Intelligent computing method and system for concrete sales

Abstract

The application discloses a kind of concrete sales intelligent computing method and system, it is related to building and logistics management technical field.The application is accurate to predict customer demand and optimize production quantity and raw material proportioning by dynamically adjusting production plan, the problem that production surplus or deficiency caused by large demand fluctuation in traditional production is solved, inventory cost is reduced and production efficiency is improved, in transportation link, real-time monitoring traffic condition and dynamically optimizing transportation route, transportation efficiency and delivery punctuality are significantly improved, transportation cost is reduced and concrete quality is ensured;In production process, raw material proportioning and concrete state are monitored in real time using sensor and machine learning algorithm, production parameters are dynamically adjusted, the quality fluctuation problem caused by inaccurate proportioning or not timely monitoring in traditional production is solved, and the stability of product quality is ensured.

Description

Technical Field

[0001] This invention relates to the field of construction and logistics management technology, specifically to an intelligent calculation method and system for concrete sales. Background Technology

[0002] In the concrete sales process, traditional production and transportation planning often relies on manual experience or simple historical data analysis, making it difficult to cope with complex and ever-changing market demands and external environmental changes. For example, customer demand may change rapidly due to sudden weather changes or adjustments to construction schedules, while uncertainties in traffic conditions may affect transportation efficiency, leading to quality problems or delivery delays during concrete transport. Furthermore, concrete production has extremely high requirements for raw material proportions and timeliness; failure to dynamically adjust production plans based on real-time data may result in raw material waste or substandard product quality.

[0003] In real-world business scenarios, the volatility of customer demand, the unpredictability of weather changes, and the real-time dynamics of traffic conditions collectively constitute a complex multivariate optimization problem. These intertwined factors make it difficult for traditional methods to make accurate production and transportation decisions in a short period. For example, when a region experiences sudden rainfall, construction demand may plummet, while transportation routes may be disrupted by flooding or congestion. Furthermore, the physical properties of concrete dictate that its production and transportation processes must be strictly controlled within a specific timeframe; otherwise, its strength and durability may be affected. However, traditional inventory management and transportation scheduling methods are typically based on static rules and cannot respond to changes in the external environment in real time, leading to overproduction or undersupply. This contradiction is particularly pronounced during peak demand periods or in unforeseen circumstances.

[0004] Therefore, how to dynamically optimize concrete production, transportation, and inventory management in real time under changing customer demands, weather conditions, and traffic conditions has become a complex technical problem that urgently needs to be solved. This problem not only involves the fusion and analysis of multi-source data, but also requires making efficient and accurate decisions in a short period of time to ensure concrete quality, reduce production costs, and improve delivery efficiency. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent calculation method and system for concrete sales. By dynamically optimizing production plans, adjusting transportation routes in real time, and intelligently monitoring the production process, it solves the problems of high cost, low efficiency, and unstable quality caused by demand fluctuations, traffic uncertainties, and insufficient production monitoring in traditional concrete sales.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] This application provides an intelligent calculation method for concrete sales, including the following steps:

[0008] Obtain customer demand data, including order volume, delivery time, and construction location information, and analyze demand fluctuation characteristics by combining historical demand change trends;

[0009] Real-time weather information, including rainfall, temperature and wind speed, is obtained from meteorological data sources to predict the impact of weather changes on construction needs in the near future.

[0010] Real-time traffic data, including road congestion, accident information, and construction sites, is obtained through traffic monitoring systems to assess the feasibility of transportation routes.

[0011] The characteristics of customer demand fluctuations, weather change forecasts, and traffic condition assessments are input into a multivariate optimization model to dynamically adjust the production plan and determine the production volume and raw material ratio. Based on the production plan results and traffic condition data, a path optimization algorithm is used to generate the optimal transportation route.

[0012] Real-time monitoring of inventory status triggers production plan adjustments when inventory levels fall below a preset threshold to avoid supply shortages; when inventory levels exceed a preset threshold, transportation scheduling is optimized to reduce inventory backlog; simultaneously, during the production process, raw material ratios and concrete condition data are acquired through sensors, and production parameters are adjusted when ratio deviations or timeliness anomalies are detected.

[0013] During transportation, the vehicle location and concrete condition are tracked in real time. If a transportation delay or abnormal concrete quality is detected, the transportation route is recalculated. Production, transportation, and inventory data are aggregated and the parameters of the multivariate optimization model are updated.

[0014] Furthermore, acquire customer demand data, including order volume, delivery time, and construction location information, and analyze demand fluctuation characteristics by combining historical demand trends. Specifically, this includes:

[0015] Acquire customer order volume, delivery time and construction location information, store it in a preset database, extract historical demand data from the database, and calculate historical demand change trend lines;

[0016] Based on historical demand change trend lines, the characteristic value of demand fluctuation is determined. When the characteristic value of demand fluctuation exceeds a preset threshold, an early warning signal is generated.

[0017] Using time series analysis, we predict future order volume trends. Based on the prediction results and construction site information, we determine the optimal delivery time and generate a demand management plan by combining demand fluctuation characteristics and prediction results.

[0018] Furthermore, real-time weather information, including rainfall, temperature, and wind speed, is obtained from meteorological data sources to predict the impact of future weather changes on construction needs, specifically including:

[0019] Real-time data on rainfall, temperature, and wind speed are obtained using a real-time interface of a meteorological data source. A time-series database is established to store the meteorological data. Based on the time-series characteristics of the meteorological data, a sliding window model is constructed to extract the trend characteristics of weather changes and to calculate the rate of change and the degree of impact.

