Mineral aggregate automatic dumping car collaborative unloading system based on unloading attitude sensing

Abstract

The invention relates to the field of automatic dumping car control, aims at solving the problem that a traditional mineral aggregate automatic dumping car is single in sensing information during operation and cannot adapt to nonlinear characteristics of dynamic changes in the unloading process, and particularly relates to a collaborative unloading system of a mineral aggregate automatic dumping car based on unloading attitude sensing. Comprising a discharging instruction control module, a collaborative sensing layer, a discharging system decision module and an early warning output module. According to the method, through real-time collection of accurate and complete vehicle body posture state data and pressure proportion change conditions of all point positions, accurate capture of dynamic changes of weight distribution in the unloading process is achieved, through fusion of the vehicle body posture state data and a dynamic sensing result of the weight distribution, multi-condition threshold judgment and dynamic feature analysis are conducted, and the real-time monitoring of the weight distribution in the unloading process is achieved. Vehicle body inclination angle early warning, vehicle door opening degree abnormal early warning, unloading abnormal early warning and pressure distribution early warning are obtained, and various risk working conditions in the unloading process are comprehensively covered.

Description

Technical Field

[0001] This invention relates to the field of self-tipping vehicle control, specifically to a collaborative unloading system for ore self-tipping vehicles based on unloading posture perception. Background Technology

[0002] Self-tipping trolleys for ore are key equipment for transporting and unloading bulk materials and are widely used in mining, metallurgy and other fields. The unloading process of self-tipping trolleys largely depends on the experience of operators, who control the lifting mechanism to raise the trolley and open the side door to dump the material.

[0003] Currently, traditional control methods typically monitor only a single parameter, such as the pressure of the lifting cylinder or the angle sensor signal. They lack multi-dimensional collaborative perception of the dynamic changes in vehicle posture and weight distribution during unloading. With the development of automation and intelligent technologies, attempts have been made to introduce vision or force sensors for status monitoring. However, these methods still rely on single-source perception information and fail to integrate multi-source data such as vision and pressure. This makes it difficult to fully reflect the actual state during unloading, resulting in simple decision-making logic that often relies on fixed thresholds for alarms. This makes it impossible to adapt to the non-linear characteristics of dynamic changes during unloading.

[0004] The aforementioned technical defects can easily lead to problems such as excessive vehicle tilting, mismatch between the movement of the cargo box and the side door, material residue, or uneven pouring during the unloading process. These issues not only affect unloading efficiency but also pose safety hazards. Therefore, a solution is proposed. Summary of the Invention

[0005] This invention achieves precise capture of dynamic changes in weight distribution during unloading by real-time acquisition of accurate and complete vehicle posture data and pressure ratio changes at various points. By integrating vehicle posture data and dynamic weight distribution perception results, multi-condition threshold judgment and dynamic feature analysis are performed to comprehensively cover various risk conditions during unloading, thereby solving the technical defects mentioned in the background technology and proposing a collaborative unloading system for ore self-tipping trucks based on unloading posture perception.

[0006] The objective of this invention can be achieved through the following technical solution: a collaborative unloading system for ore self-tipping trucks based on unloading posture perception, comprising an unloading command control module, a collaborative perception layer, an unloading system decision module, and an early warning output module; The unloading command control module can receive unloading commands and send them to the collaborative perception layer; The collaborative perception layer executes the unloading operation according to the unloading command, and the collaborative perception layer includes a vehicle posture perception module and a weight distribution dynamic perception module. The vehicle posture perception module collects the vehicle posture in real time during the unloading process and constructs vehicle posture state data. The weight distribution dynamic sensing module can divide the unloading process into stages and collect the vehicle weight distribution at each stage. Based on the development trend and vehicle weight distribution at different stages, a comprehensive analysis is performed to obtain the weight distribution dynamic sensing results. The unloading collaborative decision-making module performs verification and analysis based on the vehicle body posture status data and weight distribution dynamic perception results, judges the risk level of the vehicle body unloading process, and sends the risk level to the early warning output module. The early warning output module issues and outputs early warnings based on the degree of risk.

[0007] In a preferred embodiment of the present invention, the unloading command control module can generate a notification signal when the self-tipping vehicle is unloading, and send the notification signal to the collaborative perception layer and the unloading collaborative decision module. After the collaborative perception layer obtains the notification signal, it collects the vehicle body posture and weight distribution during the unloading process.

