Method and system for correcting multiple groups of belt weighers

Abstract

The embodiment of the invention provides a multi-group belt weigher correction method and system, and belongs to the technical field of industrial automation. The multiple groups of belt weighers at least comprise a group of ground belt weighers and a group of cantilever belt weighers. The method comprises the following steps: acquiring operation state data and operation environment parameters of each group of belt weighers; performing consistency verification based on the running state data of each group of belt weighers, and judging whether running abnormity exists or not; classifying the exception types, and calling a corresponding state correction model based on the determined exception types; wherein the abnormal types are abnormal states of a single group of belt weighers and abnormal states of a plurality of groups of belt weighers; and based on the called state correction model, executing operation state correction of each group of belt weighers. According to the scheme, cooperative correction and fault judgment of the multiple sets of belt weighers can be achieved without additional hardware transformation, the operation reliability and metering precision of the belt weighers are remarkably improved, the method adapts to the complex working condition environment, and the correction efficiency and the automation degree are improved.

Description

Technical Field

[0001] The invention relates to the technical field of industrial automation, and in particular to a calibration method for multiple belt scales and a calibration system for multiple belt scales. Background Art

[0002] Material conveying equipment is a basic equipment widely used in modern industrial production. As the core metering device in the conveying system, belt scales are widely used in ports, mines, power plants, metallurgy and other industries to perform real-time, continuous and cumulative weighing of bulk materials. The metering accuracy of belt scales directly affects material flow control, production efficiency and economic benefits, and its stability and accuracy are particularly critical.

[0003] Traditional belt scales are generally installed on the frame of the conveyor belt. The weight of the material on the belt and the running speed are measured through gravity sensors and speed sensors, and the real-time flow and cumulative flow are obtained by combining the calculation model. However, due to the long-term operation of the belt scale under complex working conditions, such as extreme environments such as high temperature, high humidity, vibration, as well as factors such as the installation error of the belt scale itself and sensor drift, its measurement accuracy is easily affected. Therefore, the belt scale needs to be calibrated regularly during the initial installation and operation. Traditional calibration methods mainly include weight calibration and physical calibration. These methods are more applicable to ground belt scales installed in fixed positions, but for stacker-reclaimer cantilever belt scales that require frequent pitching movements, the traditional calibration methods are limited in effect due to significant changes in their position and angle.

[0004] Existing technologies attempt to improve accuracy by increasing the length of the belt scale, increasing stiffness, or improving sensor performance, but these measures increase equipment costs and maintenance complexity. In addition, although some algorithms attempt to improve accuracy through data filtering, model correction, etc., these methods are difficult to effectively compensate for the errors caused by changes in the belt inclination angle under dynamic operating conditions, resulting in unstable correction effects.

[0005] Therefore, how to propose an efficient, accurate and automated correction method for the problem of inclination angle change of the stacker-reclaimer cantilever belt scale without increasing hardware costs and changing the existing equipment structure, so as to achieve coordinated correction and fault diagnosis of multiple groups of belt scales, is a key problem that needs to be urgently solved in the current technical field. Summary of the invention

[0006] The purpose of the embodiments of the present invention is to provide a method for calibrating multiple belt scales, so as to at least solve the problems of insufficient efficiency and low accuracy in existing solutions.

[0007] To achieve the above object, a multi-group belt scale calibration method is provided in the first aspect of the present invention. The multi-group belt scales include at least one ground belt scale and one cantilever belt scale. The method includes: collecting the operation status data and operation environment parameters of each group of belt scales; performing consistency verification based on the operation status data of each group of belt scales to determine whether there is an abnormal operation; classifying the abnormal types and calling the corresponding status correction model based on the determined abnormal types; where the abnormal types are single-group belt scale status abnormality and multi-group belt scale status abnormality; performing operation status correction on each group of belt scales based on the called status correction model.

[0008] Optionally, the operation status data includes any one or more of instantaneous flow rate, belt speed, and cumulative material flow weight; the operation environment parameters include any one or more of temperature, humidity, and vibration data, and belt tension, inclination angle, and material distribution data collected by non-contact devices.

[0009] Optionally, the consistency verification includes: calculating the time error based on the data of the ground belt scale and the cantilever belt scale, and judging the time error status by setting a time error threshold; if the time error exceeds the time error threshold, record the abnormal information and generate an alarm signal.

