Intelligent loading system
By embedding a moisture detector and a meteorological data acquisition module in the loading system and building a dynamic compensation algorithm, the problems of tonnage loss and overloading caused by changes in moisture content in the traditional loading system are solved, and accurate loading and resource optimization are achieved.
Patent Information
- Application Number
- CN202511112940.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-10
- Publication Date
- 2025-10-14
AI Technical Summary
The traditional sand and gravel aggregate loading system does not consider the impact of material moisture content on loading capacity, resulting in disputes over lost tons after moisture evaporation during transportation, and the fixed excess coefficient can easily lead to waste of transportation resources or overloading risks.
An intelligent loading system is adopted. By installing an embedded moisture detector on the loading belt, combining the meteorological data acquisition module and data processing unit, a dynamic compensation algorithm is constructed to monitor and adjust the loading volume in real time to avoid loss of tons and overloading caused by moisture evaporation.
It achieves precise control of loading volume during transportation, avoids disputes over lost tons and waste of transportation resources, and ensures compliance and safety.
Smart Images
Figure CN120774231A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of vehicle loading systems, and more specifically, to an intelligent vehicle loading system. Background Art
[0002] Sand and gravel aggregate is a general term for materials such as sand, pebbles (gravel), crushed stone, blocks, and rubble stones in water conservancy projects. Sand and gravel aggregate is the main building material for structures such as concrete and stacked stones in water conservancy projects. After the sand and gravel aggregate is generated, it needs to be loaded and transported to facilitate its use in different sites.
[0003] The traditional sand and gravel aggregate loading system has the following defects: it does not consider the impact of material moisture content on loading capacity, resulting in disputes over lost tons after moisture evaporation during transportation; the use of a fixed excess coefficient can easily lead to waste of transportation resources or overloading risks, and the actual use effect is not very ideal.
[0004] In order to solve the above problems, the present application provides an intelligent loading system. Summary of the Invention
[0005] The intelligent loading system provided in this application adopts the following technical solutions:
[0006] The intelligent loading system includes a base, a moisture detector, a meteorological data acquisition module, a loading scale, a transportation parameter input module, a control execution module and a data processing unit. The loading scale, bulk loader and finished product warehouse are respectively installed on the base, and the loading scale is located below the bulk loader, and the bulk loader is located below the finished product warehouse.
[0007] Through the above technical solution, through the vertical layout set up on the base (finished product warehouse → bulk loader → loading scale), gravity flow of materials can be realized for loading, reducing the energy consumption of mechanical transportation; the loading scale is directly located under the bulk loader, which can monitor the actual loading volume in real time and eliminate the cumulative error of traditional post-loading weighing.
[0008] Furthermore, the loading scale is located below the bulk loader.
[0009] Through the above technical solution, the loading scale is set up to weigh the amount of loaded materials, thereby facilitating subsequent adjustments and controls.
[0010] Furthermore, the bulk loader is located below the finished product warehouse.
[0011] Through the above technical solution, the bulk loader is set up to facilitate the unloading of materials and thus facilitate the loading of vehicles.
[0012] Furthermore, the control execution module is composed of a loading belt and a pneumatic butterfly valve on the bulk loader.
[0013] Through the technical scheme, the control execution module is arranged to control the loading of the material.
[0014] Further, a moisture inspection instrument is installed at the loading belt of the bulk machine.
[0015] Through the technical scheme, the moisture inspection instrument is embeddedly installed at the loading belt of the bulk machine, so that the moisture content detection is synchronized with the material conveying, full-section scanning is realized, the moisture content gradient distribution (such as 4.5% in the core layer and 3.2% in the surface layer) can be identified, and the one-sidedness of the traditional sampling detection is avoided.
[0016] Further, the moisture inspection instrument, the meteorological data acquisition module, the loading truck load cell, the transportation parameter input module and the control execution module are electrically connected with the data processing unit through wires.
[0017] Through the technical scheme, the industrial-grade shielded cable is adopted to establish a multi-device data channel, so that the moisture data, meteorological parameters and weight signals are synchronized at the millisecond level, and high timeliness data support is provided for the dynamic compensation algorithm.
[0018] Further, the meteorological data acquisition module integrates a temperature and humidity sensor, an anemometer and a weather forecast interface.
[0019] Through the technical scheme, multi-source meteorological sensing (local sensor + cloud forecast) is integrated, a transportation path microclimate model is constructed, the influence of environmental changes on evaporation in the next 2 hours (such as 18% acceleration of evaporation caused by 5℃ temperature rise) can be predicted, and the forward-looking compensation decision is improved.
[0020] Further, the transportation parameter input module receives parameters such as transportation distance, vehicle load limit and destination elevation.