[0008] In a preferred embodiment of the present invention, the vehicle body posture perception module performs mark recognition based on image data, marks and recognizes the vehicle body and side doors of the vehicle compartment in the image, and obtains the opening degree of the side doors of the vehicle compartment and the tilt angle of the vehicle body. The vehicle body posture sensing module obtains the cylinder's operating data through the lifting cylinder, and then analyzes the lifting distance of the cylinder to obtain the lifting angle of the vehicle body. The vehicle body posture perception module records the opening degree of the side door of the carriage, the tilt angle of the vehicle body, and the lifting angle of the carriage as vehicle body posture state data.

[0009] In a preferred embodiment of the present invention, the weight distribution dynamic sensing module collects pressure at multiple connection points between the vehicle body and the passenger compartment, and constructs an instantaneous pressure distribution based on the pressure at different points collected. The weight distribution dynamic sensing module obtains multiple instantaneous pressure distributions distributed in time sequence according to a set frequency, and constructs an instantaneous sample library; The weight distribution dynamic sensing module dynamically constructs the pressure migration trend based on the instantaneous sample library, thereby obtaining the pressure change at different points, and calculates the total pressure at multiple points and the proportion of pressure at different points in the total pressure, thus obtaining the pressure proportion change at different points.

[0010] As a preferred embodiment of the present invention, the weight distribution dynamic sensing module statistically analyzes the total pressure of all points in the instantaneous sample library at each moment to obtain the total pressure variation at different times. The weight distribution dynamic sensing module records the changes in the pressure percentage at different points and the changes in total pressure as the weight distribution dynamic sensing results.

[0011] In a preferred embodiment of the present invention, the unloading collaborative decision-making module obtains vehicle body posture state data and weight distribution dynamic perception results through the collaborative perception layer. The unloading collaborative decision-making module performs threshold judgment on the vehicle body tilt angle in the vehicle body posture state data. If the vehicle body tilt angle is greater than the set threshold, a vehicle body tilt warning is generated. The unloading collaborative decision-making module calculates the difference between the vehicle body lifting angle and the side door opening of the cargo box to obtain the lifting difference. If the vehicle body lifting angle is greater than the side door opening of the cargo box and the lifting difference is greater than a set threshold, a door opening warning is issued. The unloading collaborative decision-making module integrates the vehicle body lifting angle over time to obtain the cumulative lifting amount of the cargo box. At the same time, it performs normalization processing based on the cumulative lifting amount of the cargo box and the change in total pressure to obtain the lifting characteristic amount and the remaining total pressure. If the lifting characteristic amount is greater than the remaining total pressure, an unloading anomaly warning is issued. The unloading collaborative decision-making module compares and analyzes the changes in pressure ratio at different points to obtain the pressure ratio difference at different points, and records the largest pressure ratio difference at all times as the analysis sample. If the analysis sample is greater than the set threshold, a pressure distribution warning is issued.

[0012] In a preferred embodiment of the present invention, the early warning output module obtains all generated early warnings through unloading collaborative decision-making, and generates corresponding output signals according to different early warnings, which are then output through a display device or an alarm device.

[0013] Compared with the prior art, the beneficial effects of the present invention are: In this invention, complete vehicle body posture data is formed by accurately acquiring the opening angle of the side door of the cargo compartment, the tilt angle of the vehicle body, and the lifting angle of the cargo compartment. Then, by using multi-point pressure data at the connection points between the vehicle body and the cargo compartment, an instantaneous pressure distribution sample library is constructed. This allows for the analysis of pressure migration trends and changes in the pressure ratio at each point, enabling precise capture of dynamic changes in weight distribution during unloading. By integrating vehicle body posture data with dynamic weight distribution perception results, multi-condition threshold judgment and dynamic feature analysis are performed to obtain warnings for vehicle body tilt angle, abnormal door opening, abnormal unloading, and pressure distribution, comprehensively covering various risk conditions during the unloading process. Attached Figure Description

