Multi-machine load system for real-time total load monitoring on construction equipment

Abstract

A multi-machine payload system integrates load measurements from construction equipment to provide an estimate of total load when loading a truck. The system combines real-time load data from a cold planer with a conveyor-based measurement system and a support machine such as a wheel loader, using GPS and wireless communication to determine machine-to-truck association. For the cold planer, the system uses force sensors and hydraulic pressure sensors to measure material delivery, while the wheel loader uses boom angle and cylinder pressure to determine load. The system processes the combined load data to track cumulative total weight, compare it to truck weight limits, and generate alerts when approaching limits. The system can be implemented using a single-machine controller, machine-to-machine communication, or a server.

Description

Technical Field

[0001] This invention relates to a load monitoring system for construction equipment, specifically a system that combines real-time load measurements from multiple machines during material loading operations. Background Technology

[0002] Construction equipment such as cold milling machines, wheel loaders, and excavators are used in road construction and maintenance operations. Cold milling machines, also known as road milling machines or road turners, use a rotating milling drum to remove layers from the asphalt surface; the milled material is then transported to transport vehicles via a conveyor system. These machines can use various measurement systems incorporating load sensors, pressure sensors, and speed monitoring devices to track the movement of the material during operation. Modern construction equipment is characterized by monitoring systems that use a combination of mechanical and electronic sensors to measure operating parameters such as conveyor belt speed, hydraulic pressure, and material weight. Transport vehicles (such as trailer trucks) are used to remove the milled material from the site, and their operation is constrained by various weight limitations and efficiency considerations.

[0003] U.S. Patent No. 10,539,451 discloses a production measurement system for a cold milling machine that uses a hydraulic motor to drive a conveyor. However, this patent does not describe the integration of load data from multiple machines loaded onto the same truck. Therefore, there is a need for a method that can combine real-time load data from both the cold milling machine and support machines such as wheel loaders to provide cumulative gross weight monitoring. Summary of the Invention

[0004] This document discloses methods, systems, and apparatus for monitoring and managing load measurements on multiple construction machines during material loading operations. More specifically, the invention relates to a load measurement system that integrates data from cold milling machines with conveyor-based measurement systems and support machines such as wheel loaders, excavators, mini track loaders, and skid steer loaders to provide a real-time total load estimate as material is loaded into transport vehicles. This system and method enable accurate tracking of cumulative loads from multiple loading sources while preventing transport vehicles from being overloaded beyond weight limits.

[0005] In some implementations, the load monitoring system receives first load data from the cold milling machine's measurement system and second load data from the support machine's measurement system, the second load data relating to the material loaded into the transport vehicle. Machine identification is determined using manual operator input, GPS location data, and / or proximity detection. The load data are combined to determine the cumulative gross weight in real time. The gross weight is compared to a predetermined weight limit. An alarm is generated when the weight limit is approached. The cumulative gross weight, the transport vehicle's remaining capacity, machine identification, and / or alarm can be sent to a computer device. This system is capable of tracking cumulative loads from multiple loading sources while preventing the transport vehicle from exceeding weight limits. The support machine may include a wheel loader, excavator, mini tracked loader, or skid steer loader operating in conjunction with the cold milling machine.

[0006] In some implementations, the computer system determines that a cold milling machine is estimating the weight of material to be loaded into a transport vehicle, and that a support machine (e.g., a bulldozer or wheel loader) is also loading material into the same transport vehicle. Load information from both measurement systems is combined to determine the cumulative gross weight in real time. Machine identification is achieved using GPS data, short-range wireless communication, and / or cellular networks. When the cumulative gross weight approaches a predetermined weight limit, an alarm is generated to prevent overloading.

[0007] In some implementations, load capacity and location data of the transport vehicle are received. The computer system can use a GPS module and wireless communication to determine the real-time location of the cold milling machine and the load data transmitted by the supporting machine. The remaining load capacity of the transport vehicle is determined by analyzing the load capacity data and real-time load measurements received from the measurement system of the cold milling machine-based conveyor. When loading material using wireless data exchange, the computer system can continuously update the capacity calculation. A loading sequence is generated by analyzing the GPS location data of the transport vehicle and the machine, while simultaneously incorporating the remaining load capacity calculation. Based on the relative positions and material conveying capabilities of the cold milling machine and the supporting machine, machine identities and optimal loading sequences are established between the cold milling machine and the supporting machine. The loading sequence assigns a coordinated target load amount to each machine to effectively utilize remaining capacity while preventing overloading. Attached Figure Description

[0008] Figure 1 This is a diagram illustrating the environment of several example machines, including during material loading operations, according to some aspects of this technology.

