Controller system of full-automatic intelligent multi-point verification electronic belt scale

Abstract

The invention relates to the technical field of electronic belt scales, and discloses a controller system of a full-automatic intelligent multi-point verification electronic belt scale, which comprises a multi-point data acquisition module, a dynamic error analysis module, an intelligent calibration module and a closed-loop verification module, and the synchronous acquisition unit is configured to synchronously acquire weighing signals, belt tension signals, speed signals and vibration signals of the weighing sensor, the tension detection unit and the vibration sensor. According to the invention, by deploying a plurality of groups of integrated verification units along two sides of the belt in a distributed manner, in-situ, synchronous and high-density acquisition of belt load, three-dimensional tension and vibration signals is realized, and a high-spatial-resolution physical field sensing network is constructed from a data source; therefore, the system can comprehensively capture local abnormal and spatial distribution characteristics such as belt deviation, uneven material distribution, carrier roller state difference and the like which are difficult to find by traditional single-point detection, and a reliable data foundation is laid for deep analysis of error causes.

Description

Technical Field

[0001] This invention relates to the field of electronic belt scale technology, and more particularly to a controller system for a fully automatic intelligent multi-point calibration electronic belt scale. Background Technology

[0002] As a dynamic and continuous metering device, the long-term operating accuracy of electronic belt scales is easily affected by a combination of factors, including uneven belt tension, idler wear, material uneven loading, and environmental vibration. Traditional calibration methods often rely on periodic shutdowns and static or quasi-static calibration using chain codes or physical objects. This is not only inefficient and affects production continuity, but also fails to capture and compensate for dynamic, time-varying errors with spatial propagation characteristics under complex actual working conditions.

[0003] Existing online monitoring technologies are often based on single-point or limited sensor data and employ compensation strategies that rely on overall averaging or fixed models. This makes it difficult to accurately locate the source of error and identify the propagation path of error in the mechanical structure, resulting in delayed compensation, limited effectiveness, and a lack of predictive ability for the overall performance degradation trend of belt scales.

[0004] An existing patent discloses an electronic belt scale physical measurement and verification system (CN119779462A). The technology disclosed in this existing patent relies on a single or a small number of sensors and specific mechanical structures (such as the deflection of an arched frame) to indirectly calculate tension changes, making it difficult to comprehensively and in real time capture local dynamic features such as belt misalignment, uneven material distribution, and abnormal idler roller status; the calibration method is based on static or preset models, which cannot accurately identify the source of error and its propagation path in the mechanical structure, resulting in delayed compensation and limited effectiveness. Summary of the Invention

[0005] This invention provides a controller system for a fully automatic intelligent multi-point calibration electronic belt scale to solve existing technical problems, thereby solving the difficulties in accurately locating the source of error and identifying the propagation path of error in the mechanical structure.

[0006] To solve the above-mentioned technical problems, according to one aspect of the present invention, more specifically, a fully automatic intelligent multi-point calibration electronic belt scale, includes a fixed platform, a controller, and two sets of load-bearing calibration units, wherein the controller is located in the middle of the fixed platform, and the two sets of load-bearing calibration units are respectively located on both sides of the fixed platform.

[0007] Each load-bearing verification unit group includes a left verification unit, a middle verification unit, and a right verification unit.

[0008] Furthermore, the controller includes a multi-point data acquisition module, a dynamic error analysis module, an intelligent calibration module, and a closed-loop verification module;

[0009] The left-side verification unit, the middle verification unit, and the right-side verification unit all include a weighing sensor, a tension detection unit, and a vibration sensor.

[0010] The controller system for the fully automatic intelligent multi-point calibration electronic belt scale includes: a multi-point data acquisition module, a dynamic error analysis module, an intelligent calibration module, and a closed-loop verification module.

[0011] The multi-point data acquisition module is configured to simultaneously acquire weighing signals, belt tension signals, speed signals, and vibration signals from the weighing sensor, tension detection unit, and vibration sensor.