[0020] For key indicators of construction demand, establish a mapping table between weather changes and construction demand, determine the impact weight of weather changes on construction demand, and when the predicted weather changes exceed the preset threshold, call the decision tree algorithm to evaluate the degree of change in construction demand and output construction adjustment suggestions.

[0021] Based on the correlation between the rate of weather change and the impact of construction demand, a linear regression model is used to predict the fluctuation range of future construction demand. By combining the weather change forecast results and the construction demand forecast values, a construction demand adjustment plan is generated, which includes construction schedule, resource allocation and safety measures.

[0022] By monitoring the deviation between meteorological data and prediction models in real time, the construction demand adjustment plan is dynamically updated.

[0023] Furthermore, real-time traffic data, including road congestion, accident information, and construction zones, is obtained through traffic monitoring systems to assess the feasibility of transportation routes. Specifically, this includes:

[0024] Real-time traffic data is collected through a traffic monitoring system to obtain information on road congestion, accident locations, and construction sections. Data fusion technology is then used to integrate the collected traffic data and generate a complete road network status map.

[0025] When the road congestion exceeds the preset threshold, the road section is marked as an impassable area. When the accident site or construction section is located on the transportation route, an alternative route is replanned.

[0026] Based on the replanned transportation routes, the estimated travel time and route length are calculated. Machine learning algorithms are used to analyze historical traffic data, predict future traffic changes, and output real-time navigation solutions and traffic warning information.

[0027] Furthermore, the characteristics of customer demand fluctuations, weather change forecasts, and traffic condition assessments are input into a multivariate optimization model to dynamically adjust the production plan and determine production volume and raw material ratios. Specifically, this includes:

[0028] Customer demand data is obtained based on the volatility of customer demand, and combined with weather forecast results and traffic condition assessment results, and then integrated into a multivariate optimization model.

[0029] A multivariate optimization model is used to dynamically adjust the production plan based on the fluctuation of customer demand, weather forecast results, and traffic condition assessment results, so as to obtain the production plan adjustment value. Based on the production plan adjustment value, the production plan is dynamically adjusted to determine the production volume and raw material ratio.

[0030] When the production plan adjustment value exceeds the preset threshold, the production quantity and raw material ratio are re-optimized to ensure the dynamic adaptability of the production plan. The production plan adjustment value is iteratively optimized through a multivariate optimization model to obtain the optimal production quantity and raw material ratio.

[0031] Based on the optimal production volume and raw material ratio, production plan execution instructions are generated and output to the production system. Then, machine learning algorithms are used to train the multivariate optimization model.

[0032] Furthermore, based on the production plan results and traffic data, a route optimization algorithm is used to generate the optimal transportation route, specifically including:

[0033] Obtain production plan results, extract time nodes and cargo quantities for transportation tasks, and collect real-time traffic information from the traffic data platform, including road congestion levels and travel times;

[0034] Production plans and traffic data are integrated to form a dataset linking transportation tasks and road conditions. A path optimization algorithm is then used to calculate the shortest path and shortest time combination for transportation tasks.

[0035] If there are congested sections in the optimal route, the alternative route is recalculated and the optimal transportation route plan is generated again, including route planning and time estimation.

[0036] The optimal transportation route is matched with the production plan to determine the final transportation task execution plan.

[0037] Furthermore, inventory status is monitored in real time. When inventory levels fall below a preset threshold, production plan adjustments are triggered to avoid supply shortages; when inventory levels exceed the preset threshold, transportation scheduling is optimized, specifically including:

[0038] A pre-set inventory monitoring system is used to collect inventory data in real time. The collected inventory is compared with the pre-set high and low thresholds. When the inventory is lower than the low threshold, the production adjustment is calculated based on historical demand data, and a production plan adjustment scheme is generated. When the inventory is higher than the high threshold, the transportation optimization is calculated based on the current transportation capacity and inventory distribution.

[0039] When the inventory level is below the low threshold, a time series forecasting algorithm is used to predict future demand based on historical demand data. Combined with the current inventory level, the production adjustment amount is calculated, a production plan adjustment instruction is generated, and it is transmitted to the production management system to trigger the production plan adjustment process.

[0040] When the inventory level is higher than the high threshold, a linear programming algorithm is used to optimize the transportation route and transportation volume based on the current inventory distribution and transportation capacity. This information is then transmitted to the transportation management system to execute the transportation scheduling optimization process.

[0041] Furthermore, data on raw material proportions and concrete condition are acquired through sensors. If a deviation in the proportions or an abnormality in timeliness is detected, production parameters are adjusted, specifically including:

[0042] The system acquires the proportion of raw materials and the state of concrete through sensors. It uses a preset threshold to determine whether the collected data is within the allowable range. When the detected value exceeds the preset deviation value, a real-time adjustment mechanism is triggered to calculate the adjustment amount and update the production parameters.

[0043] Based on the updated production parameters, the ratio and status values ​​are re-collected to determine whether the preset maintenance level is met. If the timeliness is abnormal, time series analysis is used to predict future status values ​​and adjust production parameters based on the prediction results.

[0044] By using regression models in machine learning algorithms to analyze the relationship between ratio values ​​and state values ​​in historical data, production parameters can be optimized. However, if the adjusted production parameters still cannot meet the maintenance requirements, clustering algorithms are used to classify abnormal data, identify the causes of abnormalities, and adjust production parameters based on the abnormality classification results.

[0045] Furthermore, during transportation, the vehicle location and concrete condition are tracked in real time. If transportation delays or concrete quality abnormalities are detected, the transportation route is recalculated to ensure delivery efficiency. Production, transportation, and inventory data are aggregated to update the parameters of the multivariate optimization model, specifically including:

[0046] GPS technology is used to obtain vehicle location information, and sensors are used to monitor the concrete condition. When the vehicle location deviates from the preset route or the concrete condition is abnormal, the transport route recalculation module is triggered.

