Assembly material management system based on data analysis

Abstract

The present invention relates to the technical field of material management, and specifically to an assembled material management system based on data analysis, wherein the system comprises a project inventory analysis module, a material supply sorting module, an order optimization module, and a market trend analysis module. The present invention adjusts the inventory level according to the material demand assessment through historical data analysis and project demand forecasting, reduces the risk of excess or shortage of materials, improves material utilization and cost efficiency, ensures the timeliness and accuracy of material supply by assessing the supply priority of materials and adjusting the supply sequence, accelerates the assembly speed of building components, optimizes and adjusts the order quantity according to the material consumption rate, prevents material shortages, adjusts the inventory quantity through market trend analysis, makes the material supply more in line with market dynamics, avoids potential losses caused by seasonal changes and price fluctuations, improves resource utilization, and ensures efficient supply of materials.

Description

Technical Field

[0001] The present invention relates to the technical field of material management, and in particular to an assembly-type material management system based on data analysis. Background Art

[0002] The field of materials management technology involves methods and systems for the procurement, storage, distribution and control of materials in a variety of industrial environments such as manufacturing, construction, and logistics. The core goal of the field of materials management is to optimize material flow, reduce inventory costs, improve resource utilization, and ensure the timely supply of materials on the production line. Material management systems usually include inventory control, demand forecasting, supply chain coordination, and the handling of waste and recycled materials. Material management aims to reduce inventory costs, improve material utilization, and ensure the timely supply of materials on the production line. This technology also involves the classification, labeling and real-time monitoring of materials to ensure the visualization and efficient management of material flow.

[0003] Among them, the prefabricated material management system is a material management framework specifically designed to support prefabricated construction. The system supports a fast and efficient building assembly process by precisely controlling the procurement, storage, transmission and use of materials. The main purpose of the system is to reduce material waste during the assembly process, speed up the assembly of building components, and ensure that the quality and supply time of the required materials meet project requirements. By analyzing material requirements, inventory levels can be optimized and the risk of excess or shortage can be reduced.

[0004] Traditional management systems lack sensitivity to project needs and are unable to accurately predict material demand, which can lead to excess or shortage of materials. Excess materials will cause waste of funds and irrational use of storage space, while material shortages will lead to project delays and affect the overall progress of the project. Traditional systems fail to effectively combine market data with project needs, making it impossible to flexibly respond to market price fluctuations or seasonal changes during material procurement and inventory management, increasing project costs and operating risks. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an assembly material management system based on data analysis.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: an assembly material management system based on data analysis, the system comprising:

[0007] The project inventory analysis module extracts the prefabricated material consumption data of similar projects based on the historical project records and the current project type, and evaluates the amount of prefabricated materials required for the current project based on the current project scale. It also adjusts the prefabricated material inventory level based on the current inventory status to match the material demand and obtain the project inventory adjustment results.

[0008] The material supply sorting module extracts dependency data of prefabricated materials based on the project inventory adjustment result, evaluates the importance of differentiated prefabricated materials, evaluates the supply priority of differentiated prefabricated materials in combination with the construction sequence of the engineering project, adjusts the supply sequence of differentiated prefabricated materials, and obtains supply sequence adjustment information;

[0009] The order optimization module obtains the consumption data of the differentiated prefabricated materials in the engineering project based on the supply sequence adjustment information and the project inventory adjustment result, evaluates the consumption rate of the prefabricated materials, and adjusts the order quantity of the differentiated prefabricated materials according to the current inventory of materials to obtain order quantity optimization information;

[0010] The market trend analysis module identifies market trends based on the order quantity optimization information, combined with historical engineering project data and market data, and adjusts the inventory of prefabricated materials according to seasonal changes and material price fluctuations to obtain inventory optimization results.

