Concrete sales intelligent calculation method and system

Abstract

The invention discloses an intelligent calculation method and system for concrete sales, and relates to the technical field of building and logistics management. By dynamically adjusting the production plan, accurately predicting the customer demand and optimizing the production capacity and the raw material ratio, the problem of excessive or insufficient production caused by large demand fluctuation in traditional production is solved, the inventory cost is reduced, the production efficiency is improved, the traffic condition is monitored in real time and the transportation route is dynamically optimized in the transportation link, and the transportation efficiency is improved. The transportation efficiency and the delivery punctuality are obviously improved, the transportation cost is reduced, and the concrete quality is ensured; in the production process, the raw material ratio and the concrete state are monitored in real time through a sensor and a machine learning algorithm, production parameters are dynamically adjusted, the quality fluctuation problem caused by inaccurate ratio or untimely monitoring in traditional production is solved, and the stability of product quality is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of construction and logistics management, and in particular to an intelligent calculation method and system for concrete sales. Background Art

[0002] In the concrete sales process, traditional production and transportation plans often rely on manual experience or simple historical data analysis, making them incapable of responding to complex and volatile market demands and external environmental changes. For example, customer demand can rapidly change due to sudden weather changes or adjustments to construction schedules, while uncertain traffic conditions can affect transportation efficiency, leading to quality issues during transportation or delivery delays. Furthermore, concrete production requires extremely high raw material ratios and timeliness. Failure to dynamically adjust production plans based on real-time data can result in raw material waste or substandard product quality.

[0003] In actual business scenarios, the volatility of customer demand, the unpredictability of weather changes, and the real-time dynamics of traffic conditions together constitute a complex multivariable optimization problem. These intertwined factors make it difficult for traditional methods to make accurate production and transportation decisions in a short period of time. For example, when a sudden rainfall occurs in a certain area, construction demand may drop sharply, while transportation routes may be blocked by waterlogging or congestion. In addition, the physical properties of concrete dictate that its production and transportation processes must be strictly controlled within a specific time frame, otherwise its strength and durability may be affected. However, traditional inventory management and transportation scheduling methods are usually based on static rules and cannot respond to changes in the external environment in real time, resulting in overproduction or insufficient supply. This contradiction is particularly prominent during peak demand periods or emergencies.

[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 needs to be solved urgently. This problem not only involves the integration 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 the present 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, the method solves the problems of high cost, low efficiency, and unstable quality in traditional concrete sales caused by demand fluctuations, traffic uncertainty, and insufficient production monitoring.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] This application provides a concrete sales intelligent calculation method, comprising the following steps:

[0008] Obtain customer demand data, including order quantity, delivery time, and construction location information, and analyze demand fluctuation characteristics based on historical demand trends;

[0009] Obtain real-time weather information from meteorological data sources, including rainfall, temperature, and wind speed, to predict the impact of weather changes on construction needs over the next period of time;

[0010] Obtain real-time traffic data through traffic monitoring systems, including road congestion, accident information, and construction sections, to assess the feasibility of transportation routes;

[0011] Customer demand fluctuations, weather forecasts, and traffic condition assessments are input into a multivariable optimization model to dynamically adjust production plans and determine production volumes and raw material ratios. Based on the production plan results and combined with traffic data, a path optimization algorithm is used to generate the optimal transportation route.

[0012] Real-time monitoring of inventory status. When inventory levels fall below a preset threshold, production plan adjustments are triggered to avoid supply shortages. When inventory levels rise above a preset threshold, transportation scheduling is optimized to reduce inventory backlogs. During the production process, sensors are used to obtain data on raw material ratios and concrete status. If deviations in ratios or timeliness are detected, production parameters are adjusted.

[0013] During the transportation process, the vehicle location and concrete status are tracked in real time. When transportation delays or abnormal concrete quality are found, the transportation route is recalculated; production, transportation and inventory data are aggregated and the multivariable optimization model parameters are updated.

[0014] Furthermore, we obtain customer demand data, including order quantity, delivery time, and construction location information, and analyze demand fluctuation characteristics based on historical demand trends, including:

[0015] Obtain customer order quantity, delivery time, and construction location information, store them in a preset database, extract historical demand data from the database, and calculate the historical demand change trend line;

[0016] Determine the demand fluctuation characteristic value based on the historical demand change trend line. When the demand fluctuation characteristic value exceeds the preset threshold, an early warning signal is generated.