[0010] Optionally, the consistency verification further includes: calculating the weight error based on the cumulative material flow weight of the ground belt scale and the cantilever belt scale, and judging the weight error status by setting a weight error threshold; if the weight error exceeds the weight error threshold, adjust the proportional parameter of the cantilever belt scale, and recalculate the cumulative material flow weight based on the adjusted proportional parameter.

[0011] Optionally, the rule for the status correction model of single-group belt scale status abnormality to perform operation status correction on each group of belt scales is: at the set reference point of the belt where the ground belt scale is located, record the time deviation between the two by calculating the time difference between the material flow of the cantilever belt scale and the ground belt scale reaching the reference point; set a time error threshold, where the time error threshold includes a normal range threshold, a deviation range threshold, and a fault range threshold; when the time deviation is within the normal range threshold, do not adjust the proportional parameter and perform weight error correction; when the time deviation exceeds the deviation range threshold, generate an alarm signal and perform correction based on the data of the group of belt scales that reaches the reference point first; when the time deviation exceeds the fault range threshold, stop the correction operation, generate a fault alarm and prompt the staff to perform maintenance.

[0012] Optionally, the correction rule for the weight error is as follows: calculate the cumulative material flow weight of the ground belt scale and the cantilever belt scale at the reference point, and record the weight ratio between the two; set a weight error threshold, where the weight error threshold includes a normal range threshold, a deviation range threshold, and a fault range threshold; when the weight ratio is within the normal range threshold, maintain the existing proportional parameters; when the weight ratio exceeds the deviation range threshold, adjust the proportional parameters of the cantilever belt scale based on the ground belt scale, and update the cumulative material flow weight based on the adjusted proportional parameters; when the weight ratio exceeds the fault range threshold, generate a fault alarm signal and pause the correction operation to prompt the staff to check.

[0013] Optionally, the state correction model for the abnormal states of multiple groups of belt scales is constructed by combining a rule-driven algorithm and a data-driven algorithm; wherein, the rule-driven algorithm establishes a mapping relationship between the proportional parameters of the belt scale and the environmental parameters based on historical operation data; the data-driven algorithm processes data noise using Kalman filtering based on real-time collected data, and optimizes the proportional parameters through a non-linear mapping.

[0014] Optionally, the state correction model for the abnormal states of multiple groups of belt scales constructs a virtual operation model of the belt scale through digital twin technology; the virtual operation model is used to simulate the real-time state and dynamic changes of the material flow of the belt scale, and optimize the setting of the proportional parameters based on the simulation results; the optimized correction parameters are synchronized to each group of belt scales through a cloud computing platform to achieve distributed collaborative correction.

[0015] The second aspect of the present invention provides a multi-group belt scale correction system, where the multi-group belt scales include at least one group of ground belt scales and one group of cantilever belt scales, and the system includes: a collection unit for collecting the operation status data and operation environment parameters of each group of belt scales; a determination unit for performing consistency verification based on the operation status data of each group of belt scales to determine whether there is an abnormal operation; a classification unit for classifying the abnormal types and calling the corresponding state correction model based on the determined abnormal type; wherein, the abnormal types are the abnormal state of a single group of belt scales and the abnormal state of multiple groups of belt scales; a correction unit for performing the operation status correction of each group of belt scales based on the called state correction model.

[0016] On the other hand, the present invention provides a computer-readable storage medium, which stores instructions thereon that, when running on a computer, cause the computer to execute the above-mentioned multi-group belt scale correction method.

[0017] Through the above technical solution, the multi-group belt scale calibration method proposed by the present invention can effectively identify the abnormal states of single-group or multi-group belt scales by collecting the operation state data and operation environment parameters of each group of belt scales and monitoring the operation states of the ground belt scale and the cantilever belt scale in real time. Through the consistency verification method, the abnormal types can be quickly judged and classified processing can be performed on the abnormalities. Combining with the preset state calibration model, accurate calibration can be performed on different types of abnormalities, thereby solving the problem of unstable calibration accuracy caused by changes in the inclination angle, dynamic operating conditions, and complex environments in the traditional method. This method can achieve the collaborative calibration and fault judgment of multi-group belt scales without additional hardware modification, significantly improving the operation reliability and measurement accuracy of the belt scales, adapting to complex working conditions, and improving the calibration efficiency and automation level.

