Storage concrete remaining amount monitoring method and system

Abstract

The invention relates to the field of intelligent warehouse management, and discloses a warehouse concrete remaining amount monitoring method and system, and the method comprises the following steps: data collection: collecting the height data of a concrete surface and the humidity and temperature external environment data of a warehouse environment in real time through a multi-point sensor; stacking curved surface reconstruction: carrying out dynamic modeling and numerical solution on the concrete stacking surface based on a nonlinear partial differential equation model; dynamic density modeling: calculating the density distribution of the concrete in the storage container through a density evolution model; and volume calculation: calculating the real-time storage allowance of the concrete in combination with the accumulation curved surface data and the density distribution. By adopting an accumulation curved surface modeling technical scheme based on a nonlinear partial differential equation, through dynamically describing the form change of the concrete accumulation surface and combining numerical solution, precise modeling of a complex surface is realized, the problem of insufficient surface modeling precision under the condition of complex accumulation form is solved, and the modeling precision of the concrete accumulation surface is improved. And the reliability of margin calculation is obviously improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent warehouse management, and specifically to a method and system for monitoring the remaining amount of concrete in a warehouse. Background Art

[0002] During the construction process of engineering projects, the concrete mixing plant involves a large variety of materials in large quantities, such as stones, cement, fly ash, etc. of different specifications; for the use and inventory management of materials, in the field of concrete warehouse management, real-time monitoring of the remaining amount of stored concrete is an important link to ensure the smooth progress of the production process. Traditional methods for monitoring the remaining amount of concrete mostly rely on manual measurement or simple volume estimation methods.

[0003] The prior art usually uses linear or geometric models to estimate the stacking surface of concrete. For example, the stacking form is simplified into regular conical or parabolic forms for quick calculation of the remaining amount. This method has significant deficiencies in practical applications because the surface form of concrete during storage is affected by factors such as vibration, gravity flow, and external loading, forming a complex non-linear stacking surface. Existing models are difficult to accurately capture these dynamic changes, resulting in large errors in the calculation results, especially in scenarios where the shape of the storage container is complex or the non-regularity of the stacking surface is strong.

[0004] The prior art usually assumes that the density field of concrete is evenly distributed, which ignores the dynamic change characteristics of density with environmental conditions (such as humidity, temperature). However, during the storage of concrete, its particles will be affected by gravity compaction or humidity changes, resulting in local density fluctuations.

[0005] In view of the deficiencies of the prior art, the present invention provides a method and system for monitoring the remaining amount of concrete in a warehouse. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a method and system for monitoring the remaining amount of concrete in a warehouse, which solves the problem that the surface form of concrete during storage is affected by factors such as vibration, gravity flow, and external loading, forming a complex non-linear stacking surface, and existing models are difficult to accurately capture these dynamic changes, resulting in large errors in the calculation results.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for monitoring the remaining amount of concrete in a warehouse, including the following steps: Data acquisition: Real-time acquisition of the height data of the concrete surface and the external environmental data of humidity and temperature in the warehouse environment through a multi-point sensor; Reconstruction of the stacking surface: Dynamically modeling and numerically solving the concrete stacking surface based on a non-linear partial differential equation model; Dynamic density modeling: Calculate the density distribution of concrete in the storage container through the density evolution model; Volume calculation: Combine the stacking surface data and density distribution to calculate the real-time storage surplus of concrete; Surplus correction and warning: Correct the error of surplus calculation based on the topology optimization model, and trigger overstock or shortage alarms according to the preset threshold.

[0008] Furthermore, by deploying multi-point sensors on the top and side walls of the storage container, the height data of the concrete surface is collected in real time, and the external environmental data such as humidity and temperature is obtained in combination with environmental sensors for subsequent dynamic parameter adjustment and calculation; based on the dynamic changes in the surface morphology during the concrete stacking process, a nonlinear partial differential equation model is established to describe the flow, dispersion, and roughness characteristics of the stacking surface. The dynamic height distribution of the concrete stacking surface is obtained through numerical solution, and the stacking area is defined in combination with the boundary conditions; further, a dynamic evolution model of the density field is established through the Fokker-Planck equation, considering the variation law of density with the velocity field and diffusion term, and the diffusion coefficient is dynamically adjusted based on environmental data such as humidity and temperature. The distribution of concrete density in the storage space is solved using numerical methods; on this basis, combining the stacking surface height data and density distribution, the real-time storage surplus is calculated by performing a three-dimensional space integral on the storage area. At the same time, the topology optimization model is used to correct the volume calculation, and the gradient descent method is used to optimize the coupling relationship between the container geometry and the stacking area to ensure the accuracy of the surplus calculation; finally, the corrected surplus is compared with the preset inventory threshold to trigger overstock or shortage alarms, and real-time replenishment or adjustment suggestions are provided in combination with the prediction of inventory change trends.

