Decoration engineering monitoring method, electronic equipment and program product

Abstract

The invention provides a decoration engineering monitoring method, electronic equipment and a program product. The method comprises the steps of determining a construction plan comprising at least one construction node and a construction time node corresponding to the construction node based on an initial construction period of a started decoration order; generating a quotation list containing at least one auxiliary material quotation by using the construction node and the construction time node; establishing an association relationship between the auxiliary material quotation in the quotation sheet and the construction node or the construction time node in the construction plan; calculating purchase order generation time according to the construction time node, the auxiliary material supplier delivery cycle and the auxiliary material stocking buffer period; performing decoration auxiliary material demand prediction by using the multi-dimensional influence factors in the started decoration order, the non-started decoration order and the finished decoration order to obtain an overall auxiliary material demand prediction result for the plurality of decoration orders; and real-time monitoring of the decoration project is realized based on the construction plan, the purchase order generation time and the overall auxiliary material demand prediction result.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more particularly to methods, electronic devices and program products for monitoring decoration projects. Background Technology

[0002] Current renovation project management techniques rely on the project manager's personal experience for project schedule forecasting, lacking standardized quantitative models based on objective factors such as house area, house type, and renovation package. This leads to inaccuracies in initial construction plans and construction timelines. Furthermore, there is a lack of strong correlation between quotations and construction milestones / timelines, making it impossible to determine the precise timing of material requirements.

[0003] Order generation time cannot be scientifically calculated based on demand timing, auxiliary material supplier delivery cycles, and auxiliary material preparation buffer periods. This often leads to premature material procurement resulting in inventory backlog, or delayed procurement causing construction interruptions. Auxiliary material demand forecasting relies solely on historical data and fails to effectively utilize information from signed but not yet started renovation orders, making it difficult to achieve forward-looking and accurate replenishment decisions.

[0004] Furthermore, renovation projects involve numerous and fragmented stages. There is a lack of real-time monitoring and dynamic linkage mechanisms between construction progress, price changes, order status, and auxiliary material inventory. If actual progress deviates or user needs change, the inability to promptly adjust construction plans, order generation times, and auxiliary material demand forecasts can easily lead to a chain reaction of problems such as project delays, incorrect / stagnant material orders, and inventory overstocking or shortages, ultimately resulting in increased costs and delivery risks. Therefore, an accurate method for monitoring renovation projects is urgently needed. Summary of the Invention

[0005] This disclosure provides methods, electronic equipment, and software products for monitoring renovation projects.

[0006] According to a first aspect of this disclosure, a method for monitoring renovation projects is provided. The method specifically includes: determining a construction plan containing at least one construction node and the corresponding construction time node based on the initial construction period of an initiated renovation order; generating a quotation containing quotations for at least one auxiliary material using the construction node and construction time node; establishing a correlation between the auxiliary material quotations in the quotation and the construction node or construction time node in the construction plan; calculating the purchase order generation time based on the construction time node, the auxiliary material supplier's delivery cycle, and the auxiliary material preparation buffer period; predicting the demand for renovation auxiliary materials using multi-dimensional influencing factors from initiated, non-initiated, and completed renovation orders to obtain an overall auxiliary material demand prediction result for multiple renovation orders; wherein the multi-dimensional influencing factors include at least one of the following: renovation package, house area, house type, planned construction period, and city; and achieving real-time monitoring of the renovation project based on the construction plan, the purchase order generation time, and the overall auxiliary material demand prediction result.

[0007] As described above, by establishing a strong correlation between quotations and construction milestones, the system solves the fundamental problem in traditional renovation management: not knowing when to buy materials or what they will be used for. This correlation allows the system to clearly define the demand time for each material, providing accurate timing data for subsequent JIT ordering and change impact analysis, significantly improving the accuracy of renovation material supply. Furthermore, incorporating unstarted renovation orders into auxiliary material demand forecasting introduces future project pipeline characteristics. This feature enables the system to proactively capture short-term, deterministic demand pulses, significantly improving forecast accuracy and timeliness. For example, the system can anticipate a large number of luxury package projects starting construction within a certain period, allowing for advance preparation of the necessary auxiliary materials to avoid shortages due to short-term demand pulses. The dynamic linkage between construction plans, purchase order generation time, and renovation auxiliary material demand forecasting constructs a complete closed-loop management system. When construction progress changes or quotations change, the system can automatically adjust subsequent material requirements and procurement plans, ensuring the entire project remains in optimal condition.

[0008] According to at least one embodiment of this disclosure, the prediction method based on the initial construction period of an initiated renovation order includes: determining the renovation package, house area, house type, planned construction period, and city included in the renovation order as multi-dimensional influencing factors; quantifying each factor and mapping it to standard tasks in a standardized renovation work decomposition structure template; and calculating the initial construction period prediction based on the basic working hours corresponding to each standard task.

[0009] As described above, by quantifying multi-dimensional influencing factors and mapping them to standardized WBS tasks, a shift from experience-based judgment to data-driven project schedule forecasting has been achieved. This implementation eliminates reliance on the project manager's subjective experience. By precisely quantifying the impact of each influencing factor on specific construction tasks, project schedule forecasting becomes more objective and scientific, significantly improving forecast accuracy. Secondly, the standardized WBS template and its association mechanism with influencing factors give the system high configurability and adaptability. Construction specifications in different regions and process standards from different companies can be adapted by adjusting the WBS template and influencing factor parameters without redesigning the entire forecasting model. Continuous optimization of base work hours and influencing factor coefficients leads to increasingly accurate forecast results.

[0010] Determining a construction plan including at least one construction node and construction time nodes corresponding to the construction nodes according to at least one embodiment of this disclosure includes: analyzing the logical dependencies between standard tasks in the decoration work decomposition structure template using the critical path method based on the basic working hours corresponding to each standard task; calculating the construction time nodes and construction nodes corresponding to each standard task based on the logical dependencies; wherein the construction time nodes include: earliest start time, earliest end time, latest start time, and latest end time; and generating a construction plan using the construction time nodes and construction nodes.

[0011] As can be seen from the above, by scientifically applying the critical path method, accurately calculating construction time nodes, and generating a visual construction plan, the project schedule management of the decoration project has been refined, standardized, and intelligent. This effectively solves the pain points of traditional methods, such as strong subjectivity in schedule prediction, lack of transparency in progress, and slow response to changes, and significantly improves project management efficiency and customer satisfaction.

[0012] According to at least one embodiment of this disclosure, establishing the association between the auxiliary material price in the quotation and the construction node or construction time node in the construction plan includes: determining the auxiliary material list for the process based on the construction node; wherein the auxiliary material list for the process includes: the type and quantity of auxiliary materials for the process; and generating a quotation containing quotation details based on the construction time node, the auxiliary material list for the process and the auxiliary material price.

[0013] As described above, by establishing a strong link between quotations and construction milestones, the timing and quantity of material requirements are controllable and clearly defined throughout the entire project. This publicly available solution, by specifying the demand time for each material, provides an accurate timeframe for subsequent Just-In-Time (JIT) ordering, ensuring close matching between material procurement and construction progress, and significantly improving the accuracy of material supply. Furthermore, the standardization and intelligent generation of auxiliary material lists for each process significantly improves the accuracy and consistency of quotations. The system recommends auxiliary material types and quantities based on historical data and process standards, reducing subjective errors and omissions from manual estimation.

[0014] According to at least one embodiment of this disclosure, the purchase order generation time is calculated based on the construction time nodes, auxiliary material supplier delivery cycles, and auxiliary material preparation buffer periods in the quotation, including: determining the auxiliary material supplier delivery cycles and auxiliary material preparation buffer periods based on the auxiliary material list for each process in the quotation; calculating the purchase order generation time using the construction time nodes, auxiliary material supplier delivery cycles, and auxiliary material preparation buffer periods, so as to send the order to the supplier at the order generation time.

[0015] As described above, by accurately calculating order generation time, the system ensures that the procurement process for materials is initiated at the most appropriate time. This avoids inventory backlog caused by premature procurement (such as bulk purchasing all materials at the start of the project) and construction interruptions caused by late procurement. Secondly, the system dynamically determines the delivery cycle of auxiliary material suppliers and the buffer period for auxiliary material preparation based on the auxiliary material list for each process, making procurement decisions more precise and personalized. The timing of procurement is dynamically adjusted according to the characteristics of different materials, suppliers, and project stages. This refined procurement strategy significantly improves the matching degree between material supply and construction schedule. The combination of the JIT-driven order placement engine and the multi-role collaborative decision-making mechanism greatly improves project collaboration efficiency. When the status of a purchase order changes (such as a supplier confirming a delayed delivery), the system automatically assesses the impact on the construction schedule and sends early warning information to the project manager and the owner. This proactive early warning and decision support mechanism allows problems to be discovered and resolved early, avoiding greater losses from reactive responses.

[0016] According to at least one embodiment of this disclosure, the method further includes: in response to a quotation change request, obtaining the supplier rules, logistics rules, and change cost rules corresponding to the changed process auxiliary material list; determining the order status and change data of the purchase order corresponding to the changed process auxiliary material list; and using the supplier rules, logistics rules, order status, and change data to determine the impact on the project time and the impact on the cost.

[0017] As described above, by establishing a dynamic mapping relationship between quotation items and supply chain execution across the entire chain, the system achieves precise traceability of the impact from quotation changes to orders. This embodiment automatically establishes a mapping relationship between quotation item IDs and purchase order item IDs, enabling accurate location of affected orders in transit the instant a change occurs, significantly improving change response speed and accuracy. Furthermore, the change impact assessment mechanism based on a multi-dimensional rule base allows the system to provide scientific and quantitative decision-making basis. This embodiment integrates supplier rules, logistics rules, and internal cost models, automatically calculating precise cost impact values ​​and project time impact days through algorithms, making decision-making more accurate.

[0018] After determining the impact on project duration and the impact on cost according to at least one embodiment of this disclosure, the method further includes: determining the corresponding approval process based on the order status; wherein the order status includes: unconfirmed, confirmed, in preparation, in transit, and in storage; generating an intervention strategy based on the order status, the impact on project duration, and the impact on cost; and performing intervention operations on the purchase order based on the intervention strategy; wherein the intervention operations include: modifying the purchase order, canceling the purchase order, or intercepting the purchase order.

[0019] As demonstrated above, the differentiated approval process based on order status significantly improves decision-making efficiency and accuracy. In this embodiment, the approval level is dynamically adjusted according to the fulfillment status of the order, aligning the approval process with the risk of changes. For example, for orders in the "unconfirmed" status, the system allows project managers to make quick decisions without waiting for multi-level approvals; while for orders in the "in transit" status, stricter approval is required to ensure that decisions fully consider the impact on all parties. This differentiated approval mechanism reduces the average change processing time by 40% while ensuring that high-risk changes are fully assessed.

[0020] According to at least one embodiment of this disclosure, the demand for auxiliary materials for decoration is predicted using initiated decoration orders, initiating decoration orders, and historical data to obtain an overall demand prediction result for auxiliary materials for multiple decoration orders. This includes: identifying the decoration package, house area, city, apartment type, and planned construction period in initiating decoration orders as core contract and project features; identifying the expected start date in initiating decoration orders as a key time anchor point, and calculating the number of days from the current predicted date for each initiating decoration order as the number of days until the expected start date; identifying the decoration package, house area, city, apartment type, planned construction period, and current construction progress status in initiated decoration orders as current project features; and inputting the core contract and project features, the number of days until the expected start date, the current project features, and the historical features corresponding to completed decoration orders into an input feature set, which is then input into the decoration auxiliary material demand prediction model to obtain the overall auxiliary material demand prediction result.

[0021] As described above, by introducing future project pipeline characteristics, truly forward-looking demand forecasting is achieved. In this embodiment, detailed information on inactive renovation orders is used as the core predictive factor to accurately capture short-term, deterministic demand pulses. Furthermore, refined modeling of multi-dimensional features enables the system to accurately depict auxiliary material consumption patterns under different project profiles. The system deeply integrates renovation-specific contract information such as renovation packages, house area, city, house type, and construction period with dynamic data such as historical consumption and seasonal trends, constructing site profile vectors through refined feature engineering. Even in data-sparse scenarios, the system can still provide highly interpretable and logically sound prediction results, making it particularly suitable for predicting new cities, new packages, or new auxiliary materials.

[0022] According to at least one embodiment of this disclosure, the core contract and project characteristics, the number of days until the expected start date, the current project characteristics, and the historical characteristics corresponding to completed decoration orders constitute an input feature set, which is input into a decoration auxiliary material demand prediction model to obtain an overall auxiliary material demand prediction result. This includes: generating a first decoration profile vector using the current project characteristics, and generating a second decoration profile vector using the core contract and project characteristics; constructing a historical decoration profile vector using historical characteristics; calculating the similarity values ​​between the first decoration profile vector, the second decoration profile vector, and the historical decoration profile vector; selecting at least one historical feature based on the similarity value to construct a similar decoration set; assigning dynamic weights to the historical features in the similar decoration set; and calculating the auxiliary material prediction result for the changed process auxiliary material list and the historical demand calculated using the dynamic weights.

[0023] As described above, the construction of the construction site profile vector enables a precise quantitative expression of the characteristics of decoration projects. In this embodiment, by integrating and weighting multi-dimensional features, a profile vector that comprehensively reflects the characteristics of the project is constructed, enabling the system to accurately identify subtle differences between different projects.

[0024] At least one embodiment of this disclosure further includes calculating safety stock and replenishment quantity based on overall auxiliary material demand forecast results: The safety stock quantity is calculated using the obtained historical maximum daily consumption, average procurement lead time, and safety factor k; wherein the safety factor k is dynamically adjusted according to the stockout risk tolerance; the target inventory level is calculated using the obtained historical trend adjustment factor, safety stock quantity, and auxiliary material forecast results; wherein the historical trend adjustment factor is 1 + historical consumption growth rate or seasonal index; and the replenishment quantity is calculated using the obtained current inventory, in-transit inventory, and target inventory level; wherein the current inventory is the actual inventory quantity of the auxiliary material in the warehouse, and the in-transit inventory is the quantity of the auxiliary material that has been ordered but not yet delivered.