[0018] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification. They are used together with the following specific embodiments to explain the embodiments of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0020] Figure 1 is a flowchart of the steps of the multi-group belt scale calibration method provided by an embodiment of the present invention;

[0021] Figure 2 is a system structure diagram of the multi-group belt scale calibration system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following will describe in detail the specific embodiments of the present invention with reference to the drawings. It should be understood that the specific embodiments described herein are only for explaining and illustrating the present invention, and are not used to limit the present invention.

[0023] Figure 1 is a method flowchart of the multi-group belt scale calibration method provided by an embodiment of the present invention. As Figure 1 shown, an embodiment of the present invention provides a multi-group belt scale calibration method, and the method includes:

[0024] Step S10: Collect the operation state data and operation environment parameters of each group of belt scales.

[0025] Specifically, the operation state data includes any one or more of instantaneous flow rate, belt speed, and cumulative material flow weight; the operation environment parameters include any one or more of temperature, humidity, and vibration data, and belt tension, inclination angle, and material distribution data collected by non-contact devices.

[0026] In the embodiments of the present invention, the operating state data includes any one or more of instantaneous flow rate, belt speed, and cumulative material flow weight. The instantaneous flow rate is used to reflect the material flow rate in real time, the belt speed is used to calculate the dynamic behavior of the material flow, and the cumulative material flow weight is an accurate record of the cumulative material conveyance amount. The combination of the three can provide core data support for the operating state of the belt scale.

[0027] The operating environment parameters include any one or more of temperature, humidity, and vibration data, as well as belt tension, tilt angle, and material distribution data collected by non-contact devices. The temperature and humidity parameters are used to evaluate the working environment stability of the belt scale sensors to avoid measurement errors caused by changes in environmental conditions. The vibration data detects possible mechanical abnormalities during the operation of the belt scale through spectral analysis, such as deviations caused by external forces or equipment aging. The non-contact devices include a laser ranging device and a vision detection module. The laser ranging device collects the belt tension and tilt angle and can dynamically monitor the force state and spatial position changes of the belt. The vision detection module obtains the material distribution data through image processing technology, analyzes the material accumulation on the belt, and provides an accurate reference basis for correction.

[0028] By jointly collecting and analyzing the operating state data and environmental parameters, a global operating model of the belt scale can be established to accurately identify abnormalities caused by belt tilt, tension changes, or environmental interference. Further, the present invention adopts multi-dimensional data correlation analysis, combines the change trends of the operating state data and environmental parameters, provides the ability to detect abnormalities early, and lays a data foundation for subsequent state correction and fault judgment.

[0029] Step S20: Perform consistency verification based on the operating state data of each group of belt scales to determine whether there is an operating abnormality.

[0030] Specifically, the consistency verification includes: calculating the time error based on the data of the ground belt scale and the cantilever belt scale, and judging the time error state by setting a time error threshold; if the time error exceeds the time error threshold, record the abnormal information and generate an alarm signal.

[0031] Further, the consistency verification also includes: calculating the weight error based on the cumulative material flow weights of the ground belt scale and the cantilever belt scale, and judging the weight error state by setting a weight error threshold; if the weight error exceeds the weight error threshold, adjust the proportional parameter of the cantilever belt scale, and recalculate the cumulative material flow weight based on the adjusted proportional parameter.

[0032] In the embodiments of the present invention, the consistency verification includes the analysis and processing of time error and weight error to achieve accurate identification and dynamic correction of abnormal states.

[0033] The first step of consistency verification is to calculate the time error based on the data of the ground belt scale and the cantilever belt scale. Specifically: Set a reference point on the BJ belt at the location of the ground belt scale, record the time when the material flow passes through the ground belt scale and the cantilever belt scale to reach this reference point respectively, and calculate the time difference between the two sets of belt scales in combination with the belt speed. By setting a time error threshold, the time error is divided into a normal range, a deviation range, and a fault range. When the time error is within the normal range, it is considered that the operating states are consistent and no adjustment is required; when the time error exceeds the deviation range but is lower than the fault range, abnormal information is recorded and an alarm signal is generated. At the same time, the data of the set of belt scales that arrives at the reference point first is used as a reference for subsequent calculations; when the time error exceeds the fault range, it is regarded as a serious abnormality, the calibration operation is stopped, and the staff is prompted to perform maintenance.