[0009] Figure 2 This is a diagram illustrating an example multi-machine load system for real-time total load monitoring according to some aspects of this technology.

[0010] Figure 3This is a flowchart illustrating an example process for real-time load monitoring according to some aspects of the present technology.

[0011] Figure 4 This is a flowchart illustrating an example process for determining the cumulative total weight of a transport vehicle in real time, according to some aspects of the present technology.

[0012] Figure 5 This is a flowchart illustrating an example process for integrating load measurements from a cold milling machine and a support machine, according to some aspects of this technology.

[0013] Figure 6 This is a block diagram illustrating an example of a computer system in which at least some of the operations described herein can be implemented. Detailed Implementation

[0014] This document discloses methods, systems, and apparatus for implementing a multi-machine load system that integrates load measurements from a cold milling machine and / or support machines such as wheel loaders to deliver accurate real-time total load estimates while loading a truck. The system uses various identification methods, including GPS location tracking, wireless communication, and / or proximity detection, to combine load data from a cold milling machine using a transfer-based measurement system with data from the support machines to determine machine-to-truck association. The disclosed method uses manual operator input, GPS data, or proximity detection to determine machine identity, then combines load information to calculate the cumulative total weight in real time. The system compares the weight to predetermined weight limits and generates alerts when these limits are approached.

[0015] Key features include automatic tracking of cumulative loads from multiple loading sources, real-time weight monitoring and alerts, machine-to-machine communication capabilities, support for various machine identification methods, and the ability to prevent overloading beyond required weight limits. The system can be implemented using a standalone controller, machine-to-machine communication, or a server, providing deployment flexibility. The disclosed methods enable operators to optimize truck loading efficiency while ensuring compliance with weight limits.

[0016] Figure 1 This is a diagram illustrating an environment 100 including multiple example machines during a material loading operation, according to some aspects of the present technology. Environment 100 includes multiple systems for monitoring the loads on construction equipment during the material loading operation. Environment 100 implements a multi-machine load monitoring system that combines real-time load measurements from both a cold milling machine (e.g., cold milling machine 104) and a support machine (e.g., excavator 120) to provide an accurate total load estimate when loading a truck (e.g., transport vehicle 108).

[0017] The cold milling machine 104 operates in environment 100, using a rotary milling drum equipped with cutting tools to remove layers from an asphalt surface. Milled material 128 is conveyed via a conveyor system including a conveyor belt 152 driven by a motor 156, for transporting the milled material 128 to a transport vehicle 108. Material 124 may be the same as or different from material 128. The conveyor system includes force sensors and speed sensors; the force sensors are configured to measure the weight of the material on the conveyor belt 152, and the speed sensors measure the speed of the conveyor belt, thereby determining the material flow rate. The cold milling machine 104 includes a production measurement system that determines the mass flow rate based on multiple sensor inputs. For example, a force sensor attached to the roller assembly measures the downward force on the material on the conveyor belt 152, while an inclinometer determines the tilt angle to account for gravitational effects. The cold milling machine 104 also includes pressure sensors for monitoring hydraulic pressure upstream and downstream of the motor 156 to determine output power and material delivery status.

[0018] The wheel loader 112 operates as a support machine in conjunction with the cold milling machine 104. The wheel loader 112 includes a load measurement system that uses a boom angle sensor and a hydraulic cylinder pressure sensor to determine the load weight, accurately measuring the bucket load even when operating on inclined surfaces. The load measurement system generates bucket weight data in real time as material 124 is loaded and transported to the transport vehicle 108.

[0019] Excavator 120 and skid steer loader 116 can also operate as additional support machines in environment 100. Similar to wheel loader 112, these machines are equipped with load measurement capabilities, which are input into the overall monitoring system using wireless signal 140. Mini tracked loaders can also operate as additional support machines in environment 100. Mini tracked loaders are a type of support machine similar to skid steer loader 116, but they operate on continuous rubber tracks instead of wheels. Mini tracked loaders will be equipped with a load measurement system that uses boom angle sensors and hydraulic cylinder pressure monitoring to determine bucket load during material handling operations. Mini tracked loaders will be integrated into... Figure 1 In the wireless communication network shown, load data is transmitted and loading sequence instructions are received from computer server 132, thereby cooperating with cold milling machine 104 to handle materials.