[0047] Extract production data from the production system, transportation data from the transportation system, and inventory data from the inventory system. Input the production data, transportation data, and inventory data into the summary data module to generate a comprehensive dataset.

[0048] The model parameters of the multivariate optimization model are updated based on the comprehensive dataset. The new transportation route is calculated by optimizing the model, an updated route plan is generated, and then sent to the vehicle navigation system.

[0049] This invention provides an intelligent calculation system for concrete sales, which implements an intelligent calculation method for concrete sales, comprising:

[0050] The data acquisition and storage module is used to collect customer demand data, meteorological data, traffic data, inventory status and production process data in real time, and store the collected data in a preset database;

[0051] The demand analysis and forecasting module combines historical demand data and time series analysis methods to calculate historical demand change trend lines, analyze demand fluctuation characteristics, predict future order volume change trends, and determine the optimal delivery time based on construction site information, thereby generating a demand management plan.

[0052] The meteorological and construction impact assessment module extracts the trend characteristics of meteorological data through a sliding window model, uses decision tree algorithm and linear regression model to assess the impact of weather changes on construction demand, generates construction demand adjustment plan, and dynamically updates construction progress, resource allocation and safety measures.

[0053] The traffic condition assessment and route optimization module collects traffic data in real time, generates a road network status map, uses route optimization algorithms combined with historical traffic data to replan transportation routes, and outputs real-time navigation solutions and traffic condition warning information.

[0054] The production planning and inventory management module inputs customer demand fluctuation characteristics, weather change forecasts, and traffic condition assessments into a multivariate optimization model to dynamically adjust the production plan, determine production volume and raw material ratios, and monitor inventory status in real time. When inventory is below or above a preset threshold, it triggers production plan adjustment or transportation scheduling optimization processes, forming a closed-loop control.

[0055] The production process monitoring and adjustment module acquires real-time data on raw material proportions and concrete condition through sensors, analyzes the data using machine learning algorithms, and dynamically adjusts production parameters.

[0056] The transportation monitoring and dynamic adjustment module uses GPS technology to track the vehicle's location in real time during transportation and combines sensors to monitor the concrete condition. When transportation delays or abnormal concrete quality are detected, the transportation route is recalculated. At the same time, production, transportation, and inventory data are aggregated and the parameters of the multivariate optimization model are updated.

[0057] The beneficial effects of this invention are as follows:

[0058] This invention acquires customer demand data in real time and combines it with historical demand trends. It uses time series analysis to predict future order volume trends. At the same time, it inputs customer demand fluctuation characteristics, weather change prediction results, and traffic condition assessment results into a multivariate optimization model to dynamically adjust the production plan and determine the optimal production volume and raw material ratio. This solves the problem of overproduction or underproduction caused by large demand fluctuations and inaccurate predictions in traditional production planning. It achieves dynamic adaptability and accuracy in production planning, reduces inventory costs, and improves production efficiency and resource utilization.

[0059] By acquiring real-time traffic data such as road congestion, accident information, and construction sections through a traffic monitoring system, and combining this with production plan results, the system dynamically generates the optimal transportation route using a path optimization algorithm. During transportation, GPS technology is used to track the vehicle location and concrete condition in real time. When transportation delays or concrete quality abnormalities occur, the system automatically recalculates the transportation route and updates the navigation plan. This solves the problems of transportation delays and untimely delivery caused by unpredictable traffic conditions in traditional transportation scheduling, significantly improving transportation efficiency and on-time delivery, reducing transportation costs, and ensuring that the concrete remains in optimal condition during transportation.

[0060] By acquiring raw material mix proportions and concrete condition data in real time through sensors, and combining the data with machine learning algorithms (such as regression models and clustering algorithms), production parameters are dynamically adjusted. When a mix proportion deviation or timeliness anomaly is detected, an adjustment mechanism is automatically triggered to optimize production parameters. The clustering algorithm identifies the cause of the anomaly, solving the problems of product quality fluctuations and low production efficiency caused by inaccurate raw material mix proportions or untimely condition monitoring in traditional production. This achieves intelligent monitoring and quality control of the production process, ensuring high quality of concrete products and stability of the production process, and reducing production costs. Attached Figure Description

[0061] To better understand and implement this application, the technical solution is described in detail below with reference to the accompanying drawings.

[0062] Figure 1 This is a flowchart illustrating an intelligent calculation method for concrete sales provided in Embodiment 1 of this application.

[0063] Figure 2 A flowchart illustrating the intelligent calculation method for concrete sales to analyze demand fluctuation characteristics, provided in Embodiment 1 of this application;

[0064] Figure 3 This is a flowchart illustrating the intelligent calculation method for concrete sales provided in Embodiment 1 of this application, which predicts the impact of weather changes on construction demand.

[0065] Figure 4This is a schematic diagram of the structure of a concrete sales intelligent calculation system provided in Embodiment 2 of this application. Detailed Implementation

[0066] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description relating to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.

[0067] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0068] The following detailed description of the specific implementation methods, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided in detail.

[0069] Example 1

[0070] Please see Figures 1-3 This embodiment provides an intelligent calculation method for concrete sales, including the following steps:

[0071] S1. Obtain customer demand data, including order volume, delivery time and construction location information, and analyze demand fluctuation characteristics in combination with historical demand change trends;

[0072] Furthermore, acquire customer demand data, including order volume, delivery time, and construction location information, and analyze demand fluctuation characteristics by combining historical demand trends. Specifically, this includes:

[0073] S11. Obtain customer order quantity, delivery time and construction location information, store it in the preset database, extract historical demand data from the database, and calculate the historical demand change trend line;

[0074] S12. Based on the historical demand change trend line, determine the demand fluctuation characteristic value. When the demand fluctuation characteristic value is greater than the preset threshold, generate an early warning signal.