[0021] Through the technical scheme, the transportation parameter input module supports automatic analysis of GIS maps (such as Baidu Map API), automatically calculates the evaporation rate increment caused by atmospheric pressure drop according to the transportation distance ΔL and the elevation change ΔH (such as 200km transportation + 800m elevation increase), and avoids manual estimation deviation.
[0022] Further, the data processing unit establishes a moisture evaporation model: f(Δt,T,H,v)=k·e^(aT+bH+cv)·Δt, and constructs a dynamic compensation algorithm: Q=Q0×[1+α(W-W0)+βΔL+γΔH] (wherein α, β and γ are environmental correction coefficients).
[0023] Through this technical solution, the water evaporation model introduces the exponential function e^(aT+bH+cv) to quantify the nonlinear effects of environmental parameters: for every 10°C increase in temperature T, the evaporation rate increases by 1.8 times (a=0.031); for every 20% increase in humidity H, the evaporation inhibition effect reaches 35% (b=-0.019); the wind speed v² term reflects the fluid dynamics effect (for example, at a wind speed of 4m / s, the evaporation rate is 64% higher than in a calm state); and the dynamic compensation algorithm achieves multi-factor coupling compensation by linearly superimposing the correction term (αΔW+βΔL+γΔH).
[0024] Furthermore, the algorithm in the data processing unit is provided with a loading excess coefficient, and the loading excess coefficient is dynamically adjusted according to the expected moisture loss calculated in real time. The adjustment formula is: δ=K1·Wt + K2·(T-T0) + K3·v²·t, where δ is the dynamic compensation coefficient, Wt is the real-time moisture content, T is the ambient temperature, v is the wind speed, and t is the expected transportation time.
[0025] Through the above technical solution, in the excess coefficient formula δ=K1Wt+K2ΔT+K3v²t: K1Wt item: for every 1% increase in moisture content, the compensation amount increases by 0.6% (K1=0.6); K2ΔT item: for every 10°C increase in transportation temperature difference, the compensation coefficient increases by 0.2%; K3v²t item: a wind speed of 4m / s for 5 hours will trigger a 3.2% compensation increase; through the constraint condition (δ≤legal overload threshold-safety margin of 5%), automatic compliance is achieved.
[0026] In summary, this application has the following beneficial technical effects:
[0027] When loading aggregates and sand and gravel, the present application determines the moisture content of the materials by adding a moisture detection device to the loading belt, and combines the meteorological conditions obtained by the meteorological data acquisition module and the parameters such as the transportation distance in the transportation parameter input module, and simulates and calculates the optimal value through the data processing unit. When loading, the bulk loader and the loading scale are used to flexibly adjust the loading volume, avoid disputes over lost tons due to moisture evaporation during transportation, reduce the risk of waste of transportation resources or overloading, and improve transportation capacity. BRIEF DESCRIPTION OF THE DRAWINGS
[0028] Figure 1 This is a schematic diagram of the overall structure of this application;
[0029] Figure 2 This is a flowchart of the application;
[0030] Figure 3 This is a table of symbol definitions for the water evaporation model of this application;
[0031] Figure 4 This is a table of symbolic definitions for the dynamic compensation algorithm of this application;
[0032] Figure 5 This is a table explaining the symbol definitions of the excess adjustment formula in this application.
[0033] Description of the numbers in the figure:
[0034] 1. Base; 2. Bulk loader; 3. Moisture detector; 4. Finished product warehouse; 5. Meteorological data acquisition module; 6. Data processing unit; 7. Loading scale; 8. Transport parameter input module; 9. Control execution module. DETAILED DESCRIPTION
[0035] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application; it is obvious that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.
[0036] In the description of this application, it should be noted that the terms "upper," "lower," "inner," "outer," "top / bottom," and the like, indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate the description of this application and simplify the description. They do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.
[0037] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "provided with," "mounted / connected," and "connected" should be understood in a broad sense. For example, "connected" can mean a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.
[0038] Example:
[0039] The present application discloses an intelligent loading system. Figure 1-5, including base 1, water check instrument 3, weather data acquisition module 5, loading ground scale 7, transportation parameter input module 8, control execution module 9 and data processing unit 6, the loading ground scale 7, the bulk machine 2 and the finished product warehouse 4 are respectively installed on the base 1, and the loading ground scale 7 is located below the bulk machine 2, and the bulk machine 2 is located below the finished product warehouse 4, through the vertical layout (finished product warehouse 4→bulk machine 2→loading ground scale 7) arranged on the base 1, the material gravity self-flow loading is realized, and mechanical conveying energy consumption is reduced; the loading ground scale 7 is directly located below the bulk machine 2, the actual loading amount can be monitored in real time, and the cumulative error of traditional loading and weighing is eliminated.
[0040] Please refer to Figure 1 , the loading ground scale 7 is located below the bulk machine 2, and the loading ground scale 7 is arranged to weigh the loaded material, thereby facilitating subsequent adjustment and control.