[0011] The present invention is improved in that the method for evaluating the amount of prefabricated materials required for the current engineering project is:

[0012] Based on historical project records and the current project type, extract the prefabricated material consumption data of similar projects and obtain the average consumption data of prefabricated materials in the project;

[0013] Based on the average consumption data and the current project scale, the formula is:

[0014]

[0015] Calculate the amount of prefabricated materials V required for the current project and obtain material demand information, where C avg is the average prefabricated material consumption of similar historical projects, β is the adjustment coefficient for scale deviation, γ is the logarithmic adjustment coefficient, δ is the adjustment parameter for scale growth sensitivity, q is the scale of the current project, is the statistical average of the historical project sizes, and V is the amount of prefabricated materials required for the current project.

[0016] The present invention is improved in that the steps of obtaining the item inventory adjustment result are:

[0017] Based on the amount V of prefabricated materials required for the current project, extract the current inventory data to obtain the amount S of prefabricated materials in the current inventory;

[0018] Based on the quantity S of fabricated materials in the current inventory, by the formula:

[0019]

[0020] Calculate the inventory adjustment amount ΔS, use the inventory adjustment amount ΔS to adjust the prefabricated material inventory, and obtain the project inventory adjustment result, where S is the number of prefabricated materials in the current inventory, V is the amount of prefabricated materials required for the current project, ΔS is the inventory adjustment amount, and ∈ is the dynamic adjustment coefficient.

[0021] The present invention is improved in that the method for evaluating the importance of differentiated prefabricated materials is.

[0022] Extract the correlation data between materials according to the dependency data table of prefabricated materials;

[0023] Based on the correlation data between the materials, the formula is:

[0024]

[0025] Calculate the importance score W for each material i , according to the importance score W i The value is used to evaluate the importance of each material, where R i is the correlation between material i and other types of materials, n is the total number of prefabricated material types required in the project, W i is the importance score of material i, σ is the adjustment coefficient of the material importance score, μ is the average value of the material association value, and j and i are the material type indexes.

[0026] The present invention is improved in that the step of obtaining the supply sequence adjustment information is:

[0027] Based on the construction sequence data of the engineering project, the use sequence of each prefabricated material in the construction is extracted to obtain the construction sequence data.

[0028] Based on the importance score of the material W i And construction time series data, through the formula:

[0029]

[0030] Calculate the supply priority P of each material i , and according to the supply priority P i The value is adjusted to adjust the supply order of differentiated prefabricated materials and obtain the supply order adjustment information, where max(X j ) is the value of the latest used sequence in the material, W i is the importance score of material i, X i is the usage sequence of material i in construction, θ is the dynamic adjustment factor, μ is the average value of the material usage sequence, P i is the supply priority of material i, and j and i are the material indexes.

[0031] The present invention is improved in that the method for evaluating the consumption rate of the assembled material is:

[0032] Collect the consumption data of prefabricated materials in the project, including the statistical start time, statistical end time and material consumption of each material, and obtain consumption-related data;

[0033] Based on the consumption-related data, by the formula:

[0034]

[0035] Calculate the consumption rate Z of each material i , where α is the consumption adjustment coefficient, β is the time impact elasticity coefficient, γ is the time acceleration factor, T start,i is the statistical start time of material i, T end,i is the statistical deadline of material i, Q i is the consumption of material i, Z i is the consumption rate of material i, and e is the base of the natural logarithm.

[0036] The present invention is improved in that the steps of obtaining the order quantity optimization information are as follows:

[0037] Based on the consumption rate Z of each material i , extract the current inventory of materials and obtain order adjustment related data;

[0038] Based on the order adjustment associated data, by the formula:

[0039]

[0040] Calculate the adjusted order quantity O i , get the order quantity optimization information, where T p is the expected project duration, δ is the demand forecast adjustment factor, ∈ is the buffer inventory factor, τ is the time adjustment factor, and Z i is the consumption rate of material i, S i is the quantity of material i in current inventory, O i is the adjusted order quantity of material i.