[0017] Use time series analysis methods to predict future order volume trends. Determine the optimal delivery time based on the forecast results and construction site information. Combine demand fluctuation characteristic values with forecast results to generate a demand management plan.

[0018] Furthermore, 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 coming period, including:

[0019] Use the real-time interface of the meteorological data source to obtain real-time data on rainfall, temperature, and wind speed, establish a time series database to store meteorological data, and build a sliding window model based on the time series characteristics of meteorological data to extract trend characteristics of weather changes and calculate the rate of change and impact.

[0020] For key indicators of construction demand, a mapping relationship table is established between weather changes and construction demand, and the weight of the impact of weather changes on construction demand is determined. When the predicted weather change exceeds the preset threshold, a decision tree algorithm is used to evaluate the degree of change in construction demand and output construction adjustment suggestions;

[0021] Based on the correlation between 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 forecast results with the construction demand forecast, a construction demand adjustment plan is generated, including construction progress, resource allocation, and safety measures.

[0022] By real-time monitoring of the deviation between meteorological data and the forecast model, the construction demand adjustment plan is dynamically updated.

[0023] Furthermore, the traffic monitoring system is used to obtain real-time traffic condition data, including road congestion, accident information, and construction sections, to assess the feasibility of transportation routes, including:

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

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

[0026] Based on the re-planned transport route, the estimated travel time and path length are calculated, and machine learning algorithms are used to analyze historical traffic data, predict changes in road conditions in future time periods, and output real-time navigation plans and road condition warning information.

[0027] Furthermore, customer demand fluctuation characteristics, weather change forecast results, and traffic condition assessment results are input into the multivariable optimization model to dynamically adjust the production plan and determine the production volume and raw material ratio, including:

[0028] Obtain customer demand data based on customer demand volatility, combine weather forecast results with traffic condition assessment results, and integrate them into a multivariate optimization model;

[0029] A multivariable optimization model is used to dynamically adjust customer demand volatility, weather forecast results, and traffic condition assessment results to obtain production plan adjustment values. Based on the production plan adjustment values, 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 volume 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 multivariable optimization model to obtain the optimal production volume 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, and then the multivariable optimization model is trained using machine learning algorithms.

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

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

[0034] Integrate production plans and traffic data to form a data set related to transportation tasks and road conditions. Use a path optimization algorithm to calculate the shortest path and shortest time combination for transportation tasks.

[0035] When there is a congested section in the optimal route, the alternative route is recalculated and the optimal transportation route plan is generated, including path planning and time estimation;

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

[0037] Furthermore, the inventory status is monitored in real time. When the inventory level falls below a preset threshold, the production plan is adjusted to avoid supply shortages. When the inventory level rises above the preset threshold, transportation scheduling is optimized, including:

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

[0039] When inventory levels fall below a lower threshold, a time series forecasting algorithm is used to predict future demand based on historical demand data. Combined with current inventory levels, the production adjustment amount is calculated, and a production plan adjustment instruction is generated and transmitted to the production management system, triggering 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 routes and transportation volumes based on the current inventory distribution and transportation capacity, and the data is transmitted to the transportation management system to execute the transportation scheduling optimization process.

[0041] Furthermore, sensors are used to obtain data on raw material ratios and concrete status. When ratio deviations or timeliness anomalies are detected, production parameters are adjusted, including:

[0042] Sensors are used to obtain the raw material ratio and concrete status values, and a preset threshold is used to determine whether the collected amount 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, re-collect the ratio value and status value to determine whether they meet the preset maintenance quantity. If the timeliness is abnormal, use the time series analysis method to predict the future status value and adjust the production parameters according to the prediction results;

[0044] Through the regression model in the machine learning algorithm, the relationship between the ratio value and the status value in the historical data is analyzed to optimize the production parameters. However, if the adjusted production parameters still cannot meet the maintenance quantity, the clustering algorithm is used to classify the abnormal data, identify the cause of the abnormality, and adjust the production parameters based on the abnormal classification results.