[0009] Preferably, the steps of reconstructing the stacking surface include: Describe the dynamic changes of the concrete surface through the following nonlinear partial differential equation: The temporal change of the surface height is jointly determined by the surface flow, dispersion effect, and roughness characteristics, and the surface model adjusts the surface morphology according to the environmental loading.

[0010] Furthermore, the dynamic changes of the concrete stacking surface are described by establishing a nonlinear partial differential equation. Specifically, the height of the stacking surface evolves over time under the influence of concrete particle flow, gravity, and external environmental disturbances. Its surface morphology is driven by the velocity field and is simultaneously restricted by the dispersion effect to smooth the abrupt change region and roughness characteristics. The model introduces an external loading term to dynamically adapt to the environmental condition changes during the stockpiling process. The partial differential equation is discretized using numerical solution methods (such as the finite difference method), and the spatial distribution of the surface height is obtained through iterative calculation. At the same time, the stacking area is defined in combination with the container boundary conditions to ensure the physical rationality and accuracy of the reconstructed surface.

[0011] Preferably, in the density modeling step, the dynamic change of the concrete density satisfies the following conditions: The evolution of the density field is affected by the flow velocity field, and the dynamic adjustment of humidity and temperature on the density is characterized by the diffusion coefficient. The density distribution is obtained by numerical solution.

[0012] Furthermore, a density evolution model based on the Fokker-Planck equation is established to describe the distribution of the concrete density field and its dynamic change over time. The evolution of the density is driven by the flow velocity field, which characterizes the flow behavior of concrete particles during the stacking process. At the same time, the diffusion relative density is introduced for dynamic adjustment. The diffusion coefficient is closely related to the environmental humidity and temperature, and the coupling effect of humidity and temperature on the density change is expressed by a dynamic function. The model is discretely solved by numerical methods (such as the finite volume method), and the dynamic evolution result of the density distribution in the storage container is calculated in real time. Finally, the density field distribution of each region is obtained as the basic data for subsequent volume calculation.

[0013] Preferably, in the volume calculation step, the storage surplus of the concrete is calculated by the following formula: Based on the reconstructed stacking surface Z(x, y, t) and the dynamic density distribution ρ(x, y, z, t), the actual storage surplus V of the concrete is obtained by three-dimensional space integration. The specific calculation formula is: where Ω is the three-dimensional storage area of the container, the integration range is determined by the space below the stacking surface, the dynamic height Z(x, y, t) of the stacking surface is modeled by a nonlinear partial differential equation and obtained by numerical solution, and the density distribution ρ(x, y, z, t) is evolved from the Fokker-Planck equation.

[0014] Furthermore, based on the reconstructed stacking surface Z(x, y, t) and the dynamic density distribution ρ(x, y, z, t), the actual storage surplus V of the concrete is obtained by three-dimensional space integration. The specific calculation formula is: where Ω is the three-dimensional storage area of the container, the integration range is determined by the space below the stacking surface, the dynamic height Z(x, y, t) of the stacking surface is modeled by a nonlinear partial differential equation and obtained by numerical solution, and the density distribution ρ(x, y, z, t) is evolved from the Fokker-Planck equation. The above integral is discretely solved by the finite volume method, and the height distribution of the stacking surface is weighted by combining the dynamic density field to accurately reflect the actual storage state of the concrete and correct the possible density deviation and environmental impact.

[0015] The Fokker-Planck equation is a partial differential equation used to describe the evolution of the particle distribution function over time, and is typically used to study stochastic processes, diffusion phenomena, and changes in state distributions in complex systems. The equation can be expressed as: where: represents the rate of change of the density field ρ(x, y, z, t) with respect to time t, and this term reflects the dynamic changes in the density field during the storage process. is the convection term, which represents the spatial transfer of density over time under the action of the flow velocity field v. The flow velocity field v can be determined by physical factors such as the angle of repose, the direction of gravity, and external loading; DΔρ is the diffusion term, which represents the density field gradually becoming uniform over time under the action of the diffusion coefficient D. The diffusion coefficient D can be a constant or can dynamically depend on environmental parameters such as humidity and temperature.