[0075] S13. Using time series analysis, predict future order volume trends. Based on the prediction results and construction site information, determine the optimal delivery time. Combine demand fluctuation characteristics and prediction results to generate a demand management plan.

[0076] The process involves using the ratio of the standard deviation to the mean of historical demand data (coefficient of variation) to quantify demand fluctuations. A threshold is set based on the statistical distribution of historical data, such as the mean plus twice the standard deviation, to identify abnormal fluctuations. Secondly, the optimal delivery time is determined by combining forecast results and construction site information. Time series analysis methods (such as the ARIMA model) are used to predict future order volume trends. Based on the geographical location and transportation time of the construction site, a linear programming algorithm is used to optimize the delivery time, ensuring that customer needs are met while reducing transportation costs.

[0077] The specific content of the demand management solution includes: when the characteristic value of demand fluctuation exceeds the threshold, an early warning signal is generated and the inventory adjustment and production plan change process is triggered; at the same time, production parameters are optimized through machine learning algorithms to ensure supply and demand balance. The entire process uses a data verification mechanism to verify the rationality of the prediction and optimization results, ensuring the scientific nature and operability of the solution, thereby effectively solving technical problems such as large demand fluctuations, inaccurate predictions, and difficulties in optimizing delivery time in the concrete industry.

[0078] Specifically, by monitoring demand fluctuations, generating early warning signals, and combining forecast results with construction site information to optimize delivery times, a demand management solution is ultimately generated. This solves the technical problems faced by the concrete industry in demand management, such as large demand fluctuations, inaccurate forecasts, and difficulties in optimizing delivery times. Through intelligent analysis and decision support, the timeliness and accuracy of demand response are improved, inventory management is optimized, risks caused by demand fluctuations are reduced, and the company's market competitiveness is enhanced.

[0079] S2. Obtain real-time weather information from meteorological data sources, including rainfall, temperature and wind speed, and predict the impact of weather changes on construction needs in the future.

[0080] Furthermore, real-time weather information, including rainfall, temperature, and wind speed, is obtained from meteorological data sources to predict the impact of future weather changes on construction needs, specifically including:

[0081] S21. Use the real-time interface of the meteorological data source to obtain real-time data of rainfall, temperature and wind speed, establish a time series database to store meteorological data, construct a sliding window model based on the time series characteristics of meteorological data to extract the trend characteristics of weather changes, and calculate the rate of change and impact.

[0082] S22. For key indicators of construction needs, establish a mapping table between weather changes and construction needs, determine the impact weight of weather changes on construction needs, and when the predicted weather changes exceed the preset threshold, call the decision tree algorithm to evaluate the degree of change in construction needs and output construction adjustment suggestions.

[0083] S23. Based on the correlation between the weather change rate and the impact of construction demand, a linear regression model is used to predict the fluctuation range of future construction demand. By combining the weather change prediction results and the construction demand prediction values, a construction demand adjustment plan is generated, which includes construction schedule, resource allocation and safety measures.

[0084] S24. By monitoring the deviation between meteorological data and prediction models in real time, the construction demand adjustment plan is dynamically updated to ensure that the plan keeps pace with weather changes.

[0085] The process involves several key steps. First, the sliding window model's parameters are clearly defined, including window width and step size. For example, a 7-day window width and a 1-day step size are used to extract trend characteristics from meteorological data. Second, a mapping table between weather changes and construction needs can be constructed using historical data statistics to determine the specific impact weights of factors such as rainfall, temperature, and wind speed on construction progress, resource requirements, and safety measures. Third, the input variables for the linear regression model include the rate of weather change and the degree of influence of construction needs, with the prediction target being the fluctuation range of future construction needs. Finally, a dynamic adjustment mechanism can be adopted to monitor the deviation between meteorological data and the prediction model in real time. For instance, if the deviation exceeds a preset threshold, the model can be retrained or the construction plan adjusted to ensure that the plan remains synchronized with weather changes.

[0086] Specifically, by acquiring meteorological data in real time and combining it with technologies such as sliding window models, decision tree algorithms, and linear regression models, the impact of weather changes on construction needs is predicted, and construction needs adjustment plans are dynamically generated. This effectively solves the problems of schedule delays, unreasonable resource allocation, and safety risks caused by weather uncertainty in traditional construction management, improves the scientific nature and flexibility of construction management, and ensures the optimization of construction progress and resource utilization.

[0087] S3. Obtain real-time traffic data through the traffic monitoring system, including road congestion, accident information, and construction sections, and assess the feasibility of transportation routes;

[0088] Furthermore, real-time traffic data, including road congestion, accident information, and construction zones, is obtained through traffic monitoring systems to assess the feasibility of transportation routes. Specifically, this includes:

[0089] Real-time traffic data is collected through a traffic monitoring system to obtain information on road congestion, accident locations, and construction sections. Data fusion technology is then used to integrate the collected traffic data and generate a complete road network status map.

[0090] When the road congestion exceeds the preset threshold, the road section is marked as an impassable area. When the accident site or construction section is located on the transportation route, an alternative route is replanned.

[0091] Based on the replanned transportation routes, the estimated travel time and route length are calculated. Machine learning algorithms are used to analyze historical traffic data, predict future traffic changes, and output real-time navigation solutions and traffic warning information.