[0045] Furthermore, during the transportation process, 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 to ensure delivery efficiency. Production, transportation, and inventory data are aggregated to update the multivariable optimization model parameters, including:

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

[0047] Extract production data from the production system, extract transportation data from the transportation system, extract inventory data from the inventory system, and input the production data, transportation data, and inventory data into the summary data module to generate a comprehensive data set;

[0048] The model parameters of the multivariable optimization model are updated based on the comprehensive data set. The new transportation route is calculated through the optimization model, and an updated route plan is generated and sent to the vehicle navigation system.

[0049] The present invention provides a concrete sales intelligent calculation system for implementing a concrete sales intelligent calculation method, including:

[0050] Data collection and storage module, used to collect customer demand data, weather 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 with time series analysis methods to calculate historical demand trend lines, analyze demand fluctuation characteristics, predict future order volume trends, and determine the optimal delivery time based on construction site information to generate demand management plans;

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

[0053] Traffic condition assessment and route optimization module collects traffic data in real time, generates a road network status diagram, uses a route optimization algorithm combined with historical traffic data to replan transportation routes, and outputs real-time navigation solutions and road 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 multivariable optimization model to dynamically adjust production plans, determine production volume and raw material ratios, and monitor inventory status in real time. When inventory falls below or exceeds a preset threshold, it triggers production plan adjustments or transportation scheduling optimization processes, forming a closed-loop control process.

[0055] The production process monitoring and adjustment module uses sensors to obtain real-time data on raw material ratios and concrete status, uses machine learning algorithms to analyze the data, 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 status of concrete. When transportation delays or abnormal concrete quality are found, the transportation route is recalculated. At the same time, production, transportation and inventory data are summarized and the multivariable optimization model parameters are updated.

[0057] The beneficial effects of the present invention are:

[0058] The present invention obtains customer demand data in real time and combines it with historical demand change trends, using time series analysis methods to predict future order volume trends. At the same time, customer demand fluctuation characteristics, weather change prediction results, and traffic condition assessment results are input 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 plans, achieves dynamic adaptability and accuracy of production plans, reduces inventory costs, and improves production efficiency and resource utilization.

[0059] The traffic monitoring system obtains real-time traffic data such as road congestion, accident information, and construction sections. Combined with production plan results, a path optimization algorithm is used to dynamically generate the optimal transportation route. During transportation, GPS technology is used to track vehicle location and concrete status in real time. When transportation delays or abnormal concrete quality 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 improves transportation efficiency and delivery punctuality, reduces transportation costs, and ensures that the concrete remains in optimal condition during transportation.

[0060] Real-time data on raw material proportions and concrete status are acquired through sensors, and machine learning algorithms (such as regression models and clustering algorithms) are used to analyze the data and dynamically adjust production parameters. When a proportion deviation or timeliness anomaly is detected, the adjustment mechanism is automatically triggered to optimize production parameters, and the cause of the anomaly is identified through a clustering algorithm. This solves the problems of product quality fluctuations and low production efficiency caused by inaccurate raw material proportions or untimely status monitoring in traditional production, realizes intelligent monitoring and quality control of the production process, ensures the high quality of concrete products and the stability of the production process, and reduces production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] For better understanding and implementation, the technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0062] Figure 1 A flowchart of an intelligent calculation method for concrete sales provided in Example 1 of this application;

[0063] Figure 2 A flowchart of an intelligent calculation method for concrete sales analyzing demand fluctuation characteristics provided in Example 1 of the present application;

[0064] Figure 3 A flowchart of an intelligent calculation method for concrete sales, provided in Example 1 of the present application, for predicting the impact of weather changes on construction demand;

[0065] Figure 4This is a structural diagram of a concrete sales intelligent computing system provided in Example 2 of this application. DETAILED DESCRIPTION

[0066] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present application. Rather, they are merely examples of methods and systems consistent with certain aspects of the present application, as detailed in the appended claims.

[0067] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are 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 encompasses any and all possible combinations of one or more of the associated listed items.

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

[0069] Example 1

[0070] See also Figure 1-Figure 3 This embodiment provides a method for intelligent calculation of concrete sales, including the following steps:

[0071] S1. Obtain customer demand data, including order quantity, delivery time, and construction location information, and analyze demand fluctuation characteristics based on historical demand trends;

[0072] Furthermore, we obtain customer demand data, including order quantity, delivery time, and construction location information, and analyze demand fluctuation characteristics based on historical demand trends, including:

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

[0074] S12. Determine a demand fluctuation characteristic value based on a historical demand change trend line, and generate an early warning signal when the demand fluctuation characteristic value is greater than a preset threshold;

[0075] S13. Use time series analysis methods to predict future order volume trends, determine the optimal delivery time based on the forecast results and construction site information, and generate a demand management plan based on the demand fluctuation characteristic values and forecast results.

