Intelligent supervision method and system for decoration engineering progress

Abstract

The application relates to the technical field of supervision management, and discloses a decoration engineering progress intelligent supervision method and system. The method comprises the following steps: decomposing a three-dimensional building model of a decoration engineering into supervision units according to physical space, and associating a plan bill of materials and a plan operation area corresponding to each supervision unit; collecting the warehouse consumption rate of each material in a construction site of the decoration engineering, inversely deducing the theoretical area increment sequence of a corresponding working procedure from the warehouse consumption rate, and synchronously collecting the actual area increment sequence of a current working procedure on the supervision unit; forcibly making the theoretical area increment sequence and the actual area increment sequence dynamically converge on a time axis to obtain a convergence residual error, and calculating the real-time completion area of the current working procedure according to the convergence residual error. The application can solve the problems of the existing progress supervision method, such as response lag, insufficient precision and difficulty in adapting to the dynamic management requirements of complex decoration engineering.

Description

Technical Field

[0001] This invention relates to the field of supervision and management technology, and in particular to an intelligent supervision method and system for the progress of decoration and renovation projects. Background Technology

[0002] In decoration and renovation project management, progress monitoring is a crucial link in ensuring timely project delivery. Traditional progress monitoring methods typically rely on manual inspections, paper reports, or overall project management software to compare the planned and actual completed areas of each process. However, monitoring is often based on the entire floor or area, making it difficult to accurately pinpoint progress deviations in specific physical units. Furthermore, there is a lack of effective correlation between material consumption data and actual process progress, making it impossible to use the rate of material consumption to deduce the theoretical completed area. Secondly, most methods only compare static deviations and do not consider the dynamic convergence process of theoretical and actual progress on the time axis, resulting in the inability to correct accumulated errors in a timely manner and inaccurate calculation of the progress deviation coefficient. Furthermore, when the deviation exceeds the allowable range, it is difficult to automatically identify whether the dominant factor causing the deviation is a material supply problem or an on-site execution problem, leading to blind adjustment measures and difficulty in achieving hierarchical scheduling of material delivery priorities.

[0003] The aforementioned shortcomings make existing progress monitoring methods slow to respond and lacking in accuracy, making them difficult to adapt to the dynamic management needs of complex decoration projects. Summary of the Invention

[0004] This invention provides an intelligent monitoring method and system for the progress of decoration and renovation projects, the main purpose of which is to address the problems raised in the background section above.

[0005] To achieve the above objectives, the present invention provides an intelligent monitoring method for the progress of decoration and renovation projects, comprising: S1: Deconstruct the three-dimensional architectural model of the decoration project into monitoring units according to physical space, and associate the planned bill of materials and planned work area corresponding to each monitoring unit; S2: Collect the outbound consumption rate of each material at the construction site in the decoration project, reverse the outbound consumption rate to deduce the theoretical area increment sequence of the corresponding process, and simultaneously collect the actual area increment sequence of the current process on the monitoring unit. S3: Force the theoretical area increment sequence and the actual area increment sequence to converge dynamically on the time axis to obtain the convergence residual. Calculate the real-time completed area of ​​the current process based on the convergence residual and generate a progress deviation coefficient relative to the planned work area. S4: When the schedule deviation coefficient exceeds the allowable range, the dominant factor causing the deviation is identified as either material constraint type or execution constraint type based on the direction of the convergence residual. S5: Associate multiple monitoring units that are spatially adjacent and have the same process and have the aforementioned schedule deviation coefficient, generate a work surface early warning area, and estimate the resource competition intensity of the work surface early warning area; S6: Generate process adjustment instructions and material delivery priority instructions based on the dominant factors and the intensity of resource competition.

[0006] Preferably, the step of deconstructing the three-dimensional architectural model of the decoration project into monitoring units according to physical space, and associating each monitoring unit with a planned bill of materials and a planned work area, includes: The three-dimensional building model is divided into units based on its physical boundaries to obtain monitoring units; Read the construction drawings and material budget sheets corresponding to the monitoring unit to obtain the planned work area and planned material list.

[0007] Preferably, the step of reverse-engineering the outbound consumption rate into a theoretical area increment sequence for the corresponding process includes: Separate the various materials belonging to the same process from the outbound consumption rate, calculate the offset of the consumption rate corresponding to each pair of materials, and construct the consumption deviation matrix of the materials within the same process; The material pair with the smallest offset in the consumption deviation matrix is ​​marked, and the average consumption rate of the material pair is marked as the effective construction rate to filter out the pseudo rate caused by the distribution fluctuation of the single material. The effective construction driving rate is integrated over the time axis of the decoration project to obtain a rate integral curve. The net driving accumulation is obtained by removing the time periods when the rate is continuously zero on the rate integral curve and subtracting the corresponding area calculation value. The material pair with the smallest offset is used as the baseline synchronization pair, and the unit area consumption quota of any one of the materials in the baseline synchronization pair is determined in the planned bill of materials. Divide the net driving accumulation by the unit area consumption quota to obtain the theoretical area increment sequence of the current process.

[0008] Preferably, the step of forcing the theoretical area increment sequence and the actual area increment sequence to converge dynamically on the time axis to obtain convergence residuals includes: Starting from the initial point, the cumulative difference sequence between the theoretical area increment sequence and the actual area increment sequence is determined point by point; Mark the local maxima and local minima in the cumulative difference sequence to obtain the deviation extreme value anchor point sequence; The time interval between two adjacent extreme deviation anchor points in the deviation extreme anchor point sequence is taken as the elastic deformation segment. Keeping the theoretical area increment at the starting anchor point in the elastic deformation segment constant, calculate the ratio of the theoretical area increment to the actual area increment at the ending anchor point in the elastic deformation segment. By multiplying the theoretical area increment within the elastic deformation segment by a ratio, the theoretical area increment at the endpoint is forcibly aligned with the actual area increment to obtain the theoretical fitting sequence. Extract the value of the last sampled point in the theoretical fitting sequence, and calculate the algebraic difference between this value and the actual area increment at the same time to obtain the convergence residual.

