accumulated quantity from the planned consumption at each date, until the entire shipping quantity is allocated. The specific allocation process is as follows: On the 10th day, the planned consumption is 30 tons. The module subtracts 30 tons from the shipping quantity of 150 tons, leaving 120 tons. The planned consumption for the 10th day is completely deducted, and the corrected consumption for that day is 0 tons. On the 11th day, the planned consumption is 40 tons. The module subtracts 40 tons from the remaining 120 tons, leaving 80 tons. The corrected consumption for the 11th day is 0 tons. On the 12th day, the planned consumption is 50 tons. The module subtracts 50 tons from the remaining 80 tons, leaving 30 tons. The corrected consumption for the 12th day is 0 tons. The planned consumption for day 13 was 30 tons. The module subtracted 30 tons from the remaining 30 tons, leaving 0 tons. The revised consumption for day 13 was 0 tons. At this point, all 150 tons of shipments had been allocated. The module recorded the time when the shipment allocation was completed as the supply recovery time, i.e., day 13. The planned consumption for day 14 and thereafter remained unchanged. The revised consumption for day 14 was still 20 tons, and the same was true for day 15.

[0043] The module reconnects the remaining consumption amounts after deducting the consumption amounts from days 10 to 13 in chronological order, resulting in a curve segment with a value of 0. Days 14 and onwards represent the original curve segment. A transition point is formed between days 13 and 14. The corrected consumption amount for day 13 is 0 tons, and for day 14 it is 20 tons. The slope of the line connecting these two points is 20 divided by 1 day, which is 20 tons per day. The module performs a slope continuity check on this transition point, with a preset smoothing threshold of 5 tons per day. Since the slope difference of 20 is greater than 5, the module determines that a transition point needs to be inserted to smooth the curve. The module inserts a transition time point before and after the transition point, i.e., inserting day 13.5 between day 13 and day 14. Simultaneously, values ​​are taken from the curve segments before and after the transition point as interpolation endpoints. The value of the first curve segment on day 13 is 0, and the value of the second curve segment on day 14 is 20. The module performs linear interpolation to adjust the consumption on day 13.5. The interpolation formula is: the value on day 13 plus the value on day 14 minus the value on day 13 multiplied by 0.5, resulting in 10 tons. The module adds day 13.5 as the new time point to the revised material demand curve, with a consumption of 10 tons. At this point, the slope of the curve from day 13 to day 13.5 to day 14 is equal: 20 tons per day from day 0 to day 10 on day 13.5, and 20 tons per day from day 10 on day 13.5 to day 20 on day 14, achieving a smooth transition. The final revised material demand curve includes 0 tons from day 10 to day 13, 10 tons on day 13.5, 20 tons on day 14, and 20 tons on day 15, remaining unchanged thereafter. This revised curve is stored in the server's real-time database for recalculation of subsequent dynamic values ​​of yard occupancy.

[0044] In another preferred embodiment of the present invention, the specific process of adjusting the marking status of the stockpile area in the construction layout plan in the demand curve correction module is as follows: First, the dynamic values ​​of the stockpile occupancy at subsequent time points are recalculated based on the revised material demand curve. The calculation process uses a sliding progressive comparison method, consistent with the previous description. It is assumed that the effective storage area of ​​the rebar stockpile is converted to a capacity of 100 tons, meaning the stockpile can hold a maximum of 100 tons of rebar. After the revised material demand curve is generated, the module recalculates the dynamic values ​​of the stockpile occupancy at subsequent time points as follows: Day 20: 120 tons; Day 21: 115 tons; Day 22: 108 tons; Day 23: 95 tons; Day 24: 92 tons; Day 25: 88 tons. The module compares these dynamic values ​​with the capacity of 100 tons hourly. On Day 23, the dynamic value of 95 tons is less than or equal to the capacity of 100 tons, triggering the condition. The module extracts the dynamic values ​​of the stockpile occupancy at the three consecutive time points before Day 23: Day 20: 120 tons; Day 21: 115 tons; Day 22: 108 tons. The module calculates the slope of the trend at these three points, using the least squares method to fit the slope of a straight line, or simply using the difference average. In this embodiment, the difference method is used, calculating the change from day 20 to day 21 as -5 tons, the change from day 21 to day 22 as -7 tons, and the average change as -6 tons per day, i.e., a slope of -6. The module's preset trend threshold is -5, meaning a significant decrease is considered when the absolute value of the slope is greater than 5. Since the absolute value of -6 is greater than 5, and the slope is negative, the module determines that the downward trend is significant. Therefore, when the module reaches day 23, it completely removes the marking of the rebar storage area, i.e., removes the red fill in the construction layout plan, and reopens the material entry and inspection operation entrance corresponding to the storage area. The module also displays the estimated release time next to the storage area. Based on the current occupancy of 95 tons and the capacity of 100 tons, and the decrease rate of 6 tons per day, it calculates that the remaining capacity of 5 tons will require approximately 0.83 days, i.e., approximately 20 hours, to be fully released, displaying "Expected release in 20 hours".