[0076] Among them, the ratio of the standard deviation to the mean of historical demand data (coefficient of variation) is used to quantify the degree of demand fluctuation, and a threshold is set according to 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 the forecast results and construction site information, and 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, the delivery time is optimized through a linear programming algorithm to ensure that transportation costs are reduced while meeting customer needs.

[0077] The specific contents of the demand management plan include: when the demand fluctuation characteristic value 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 to ensure the scientific nature and operability of the plan, 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 optimizing delivery times based on forecast results and construction site information, a demand management plan is ultimately generated, solving 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, the 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 weather changes on construction needs in the coming period, including:

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

[0082] S22. For key indicators of construction demand, a mapping table is established between weather changes and construction demand, and the weight of the impact of weather changes on construction demand is determined. When the predicted weather change exceeds a preset threshold, a decision tree algorithm is used to evaluate the degree of change in construction demand and output construction adjustment suggestions.

[0083] S23. Based on the correlation between weather change rate and the impact of construction demand, a linear regression model is used to predict the fluctuation range of future construction demand. The weather change forecast results are combined with the construction demand forecast value to generate a construction demand adjustment plan, including construction schedule, resource allocation and safety measures;

[0084] S24. By real-time monitoring of the deviation between meteorological data and forecast models, the construction demand adjustment plan is dynamically updated to ensure that the plan remains synchronized with weather changes.

[0085] Among them, the parameter settings of the sliding window model are clarified, including the window width and step size, for example, a window width of 7 days and a step size of 1 day, to extract trend characteristics of meteorological data. Secondly, a mapping relationship table between weather changes and construction needs can be constructed through 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. Thirdly, the input variables of the linear regression model include the weather change rate and the impact of construction needs, and the prediction target is the fluctuation range of future construction needs. Finally, when monitoring the deviation between meteorological data and the prediction model in real time, a dynamic adjustment mechanism can be adopted. For example, when the deviation exceeds a preset threshold, the model can be retrained or the construction plan can be adjusted to ensure that the plan keeps pace with weather changes.

[0086] Specifically, by acquiring meteorological data in real time and combining it with technical means such as sliding window models, decision tree algorithms, and linear regression models, the impact of weather changes on construction needs is predicted, and construction demand adjustment plans are dynamically generated. This effectively solves the problems of progress delays, irrational resource allocation, and safety risks caused by meteorological uncertainties 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, to assess the feasibility of transportation routes;

[0088] Furthermore, the traffic monitoring system is used to obtain real-time traffic condition data, including road congestion, accident information, and construction sections, to assess the feasibility of transportation routes, including:

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

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

[0091] Based on the re-planned transport route, the estimated travel time and path length are calculated, and machine learning algorithms are used to analyze historical traffic data, predict changes in road conditions in future time periods, and output real-time navigation plans and road condition warning information.

[0092] Among them, real-time traffic data, including road congestion levels, accident points and construction section information, is collected through the traffic monitoring system, and data fusion technology is used to integrate multi-source data to generate a complete road network status diagram; when the road congestion level exceeds the preset threshold, the section is marked as an impassable area; if the accident point or construction section is located on the transportation route, the A* algorithm is called to re-plan an alternative route to ensure that the obstacle area is avoided; based on the re-planned route, the estimated travel time and path length are calculated, and the long short-term memory network (LSTM) or graph neural network (GNN) is used to analyze historical traffic data and predict road condition changes in future time periods, thereby determining the transportation route; finally, the system outputs real-time navigation plans and road condition warning information, and at the same time, by real-time monitoring of the deviation between meteorological data and the prediction model, the transportation plan is dynamically updated to ensure the feasibility and real-time performance of the transportation route.