[0009] Preferably, the step of calculating the real-time completed area of ​​the current process based on the convergence residual and generating a progress deviation coefficient relative to the planned work area includes: The convergence residual is summed with the value of the last point of the actual area increment sequence to obtain the real-time completed area. The progress deviation coefficient is obtained by dividing the difference between the real-time completed area and the planned work area by the planned work area.

[0010] Preferably, identifying the dominant factor causing the deviation as either material-constrained or execution-constrained based on the direction of the convergence residual includes: Calculate the planned area increment of the current process within the corresponding time period; By comparing the last point value of the theoretical area increment sequence with the planned area increment, the material supply satisfaction rate is obtained; When the material supply satisfaction rate is lower than the first preset threshold, the dominant factor of the deviation is determined to be material constraint type. When the material supply satisfaction rate is higher than or equal to the first preset threshold and the convergence residual is greater than the second preset threshold, the deviation dominant factor is determined to be execution constraint type in order to obtain the dominant factor.

[0011] Preferably, the step of associating multiple monitoring units that are spatially adjacent and have the same process and possess the aforementioned progress deviation coefficient to generate a work surface early warning area includes: Obtain all monitoring units that have the aforementioned schedule deviation coefficient, and merge the monitoring units with the same process type and adjacent boundaries into a connected group to obtain a monitoring unit group; Extract the minimum outer contour of the geometric boundary corresponding to all regulatory units within each regulatory unit cluster; The area enclosed by the minimum outer contour is marked as the work surface warning area.

[0012] Preferably, the step of estimating the resource competition intensity in the early warning area of ​​the work surface includes: The common material types and planned total consumption per unit time of all monitored units within the warning area of ​​the work surface are statistically analyzed to obtain the demand load density. The estimated value of resource competition intensity is obtained by multiplying the demand load density by the average absolute value of the progress deviation coefficient corresponding to the monitoring unit in the early warning area of ​​the work surface.

[0013] Preferably, the step of generating process adjustment instructions and material delivery priority instructions based on the dominant factors and the intensity of resource competition includes: Generate process adjustment instructions based on the aforementioned key factors; The resource competition intensity is numerically compared with the estimated resource competition intensity of other work area warning zones in the same project; Based on the numerical comparison results, the warning areas of the work surface are sorted from high to low to obtain material delivery priority instructions.

[0014] A system for intelligent monitoring of the progress of decoration and renovation projects, used to implement the intelligent monitoring method for the progress of decoration and renovation projects as described in any one of items 1-9, the system comprising: Data acquisition module: used to deconstruct the three-dimensional building model of the decoration project into monitoring units according to the physical space, and associate the planned material list and planned work area corresponding to each monitoring unit; Derivation module: Collects the outbound consumption rate of each material at the construction site in the decoration project, reverse-engineers the outbound consumption rate into the theoretical area increment sequence of the corresponding process, and simultaneously collects the actual area increment sequence of the current process on the monitoring unit; Coefficient determination module: Forces the theoretical area increment sequence and the actual area increment sequence to converge dynamically on the time axis to obtain the convergence residual, calculates the real-time completed area of ​​the current process based on the convergence residual, and generates the progress deviation coefficient relative to the planned operation area. Factor determination module: When the schedule deviation coefficient exceeds the allowable range, the dominant factor causing the deviation is identified as either material constraint type or execution constraint type based on the direction of the convergence residual. Intensity determination module: Associate multiple monitoring units that are spatially adjacent and have the same process and have the aforementioned schedule deviation coefficient, generate a work surface early warning area, and estimate the resource competition intensity of the work surface early warning area; Instruction determination module: Generates process adjustment instructions and material delivery priority instructions based on the dominant factors and the intensity of resource competition.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention transforms overall fuzzy supervision into precise local control by deconstructing a three-dimensional building model into fine-grained monitoring units according to physical space and associating each unit with a planned bill of materials and planned work area. It collects the outbound consumption rate of various materials at the construction site, reversely derives the theoretical area increment sequence for the corresponding process, and simultaneously collects the actual area increment sequence, thus establishing a quantitative mapping relationship between material consumption and area progress. This overcomes the defect of separating material data and progress data. Secondly, by forcing the theoretical and actual area increment sequences to converge dynamically on the time axis, the convergence residual is calculated to determine the real-time completed area of ​​the current process and generate a progress deviation coefficient relative to the planned work area. This avoids the lag and distortion caused by static comparison, improving the accuracy and response speed of the progress deviation coefficient. Based on the direction of the convergence residual, the dominant factors causing the deviation are automatically identified as material-constrained or execution-constrained, providing a basis for subsequent adjustments.