[0014] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0015] Figure 1 This is a system block diagram of the present invention; Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0016] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: Please refer to Figure 1 - Figure 2 As shown, the ore self-tipping truck collaborative unloading system based on unloading posture perception includes an unloading command control module, a vehicle posture perception module, a weight distribution dynamic perception module, an unloading system decision module, and an early warning output module. The unloading command control module can obtain unloading commands through the network and control the ore self-tipping truck to unload according to the unloading commands. At the same time, the unloading command control module can generate notification signals and send the notification signals to the collaborative perception layer and the unloading collaborative decision-making module. After obtaining the notification signals, the collaborative perception layer and the unloading collaborative decision-making module collect the vehicle posture and weight distribution during the unloading process. The collaborative perception layer includes a vehicle posture perception module and a weight distribution dynamic perception module. The vehicle posture perception module perceives the vehicle posture through multiple sensors, while the weight distribution dynamic perception module perceives the vehicle weight distribution through a sensor array. Specifically, the vehicle posture perception module can acquire image data through camera equipment and obtain cylinder operation data through lifting cylinder; The vehicle posture perception module performs label recognition based on image data, marking and recognizing the vehicle body and side doors of the vehicle compartment in the image. At the same time, it establishes horizontal and vertical reference lines to identify the motion angles of the vehicle body and side doors, and obtains the opening degree of the side doors and the tilt angle of the vehicle body. The vehicle body posture perception module obtains the operating data of the lifting cylinder through the lifting cylinder, and analyzes it based on the lifting distance of the cylinder to obtain the lifting angle of the vehicle body. The vehicle body attitude perception module records the opening degree of the side door of the carriage, the tilt angle of the vehicle body, and the lifting angle of the carriage as vehicle body attitude state data; The weight distribution dynamic sensing module collects pressure data through pressure sensors distributed at multiple connection points between the vehicle body and the passenger compartment, thereby obtaining the pressure conditions at different points and constructing an instantaneous pressure distribution based on the collected pressure conditions at different points. The weight distribution dynamic sensing module collects pressure data at different points multiple times according to the set collection frequency, thereby obtaining multiple instantaneous pressure distributions distributed in time sequence and constructing an instantaneous sample library. The weight distribution dynamic sensing module dynamically constructs the pressure migration trend based on the instantaneous sample library, thereby obtaining the pressure change at different points, and calculates the total pressure at multiple points and the proportion of pressure at different points in the total pressure, thus obtaining the pressure proportion change at different points. Meanwhile, the weight distribution dynamic sensing module will statistically analyze the total pressure of all points in the instantaneous sample library at each moment to obtain the total pressure changes at different times. The weight distribution dynamic sensing module records the changes in the pressure percentage at different points and the total pressure changes as a result of the weight distribution dynamic sensing. The unloading collaborative decision-making module obtains vehicle posture status data and weight distribution dynamic perception results through the collaborative perception layer. The unloading collaborative decision-making module judges the vehicle tilt angle in the vehicle posture status data by threshold. If the vehicle tilt angle is greater than the set threshold, a vehicle tilt warning is generated. If the vehicle tilt angle is not greater than the set threshold, no reaction is made. The unloading collaborative decision-making module calculates the difference between the vehicle body lifting angle and the side door opening of the cargo box to obtain the lifting difference. If the vehicle body lifting angle is greater than the side door opening of the cargo box and the lifting difference is greater than the set threshold, a door opening warning is issued; otherwise, no response is made. The unloading collaborative decision-making module integrates the vehicle body lifting angle over time to obtain the cumulative lifting amount of the cargo box. At the same time, it obtains the total pressure change in the dynamic perception results of weight distribution. The module performs normalization processing based on the cumulative lifting amount of the cargo box and the total pressure change to obtain the lifting characteristic amount and the remaining total pressure. If the lifting characteristic amount is greater than the remaining total pressure, an unloading anomaly warning is issued. If the lifting characteristic amount is not greater than the remaining total pressure, no reaction is taken. The unloading collaborative decision-making module compares and analyzes the changes in pressure ratio at different points to obtain the pressure ratio difference at different points. It records the largest pressure ratio difference at all times as the analysis sample. If the analysis sample is greater than the set threshold, a pressure distribution warning is issued. If the analysis sample is not greater than the set threshold, no reaction is taken. The early warning output module acquires all generated early warnings through unloading collaborative decision-making, and generates corresponding output signals based on different early warnings, which are then output through display devices or alarm devices.

[0018] Thresholds, preset values, or preset ranges are set for result comparison and analysis to determine whether they are good or bad. The value of these thresholds is determined by a combination of large-scale model analysis of sample data and human experience. They can also be adjusted appropriately based on seasonal or rational factors.