[0092] The system collects real-time traffic data, including road congestion levels, accident locations, and construction zone information, through a traffic monitoring system. It then integrates multi-source data using data fusion technology to generate a complete road network status map. When road congestion exceeds a preset threshold, the affected road segment is marked as impassable. If an accident location or construction zone is located on the transport route, the A* algorithm is used to replan an alternative route, ensuring avoidance of obstructed areas. Based on the replanned route, estimated travel time and path length are calculated. Historical traffic data is analyzed using Long Short-Term Memory (LSTM) or Graph Neural Network (GNN) to predict future traffic conditions, thereby determining the transport route. Finally, the system outputs real-time navigation plans and traffic warnings. Simultaneously, by monitoring the deviation between meteorological data and the prediction model in real time, the system dynamically updates the transport plan to ensure the feasibility and real-time nature of the transport route.

[0093] Specifically, by acquiring real-time traffic monitoring data and combining it with machine learning algorithms, dynamic optimization and intelligent navigation of transportation routes are achieved, significantly improving the efficiency and reliability of logistics transportation. It can quickly respond to emergencies such as road congestion, accidents, and construction, replan transportation routes in real time, and provide traffic condition warnings. This solves problems such as transportation delays, unreasonable route selection, and resource waste caused by unpredictable traffic conditions in traditional transportation, ensuring that goods can be delivered to their destination efficiently and safely, while reducing transportation costs and management complexity.

[0094] S4. Input the characteristics of customer demand fluctuations, weather change forecasts, and traffic condition assessments into a multivariate optimization model to dynamically adjust the production plan and determine the production volume and raw material ratio. Based on the production plan results and combined with traffic condition data, use a path optimization algorithm to generate the optimal transportation route to ensure transportation efficiency and on-time delivery.

[0095] Furthermore, the characteristics of customer demand fluctuations, weather change forecasts, and traffic condition assessments are input into a multivariate optimization model to dynamically adjust the production plan and determine production volume and raw material ratios. Specifically, this includes:

[0096] Customer demand data is obtained based on the volatility of customer demand, and combined with weather forecast results and traffic condition assessment results, and then integrated into a multivariate optimization model.

[0097] A multivariate optimization model is used to dynamically adjust the production plan based on the fluctuation of customer demand, weather forecast results, and traffic condition assessment results, so as to obtain the production plan adjustment value. Based on the production plan adjustment value, the production plan is dynamically adjusted to determine the production volume and raw material ratio.

[0098] When the production plan adjustment value exceeds the preset threshold, the production quantity and raw material ratio are re-optimized to ensure the dynamic adaptability of the production plan. The production plan adjustment value is iteratively optimized through a multivariate optimization model to obtain the optimal production quantity and raw material ratio.

[0099] Based on the optimal production volume and raw material ratio, production plan execution instructions are generated and output to the production system. Then, machine learning algorithms are used to train the multivariate optimization model to improve the accuracy of dynamic adjustment of the production plan.

[0100] Specifically, by inputting customer demand fluctuation characteristics, weather change forecasts, and traffic condition assessments into a multivariate optimization model, dynamic adjustments to production plans are achieved, solving the problems of low production efficiency and resource waste caused by demand uncertainty, weather influences, and traffic condition changes in traditional production planning.

[0101] Furthermore, based on the production plan results and traffic data, a route optimization algorithm is used to generate the optimal transportation route, specifically including:

[0102] Obtain production plan results, extract time nodes and cargo quantities for transportation tasks, and collect real-time traffic information from the traffic data platform, including road congestion levels and travel times;

[0103] Production plans and traffic data are integrated to form a dataset linking transportation tasks and road conditions. A path optimization algorithm is then used to calculate the shortest path and shortest time combination for transportation tasks.

[0104] If there are congested sections in the optimal route, the alternative route is recalculated to ensure transportation efficiency, and then the optimal transportation route plan is generated, including route planning and time estimation.

[0105] The optimal transportation route is matched with the production plan to determine the final transportation task execution plan.

[0106] Specifically, by combining production planning results with real-time traffic data and employing route optimization algorithms, the system achieves efficient scheduling and optimal route planning for transportation tasks. It can dynamically generate optimal transportation routes, ensuring the efficient execution of transportation tasks. This solves the problems of low transportation efficiency and time delays caused by road congestion and information lag in traditional transportation scheduling. Through the integration of real-time traffic information and production plans, the system can quickly respond to traffic changes, recalculate alternative routes, and generate optimal transportation solutions that include route planning and time estimation. This significantly improves the flexibility and reliability of transportation, and optimizes logistics costs and delivery times.

[0107] S5. Real-time monitoring of inventory status. When the inventory level is lower than the preset threshold, production plan adjustments are triggered to avoid supply shortages. When the inventory level is higher than the preset threshold, transportation scheduling is optimized to reduce inventory backlog. At the same time, during the production process, raw material ratio and concrete condition data are obtained through sensors. When a ratio deviation or timeliness abnormality is detected, production parameters are adjusted to ensure product quality.

[0108] Furthermore, inventory status is monitored in real time. When inventory levels fall below a preset threshold, production plan adjustments are triggered to avoid supply shortages; when inventory levels exceed the preset threshold, transportation scheduling is optimized, specifically including:

[0109] A pre-set inventory monitoring system is used to collect inventory data in real time. The collected inventory is compared with the pre-set high and low thresholds. When the inventory is lower than the low threshold, the production adjustment is calculated based on historical demand data, and a production plan adjustment scheme is generated. When the inventory is higher than the high threshold, the transportation optimization is calculated based on the current transportation capacity and inventory distribution.