[0016] The non-linear partial differential equation for the stacking surface modeling is: where: is the rate of change of the surface height with respect to time, reflecting the dynamic characteristics of the stacking process. is the flow term, which describes the diffusion of concrete particles to other regions of the surface under the action of the flow velocity field v. αΔZ: surface dispersion term, which controls the smoothness of the surface and reduces local mutations. is the non-linear roughness term, which captures the microscopic surface characteristics formed during the stacking process of concrete particles. f(x, y): external loading term, which reflects the influence of external factors (such as the dumping direction and strength of the material) on the stacking surface.

[0017] Preferably, the margin correction step uses a topology optimization method for error correction, specifically including: Construct an optimization function with the difference between the stacking surface and the geometric shape of the container bottom as the objective. Based on the gradient descent method, adjust the coupling relationship between the container shape and the stacking area.

[0018] Furthermore, first construct an objective function, with the difference between the stacking surface Z(x, y, t) and the geometric shape S(x, y) of the container bottom as the optimization objective, and optimize the coupling relationship between the container shape and the concrete stacking area by minimizing the error between the two. The form of the objective function is: F(χ) = ∫ Ω ∥Z(x, y, t) - S(x, y)∥ 2 χ(x, y, z)dxdy dz Among them, χ(x, y, z) is the topological optimization variable, and its value range is 0 or 1, which is used to identify the area actually occupied by the concrete; then, based on the gradient descent method, the partial derivative of the objective function with respect to the optimization variable is calculated to update the topological variable. Where η is the learning rate. By iterating multiple times, the matching degree between the actual stacking area of the concrete and the geometric shape of the container is gradually optimized, and the projection method is used to constrain the variable χ within the physical range, finally realizing the correction of the calculation error of the concrete storage margin and improving the accuracy and robustness of the volume estimation.

[0019] Preferably, the sensors in the data acquisition step include ultrasonic sensors, radar sensors, and environmental sensors, which are respectively used to collect the concrete height data and environmental data.

[0020] Preferably, in the margin correction step, the calculation parameters, including the concrete flow rate, diffusion coefficient, and roughness, are dynamically adjusted by minimizing the error between the sensor measurement value and the model prediction value.

[0021] Furthermore, the sensors include ultrasonic sensors, radar sensors, and environmental sensors. The ultrasonic sensor is used to measure the height data of the concrete stacking surface in real time. The radar sensor is used to monitor the large-range surface morphology changes to enhance the data accuracy. The environmental sensor is used to collect environmental parameters such as humidity and temperature. These data are uploaded to the data processing system in real time through the wireless transmission module, providing the basic input for subsequent modeling. And the sensor layout positions are optimized to cover the key areas of the entire storage container, ensuring the accuracy and comprehensiveness of the height data and environmental data. Preferably, in the margin correction step, by dynamically analyzing the error between the actual measurement value of the sensor and the model prediction value, the key calculation parameters such as the concrete flow rate, diffusion coefficient, and roughness are adjusted to optimize the model's description of the stacking dynamic process and density change, correct the deviation caused by environmental fluctuations or data noise in the calculation, and finally improve the overall accuracy of the margin estimation and the robustness of the model.

[0022] Preferably, the alarm step includes: Triggering overstocking and shortage alarms according to the calculated real-time storage margin; Generating a prediction of the inventory change trend for the alarm status and providing suggestions for replenishment or storage adjustment.

[0023] Preferably, the margin calculation step combines real-time environmental data and historical records to dynamically adjust the modeling parameters to improve the prediction accuracy.

[0024] Furthermore, based on the calculated real-time storage margin, an early warning threshold is set. When the margin exceeds the preset storage upper limit, an overstock alarm is triggered to remind the operator to adjust the material storage plan to avoid potential safety hazards caused by overloading. When the margin is lower than the preset storage lower limit, a shortage alarm is triggered to indicate that the insufficient materials may affect the production plan, and relevant information on the alarm status is generated. The system generates a prediction curve of inventory changes by analyzing the current margin data, historical inventory records, and usage trends, provides a visual trend report for management personnel, and gives replenishment suggestions or storage adjustment plans in combination with the future demand and supply cycle output by the model to optimize the material management decision. Preferably, the margin calculation step combines the real-time collected environmental data and historical records to dynamically adjust the modeling parameters, such as the diffusion coefficient of density and the roughness characteristics of the accumulation surface, to ensure that the calculation results can reflect the impact of changes in the storage environment on the material distribution in real time, thereby significantly improving the accuracy and reliability of the prediction.