[0012] The dynamic error analysis module is configured to perform spatiotemporal alignment and fusion analysis based on the multimodal data collected by the multi-point data acquisition module, and identify local and global error characteristics.

[0013] The intelligent calibration module is configured to dynamically calculate and distribute differentiated calibration coefficients for different calibration points based on the output results of the dynamic error analysis module.

[0014] The closed-loop verification module is configured to re-collect data after calibration, verify the calibration effect, and iteratively optimize the calibration parameters.

[0015] Furthermore, the dynamic error analysis module includes:

[0016] The spatiotemporal alignment unit is configured to perform millisecond-level synchronization of multimodal data streams at multiple verification points based on preset sensor spatial coordinates and signal propagation delay.

[0017] The error propagation modeling unit is configured to construct a dynamic connection topology diagram of the belt conveyor structure and calculate the error propagation coefficient between adjacent verification points. The formula is:

[0018] ;

[0019] in, , The average weighing signal of adjacent verification points i and j; For tension difference; The difference is due to vibrational energy. , , This represents the full-scale value of the corresponding physical quantity; , , These are adjustable weighting coefficients.

[0020] Furthermore, the intelligent calibration module includes:

[0021] The calibration coefficient calculation unit is configured for each calibration point. Calculate its dynamic calibration coefficient. The formula is:

[0022] ;

[0023] in, For reference standard values; Verification point The real mean; , This is the error propagation coefficient between the input and output points at this point; The maximum transfer factor; To transmit the compensation coefficient; This is the time interval since the last calibration; This is the time decay coefficient;

[0024] The differentiated distribution unit is configured to distribute the calculated calibration coefficients. Data is sent in real time to the weighing sensors at the corresponding calibration points.

[0025] Furthermore, the weighing sensor, tension detection unit, and vibration sensor are all evenly distributed along the belt bearing section;

[0026] The tension detection unit is a triaxial force sensor, which is installed on the idler bracket or under the belt at the corresponding calibration point to detect the longitudinal tension, lateral force and vertical pressure of the belt at that point in real time.

[0027] Furthermore, the closed-loop verification module is configured as follows:

[0028] After the intelligent calibration module issues new parameters, the first round of data collection is initiated.

[0029] Calculate the error propagation coefficient before and after calibration. and dynamic calibration coefficient ;

[0030] If the error propagation coefficient and dynamic calibration coefficient If the improvement rate exceeds the preset threshold, the calibration is deemed valid; otherwise, an iterative optimization process is triggered to adjust the calibration measurement.

[0031] Furthermore, the controller system of the fully automatic intelligent multi-point calibration electronic belt scale also includes:

[0032] The self-learning and prediction module is configured to establish a belt scale performance degradation model based on historical calibration data, error analysis results, and equipment operation logs, and predict the time point and key parameters that need to be calibrated in the future.

[0033] The visualization and interaction module is configured to display multi-point calibration data, error propagation topology, calibration process, and verification results in a graphical manner.

[0034] The controller system for the fully automatic intelligent multi-point calibration electronic belt scale provided by this invention has the following advantages compared to existing technologies:

[0035] 1. This invention achieves in-situ, synchronous, and high-density acquisition of belt load, three-dimensional tension, and vibration signals by distributing multiple integrated verification units along both sides of the belt. It constructs a high spatial resolution physical field sensing network from the data source, enabling the system to comprehensively capture local anomalies and spatial distribution characteristics that are difficult to detect by traditional single-point detection, such as belt misalignment, uneven material distribution, and differences in idler roller condition. This lays a reliable data foundation for in-depth analysis of the causes of errors.

[0036] 2. This invention can analyze the source and transmission path of errors in the mechanical structure of belt scales in real time, and generate differentiated calibration coefficients with spatiotemporal correlation accordingly. It realizes a fundamental transformation from "overall mean compensation" to "precise source correction and propagation suppression", significantly improving the pertinence and timeliness of calibration, effectively curbing the chain diffusion of errors, and thus maintaining high measurement accuracy under complex dynamic working conditions.