[0041] The present invention is improved in that the steps of obtaining the inventory optimization result are:

[0042] Based on historical project data and market data, extract material consumption trends and price fluctuation information to obtain market trend indicators;

[0043] Based on the market trend indicators, combined with seasonal changes and material price fluctuations, the formula:

[0044]

[0045] Calculate the adjusted inventory S adj , and obtain the inventory optimization result, where S curr is the current inventory, M t Is a market trend indicator, J f is the seasonal adjustment coefficient, P f is the price fluctuation factor, λ is the dynamic adjustment factor, S adj is the adjusted inventory, and e is the base of the natural logarithm.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] In the present invention, through historical data analysis and project demand forecasting, the inventory level is adjusted according to the material demand assessment, the risk of material surplus or shortage is reduced, the material utilization rate and cost efficiency are improved, the supply priority of the material is evaluated and the supply sequence is adjusted to ensure the timeliness and accuracy of the material supply, accelerate the assembly speed of the building components, optimize the order quantity according to the material consumption rate, prevent material shortages, adjust the inventory through market trend analysis, make the material supply more in line with market dynamics, avoid potential losses caused by seasonal changes and price fluctuations, improve resource utilization, and ensure the efficient supply of materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a system flow chart of the present invention;

[0049] Figure 2 A flow chart for evaluating the amount of fabricated materials required for a current engineering project for the present invention;

[0050] Figure 3 A flow chart for obtaining the project inventory adjustment result for the present invention;

[0051] Figure 4 Flowchart for evaluating the importance of differentiated fabricated materials for the present invention;

[0052] Figure 5 A flow chart for obtaining supply sequence adjustment information of the present invention;

[0053] Figure 6 A flow chart for evaluating the consumption rate of fabricated materials for the present invention;

[0054] Figure 7 A flow chart for obtaining order quantity optimization information for the present invention;

[0055] Figure 8 A flow chart for obtaining inventory optimization results for the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0058] Example

[0059] See also Figure 1 The present invention provides a technical solution: an assembly material management system based on data analysis, the system comprising:

[0060] The project inventory analysis module extracts the prefabricated material consumption data of similar projects based on the historical project records and the current project type, and evaluates the amount of prefabricated materials required for the current project based on the current project scale. It also adjusts the prefabricated material inventory level based on the current inventory status to match the material demand and obtain the project inventory adjustment results.

[0061] The material supply sorting module extracts the dependency data of prefabricated materials based on the project inventory adjustment results, evaluates the importance of differentiated prefabricated materials, evaluates the supply priority of differentiated prefabricated materials in combination with the construction sequence of the project, adjusts the supply sequence of differentiated prefabricated materials, and obtains the supply sequence adjustment information;

[0062] The order optimization module obtains the consumption data of differentiated prefabricated materials in the project based on the supply sequence adjustment information and the project inventory adjustment results, evaluates the consumption rate of prefabricated materials, and adjusts the order quantity of differentiated prefabricated materials according to the current inventory of materials to obtain order quantity optimization information;

[0063] The market trend analysis module optimizes order quantity information, combines historical project data with market data, identifies market trends, and adjusts the inventory of prefabricated materials according to seasonal changes and material price fluctuations to obtain inventory optimization results.

[0064] The project inventory adjustment results include the adjusted material inventory level, expected material demand difference and inventory backup strategy. The supply sequence adjustment information includes the material supply list and material supply time point. The order quantity optimization information includes the adjusted order quantity and material consumption rate. The inventory optimization results include market demand forecast, price fluctuation response measures and seasonal inventory adjustment information.

[0065] See also Figure 2 , the method for evaluating the amount of prefabricated materials required for the current engineering project is:

[0066] Based on historical project records and the current project type, extract the prefabricated material consumption data of similar projects and obtain the average consumption data of prefabricated materials in the project;

[0067] Based on the average consumption data and the current project scale, the formula is:

[0068]

[0069] Calculate the amount of prefabricated materials V required for the current project and obtain material demand information, where C avg is the average prefabricated material consumption of similar historical projects, β is the adjustment coefficient for scale deviation, γ is the logarithmic adjustment coefficient, δ is the adjustment parameter for scale growth sensitivity, q is the scale of the current project, is the statistical average of the historical project sizes, and V is the amount of prefabricated materials required for the current project.