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

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

[0095] Furthermore, customer demand fluctuation characteristics, weather change forecast results, and traffic condition assessment results are input into the multivariable optimization model to dynamically adjust the production plan and determine the production volume and raw material ratio, including:

[0096] Obtain customer demand data based on customer demand volatility, combine weather forecast results with traffic condition assessment results, and integrate them into a multivariate optimization model;

[0097] A multivariable optimization model is used to dynamically adjust customer demand volatility, weather forecast results, and traffic condition assessment results to obtain production plan adjustment values. Based on the production plan adjustment values, 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 volume 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 multivariable optimization model to obtain the optimal production volume 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. Machine learning algorithms are then used to train the multivariable optimization model to improve the accuracy of dynamic adjustment of the production plan.

[0100] Specifically, by inputting the customer demand fluctuation characteristics, weather change forecast results and traffic condition assessment results into the multivariable optimization model, dynamic adjustment of the production plan is achieved, solving the problems of low production efficiency and resource waste caused by demand uncertainty, weather impact and traffic condition changes in traditional production plans.

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

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

[0103] Integrate production plans and traffic data to form a data set related to transportation tasks and road conditions. Use a path optimization algorithm to calculate the shortest path and shortest time combination for transportation tasks.

[0104] When 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 path planning and time estimation;

[0105] Match the optimal transportation route plan 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 a path optimization algorithm, efficient transportation scheduling and optimal path planning are achieved. The system can dynamically generate optimal transportation routes to ensure efficient execution of transportation tasks. This addresses the issues of low transportation efficiency and time delays caused by road congestion and information lags in traditional transportation scheduling. By integrating real-time traffic information with production plans, the system can quickly respond to traffic changes, recalculate alternative routes, and generate an optimal transportation plan that includes path planning and time estimation, significantly improving transportation flexibility and reliability, and optimizing logistics costs and delivery times.

[0107] S5. Real-time monitoring of inventory status. When inventory levels fall below a preset threshold, production plan adjustments are triggered to avoid supply shortages. When inventory levels rise above a preset threshold, transportation scheduling is optimized to reduce inventory backlogs. During the production process, sensors are used to obtain data on raw material mix ratios and concrete status. If mix ratio deviations or timeliness anomalies are detected, production parameters are adjusted to ensure product quality.

[0108] Furthermore, the inventory status is monitored in real time. When the inventory level falls below a preset threshold, the production plan is adjusted to avoid supply shortages. When the inventory level rises above the preset threshold, transportation scheduling is optimized, including:

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

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

[0111] When inventory levels are higher than a high threshold, a linear programming algorithm is used to optimize transportation routes and volumes based on current inventory distribution and transportation capacity, and the data is transmitted to the transportation management system to execute the transportation scheduling optimization process.

[0112] In the process of generating production adjustments and transportation optimizations, a data verification mechanism is used to verify the rationality of the calculated adjustments and optimizations. If abnormal values are found, recalculation is performed to ensure that the generated solutions meet actual business needs.

[0113] The generated production plan adjustment plan and transportation scheduling optimization plan are transmitted to the corresponding management system respectively, triggering 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 real-time monitoring of inventory status and comparing it with preset high and low thresholds, the system automatically triggers production plan adjustments or transportation scheduling optimization; when the inventory is lower than the low threshold, the production adjustment amount is calculated based on the time series prediction algorithm combined with historical demand data, and a production plan adjustment instruction is generated and transmitted to the production management system; when the inventory is higher than the high threshold, the linear programming algorithm is used to optimize the transportation route and transportation volume and transmit it to the transportation management system; the entire process ensures the rationality of the adjustment amount and optimization amount through a data verification mechanism, and feeds back the execution results to the inventory monitoring system to form a closed-loop control, effectively avoiding supply shortages or inventory backlogs, and improving the collaborative efficiency of production and transportation.

[0115] Furthermore, sensors are used to obtain data on raw material ratios and concrete status. When ratio deviations or timeliness anomalies are detected, production parameters are adjusted, including:

[0116] Sensors are used to obtain the raw material ratio and concrete status values, and a preset threshold is used to determine whether the collected amount 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, re-collect the ratio value and status value to determine whether they meet the preset maintenance quantity. If the timeliness is abnormal, use the time series analysis method to predict the future status value and adjust the production parameters according to the prediction results;

[0118] The regression model in the machine learning algorithm analyzes the relationship between the ratio value and the status value in the historical data and optimizes the production parameters. However, if the adjusted production parameters still cannot meet the maintenance volume, the clustering algorithm is used to classify the abnormal data and identify the cause of the abnormality.