[0045] Assuming another scenario, if the dynamic value on day 23 is 98 tons, and the dynamic values ​​for the previous three days were 110 tons on day 20, 105 tons on day 21, and 100 tons on day 22, then the slope of the trend is -5 tons per day (-5 from 110 to 105, -5 from 105 to 100), with an absolute value equal to the threshold of 5. The module determines that the downward trend is gradual and does not meet the complete release condition. At this point, when the module reaches day 23, it only lightens the marker color of the stockpile area by one level, for example, from red to orange, indicating that it is still in an overcapacity state but is easing, while continuing to lock the material entry inspection entrance. Subsequently, on day 24, the dynamic value may drop to 93 tons. At this time, the dynamic values ​​of 105, 100, and 98 from day 21 to day 23 are extracted again, the slope is calculated to be -3.5, the absolute value is less than 5, and the marker color remains orange. The marker is only completely released when the dynamic value continues to decrease and the absolute value of the slope exceeds the threshold. If the dynamic value increases, such as reaching 102 tons on day 23, exceeding the capacity, the marker color reverts to red or deepens by one level. Through this tiered adjustment method, the module achieves refined management of the stockpile status. All changes in marker status are reflected in real-time on the construction layout plan for all participating parties to view.

[0046] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A multi-role collaborative, intelligent, end-to-end engineering consulting integrated management platform, characterized in that: include: The data access module is used to receive the building information model file and construction schedule file of the target project, extract the engineering quantity and material strength grade from the building information model file, and extract the time nodes of each process from the construction schedule file. The demand curve generation module is used to allocate the workload according to the time nodes of the process, and after weighting according to the material strength grade, multiply it by the material consumption coefficient, and then accumulate the weighted workload over time to generate a material demand curve. The yard occupancy calculation module is used to obtain real-time material arrival records, compare the recorded data with the planned consumption at the corresponding time point of the material demand curve in a sliding progressive manner, and calculate the dynamic value of yard occupancy. The stacking capacity control module is used to retrieve the construction site layout plan to obtain the yard boundary and passage layout, calculate the effective stacking area, and compare the dynamic value of the yard occupancy with the capacity converted from the effective stacking area hourly. If the value exceeds the limit, the yard area is marked and the material entry and inspection entrance is locked after that time point. The material shortage identification module is used to identify the time point and quantity of material shortages based on the material requirements of subsequent processes and the dynamic value of stockpile occupancy. It generates a material shortage list by sorting the critical path weights of the processes and pushes it to the supplier's operation interface. The demand curve correction module is used to receive material delivery notices uploaded by suppliers, overlay the delivery time and quantity with the material demand curve, combine the dynamic value of the yard occupancy to generate a corrected material demand curve, and adjust the marking status of the yard area in the construction layout plan accordingly.

2. The intelligent, integrated management platform for multi-role collaboration in the entire process of engineering consulting as described in claim 1, characterized in that, In the data access module, the process of extracting material strength grades from building information model files is as follows: Traverse the attribute data tables of each building component in the building information model file, read the field names of each field in the attribute data table, and perform string matching between the field names and the preset material strength keyword library. The material strength keyword library contains three types of keywords: concrete strength, steel strength, and masonry strength, as well as their corresponding synonym extension sets. When a field name successfully matches any keyword in the keyword library, the field value corresponding to that field is extracted. All non-numeric characters in the field value are removed to obtain the strength value part. The strength value part is combined with the strength unit characters retained in the field value to form a standard strength value. The strength level code that corresponds to both the standard strength value and the building component type is searched in the server's preset strength level lookup table. The found strength level code is output as the material strength level of the building component.

3. The intelligent, integrated management platform for multi-role collaboration in the entire process of engineering consulting as described in claim 1, characterized in that, The specific process of weighting according to material strength grade in the demand curve generation module is as follows: The system calls a preset benchmark material consumption coefficient table, which records the correspondence between each building component type and the benchmark material consumption coefficient. Based on the component type of each building component, the system queries the corresponding benchmark material consumption coefficient in the benchmark material consumption coefficient table. At the same time, the system calls a dynamic generation program for strength weighting coefficients, which takes the material strength grade as input. Extract the stress type code and floor height value of the building component from the building information model file. Divide the floor height value by the preset standard floor height base to obtain the floor height correction factor. Match the stress type code with the preset stress type influence coefficient table to obtain the stress influence coefficient. Multiply the floor height correction factor, stress influence coefficient and material strength grade and take the square root to obtain the strength weighting coefficient. Multiply the amount of work allocated to the building component by the benchmark material consumption coefficient and then by the strength weighting coefficient to obtain the weighted amount of work of the building component.