[0016] This invention automatically generates a work area warning zone by associating multiple spatially adjacent and identical monitoring units with schedule deviation coefficients, and estimates the resource competition intensity of the zone. This allows for the rapid identification of work areas requiring key intervention, while also quantifying the competitive pressure between different zones for shared materials, thus avoiding resource allocation imbalances caused by independent management of a single unit. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an intelligent monitoring method for the progress of decoration and renovation projects according to an embodiment of the present invention. Figure 2 A functional module diagram of an intelligent monitoring method system for decoration and renovation project progress provided in an embodiment of the present invention; The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides an intelligent monitoring method for the progress of decoration and renovation projects. The executing entity of this intelligent monitoring method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the intelligent monitoring method for the progress of decoration and renovation projects can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating an intelligent monitoring method for the progress of a decoration and renovation project according to an embodiment of the present invention. In this embodiment, the intelligent monitoring method for the progress of a decoration and renovation project includes: S1: Deconstruct the three-dimensional architectural model of the decoration project into monitoring units according to physical space, and associate the planned bill of materials and planned work area corresponding to each monitoring unit; In this embodiment, the step of deconstructing the three-dimensional architectural model of the decoration project into monitoring units according to physical space, and associating the planned bill of materials and planned work area corresponding to each monitoring unit, includes: The three-dimensional building model is divided into units based on its physical boundaries to obtain monitoring units; Read the construction drawings and material budget sheets corresponding to the monitoring unit to obtain the planned work area and planned material list.

[0021] Specifically, a three-dimensional building model refers to a three-dimensional digital model created using architectural design software such as Revit and ArchiCAD before the start of a decoration and renovation project. It expresses the spatial location, size, and geometric relationships between each wall, column, and floor slab in the building.

[0022] Physical boundaries refer to the real, indivisible structural boundaries in the three-dimensional building model, such as the outline of a room enclosed by four walls, or the structural partitions between different functional areas.

[0023] A regulatory unit refers to the smallest and most basic independent spatial management unit obtained by decomposing the three-dimensional building model according to the aforementioned physical boundaries.

[0024] Construction drawings are detailed construction drawings created for each specific regulatory unit. They typically include floor plans, elevations, and sections, and indicate the specific items to be constructed within that unit and their corresponding geometric dimensions.

[0025] A material budget sheet is a list of materials used in advance, calculated based on the construction drawings and decoration process requirements, to complete all operations within the supervision unit.

[0026] The planned work area refers to the total area of ​​decoration and renovation work to be completed within the supervised unit, which is directly measured and calculated from the construction drawings. For example, if the floor of a room needs to be tiled, the planned work area is the length multiplied by the width of the room floor; if the walls need to be painted with latex paint, the planned work area is the sum of the areas of the four walls, excluding the area of ​​door and window openings.

[0027] The planned bill of materials is a complete list of all materials required to complete the planned work area of ​​the monitoring unit, which is read from the material budget table.

[0028] In detail, based on the inherent and unchangeable physical boundaries in the three-dimensional building model, such as the enclosed spaces formed by building components such as walls, floors, and columns, like individual rooms or clearly defined construction zones, the complete three-dimensional model is decomposed into discrete and minimized monitoring units through geometric segmentation.

[0029] Furthermore, based on the unique spatial location identifier corresponding to each of the identified regulatory units, the construction drawings for that specific location are precisely retrieved from the project database, such as the floor plan and elevation of the room, which indicate the specific scope of decoration and renovation required for that area and the material budget, such as a material usage list prepared separately for that room.

[0030] By directly reading the quantitative data in these files, the total area of ​​work to be completed by the regulatory unit, i.e. the planned work area, and the total types and quantities of materials required to complete the planned work area, i.e. the planned bill of materials, are obtained.

[0031] S2: Collect the outbound consumption rate of each material at the construction site in the decoration project, reverse the outbound consumption rate to deduce the theoretical area increment sequence of the corresponding process, and simultaneously collect the actual area increment sequence of the current process on the monitoring unit. In this embodiment, the step of reverse-engineering the outbound consumption rate into a theoretical area increment sequence for the corresponding process includes: Separate the various materials belonging to the same process from the outbound consumption rate, calculate the offset of the consumption rate corresponding to each pair of materials, and construct the consumption deviation matrix of the materials within the same process; The material pair with the smallest offset in the consumption deviation matrix is ​​marked, and the average consumption rate of the material pair is marked as the effective construction rate to filter out the pseudo rate caused by the distribution fluctuation of the single material. The effective construction driving rate is integrated over the time axis of the decoration project to obtain a rate integral curve. The net driving accumulation is obtained by removing the time periods when the rate is continuously zero on the rate integral curve and subtracting the corresponding area calculation value. The material pair with the smallest offset is used as the baseline synchronization pair, and the unit area consumption quota of any one of the materials in the baseline synchronization pair is determined in the planned bill of materials. Divide the net driving accumulation by the unit area consumption quota to obtain the theoretical area increment sequence of the current process.

[0032] Specifically, the outbound consumption rate refers to the instantaneous speed at which each type of material, such as cement, tiles, and paint, is drawn from the warehouse or storage yard and consumed at the construction site. It is usually measured by the amount consumed per unit time.

[0033] Materials used in the same process refer to all different types of materials that must be used simultaneously or sequentially to complete a specific decoration and construction process, such as wall plastering, floor leveling, and tile laying. For example, the tile laying process requires tiles, cement, sand, and adhesive.

[0034] Offset refers to the absolute difference between the outbound consumption rates of two different materials at the same point in time or within a time window.

[0035] The consumption deviation matrix is ​​a two-dimensional data table in which the rows and columns represent the types of materials belonging to the same process. Each cell in the table stores the calculated offset value between the corresponding row material and the column material.

[0036] A material pair refers to any combination of two different materials selected from the consumption deviation matrix.

[0037] Effective construction rate refers to the average value obtained by selecting the material pair with the smallest offset from all material pairs, and then adding the outbound consumption rates of these two materials and dividing by two.

[0038] Pseudo-rate refers to a temporary abnormally high or low rate of consumption of a single material due to reasons such as untimely delivery, inventory fluctuations, or errors in material requisition records. Such fluctuations do not reflect the actual growth of the construction surface layer.