[0037] 3. This invention incorporates a complete intelligent closed loop from data acquisition, analysis, calibration to verification, and introduces self-learning and prediction capabilities. It can autonomously build a performance degradation model based on historical operating data, predict calibration and maintenance needs in advance, and upgrade the operation and maintenance mode from post-processing and regular maintenance to predictive maintenance. This greatly enhances the system's autonomy and long-term operational stability, while reducing the risk of production interruption caused by inaccurate measurement or sudden failures.

[0038] 4. This invention presents complex multi-point multimodal data, dynamic error topology, calibration process and performance prediction results in an intuitive graphical way through a visual interactive interface, which greatly improves the user-friendliness of human-computer interaction and the transparency of status monitoring. This enables operators to quickly grasp the overall health status and key issues of the belt scale, providing clear and efficient support for optimizing process parameters and making scientific maintenance decisions. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the electronic belt scale in this invention;

[0040] Figure 2 This is a top view of the electronic belt scale in this invention;

[0041] Figure 3 This is a flowchart of the present invention.

[0042] In the diagram: 1. Fixed platform; 2. Controller; 3. Weighing and verification unit; 301. Left verification unit; 302. Middle verification unit; 303. Right verification unit. Detailed Implementation

[0043] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] Example 1

[0045] like Figure 1-2 As shown, according to one aspect of the present invention, a fully automatic intelligent multi-point calibration electronic belt scale is provided, characterized in that it includes a fixed platform 1, a controller 2 and two sets of load-bearing calibration units 3, the controller 2 being located in the middle of the fixed platform 1, and the two sets of load-bearing calibration units 3 being located on both sides of the fixed platform 1 respectively; each set of load-bearing calibration units 3 includes a left calibration unit 301, a middle calibration unit 302 and a right calibration unit 303.

[0046] The left verification unit 301, the middle verification unit 302 and the right verification unit 303 all include a weighing sensor, a tension detection unit and a vibration sensor.

[0047] By symmetrically arranging six independent load-bearing verification units (2 groups × 3 points) on both sides of the effective weighing section of the belt, each unit integrates weighing, triaxial tension, and vibration sensors, forming a highly redundant and complementary sensor network in space. This hardware innovation enables the system to synchronously and in-situ capture multi-dimensional physical field information such as load distribution, three-dimensional stress state, and mechanical vibration at different locations of the belt during operation. From the source of data acquisition, it overcomes the limitations of traditional single-point or small-scale sensors that cannot reflect complex working conditions such as uneven belt tension, local wear of idlers, or uneven material loading, providing a high-fidelity, high-spatial-resolution raw data foundation for subsequent error analysis and calibration.

[0048] Example 2

[0049] like Figure 3 As shown, the controller system of the fully automatic intelligent multi-point calibration electronic belt scale includes: a multi-point data acquisition module, a dynamic error analysis module, an intelligent calibration module, and a closed-loop verification module.

[0050] The multi-point data acquisition module is configured to simultaneously acquire weighing signals, belt tension signals, speed signals, and vibration signals from the weighing sensor, tension detection unit, and vibration sensor. The weighing sensor, tension detection unit, and vibration sensor are all evenly distributed along the belt bearing section. The tension detection unit is a triaxial force sensor installed on the idler bracket or under the belt at the corresponding calibration point to detect the longitudinal tension, lateral force, and vertical pressure of the belt at that point in real time.

[0051] The dynamic error analysis module is configured to perform spatiotemporal alignment and fusion analysis on multimodal data collected by the multi-point data acquisition module to identify local and global error characteristics;

[0052] The intelligent calibration module is configured to dynamically calculate and distribute differentiated calibration coefficients for different calibration points based on the output of the dynamic error analysis module.