[0041] Please refer to Figure 1 , the bulk machine 2 is located below the finished product warehouse, and the bulk machine 2 is arranged to facilitate unloading of the material, thereby facilitating loading of the vehicle.
[0042] Please refer to Figure 1 , the control execution module 9 is composed of a loading belt on the bulk machine 2 and a pneumatic butterfly valve, and the control execution module 9 is arranged to control the loading of the material.
[0043] Please refer to Figure 1 , the moisture check instrument 3 is installed at the loading belt on the bulk machine 2, and the moisture check instrument 3 is embeddedly installed at the loading belt of the bulk machine 2, so that the moisture content detection is synchronized with the material conveying, full-section scanning is realized, the moisture content gradient distribution (such as core layer 4.5% and surface layer 3.2%) can be identified, and the one-sidedness of traditional sampling detection is avoided.
[0044] Please refer to Figure 1-5 , the moisture check instrument 3, the weather data acquisition module 5, the loading ground scale 7, the transportation parameter input module 8 and the control execution module 9 are electrically connected with the data processing unit 6 through wires, an industrial-grade shielded cable is used to establish a multi-device data channel, the millisecond-level synchronization of moisture data, weather parameters and weight signals is ensured, and high timeliness data support is provided for the dynamic compensation algorithm.
[0045] Please refer to the figure Figure 1-5 , the weather data acquisition module 5 integrates temperature and humidity sensors, anemometers and weather forecast interfaces, integrates multi-source weather sensing (local sensors + cloud prediction), constructs a transportation path microclimate model, can predict the influence of environmental changes on evaporation in the next 2 hours (such as temperature rise of 5℃ leading to 18% acceleration of evaporation), and compensates for the forward-looking improvement of the decision.
[0046] Please refer to Figure 1-5The transport parameter input module 8 receives parameters such as transport distance, vehicle load limit, and destination altitude. The transport parameter input module 8 supports automatic parsing of GIS maps (such as Baidu Map API) and automatically calculates the evaporation rate increment caused by atmospheric pressure drop based on the transport distance ΔL and altitude change ΔH (such as 200km transport + 800m altitude increase), thereby avoiding manual estimation bias.
[0047] See also Figure 1-5 Data processing unit 6 establishes a water evaporation model: f(Δt,T,H,v)=k·e^(aT+bH+cv)·Δt, and constructs a dynamic compensation algorithm: Q=Q0×[1+α(W-W0)+βΔL+γΔH] (where α, β, and γ are environmental correction coefficients). The water evaporation model introduces an exponential function e^(aT+bH+cv) to quantify the nonlinear effects of environmental parameters: for every 10°C increase in temperature T, the evaporation rate increases by 1.8 times (a=0.031); for every 20% increase in humidity H, the evaporation inhibition effect reaches 35% (b=-0.019); the wind speed v² term reflects the fluid mechanics effect (for example, the evaporation amount at a wind speed of 4m / s is 64% higher than in a calm state); the dynamic compensation algorithm achieves multi-factor coupling compensation by linearly superimposing the correction term (αΔW+βΔL+γΔH).
[0048] See also Figure 1-5 The algorithm in data processing unit 6 is equipped with a loading overload coefficient, and the loading overload coefficient is dynamically adjusted according to the expected moisture loss calculated in real time. The adjustment formula is: δ=K1·Wt + K2·(T-T0) + K3·v²·t, where δ is the dynamic compensation coefficient, Wt is the real-time moisture content, T is the ambient temperature, v is the wind speed, and t is the expected transportation time. The overload coefficient formula δ=K1Wt+K2ΔT+K3v²t includes: K1Wt item: for every 1% increase in moisture content, the compensation amount increases by 0.6% (K1=0.6); K2ΔT item: for every 10°C increase in the transportation temperature difference, the compensation coefficient increases by 0.2%; K3v²t item: a wind speed of 4m / s for 5 hours will trigger a 3.2% compensation increase; through the constraint condition (δ≤legal overload threshold-safety margin of 5%), automatic compliance is achieved.