[0039] A timeline is a linear coordinate system that uses the start date of the renovation project as the origin and days or hours as the scale unit to represent the progress of the project.

[0040] Integration refers to the process of accumulating and summing the continuously changing effective construction rate over a time axis. The result is represented by the area enclosed by the rate curve from the starting point to the current moment and the time axis.

[0041] The rate integral curve is a new curve obtained by integrating the effective construction rate. Its horizontal axis is the time axis, and its vertical axis is the total effective construction driving force accumulated from the start of the project to that moment.

[0042] The period during which the rate is continuously zero refers to the time interval in which multiple consecutive sampling points on the derivative of the rate integral curve are zero. This usually corresponds to a period during which no work of this process is carried out at the construction site.

[0043] Net driven cumulative amount refers to the cumulative amount driven purely by effective construction actions, obtained by removing the area estimation values ​​that may be generated during all time periods when the rate is continuously zero from the total value of the vertical axis of the rate integral curve.

[0044] The reference synchronization pair refers to the material pair with the smallest offset in the consumption deviation matrix.

[0045] The unit area consumption quota refers to the fixed quantity of a certain material required to complete the construction of each unit area, such as 1 square meter, of a certain process, as specified in the planned bill of materials according to the engineering design or construction specifications.

[0046] The theoretical area increment sequence of the current process refers to a series of area completion values ​​that change over time by converting the net driving cumulative amount into a unit area consumption quota. Each value represents the construction area of ​​the process that should theoretically be completed by the corresponding time.

[0047] In detail, from the data stream of material consumption rates collected at the construction site, the consumption rates of all materials belonging to the same construction process are separated according to the relationship between the materials and the construction process. For example, materials such as cement, sand, and tiles belong to the floor tile laying process.

[0048] These materials are paired up, and the absolute value of the difference between the consumption rates of each pair of materials at the same time is calculated, i.e., the offset. The offsets of all materials at different times are organized to form a consumption offset matrix that describes the synchronicity deviation of the consumption of each material in the process.

[0049] Scan the matrix and find the material pair with the smallest offset value. This means that the consumption rhythm of the two materials is most consistent and can best reflect the construction action. Then, take the arithmetic average of the consumption rates of the two materials in the material pair and mark the average value as the effective construction rate.

[0050] Furthermore, using the number of construction days of the renovation project as the horizontal axis and the rate value as the curve on the vertical axis, the total area under the rate curve from the start of the project to each sampling time is calculated by continuous accumulation, thus obtaining a rate integral curve that increases with time.

[0051] Identify the time periods on the curve where all rate values ​​are consistently zero, such as nighttime shutdowns, holidays, or periods of waiting for materials. Subtract the area growth corresponding to these time periods from the integration result, retaining only the cumulative amount driven by actual construction, thus obtaining the net driven cumulative amount.

[0052] The material pair with the smallest offset is used as the baseline synchronization pair. Then, any material is selected from it, and the quantity of this material required to complete the construction of a unit area is directly retrieved from the planned material list corresponding to the monitoring unit. This is the unit area consumption quota. For example, laying 1 square meter of tiles requires 0.05 bags of cement.

[0053] Divide the net driving cumulative amount by the unit area consumption quota to calculate the corresponding construction area. As time goes by, the corresponding area value can be calculated for each sampling point.

[0054] S3: Force the theoretical area increment sequence and the actual area increment sequence to converge dynamically on the time axis to obtain the convergence residual. Calculate the real-time completed area of ​​the current process based on the convergence residual and generate a progress deviation coefficient relative to the planned work area. In this embodiment, the step of forcing the theoretical area increment sequence and the actual area increment sequence to converge dynamically on the time axis to obtain the convergence residual includes: Starting from the initial point, the cumulative difference sequence between the theoretical area increment sequence and the actual area increment sequence is determined point by point; Mark the local maxima and local minima in the cumulative difference sequence to obtain the deviation extreme value anchor point sequence; The time interval between two adjacent extreme deviation anchor points in the deviation extreme anchor point sequence is taken as the elastic deformation segment. Keeping the theoretical area increment at the starting anchor point in the elastic deformation segment constant, calculate the ratio of the theoretical area increment to the actual area increment at the ending anchor point in the elastic deformation segment. By multiplying the theoretical area increment within the elastic deformation segment by a ratio, the theoretical area increment at the endpoint is forcibly aligned with the actual area increment to obtain the theoretical fitting sequence. Extract the value of the last sampled point in the theoretical fitting sequence, and calculate the algebraic difference between this value and the actual area increment at the same time to obtain the convergence residual.

[0055] Specifically, the starting point is the initial position where data comparison and accumulation begin.

[0056] The actual area increment sequence refers to the set of actual completed area values ​​at each sampling point that change over time, obtained through on-site measurement or sensor data collection.

[0057] The cumulative difference sequence refers to a sequence of differences obtained by subtracting the cumulative value of the theoretical area increment sequence up to that moment from the cumulative value of the actual area increment sequence at each sampling point, starting from the starting point, and arranged in chronological order.

[0058] A local maximum is a value at a sampling point in a cumulative difference sequence that is greater than the values ​​of the two sampling points before and after it, indicating that the deviation at that point has reached a local peak.

[0059] A local minimum refers to a value at a sampling point in a cumulative difference sequence that is less than the values ​​of the two sampling points before and after it, indicating that the deviation at that point has reached a local trough.

[0060] The deviation extreme value anchor sequence refers to a new sequence formed by extracting all local maxima and local minima from the cumulative difference sequence in chronological order.