[0025] As described above, the dynamically adjusted safety factor mechanism enables the system to flexibly respond to different risk scenarios. This disclosed solution dynamically adjusts the k-value based on the stockout risk tolerance, ensuring that the safety stock level meets business needs without excessively tying up capital. Furthermore, the introduction of a historical trend adjustment factor makes demand forecasting more accurate. The system not only considers the baseline demand forecast but also adjusts it based on seasonal fluctuations and business growth trends, making the forecast results more consistent with actual business patterns.

[0026] According to at least one embodiment of this disclosure, the steps for real-time monitoring of a decoration project based on construction plans, order generation times, and forecasts of decoration material demand specifically include: real-time collection of the actual start time and current process progress of each construction node at the construction site, and calculation of the estimated end time; when the estimated end time of any construction node deviates from the construction time node corresponding to the construction node by more than a preset threshold, or when the quotation is changed, a decoration project re-evaluation operation is triggered; wherein, the decoration project re-evaluation operation includes: re-estimating the construction time node corresponding to the construction plan, re-estimating the demand for decoration materials, and re-estimating the order generation time.

[0027] As described above, the real-time progress monitoring mechanism enables precise management of the decoration project. In this embodiment, by collecting construction data in real time through multiple channels and combining it with a scientific model for calculating the estimated completion time, the actual status of the project can be accurately reflected. Furthermore, the intelligent triggering mechanism based on preset thresholds avoids overreaction and underreaction. The system does not adjust for every minor deviation, but rather sets reasonable thresholds based on process characteristics and industry experience, triggering a reassessment operation only when the deviation reaches a level that affects the overall project objective.

[0028] According to a second aspect of this disclosure, an electronic device is provided, comprising: a memory storing execution instructions; and a processor executing the execution instructions stored in the memory, such that the processor performs the method described in the first aspect of any embodiment of this disclosure.

[0029] According to a third aspect of this disclosure, a readable storage medium is provided, wherein executable instructions are stored therein, which, when executed by a processor, are used to implement the method described in the first aspect of any embodiment of this disclosure.

[0030] According to a fourth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect of any embodiment of this disclosure. Attached Figure Description

[0031] The accompanying drawings illustrate exemplary embodiments of the present disclosure and, together with the description thereof, serve to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are incorporated in and constitute a part of this specification.

[0032] Figure 1 This is a flowchart illustrating a method for monitoring a renovation project provided in this disclosure.

[0033] Figure 2 A flowchart illustrating the initial project duration prediction method provided in this embodiment of the disclosure.

[0034] Figure 3 A flowchart illustrating the method for determining construction plans and construction time nodes provided in this embodiment of the disclosure.

[0035] Figure 4 This is a flowchart illustrating the method for establishing association relationships provided in the embodiments of this disclosure.

[0036] Figure 5 A flowchart illustrating the method for determining order generation time provided in this embodiment of the disclosure.

[0037] Figure 6 The procedure for analyzing the impact of price change provided in this embodiment of the disclosure.

[0038] Figure 7 This is a flowchart illustrating a differentiated processing method for different order statuses provided in an embodiment of this disclosure.

[0039] Figure 8 This is a flowchart illustrating the prediction method provided in an embodiment of the present disclosure.

[0040] Figure 9 This is a flowchart illustrating the auxiliary material prediction method provided in an embodiment of the present disclosure.

[0041] Figure 10 This is a flowchart illustrating the method for calculating safety stock and replenishment quantity provided in embodiments of this disclosure.

[0042] Figure 11 This is a flowchart illustrating the real-time monitoring method provided in an embodiment of the present disclosure.

[0043] Figure 12 This is a schematic block diagram of a renovation project monitoring device according to one embodiment of the present disclosure.

[0044] Figure 13 This is a schematic block diagram of an electronic device according to one embodiment of the present disclosure. Detailed Implementation

[0045] The present disclosure will now be described in further detail with reference to the accompanying drawings and examples. It should be understood that the specific examples described herein are for illustrative purposes only and are not intended to limit the scope of the disclosure. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present disclosure are shown in the accompanying drawings.

[0046] It should be noted that, where there is no conflict, the embodiments and features described in this disclosure can be combined with each other. The technical solutions of this disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0047] Figure 1 This is a flowchart illustrating a method for monitoring renovation projects provided in this disclosure. Figure 1 The method shown includes steps 101 to 106. This method can be executed by an electronic device such as a client (which can be a client of an application server in a distributed system).

[0048] Specifically, Figure 1 The method shown includes: Step 101: Based on the initial construction period of the initiated renovation order, determine a construction plan that includes at least one construction node, and the construction time node corresponding to the construction node; wherein the initiated renovation order includes: renovation package, house area, house type, planned construction period and at least one city.

[0049] First, it's important to clarify that "initiated renovation orders" refer to renovation projects already under construction, including but not limited to key information such as renovation packages, house area, apartment type, planned construction period, and city. This information forms the foundation of the project and serves as the basis for all subsequent forecasts and decisions. Renovation packages refer to the customer's chosen service level, such as an economy package, a comfort package, or a luxury package, or simply labeled Package A, Package B. Personalized packages can be created when designing them. House area refers to the building area of ​​the house to be renovated, directly affecting the amount of most auxiliary materials used and the working hours for some procedures. Apartment type includes high-rise apartments, townhouses, and old house renovations, with significant differences in structure and the complexity of water and electricity modifications among different types. City information affects local construction standards, climate-adaptable material selection, and logistics costs. For example, core information for an initiated renovation order includes renovation package (Package B), house area (120 square meters), apartment type (high-rise apartment), planned construction period (90 days), and city (Beijing).

[0050] Construct a standardized Work Breakdown Structure Knowledge Base (WBS) template for the renovation project, breaking it down into major stages such as plumbing and electrical work, masonry, carpentry, painting, and installation, as well as specific construction nodes under each stage (e.g., the plumbing and electrical work stage includes wall grooving, wiring, and installation of low-voltage boxes).

[0051] The system maintains a multi-dimensional influencing factor library, quantifying factors such as renovation packages, house area, apartment type, planned construction period, and city, and mapping them to the standard tasks in the standardized renovation work breakdown structure (WBS) template, calculating the base work hours for each standard task. For example, the house area factor causes a 20% increase in base work hours for the masonry stage, and the apartment type in a high-rise building causes a 15% increase in base work hours for the plumbing and electrical renovation stage.

[0052] Based on the calculation of the basic working hours of each standard task, the Critical Path Method (CPM) is used to automatically parse the pre- and post-task logical dependencies between WBS standard tasks, calculate the earliest start time, earliest end time, latest start time, and latest end time of each standard task, identify the critical path (such as water and electricity renovation, masonry, and carpentry as critical paths), and generate a visual view (such as a Gantt chart).

[0053] The final output includes a detailed construction plan and construction timeline (i.e., milestones for each important process), for example: water and electricity renovation phase: December 1-15, 2025; masonry phase: December 16, 2025-January 5, 2026; and completion date: February 28, 2026.

[0054] Step 102: Generate a quotation containing at least one auxiliary material price using construction milestones and construction timelines. Step 103: Establish the association between the auxiliary material prices in the quotation and the construction milestones or construction timelines in the construction plan.

[0055] The quotation not only includes detailed information such as product SKUs, specifications, quantities, unit prices, and brands, but also establishes a strong correlation between quotation items (i.e., a list of various auxiliary materials) and specific construction milestones / stages. For example, when the quotation includes tiles, the system explicitly associates it with the masonry process, ensuring that this auxiliary material must arrive before the masonry process begins. This correlation essentially establishes a correspondence between auxiliary materials (i.e., various supplies) and time, enabling the system to clearly understand when a material is needed, providing indispensable time information for all subsequent forecasts, order placements, and early warnings.

[0056] For example, the designer generates a quotation based on the construction plan and timeline data. The quotation includes main and auxiliary materials, such as "XX brand tiles, area 200 square meters, unit price 150 yuan / square meter". The system strongly binds each material item in the quotation to a specific construction node or timeline in the construction plan, establishing a correlation. For example, "XX brand tiles" is bound to the "wall and floor tile laying" process in the masonry stage, with a required date of January 3, 2026 (3 days before the start of the masonry stage to allow for installation buffer); "YY brand electrical wires" are bound to the "wiring" process in the water and electricity renovation stage, with a required date of December 10, 2025. This clearly defines the required timeline for each material item.

[0057] Step 104: Calculate the purchase order generation time based on the construction time nodes, the delivery cycle of auxiliary material suppliers, and the buffer period for auxiliary material preparation.

[0058] Purchase order generation time = Construction timeline - Auxiliary material supplier delivery cycle - Auxiliary material preparation buffer period. The construction timeline is the material requirement time determined by linking the quotation with the construction schedule. The auxiliary material supplier delivery cycle refers to the average time from order placement to material arrival, dynamically determined based on different suppliers and material types. The auxiliary material preparation buffer period is a time reserved to cope with uncertainties and can be adjusted based on historical data and risk appetite. This calculation process realizes a true Just-In-Time (JIT) production model, initiating procurement at the most appropriate time, avoiding inventory backlog caused by early procurement and preventing construction interruptions due to late procurement.

[0059] For example, for "XX brand tiles": the demand date is January 3, 2026; the average supplier delivery time is 7 days; and the buffer period for auxiliary material preparation is 3 days. Furthermore, the order generation time is calculated as follows: Order generation time = January 3, 2026 - 7 days - 3 days = December 24, 2025. That is, the JIT-driven order engine for this tile will be automatically triggered on December 24, 2025, generating a purchase order.

[0060] The renovation package, house area, city, house type, and planned construction period of the initiated renovation order are used as the current project characteristics. At the same time, the common characteristics of all renovation orders that have not yet started within the next 90 days, as well as the expected start date and the number of days until the expected start date, are obtained as the pipeline characteristics of future projects.

[0061] For each auxiliary material (such as electrical wire), a site profile vector is constructed. The comprehensive similarity between this vector and historical completed project vectors is calculated (accurate matching of categorical features, Gaussian similarity of numerical features). The top N similar historical projects are selected, and dynamic weights are assigned (60% similarity + 30% time decay + 10% quality factor). The weighted average historical consumption is used to obtain the baseline demand forecast, which is then multiplied by a trend adjustment factor to obtain the final demand forecast. For example, the predicted electrical wire demand for an initiated renovation order is 520 meters, and the aggregated electrical wire demand for all initiating orders in the next 30 days is 12,500 meters.

[0062] Step 105: Utilize the multidimensional influencing factors in initiated, uninitiated, and completed renovation orders to predict the demand for auxiliary renovation materials, and obtain the overall auxiliary material demand prediction results for multiple renovation orders; among which, the multidimensional influencing factors include at least one of the following: renovation package, house area, house type, planned construction period, and city.

[0063] Unstarted renovation orders refer to renovation projects for which a contract has been signed with the company and a deposit has been paid, but which have not yet officially started. These orders include key information such as the expected start date, renovation package, and house area.

[0064] The system uses this information as pipeline features for future projects and inputs it into the auxiliary material demand prediction model. This model deeply integrates decoration-specific contract information such as package details, area, city, apartment type, and construction period with dynamic data such as historical consumption and seasonal trends. It constructs a site profile vector through refined feature engineering, calculates the similarity between the new project and historical projects, and predicts the auxiliary material demand for the new project based on a similarity-weighted weighted historical consumption data. For example, for a new project P_new to be predicted, the system extracts its key features (such as luxury package X, 120 square meters, Beijing, high-rise apartment), compares them with each completed project P_hist_j in the historical database, calculates a similarity score, and then predicts the auxiliary material demand for the project based on a similarity-weighted weighted historical consumption data. This prediction method is particularly suitable for cold starts (such as new cities or new packages) or data-sparse scenarios, providing highly interpretable and business-logical prediction results. Furthermore, it helps improve the accuracy of decoration auxiliary material demand prediction.

[0065] In practical applications, to monitor the real-time progress of construction projects, a mobile app can be used to collect actual progress data (worker clock-ins, project manager photo uploads, and automatic recording by IoT devices). If, on December 10, 2025, it is discovered that the water and electricity renovation phase is only 60% complete (the plan is 85%), exceeding the 10% deviation threshold, the system automatically recalculates the remaining construction period using the remaining workload method, postponing the start date of the masonry phase to December 20, 2025. The system also recalculates the order generation time for all affected materials (e.g., adjusting the tile order generation time to December 30, 2025) and re-executes the forecast for auxiliary construction material demand (incorporating the project's extended construction period information into future project pipeline characteristics).

[0066] If a user changes the quotation during the period (such as changing the tile brand), the system queries the status of orders in transit based on the correlation, generates a change impact report, and automatically intercepts / modifies the order after approval, thus achieving closed-loop monitoring.

[0067] Step 106: Real-time monitoring of the decoration project is achieved based on the construction plan, the time of purchase order generation, and the overall auxiliary material demand forecast.

[0068] In practical applications, the system tracks construction progress in real time, automatically recalculating the remaining construction period when the actual progress deviates from the plan by more than a threshold; it monitors order status (confirmation, preparation, shipment, arrival) to ensure that material arrivals match construction needs; it periodically assesses inventory status to determine whether a replenishment process needs to be triggered; it captures quotation changes, analyzes the impact of changes on generated orders, and generates change impact analysis reports; and it updates construction plans and auxiliary material demand forecasts based on approved changes. This monitoring data is shared through a unified data platform, forming a complete closed loop of schedule-driven processes, demand determination, material supply, progress feedback, and dynamic adjustments.

[0069] Based on the publicly available solutions described above, by establishing a strong correlation between quotations and construction milestones, the system solves the fundamental problem in traditional renovation management: not knowing when to buy materials or what they will be used for. This correlation allows the system to clearly define the demand time for each material, providing accurate timing data for subsequent JIT ordering and change impact analysis, and significantly improving the accuracy of renovation material supply.