[0034] Furthermore, the consistency verification also includes calculating the weight error based on the cumulative material flow weights of the ground belt scale and the cantilever belt scale. Specifically: Obtain the cumulative material flow weights of the ground belt scale and the cantilever belt scale at the reference point respectively, and calculate the weight ratio between the two. By setting a weight error threshold, the weight error is also divided into a normal range, a deviation range, and a fault range. When the weight ratio is within the normal range, the existing proportional parameters are maintained; when the weight ratio exceeds the deviation range but is lower than the fault range, an alarm signal is generated, and the proportional parameters of the cantilever belt scale are adjusted based on the ground belt scale. At the same time, the cumulative material flow weight is recalculated based on the adjusted proportional parameters; when the weight ratio exceeds the fault range, it is regarded as abnormal equipment operation, the calibration operation is stopped, and an alarm prompt is generated.

[0035] Through the double consistency verification of time error and weight error, the solution of the present invention can judge the operating states of each group of belt scales in real time, ensure the data consistency between the ground belt scale and the cantilever belt scale, thereby improving the accuracy and stability of the coordinated operation of multiple groups of belt scales. Especially in the complex working conditions where the inclination angle of the cantilever belt scale of the stacker-reclaimer changes due to the pitching action, by using the early detection ability of time error, abnormalities can be quickly discovered and cumulative errors can be reduced; through weight error correction, the proportional parameters of the cantilever belt scale can be dynamically adjusted to effectively compensate for the deviation caused by the angle change. The overall solution does not require changing the equipment structure, reduces the maintenance cost, and at the same time significantly improves the calibration efficiency and reliability through the automatic calibration method, providing strong support for the intelligent operation of belt scales in complex environments.

[0036] Step S30: Classify the abnormal types and call the corresponding state correction model based on the determined abnormal types.

[0037] Specifically, the abnormal types are the abnormal state of a single group of belt scales and the abnormal state of multiple groups of belt scales.

[0038] In the embodiments of the present invention, the abnormal types include the abnormal state of a single group of belt scales and the abnormal state of multiple groups of belt scales. For the abnormal state of a single group of belt scales, such as the time error or weight error abnormality caused by the change of the inclination angle of the cantilever belt scale, a single-group state correction model is called, and independent correction is performed by adjusting the proportional parameter of this group of belt scales; for the abnormal state of multiple groups of belt scales, such as the total material flow deviation caused by the pitching action of the stacker-reclaimer, a multi-group state correction model is called, and collaborative correction is performed by combining the time deviation and weight deviation data of the ground belt scale and the cantilever belt scale. Through this classification and correction method, the pertinence and accuracy of the correction can be improved, the influence of abnormal propagation can be reduced, the efficient collaborative operation of multiple groups of belt scales can be realized, and the overall measurement accuracy and operation stability can be improved.

[0039] Step S40: Based on the called state correction model, perform the operation state correction of each group of belt scales.

[0040] Specifically, the rule for the state correction model of the abnormal state of a single group of belt scales to perform the operation state correction of each group of belt scales is as follows: At the belt set reference point where the ground belt scale is located, calculate the time difference between the material flow of the cantilever belt scale and the ground belt scale reaching the reference point, and record the time deviation between the two; set the time error threshold, where the time error threshold includes the normal range threshold, the deviation range threshold, and the fault range threshold; when the time deviation is within the normal range threshold, do not adjust the proportional parameter, and perform the weight error correction; when the time deviation exceeds the deviation range threshold, generate an alarm signal, and perform correction based on the data of the group of belt scales that reaches the reference point first; when the time deviation exceeds the fault range threshold, stop the correction operation, generate a fault alarm, and prompt the staff to perform maintenance.

[0041] Furthermore, the correction rule for the weight error is as follows: Calculate the cumulative material flow weight of the ground belt scale and the cantilever belt scale at the reference point, and record the weight ratio between the two; set the weight error threshold, where the weight error threshold includes the normal range threshold, the deviation range threshold, and the fault range threshold; when the weight ratio is within the normal range threshold, maintain the existing proportional parameter; when the weight ratio exceeds the deviation range threshold, adjust the proportional parameter of the cantilever belt scale based on the ground belt scale, and update the cumulative material flow weight based on the adjusted proportional parameter; when the weight ratio exceeds the fault range threshold, generate a fault alarm signal, and pause the correction operation to prompt the staff to perform inspection.