[0025] A storage concrete margin monitoring system, comprising: Data acquisition module: including multi-point height sensors and environmental sensors for real-time collecting the concrete surface height data and environmental parameters; Data processing module: including a non-linear partial differential equation solving unit for accumulation surface modeling, a density dynamic evolution calculation unit, and a volume calculation and topology optimization unit; Alarm module: triggering an overstock or shortage alarm based on the margin calculation result and the preset threshold; Display module: presenting the 3D concrete model, density distribution, and margin status information in real time.

[0026] Furthermore, there is a data acquisition module, which consists of a multi-point height sensor and an environmental sensor. The multi-point height sensor is arranged on the top and side walls of the storage container and is used to collect the height data of the concrete accumulation surface in real time and monitor the dynamic changes of the accumulation surface through high-frequency sampling. The environmental sensor is used to collect external parameters such as humidity and temperature, providing key environmental inputs for the density dynamic evolution model; a data processing module, which includes an accumulation surface modeling unit, a density dynamic evolution calculation unit, and a volume calculation and topology optimization unit. The accumulation surface modeling unit describes the dynamic evolution of the concrete surface morphology based on non-linear partial differential equations. The density dynamic evolution calculation unit dynamically adjusts the density distribution by combining the flow velocity field and environmental factors. The volume calculation and topology optimization unit accurately calculates the storage margin and corrects errors according to the surface morphology and density distribution; an alarm module, which compares the margin calculation result with a preset threshold, automatically triggers an overstock alarm or a shortage alarm, supports real-time push of alarm information, and generates replenishment or adjustment suggestions by combining the inventory change trend prediction; a display module, which is used to visually present the three-dimensional concrete accumulation model, dynamic density distribution, and real-time margin information, and at the same time display the inventory warning status and trend prediction results, providing comprehensive decision-making support for management personnel.

[0027] The present invention provides a method and system for monitoring the remaining amount of concrete in storage. It has the following beneficial effects: 1. By adopting the technical solution of accumulation surface modeling based on non-linear partial differential equations, the present invention realizes accurate modeling of complex surfaces by dynamically describing the morphological changes of the concrete accumulation surface and combining numerical solutions. Compared with the technical solutions in the prior art that use linear or simple geometric assumptions to estimate the accumulation surface, the problem of insufficient surface modeling accuracy in the case of complex accumulation forms is solved, and the reliability of margin calculation is significantly improved.

[0028] 2. By adopting the technical solution of density dynamic evolution modeling based on the Fokker-Planck equation, the present invention can dynamically adjust the density field distribution by combining environmental factors such as temperature and humidity changes, and realizes accurate description of the non-uniform characteristics of the density field. Compared with the technical solutions in the prior art that assume a uniform density field distribution, the problem of large margin estimation errors caused by the inability to reflect actual density changes is solved, and the physical accuracy and adaptability of the model are improved.

[0029] 3. By adopting the technical solution of margin correction combined with topology optimization, the present invention effectively corrects the deviation in volume calculation by constructing an optimization objective function and using the gradient descent method to couple and optimize the container shape and the concrete accumulation area. Compared with the technical solutions in the prior art that directly estimate based on geometric volume formulas, the problem of inaccurate margin calculation under complex geometric shape conditions is solved, and the accuracy and robustness of margin monitoring are significantly improved. Description of the Drawings

[0030] Figure 1 is the flow chart of the present invention; Figure 2 is the system framework diagram of the present invention. Specific embodiments

[0031] Next, in combination with the accompanying drawings of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0032] Please refer to the attached Figure 1 - attached Figure 2 , the embodiment of the present invention provides a method for monitoring the remaining amount of concrete in a warehouse, including the following steps: Data acquisition: Real-time acquisition of the height data of the concrete surface and the external environmental data such as humidity and temperature of the warehouse environment through multi-point sensors; Stacked surface reconstruction: Dynamically model and numerically solve the concrete stacking surface based on the non-linear partial differential equation model; Dynamic density modeling: Calculate the density distribution of concrete in the storage container through the density evolution model; Volume calculation: Combine the stacked surface data and the density distribution to calculate the real-time storage remaining amount of concrete; Remaining amount correction and warning: Correct the remaining amount calculation error based on the topology optimization model, and trigger overstock or shortage alarms according to the preset threshold.

[0033] Data acquisition is the basic step for the present invention to realize real-time monitoring of the remaining amount of concrete, aiming to provide necessary initial data support for subsequent stacked surface reconstruction, dynamic density evolution modeling and volume calculation. By accurately acquiring the height information of the concrete stacking surface and the external environmental parameters, the accuracy and robustness of the subsequent model construction can be ensured. In some embodiments, in order to enhance the integrity and reliability of the acquired data, a multi-point distributed sensor layout and a dynamic sampling strategy are adopted to capture the dynamic changes of the concrete surface and environmental parameters in real time.