[0070] Furthermore, incorporating pending renovation orders into the auxiliary material demand forecast introduces future project pipeline characteristics. This feature allows the system to proactively capture short-term, deterministic demand pulses, significantly improving forecast accuracy and timeliness. For example, the system can anticipate a large number of luxury package projects starting construction within a certain period, allowing for advance preparation of the necessary auxiliary materials to avoid stockouts caused by short-term demand pulses. The dynamic linkage between construction plans, purchase order generation time, and auxiliary material demand forecasting constructs a complete closed-loop management system. When construction progress changes or pricing changes occur, the system can automatically adjust subsequent material requirements and procurement plans to ensure the entire project remains in optimal condition.

[0071] In one or more embodiments of this disclosure, such as Figure 2 This is a flowchart illustrating the method for predicting the initial project duration provided in an embodiment of this disclosure. Figure 2 As shown, the prediction of the initial construction period for a renovation order includes: Step 201: Identifying the renovation package, house area, house type, planned construction period, and city included in the renovation order as multi-dimensional influencing factors. Step 202: Quantifying each factor and mapping it to standard tasks in a standardized renovation work breakdown structure template. Step 203: Calculating the initial construction period based on the basic working hours corresponding to each standard task.

[0072] In practical applications, multi-dimensional influencing factors refer to the key factors affecting the construction period of a renovation project, including but not limited to renovation packages, house area, house type, planned construction period, and city.

[0073] As mentioned earlier, a standardized Work Breakdown Structure (WBS) template refers to a hierarchical structure that breaks down a renovation project into a series of standard construction tasks. This template defines all possible construction milestones involved in a renovation project, such as plumbing and electrical work, masonry, and carpentry. Each process is further subdivided into more specific task units, forming a complete task tree structure. Each task unit has a clear definition, scope of work, and standard execution process.

[0074] The standard tasks mentioned here refer to specific construction nodes defined in the WBS template, such as bathroom waterproofing and living room wall leveling. Each standard task corresponds to a basic work hour value, which is the baseline time required to complete the task under standard conditions.

[0075] The baseline man-hours referred to here are the benchmark time required to complete a specific standard task under standard construction conditions, without considering any adjustments for influencing factors. Baseline man-hours are derived through statistical analysis of historical project data and reflect task execution efficiency under ideal conditions.

[0076] The specific steps for initial project duration forecasting based on initiated renovation orders are as follows: First, extract key information from the initiated renovation orders to determine the renovation package, house area, apartment type, planned construction period, and city as multi-dimensional influencing factors. For example, the system may identify an order that selected Package B, has a house area of ​​120 square meters, is a high-rise apartment, has a planned construction period of 90 days, and is located in Beijing.

[0077] The system quantifies each influencing factor. For numerical factors (such as house area), the system applies a specific function for transformation; for example, house area factor = k1 × area, where k1 is a coefficient calibrated based on historical data. For categorical factors (such as renovation packages, apartment types, and cities), the system maps them to numerical weights; for example, Package B corresponds to a weight of 1.3, the Comfort Package to a weight of 1.1, and the Economy Package to a weight of 1.0. This quantification ensures that different types of factors can participate uniformly in subsequent calculations.

[0078] Furthermore, the quantified influencing factors are mapped to standard tasks in a standardized Work Breakdown Structure (WBS) template. The WBS template contains numerous standard tasks, each with association rules established with specific influencing factors. For example, the plumbing and electrical renovation task is associated with the house area, apartment type, and renovation package; the wall leveling task is primarily affected by the house area and apartment type. Based on the specific factor values ​​in the order, the system determines which standard tasks need to be included in the project's construction plan and calculates adjustment coefficients for each task.

[0079] Next, the system calculates the actual planned working hours for each standard task based on the corresponding basic working hours and the adjustment coefficients for influencing factors. The specific calculation formula is: Actual planned working hours = Basic working hours × Adjustment coefficient 1 × Adjustment coefficient 2 × ... × Adjustment coefficient n, where each adjustment coefficient corresponds to the quantified value of an influencing factor. For example, for the water and electricity renovation task, the basic working hours are 5 days, the house area adjustment coefficient is 1.2 (based on 120 square meters), the apartment type adjustment coefficient is 1.1 (high-rise apartments are more complex than ordinary apartments), and the decoration package adjustment coefficient is 1.3 (luxury packages have higher requirements than economy packages). Then, the actual planned working hours for this task = 5 × 1.2 × 1.1 × 1.3 = 8.58 days.

[0080] In one alternative approach, the Critical Path Method (CPM) can be applied to intelligently schedule all tasks. The specific task scheduling process will be explained in detail in subsequent embodiments and will not be repeated here. Considering the logical dependencies between tasks (e.g., plumbing and electrical work must be completed before wall leveling), the earliest / latest start / end times of each task are calculated, the critical path is identified, and the initial project duration prediction results are obtained. The total duration of tasks on the critical path represents the shortest possible project duration. Based on this, the system generates a detailed construction plan and construction time nodes.

[0081] Based on the aforementioned publicly available solutions, by quantifying multi-dimensional influencing factors and mapping them to standardized WBS tasks, a shift from experience-based judgment to data-driven project schedule forecasting has been achieved. This embodiment eliminates reliance on the project manager's subjective experience. By precisely quantifying the impact of each influencing factor on specific construction tasks, project schedule forecasting becomes more objective and scientific, significantly improving forecast accuracy. For example, the system can accurately identify that a "luxury package for a 120-square-meter high-rise apartment" requires 20% more time for water and electricity renovations compared to an "economic package for a 100-square-meter ordinary residence." Such refined calculations are difficult to achieve manually.

[0082] Secondly, the standardized WBS template and its correlation mechanism with influencing factors give the system a high degree of configurability and adaptability. Construction specifications in different regions and process standards in different companies can be adapted by adjusting the WBS template and influencing factor parameters, without needing to redesign the entire prediction model. As companies accumulate more historical data, they can continuously optimize the base working hours and influencing factor coefficients, making the prediction results increasingly accurate and forming a virtuous cycle driven by data.

[0083] In one or more embodiments of this disclosure, such as Figure 3 This is a flowchart illustrating the method for determining construction plans and construction timelines provided in this embodiment of the disclosure. Figure 3As shown, determining a construction plan containing at least one construction node and the corresponding construction time nodes for each construction node includes: Step 301: Based on the basic work hours corresponding to each standard task, automatically parse the logical dependencies between standard tasks in the decoration work breakdown structure template using the critical path method. Step 302: Based on the logical dependencies, calculate the construction time nodes and construction nodes corresponding to each standard task; wherein, the construction time nodes include: earliest start time, earliest end time, latest start time, and latest end time. Step 303: Generate a construction plan using the construction time nodes and construction nodes (e.g., a visualized Gantt chart).

[0084] In practical applications, the Critical Path Method (CPM) mentioned here is a project management technique used to determine the optimal execution sequence and timeline of tasks in a project. In this solution, CPM is specifically applied to the decoration engineering field, identifying the sequence of critical tasks that determines the overall project duration by analyzing the dependencies between tasks.

[0085] The logical dependencies mentioned here refer to the sequential constraints and parallel possibilities between renovation and construction tasks. For example, plumbing and electrical work must be completed before wall leveling, while kitchen and bathroom ceiling installations can be done concurrently. These dependencies are predefined in a standardized Work Breakdown Structure (WBS) template and are typically stored as a list of preceding task IDs and their lag times. For example, [{task_id:ID1,lag_days:0},{task_id:ID2,lag_days:1}] indicates that task ID1 can start immediately after completion, while task ID2 requires a one-working-day delay before it can start.

[0086] The construction time nodes mentioned here include four key time parameters: Earliest Start Time (ES) refers to the earliest time a task can begin without affecting the completion of preceding tasks; Earliest Finish Time (EF) equals the earliest start time plus the task's duration; Latest Start Time (LS) refers to the latest time a task can begin without affecting the overall project schedule; and Latest Finish Time (LF) equals the latest start time plus the task's duration. These time parameters together constitute the task's time window, and tasks with the earliest and latest finish times equal form the critical path.

[0087] When presenting a construction plan to users, various visualization methods can be used. For example, a visual Gantt chart can be used. This visual Gantt chart is a bar chart that intuitively displays the project schedule. The horizontal axis represents time, and the vertical axis represents tasks. Each horizontal bar represents a task, its length represents the task's duration, and its position represents the task's time arrangement. In this embodiment, the Gantt chart specifically marks critical path tasks and core milestone nodes (such as water and electricity acceptance, final acceptance, etc.).

[0088] In practical applications, the specific steps for generating construction plans and construction time nodes are as follows: First, based on the basic work hours corresponding to each standard task, the Critical Path Method (CPM) is used to automatically parse the logical dependencies between standard tasks in the work breakdown structure (WBS) template. The system reads the predefined prerequisite task dependencies in the WBS template and constructs a task dependency network diagram. For example, the bathroom waterproofing task may depend on the completion of the plumbing and electrical work, while the wall leveling task may depend on the completion of both the plumbing and electrical work and the floor leveling tasks. The system traverses all tasks using a depth-first search algorithm to establish a complete task dependency network.

[0089] The CMP calculation process consists of two phases: forward traversal to calculate the earliest time parameter and backward traversal to calculate the latest time parameter. In the forward traversal phase, the system starts with the initial task that has no predecessor tasks and calculates the earliest start time and earliest end time for each task sequentially. For each task, its earliest start time is equal to the maximum of the earliest end times of all its predecessor tasks; its earliest end time is equal to the earliest start time plus the task's base time.

[0090] For example, the task network is first sorted topologically to ensure that all its predecessor tasks have been computed before a task is computed.

[0091] Calculate the earliest start workday offset (ES_workday_offset):

[0092] For a starting task without any preceding tasks, its earliest start_working day offset = 0 (i.e., the 0th working day relative to the start of the project).

[0093] For tasks with prerequisite tasks, the earliest start_workday offset is calculated as MAX(predetermined task.earliest finish_workday offset + lag workdays from the prerequisite task to the current task), taking the maximum value from all prerequisite tasks.

[0094] For example, if task B has two prerequisite tasks A1 and A2: A1's earliest completion workday offset = 5, and the lag workday from A1 to B = 0. A2's earliest completion workday offset = 6, and the lag workday from A2 to B = 1. Then, B's earliest start workday offset = MAX(5+0,6+1) = 7. Calculate the earliest completion workday offset (EF_workday_offset): Earliest completion workday offset = Earliest start workday offset + Planned duration workdays. Note that the workday offset is relative to the project start date and does not consider weekends and holidays; it is only used for internal calculations.

[0095] During the reverse traversal phase, the system starts with the last completed task that has no subsequent tasks and calculates the latest end time and latest start time for each task in turn. For each task, its latest end time is equal to the minimum of the latest start times of all subsequent tasks; the latest start time is equal to the latest end time minus the basic working hours of that task.

[0096] For example, to determine the planned project completion calendar date: Planned project completion calendar date = MAX(Planned completion calendar dates for all tasks). Calculate the latest completion calendar date (LF_Date): For a terminating task with no successor, its latest completion calendar date = Planned project completion calendar date. For a task with successor tasks, its latest completion calendar date = MIN(Latest start date of successor tasks - Lag workdays from current task to successor task - Reverse perspective).

[0097] It's important to note that the lag working days in the reverse calculation need to be converted to calendar days. For example, if the lag working days from task A to task B is 2, then in the reverse calculation, the latest completion date of task A should be 2 working days earlier than the latest start date of task B.

[0098] Calculate the latest start calendar date (LS_Date): Latest start calendar date = function_reverse_calculate_calculate_date(latest_complete_date,_planned_duration_days,_hours_calendar). The function_reverse_calculate_date is the opposite of the forward calculation; it starts from the target date, counts backward by a specified number of working days, skips non-working days, and obtains the starting date.

[0099] The critical path is the sequence of tasks in a project where the earliest finish time equals the latest finish time in terms of time parameters. Any delay on this path will directly lead to an extension of the overall project duration. The system will also identify key milestone nodes, such as water and electricity acceptance and final acceptance, which typically represent the completion of important construction phases.

[0100] The system generates a construction plan using calculated construction timelines and milestones, presenting it as a visual Gantt chart. In the Gantt chart, each task is represented by a horizontal bar, its position and length precisely reflecting the planned start time, end time, and duration of that task. To enhance the chart's visual appeal, critical path tasks are highlighted with a special color (e.g., red), and key milestones are marked with diamond-shaped markers on the timeline. The system also provides a detailed task information table alongside the Gantt chart, including task name, responsible person, planned duration, and earliest / latest start / end times. For project managers and owners, the system offers an interactive interface allowing them to view detailed information about specific tasks, such as prerequisites, successors, and resource requirements.

[0101] Based on the publicly available solutions described above, by applying the Critical Path Method (CPM) to automatically resolve task dependencies, the system can scientifically identify the sequence of critical tasks that determine the overall project duration. The algorithm automatically analyzes the logical relationships between all tasks, ensuring comprehensive and accurate identification of the critical path, providing a scientific basis for schedule prediction. Furthermore, the precise calculation of the earliest / latest start / end times for each task provides greater flexibility and decision-making space for project management. The system not only provides the planned times for tasks but also clarifies the time windows during which tasks can fluctuate (i.e., the difference between the latest and earliest start times), enabling project managers to allocate resources rationally and make flexible adjustments to non-critical path tasks without affecting the overall schedule. This refined time management capability is unattainable with traditional experience-based methods. In summary, this embodiment, through the scientific application of the Critical Path Method, precise calculation of construction time nodes, and generation of a visualized construction plan, achieves refined, standardized, and intelligent management of the renovation project schedule. It effectively solves the pain points of traditional methods, such as strong subjectivity in schedule prediction, lack of transparency in progress, and slow response to changes, significantly improving project management efficiency and customer satisfaction.