[0119] According to the abnormal classification results, the production parameters are adjusted to ensure that the collection volume is stable within the preset deviation value range.

[0120] Specifically, sensors capture real-time data on raw material mix proportions and concrete status, combined with machine learning algorithms to dynamically adjust and optimize production parameters, significantly improving the quality and efficiency of concrete production. The system can quickly respond to mix deviations and timeliness anomalies, ensuring product quality through real-time adjustments to production parameters. This addresses the issues of product quality fluctuations and low production efficiency in traditional production caused by inaccurate mix proportions or untimely status monitoring. By analyzing historical data through regression models, the system optimizes 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 difficulties.

[0121] S6. During transportation, the vehicle location and concrete status are tracked in real time. When transportation delays or abnormal concrete quality are found, the transportation route is recalculated to ensure delivery efficiency. Production, transportation and inventory data are aggregated and multivariable optimization model parameters are updated to improve model prediction accuracy and decision-making efficiency.

[0122] Furthermore, during the transportation process, 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 to ensure delivery efficiency. Production, transportation, and inventory data are aggregated to update the multivariable optimization model parameters, including:

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

[0124] Extract production data from the production system, extract transportation data from the transportation system, extract inventory data from the inventory system, and input the production data, transportation data, and inventory data into the summary data module to generate a comprehensive data set;

[0125] The model parameters of the multivariable optimization model are updated based on the comprehensive data set. The new transport route is calculated through the optimization model, and an updated route plan is generated. The updated route plan is then sent to the vehicle navigation system to complete the transport route update.

[0126] Specifically, by real-time monitoring of vehicle location and concrete status, the system can quickly respond to transportation delays or abnormal concrete quality during transportation, dynamically adjusting transportation routes and optimizing delivery efficiency. This eliminates the delays and quality risks caused by information lags in traditional transportation, improving logistics efficiency and customer satisfaction.

[0127] Example 2

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

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

[0130] The demand analysis and forecasting module combines historical demand data with time series analysis methods to calculate historical demand trend lines, analyze demand fluctuation characteristics, predict future order volume trends, and determine the optimal delivery time based on construction site information to generate demand management plans;

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

[0132] Traffic condition assessment and route optimization module collects traffic data in real time, generates a road network status diagram, uses a route optimization algorithm (such as the A* algorithm) combined with historical traffic data (predicted by LSTM or GNN models) to replan transportation routes, and outputs real-time navigation solutions and road 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 multivariable optimization model to dynamically adjust production plans, determine production volume and raw material ratios, and monitor inventory status in real time. When inventory falls below or exceeds a preset threshold, it triggers production plan adjustments or transportation scheduling optimization processes, forming a closed-loop control process.

[0134] The production process monitoring and adjustment module uses sensors to obtain real-time data on raw material ratios and concrete status, uses machine learning algorithms (such as regression models and clustering algorithms) to analyze the data, 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 the vehicle's location in real time during transportation, and combines sensors to monitor the status of concrete. When transportation delays or abnormal concrete quality are found, the transportation route is recalculated to ensure delivery efficiency. At the same time, production, transportation and inventory data are aggregated, and multivariable optimization model parameters are updated to improve model prediction accuracy and decision-making efficiency.

[0136] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A concrete sales intelligent calculation method, characterized by: The steps include: Obtain customer demand data, including order quantity, delivery time, and construction location information, and analyze demand fluctuation characteristics based on historical demand trends; Obtain real-time weather information from meteorological data sources, including rainfall, temperature, and wind speed, to predict the impact of weather changes on construction needs over the next period of time; Obtain real-time traffic data through traffic monitoring systems, including road congestion, accident information, and construction sections, to assess the feasibility of transportation routes; Customer demand fluctuations, weather forecasts, and traffic condition assessments are input into a multivariable optimization model to dynamically adjust production plans and determine production volumes and raw material ratios. Based on the production plan results and combined with traffic data, a path optimization algorithm is used to generate the optimal transportation route. Real-time monitoring of inventory status. When inventory levels fall below a preset threshold, production plan adjustments are triggered to avoid supply shortages. When inventory levels exceed a preset threshold, transportation scheduling is optimized to reduce inventory backlogs. During the production process, sensors are used to obtain data on raw material ratios and concrete status. If deviations in ratios or timeliness are detected, production parameters are adjusted. During the transportation process, the vehicle location and concrete status are tracked in real time. When transportation delays or abnormal concrete quality are found, the transportation route is recalculated; production, transportation and inventory data are aggregated and the multivariable optimization model parameters are updated.