[0034] In this embodiment, the data acquisition step specifically includes the following contents: Deployment and layout of sensors Specifically, multi-point height sensors and environmental sensors are respectively arranged on the top and side walls of the concrete storage container.

[0035] The height sensors may include, but are not limited to, ultrasonic sensors and radar sensors, which are respectively used to collect the height information of the concrete stacking surface. Among them, the ultrasonic sensor is mainly used to accurately measure the local height of the stacking surface points and is suitable for accurate detection within a relatively short distance; the radar sensor can cover a larger range and is applicable to the monitoring of the overall height distribution of the container.

[0036] The environmental sensors may include humidity sensors and temperature sensors, which are used to collect the humidity and temperature information inside and outside the storage container in real time. These environmental parameters directly affect the dynamic changes and diffusion characteristics of the concrete density field.

[0037] As an option, the specific layout positions of the sensors can be optimized according to the shape and size of the storage container. For example, multiple height sensors are evenly arranged on the top of the container to cover the key areas of the stacking surface, and a certain number of height sensors are arranged on the side walls to monitor the changes in the stacking height of the boundary areas.

[0038] In a possible implementation, a cross-shaped sensor array can be set on the top of the container to capture the dynamic changes in the core area of the stacking surface, and the side wall sensors are arranged vertically to monitor the boundary transition area of the stacking surface.

[0039] It should be noted that the above layout method can ensure the full coverage and high accuracy of the stacking surface data, and at the same time avoid the generation of data acquisition blind spots.

[0040] Dynamic processing of data acquisition In order to adapt to the dynamic changes in the height and shape of the concrete stacking surface, the present invention adopts a real-time dynamic sampling strategy.

[0041] Specifically, the sensors will collect the concrete surface height data at fixed time intervals (such as every second), and increase the sampling frequency when the height changes violently. For example, when a new batch of concrete materials is poured into the storage container, the system will automatically trigger the high-frequency sampling mode to ensure the capture of key data during the rapid change process of the stacking surface.

[0042] As an option, the sampling interval can be adaptively adjusted according to the working state of the storage container. For example, when there is no new material added for a long time, the system will enter the low-frequency sampling mode to reduce energy consumption and data redundancy.

[0043] Preliminary processing of sensor data The data collected by the sensors is preliminarily processed through the data acquisition module, mainly including data cleaning, outlier removal and data formatting.

[0044] Data cleaning is to remove abnormal readings caused by external interferences (such as vibration, environmental noise) to ensure the credibility of the data.

[0045] Outlier rejection adopts a threshold method based on historical data. For example, data points with too large a deviation from the previous and subsequent sampled values are marked as outliers and rejected.

[0046] Data formatting then organizes the data from multi-point sensors into a unified format for subsequent processing modules to perform surface modeling and density evolution analysis.

[0047] It should be noted that the data after preliminary processing not only improves the accuracy and consistency of the data but also lays a good foundation for subsequent model solving.

[0048] Collection and integration of environmental data The humidity and temperature data collected by environmental sensors are timestamp-synchronized with the stacked surface height data collected by height sensors.

[0049] Exemplarily, the humidity data is input into the density evolution model in real time to dynamically adjust the density diffusion coefficient to ensure that the model can reflect the true state of the concrete in the storage container; the temperature data is used to correct the temperature dependence of the diffusion characteristics. For example, in a high-temperature environment, the density dispersion process may be accelerated.

[0050] It should be noted that the dynamic input of these environmental data greatly enhances the adaptability of the model to external changes and ensures the accuracy of subsequent density modeling and volume calculation.

[0051] Extended technical content As a possible extended embodiment, the signals of the height sensor and the environmental sensor can also be fused. For example, by fusing the height data collected by the sensor and the humidity and temperature data, a higher-dimensional description of the stacked surface characteristics can be generated to further improve the accuracy of surface modeling.

[0052] In addition, in some special scenarios, such as when multiple different granular materials are stored in the container, multiple types of sensors can be introduced to work together to achieve hierarchical monitoring of the multi-layer stacked surface.

[0053] Through the above steps, the data acquisition module can provide the initial data of the concrete stacked surface and environmental parameters in real time and accurately, providing reliable data support for the stacked surface reconstruction, dynamic density modeling, and volume calculation in the subsequent steps. It should be noted that the data acquisition strategy of the present invention has both dynamic response ability and high efficiency and can adapt to complex storage environment conditions.