[0102] In one or more embodiments of this disclosure, such as Figure 4 This is a flowchart illustrating the method for establishing association relationships provided in embodiments of this disclosure. Figure 4 As shown, establishing the correlation between auxiliary material prices in the quotation and construction nodes or construction time nodes in the construction plan includes: Step 401: Determine the list of auxiliary materials for each process based on the construction nodes; wherein, the list of auxiliary materials for each process includes: the types and quantities of auxiliary materials for each process. Step 402: Generate a quotation containing detailed quotations based on the construction time nodes, the list of auxiliary materials for each process, and the auxiliary material prices.

[0103] In practical applications, a process auxiliary material list refers to a detailed list of auxiliary materials required for a specific construction node in a renovation project, including the types and quantities of auxiliary materials for each process. For example, the water and electricity renovation process may require auxiliary materials such as wires, water pipes, and junction boxes. The system will automatically recommend a standard auxiliary material list and quantities for this process based on a standardized Work Breakdown Structure (WBS) template and historical project data. Construction time nodes refer to the expected start and end times of each process in the construction plan. These time points are calculated using the Critical Path Method (CPM), taking into account the logical dependencies between tasks and non-working days in the work calendar. Furthermore, a clear association can be established between auxiliary material items in the quotation and specific construction nodes or construction time nodes, ensuring that each material has a definite usage time and scenario. The demand time point refers to the time when materials must arrive, determined by the construction time node of the associated construction node, and is a key parameter for subsequent Just-In-Time (JIT) ordering.

[0104] The specific steps for establishing the correlation between the quotation and construction nodes or time nodes in the construction plan are as follows: First, obtain the standard auxiliary material requirements for each construction node from the standardized Work Breakdown Structure (WBS) template. For example, when the system identifies the water and electricity renovation construction node, it will automatically load the standard auxiliary material list related to this process, including the types and standard quantities of auxiliary materials such as wires, PVC pipes, and junction boxes. These standard quantities are calculated based on historical project data and process standards. For example, the standard quantity of wires may be calculated based on the house area and the complexity of the circuit design, using the formula: wire length = k1 × house area + k2 × number of sockets, where k1 and k2 are coefficients calibrated based on historical data. In practical applications, it can also support fine-tuning according to the actual project characteristics, such as adjusting the quantity of auxiliary materials to consider special apartment structures or personalized customer needs.

[0105] Furthermore, based on the construction timeline, the list of auxiliary materials for each process, and the quotations for those materials, a quotation with detailed pricing is generated. The system obtains the estimated start and end times for each construction node from the construction plan. For each auxiliary material in the list of auxiliary materials for each process, the system queries the current supplier's quotation database to obtain the latest SKU, specifications, unit price, and brand information. The system combines this information with the construction timeline to generate a detailed quotation. For example, for electrical wiring accessories in the water and electricity renovation process, the quotation will clearly list: SKU code, specifications (e.g., 2.5mm²). 2 The quotation includes information such as the quantity (e.g., 150 meters), unit price (e.g., 5 yuan / meter), brand, and related construction milestones (e.g., water and electricity renovation) and required timeframe (e.g., March 8, 2024). This detailed quotation not only includes basic material information but also clarifies the material's usage scenario and time requirements.

[0106] Furthermore, each auxiliary material item in the quotation is strongly linked to a specific construction node or time node in the construction plan. Specifically, the system establishes a two-way association between each auxiliary material item (such as electrical wiring) in the quotation and a construction node (water and electricity renovation). This association is not a simple tag association, but rather establishes a clear temporal relationship: the auxiliary material must be prepared before the water and electricity renovation process begins. The system records this association through data structures, for example, creating a mapping relationship between quotation item ID, construction node ID, and required time offset. Here, the required time offset indicates how many days before the start time of the process the material needs to arrive (e.g., 2 working days in advance). For a complete renovation project, the system will establish similar strong binding relationships for hundreds of quotation items, forming a refined material-time matrix.

[0107] The system then calculates the required time point as: Expected start time of related processes - Material preparation buffer period. The material preparation buffer period takes into account the installation preparation time of materials; for example, tiles need to arrive one day in advance for cutting and pre-arranging before tiling. The system automatically marks the required time point in the quotation and construction plan, allowing project managers and purchasing personnel to clearly understand the arrival time requirements of each material. For example, if the expected start time of the masonry work is March 10, 2024, and the material preparation buffer period for tiles is one day, the system will determine the required time point for tiles as March 9, 2024.

[0108] Based on the aforementioned publicly available solutions, it is clear that by establishing a strong link between quotations and construction milestones, the timing and quantity of material requirements are controllable and clearly defined throughout the entire project. This publicly available solution, by specifying the demand time for each material, provides an accurate timeframe for subsequent Just-In-Time (JIT) ordering, ensuring close matching between material procurement and construction progress, and significantly improving the accuracy of material supply. Furthermore, the standardization and intelligent generation of auxiliary material lists for each process significantly improves the accuracy and consistency of quotations. The system recommends the types and quantities of auxiliary materials based on historical data and process standards, reducing subjective errors and omissions from manual estimation. For example, the system can accurately calculate the amount of electrical wiring and water pipes needed for plumbing and electrical renovations in a 120-square-meter house, without significant deviations due to differences in the experience of different designers.

[0109] In one or more embodiments of this disclosure, such as Figure 5 This is a flowchart illustrating the method for determining order generation time provided in an embodiment of this disclosure. Figure 5 As shown, the purchase order generation time is calculated based on the construction time nodes, auxiliary material supplier delivery cycles, and auxiliary material preparation buffer periods in the quotation. This includes: Step 501: Determining the auxiliary material supplier delivery cycles and auxiliary material preparation buffer periods based on the auxiliary material list for each process in the quotation. Step 502: Calculating the purchase order generation time using the construction time nodes, auxiliary material supplier delivery cycles, and auxiliary material preparation buffer periods, so that the order can be sent to the supplier at the order generation time.

[0110] It should be noted that the delivery cycle of auxiliary material suppliers refers to the average time required from placing a purchase order with the supplier to the actual delivery of the materials to the construction site, including the number of working days required for the supplier to process the order, prepare production, and handle logistics and transportation. This cycle is dynamically determined based on factors such as the supplier's historical performance data, material type, and region. For example, the delivery cycle for ordinary tiles may be 5 working days, while the delivery cycle for customized cabinets may be as long as 30 working days.

[0111] The auxiliary material preparation buffer period refers to the extra time reserved to cope with supply chain uncertainties, and is used to deal with possible issues such as supplier delays, logistics anomalies, and quality inspection. It is usually set to 1-3 working days based on the supplier's historical performance and the importance of the material.

[0112] The JIT-driven order engine mentioned here can be understood as a procurement triggering mechanism based on the lean production concept. It triggers procurement needs at the most appropriate time, avoiding inventory backlog caused by early procurement and preventing construction interruptions caused by late procurement.

[0113] The specific steps for calculating the purchase order generation time based on the construction timeline, auxiliary material supplier delivery cycle, and auxiliary material preparation buffer period in the quotation are as follows: First, the system queries the detailed information of each auxiliary material item in the quotation, including SKU, specifications, brand, and quantity. Then, based on this information, it retrieves the corresponding delivery cycle from the supplier database. For example, for "XX brand 2.5mm..." 2 The system identifies "single-core copper wire" as a standard wire category and finds that supplier A has a delivery cycle of 3 working days, while supplier B has a delivery cycle of 5 working days. Simultaneously, the system determines the auxiliary material preparation buffer period based on the material's importance and the supplier's historical performance: for materials essential to critical processes or suppliers with a historical fulfillment rate below 90%, the buffer period is set at 2 working days; for non-critical materials or suppliers with high fulfillment rates, the buffer period is set at 1 working day. The system also considers special factors that may cause logistical delays, such as holidays or severe weather forecasts.

[0114] Furthermore, the system calculates the purchase order generation time using construction time nodes, auxiliary material supplier delivery cycles, and auxiliary material preparation buffer periods. The calculation formula is: Order Generation Time = Demand Time Point - Auxiliary Material Supplier Delivery Cycle - Auxiliary Material Preparation Buffer Period. For example, if the expected start date of the masonry project is March 10, 2024, and the material preparation buffer period for tiles is 1 day, then the demand time point for tiles is March 9, 2024. Assuming the selected tile auxiliary material supplier has a delivery cycle of 5 working days and an auxiliary material preparation buffer period of 1 working day, then the order generation time = March 9, 2024 - 5 working days - 1 working day = March 3, 2024. The system automatically converts this calculation result into the actual calendar date, taking into account weekends and holidays.

[0115] Furthermore, when the system time reaches the order generation time, the JIT-driven order placement engine is activated and performs the following operations: First, it checks the project / center inventory; if applicable inventory is available, it generates a transfer instruction; if inventory is insufficient, it generates a purchase request based on the intelligent order merging / splitting strategy. The order merging logic automatically combines multiple material requests from the same supplier, with similar expected delivery dates and the same delivery address into a single purchase order to optimize logistics costs and management efficiency. The order splitting logic intelligently splits a comprehensive request into multiple purchase orders based on differences in suppliers and significant differences in delivery cycles for different materials. The system also automatically matches the optimal supplier for the purchase request based on preset rules (such as historical cooperation prices, on-time delivery rate, quality scores, etc.).

[0116] Purchase orders contain detailed material information (SKU, specifications, quantity, brand), expected delivery date, and delivery address, and are sent to suppliers via API or standardized data interfaces (such as EDI or email). The system simultaneously tracks the entire lifecycle of the order, from "Pending Supplier Confirmation" to "Inbound / Verified," including key milestones such as "Confirmed," "In Production," "Shipped," "Transportation in Progress," and "Received."

[0117] Based on the publicly available solutions, the system ensures that the procurement process starts at the most appropriate time by accurately calculating order generation time. This avoids inventory backlog caused by premature procurement (such as bulk purchasing all materials at the start of the project) and construction interruptions caused by late procurement. Secondly, the system dynamically determines the delivery cycle and buffer period for auxiliary material suppliers based on the auxiliary material list, making procurement decisions more precise and personalized. The timing of procurement is dynamically adjusted according to the characteristics of different materials, suppliers, and project stages. For example, for fragile materials (such as tiles), the system sets a longer buffer period for auxiliary material preparation; for suppliers with high fulfillment rates, the system appropriately shortens the buffer period. This refined procurement strategy significantly improves the matching degree between material supply and construction progress. The combination of the JIT-driven order engine and the multi-role collaborative decision-making mechanism greatly improves project collaboration efficiency. When the status of a purchase order changes (such as a supplier confirming a delayed delivery), the system automatically assesses the impact on the construction progress and sends early warning information to the project manager and owner. This proactive early warning and decision support mechanism allows problems to be discovered and resolved early, avoiding greater losses from reactive responses.

[0118] In one or more embodiments of this disclosure, such as Figure 6 This document describes a process for analyzing the impact of price change changes in embodiments of this disclosure. Figure 6As shown, the specific steps include: Step 601: In response to the quotation change request, obtain the supplier rules, logistics rules, and change cost rules corresponding to the changed auxiliary material list for the process. Step 602: Determine the order status and change data of the purchase order corresponding to the changed auxiliary material list for the process. Step 603: Using the supplier rules, logistics rules, order status, and change data, determine the impact on the project timeline and the impact on costs.

[0119] It should be noted that the supplier rules mentioned here refer to the relevant policies and restrictions for cooperating with suppliers, including but not limited to order modification / cancellation windows, minimum order quantities (MOQ), return and exchange policies, and related fees (such as rush fees, cancellation fees, return handling fees / damage fees, etc.). These rules can be preset by the system or obtained in real time from the supplier's system via API.

[0120] The logistics rules mentioned here refer to policies and restrictions related to logistics and transportation, including the feasibility of interception by cooperating logistics providers (according to different transportation stages), interception fees, and rerouting fees. Change cost rules refer to an internal calculation model used to quantitatively assess the impact of price changes on the total project cost, including factors such as secondary handling fees, temporary storage fees, and estimates of indirect losses caused by project delays.

[0121] Order status refers to the stage of a purchase order in its lifecycle, including key statuses such as "unconfirmed", "confirmed", "preparing for shipment", "in transit", and "in storage". These statuses reflect the actual progress of materials in the supply chain.

[0122] The detailed processing steps for responding to quotation change requests are as follows: When an interior designer or user modifies the quotation in the system (such as deleting, adding, or changing auxiliary material items), the system automatically captures this change event and identifies the specific auxiliary material items involved in the change. For each changed auxiliary material item, the system queries a preset rule base or obtains the latest rules from relevant suppliers in real time via an API interface. For example, when a user changes "Brand A 2.5mm..." 2 The "single-core copper wire" has been replaced with "Brand B 2.5mm". 2 When selecting "single-core copper wire," the system will query the supplier rules for both brands of wire: Supplier A may stipulate that orders can be cancelled free of charge within 24 hours of confirmation, and a 15% cancellation fee will be charged after 24 hours; Supplier B may stipulate that orders cannot be cancelled after confirmation, but the quantity can be modified. Simultaneously, the system will retrieve relevant logistics rules, such as the current logistics stage (whether the goods have been shipped), the feasibility of interception, and the associated costs. The system will also invoke change cost rules, which include internal cost parameters such as secondary handling fees (50 yuan / time), temporary storage fees (calculated at 1% of the material value / day), and indirect losses due to project delays (2000 yuan / day).

[0123] Furthermore, based on the strong binding relationship between the established quotation and construction nodes, the system queries whether the changed auxiliary material items have been associated with any purchase orders. Specifically, the system establishes a dynamic mapping relationship between "Quotation Item ID," "Purchase Order Item ID," "Inventory Batch ID," and "Logistics Waybill Number," enabling end-to-end tracking from quotation to supply chain execution. For each changed quotation item, the system queries the current status of its associated purchase order items in real time. For example, if a user decides to replace the A-brand tiles already ordered in the original quotation with B-brand tiles, the system will find that the A-brand tiles are associated with purchase order PO123, and the current status is "Preparing Stock" (i.e., the supplier has confirmed the order and started preparing materials). The system also collects change data, including a detailed comparison between the original and new auxiliary material items: differences in brand, specifications, unit price, quantity, as well as changes in associated construction nodes and demand time points.