[0020] Environment 100 includes a computer server 132, which can implement a scheduling system for coordinating material loading operations. The load monitoring system and scheduling system can be implemented as a single system or as separate systems. The load monitoring system or scheduling system can also be implemented on a mobile device 148. Each support machine includes sensors for measuring bucket angle, hydraulic pressure, and load weight, and transmitting this information to the computer server 132, which can receive the information using wireless signals 136.

[0021] The scheduling system receives load and location data (e.g., from GPS module 144) from supporting machines and transport vehicles, and determines the loading sequence based on machine locations and the remaining load capacity of the transport vehicles. For example, the scheduling system tracks GPS location data to determine the relative positions between machines and transport vehicles. In one implementation, the scheduling system determines a loading sequence with specified machine identities, loading order, and target load amounts. The scheduling system can determine an estimated loading completion time based on the mass flow rate from the cold milling machine 104 and the material loading rate of the supporting machines. The scheduling system can continuously update the sequence in real time as machines move and loads change, and transmits the updates using wireless signal 140.

[0022] The scheduling system implements algorithms to coordinate material loading operations on machines 104 and 112. For example, dynamic priority scheduling can assign priorities to machines based on their current load, proximity to transport vehicle 108, and estimated completion time. As conditions change, a load monitoring system implemented on computer server 132 is used to update priorities. In some examples, nearest neighbor sorting uses location data from GPS module 144 and proximity detection from short-range wireless modules to optimize the loading sequence by reducing the travel distance between machines 104 and 112 and transport vehicle 108 while maintaining load targets. Predictive load balancing can be used to analyze historical mass flow rates from cold milling machine 104 and bucket weight data from support machines such as wheel loader 112 to determine loading sequence and material delivery rates.

[0023] When approaching weight limits, multi-machine collaborative processing can be used to coordinate reduced material delivery rates across all loaders. Real-time scheduling optimization can be used to determine when to schedule fully loaded transport vehicles and request empty vehicles based on the current loading progress monitored using force and speed sensors. The scheduling system takes into account weight limits and remaining capacity data of transport vehicles transmitted to mobile device 148 via wireless signal 140. Adaptive sequence planning can be used to automatically adjust the loading sequence based on machine availability, load measurement accuracy levels, and transport vehicle requirements tracked using vision recognition system 208. The scheduling algorithm can determine the loading sequence to prevent overloading while improving efficiency, and operators monitor progress using electronic displays.

[0024] In some implementations, the identification system uses manual operator input, GPS module 144 tracking, and proximity detection using short-range wireless signals to determine the association between the machine and the truck. Short-range wireless signal technology enables direct communication between machines 104, 112, mobile device 148, and computer server 132 in environment 100. The dispatch system can use wireless communication between the machines, computer server 132, and mobile device 148 to achieve real-time sharing of load data, machine location, and loading status updates. For example, the dispatch system uses wireless modules to transmit load data, location information, and status updates between the cold milling machine 104, support machines 112, 116, and transport vehicle 108.

[0025] Wireless technologies can include Bluetooth, WiFi, and other short-range wireless protocols capable of close-range detection between machines and trucks. Short-range wireless modules establish machine-to-machine connections to share real-time load measurements, bucket weight data, and loading sequence updates. This wireless communication infrastructure allows for rapid data exchange to coordinate loading operations and prevent vehicle overloading. The dispatch system can automatically detect when a machine is within communication range to determine machine-to-truck association and enable load data sharing. Multiple wireless protocols can be used simultaneously to ensure reliable data transmission on the site while maintaining low latency for critical real-time monitoring functions.

[0026] Computer server 132 can implement an accuracy determination algorithm to evaluate the accuracy of load measurements based on sensor inputs and operating conditions. For example, the scheduling system applies appropriate accuracy tolerances to load measurements, adjusting the calculation results to maintain reliable weight determination under various conditions. This accuracy monitoring enables the scheduling system to provide a confidence level for load measurements and adjust control parameters accordingly. For example, an acceptable error range within the sensor measurement can be determined, typically expressed as a positive / negative value around the true value, and can be evaluated through calibration, statistical analysis of repeated measurements, and consideration of environmental conditions and sensor characteristics such as nonlinearity, hysteresis, and repeatability.