[0070] formula:

[0071]

[0072] The meaning and acquisition method of parameters:

[0073] C avg : Represents the average prefabricated material consumption of similar historical projects. It is obtained by summarizing the prefabricated material consumption of similar projects in historical data and calculating its average value.

[0074] β: Adjustment coefficient for scale deviation, which determines the sensitivity of material quantity estimation. The coefficient is obtained by analyzing the sensitivity of the project scale deviation from the average scale. Its value can be determined by regression analysis of historical data to adjust the impact of scale changes on material consumption.

[0075] γ: Logarithmic adjustment coefficient, used to smooth the impact of scale growth. The most appropriate coefficient is determined by analyzing the nonlinear impact of scale growth on material demand in historical data and minimizing the error.

[0076] δ: Adjustment parameter for scale growth sensitivity. The parameter reflects the degree of impact of project scale growth on material consumption. By statistically analyzing historical project data, it is determined how material demand changes for each unit increase in project scale.

[0077] q: The scale of the current project, obtained directly from the project planning document or project proposal.

[0078] The statistical average of historical project sizes, calculated using historical project data.

[0079] Calculation example:

[0080] Assume that the current project size q = 100 square meters and the average size of historical projects Square meter, historical average material consumption C avg =200 units, adjustment coefficient β = 0.05, logarithmic adjustment coefficient γ = 0.1, scale sensitivity adjustment parameter δ = 0.03.

[0081] Calculation process:

[0082]

[0083] The calculated V = 289.54 is the estimated amount of prefabricated materials based on the current project scale. The value reflects the material demand of the current project based on historical data.

[0084] See also Figure 3 , the steps to obtain the project inventory adjustment results are:

[0085] Based on the amount of prefabricated materials V required for the current project, extract the current inventory data and obtain the amount S of prefabricated materials in the current inventory;

[0086] Based on the quantity S of fabricated materials in the current inventory, by the formula:

[0087]

[0088] Calculate the inventory adjustment amount ΔS, use the inventory adjustment amount ΔS to adjust the prefabricated material inventory, and obtain the project inventory adjustment result, where S is the number of prefabricated materials in the current inventory, V is the amount of prefabricated materials required for the current project, ΔS is the inventory adjustment amount, and ∈ is the dynamic adjustment coefficient.

[0089] formula:

[0090]

[0091] The meaning and acquisition method of parameters:

[0092] S: The number of prefabricated materials currently in stock, obtained directly from inventory management data, usually updated through regular inventory counts to ensure the accuracy of the information.

[0093] V: The amount of prefabricated materials required for the current project, calculated in the previous steps.

[0094] ∈: Dynamic adjustment coefficient, obtained by analyzing the frequency of changes in demand and inventory differences in historical data and their impact on inventory adjustment strategies. The coefficient is used to adjust the sensitivity of inventory replenishment, allowing inventory management to respond more flexibly to changes in market and project demand.

[0095] Calculation example:

[0096] Assume that the estimated material demand V of the current project is 500 units, the current inventory S is 400 units, and the dynamic adjustment coefficient ∈ is 0.1.

[0097] The calculation steps are as follows:

[0098] Calculate the difference between demand and current stock:

[0099] VS = 500-400 = 100

[0100] Calculate the adjusted inventory level ΔS:

[0101]

[0102] The calculated result ΔS = 102 units means that 102 units of prefabricated materials need to be added to the inventory to meet the needs of the current project.

[0103] See also Figure 4 , the method to assess the importance of differentiated prefabricated materials is.