[0124] By utilizing supplier rules, logistics rules, order status, and change data, the system determines the impact on construction period and cost. Specifically, the system comprehensively analyzes each change item using the `evaluate_change` function: For deleted auxiliary material items, the system determines whether the order can be cancelled or modified based on supplier rules and order status, and the potential costs; for newly added auxiliary material items, the system calculates the new demand timeline and assesses whether the auxiliary material supplier's delivery cycle meets the construction schedule requirements; for modified auxiliary material items, the system compares the differences between the old and new materials and assesses the comprehensive impact on cost and construction period. For example, if order PO123 for brand A tiles is in "preparing stock," and supplier rules stipulate that cancelling an order in this state requires a 15% cancellation fee, the system calculates the cost impact as 5000 yuan × 15% = 750 yuan; simultaneously, for newly added brand B tiles, the system calculates its delivery cycle as 7 days, while the "masonry process" is scheduled to start in 5 days, resulting in a 2-day construction period gap, therefore the construction period impact is "potentially delayed by 2 days." The system also assesses whether materials in transit can be intercepted based on logistics rules, such as whether shipped orders can be intercepted and returned at specific transit stations, and what the related costs would be.

[0125] The system generates a change impact report that includes cost impact, schedule impact, and intervention recommendations for orders in transit. The report details the specific impact of each change: cost impact includes changes in direct material costs, cancellation / modification fees, and potential additional logistics costs; schedule impact includes the matching of material arrival times with construction milestones and potential delays; intervention recommendations provide differentiated strategies based on order status. For example, for orders in the "Unconfirmed" status, it recommends "immediately cancel and reorder"; for orders in the "Preparing" status, it recommends "paying an X% cancellation fee to cancel the order"; and for orders in the "Transiting" status, it recommends "intercepting at logistics transit station Y, with an estimated interception fee of Z yuan." The report also provides comprehensive recommendations, such as "It is recommended to accept a 750 yuan cancellation fee to avoid a 2-day delay, with a total cost impact of +3750 yuan (-5000 + 750 + 8000)."

[0126] Based on the publicly available solutions described above, by establishing a dynamic mapping relationship between quotation items and supply chain execution, the system achieves precise tracking of the impact of quotation changes on orders. This embodiment, by automatically establishing a mapping relationship between quotation item IDs and purchase order item IDs, can accurately locate affected orders in transit the moment a change occurs, significantly improving the speed and accuracy of change response. For example, within seconds of a user submitting a tile brand change request, the system can identify which purchase order is affected by the change and its current status, providing timely and accurate information support for subsequent decision-making. Furthermore, the change impact assessment mechanism based on a multi-dimensional rule base enables the system to provide scientific and quantitative decision-making basis. This embodiment integrates supplier rules, logistics rules, and internal cost models, automatically calculating precise cost impact values ​​and project duration impact days through algorithms, making decisions more accurate. In summary, this embodiment, through scientifically constructing a rule base, accurately tracking order status, quantitatively assessing change impact, and generating differentiated intervention strategies, achieves intelligent processing of quotation changes for decoration projects, effectively solving pain points such as slow order change response, inaccurate impact assessment, and lack of decision-making basis, significantly reducing change costs.

[0127] In one or more embodiments of this disclosure, such as Figure 7 This is a flowchart illustrating a differentiated processing method for different order statuses provided in an embodiment of this disclosure. Figure 7 As shown, after determining the impact on the project duration and the impact on costs, the process includes: Step 701: Determine the corresponding approval process based on the order status; where order status includes: unconfirmed, confirmed, in preparation, in transit, and in stock. Step 702: Generate an intervention strategy based on the order status, the impact on the project duration, and the impact on costs. Step 703: Execute intervention operations on the purchase order based on the intervention strategy; where intervention operations include: modifying the purchase order, canceling the purchase order, or intercepting the purchase order.

[0128] It's important to clarify that the order status mentioned here refers to the specific stage of a purchase order's lifecycle, including five key statuses: "Unconfirmed" (the supplier has not yet confirmed receipt), "Confirmed" (the supplier has confirmed receipt but has not started preparing the goods), "Preparing" (the supplier has started preparing materials but has not yet shipped the goods), "Transiting" (the materials are in transit but have not yet arrived at the construction site), and "Inbound" (the materials have arrived at the construction site and been inspected and accepted). These statuses reflect the actual progress of materials in the supply chain and are crucial for determining intervention strategies. Intervention strategies can be understood as specific operational plans developed for specific order statuses and the impact of changes, including specific operational instructions and execution conditions for order modification, cancellation, or interception. The approval process refers to the decision-making path determined based on the order status and the degree of impact of changes, including which roles need approval, the approval order, and the timeout handling mechanism.

[0129] In practical applications, the specific processing steps after determining the impact on project duration and cost are as follows: First, determine the corresponding approval process based on the order status. The system divides the order status into five key stages and sets different approval rules for each stage. For orders in the "Unconfirmed" status, since no actual performance has occurred, the system sets a simplified approval process, usually requiring only single-person approval from the project manager. For orders in the "Confirmed" status, the system requires dual confirmation from the project manager and the user, as the supplier may have already begun preparing materials. For orders in the "Preparing Stock" and "Transporting Stock" statuses, since actual performance costs have been incurred, the system sets a stricter approval process, requiring three levels of approval from the project manager, user, and regional manager. For orders in the "In Stock" status, since the materials have arrived at the construction site, the system requires additional approval from the finance department to assess the feasibility of returns or transfers. The system also sets timeout reminders and automatic escalation mechanisms for each approval node. For example, if the project manager does not process the approval request within 24 hours, the system will automatically escalate it to the regional manager for processing. All approval actions and opinions are fully recorded, forming a traceable approval chain, providing data support for subsequent analysis.

[0130] The system performs a comprehensive analysis of each change by evaluating intervention strategy functions: For orders in the unconfirmed state, the system recommends immediate cancellation and reordering, as this usually incurs no additional costs. For confirmed orders, the system calculates the cancellation fee percentage (typically 5%-10%) based on supplier rules and recommends paying an X% cancellation fee to cancel the order. For orders in the "preparing for shipment" state, the system calculates the percentage of costs already incurred by the supplier and recommends paying a Y% cancellation fee to cancel the order or modify the order quantity. For orders in transit, the system assesses the feasibility of interception based on logistics rules and recommends interception at logistics transit station Z, with an estimated interception fee of W yuan. For orders already in the warehouse, the system recommends negotiating with the supplier for returns or reassignment to other projects.

[0131] In addition, the system also considers the impact on the project schedule. If the number of days affected by the schedule exceeds a threshold (e.g., 2 days), intervention strategies that can guarantee the schedule will be prioritized, even if the cost is slightly higher. If the cost impact exceeds a threshold (e.g., 5000 yuan), intervention strategies that can control costs will be prioritized. The intervention strategies generated by the system include specific operation instructions, estimated costs, execution time, and alternative solutions, forming structured strategy recommendations.

[0132] For suppliers or logistics platforms with established API connections, the system automatically sends corresponding operation instructions: For order modifications, the system sends a modification request to the supplier's API, including information such as the new SKU, specifications, and quantity; for order cancellations, the system sends a cancellation request to the supplier's API, along with the reason for cancellation and a compensation plan; for order interceptions, the system sends an interception instruction to the logistics platform's API, specifying the interception location and subsequent handling methods. The system tracks the operation results in real time. If the API returns success, the order status is updated; if it returns failure, a new alternative strategy is generated based on the reason for the failure.

[0133] For cases where API integration is not established, the system generates a structured change processing task sheet containing all necessary information and suggested actions. This task sheet is then pushed to designated roles (such as purchasing staff or project managers) via the messaging system for manual communication and action. These individuals are required to promptly update the system with the processing results and actual costs / time incurred. The system also includes an operation monitoring mechanism; if no feedback on the operation results is received within a preset time, an alert will be triggered and the processing will be escalated.

[0134] The system records the execution results and actual impact of intervention operations for continuous optimization of the decision-making model. Specifically, the system automatically collects data such as the actual cost (including direct and indirect costs), actual project duration impact, and supplier response time of intervention operations, and compares and analyzes these data with the predicted results. For example, the system records whether the actual cancellation cost of orders in the "preparing for shipment" status matches the prediction, and the success rate of intercepting orders in the "in transit" status. This data is used to optimize the supplier rule base, logistics rule base, and internal cost model, enabling the system to more accurately predict the impact of future changes. The system also establishes change effectiveness evaluation indicators, such as "change processing time" (the time from change request to completion) and "change cost savings rate" (the ratio of actual cost savings to the potential maximum cost), to measure and improve the change processing workflow.

[0135] Based on the publicly available solutions, it is evident that the differentiated approval process based on order status significantly improves decision-making efficiency and accuracy. In this embodiment, the approval level is dynamically adjusted according to the fulfillment status of the order, ensuring that the approval process matches the risk of changes. For example, for orders in the "unconfirmed" status, the system allows project managers to make quick decisions without waiting for multi-level approvals; while for orders in the "in transit" status, stricter approval is required to ensure that the decision fully considers the impact on all parties. This differentiated approval mechanism reduces the average change processing time by 40% while ensuring that high-risk changes are fully assessed. Furthermore, the intervention strategy generation mechanism based on multi-dimensional data enables scientific decision support. The system not only considers the order status but also integrates the impact on schedule and cost to generate the optimal intervention strategy. For example, when an order in the "preparing for shipment" status faces a change, the system can not only calculate the cancellation fee percentage but also assess the impact of the change on the schedule, thereby recommending a quantitative decision-making scheme of "paying X% cancellation fee to avoid a Y-day delay in schedule." By scientifically classifying order status, dynamically determining approval processes, generating differentiated intervention strategies, and automating execution, the system achieves precision, efficiency, and intelligence in handling changes to renovation projects. This effectively addresses pain points in traditional methods, such as slow change response, lack of basis for decision-making, and low execution efficiency, significantly reducing change costs, ensuring project schedules, and improving customer satisfaction.

[0136] In one or more embodiments of this disclosure, such as Figure 8 This is a schematic flowchart illustrating the prediction method provided in an embodiment of this disclosure. Figure 8As shown, the specific steps for forecasting the demand for auxiliary decoration materials using initiated decoration orders, initiating decoration orders, and historical data are as follows: Step 801: Determine the decoration package, house area, city, house type, and planned construction period of initiating decoration orders as core contract and project features. Step 802: Determine the expected start date of initiating decoration orders as key time anchors, and calculate the number of days from the current forecast date for each initiating decoration order as the number of days until the expected start date. Step 803: Determine the decoration package, house area, city, house type, planned construction period, and current construction progress status of initiated decoration orders as current project features. Step 804: Combine the core contract and project features, the number of days until the expected start date, the current project features, and the corresponding historical features of completed decoration orders to form the input feature set, input it into the auxiliary decoration material demand forecasting model, and obtain the overall auxiliary material demand forecast result.

[0137] It's important to note that "unstarted renovation orders" refer to renovation projects for which contracts have been signed and deposits paid, but which have not yet officially commenced. These orders include detailed project information and the expected start date. Core contract and project characteristics describe key information about the basic attributes of the renovation project, including renovation package type, house area, city, house type, and planned construction period. These characteristics directly impact the demand for auxiliary materials. The key time anchor point is the expected start date of the unstarted renovation order, serving as an important reference point for predicting future demand release. The number of days until the expected start date is the number of days between the current forecast date and the expected start date of the unstarted renovation order, a key parameter for determining when demand will be released. Current project characteristics refer to detailed information about started renovation orders, including renovation package type, house area, city, house type, planned construction period, and current construction progress status. This information reflects the current actual status of the project. The renovation auxiliary material demand prediction model mentioned here can be understood as a prediction algorithm based on feature similarity and weighted historical averages, which can accurately predict the demand for renovation auxiliary materials by combining historical data and future project information.

[0138] In practical applications, the specific steps for predicting the demand for auxiliary renovation materials using initiated renovation orders, initiating renovation orders, and historical data are as follows: First, the renovation package type, house area, project city, house type, and planned construction period of initiating renovation orders are used as core contract and project characteristics. The system retrieves a list of all signed but not yet started projects from the CRM or contract management system for the next 30-90 days, extracting the core contract information for each project. For example, for a project expected to start next month, the system records key characteristics such as selecting "Package B," a house area of ​​120 square meters, being located in "Beijing," a house type of "high-rise apartment," and a planned construction period of 90 days. These characteristics constitute the basic profile of the project and are an important basis for predicting its auxiliary material demand. The system also standardizes these characteristics, such as converting the house area into a standardized area range and dividing the city into different climate zones, to facilitate subsequent similarity calculations.

[0139] Furthermore, the system uses the estimated start date of unstarted renovation orders as a key time anchor and calculates the number of days from the current forecast date for each unstarted renovation order as the estimated start date. For each unstarted renovation order, the system retrieves its estimated start date from the contract data and then calculates the difference in days between that date and the current forecast date. For example, if the current date is March 1, 2024, and the estimated start date of an unstarted renovation order is March 15, 2024, then the estimated start date is 14 days. The system further processes the estimated start date into categorized features such as "estimated start week" or "estimated start month" to capture seasonal demand fluctuations. These time-related features enable the system to accurately determine when future demand will be released, providing a time-dimensional reference for inventory planning.

[0140] The system then uses the renovation package type, house area, city, house type, planned construction period, and current construction progress status of the initiated renovation orders as the characteristics of the current project. For all renovation projects under construction, the system retrieves real-time data from the project management system, including basic project information and construction progress. For example, for a project that has started, the system will record information such as the selection of "Package A," a house area of ​​100 square meters, location in "Shanghai," house type "townhouse," planned construction period of 80 days, and current construction progress status "80% of water and electricity renovation completed." Crucially, the system tracks the deviation between the actual progress and planned progress for each initiated renovation order, such as "water and electricity renovation is actually 2 days behind schedule." This deviation directly affects the demand for auxiliary materials in the current stage and the timing of demand in subsequent stages.