[0061] Two adjacent extreme deviation anchor points refer to two anchor points that are immediately adjacent in time in the extreme deviation anchor point sequence, such as the first anchor point and the second anchor point, the second anchor point and the third anchor point, and so on.

[0062] A time interval refers to the continuous period of time between the time point corresponding to the first anchor point and the time point corresponding to the second anchor point among two adjacent extreme deviation anchor points.

[0063] The elastic deformation segment refers to the above time interval and all theoretical area increments within that interval as a whole segment, in which the theoretical values ​​can be stretched or compressed as a whole.

[0064] The starting anchor point refers to the earliest extreme deviation anchor point in time within an elastic deformation segment, while the ending anchor point refers to the latest extreme deviation anchor point in time within the same elastic deformation segment.

[0065] The last sampling point refers to the sampling point at the last moment of the corresponding time axis in the theoretically fitted sequence.

[0066] The actual area increment at the same time refers to the value in the actual area increment sequence that has the same timestamp as the last sampling point.

[0067] The convergence residual is the algebraic difference between the theoretical fitting sequence value and the actual area increment value calculated at the last point of the time axis after the theoretical area increment sequence is dynamically aligned with the actual area increment sequence. It represents the small residual deviation that still exists after calibration. This residual may be caused by factors such as nonlinear material loss, measurement noise, or local rework, and is used to correct the final value of the real-time completed area.

[0068] In detail, starting from the beginning of the time axis, for each sampling point, the difference between the cumulative value of the theoretical area increment sequence and the cumulative value of the actual area increment sequence up to that moment is calculated, and these differences are arranged in chronological order to form a cumulative difference sequence.

[0069] Scan this cumulative difference sequence to find all local maxima and local minima, forming a sequence of deviation extreme value anchor points. These anchor points represent the critical moments when the deviation between theoretical and actual progress turns.

[0070] The time interval between two adjacent extreme deviation anchor points in the sequence of extreme deviation anchor points is defined as an elastic deformation segment.

[0071] Keeping the theoretical area increment at the starting anchor point of the segment unchanged, calculate the ratio of the theoretical area increment at the ending anchor point of the segment to the actual area increment at the same point. Next, the theoretical area increment values ​​at all sampling points within the elastic deformation segment are multiplied by this ratio, thereby forcibly leveling the theoretical and actual values, which were originally unequal at the endpoint anchor point, to the same value.

[0072] Furthermore, the value of the last sampled point in the theoretical fitting sequence is extracted, and the algebraic difference between this value and the actual area increment at the same time is calculated. This difference is the convergence residual.

[0073] In this embodiment, the step of calculating the real-time completed area of ​​the current process based on the convergence residual and generating a progress deviation coefficient relative to the planned work area includes: The convergence residual is summed with the value of the last point of the actual area increment sequence to obtain the real-time completed area. The progress deviation coefficient is obtained by dividing the difference between the real-time completed area and the planned work area by the planned work area.

[0074] Specifically, the real-time completed area refers to the precise construction area that has actually been completed at the current moment in the current process of the monitoring unit after compensating the convergence residual to the end value of the actual area increment sequence.

[0075] The difference here refers to the value obtained by subtracting the planned work area from the real-time completed area. A positive difference indicates that the completed area exceeds the planned area, while a negative difference indicates that the planned area has not been reached.

[0076] The schedule deviation factor is the ratio obtained by dividing the difference between the area completed in real time and the planned work area by the planned work area.

[0077] In detail, the convergence residual is extracted, and the final point value is taken from the actual area increment sequence. The sum of the two values ​​is calculated, and the result is the real-time completed area of ​​the current process in the current monitoring unit at the current moment. The planned work area is read from the planning data associated with the monitoring unit, the difference between the real-time completed area and the planned work area is calculated, and then divided by the planned work area. The resulting quotient is the schedule deviation coefficient.

[0078] S4: When the schedule deviation coefficient exceeds the allowable range, the dominant factor causing the deviation is identified as either material constraint type or execution constraint type based on the direction of the convergence residual. In this embodiment, identifying the dominant factor causing the deviation as either material-constrained or execution-constrained based on the direction of the convergence residual includes: Calculate the planned area increment of the current process within the corresponding time period; By comparing the last point value of the theoretical area increment sequence with the planned area increment, the material supply satisfaction rate is obtained; When the material supply satisfaction rate is lower than the first preset threshold, the dominant factor of the deviation is determined to be material constraint type. When the material supply satisfaction rate is higher than or equal to the first preset threshold and the convergence residual is greater than the second preset threshold, the deviation dominant factor is determined to be execution constraint type in order to obtain the dominant factor.

[0079] Specifically, the current process refers to the specific construction task being carried out in the decoration and renovation project.

[0080] The corresponding time period refers to the continuous time interval from the point when the current process begins construction until the sampling time when the deviation analysis is performed.

[0081] The final value of the theoretical area increment sequence is the specific value located at the last sampling moment on the time axis in the theoretical area increment sequence.

[0082] The planned increase in area refers to the cumulative area that should be completed by the end of the corresponding time period.

[0083] The material supply satisfaction rate is the ratio obtained by dividing the last point of the theoretical area increment sequence by the planned area increment. It reflects the degree to which the actual material consumption can support the construction area relative to the planned demand area. The lower the value, the greater the material shortage.

[0084] The first preset threshold refers to a fixed numerical boundary, such as 0.8, that is pre-set and stored in the configuration to determine whether the material supply is severely insufficient.

[0085] Material-constrained schedule deviations refer to those caused by insufficient material supply.

[0086] Execution constraint type refers to a type where the main factor causing schedule deviations is poor execution of on-site construction.