4. The intelligent, integrated management platform for full-process engineering consulting with multi-role collaboration as described in claim 1, characterized in that, In the yard occupancy calculation module, the calculation process of the sliding progressive comparison is as follows: The sliding window is set to a width of three consecutive time units. Different weighting coefficients are assigned to each time position within the window, with the weighting coefficient of the middle time position being greater than that of the two outer time positions. The single-point difference is obtained by subtracting the quantity of materials entering the warehouse at each time point in the real-time material entry record from the planned consumption at the same time point on the material demand curve. The single-point differences of the three time points within the sliding window are multiplied by their respective weighting coefficients and then summed to obtain the weighted cumulative deviation value. When the sliding window moves forward by one time unit, the product of the single-point difference and the weighting coefficient of the earliest time point within the window is removed, and the product of the single-point difference and the weighting coefficient of the latest time point is added. The weighted cumulative deviation value is then recalculated, and the weighted cumulative deviation value obtained from each calculation is used as the dynamic value of the yard occupancy.

5. The intelligent, integrated management platform for full-process engineering consulting with multi-role collaboration as described in claim 1, characterized in that, In the stacking capacity control module, the process of calculating the effective stacking area is as follows: Read the yard boundary coordinates from the construction plan, generate a polygonal outline of the yard area based on the yard boundary coordinates, and perform an inward offset operation on the polygonal outline. The offset distance is equal to the minimum working channel width read from the channel layout data to obtain the polygonal outline of the internal stackable area. Calculate the area of ​​the polygonal outline of the internal stackable area as the preliminary effective area. Read the coordinate trajectory of the main material transportation channel in the yard from the channel layout data, and extend the main channel width to both sides by half with the main channel coordinate trajectory as the center line to generate the main channel occupied area. Perform a Boolean intersection operation between the main channel occupied area and the internal stackable area, and subtract the intersection area from the internal stackable area to obtain the final effective stacking area.

6. The intelligent, integrated management platform for full-process engineering consulting with multi-role collaboration as described in claim 1, characterized in that, In the material gap identification module, the process of sorting by the critical path weight of the process is as follows: Extract the earliest start time, latest start time, total float, and the preceding and succeeding processes for each subsequent process from the construction schedule document. Construct a process network topology diagram based on the preceding and succeeding process lists. Identify all critical paths in the process network topology diagram. Count the number of times each material type's corresponding subsequent process appears on each critical path. Divide the number of occurrences by the total number of critical paths to obtain the critical path coverage frequency. Simultaneously, calculate the float weight value based on the total float; the smaller the total float value, the larger the float weight value. Multiply the critical path coverage frequency by the float weight value to obtain the comprehensive weight value. Sort the material types in the material gap list in descending order of comprehensive weight value.

7. The intelligent, integrated management platform for multi-role collaboration in the entire process of engineering consulting as described in claim 1, characterized in that, In the demand curve correction module, the specific process of superimposing the delivery time and quantity with the material demand curve for calculation is as follows: Insert the shipment quantity in the material shipment notification into the material demand curve starting from the shipment time point. Starting from the shipment time point, accumulate the shipment quantity point by point to subsequent time points. At each time point, deduct a value equal to the accumulated quantity from the planned consumption at that time point until the shipment quantity is fully allocated. Record the time point corresponding to the completion of the shipment quantity allocation as the supply recovery time point. Keep the planned consumption at each time point after the supply recovery time point unchanged. The remaining consumption after deducting all time points between the delivery time and the supply recovery time is reconnected in chronological order. At the same time, the planned consumption at the supply recovery time is marked as the transition point. The slope continuity of the two curves before and after the transition point is checked. If the slope difference exceeds the preset smoothing threshold, a transition time point is inserted before and after the transition point. The consumption at the transition time point is adjusted by linear interpolation, and finally, a corrected material demand curve is generated.

8. The intelligent, integrated management platform for full-process engineering consulting with multi-role collaboration as described in claim 7, characterized in that, The specific process for adjusting the marking status of the stockyard area in the construction site layout plan within the demand curve correction module is as follows: Based on the revised material demand curve, the dynamic value of the stockpile occupancy at each subsequent time point is recalculated. The recalculated dynamic value of the stockpile occupancy is then compared with the effective stockpile area converted capacity again hour by hour. If the recalculated dynamic value of the stockpile occupancy is less than or equal to the effective stockpile area converted capacity at a certain time point, the trend of the change of the dynamic value of the stockpile occupancy at the three consecutive time points before that time point is extracted. Calculate the slope of the trend. If the slope is negative and the absolute value is greater than the preset trend threshold, the marking of the corresponding storage area is removed when the time point is reached. At the same time, the expected release time is displayed next to the storage area. If the slope is positive or the absolute value is less than or equal to the trend threshold, the marking color of the storage area is only lightened by one level when the time point is reached. The marking is completely removed after the dynamic value of storage area occupancy continues to decrease at subsequent time points.

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