[0141] Furthermore, the system incorporates core contract and project characteristics, the estimated number of days until construction begins, and the corresponding historical characteristics of the current project and historical data into an input feature set, which is then input into the decoration auxiliary material demand prediction model. The system first constructs a construction site profile vector, integrating all the above features into a unified data structure. For each category of auxiliary materials to be predicted (such as wires, water pipes, tiles, etc.), the system filters historical projects with similar characteristics from the historical database and calculates the similarity score between the new project and the historical projects. The similarity calculation uses a weighted method, assigning different weights to different features: decoration package type has a weight of 0.4, house area has a weight of 0.3, city has a weight of 0.2, and house type has a weight of 0.1. Area similarity is calculated using a Gaussian function, with σ set to 10 square meters. For classification features, exact matching is used (similarity is 1 if matched, otherwise 0). The system also considers time decay factors, assigning higher weights to newer historical projects; the time decay parameter λ is set to 0.01.

[0142] The system obtains the predicted demand for decoration auxiliary materials through a demand prediction model. In one optional scheme, the prediction model adopts an algorithm based on feature similarity and weighted historical averages, with the calculation formula: Predicted Demand = Σ(Historical Project Auxiliary Material Usage × Similarity Weight × Time Decay Weight). Specifically, the system calculates the required quantities of various auxiliary materials for each inactive and initiated decoration order, and then summarizes these demands along the time dimension to form a demand curve for a future period. For example, the system predicts that 15 new projects will start within the next 30 days, requiring a total of 2,000 rolls of electrical wire and 3,000 PVC pipes; within 45 days, the demand will increase to 3,500 rolls of electrical wire and 5,000 PVC pipes. The system also incorporates historical trend adjustment factors (such as seasonal fluctuations) and business planning adjustment coefficients (such as sales targets) to fine-tune the prediction results, ensuring that the prediction conforms to historical patterns while meeting business development needs.

[0143] Based on the publicly available solutions described above, true forward-looking demand forecasting is achieved by introducing future project pipeline characteristics. In this embodiment, detailed information on inactive renovation orders is used as the core predictive factor to accurately capture short-term, deterministic demand pulses. Furthermore, refined modeling of multi-dimensional features enables the system to accurately depict auxiliary material consumption patterns under different project profiles. The system deeply integrates renovation-specific contract information such as renovation packages, house area, city, house type, and construction period with dynamic data such as historical consumption and seasonal trends, constructing site profile vectors through refined feature engineering. Even in data-sparse scenarios, the system can provide highly interpretable and logically sound prediction results, making it particularly suitable for predicting new cities, new packages, or new auxiliary materials.

[0144] The introduction of the estimated start date enables precise time-based forecasting of demand. The system not only predicts the future quantity of auxiliary materials needed but also accurately predicts when these materials will arrive. For example, for a project with an estimated start date of 15 days, the system will include the demand in the forecast for two weeks later; for a project with an estimated start date of 5 days, it will include it in the forecast for one week later. This precise time-based forecasting allows companies to develop more scientific procurement plans and inventory strategies, avoiding inventory backlogs caused by advance procurement or construction interruptions due to late procurement.

[0145] Meanwhile, the current construction progress status of initiated renovation orders is incorporated into the forecasting model, enabling dynamic adjustments to the forecasts. The system not only considers future deterministic demand but also dynamically adjusts demand forecasts based on the actual progress of initiated projects.

[0146] In one or more embodiments of this disclosure, such as Figure 9 This is a schematic flowchart illustrating the auxiliary material prediction method provided in an embodiment of this disclosure. Figure 9 As shown, the core contract and project features, the number of days until the expected start date, the current project features, and the historical features corresponding to completed decoration orders constitute the input feature set. This set is then input into the decoration auxiliary material demand prediction model to obtain the overall auxiliary material demand prediction result. The steps include: Step 901: Generating a first decoration profile vector using the current project features, and generating a second decoration profile vector using the core contract and project features. Step 902: Constructing historical decoration profile vectors using historical features. Step 903: Calculating the similarity values ​​between the first decoration profile vector, the second decoration profile vector, and the historical decoration profile vectors. Step 904: Selecting at least one historical feature (each historical completed project) based on the similarity value to construct a similar decoration set. Step 905: Assigning dynamic weights to the historical features in the similar decoration set. Step 906: Calculating the overall auxiliary material demand prediction result based on the changed auxiliary material list and the historical demand calculated using the dynamic weights.

[0147] It's important to clarify that the "renovation profile vector" mentioned here can be understood as converting the key features of a renovation project into a numerical vector representation, used to quantitatively describe the overall characteristics of the project. This vector typically includes core features such as renovation package type, house area, city, and house type. Categorical features (e.g., package type, city, house type) are converted to numerical values ​​through one-hot encoding or label encoding, while numerical features (e.g., house area) undergo standardization. The similarity value refers to a numerical indicator measuring the degree of similarity between two renovation projects. For categorical features, exact matching is used (similarity is 1 if matched, 0 otherwise), while for numerical features (e.g., house area), a Gaussian function is used to calculate similarity. The "similar renovation set" refers to the set of historical projects whose similarity to the current project exceeds a threshold; typically, the top N historical projects by similarity are selected. The "dynamic weight" refers to the weight assigned to each historical project in the similar renovation set. This weight comprehensively considers factors such as feature similarity, time decay, and data quality to ensure that the prediction results reflect the experience of similar projects while adapting to the latest business changes.

[0148] In practical applications, the specific steps for obtaining the auxiliary material prediction results by inputting the core contract and project characteristics, the number of days until the expected start date, and the corresponding features of the current project characteristics and historical data into the auxiliary material demand prediction model are as follows: First, for renovation orders that have already started, the system integrates information such as the renovation package type, house area, city where the project is located, house type, planned construction period, and current construction progress status to generate a first renovation profile vector. For example, for a project that has already started, its first renovation profile vector can be represented as: {Package type code = 3, Standardized house area value = 1.2, City code = 5, House type code = 2, Planned construction period = 90, Construction progress = 0.65}.

[0149] For renovation orders that have not yet started, the system integrates information such as renovation package type, house area, city where the project is located, house type, and planned construction period to generate a second renovation profile vector. For example, for a project awaiting commencement, its second renovation profile vector might be represented as: {Package type code = 3, Standardized house area value = 1.2, City code = 5, House type code = 2, Planned construction period = 90}. The system also assigns weights to these features, such as a weight of 0.4 for renovation package type, 0.3 for house area, 0.2 for city, and 0.1 for house type, to reflect the degree of influence of different features on the demand for auxiliary materials.

[0150] In addition, the system extracts detailed information about completed projects from the historical database, including renovation package type, house area, city, house type, planned construction period, and actual auxiliary material consumption data. For each historical project, the system constructs a historical renovation profile vector according to the same feature coding rules and weight allocation method as the current project. For example, the historical renovation profile vector of a completed historical project can be represented as: {Package type code = 3, Standardized house area = 1.18, City code = 5, House type code = 2, Planned construction period = 88, Auxiliary material consumption = 150}. The system also records the completion date of each historical project for subsequent calculation of time decay weights.

[0151] For each project to be predicted (whether initiated or not), the system calculates its similarity score with each historical project. The similarity calculation uses a weighted method, assigning different weights to different features. For categorical features (such as package type, city, and housing type), an exact match principle is used: a match results in a similarity score of 1, otherwise 0. For numerical features (such as housing area), a Gaussian function is used to calculate the similarity, with σ set to 10 square meters. For example, if the current project's housing area is 120 square meters and the historical project's housing area is 115 square meters, then the area similarity = exp(-(120-115)). 2 / (2×10 2 The approximate similarity score is 0.882. The total similarity score is calculated as Σ(feature weight × feature similarity). The system also considers the impact of the number of days remaining until the expected start date on the similarity score. For renovation orders that have not yet started, the system adjusts the similarity weight based on the number of days remaining until the expected start date, making the prediction more consistent with the time-based demand release patterns.

[0152] Next, the system selects at least one historical feature (each historical completed project) based on the similarity score to construct a similar renovation set. The system sorts the projects by similarity score from highest to lowest and selects the top N similarity projects to form the similar renovation set. The value of N is typically dynamically determined based on business needs and data quality, generally set to 5-20. The system also sets a similarity threshold, such as 0.7; only historical projects with similarity exceeding this threshold are included in the similar renovation set. For each category of auxiliary materials, the system may use different N values ​​and thresholds to accommodate the consumption characteristics of different auxiliary materials. For example, for standard auxiliary materials such as electrical wires, the system may select more similar projects (N=15), while for specially customized auxiliary materials, the system may only select the most similar projects (N=5).

[0153] The system considers not only feature similarity but also introduces a time decay factor and a data quality factor to form a comprehensive dynamic weight. The time decay factor = exp(-λ × days_since_completion), where λ is the time decay parameter, set to 0.01, and days_since_completion is the number of days since the historical project was completed. The data quality factor is determined based on the completeness and accuracy of historical data: high-quality data (such as complete records of the construction process and accurate material consumption to the unit) is set to 1.0, medium-quality data to 0.8, and low-quality data to 0.6. The comprehensive dynamic weight = feature similarity × time decay factor × data quality factor. The system also normalizes the comprehensive dynamic weight to ensure that the sum of all weights is 1. For example, if a similar renovation set contains 3 projects with comprehensive dynamic weights of 0.4, 0.35, and 0.25 respectively, the normalized weights are 0.4 / 1.0, 0.35 / 1.0, and 0.25 / 1.0.

[0154] For each auxiliary material category, the system extracts corresponding historical auxiliary material consumption data from a set of similar renovation projects, and performs a weighted average based on dynamic weights to obtain a basic forecast value. For example, if the wire consumption of three historical projects in the set of similar renovation projects is 150, 145, and 160 respectively, and the dynamic weights are 0.4, 0.35, and 0.25 respectively, then the basic forecast value = 150 × 0.4 + 145 × 0.35 + 160 × 0.25 = 151.75. The system also incorporates historical trend adjustment factors (such as seasonal fluctuations) and business planning adjustment coefficients (such as sales targets) to fine-tune the forecast results. For auxiliary material lists of changed processes, the system adjusts the forecast results according to the type and extent of the change. For example, if a user changes from an "economy package" to a "deluxe package," the system adjusts the forecast value based on the impact coefficient of the package change on the demand for auxiliary materials (such as the deluxe package requiring 20% ​​more wires than the economy package). The system also considers the number of days until the expected start of construction, allocating the forecast demand to the corresponding time periods to form a demand curve for a future period.

[0155] Based on the aforementioned publicly available solutions, the construction of the construction site profile vector enables precise quantitative expression of the characteristics of decoration projects. In this embodiment, by integrating and weighting multi-dimensional features, a profile vector comprehensively reflecting project characteristics is constructed, allowing the system to accurately identify subtle differences between different projects. Furthermore, the numerical feature similarity calculation based on a Gaussian function ensures reasonable evaluation of continuous variables such as area. Unlike simple difference comparisons, the Gaussian function smoothly reflects the impact of area differences on auxiliary material requirements, avoiding abrupt changes in prediction results due to minor area differences. The dynamic weight allocation mechanism allows the prediction results to reflect the experience of similar projects while adapting to the latest business changes. The system not only considers feature similarity but also introduces time decay factors and data quality factors to ensure that newer, higher-quality historical data has a greater impact on the prediction results.

[0156] In one or more embodiments of this disclosure, such as Figure 10 This is a flowchart illustrating the method for calculating safety stock and replenishment quantities provided in embodiments of this disclosure. Figure 10 As shown, the specific steps for calculating safety stock and replenishment quantity based on auxiliary material forecast results include: Step 1001: Calculate the safety stock quantity using the obtained historical maximum daily consumption, average procurement lead time, and safety factor k; where the safety factor k is dynamically adjusted according to the stockout risk tolerance. Specifically, the safety stock quantity S_s is calculated as follows: Historical maximum daily consumption D_max × Average procurement lead time T_L × Safety factor k, where the safety factor k is dynamically adjusted according to the stockout risk tolerance.

[0157] Step 1002: Calculate the target inventory level using the obtained historical trend adjustment factor, safety stock level, and auxiliary material forecast results; wherein, the historical trend adjustment factor is 1 + historical consumption growth rate or seasonal index. Specifically, calculate the target inventory level I_target = auxiliary material forecast result (baseline demand forecast) P × historical trend adjustment factor TrendFactor + safety stock level S_s, where the historical trend adjustment factor TrendFactor is 1 + historical consumption growth rate or seasonal index.

[0158] Step 1003: Calculate the replenishment quantity using the obtained current inventory, in-transit inventory, and target inventory level; where the current inventory is the actual quantity of the decorative auxiliary material in the warehouse, and the in-transit inventory is the quantity of the decorative auxiliary material that has been ordered but not yet received. Specifically, calculate the replenishment quantity R = max(0, target inventory level I_target - current inventory I_current - in-transit inventory I_transit); where the current inventory I_current is the actual quantity of the decorative auxiliary material in the warehouse, and the in-transit inventory I_transit is the quantity of the decorative auxiliary material that has been ordered but not yet received.

[0159] It should be noted that the safety stock (S_s) mentioned here refers to the minimum inventory level set aside to cope with random fluctuations in demand for decoration materials and uncertainties such as supplier delivery delays, aiming to ensure that there are no stockouts during the normal replenishment cycle. The historical maximum daily consumption (D_max) refers to the maximum daily consumption of this decoration material, calculated from historical data, usually obtained by analyzing daily consumption data over the past 12 months. The average lead time (T_L) refers to the average time required from issuing a purchase order to the actual delivery of materials to the construction site, calculated in working days, including the time required for supplier order processing, production preparation, and logistics transportation. The safety factor (k) is a parameter dynamically adjusted based on stockout risk tolerance; a higher value indicates a lower tolerance for stockout risk, typically ranging from 1.0 to 2.0. The target inventory level (I_target) refers to the total inventory level required to meet future projected demand and maintain safety stock. The historical trend adjustment factor (TrendFactor) is an adjustment coefficient that takes into account seasonal fluctuations and business growth trends, used to correct baseline demand forecasts. In-transit inventory (I_transit) refers to the quantity of construction auxiliary materials that have been ordered but not yet delivered, reflecting materials that have been committed to but not yet arrived in the supply chain.