[0087] The second preset threshold refers to another fixed numerical boundary, such as 0, that is pre-set and stored in the system configuration, used to determine whether the deviation at the execution level is significant.

[0088] Dominant factors refer to the categories of primary causes of schedule deviations ultimately determined by the system after the above comparison and judgment process. In detail, based on the corresponding time period of the current process, the planned area increment that should be completed within the time period is extracted from the project plan data. The last point value in the theoretical area increment sequence is used to directly compare this value with the planned area increment, that is, the planned area increment at the same time, and the ratio of the two is calculated.

[0089] Determine whether the material supply satisfaction rate is lower than the first preset threshold. If so, it means that the actual consumption of materials is far lower than the planned demand. Insufficient material supply is the main reason for the delay. The dominant factor of the deviation is determined to be material constraint.

[0090] If not, then determine whether the convergence residual is greater than the second preset threshold. If it is, it means that although the material supply is basically met, the actual construction area growth is still significantly behind the theoretical calculation value, indicating that problems such as construction personnel, machinery or management have slowed down the progress. The dominant factor of the deviation is determined to be execution constraint type.

[0091] S5: Associate multiple monitoring units that are spatially adjacent and have the same process and have the aforementioned schedule deviation coefficient, generate a work surface early warning area, and estimate the resource competition intensity of the work surface early warning area; In this embodiment, associating multiple monitoring units that are spatially adjacent and have the same process and possess the aforementioned progress deviation coefficient to generate a work surface early warning area includes: Obtain all monitoring units that have the aforementioned schedule deviation coefficient, and merge the monitoring units with the same process type and adjacent boundaries into a connected group to obtain a monitoring unit group; Extract the minimum outer contour of the geometric boundary corresponding to all regulatory units within each regulatory unit cluster; The area enclosed by the minimum outer contour is marked as the work surface warning area.

[0092] Specifically, the work area warning area refers to a connected area formed by merging multiple spatially adjacent and identical regulatory units with progress deviation coefficients, and its geometric boundary is the range enclosed by the minimum outer contour of these units.

[0093] Work process type refers to the category name of the construction task that is being performed or planned to be performed on the supervision unit.

[0094] Adjacent boundaries refer to two regulatory units sharing a common edge, a common corner, or being in direct contact in a 3D building model.

[0095] A connected cluster refers to a set of regulatory units that are of the same type of process and are directly or indirectly connected to each other through boundary adjacency.

[0096] The regulatory unit grouping is the output result obtained after the merging operation.

[0097] Geometric boundaries refer to the spatial outline of each regulatory unit in a three-dimensional building model, describing the planar or three-dimensional extent occupied by the unit.

[0098] The minimum bounding contour refers to the closed contour with the smallest area that completely encloses the geometric boundaries of all regulatory units within a given regulatory unit cluster.

[0099] In detail, scan all the monitoring units in the entire decoration project for which the progress deviation coefficient has been calculated, filter out these units, check the process type of each monitoring unit, and determine the spatial adjacency between units with the same process type. For all monitoring units with the same process type and adjacent boundaries, merge them into a connected cluster.

[0100] Extract the geometric boundaries of each regulatory unit within the cluster in the 3D building model, and calculate the smallest convex polygon or smallest bounding rectangle that can completely enclose all these geometric boundaries. The contour is characterized by having the smallest area and being able to cover all units within the cluster.

[0101] Mark the area enclosed by this minimum outer contour in the model and define it as the work surface warning area.

[0102] In this embodiment, estimating the resource competition intensity in the early warning area of ​​the work surface includes: The common material types and planned total consumption per unit time of all monitored units within the warning area of ​​the work surface are statistically analyzed to obtain the demand load density. The estimated value of resource competition intensity is obtained by multiplying the demand load density by the average absolute value of the progress deviation coefficient corresponding to the monitoring unit in the early warning area of ​​the work surface.

[0103] Specifically, shared material types refer to the same type of material that needs to be used to complete the current process in multiple monitoring units within the warning area of ​​the work surface.

[0104] The planned total consumption per unit time refers to the total quantity of a certain common material that all monitored units within the warning area of ​​the work site should consume according to the schedule within a unit of time, such as a day or an hour.

[0105] Demand load density refers to the numerical value obtained by statistically analyzing the types of shared materials and their planned total consumption per unit time within the warning area of ​​the work site, describing the density of material demand per unit time and per unit space.

[0106] The schedule deviation factor is the ratio obtained by dividing the difference between the real-time completed area and the planned work area by the planned work area. It is used to quantify the degree of schedule deviation from the plan.

[0107] The average absolute value refers to the arithmetic mean obtained by taking the absolute values ​​of the progress deviation coefficients of all monitored units within the warning area of ​​the work surface, summing these absolute values, and dividing by the number of monitored units. It reflects the average severity of the overall progress deviation in the area.

[0108] The estimated value of resource competition intensity is the value obtained by multiplying the demand load density by the average absolute value of the schedule deviation coefficient.

[0109] In detail, the system locks down the already generated work area warning zone, traverses all monitoring units within the zone, and identifies the types of materials necessary for the current process in each monitoring unit and used by multiple units, such as cement, tiles, or paint needed by multiple rooms at the same time.

[0110] Furthermore, the planned total consumption of each shared material per unit time is statistically analyzed, and these statistical results are summarized to obtain a demand load density that describes the intensity of material demand in the region per unit time.

[0111] Calculate the absolute value of the schedule deviation coefficient, and then take the arithmetic mean of these absolute values ​​to obtain the average schedule deviation of the area, which is the average absolute value of the schedule deviation coefficient.

[0112] Multiplying the demand load density by this average absolute value yields the estimated value of resource competition intensity.