2. The intelligent calculation method for concrete sales according to claim 1, characterized in that: Obtain customer demand data, including order quantity, delivery time, and construction location information. Combined with historical demand trends, analyze demand fluctuation characteristics, including: Obtain customer order quantity, delivery time, and construction location information, store them in a preset database, extract historical demand data from the database, and calculate the historical demand change trend line; Determine the demand fluctuation characteristic value based on the historical demand change trend line. When the demand fluctuation characteristic value exceeds the preset threshold, an early warning signal is generated. Use time series analysis methods to predict future order volume trends. Determine the optimal delivery time based on the forecast results and construction site information. Combine demand fluctuation characteristic values with forecast results to generate a demand management plan.

3. The intelligent calculation method for concrete sales according to claim 1, characterized in that: 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 over the next period of time, including: Use the real-time interface of the meteorological data source to obtain real-time data on rainfall, temperature, and wind speed, establish a time series database to store meteorological data, and build a sliding window model based on the time series characteristics of meteorological data to extract trend characteristics of weather changes and calculate the rate of change and impact. For key indicators of construction demand, a mapping relationship table is established between weather changes and construction demand, and the weight of the impact of weather changes on construction demand is determined. When the predicted weather change exceeds the preset threshold, a decision tree algorithm is used to evaluate the degree of change in construction demand and output construction adjustment suggestions; Based on the correlation between 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 forecast results with the construction demand forecast, a construction demand adjustment plan is generated, including construction progress, resource allocation, and safety measures. By real-time monitoring of the deviation between meteorological data and the forecast model, the construction demand adjustment plan is dynamically updated.

4. The intelligent calculation method for concrete sales according to claim 1, characterized in that: The traffic monitoring system obtains real-time traffic data, including road congestion, accident information, and construction sections, to assess the feasibility of transportation routes, including: The traffic monitoring system collects real-time traffic data to obtain information on road congestion, accident locations, and construction sections. Data fusion technology is used to integrate the collected traffic data to generate a complete road network status map. When the road congestion exceeds a preset threshold, the road section is marked as a no-traffic area. When an accident site or construction section is located on a transport route, an alternative route is replanned. Based on the re-planned transport route, the estimated travel time and path length are calculated, and machine learning algorithms are used to analyze historical traffic data, predict changes in road conditions in future time periods, and output real-time navigation plans and road condition warning information.

5. The intelligent calculation method for concrete sales according to claim 1, characterized in that: Input customer demand fluctuation characteristics, weather forecast results, and traffic condition assessment results into a multivariate optimization model to dynamically adjust production plans and determine production volume and raw material ratios. Specifically, this includes: Obtain customer demand data based on customer demand volatility, combine weather forecast results with traffic condition assessment results, and integrate them into a multivariate optimization model; A multivariable optimization model is used to dynamically adjust customer demand volatility, weather forecast results, and traffic condition assessment results to obtain production plan adjustment values. Based on the production plan adjustment values, 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 volume 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 multivariable optimization model to obtain the optimal production volume 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, and then the multivariable optimization model is trained using machine learning algorithms.

6. The intelligent calculation method for concrete sales according to claim 1, characterized in that: Based on the production plan results and combined with traffic data, a path optimization algorithm is used to generate the optimal transportation route, including: Obtain production plan results, extract transportation task time nodes and cargo quantities, and collect real-time traffic information from the traffic data platform, including road congestion levels and travel time; Integrate production plans and traffic data to form a data set related to transportation tasks and road conditions. Use a path optimization algorithm to calculate the shortest path and shortest time combination for transportation tasks. When there is a congested section in the optimal route, the alternative route is recalculated and the optimal transportation route plan is generated, including path planning and time estimation; Match the optimal transportation route plan with the production plan to determine the final transportation task execution plan.