[0104] Extract the correlation data between materials according to the dependency data table of prefabricated materials;

[0105] Based on the correlation data between materials, the formula is:

[0106]

[0107] Calculate the importance score W for each material i , according to the importance score W i The value is used to evaluate the importance of each material, where R i is the correlation between material i and other types of materials, n is the total number of prefabricated material types required in the project, W i is the importance score of material i, σ is the adjustment coefficient of the material importance score, μ is the average value of the material association value, and j and i are the material type indexes.

[0108] formula:

[0109]

[0110] The meaning and acquisition method of parameters:

[0111] R i : represents the correlation between the i-th material and other materials, usually obtained from the dependency data table of prefabricated materials. The data is usually obtained through the analysis of historical engineering projects and can be set through expert evaluation.

[0112] n: represents the total number of different types of prefabricated materials involved in the project. It is directly calculated from the project's bill of materials and reflects the complexity and material diversity of the project.

[0113] μ: The average value of all material correlation values. It is calculated by adding all R i The sum of the values ​​and division by the total number of material types n provides a benchmark for measuring the overall project material relevance.

[0114] σ: Adjustment coefficient of material importance score, reflecting the variability of importance scores between different materials. This coefficient is determined based on the standard deviation of material importance in historical projects and is used to adjust the impact caused by the standard deviation of material importance.

[0115] Calculation example:

[0116] Assume that the project involves 3 different fabricated materials with correlations R1 = 10, R2 = 15, and R3 = 5. Calculate the average μ and adjustment factor σ = 0.2 of all material correlations.

[0117] Calculate the average correlation μ:

[0118]

[0119] Calculate the importance score W for each material i :

[0120]

[0121] Perform the calculations for each material:

[0122] For R1 = 10:

[0123]

[0124] For R2=15:

[0125]

[0126] For R3=5:

[0127]

[0128] The results W1=33.33, W2=55, and W3=18.34 represent the importance scores of each material after adjustment based on its relevance to the project schedule and the degree of its deviation from the average relevance.

[0129] See also Figure 5 , the steps to obtain the supply sequence adjustment information are:

[0130] Based on the construction sequence data of the engineering project, the use sequence of each prefabricated material in the construction is extracted to obtain the construction sequence data.

[0131] Based on the importance score of the material i And construction time series data, through the formula:

[0132]

[0133] Calculate the supply priority P of each material i , and according to the supply priority P i The value is adjusted to adjust the supply order of differentiated prefabricated materials and obtain the supply order adjustment information, where max(X j ) is the value of the latest used sequence in the material, W i is the importance score of material i, X i is the usage sequence of material i in construction, θ is the dynamic adjustment factor, μ is the average value of the material usage sequence, P i is the supply priority of material i, and j and i are the material indexes.

[0134] formula:

[0135]

[0136] The meaning and acquisition method of parameters:

[0137] W i : The importance score of material i, calculated by the previous step.

[0138] X i : The usage sequence of material i in construction is directly obtained from the project management data, indicating the scheduled usage time point of each material according to the actual project progress.

[0139] θ: Dynamic adjustment factor, determined based on project urgency or other relevant factors, used to adjust the impact of time sensitivity in the priority algorithm and increase or decrease the adjustment flexibility of the priority.

[0140] μ: The average value of all material usage time series, calculated by all X iThe average is used to measure the average usage time and adjust the priority of individual materials.

[0141] max(X j ): The latest usage time value of all materials, by comparing all X i The maximum value in is obtained and is used in the formula to ensure the reflection of time urgency.

[0142] Calculation example:

[0143] Assume that the project involves three materials, whose importance scores are W1=30, W2=50, W3=20, and their usage sequences are X1=5, X2=15, X3=10.

[0144] Assume that the dynamic adjustment factor θ=0.1.

[0145] Calculate the average μ of all material usage timings and the latest usage timing max(X j ):

[0146]

[0147] max(X j )=15

[0148] Apply the formula to calculate the supply priority P of each material i :

[0149]

[0150] Perform the calculations for each material:

[0151] For W1=30, X1=5:

[0152]

[0153] For W2=50, X2=15:

[0154]

[0155] For W3=20, X3=10:

[0156]

[0157] The calculation results P1=30.16, P2=52.89, P3=20 represent the supply priority of each material calculated based on its importance and the urgency of its usage sequence.