[0113] S6: Generate process adjustment instructions and material delivery priority instructions based on the dominant factors and the intensity of resource competition.

[0114] In this embodiment, the step of generating process adjustment instructions and material delivery priority instructions based on the dominant factors and the intensity of resource competition includes: Generate process adjustment instructions based on the aforementioned key factors; The resource competition intensity is numerically compared with the estimated resource competition intensity of other work area warning zones in the same project; Based on the numerical comparison results, the warning areas of the work surface are sorted from high to low to obtain material delivery priority instructions.

[0115] Specifically, when the dominant factor is material constraint, the instruction is to suspend the current process or switch to another available process; when the dominant factor is execution constraint, the instruction is to add a shift or extend the working time.

[0116] Other warning areas for the same work surface in the same project refer to all warning areas generated by other adjacent and identical regulatory units in the same decoration project, excluding the warning area currently being processed.

[0117] Material delivery priority instructions refer to using the sorted list of warning areas on the work surface as the instruction content, clearly informing material delivery personnel that the higher the ranking of an area, the higher its material delivery priority level, and it should be arranged for delivery first.

[0118] In detail, the corresponding process adjustment instructions are generated based on the dominant factors, the estimated value of resource competition intensity of the current work surface warning area is read, and the estimated value of resource competition intensity of each of the other work surface warning areas in the same decoration project is obtained.

[0119] The data are arranged in descending order of value. Based on this comparison result, all warning areas of the work surface are sorted from high to low, and the sorted list of areas is used as the content of the material delivery priority instruction.

[0120] like Figure 2 The diagram shown is a functional module diagram of an intelligent monitoring method system for the progress of decoration and renovation projects provided in an embodiment of the present invention.

[0121] The intelligent monitoring method system 100 for the progress of decoration and renovation projects described in this invention can be installed in an electronic device. Depending on the functions implemented, the intelligent monitoring method system 100 for the progress of decoration and renovation projects may include a data acquisition module 101, a derivation module 102, a coefficient determination module 103, a factor determination module 104, an intensity determination module 105, and an instruction determination module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0122] In this embodiment, the functions of each module / unit are as follows: Data acquisition module 101: used to deconstruct the three-dimensional building model of the decoration project into monitoring units according to physical space, and associate the planned material list and planned work area corresponding to each monitoring unit; Derivation module 102: Collects the outbound consumption rate of each material at the construction site in the decoration project, reverse-engineers the outbound consumption rate into the theoretical area increment sequence of the corresponding process, and simultaneously collects the actual area increment sequence of the current process on the monitoring unit; Coefficient determination module 103: Forces the theoretical area increment sequence and the actual area increment sequence to converge dynamically on the time axis to obtain the convergence residual, calculates the real-time completed area of ​​the current process based on the convergence residual, and generates the progress deviation coefficient relative to the planned operation area. Factor determination module 104: When the schedule deviation coefficient exceeds the allowable range, the dominant factor causing the deviation is identified as either material constraint type or execution constraint type based on the direction of the convergence residual. Intensity determination module 105: Associates multiple monitoring units that are spatially adjacent and have the same process and have the aforementioned schedule deviation coefficient, generates a work surface early warning area, and estimates the resource competition intensity of the work surface early warning area; Instruction determination module 106: Generates process adjustment instructions and material delivery priority instructions based on the dominant factors and the intensity of resource competition.

[0123] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0124] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0126] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0127] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for intelligent monitoring of the progress of decoration and renovation projects, characterized in that, The method includes: S1: Deconstruct the three-dimensional architectural model of the decoration project into monitoring units according to physical space, and associate the planned bill of materials and planned work area corresponding to each monitoring unit; S2: Collect the outbound consumption rate of each material at the construction site in the decoration project, reverse the outbound consumption rate to deduce the theoretical area increment sequence of the corresponding process, and simultaneously collect the actual area increment sequence of the current process on the monitoring unit. S3: Force the theoretical area increment sequence and the actual area increment sequence to converge dynamically on the time axis to obtain the convergence residual. Calculate the real-time completed area of ​​the current process based on the convergence residual and generate a progress deviation coefficient relative to the planned work area. S4: When the schedule deviation coefficient exceeds the allowable range, the dominant factor causing the deviation is identified as either material constraint type or execution constraint type based on the direction of the convergence residual. S5: Associate multiple monitoring units that are spatially adjacent and have the same process and have the aforementioned schedule deviation coefficient, generate a work surface early warning area, and estimate the resource competition intensity of the work surface early warning area; S6: Generate process adjustment instructions and material delivery priority instructions based on the dominant factors and the intensity of resource competition.

2. The intelligent monitoring method for the progress of decoration and renovation projects as described in claim 1, characterized in that, The process of deconstructing the three-dimensional architectural model of the renovation project into monitoring units according to physical space, and associating each monitoring unit with a planned bill of materials and a planned work area, includes: The three-dimensional building model is divided into units based on its physical boundaries to obtain monitoring units; Read the construction drawings and material budget sheets corresponding to the monitoring unit to obtain the planned work area and planned material list.

3. The intelligent monitoring method for the progress of decoration and renovation projects as described in claim 1, characterized in that, The step of reverse-engineering the outbound consumption rate into a theoretical area increment sequence for the corresponding process includes: Separate the various materials belonging to the same process from the outbound consumption rate, calculate the offset of the consumption rate corresponding to each pair of materials, and construct the consumption deviation matrix of the materials within the same process; The material pair with the smallest offset in the consumption deviation matrix is ​​marked, and the average consumption rate of the material pair is marked as the effective construction rate to filter out the pseudo rate caused by the distribution fluctuation of the single material. The effective construction driving rate is integrated over the time axis of the decoration project to obtain a rate integral curve. The net driving accumulation is obtained by removing the time periods when the rate is continuously zero on the rate integral curve and subtracting the corresponding area calculation value. The material pair with the smallest offset is used as the baseline synchronization pair, and the unit area consumption quota of any one of the materials in the baseline synchronization pair is determined in the planned bill of materials. Divide the net driving accumulation by the unit area consumption quota to obtain the theoretical area increment sequence of the current process.