7. The intelligent calculation method for concrete sales according to claim 1, characterized in that: Real-time monitoring of inventory status. When inventory levels fall below a preset threshold, production plan adjustments are triggered to avoid supply shortages. When inventory levels are higher than a preset threshold, transportation scheduling is optimized, including: A preset inventory monitoring system is used to collect inventory data in real time. The collected inventory data is compared with preset high and low thresholds. When the inventory level is lower than the low threshold, the production adjustment amount is calculated based on historical demand data and a production plan adjustment plan is generated. When the inventory level is higher than the high threshold, the transportation optimization amount is calculated based on the current transportation capacity and inventory distribution. When inventory levels fall below a lower threshold, a time series forecasting algorithm is used to predict future demand based on historical demand data. Combined with current inventory levels, the production adjustment amount is calculated, and a production plan adjustment instruction is generated and transmitted to the production management system, triggering the production plan adjustment process. When the inventory level is higher than the high threshold, a linear programming algorithm is used to optimize the transportation routes and transportation volumes based on the current inventory distribution and transportation capacity, and the data is transmitted to the transportation management system to execute the transportation scheduling optimization process.

8. The intelligent calculation method for concrete sales according to claim 1, characterized in that: Sensors are used to obtain data on raw material ratios and concrete status. When ratio deviations or timeliness anomalies are detected, production parameters are adjusted, including: Sensors are used to obtain the raw material ratio and concrete status values, and a preset threshold is used to determine whether the collected amount 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, re-collect the ratio value and status value to determine whether they meet the preset maintenance quantity. If the timeliness is abnormal, use the time series analysis method to predict the future status value and adjust the production parameters according to the prediction results; Through the regression model in the machine learning algorithm, the relationship between the ratio value and the status value in the historical data is analyzed to optimize the production parameters. However, if the adjusted production parameters still cannot meet the maintenance quantity, the clustering algorithm is used to classify the abnormal data, identify the cause of the abnormality, and adjust the production parameters based on the abnormal classification results.

9. The intelligent calculation method for concrete sales according to claim 1, characterized in that: During transportation, the vehicle location and concrete status are tracked in real time. If transportation delays or abnormal concrete quality are detected, the transportation route is recalculated to ensure delivery efficiency. Production, transportation, and inventory data are aggregated to update the multivariable optimization model parameters, including: GPS technology is used to obtain vehicle location information, and sensors are used to monitor the concrete status. When the vehicle position deviates from the preset route or the concrete status is abnormal, the transport route recalculation module is triggered; Extract production data from the production system, extract transportation data from the transportation system, extract inventory data from the inventory system, and input the production data, transportation data, and inventory data into the summary data module to generate a comprehensive data set; The model parameters of the multivariable optimization model are updated based on the comprehensive data set. The new transportation route is calculated through the optimization model, and an updated route plan is generated and sent to the vehicle navigation system.

10. A concrete sales intelligent calculation system, used to implement the concrete sales intelligent calculation method according to any one of claims 1 to 9, characterized in that: include: Data collection and storage module, used to collect customer demand data, weather 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 with time series analysis methods to calculate historical demand trend lines, analyze demand fluctuation characteristics, predict future order volume trends, and determine the optimal delivery time based on construction site information to generate demand management plans; The weather and construction impact assessment module uses a sliding window model to extract trend characteristics of meteorological data, and uses a decision tree algorithm and linear regression model to evaluate the impact of weather changes on construction needs. It generates a construction demand adjustment plan and dynamically updates the construction progress, resource allocation, and safety measures. Traffic condition assessment and route optimization module collects traffic data in real time, generates a road network status diagram, uses a route optimization algorithm combined with historical traffic data to replan transportation routes, and outputs real-time navigation solutions and road condition warning information; The production planning and inventory management module inputs customer demand fluctuation characteristics, weather change forecasts, and traffic condition assessments into a multivariable optimization model to dynamically adjust production plans, determine production volume and raw material ratios, and monitor inventory status in real time. When inventory falls below or exceeds a preset threshold, it triggers production plan adjustments or transportation scheduling optimization processes, forming a closed-loop control process. The production process monitoring and adjustment module uses sensors to obtain real-time data on raw material ratios and concrete status, uses machine learning algorithms to analyze the data, 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 status of concrete. When transportation delays or abnormal concrete quality are found, the transportation route is recalculated. At the same time, production, transportation and inventory data are summarized and the multivariable optimization model parameters are updated.

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