[0053] The closed-loop verification module is configured to re-collect data after calibration to verify the calibration effect and iteratively optimize the calibration parameters. The closed-loop verification module is configured as follows: after the intelligent calibration module issues new parameters, it initiates the first round of data acquisition; calculates the error propagation coefficient and dynamic calibration coefficient before and after calibration; if the improvement rate of both the error propagation coefficient and the dynamic calibration coefficient exceeds a preset threshold, the calibration is deemed valid; otherwise, an iterative optimization process is triggered to adjust the calibration measurement.

[0054] In this embodiment, the dynamic error analysis module includes: a spatiotemporal alignment unit configured to perform millisecond-level synchronization of multimodal data streams from multiple verification points based on preset sensor spatial coordinates and signal propagation delay; and an error propagation modeling unit configured to construct a dynamic connection topology diagram of the conveyor belt structure and calculate the error propagation coefficient between adjacent verification points. The formula is:

[0055] ;

[0056] in, , The average weighing signal of adjacent verification points i and j; For tension difference; The difference is due to vibrational energy. , , This represents the full-scale value of the corresponding physical quantity; , , These are adjustable weighting coefficients.

[0057] The intelligent calibration module includes: a calibration coefficient calculation unit, configured for each calibration point. Calculate its dynamic calibration coefficient. The formula is:

[0058] ;

[0059] in, For reference standard values; Verification point The real mean; , This is the error propagation coefficient between the input and output points at this point; The maximum transfer factor; To transmit the compensation coefficient; This is the time interval since the last calibration; This is the time decay coefficient;

[0060] The differentiated distribution unit is configured to distribute the calculated calibration coefficients. Data is sent in real time to the weighing sensors at the corresponding calibration points.

[0061] The implementation principle of the intelligent closed-loop calibration method based on the dynamic error propagation model is as follows: The system not only collects multimodal data, but more importantly, it constructs a dynamic connection topology diagram of the conveyor belt mechanism and uses quantitative formulas (such as the error propagation coefficient and dynamic calibration coefficient) to calculate the propagation path and intensity of errors in the structure in real time. The principle lies in treating the conveyor belt as a dynamic system where errors propagate between mechanical connection points. The system identifies the error source and main propagation path through algorithms, and generates differentiated, spatiotemporally correlated calibration coefficients accordingly. This allows for precise correction of the error source and inhibits its propagation downstream. This method fundamentally changes the traditional passive "detection-overall compensation" mode, achieving adaptive, precise positioning, and active error suppression, significantly improving the effectiveness and efficiency of calibration.

[0062] Example 3

[0063] like Figure 3 As shown, the controller system of the fully automatic intelligent multi-point calibration electronic belt scale also includes: a self-learning and prediction module, configured to establish a belt scale performance degradation model based on historical calibration data, error analysis results and equipment operation logs, and predict the time point and key parameters that need to be calibrated in the future; and a visualization interaction module, configured to display multi-point calibration data, error propagation topology, calibration process and verification results in a graphical manner.

[0064] The implementation principle of intelligent operation and maintenance extension with self-learning and predictive capabilities: Based on the completion of real-time calibration closed loop, the system introduces a self-learning and predictive module to continuously correlate historical calibration data, error analysis results, and equipment operation logs. Through machine learning or statistical models, it establishes performance degradation models for key components of the belt scale (such as sensors, idlers, and belts). The principle is to use long-term operating data to fit performance degradation trends, thereby achieving predictive maintenance, providing early warnings, and planning calibration or maintenance activities. Combined with a visualization and interaction module, complex multi-point, multi-modal data, dynamic error topology, and prediction results are presented graphically. This innovation upgrades the system from a "real-time calibration" tool to an "intelligent health management" platform, significantly reducing the risk of unplanned downtime, optimizing operation and maintenance costs, and improving the transparency and manageability of the system status.

[0065] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A fully automatic intelligent multi-point calibration electronic belt scale, characterized in that, It includes a fixed platform (1), a controller (2) and two sets of load-bearing verification units (3). The controller (2) is located in the middle of the fixed platform (1), and the two sets of load-bearing verification units (3) are located on both sides of the fixed platform (1). Each load-bearing verification unit (3) includes a left verification unit (301), a middle verification unit (302), and a right verification unit (303).