[0042] In the embodiments of the present invention, the state correction model of the abnormal state of a single group of belt scales realizes the precise correction of the abnormal state through the step-by-step processing of the time error and the weight error, ensuring the accuracy and stability of the operation of the belt scale. Specifically, the state correction model follows the following rules:

[0043] 1) Time error correction rule: First, set a reference point on the belt where the ground belt scale is located (such as the BJ belt). By synchronously collecting the operation data of the cantilever belt scale and the ground belt scale, calculate the time difference for the material flow to reach the reference point from the cantilever belt scale, and record the time deviation between the two sets of belt scales. Set the time error threshold, and divide the time error into normal range threshold, deviation range threshold, and fault range threshold. When the time deviation is within the normal range threshold, it is considered that the operation states of the two sets of belt scales are consistent, and there is no need to adjust the proportional parameter of the cantilever belt scale. Directly perform the weight error correction; when the time deviation exceeds the deviation range threshold but is lower than the fault range threshold, generate an alarm signal, and perform time deviation correction based on the data of the belt scale that reaches the reference point first to ensure the calculation accuracy of the material flow position; when the time deviation exceeds the fault range threshold, it is regarded as a serious abnormality, stop the correction operation, and at the same time generate a fault alarm signal to prompt the staff to check and repair.

[0044] 2) Weight error correction rule: After completing the time error correction, further perform the weight error correction. Specifically: Calculate the weight ratio between the two by the cumulative material flow weight of the ground belt scale and the cantilever belt scale at the reference point, and record the deviation of this ratio. Set the weight error threshold, and divide the weight ratio into normal range threshold, deviation range threshold, and fault range threshold. When the weight ratio is within the normal range threshold, keep the existing proportional parameter of the cantilever belt scale without further adjustment; when the weight ratio exceeds the deviation range threshold but is lower than the fault range threshold, adjust the proportional parameter of the cantilever belt scale based on the ground belt scale, and update the cumulative material flow weight by calculating the corrected proportional parameter; when the weight ratio exceeds the fault range threshold, suspend the correction operation, generate a fault alarm signal, and prompt the staff to check and adjust.

[0045] Preferably, the state correction model for the abnormal states of multiple groups of belt scales is constructed by combining a rule-driven algorithm and a data-driven algorithm; among them, the rule-driven algorithm establishes a mapping relationship between the proportional parameter of the belt scale and the environmental parameter based on historical operation data; the data-driven algorithm processes the data noise by using Kalman filtering based on the real-time collected data, and optimizes the proportional parameter through a non-linear mapping.

[0046] Furthermore, the state correction model for the abnormal states of multiple groups of belt scales constructs a virtual operation model of the belt scale through digital twin technology; the virtual operation model is used to simulate the real-time state of the belt scale and the dynamic change of the material flow, and optimize the proportional parameter setting based on the simulation result; the optimized correction parameters are synchronized to each group of belt scales through the cloud computing platform to achieve distributed collaborative correction.

[0047] In the embodiments of the present invention, the rule-driven algorithm establishes a mapping relationship between the scale parameters of the belt scale and the environmental parameters based on historical operation data. By collecting and storing the long-term operation data of each group of belt scales, including instantaneous flow rate, belt speed, cumulative material flow weight, and operation environmental parameters such as temperature, humidity, and vibration, the variation law between the scale parameters of the belt scale and the environmental parameters is extracted. Specifically, statistical analysis methods are used to calculate the influence weight of the environmental parameters on the scale parameters of the belt scale, and a rule table or empirical formula is generated to guide the calibration process. For example, when the vibration parameter exceeds a specific threshold, the dynamic correction factor of the scale parameter is adjusted through the rule table to compensate for the error caused by vibration. This rule-based calibration method has the characteristics of being intuitive and easy to interpret, and provides initial calibration parameters for the data-driven algorithm.

[0048] Furthermore, the data-driven algorithm processes data noise using Kalman filtering based on real-time collected data, and optimizes the scale parameters through non-linear mapping. Specifically, the operation state data of the ground belt scale and the cantilever belt scale are collected in real time, including time deviation and weight deviation. Kalman filtering is used to remove the random noise and abnormal fluctuations in the data to ensure the stability of the input data. On this basis, the data-driven algorithm optimally models the relationship between the operation state data and the scale parameters through a non-linear mapping model (such as a multi-layer neural network or a support vector machine). The optimized model can dynamically adjust the scale parameters to adapt to complex environmental changes, such as dynamic errors caused by changes in the belt inclination angle or uneven material distribution.