[0019] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A collaborative unloading system for ore self-tipping carts based on unloading posture perception, characterized in that, It includes a discharge command control module, a collaborative perception layer, a discharge system decision module, and an early warning output module; The unloading command control module can receive unloading commands and send them to the collaborative perception layer; The collaborative perception layer executes the unloading operation according to the unloading command, and the collaborative perception layer includes a vehicle posture perception module and a weight distribution dynamic perception module. The vehicle posture sensing module collects the vehicle posture in real time during the unloading process and constructs vehicle posture state data. The weight distribution dynamic sensing module can divide the unloading process into stages and collect the vehicle weight distribution at each stage. Based on the development trend and vehicle weight distribution at different stages, a comprehensive analysis is performed to obtain the weight distribution dynamic sensing results. The unloading collaborative decision-making module performs verification and analysis based on the vehicle body posture status data and weight distribution dynamic perception results, judges the risk level of the vehicle body unloading process, and sends the risk level to the early warning output module. The early warning output module issues and outputs early warnings based on the degree of risk.

2. The ore self-tipping collaborative unloading system based on unloading posture perception according to claim 1, characterized in that, The unloading command control module can generate a notification signal when the self-tipping vehicle is unloading, and send the notification signal to the collaborative perception layer and the unloading collaborative decision-making module. After the collaborative perception layer obtains the notification signal, it collects the vehicle body posture and weight distribution during the unloading process.

3. The ore self-tipping collaborative unloading system based on unloading posture perception according to claim 1, characterized in that, The vehicle posture perception module performs label recognition based on image data, and performs label recognition on the vehicle body and side doors of the carriage in the image to obtain the opening degree of the side doors of the carriage and the tilt angle of the vehicle body. The vehicle body posture sensing module obtains the cylinder's operating data through the lifting cylinder, and then analyzes the lifting distance of the cylinder to obtain the lifting angle of the vehicle body. The vehicle body posture perception module records the opening degree of the side door of the carriage, the tilt angle of the vehicle body, and the lifting angle of the carriage as vehicle body posture state data.

4. The ore self-tipping collaborative unloading system based on unloading posture perception according to claim 1, characterized in that, The weight distribution dynamic sensing module collects pressure data at multiple connection points between the vehicle body and the passenger compartment, and constructs an instantaneous pressure distribution based on the pressure data collected at different points. The weight distribution dynamic sensing module obtains multiple instantaneous pressure distributions distributed in time sequence according to a set frequency, and constructs an instantaneous sample library; The weight distribution dynamic sensing module dynamically constructs the pressure migration trend based on the instantaneous sample library, thereby obtaining the pressure change at different points, and calculates the total pressure at multiple points and the proportion of pressure at different points in the total pressure, thus obtaining the pressure proportion change at different points.

5. The ore self-tipping collaborative unloading system based on unloading posture perception according to claim 1, characterized in that, The weight distribution dynamic sensing module will statistically analyze the total pressure of all points in the instantaneous sample library at each moment to obtain the total pressure variation at different times. The weight distribution dynamic sensing module records the changes in the pressure percentage at different points and the changes in total pressure as the weight distribution dynamic sensing results.

6. The ore self-tipping collaborative unloading system based on unloading posture perception according to claim 1, characterized in that, The unloading collaborative decision-making module obtains vehicle posture state data and weight distribution dynamic perception results through the collaborative perception layer. The unloading collaborative decision-making module judges the vehicle tilt angle in the vehicle posture state data by threshold. If the vehicle tilt angle is greater than the set threshold, a vehicle tilt warning is generated. The unloading collaborative decision-making module calculates the difference between the vehicle body lifting angle and the side door opening of the cargo box to obtain the lifting difference. If the vehicle body lifting angle is greater than the side door opening of the cargo box and the lifting difference is greater than a set threshold, a door opening warning is issued. The unloading collaborative decision-making module integrates the vehicle body lifting angle over time to obtain the cumulative lifting amount of the cargo box. At the same time, it performs normalization processing based on the cumulative lifting amount of the cargo box and the change in total pressure to obtain the lifting characteristic amount and the remaining total pressure. If the lifting characteristic amount is greater than the remaining total pressure, an unloading anomaly warning is issued. The unloading collaborative decision-making module compares and analyzes the changes in pressure ratio at different points to obtain the pressure ratio difference at different points, and records the largest pressure ratio difference at all times as the analysis sample. If the analysis sample is greater than the set threshold, a pressure distribution warning is issued.

7. The ore self-tipping collaborative unloading system based on unloading posture perception according to claim 1, characterized in that, The early warning output module acquires all generated early warnings through unloading collaborative decision-making, and generates corresponding output signals based on different early warnings, which are then output through display devices or alarm devices.

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