[0158] See also Figure 6 , the method for evaluating the consumption rate of fabricated materials is:

[0159] Collect the consumption data of prefabricated materials in the project, including the statistical start time, statistical end time and material consumption of each material, and obtain consumption-related data;

[0160] Based on consumption-related data, the formula is:

[0161]

[0162] Calculate the consumption rate Z of each material i , where α is the consumption adjustment coefficient, β is the time impact elasticity coefficient, γ is the time acceleration factor, T start,i is the statistical start time of material i, T end,i is the statistical deadline of material i, Q i is the consumption of material i, Z i is the consumption rate of material i, and e is the base of the natural logarithm.

[0163] formula:

[0164]

[0165] The meaning and acquisition method of parameters:

[0166] T start,i and T end,i :Get the statistical start time and statistical end time of prefabricated material consumption from the project progress report. The time point is determined by actually monitoring the project progress and material usage records.

[0167] Q i :The consumption of each material is obtained through material management data records. It is an actual measurement value that reflects the total amount of a specific material consumed by the project in a specific period of time.

[0168] α: Consumption adjustment coefficient, used to adjust the consumption rate calculation according to the characteristics of the material. This is a coefficient set based on the material type and usage experience to adapt to the consumption characteristics of different materials.

[0169] β: Time-influence elasticity coefficient, adjusted according to the degree of time influence during the material use process. This coefficient is obtained through historical data analysis based on the time sensitivity of material consumption.

[0170] γ: Time acceleration factor, which indicates the effect of time changes on the consumption rate. It is an empirical coefficient used to adjust the increase or decrease of the material consumption rate over time.

[0171] Calculation example:

[0172] Assumption data: T start,i = 0 days, T end,i= 10 days, total consumption Q i =100 units, α=1.0, and γ=0.05.

[0173] Calculate the usage duration ΔT i :

[0174] ΔT i =T end,i -T start,i =10-0=10 days

[0175] Apply the formula to calculate the consumption rate Z i :

[0176]

[0177] Calculation result Z i =13.09 units / day means that within a given 10 days, the average consumption rate of the material is about 13.09 units per day, and the rate reflects the consumption of materials in the engineering project.

[0178] See also Figure 7 , the steps to obtain order quantity optimization information are:

[0179] Based on the consumption rate Z of each material i , extract the current inventory of materials and obtain order adjustment related data;

[0180] Adjust the associated data based on the order, through the formula:

[0181]

[0182] Calculate the adjusted order quantity O i , get the order quantity optimization information, where T p is the expected project duration, δ is the demand forecast adjustment factor, ∈ is the buffer inventory factor, τ is the time adjustment factor, and Z i is the consumption rate of material i, S i is the quantity of material i in current inventory, O i is the adjusted order quantity of material i.

[0183] formula:

[0184]

[0185] The meaning and acquisition method of parameters:

[0186] Z i : The consumption rate of material i, obtained by the previous step.

[0187] S i: The quantity of material i in the current inventory, obtained directly from the inventory management data. It shows the available material stock at the time of calculation.

[0188] T p : Estimated project duration, obtained from the project plan document. It is the estimated time required for the project from start to finish.

[0189] δ: Demand forecast adjustment factor, which is determined based on historical data analysis and future forecasts. This factor helps adjust the order volume to match the expected demand.

[0190] ∈: Buffer stock factor, used to add a certain amount of safety stock to the order calculation to prevent supply chain disruptions or sudden increases in demand. This factor is determined based on risk assessment and the responsiveness of the supply chain.

[0191] τ: Time adjustment factor, used to adjust the order quantity according to changes in project progress, ensuring that the order quantity can adapt to changes in project demand over time.