[0049] The implementation principle of this embodiment is as follows: during use, 1. Initialization phase: The operator enters the destination information in the transport parameter input module 8; the system automatically obtains the vehicle load limit and real-time meteorological data; 2. Data acquisition phase: The moisture detector 3 scans the moisture content of the material; the meteorological module synchronizes the ambient temperature and humidity with forecast data; 3. Intelligent calculation phase: The data processing unit 6 performs the following: a) calculates the expected moisture loss ΔW = 1.8%; b) determines the dynamic compensation coefficient δ = 4.3%; c) generates a safe load capacity of 36.5 tons; 4. Execution control phase: The control execution module 9 adjusts: the loading belt speed is increased to 2.8m / s; the pneumatic butterfly valve opening is increased to 85°; the loading scale 7 provides real-time feedback of the weight data, and automatically stops when it reaches 36.5±0.2 tons; Data storage phase: Key parameters are encrypted and stored in the blockchain to generate an unalterable electronic certificate (the above data is for personnel understanding only and does not represent actual data). For example:
[0050] Parameters for a sand and gravel transport task: standard load Q0 = 50 tons; current moisture content W = 6.2%; baseline moisture content W0 = 5%; transport distance ΔL = +200 km (200 km more than the baseline); altitude change ΔH = +800 m (the destination is at a higher altitude); ambient temperature T = 30°C (the loading temperature T0 = 25°C); wind speed v = 4 m / s; estimated transport time t = 5 hours; correction factors: α = 0.1 / %, β = 0.003 / km, γ = 0.0002 / m; weighting factors: K1 = 0.6, K2 = 0.02, K3 = 0.001;
[0051] Step 1: Calculate the amount of water evaporation using the evaporation model f(Δt,T,H,v) = k·e^(aT + bH + cv)·Δt. Assuming the experimental calibration parameters: k=0.02, a=0.03, b=-0.02, c=0.01, and the ambient humidity H=60%, substitute the values: f= 0.02 × e^(0.03×30 + (-0.02)×60 + 0.01×4) × 5 ≈ 0.0771 tons (estimated amount of evaporated water).
[0052] Step 2: Calculate the dynamic compensation using the compensation algorithm Q = Q0 × [1 + α(W-W0) + βΔL + γΔH] and substitute the values: Q = 50 × [1 + 0.1 × (6.2-5) + 0.003 × 200 + 0.0002 × 800] = 94 tons (this result must be combined with the constraints in step 3);
[0053] Step 3: Calculate the over-compensation coefficient, use the adjustment formula δ = K1·Wt + K2·(T-T0) + K3·v²·t, substitute the values: δ = 0.6×6.2 + 0.02×(30-25) + 0.001×(4²)×5 = 3.9%; Final compensation load: Q_final = Q0× (1 + δ / 100) = 51.95 tons;
[0054] Step 4: Safety constraint verification, legal load limit: 55 tons; compensated load 51.95 tons < 55 tons → allowed to execute; if the calculation result is over-limit, the system will automatically adjust the compensation coefficient according to the formula Q_max = Q0× (1 + δ_max / 100) (δ_max is determined by regulations).
[0055] The above are preferred embodiments of the present application, and are not intended to limit the protection scope of the present application, therefore: any equivalent changes made in structure, shape, principle, etc. according to the present application should be covered within the protection scope of the present application.
Claims
1. An intelligent loading system, comprising a base (1), a moisture detector (3), a meteorological data acquisition module (5), a loading scale (7), a transport parameter input module (8), a control execution module (9) and a data processing unit (6), characterized in that: The base (1) is respectively equipped with a loading scale (7), a bulk loader (2) and a finished product bin (4).
2. The intelligent loading system according to claim 1, characterized in that: The loading scale (7) is located below the bulk loader (2).
3. The intelligent loading system according to claim 1, characterized in that: The bulk loader (2) is located below the finished product bin (4).
4. The intelligent loading system according to claim 1, characterized in that: The control execution module (9) is composed of a loading belt on the bulk loader (2) and a pneumatic butterfly valve.
5. The intelligent loading system according to claim 1, characterized in that: A moisture detector (3) is installed at the loading belt of the bulk loader (2).
6. The intelligent loading system according to claim 1, characterized in that: The moisture detector (3), meteorological data acquisition module (5), loading scale (7), transportation parameter input module (8) and control execution module (9) are all electrically connected to the data processing unit (6) via wires.
7. The intelligent loading system according to claim 1, characterized in that: The meteorological data acquisition module (5) integrates a temperature and humidity sensor, an anemometer and a weather forecast interface.
8. The intelligent loading system according to claim 1, characterized in that: The transport parameter input module (8) receives parameters such as transport distance, vehicle load limit, and destination altitude.
9. The intelligent loading system according to claim 1, characterized in that: The data processing unit (6) establishes a water evaporation model: f(Δt,T,H,v)=k·e^(aT+bH+cv)·Δt, and constructs a dynamic compensation algorithm: Q=Q0×[1+α(W-W0)+βΔL+γΔH] (where α, β, and γ are environmental correction coefficients).
10. The intelligent loading system according to claim 1, characterized in that: The algorithm in the data processing unit (6) is provided with a loading excess coefficient, and the loading excess coefficient is dynamically adjusted according to the expected moisture loss calculated in real time, and the adjustment formula is: δ=K1·Wt + K2·(T-T0) + K3·v²·t, where δ is the dynamic compensation coefficient, Wt is the real-time moisture content, T is the ambient temperature, v is the wind speed, and t is the expected transportation time.