[0049] Furthermore, the state correction model for the abnormal states of multiple groups of belt scales constructs a virtual operation model of the belt scale through digital twin technology. The digital twin model is based on the real-time collected data of the belt scale, and combines the outputs of the rule-driven algorithm and the data-driven algorithm to simulate the operation state and dynamic changes of the material flow of the belt scale. The virtual operation model can reproduce the dynamic behavior of the belt scale under different environmental parameters and operating conditions, and provide a verification and optimization platform for calibration parameters. For example, the virtual model can simulate the influence of the pitching action of the cantilever belt scale on the time deviation and weight deviation of the material flow, and find the optimal scale parameter setting through multiple rounds of simulation, so as to improve the accuracy and reliability of calibration.

[0050] Specifically, the optimized calibration parameters are synchronized to each group of belt scales through the cloud computing platform to achieve distributed collaborative calibration. Specifically, the cloud platform integrates the historical data, real-time operation data, and virtual model outputs of each group of belt scales, generates a unified calibration strategy, and sends the calibration parameters to each belt scale device through wireless communication. The distributed collaborative calibration mechanism ensures the accuracy consistency of each group of belt scales during collaborative operation, and at the same time supports the status monitoring and abnormal warning functions of multiple devices.

[0051] Based on the solution of the present invention, by combining the rule-driven algorithm and the data-driven algorithm, the state correction model not only has the global adaptation ability based on historical data, but also can dynamically respond to the changes of real-time data, realizing the accurate correction of the abnormal states of multiple groups of belt scales. The introduction of digital twin technology provides a visualization and verification tool for the optimization of proportional parameters under complex working conditions, further improving the accuracy and robustness of the correction process. Cloud distributed collaborative calibration ensures the consistency of the operating states of each group of belt scales and the rapid deployment of calibration strategies, meets the requirements of multi-device collaborative operation, and significantly improves the intelligent level and operating efficiency. The overall solution does not require additional hardware investment, reduces the maintenance cost, and provides a reliable guarantee for the long-term stable operation of multiple groups of belt scales.

[0052] Figure 2 It is the system structure diagram of the multi-group belt scale correction system provided by an embodiment of the present invention. As Figure 2 shown, an embodiment of the present invention provides a multi-group belt scale correction system, and the system includes: a collection unit, configured to collect the operating state data and operating environment parameters of each group of belt scales; a determination unit, configured to perform consistency verification based on the operating state data of each group of belt scales and determine whether there is an operating abnormality; a classification unit, configured to classify the abnormal types and call the corresponding state correction model based on the determined abnormal types; wherein, the abnormal types are the abnormal state of a single group of belt scales and the abnormal state of multiple groups of belt scales; a correction unit, configured to perform the correction of the operating states of each group of belt scales based on the called state correction model.

[0053] An embodiment of the present invention also provides a computer-readable storage medium, on which instructions are stored, and when running on a computer, the instructions cause the computer to execute the above-mentioned multi-group belt scale correction method.

[0054] Those skilled in the art can understand that all or part of the steps in the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program is stored in a storage medium, including several instructions for causing a single-chip microcomputer, a chip or a processor to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical discs that can store program codes.

[0055] The optional embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. In addition, it should be noted that, in the above specific embodiments, the various specific technical features described can be combined in any appropriate manner without conflict. To avoid unnecessary repetition, the embodiments of the present invention will not separately describe various possible combination methods.

[0056] In addition, any combination can be made between various different embodiments of the present invention as long as it does not violate the idea of the embodiments of the present invention, and it should also be regarded as the content disclosed in the embodiments of the present invention.

Claims

1. A method for calibrating multiple belt scales, characterized in that: The plurality of belt scales include at least one ground belt scale and one cantilever belt scale, and the method includes: Collect the operating status data and operating environment parameters of each group of belt scales; Perform consistency check based on the operating status data of each group of belt scales to determine whether there is any operating abnormality; Classify the abnormal type and call the corresponding state correction model based on the determined abnormal type; wherein, The abnormality types are single-group belt scale state abnormality and multi-group belt scale state abnormality; Based on the called state correction model, the operating state correction of each group of belt scales is performed.