[0192] Calculation example:

[0193] Assuming the following materials for the project, the specific parameters are as follows:

[0194] Materials A-Z A = 20 units / month, current inventory S A = 100 units, project duration T p = 6 months. Demand forecast adjustment coefficient δ = 1.1, buffer inventory coefficient ∈ = 0.1, time adjustment factor τ = 0.05.

[0195] Calculate the adjusted order quantity O for material A A :

[0196] Calculate the expected total consumption, taking into account demand adjustments:

[0197] δ·Z A ·T p =1.1·20·6=132 units

[0198] Calculate order quantities, including buffer stock and time adjustments:

[0199]

[0200] The calculations show that in order to meet project demand and maintain appropriate safety stock, an order of approximately 35.43 units of material A needs to be placed next.

[0201] See also Figure 8 , the steps to obtain inventory optimization results are:

[0202] Based on historical project data and market data, extract material consumption trends and price fluctuation information to obtain market trend indicators;

[0203] Based on market trend indicators, combined with seasonal changes and material price fluctuations, the formula:

[0204]

[0205] Calculate the adjusted inventory S adj , and obtain the inventory optimization result, where S curr is the current inventory, M t Is a market trend indicator, J f is the seasonal adjustment coefficient, P f is the price fluctuation factor, λ is the dynamic adjustment factor, S adj is the adjusted inventory, and e is the base of the natural logarithm.

[0206] formula:

[0207]

[0208] Parameter meaning and acquisition method

[0209] S curr : Current inventory, obtained directly from inventory management data, indicating the quantity of prefabricated materials currently available in the warehouse.

[0210] M t :Market trend indicators are obtained by analyzing historical data and market forecast reports, reflecting the changing trend of market demand.

[0211] J f : Seasonal adjustment factor, adjusted according to historical seasonal consumption patterns, based on the impact of seasonal changes on material demand, derived from historical data and seasonal analysis.

[0212] P f :Price volatility factor, adjusted according to current market price trends and forecasts, reflects the fluctuation of material prices, and is obtained from market price data and forecast reports.

[0213] λ: Dynamic adjustment factor, adjusted according to the sensitivity of market changes, used to reflect the flexibility of market changes, determined from market sensitivity analysis and historical data.

[0214] S adj : Adjusted inventory volume, calculated using a formula, is used to guide inventory management decisions and ensure that inventory levels meet market and project needs.

[0215] Calculation example:

[0216] Assume that the current inventory of the item is S curr =1000 units, market trend indicator M t =0.2, seasonal adjustment coefficient J f =1.1, price fluctuation factor P f =0.05, dynamic adjustment factor λ=0.1.

[0217] Calculate the dynamic adjustment part:

[0218]

[0219] Substitute into the formula to calculate the adjusted inventory S adj :

[0220]

[0221] Calculation result S adj =1326.6 units, which means that after considering market trends, seasonal adjustments and price fluctuations, the adjusted inventory quantity of the project is 1326.6 units.