[0054] Step: Stacked surface reconstruction The reconstruction of the accumulation surface is one of the key steps of the present invention. By accurately modeling the dynamic morphology of the concrete accumulation surface, it provides a geometric basis for subsequent density modeling and volume calculation. The dynamic changes of the accumulation surface are jointly affected by the material pouring method, gravity flow, external disturbances, and environmental factors. Therefore, accurately describing and solving the morphology of the accumulation surface is crucial. The present invention models the dynamic evolution process of the accumulation surface morphology by establishing a mathematical model based on nonlinear partial differential equations (PDEs) and uses numerical methods for solution, realizing the reconstruction of the accumulation surface in complex scenarios.

[0055] In this embodiment, the reconstruction of the accumulation surface includes the specific implementation of mathematical modeling, parameter setting, numerical discretization, and solution process.

[0056] Mathematical Modeling In this embodiment, the dynamic changes of the accumulation surface Z(x, y, t) are described by the following nonlinear partial differential equation: It should be noted that each term in the above equation corresponds to the main influencing factors of the accumulation surface: Describes the rate of change of the accumulation surface over time, Reflects the flow characteristics of concrete particles under the action of gravity and the material pouring speed. The flow velocity field v determines the dynamic direction and rate of accumulation. αΔZ is the surface diffusion term, mainly used to smooth local mutation regions and reduce the discontinuity of the surface morphology. Is the roughness characteristic description term, capturing the fine characteristics of the accumulation surface. f(x, y) is the external loading term, describing the morphological disturbance caused to the accumulation surface during the material pouring process. In some embodiments, the direction of the flow velocity field v can be calculated based on gravity and the material pouring angle, and its magnitude is proportional to the material conveying rate. The external loading term f(x, y) can be dynamically updated according to the pouring time and area recorded by the sensor to ensure the accurate description of the dynamic accumulation process by the model.

[0057] Parameter Setting In this embodiment, each parameter is set according to experimental calibration and the actual environment: the surface diffusion coefficient α is adjusted by experimentally measuring the continuity of the accumulation surface and is suitable for smoothing processing. The magnitude of the roughness parameter β is related to the average diameter of concrete particles. Larger particles correspond to higher roughness. The initial condition Z(x, y, 0) is set according to the initial accumulation height collected by the sensor. The boundary condition adopts the Dirichlet boundary condition, assuming that the accumulation surface is zero at the container edge, that is It should be noted that these parameters can be adjusted according to the actual container shape and concrete particle characteristics to adapt to different application scenarios.

[0058] Numerical Discretization To solve the above partial differential equation, in this embodiment, the finite difference method (FDM) is used to discretize space and time.

[0059] Spatial discretization: The central difference scheme is used to and ΔZ for spatial discretization to generate a numerical form at discrete grid points. The grid division density is adjusted according to the container size and the complexity of the stacking surface. Usually, a finer grid is adopted in the area where the stacking surface changes rapidly.

[0060] Temporal discretization: The explicit Runge-Kutta method is used for temporal stepping to discretize the temporal derivative to ensure the stability and accuracy of the calculation. The time step is dynamically adjusted according to the stacking speed and the change of the flow velocity field to ensure the convergence of the solution process. In a possible implementation, a three-dimensional grid is used to accurately discretize the stacking surface inside the container, and the grid point density is increased in the area where the curved surface changes violently to improve the local accuracy of the solution.

[0061] Numerical solution The discretized equation is solved iteratively to obtain the dynamic height distribution of the stacking surface. The specific implementation process includes the following: after setting the initial conditions, the height Z(x, y, t) of the stacking surface is iteratively calculated based on the time step Δt. In each time period, the model results are corrected by combining the height data collected by the sensor to reduce error accumulation. During the solution process, it is checked whether the surface morphology meets the convergence condition, such as the height change in two consecutive iterations is less than the set threshold. In an exemplary embodiment, the solution efficiency is optimized by parallel computing, such as distributing the grid points to multiple computing nodes for simultaneous processing.

[0062] Model dynamic adjustment In this embodiment, to enhance the adaptability of the stacking surface modeling, the model supports dynamic parameter adjustment. For example, in a high-humidity environment, the diffusivity of the concrete surface will increase, so the value of the diffusivity coefficient α can be dynamically increased. It should be noted that these dynamic adjustments are updated in real time according to the environmental data collected by the sensor to ensure that the model can reflect the actual dynamic changes of the stacking surface.