2. The method for calibrating multiple belt scales according to claim 1, characterized in that: The operating status data includes: Any one or more of instantaneous flow rate, belt speed and accumulated material flow weight; The operating environment parameters include: Temperature, humidity and vibration data, and any one or more of belt tension, inclination angle and material distribution data collected by non-contact equipment.

3. The method for calibrating multiple belt scales according to claim 2, characterized in that: The consistency check includes: Calculate the time error based on the data of the ground belt scale and the cantilever belt scale, and determine the time error state by setting the time error threshold; If the time error exceeds the time error threshold, the abnormal information is recorded and an alarm signal is generated.

4. The method for calibrating multiple belt scales according to claim 3, characterized in that: The consistency check also includes: Calculate the weight error based on the accumulated material flow weight of the ground belt scale and the cantilever belt scale, and determine the weight error status by setting the weight error threshold; If the weight error exceeds the weight error threshold, the proportional parameters of the cantilever belt scale are adjusted, and the accumulated material flow weight is recalculated based on the adjusted proportional parameters.

5. The method for calibrating multiple belt scales according to claim 1, characterized in that: The state correction model for abnormal state of a single group of belt scales executes the following rules for the operation state correction of each group of belt scales: Set a reference point on the belt where the ground belt scale is located, calculate the time difference between the cantilever belt scale and the ground belt scale when the material flow reaches the reference point, and record the time deviation between the two; Setting a time error threshold, wherein the time error threshold includes a normal range threshold, a deviation range threshold and a fault range threshold; When the time deviation is within the normal range threshold, the proportional parameters are not adjusted and weight error correction is performed; When the time deviation exceeds the deviation range threshold, an alarm signal is generated, and correction is performed based on a set of belt scale data that arrives at the reference point first; When the time deviation exceeds the fault range threshold, the correction operation is stopped, a fault alarm is generated, and the staff is prompted to perform maintenance.

6. The method for calibrating multiple belt scales according to claim 5, characterized in that: The correction rule for the weight error is: Calculate the accumulated material flow weight of the ground belt scale and the cantilever belt scale at the reference point, and record the weight ratio between the two; Setting a weight error threshold, wherein the weight error threshold includes a normal range threshold, a deviation range threshold and a fault range threshold; When the weight ratio is within the normal range threshold, the existing ratio parameters are maintained; When the weight ratio exceeds the deviation range threshold, the proportional parameters of the cantilever belt scale are adjusted based on the ground belt scale, and the accumulated material flow weight is updated based on the adjusted proportional parameters; When the weight ratio exceeds the fault range threshold, a fault alarm signal is generated and the correction operation is suspended to prompt the staff to check.

7. The method for calibrating multiple belt scales according to claim 1, characterized in that: The state correction model of the abnormal state of the multiple groups of belt scales is constructed by combining the rule-driven algorithm and the data-driven algorithm; wherein, The rule-driven algorithm establishes a mapping relationship between belt scale scale parameters and environmental parameters based on historical operation data; The data-driven algorithm uses Kalman filtering to process data noise based on real-time collected data, and optimizes proportional parameters through nonlinear mapping.

8. The method for calibrating multiple belt scales according to claim 7, characterized in that: The state correction models of the multiple groups of belt scale abnormal states construct a virtual operation model of the belt scale through digital twin technology; The virtual operation model is used to simulate the real-time state of the belt scale and the dynamic changes of the material flow, and optimize the proportional parameter settings based on the simulation results; The optimized correction parameters are synchronized to each group of belt scales through the cloud computing platform to achieve distributed collaborative correction.

9. A multi-group belt scale calibration system, characterized in that: The plurality of belt scales include at least one ground belt scale and one cantilever belt scale, and the system includes: A collection unit is used to collect the operating status data and operating environment parameters of each group of belt scales; A determination unit, used to perform consistency check based on the operation status data of each group of belt scales to determine whether there is an operation abnormality; A classification unit is used to classify the abnormal type and call the corresponding state correction model based on the determined abnormal type; wherein, The abnormality types are single-group belt scale state abnormality and multi-group belt scale state abnormality; The correction unit is used to perform operation state correction of each group of belt scales based on the called state correction model.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the method for calibrating multiple belt scales as described in any one of claims 1 to 8.

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