[0160] In practical applications, the specific steps for calculating safety stock and replenishment quantities based on auxiliary material forecasting results are as follows: First, calculate the safety stock quantity S_s = historical maximum daily consumption D_max × average procurement lead time T_L × safety factor k. The system extracts daily auxiliary material consumption data from the historical database for the past 12 months and selects the maximum daily consumption as D_max. For example, for "Brand A 2.5mm..." 2The system identifies a single-core copper wire as having a historical maximum daily consumption of 200 rolls. It also calculates the average lead time (T_L) for this auxiliary material from the supplier's fulfillment history, such as 5 working days. The safety factor (k) is dynamically determined based on business strategy and risk appetite: for auxiliary materials essential to critical processes or suppliers with historical fulfillment rates below 90%, k is set to 1.8; for non-critical auxiliary materials or suppliers with high fulfillment rates, k is set to 1.2. For example, the calculated value is S_s = 200 rolls × 5 days × 1.5 = 1500 rolls. The system regularly updates D_max and T_L to ensure that safety stock levels reflect the latest business conditions. When market conditions or supplier performance change significantly, the system automatically adjusts the k value, such as appropriately increasing it during holidays or periods of supply chain strain.

[0161] The system calculates the target inventory level I_target = auxiliary material forecast result (baseline demand forecast) P × historical trend adjustment factor + safety stock S_s. The auxiliary material forecast result P is obtained, which is the baseline demand forecast. The historical trend adjustment factor is calculated by analyzing data from the same period over the past three years, and the formula is: TrendFactor = 1 + historical consumption growth rate or seasonal index. For example, if historical data shows that auxiliary material consumption in March each year is 5% higher than the average, then the TrendFactor for March is 1.05; if the business grows at a rate of 10% per year, then the TrendFactor is 1.10. The system comprehensively considers seasonality and business growth factors to calculate the final TrendFactor value. For example, if the baseline demand forecast P = 3000 rolls, TrendFactor = 1.05, and safety stock S_s = 1500 rolls, then I_target = 3000 × 1.05 + 1500 = 4650 rolls. For newly launched auxiliary material categories, the system will use historical trend data or industry averages of alternative categories as a reference to ensure the continuity and rationality of the predictions.

[0162] The system calculates the replenishment quantity R = max(0, target inventory level I_target - current inventory I_current - in-transit inventory I_transit). The system obtains the current inventory I_current in real time, which is the actual quantity of the decorative auxiliary material in the warehouse, through the WMS (Warehouse Management System) interface. Simultaneously, the system obtains the in-transit inventory I_transit, which is the quantity of the decorative auxiliary material that has been ordered but not yet received, including orders confirmed by the supplier but not yet shipped, and orders currently in transit. For example, if the target inventory level I_target = 4650 rolls, the current inventory I_current = 2000 rolls, and the in-transit inventory I_transit = 1500 rolls, then the replenishment quantity R = max(0, 4650 - 2000 - 1500) = 1150 rolls. The system also considers the minimum order quantity (MOQ) and packaging specifications, making appropriate adjustments to the calculated replenishment quantity. For example, if the minimum order quantity for the auxiliary material is 500 rolls, then 1150 rolls will be adjusted to 1500 rolls; if the packaging specification is 100 rolls / box, then 1150 rolls will be adjusted to 1200 rolls. The system will also check whether the replenishment quantity exceeds the maximum inventory level to avoid over-replenishment.

[0163] The system compares the calculated replenishment quantity with preset replenishment trigger conditions. When the replenishment quantity exceeds the minimum replenishment threshold, a replenishment suggestion is generated. This suggestion includes detailed material information, the suggested replenishment quantity, and the expected delivery date, and is pushed to purchasing personnel via the messaging system. Purchasing personnel can view the replenishment suggestion in the system and make adjustments based on actual conditions. The system provides auxiliary decision-making information, such as the cost impact of different replenishment quantities and changes in inventory turnover. After confirmation, the system generates a purchase requisition or order with one click and pushes it to the Enterprise Resource Planning (ERP) system, completing the replenishment process. The system also continuously tracks order status and automatically updates inventory data after auxiliary materials are received, forming a complete business loop. For urgent replenishment needs, the system also provides an expedited channel to support rapid approval and execution.

[0164] Based on the publicly available solutions, the dynamically adjusted safety factor mechanism enables the system to flexibly respond to different risk scenarios. This publicly available solution dynamically adjusts the k-value based on the stockout risk tolerance, ensuring that the safety stock level meets business needs without excessively tying up capital. Furthermore, the introduction of a historical trend adjustment factor makes demand forecasting more accurate. The system not only considers the baseline demand forecast but also adjusts it in conjunction with seasonal fluctuations and business growth trends, making the forecast results more consistent with actual business patterns. For example, the system can identify the increased demand for auxiliary materials during the peak decoration season after the Spring Festival each year and increase inventory preparation in advance; it can also capture the demand growth trend brought about by business expansion, avoiding stockouts caused by increased demand. The replenishment quantity calculation formula scientifically balances the relationship between demand and inventory. By comparing the target inventory level with the current inventory and in-transit inventory, the system accurately calculates the actual replenishment quantity needed, avoiding over-replenishment or under-replenishment. For example, when the system detects a large amount of in-transit inventory, it will reduce the replenishment quantity accordingly to prevent inventory backlog; when the system detects that the inventory is below the safety level, it will increase the replenishment quantity to ensure supply security. This precise replenishment decision-making keeps inventory levels consistently within an optimal range, meeting construction needs while minimizing capital tied up.

[0165] Furthermore, the replenishment quantity adjustment mechanism, which considers minimum order quantities and packaging specifications, improves the feasibility of practical operations. The system not only calculates the theoretical replenishment quantity but also makes reasonable adjustments based on the supplier's minimum order quantity requirements and the material's packaging specifications, making replenishment recommendations more practical. For example, the system might adjust the replenishment quantity from 1150 rolls to 1200 rolls (12 boxes, 100 rolls per box), satisfying both the minimum order quantity requirement and the packaging specifications, reducing the need for splitting and waste.

[0166] In one or more embodiments of this disclosure, such as Figure 11 This is a flowchart illustrating the real-time monitoring method provided in an embodiment of this disclosure. Figure 11 As shown, the steps for real-time monitoring of a renovation project based on construction plans, order generation times, and forecasts of auxiliary material needs include: Step 1101: Real-time collection of the actual start time and current progress of each construction node at the construction site, and calculation of the estimated end time. Step 1102: When the estimated end time of any construction node deviates from the corresponding construction time node by more than a preset threshold, or when the quotation changes, a renovation project reassessment operation is triggered; wherein, the renovation project reassessment operation includes: re-estimating the construction time node corresponding to the construction plan, re-estimating the demand for auxiliary materials, and re-estimating the order generation time.

[0167] It should be noted that the preset threshold refers to the allowable deviation range for the project duration set by the system. This is typically set to 1-3 working days based on the importance of the work process and industry experience. Exceeding this range triggers a recalculation mechanism. The estimated end time refers to the predicted completion time based on the current work process progress and remaining workload. The calculation formula is: Estimated End Time = Current Time + (1 - Current Progress) × Remaining Workload / Current Work Efficiency. The renovation project recalculation operation refers to the automatic recalculation process of the construction plan, auxiliary material requirements, and order generation time triggered by the system when significant deviations or changes are detected, ensuring consistency across all aspects of the project.

[0168] In practical applications, the specific steps for real-time monitoring of decoration projects based on construction plans, order generation times, and forecasts of auxiliary material demand are as follows: First, the system collects the actual start time and current progress of each construction node at the construction site in real time and calculates the estimated end time. The system obtains construction progress data through multiple channels: for construction teams that support mobile terminals, the system provides a dedicated APP, allowing construction personnel to scan codes to check in or manually input progress when key nodes are completed; for construction sites with deployed IoT devices, the system automatically collects construction progress through smart cameras, RFID tags, and other devices; at the same time, the system also integrates the project manager's regular progress reports. For each construction node, the system records its actual start time (accurate to the hour) and collects the current progress percentage through input by construction personnel or automated devices. Based on this data, the system calculates the estimated end time, taking into account factors such as current progress, remaining workload, and historical work efficiency. For example, for the water and electricity renovation project, the system detected that the actual start time was 08:00 on March 1, 2024, the current progress was 60%, the remaining workload was 40%, and the current team's work efficiency was calculated to be 15% of the workload per day based on historical data. Therefore, the estimated end time = 08:00 on March 1, 2024 + (40% ÷ 15%) days ≈ 08:00 on March 4, 2024.

[0169] The system continuously monitors the deviation between the estimated completion time and the planned completion time for each construction node. The system sets monitoring rules for each construction node, triggering an early warning mechanism when the deviation exceeds a preset threshold. The preset threshold is dynamically adjusted based on the characteristics of the process: for processes on the critical path, the threshold is set to 1 working day; for processes on non-critical paths, the threshold is set to 2-3 working days. For example, if the planned completion time for the water and electricity renovation process is 18:00 on March 3, 2024, while the system calculates an estimated completion time of 08:00 on March 5, 2024, the deviation is 1.5 working days, exceeding the preset threshold of 1 working day for critical processes, thus triggering a reassessment. The system also monitors quotation change events in real time. When a designer or user modifies a quotation in the system, the system immediately identifies the changes and assesses their impact.

[0170] When the system detects that the estimated end time of a construction node deviates from the actual construction timeline by more than a preset threshold, or when the quotation changes, it immediately triggers a recalculation of the renovation project. The recalculation process includes three key steps: re-estimating the construction timelines corresponding to the construction plan, re-estimating the demand for auxiliary materials, and re-estimating the order generation time. When re-estimating the construction plan, the system uses the latest actual progress data and applies the Critical Path Method (CPM) for dynamic recalculation, updating the earliest / latest start and end times of all subsequent processes and re-identifying the critical path. For example, if the plumbing and electrical work is delayed by two days, the system will postpone the start time of the masonry work and recalculate the schedule for all subsequent processes. When re-estimating the demand for auxiliary materials, the system combines the updated construction plan, inactive renovation orders, and historical data, using a method based on feature similarity and weighted historical averages to recalculate the demand for each auxiliary material. For example, if a delay in the construction plan leads to a postponement of the masonry work, the system will correspondingly postpone the demand timeline for auxiliary materials such as tiles and adjust the demand quantities. When re-estimating order generation time, the system recalculates the purchase order generation time based on the updated construction plan and demand time points. The formula is: New order generation time = New demand time point - Auxiliary material supplier delivery cycle - Auxiliary material preparation buffer period. For example, if the demand time for tiles is delayed by 2 days, the system will correspondingly delay the order generation time to avoid inventory backlog caused by premature procurement.

[0171] The system synchronizes the reassessment results with relevant parties and provides decision support. It generates detailed reassessment reports, including key information such as changes in schedule, adjustments to auxiliary material requirements, and changes in order creation times, and pushes these reports to project managers, procurement personnel, and the owner via messaging. For critical changes, the system offers multiple adjustment options, such as: Option A adjusts the construction sequence to shorten the schedule, Option B increases construction personnel to accelerate progress, and Option C accepts a schedule extension while maintaining the original plan. The system also automatically updates a visual Gantt chart, highlighting affected construction nodes and critical path changes, allowing all parties to intuitively understand the project status. For purchase orders that have been generated but not yet executed, the system provides specific operational suggestions based on the reassessment results, such as modifying orders, canceling orders, or intercepting orders in transit.

[0172] Based on the above scheme, it can be seen that the real-time progress monitoring mechanism achieves precise management of the decoration project. In this embodiment, by collecting construction data in real time through multiple channels and combining it with a scientific estimated completion time calculation model, the actual status of the project can be accurately reflected. For example, the system can identify that the actual progress of the water and electricity renovation process is 15% slower than planned and predict possible delays two days in advance, enabling the project manager to take timely intervention measures. In addition, the intelligent triggering mechanism based on preset thresholds avoids overreaction and underreaction. The system does not adjust for every minor deviation, but sets reasonable thresholds based on process characteristics and industry experience, triggering a reassessment operation only when the deviation reaches a level that affects the overall project goal.

[0173] Based on any of the above embodiments, this disclosure also provides a renovation project monitoring device. This device can be applied to a client. Figure 12 This is a schematic block diagram of a renovation project monitoring device according to one embodiment of this disclosure. Figure 12As shown, the renovation project monitoring device includes: a plan generation module 121, used to determine a construction plan containing at least one construction node and the corresponding construction time node based on the initial construction period of the initiated renovation orders; a quotation generation module 122, used to generate a quotation containing at least one auxiliary material quotation using the construction nodes and construction time nodes; and a relationship establishment module 123, used to establish the association between the auxiliary material quotation in the quotation and the construction nodes or construction time nodes in the construction plan; a calculation module 124, used to calculate the purchase order generation time based on the construction time node, the auxiliary material supplier's delivery cycle, and the auxiliary material preparation buffer period; a prediction module 125, used to predict the demand for renovation auxiliary materials using multi-dimensional influencing factors in initiated renovation orders, inactive renovation orders, and completed renovation orders, to obtain the overall auxiliary material demand prediction result for multiple renovation orders; wherein, the multi-dimensional influencing factors include at least one of the following: renovation package, house area, house type, planned construction period, and city; and a monitoring module 126, used to achieve real-time monitoring of the renovation project based on the construction plan, the purchase order generation time, and the overall auxiliary material demand prediction result.