[0063] Extended technical content As a possible extended application, the stacking surface model in this embodiment can be used to predict the surface morphology after pouring new materials. For example, through the dynamic adjustment of the external add-in f(x,y) and combined with the input of the stacking speed, the impact of the newly added materials on the existing stacking surface can be calculated in advance, providing support for warehousing planning. In addition, the stacking surface model of the present invention can also further optimize the parameters in combination with machine learning methods. For example, by training the model with historical data to dynamically adjust the flow velocity field v and the roughness parameter β, the accuracy of surface modeling can be improved.

[0064] Through the above steps, the stacking surface reconstruction module can accurately model the concrete stacking surface in complex dynamic scenarios, providing a geometric basis for subsequent density modeling and margin calculation. It should be noted that the reconstruction method of this embodiment is not only applicable to the margin monitoring of warehoused concrete, but also can be extended to other storage monitoring scenarios of granular materials.

[0065] Step: Dynamic density modeling Dynamic density modeling is a core step of the present invention. Its goal is to provide an accurate physical property basis for subsequent volume calculation by describing the density distribution and its dynamic evolution law of concrete in the storage container. The density field of concrete shows non-uniformity and dynamic change characteristics during the storage process, affected by gravity, stacking angle, particle flow, and environmental conditions (such as humidity, temperature). The present invention establishes a density evolution model based on the Fokker-Planck equation and dynamically calculates the density distribution in combination with numerical solution methods, providing reliable support for margin monitoring in complex scenarios.

[0066] In this embodiment, the implementation of dynamic density modeling includes mathematical modeling of the density field, parameter setting, numerical discretization and solution, as well as dynamic adjustment and error correction.

[0067] Mathematical modeling of the density field In this embodiment, the dynamic change of the concrete density field ρ(x,y,z,t) follows the following partial differential equation: It should be noted that each term in the equation describes the key influencing factors of the concrete density distribution: Describes the rate of change of the density field over time; is the convection term, describing the movement characteristics of density under the action of the flow velocity field v; DΔρ: is the diffusion term, describing the dispersion effect of density in the storage container, where D is the diffusion coefficient. In a possible implementation, the flow velocity field v is jointly determined by the slope of the stacking surface and the direction of gravity, and can be calculated through the stacking surface reconstruction result. The diffusion coefficient D dynamically depends on environmental parameters (such as humidity, temperature) and is represented by the following functional relationship D = D 0 +γ1 · Humidity + γ 2 · Temperature Where D 0 is the initial diffusion coefficient, and γ 1 , γ 2 are empirical parameters.

[0068] Initial Condition and Boundary Condition Setting In this embodiment, the initial condition of the density field is set as: ρ(x, y, z, 0) = ρ 0 Where ρ 0 is the initial uniform density of the concrete.

[0069] The boundary condition adopts the zero-flux boundary condition: That is, the density cannot flow out or flow into the container boundary, ensuring the overall mass conservation of the material. It should be noted that these initial conditions and boundary conditions can be adjusted according to the geometric shape of the storage container and the material properties to adapt to different application scenarios.

[0070] Numerical Discretization and Solution In this embodiment, the finite volume method (FVM) is used to discretize and solve the Fokker-Planck equation.

[0071] The storage container is divided into uniform grid cells, and the density distribution within each grid cell is integrated and converted into a discrete form. The convective term is discretized using the upwind scheme to improve the solution stability.

[0072] Spatial Discretization: Temporal Discretization: The time step adopts the implicit Euler method to enhance the convergence and stability of the calculation. In one possible implementation, the grid density can be increased in the area with large density changes (such as the high-pressure area at the bottom) through the adaptive grid refinement technology, thereby improving the calculation accuracy.

[0073] Dynamic Adjustment and Error Correction To adapt to real-time environmental changes, a dynamic parameter adjustment and error correction mechanism is introduced in this embodiment.

[0074] Dynamic Adjustment: The diffusion coefficient D is dynamically updated according to the humidity and temperature data collected by the environmental sensor in real time, ensuring that the density model can reflect the actual impact of environmental changes on the concrete density distribution. Under high humidity conditions, the value of D is increased to enhance the diffusion effect; under low temperature environments, the value of D is decreased to reflect the low fluidity of the density.

[0075] Error Correction: During the numerical solution process, the density data measured by the sensor is compared with the density distribution predicted by the model, and the error E = ∥ρ sensor - ρ model ∥.

[0076] According to the magnitude of the error, the initial conditions of the density model and the flow velocity field parameter v are adjusted to reduce the model deviation. It should be noted that this dynamic adjustment and error correction mechanism can significantly improve the accuracy and reliability of density modeling.