[0222] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. The assembly material management system based on data analysis is characterized by: The system comprises: The project inventory analysis module extracts the prefabricated material consumption data of similar projects based on the historical project records and the current project type, and evaluates the amount of prefabricated materials required for the current project based on the current project scale. It also adjusts the prefabricated material inventory level based on the current inventory status to match the material demand and obtain the project inventory adjustment results. The material supply sorting module extracts dependency data of prefabricated materials based on the project inventory adjustment result, evaluates the importance of differentiated prefabricated materials, evaluates the supply priority of differentiated prefabricated materials in combination with the construction sequence of the engineering project, adjusts the supply sequence of differentiated prefabricated materials, and obtains supply sequence adjustment information; The method for evaluating the importance of differentiated fabricated materials is: Extract the correlation data between materials according to the dependency data table of prefabricated materials; Based on the correlation data between the materials, the formula is: ; Calculate the importance score for each material , according to the importance score The magnitude of the value evaluates the importance of each material, where For materials The degree of correlation with other types of materials, is the total number of fabricated material types required in the project, is the adjustment factor for the material importance score, is the average value of the material correlation value, and Index for material type; The steps for obtaining the supply sequence adjustment information are as follows: Based on the construction sequence data of the engineering project, the use sequence of each prefabricated material in the construction is extracted to obtain the construction sequence data. Rating based on importance of the material described And construction time series data, through the formula: ; Calculate the supply priority of each material , and based on supply priorities The value is adjusted to adjust the supply order of differentiated prefabricated materials to obtain supply order adjustment information, where: is the value of the latest used sequence in the material. It is the material The importance rating of It is the material Timing of use in construction, is the dynamic adjustment factor, is the average value of the material usage time series; The order optimization module obtains the consumption data of the differentiated prefabricated materials in the engineering project based on the supply sequence adjustment information and the project inventory adjustment result, evaluates the consumption rate of the prefabricated materials, and adjusts the order quantity of the differentiated prefabricated materials according to the current inventory of materials to obtain order quantity optimization information; The method for evaluating the consumption rate of fabricated materials is: Collect the consumption data of prefabricated materials in the project, including the statistical start time, statistical end time and material consumption of each material, and obtain consumption-related data; Based on the consumption-related data, by the formula: ; Calculate the consumption rate of each material ,in, is the consumption adjustment factor, is the time-dependent elasticity coefficient, is the time acceleration factor, For materials The statistical start time, For materials The statistical deadline is For materials The consumption of is the base of natural logarithms; The step of obtaining the order quantity optimization information is as follows: based on the consumption rate of each material , extract the current inventory of materials and obtain order adjustment related data; Based on the order adjustment associated data, by the formula: ; Calculating adjusted order volume , get the order quantity optimization information, where, is the estimated project duration, is the demand forecast adjustment factor, is the buffer stock factor, is the time adjustment factor, It is the material The consumption rate, The material is currently in stock the number of The market trend analysis module identifies market trends based on the order quantity optimization information, combined with historical engineering project data and market data, and adjusts the inventory of prefabricated materials according to seasonal changes and material price fluctuations to obtain inventory optimization results.

2. The assembly material management system based on data analysis according to claim 1 is characterized in that: The method for evaluating the amount of prefabricated materials required for the current project is: Based on historical project records and the current project type, extract the prefabricated material consumption data of similar projects and obtain the average consumption data of prefabricated materials in the project; Based on the average consumption data and the current project scale, the formula is: ; Calculate the amount of prefabricated materials required for the current project , get the material demand information, where is the average prefabricated material consumption of similar historical projects, is the adjustment coefficient for the impact of scale deviation, is the logarithmic adjustment coefficient, is the tuning parameter for the sensitivity to scale growth, The scale of the current project, is the statistical average of historical project sizes, The amount of prefabricated materials required for the current project.

3. The assembly material management system based on data analysis according to claim 2 is characterized in that: The steps for obtaining the item inventory adjustment result are: Based on the amount of fabricated materials required for the current project , extract current inventory data, and obtain the number of prefabricated materials in the current inventory ; Based on the quantity of fabricated materials currently in stock , through the formula: ; Calculate inventory adjustments , using inventory adjustment Adjust the prefabricated material inventory to obtain the project inventory adjustment result, where: is the quantity of fabricated materials in current inventory, is the amount of prefabricated materials required for the current project, is the inventory adjustment, It is a dynamic adjustment coefficient.

4. The assembly material management system based on data analysis according to claim 1 is characterized in that: The steps for obtaining the inventory optimization result are: Based on historical project data and market data, extract material consumption trends and price fluctuation information to obtain market trend indicators; Based on the market trend indicators, combined with seasonal changes and material price fluctuations, the formula: ; Calculating Adjusted Inventory , and obtain the inventory optimization result, where is the current inventory, It is a market trend indicator. is the seasonal adjustment factor, is the price volatility factor, is the dynamic adjustment factor, is the base of natural logarithms.

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