[0110] When the inventory level is below the low threshold, a time series forecasting algorithm is used to predict future demand based on historical demand data. Combined with the current inventory level, the production adjustment amount is calculated, a production plan adjustment instruction is generated, and it is transmitted to the production management system to trigger the production plan adjustment process.

[0111] For cases where the inventory level exceeds the high threshold, a linear programming algorithm is used to optimize the transportation route and transportation volume based on the current inventory distribution and transportation capacity. This information is then transmitted to the transportation management system to execute the transportation scheduling optimization process.

[0112] During the process of generating production adjustment quantities and transportation optimization quantities, a data verification mechanism is adopted to verify the rationality of the calculated adjustment quantities and optimization quantities. If anomalies are found, the calculation is recalculated to ensure that the generated solutions meet the actual business needs.

[0113] The generated production plan adjustment scheme and transportation scheduling optimization scheme are transmitted to the corresponding management system to trigger the corresponding business execution process. At the same time, the execution results are fed back to the inventory monitoring system to form a closed-loop control.

[0114] Specifically, by monitoring inventory status in real time and comparing it with preset high and low thresholds, the system automatically triggers production plan adjustments or transportation scheduling optimizations. When inventory is below the low threshold, the system calculates production adjustment amounts based on time series forecasting algorithms combined with historical demand data, generates production plan adjustment instructions, and transmits them to the production management system. When inventory is above the high threshold, the system optimizes transportation routes and volumes using linear programming algorithms and transmits them to the transportation management system. The entire process ensures the rationality of adjustment and optimization amounts through a data verification mechanism and feeds the execution results back to the inventory monitoring system, forming a closed-loop control that effectively avoids supply shortages or inventory backlogs and improves the collaborative efficiency of production and transportation.

[0115] Furthermore, data on raw material proportions and concrete condition are acquired through sensors. If a deviation in the proportions or an abnormality in timeliness is detected, production parameters are adjusted, specifically including:

[0116] The system acquires the proportion of raw materials and the state of concrete through sensors. It uses a preset threshold to determine whether the collected data is within the allowable range. When the detected value exceeds the preset deviation value, a real-time adjustment mechanism is triggered to calculate the adjustment amount and update the production parameters.

[0117] Based on the updated production parameters, the ratio and status values ​​are re-collected to determine whether the preset maintenance level is met. If the timeliness is abnormal, time series analysis is used to predict future status values ​​and adjust production parameters based on the prediction results.

[0118] By using regression models in machine learning algorithms, the relationship between ratio values ​​and state values ​​in historical data is analyzed to optimize production parameters. However, if the adjusted production parameters still cannot meet the maintenance requirements, clustering algorithms are used to classify abnormal data and identify the causes of the abnormalities.

[0119] Based on the anomaly classification results, adjust the production parameters to ensure that the collected data remains stable within the preset deviation range.

[0120] Specifically, by acquiring real-time data on raw material proportions and concrete condition using sensors, and combining this with machine learning algorithms, the system enables dynamic adjustment and optimization of production parameters, significantly improving the quality and efficiency of concrete production. It can quickly respond to proportion deviations and timeliness anomalies, ensuring product quality through real-time adjustments to production parameters. This solves the problems of product quality fluctuations and low production efficiency caused by inaccurate proportions or untimely condition monitoring in traditional production. Through regression model analysis of historical data, the system can optimize production parameters, while clustering algorithms are used to identify and process abnormal data, ensuring the stability and reliability of the production process. This not only improves the quality of concrete products but also reduces production costs and management complexity.

[0121] S6. During transportation, track vehicle location and concrete status in real time. If transportation delays or concrete quality abnormalities are detected, recalculate the transportation route to ensure delivery efficiency. Summarize production, transportation, and inventory data, update multivariate optimization model parameters, and improve model prediction accuracy and decision-making efficiency.

[0122] Furthermore, during transportation, the vehicle location and concrete condition are tracked in real time. If transportation delays or concrete quality abnormalities are detected, the transportation route is recalculated to ensure delivery efficiency. Production, transportation, and inventory data are aggregated to update the parameters of the multivariate optimization model, specifically including:

[0123] GPS technology is used to obtain vehicle location information, and sensors are used to monitor the concrete condition. When the vehicle location deviates from the preset route or the concrete condition is abnormal, the transport route recalculation module is triggered.

[0124] Extract production data from the production system, transportation data from the transportation system, and inventory data from the inventory system. Input the production data, transportation data, and inventory data into the summary data module to generate a comprehensive dataset.

[0125] The model parameters of the multivariate optimization model are updated based on the comprehensive dataset. The new transportation route is calculated by optimizing the model, an updated route plan is generated, and then sent to the vehicle navigation system to complete the transportation route update.

[0126] Specifically, by monitoring vehicle location and concrete condition in real time, the system can quickly respond to transportation delays or concrete quality abnormalities during transport, dynamically adjust transportation routes, and optimize delivery efficiency. This solves the delays and quality risks caused by information lag in traditional transportation, improving logistics efficiency and customer satisfaction.

[0127] Example 2

[0128] Please see Figure 4 This embodiment provides a concrete sales intelligent calculation system for implementing a concrete sales intelligent calculation method, including:

[0129] The data acquisition and storage module is used to collect customer demand data (order volume, delivery time, construction location), meteorological data (rainfall, temperature, wind speed), traffic data (road congestion, accident information, construction sections), as well as inventory status and production process data (raw material ratio, concrete condition) in real time, and store the collected data in a preset database.

[0130] The demand analysis and forecasting module combines historical demand data and time series analysis methods to calculate historical demand change trend lines, analyze demand fluctuation characteristics, predict future order volume change trends, and determine the optimal delivery time based on construction site information, thereby generating a demand management plan.