4. The intelligent monitoring method for the progress of decoration and renovation projects as described in claim 3, characterized in that, The forced convergence of the theoretical area increment sequence and the actual area increment sequence on the time axis to obtain the convergence residual includes: Starting from the initial point, the cumulative difference sequence between the theoretical area increment sequence and the actual area increment sequence is determined point by point; Mark the local maxima and local minima in the cumulative difference sequence to obtain the deviation extreme value anchor point sequence; The time interval between two adjacent extreme deviation anchor points in the deviation extreme anchor point sequence is taken as the elastic deformation segment. Keeping the theoretical area increment at the starting anchor point in the elastic deformation segment constant, calculate the ratio of the theoretical area increment to the actual area increment at the ending anchor point in the elastic deformation segment. By multiplying the theoretical area increment within the elastic deformation segment by a ratio, the theoretical area increment at the endpoint is forcibly aligned with the actual area increment to obtain the theoretical fitting sequence. Extract the value of the last sampled point in the theoretical fitting sequence, and calculate the algebraic difference between this value and the actual area increment at the same time to obtain the convergence residual.

5. The intelligent monitoring method for the progress of decoration and renovation projects as described in claim 1, characterized in that, The step of calculating the real-time completed area of ​​the current process based on the convergence residual and generating a progress deviation coefficient relative to the planned work area includes: The convergence residual is summed with the value of the last point of the actual area increment sequence to obtain the real-time completed area. The progress deviation coefficient is obtained by dividing the difference between the real-time completed area and the planned work area by the planned work area.

6. The intelligent monitoring method for the progress of decoration and renovation projects as described in claim 5, characterized in that, The step of identifying the dominant factor causing the deviation as either material-constrained or execution-constrained based on the direction of the convergence residual includes: Calculate the planned area increment of the current process within the corresponding time period; By comparing the last point value of the theoretical area increment sequence with the planned area increment, the material supply satisfaction rate is obtained; When the material supply satisfaction rate is lower than the first preset threshold, the dominant factor of the deviation is determined to be material constraint type; When the material supply satisfaction rate is higher than or equal to the first preset threshold and the convergence residual is greater than the second preset threshold, the deviation dominant factor is determined to be execution constraint type to obtain the dominant factor.

7. The intelligent monitoring method for the progress of decoration and renovation projects as described in claim 1, characterized in that, The step of associating multiple monitoring units that are spatially adjacent and have the same process and have the aforementioned progress deviation coefficient to generate a work surface early warning area includes: Obtain all monitoring units that have the aforementioned schedule deviation coefficient, and merge the monitoring units with the same process type and adjacent boundaries into a connected group to obtain a monitoring unit group; Extract the minimum outer contour of the geometric boundary corresponding to all regulatory units within each regulatory unit cluster; The area enclosed by the minimum outer contour is marked as the work surface warning area.

8. The intelligent monitoring method for the progress of decoration and renovation projects as described in claim 7, characterized in that, The estimation of resource competition intensity in the early warning area of ​​the work surface includes: The common material types and planned total consumption per unit time of all monitored units within the warning area of ​​the work surface are statistically analyzed to obtain the demand load density. The estimated value of resource competition intensity is obtained by multiplying the demand load density by the average absolute value of the progress deviation coefficient corresponding to the monitoring unit in the early warning area of ​​the work surface.

9. The intelligent monitoring method for the progress of decoration and renovation projects as described in claim 1, characterized in that, The process adjustment instructions and material delivery priority instructions generated based on the dominant factors and the intensity of resource competition include: Generate process adjustment instructions based on the aforementioned key factors; The resource competition intensity is numerically compared with the estimated resource competition intensity of other work area warning zones in the same project; Based on the numerical comparison results, the warning areas of the work surface are sorted from high to low to obtain material delivery priority instructions.

10. A method and system for intelligent monitoring of the progress of decoration and renovation projects, used to implement the intelligent monitoring method for the progress of decoration and renovation projects as described in any one of claims 1-9, characterized in that, The system includes: Data acquisition module: used to deconstruct the three-dimensional building model of the decoration project into monitoring units according to physical space, and associate the planned material list and planned work area corresponding to each monitoring unit; Derivation module: Collects the outbound consumption rate of each material at the construction site in the decoration project, reverse-engineers the outbound consumption rate into the theoretical area increment sequence of the corresponding process, and simultaneously collects the actual area increment sequence of the current process on the monitoring unit; Coefficient determination module: Forces the theoretical area increment sequence and the actual area increment sequence to converge dynamically on the time axis to obtain the convergence residual, calculates the real-time completed area of ​​the current process based on the convergence residual, and generates the progress deviation coefficient relative to the planned operation area. Factor determination module: When the schedule deviation coefficient exceeds the allowable range, the dominant factor causing the deviation is identified as either material constraint type or execution constraint type based on the direction of the convergence residual. Intensity determination module: Associate multiple monitoring units that are spatially adjacent and have the same process and have the aforementioned schedule deviation coefficient, generate a work surface early warning area, and estimate the resource competition intensity of the work surface early warning area; Instruction determination module: Generates process adjustment instructions and material delivery priority instructions based on the dominant factors and the intensity of resource competition.

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