2. The fully automatic intelligent multi-point calibration electronic belt scale according to claim 1, characterized in that: The left verification unit (301), the middle verification unit (302), and the right verification unit (303) all include a weighing sensor, a tension detection unit, and a vibration sensor.

3. A controller system for a fully automatic intelligent multi-point calibration electronic belt scale, applied to the fully automatic intelligent multi-point calibration electronic belt scale as described in claim 1, wherein the controller system for the fully automatic intelligent multi-point calibration electronic belt scale comprises: Multi-point data acquisition module, dynamic error analysis module, intelligent calibration module, and closed-loop verification module: The multi-point data acquisition module is configured to simultaneously acquire weighing signals, belt tension signals, speed signals, and vibration signals from the weighing sensor, tension detection unit, and vibration sensor. The dynamic error analysis module is configured to perform spatiotemporal alignment and fusion analysis based on the multimodal data collected by the multi-point data acquisition module, and identify local and global error characteristics. The intelligent calibration module is configured to dynamically calculate and distribute differentiated calibration coefficients for different calibration points based on the output results of the dynamic error analysis module. The closed-loop verification module is configured to re-collect data after calibration, verify the calibration effect, and iteratively optimize the calibration parameters.

4. The controller system for the fully automatic intelligent multi-point calibration electronic belt scale according to claim 3, characterized in that: The dynamic error analysis module includes: The spatiotemporal alignment unit is configured to perform millisecond-level synchronization of multimodal data streams at multiple verification points based on preset sensor spatial coordinates and signal propagation delay. The error propagation modeling unit is configured to construct a dynamic connection topology diagram of the belt conveyor structure and calculate the error propagation coefficient between adjacent verification points. The formula is: ； in, , The average weighing signal of adjacent verification points i and j; For tension difference; The difference is due to vibrational energy. , , This represents the full-scale value of the corresponding physical quantity; , , These are adjustable weighting coefficients.

5. The controller system for the fully automatic intelligent multi-point calibration electronic belt scale according to claim 1, characterized in that: The intelligent calibration module includes: The calibration coefficient calculation unit is configured for each calibration point. Calculate its dynamic calibration coefficient. The formula is: ； in, For reference standard values; Verification point The real mean; , This is the error propagation coefficient between the input and output points at this point; The maximum transfer factor; To transmit the compensation coefficient; This is the time interval since the last calibration; This is the time decay coefficient; The differentiated distribution unit is configured to distribute the calculated calibration coefficients. Data is sent in real time to the weighing sensors at the corresponding calibration points.

6. The controller system for the fully automatic intelligent multi-point calibration electronic belt scale according to claim 1, characterized in that: The weighing sensor, tension detection unit and vibration sensor are all evenly distributed along the belt bearing section. The tension detection unit is a triaxial force sensor, which is installed on the idler bracket or under the belt at the corresponding calibration point to detect the longitudinal tension, lateral force and vertical pressure of the belt at that point in real time.

7. The controller system for the fully automatic intelligent multi-point calibration electronic belt scale according to claim 1, characterized in that: The closed-loop verification module is configured as follows: After the intelligent calibration module issues new parameters, the first round of data collection is initiated. Calculate the error propagation coefficient before and after calibration. and dynamic calibration coefficient ; If the error propagation coefficient and dynamic calibration coefficient If the improvement rate exceeds the preset threshold, the calibration is deemed valid; otherwise, an iterative optimization process is triggered to adjust the calibration measurement.

8. The controller system for the fully automatic intelligent multi-point calibration electronic belt scale according to claim 1, characterized in that: The controller system for the fully automatic intelligent multi-point calibration electronic belt scale also includes: The self-learning and prediction module is configured to establish a belt scale performance degradation model based on historical calibration data, error analysis results, and equipment operation logs, and predict the time point and key parameters that need to be calibrated in the future. The visualization and interaction module is configured to display multi-point calibration data, error propagation topology, calibration process, and verification results in a graphical manner.

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