[0131] The meteorological and construction impact assessment module extracts the trend characteristics of meteorological data through a sliding window model, uses decision tree algorithm and linear regression model to assess the impact of weather changes on construction demand, generates construction demand adjustment plan, and dynamically updates construction progress, resource allocation and safety measures.

[0132] The traffic condition assessment and route optimization module collects traffic data in real time, generates a road network status map, and uses route optimization algorithms (such as the A* algorithm) combined with historical traffic data (predicted through LSTM or GNN models) to replan transportation routes, outputting real-time navigation solutions and traffic condition warning information to ensure transportation efficiency and on-time delivery.

[0133] The production planning and inventory management module inputs customer demand fluctuation characteristics, weather change forecasts, and traffic condition assessments into a multivariate optimization model to dynamically adjust the production plan, determine production volume and raw material ratios, and monitor inventory status in real time. When inventory is below or above a preset threshold, it triggers production plan adjustment or transportation scheduling optimization processes, forming a closed-loop control.

[0134] The production process monitoring and adjustment module acquires raw material mix ratio and concrete condition data in real time through sensors, analyzes the data using machine learning algorithms (such as regression models and clustering algorithms), and dynamically adjusts production parameters to ensure product quality and production efficiency.

[0135] The transportation monitoring and dynamic adjustment module uses GPS technology to track vehicle location in real time during transportation and combines sensors to monitor concrete condition. When transportation delays or abnormal concrete quality are detected, the transportation route is recalculated to ensure delivery efficiency. At the same time, production, transportation and inventory data are aggregated to update the parameters of the multivariate optimization model, thereby improving the model's prediction accuracy and decision-making efficiency.

[0136] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A smart calculation method for concrete sales, characterized in that: Includes the following steps: Obtain customer demand data, including order volume, delivery time, and construction location information, and analyze demand fluctuation characteristics by combining historical demand change trends; Real-time weather information, including rainfall, temperature and wind speed, is obtained from meteorological data sources to predict the impact of weather changes on construction needs in the near future. This includes: using real-time interfaces of meteorological data sources to obtain real-time data on rainfall, temperature, and wind speed; establishing a time-series database to store meteorological data; constructing a sliding window model to extract trend features of weather changes based on the time-series characteristics of meteorological data; and calculating the rate of change and impact. For key indicators of construction demand, establish a mapping table between weather changes and construction demand, determine the impact weight of weather changes on construction demand, and when the predicted weather changes exceed the preset threshold, call the decision tree algorithm to evaluate the degree of change in construction demand and output construction adjustment suggestions. Based on the correlation between the rate of weather change and the impact of construction demand, a linear regression model is used to predict the fluctuation range of future construction demand. By combining the weather change forecast results and the construction demand forecast values, a construction demand adjustment plan is generated, which includes construction schedule, resource allocation and safety measures. By monitoring the deviation between meteorological data and prediction models in real time, the construction demand adjustment plan is dynamically updated. Real-time traffic data, including road congestion, accident information, and construction sites, is obtained through traffic monitoring systems to assess the feasibility of transportation routes. The characteristics of customer demand fluctuations, weather change forecasts, and traffic condition assessments are input into a multivariate optimization model to dynamically adjust the production plan and determine the production volume and raw material ratio. Based on the production plan results and traffic condition data, a path optimization algorithm is used to generate the optimal transportation route. This includes: obtaining customer demand data based on the volatility of customer demand, combining weather forecast results and traffic condition assessment results, and integrating them into a multivariate optimization model; A multivariate optimization model is used to dynamically adjust the production plan based on the fluctuation of customer demand, weather forecast results, and traffic condition assessment results, so as to obtain the production plan adjustment value. Based on the production plan adjustment value, the production plan is dynamically adjusted to determine the production volume and raw material ratio. When the production plan adjustment value exceeds the preset threshold, the production quantity and raw material ratio are re-optimized to ensure the dynamic adaptability of the production plan. The production plan adjustment value is iteratively optimized through a multivariate optimization model to obtain the optimal production quantity and raw material ratio. Based on the optimal production volume and raw material ratio, production plan execution instructions are generated and output to the production system. Then, machine learning algorithms are used to train the multivariate optimization model. Real-time monitoring of inventory status triggers production plan adjustments when inventory levels fall below a preset threshold to avoid supply shortages; when inventory levels exceed a preset threshold, transportation scheduling is optimized to reduce inventory backlog; simultaneously, during the production process, raw material ratios and concrete condition data are acquired through sensors, and production parameters are adjusted when ratio deviations or timeliness anomalies are detected. This includes: using a pre-set inventory monitoring system to collect inventory data in real time, comparing the collected inventory with pre-set high and low thresholds; when the inventory is below the low threshold, calculating the production adjustment amount based on historical demand data and generating a production plan adjustment scheme; when the inventory is above the high threshold, calculating the transportation optimization amount based on the current transportation capacity and inventory distribution. When the inventory level is below the low threshold, a time series forecasting algorithm is used to predict future demand based on historical demand data. Combined with the current inventory level, the production adjustment amount is calculated, a production plan adjustment instruction is generated, and it is transmitted to the production management system to trigger the production plan adjustment process. For cases where the inventory level exceeds the high threshold, a linear programming algorithm is used to optimize the transportation route and transportation volume based on the current inventory distribution and transportation capacity. This information is then transmitted to the transportation management system to execute the transportation scheduling optimization process. During transportation, the vehicle location and concrete status are tracked in real time. If a transportation delay or abnormal concrete quality is detected, the transportation route is recalculated. Production, transportation and inventory data are summarized and the parameters of the multivariate optimization model are updated. These include: using GPS technology to obtain vehicle location information, monitoring the concrete condition through sensors, and triggering a transport route recalculation module when the vehicle location deviates from the preset route or the concrete condition is abnormal; Extract production data from the production system, transportation data from the transportation system, and inventory data from the inventory system. Input the production data, transportation data, and inventory data into the summary data module to generate a comprehensive dataset. The model parameters of the multivariate optimization model are updated based on the comprehensive dataset. The new transportation route is calculated by optimizing the model, an updated route plan is generated, and then sent to the vehicle navigation system.