[0077] Extended technical content As an extended application, the density modeling method in this embodiment can be used to predict the density changes under different storage times and environmental conditions. For example, by increasing the time dependence D(t) of the diffusion coefficient, the dispersion effect of density during long-term storage can be simulated, providing technical support for the material management in long-term warehousing. In addition, the density modeling method of the present invention can also be combined with machine learning techniques to optimize the parameters of the diffusion coefficient D and the flow velocity field v. For example, by training the model with historical data, the changing trend of the density field can be dynamically predicted, further improving the intelligent level of the monitoring system.

[0078] Through the above density modeling process, this embodiment realizes the dynamic calculation of the concrete density field, providing an accurate physical input basis for the volume calculation in complex scenarios. It should be noted that the dynamic density modeling method of the present invention is not only applicable to the monitoring of concrete, but also can be extended to other scenarios of granular materials or liquid storage.

[0079] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the remaining amount of stored concrete, characterized in that: The following steps are involved: Data collection: Multi-point sensors are used to collect real-time data on the height of the concrete surface and external environmental data such as humidity and temperature of the storage environment; Deposition surface reconstruction: Dynamic modeling and numerical solution of concrete accumulation surface based on nonlinear partial differential equation model; Dynamic density modeling: Calculate the density distribution of concrete in storage containers through density evolution model; Volume calculation: Combine the accumulation surface data and density distribution to calculate the real-time storage margin of concrete; Redundancy correction and early warning: Correct the redundancy calculation error based on the topology optimization model, and trigger overstock or shortage alarm according to the preset threshold.

2. A method for monitoring the remaining amount of stored concrete according to claim 1, characterized in that: The step of reconstructing the stacked surface comprises: The dynamic changes of the concrete surface are described by nonlinear partial differential equations: The temporal variation of surface height is determined by surface flow, diffusion effects and roughness characteristics, and the surface model adjusts the surface morphology according to environmental loading.

3. A method for monitoring the remaining amount of stored concrete according to claim 1, characterized in that: In the density modeling step, the dynamic change of concrete density satisfies the following conditions: The evolution of the density field is affected by the velocity field, and the dynamic adjustment of humidity and temperature to density is characterized by the diffusion coefficient. The density distribution is obtained by numerical solution.

4. A method for monitoring the remaining amount of stored concrete according to claim 1, characterized in that: In the volume calculation step, the storage margin of concrete is calculated by the following formula: Based on the reconstructed stacking surface Z(x, y, t) and the dynamic density distribution ρ(x, y, z, t), the actual storage margin V of concrete is obtained by three-dimensional spatial integration. The specific calculation formula is: Where Ω is the three-dimensional storage area of ​​the container, the integration range is determined by the space below the stacking surface, the dynamic height Z(x, y, t) of the stacking surface is modeled by a nonlinear partial differential equation and numerically solved, and the density distribution ρ(x, y, z, t) is evolved from the Fokker-Planck equation.

5. The method for monitoring the remaining amount of stored concrete according to claim 1, characterized in that: The margin correction step adopts a topology optimization method to perform error correction, specifically comprising: Construct an optimization function targeting the difference in geometric shapes between the stacking surface and the container bottom; The coupling relationship between the container shape and the stacking area is adjusted based on the gradient descent method.

6. A method for monitoring the remaining amount of stored concrete according to claim 1, characterized in that: The sensors in the data collection step include ultrasonic sensors, radar sensors and environmental sensors, which are used to collect concrete height data and environmental data respectively.

7. A method for monitoring the remaining amount of stored concrete according to claim 1, characterized in that: In the residual correction step, the calculation parameters including concrete flow velocity, diffusion coefficient and roughness are dynamically adjusted by minimizing the error between the sensor measurement value and the model prediction value.

8. The method for monitoring the remaining amount of stored concrete according to claim 1, characterized in that: The alarm step includes: triggering over-storage and shortage alarms according to the calculated real-time storage margin; Generate inventory change trend forecasts for alarm status and provide replenishment or storage adjustment suggestions.

9. The method for monitoring the remaining amount of stored concrete according to claim 1, characterized in that: The margin calculation step combines real-time environmental data and historical records to dynamically adjust modeling parameters to improve prediction accuracy.

10. A storage concrete surplus monitoring system, according to a storage concrete surplus monitoring method according to claims 1-9, characterized in that: include: Data acquisition module: including multi-point height sensors and environmental sensors, used to collect concrete surface height data and environmental parameters in real time; Data processing module: including nonlinear partial differential equation solving unit for stacking surface modeling, density dynamic evolution calculation unit, and volume calculation and topology optimization unit; Alarm module: triggers overstock or shortage alarm based on the balance calculation result and preset threshold; Display module: real-time presentation of concrete 3D model, density distribution and surplus status information.

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