[0174] The plan generation module 121 is used to determine the decoration package, house area, house type, planned construction period and city included in the decoration order as multi-dimensional influencing factors; after quantifying each factor, it maps it to the standard tasks of the standardized decoration work decomposition structure template; and calculates the initial construction period prediction based on the basic working hours corresponding to each standard task.

[0175] The plan generation module 121 is used to analyze the logical dependencies between standard tasks in the decoration work decomposition structure template based on the basic working hours corresponding to each standard task, using the critical path method; based on the logical dependencies, it calculates the construction time nodes and construction nodes corresponding to each standard task; among which, the construction time nodes include: earliest start time, earliest end time, latest start time, and latest end time; and uses the construction time nodes and construction nodes to generate a construction plan.

[0176] The relationship establishment module 123 is used to determine the list of auxiliary materials for each process based on the construction nodes. The list of auxiliary materials for each process includes the types and quantities of auxiliary materials. Based on the construction time nodes, the list of auxiliary materials for each process and the quotation of auxiliary materials, a quotation sheet containing quotation details is generated.

[0177] The quotation generation module 122 is used to determine the delivery cycle of auxiliary material suppliers and the buffer period for auxiliary material preparation based on the auxiliary material list of the process in the quotation; and to calculate the purchase order generation time by using the construction time node, the delivery cycle of auxiliary material suppliers and the buffer period for auxiliary material preparation, so as to send the order to the supplier at the order generation time.

[0178] The quotation generation module 122 is used to respond to quotation change requests, obtain the supplier rules, logistics rules and change cost rules corresponding to the changed process auxiliary material list; determine the order status and change data of the purchase order corresponding to the changed process auxiliary material list; and use the supplier rules, logistics rules, order status and change data to determine the impact on the project time and the impact on the cost.

[0179] The quotation generation module 122 is used to determine the corresponding approval process based on the order status; the order status includes: unconfirmed, confirmed, in preparation, in transit, and in stock; based on the order status, the impact on the production period, and the impact on the cost, an intervention strategy is generated; based on the intervention strategy, intervention operations are performed on the purchase order; the intervention operations include: modifying the purchase order, canceling the purchase order, and intercepting the purchase order.

[0180] The prediction module 125 is used to determine the renovation package, house area, city, house type, and planned construction period of the renovation orders that have not yet started as the core contract and project features; determine the expected start date of the renovation orders that have not yet started as the key time anchor point, and calculate the number of days from the current prediction date for each renovation order that has not yet started as the number of days until the expected start date; determine the renovation package, house area, city, house type, planned construction period, and current construction progress status of the renovation orders that have started as the current project features; and combine the core contract and project features, the number of days until the expected start date, the current project features, and the historical features corresponding to the completed renovation orders to form the input feature set, which is then input into the renovation auxiliary material demand prediction model to obtain the overall auxiliary material demand prediction result.

[0181] The prediction module 125 is used to generate a first decoration profile vector using the current project features, and a second decoration profile vector using the core contract and project features; to construct a historical decoration profile vector using historical features; to calculate the similarity value between the first decoration profile vector, the second decoration profile vector and the historical decoration profile vector respectively; to select at least one historical feature to construct a similar decoration set based on the similarity value; to assign dynamic weights to the historical features in the similar decoration set; and to calculate the auxiliary material prediction result for the changed process auxiliary material list and the historical demand calculated using the dynamic weights.

[0182] The forecasting module 125 is used to calculate the safety stock level using the historical maximum daily consumption, average procurement lead time, and safety factor k; where the safety factor k is dynamically adjusted according to the stockout risk tolerance; to calculate the target inventory level using the historical same-period trend adjustment factor, safety stock level, and auxiliary material forecast results; where the historical same-period trend adjustment factor is 1 + historical same-period consumption growth rate or seasonal index; and to calculate the replenishment quantity using the current inventory, in-transit inventory, and target inventory level; where the current inventory is the actual inventory quantity of the decoration auxiliary material in the warehouse, and the in-transit inventory is the quantity of the decoration auxiliary material that has been ordered but has not yet arrived.

[0183] The monitoring module 126 is used to collect the actual start time and current process progress of each construction node at the construction site in real time, and calculate the estimated end time. When the estimated end time of any construction node deviates from the construction time node corresponding to the construction node by more than a preset threshold, or when the quotation is changed, the renovation project re-estimation operation is triggered. The renovation project re-estimation operation includes: re-estimating the construction time node corresponding to the construction plan, re-estimating the demand for renovation auxiliary materials, and re-estimating the order generation time.

[0184] The specific implementation process of the functions and roles of each module in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0185] The execution subject of the decoration project monitoring method in the specific embodiments of this disclosure can be an electronic device such as a server (including a local server or a cloud server).

[0186] Therefore, based on any of the above embodiments, this disclosure also provides an electronic device that can execute the decoration project monitoring method of any of the embodiments described above.

[0187] Figure 13 This is a schematic block diagram of an electronic device according to one embodiment of the present disclosure.

[0188] The hardware architecture of the electronic device 1000 can be implemented using a bus architecture. The bus architecture can include any number of interconnect buses and bridges, depending on the specific application of the hardware and overall design constraints. Bus 1100 connects various circuits, including one or more processors 1200, memory 1300, and / or hardware modules. Bus 1100 can also connect various other circuits 1400, such as peripheral devices, voltage regulators, power management circuits, external antennas, etc.

[0189] Bus 1100 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Component (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one connection line is used in this diagram, but this does not imply that there is only one bus or only one type of bus.

[0190] This disclosure also provides a readable storage medium storing a computer program that, when executed by a processor, is used to implement the methods described above. A "readable storage medium" can be any means capable of containing, storing, communicating, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples of a readable storage medium include: an electrical connection with one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable read-only memory (CDROM), etc.

[0191] This disclosure also provides a computer program product, the methods of which can be implemented wholly or partially through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented wholly or partially as a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed, all or part of the processes or functions of this disclosure are performed.

[0192] Computer programs or instructions can be stored in a readable storage medium or transferred from one readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The readable storage medium can be any available medium capable of access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a digital video optical disc; or a semiconductor medium, such as a solid-state drive. The computer-readable storage medium can be a volatile or non-volatile storage medium, or it can include both volatile and non-volatile types of storage media.

[0193] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0194] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0195] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0196] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0197] In the description of this specification, the references to terms such as "one embodiment / mode," "some embodiments / modes," "example," "specific example," or "some examples," etc., refer to specific features, structures, or characteristics described in connection with that embodiment / mode or example, which are included in at least one embodiment / mode or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment / mode or example. Moreover, the specific features, structures, or characteristics described may be combined in any suitable manner in one or more embodiments / modes or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments / modes or examples described in this specification, as well as the features of different embodiments / modes or examples.

[0198] Those skilled in the art should understand that the above embodiments are merely for illustrating the present disclosure and are not intended to limit the scope of the disclosure. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of the present disclosure.

Claims

1. A method for monitoring decoration projects, characterized in that, The method includes: Based on the initial construction period of the initiated renovation order, determine a construction plan that includes at least one construction node, and the construction time node corresponding to the construction node; The process involves generating a quotation sheet containing the price of at least one auxiliary material using the construction nodes and construction time nodes in the construction plan; and establishing the association between the auxiliary material prices in the quotation sheet and the construction nodes and construction time nodes in the construction plan. The purchase order generation time is calculated based on the construction time nodes, the delivery cycle of auxiliary material suppliers, and the buffer period for auxiliary material preparation. The demand for auxiliary materials for renovation is predicted by using multidimensional influencing factors from the initiated renovation orders, inactive renovation orders, and completed renovation orders, resulting in an overall forecast of auxiliary material demand for multiple renovation orders. The multidimensional influencing factors include at least one of the following: renovation package, house area, house type, planned construction period, and city. Real-time monitoring of the renovation project is achieved based on the construction plan, the time of the purchase order generation, and the overall auxiliary material demand forecast results.

2. The method for monitoring decoration projects according to claim 1, characterized in that, The methods for predicting the initial construction period of the initiated renovation orders include: The renovation package, house area, house type, planned construction period, and city included in the renovation order are identified as multi-dimensional influencing factors. After quantifying each factor, it is mapped to a standardized task in the standardized decoration work decomposition structure template. The initial project duration is calculated based on the basic working hours corresponding to each of the standard tasks.

3. The method for monitoring decoration projects according to claim 2, characterized in that, The determination of the construction plan, which includes at least one construction node, and the corresponding construction time node, includes: Based on the basic working hours corresponding to each standard task, the critical path method is used to analyze the logical dependencies between the standard tasks in the decoration work breakdown structure template. Based on the logical dependencies, calculate the construction time nodes and construction nodes corresponding to each of the standard tasks; wherein, the construction time nodes include: earliest start time, earliest end time, latest start time, and latest end time; The construction plan is generated using the construction time nodes and the construction nodes.

4. The method for monitoring decoration projects according to claim 1, characterized in that, The process of establishing the association between the auxiliary material prices in the quotation and the construction nodes or construction time nodes in the construction plan includes: A list of auxiliary materials for each process is determined based on the construction nodes; wherein, the list of auxiliary materials for each process includes: the types and quantities of auxiliary materials for each process; Based on the construction timeline, the list of auxiliary materials for the specified process, and the price quotes for those materials, a price list containing detailed quotes is generated.

5. The method for monitoring decoration projects according to claim 4, characterized in that, The calculation of the purchase order generation time based on the construction time nodes, auxiliary material supplier delivery cycles, and auxiliary material preparation buffer periods includes: The delivery cycle and inventory buffer period for the auxiliary materials supplier are determined based on the list of auxiliary materials for the process in the quotation. The purchase order generation time is calculated using the construction time nodes, the delivery cycle of the auxiliary material suppliers, and the buffer period for auxiliary material preparation, so that the order can be sent to the supplier at the order generation time.

6. The method for monitoring decoration projects according to claim 5, characterized in that, Also includes: In response to the quotation change request, obtain the supplier rules, logistics rules and change cost rules corresponding to the changed auxiliary material list for the process; Determine the order status and change data of the purchase order corresponding to the changed auxiliary material list for the process; Using the supplier rules, logistics rules, order status, and change data, the impact on project duration and cost is determined.

7. The method for monitoring decoration projects according to claim 6, characterized in that, After determining the impact on schedule and cost, the following also includes: The corresponding approval process is determined based on the order status; wherein the order status includes: unconfirmed, confirmed, in preparation, in transit, and already in stock; Based on the order status, the impact on the project duration, and the impact on the cost, an intervention strategy is generated. The intervention operation is performed on the purchase order based on the intervention strategy; wherein the intervention operation includes any one of: modifying the purchase order, canceling the purchase order, or intercepting the purchase order.

8. The method for monitoring decoration projects according to claim 1, characterized in that, The process of forecasting the demand for auxiliary materials for renovations using initiated renovation orders, initiating renovation orders, and historical data yields an overall forecast result for the demand of auxiliary materials for multiple renovation orders, including: The renovation package, house area, city, house type, and planned construction period in the unstarted renovation order are identified as core contract and project characteristics; The estimated start date of the unstarted renovation orders is determined as the key time anchor point, and the number of days from the current predicted date for each unstarted renovation order is calculated as the number of days from the estimated start date; The renovation package, house area, city, house type, planned construction period, and current construction progress status of the initiated renovation order are determined as the current project characteristics. The core contract and project characteristics, the estimated number of days until construction begins, the current project characteristics, and the historical characteristics corresponding to the completed decoration orders are combined to form an input feature set, which is then input into the decoration auxiliary material demand prediction model to obtain the overall auxiliary material demand prediction result.

9. The method for monitoring decoration projects according to claim 8, characterized in that, The core contract and project characteristics, the estimated number of days until commencement, the current project characteristics, and the historical characteristics corresponding to completed renovation orders are combined to form an input feature set, which is then input into the renovation auxiliary material demand prediction model to obtain the overall auxiliary material demand prediction result, including: A first decoration profile vector is generated using the current project features, and a second decoration profile vector is generated using the core contract and project features; Construct a historical decoration profile vector using the aforementioned historical features; Calculate the similarity values ​​between the first decoration portrait vector, the second decoration portrait vector, and the historical decoration portrait vector, respectively; Based on the similarity values, at least one of the historical features is selected to construct a set of similar decorations; Assign dynamic weights to historical features in the set of similar renovations; Based on the changed list of auxiliary materials for the aforementioned processes and the historical demand calculated using dynamic weights, an overall auxiliary material demand forecast is obtained.

10. The method for monitoring decoration projects according to claim 9, characterized in that, This also includes calculating safety stock and replenishment levels based on overall auxiliary material demand forecasts: The safety stock level is calculated using the historical maximum daily consumption, average procurement lead time, and safety factor k; where the safety factor k is dynamically adjusted according to the stockout risk tolerance. The target inventory level is calculated using the obtained historical trend adjustment factor, safety stock level, and the auxiliary material forecast results; wherein, the historical trend adjustment factor is 1 + historical consumption growth rate or seasonal index. The replenishment quantity is calculated using the current inventory, in-transit inventory, and the target inventory level; wherein, the current inventory is the actual inventory quantity of the decoration auxiliary material in the warehouse, and the in-transit inventory is the quantity of the decoration auxiliary material that has been ordered but has not yet arrived.

11. The method for monitoring decoration projects according to claim 1, characterized in that, The real-time monitoring of the decoration project based on the construction plan, the order generation time, and the overall auxiliary material demand forecast results includes: The system collects the actual start time and current progress of each construction node at the construction site in real time, and calculates the estimated end time. If the estimated end time of any construction node deviates from the construction time node corresponding to the construction node by more than a preset threshold, or if the quotation is changed, a renovation project re-evaluation operation is triggered; wherein, the renovation project re-evaluation operation includes: re-evaluating the construction time node corresponding to the construction plan, re-evaluating the demand for renovation auxiliary materials, and re-evaluating the order generation time.

12. An electronic device, characterized in that, include: The memory stores execution instructions; as well as A processor that executes execution instructions stored in the memory, causing the processor to perform the method of any one of claims 1 to 11.

13. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 11.

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