2. The intelligent calculation method for concrete sales according to claim 1, characterized in that: Obtain customer demand data, including order volume, delivery time, and construction location information, and analyze demand fluctuation characteristics by combining historical demand trends. Specifically, this includes: Acquire customer order volume, delivery time and construction location information, store it in a preset database, extract historical demand data from the database, and calculate historical demand change trend lines; Based on historical demand change trend lines, the characteristic value of demand fluctuation is determined. When the characteristic value of demand fluctuation exceeds a preset threshold, an early warning signal is generated. Using time series analysis, we predict future order volume trends. Based on the prediction results and construction site information, we determine the optimal delivery time and generate a demand management plan by combining demand fluctuation characteristics and prediction results.

3. The intelligent calculation method for concrete sales according to claim 1, characterized in that: Real-time traffic data, including road congestion, accident information, and construction zones, is obtained through traffic monitoring systems to assess the feasibility of transportation routes. Specifically, this includes: Real-time traffic data is collected through a traffic monitoring system to obtain information on road congestion, accident locations, and construction sections. Data fusion technology is then used to integrate the collected traffic data and generate a complete road network status map. When the road congestion exceeds the preset threshold, the road segment is marked as a no-traffic area. When the accident site or construction section is located on the transportation route, an alternative route is replanned. Based on the replanned transportation routes, the estimated travel time and route length are calculated. Machine learning algorithms are used to analyze historical traffic data, predict future traffic changes, and output real-time navigation solutions and traffic warning information.

4. The intelligent calculation method for concrete sales according to claim 1, characterized in that: Based on the production plan results and traffic data, a route optimization algorithm is used to generate the optimal transportation route, specifically including: Obtain production plan results, extract time nodes and cargo quantities for transportation tasks, and collect real-time traffic information from the traffic data platform, including road congestion levels and travel times; Production plans and traffic data are integrated to form a dataset linking transportation tasks and road conditions. A path optimization algorithm is then used to calculate the shortest path and shortest time combination for transportation tasks. If there are congested sections in the optimal route, the alternative route is recalculated and the optimal transportation route plan is generated again, including route planning and time estimation. The optimal transportation route is matched with the production plan to determine the final transportation task execution plan.

5. The intelligent calculation method for concrete sales according to claim 1, characterized in that: Data on raw material proportions and concrete condition are acquired through sensors. When deviations in the proportions or abnormalities in timeliness are detected, production parameters are adjusted, including: The system acquires the proportion of raw materials and the state of concrete through sensors. It uses a preset threshold to determine whether the collected data is within the allowable range. When the detected value exceeds the preset deviation value, a real-time adjustment mechanism is triggered to calculate the adjustment amount and update the production parameters. Based on the updated production parameters, the ratio and status values ​​are re-collected to determine whether the preset maintenance level is met. If the timeliness is abnormal, time series analysis is used to predict future status values ​​and adjust production parameters based on the prediction results. By using regression models in machine learning algorithms to analyze the relationship between ratio values ​​and state values ​​in historical data, production parameters can be optimized. However, if the adjusted production parameters still cannot meet the maintenance requirements, clustering algorithms are used to classify abnormal data, identify the causes of abnormalities, and adjust production parameters based on the abnormality classification results.

6. A concrete sales intelligent calculation system, used to implement the concrete sales intelligent calculation method as described in any one of claims 1-5, characterized in that: include: The data acquisition and storage module is used to collect customer demand data, meteorological data, traffic data, inventory status and production process data in real time, and store the collected data in a preset database; The demand analysis and forecasting module combines historical demand data and time series analysis methods to calculate historical demand change trend lines, analyze demand fluctuation characteristics, predict future order volume change trends, and determine the optimal delivery time based on construction site information, thereby generating a demand management plan. The meteorological and construction impact assessment module extracts the trend characteristics of meteorological data through a sliding window model, uses decision tree algorithm and linear regression model to assess the impact of weather changes on construction demand, generates construction demand adjustment plan, and dynamically updates construction progress, resource allocation and safety measures. The traffic condition assessment and route optimization module collects traffic data in real time, generates a road network status map, uses route optimization algorithms combined with historical traffic data to replan transportation routes, and outputs real-time navigation solutions and traffic condition warning information. The production planning and inventory management module inputs customer demand fluctuation characteristics, weather change forecasts, and traffic condition assessments into a multivariate optimization model to dynamically adjust the production plan, determine production volume and raw material ratios, and monitor inventory status in real time. When inventory is below or above a preset threshold, it triggers production plan adjustment or transportation scheduling optimization processes, forming a closed-loop control. The production process monitoring and adjustment module acquires real-time data on raw material proportions and concrete condition through sensors, analyzes the data using machine learning algorithms, and dynamically adjusts production parameters. The transportation monitoring and dynamic adjustment module uses GPS technology to track the vehicle's location in real time during transportation and combines sensors to monitor the concrete condition. When transportation delays or abnormal concrete quality are detected, the transportation route is recalculated. At the same time, production, transportation, and inventory data are aggregated and the parameters of the multivariate optimization model are updated.

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