[0027] The mobile device 148 displays comprehensive load information to the operator, including the current fill level, remaining capacity of the transport vehicle, estimated completion time, and loading sequence details. In some embodiments, the dispatch system uses the mobile device 148 to generate an alarm when approaching weight limits and can automatically adjust the material delivery rate via the control motor 156 to optimize loading efficiency while ensuring compliance with weight limits. The alarm generation module can implement various types of alarms to notify the operator of load status and weight limits. Visual alarms displayed on an electronic display include a real-time fill level indicator, remaining capacity, and warning messages when approaching predetermined weight limits.

[0028] The dispatch system can generate audible warnings using loudspeakers, with the warning pattern or intensity changing as the fill level increases. Proximity-based alarms notify the operator using data from short-range wireless modules and / or GPS modules when the machine moves into the loading operation range. When transport vehicle 108 reaches capacity or needs to be empty, the dispatch system can send automatic dispatch alarms using mobile device 148. Weight limit alarms can be transmitted using communication modules to coordinate reduced material delivery rates on all loaders. These include automatic control signals for adjusting conveyor speeds and bucket load targets. The system also generates compliance alarms to prevent exceeding road weight limits or fleet operator restrictions. Alarms transmitted using wireless signal 136 can provide real-time status updates to computer server 132 for tracking multiple transport vehicles and recording fill levels over time.

[0029] The load monitoring system processes information received from various machines and sensors via a network. This information tracks bucket weight data from supporting machines and load measurements from the conveyor belt 152 of the cold milling machine 104. The load monitoring system combines these inputs while considering measurement accuracy to maintain accurate real-time monitoring of the cumulative total load. The conveyor system of the cold milling machine uses a motor 156 to drive the conveyor belt 152 at a controlled speed based on the desired material delivery rate. Force sensors attached to the roller assembly measure the downward force of the material 128 on the conveyor belt 152, while speed sensors monitor the belt speed for mass flow rate calculations. When the conveyor belt 152 is not delivering material, the system automatically recalibrates the measurements to ensure accuracy throughout operation.

[0030] In this implementation, the scheduling system maintains a dynamic loading sequence, taking into account machine locations tracked by GPS module 144, current load amounts, and transport vehicle requirements. For example, the scheduling system determines estimated completion times by analyzing the mass flow rate of conveyor belt 152, bucket capacity, and cycle time of each machine. This sequence is continuously updated using updates transmitted via wireless signal 140 based on real-time tracking of machine movement and progress of the target load. Support is provided for transmitting bucket weight data wirelessly each time material 124 is loaded, enabling computer server 132 to track individual bucket loads and maintain cumulative totals. The cold milling machine 104 provides continuous mass flow rate data based on measurements from conveyor belt 152, enabling real-time calculation of the transported material. These inputs, combined with accuracy tolerances, determine the cumulative total weight and remaining capacity of the transport vehicles.

[0031] The load monitoring system implements control algorithms on computer server 132 to automatically adjust the material conveying rate. When approaching weight limits, control motor 156 reduces the speed of conveyor belt 152, while simultaneously supporting machine receiving alarms via wireless signal 136 to adjust the loading rate. Cooperative control prevents overloading. Environment 100 uses integrated motor 156, conveyor belt 152, wireless signals 136 and 140, GPS module 144, and computer server 132 to achieve comprehensive load monitoring. Mobile device 148 provides operators with real-time visibility of the loading process, while the measurement and control system ensures accurate load tracking on all machines. This integrated approach enables construction operations to improve loading efficiency while ensuring compliance with weight limits through accurate monitoring of the cumulative load from all loading sources.

[0032] Figure 2 This is a diagram illustrating an example multi-machine load system 200 for real-time total load monitoring according to some aspects of the present technology. System 200 includes a load monitoring system 204 that combines real-time load measurements from a cold milling machine and a support machine to provide an accurate total load estimate when loading a transport vehicle. System 200 uses a reference... Figure 6 The components of the example computer system 600 are shown and described in more detail for implementation. Similarly, embodiments of the example system 200 may include different and / or additional components, or may be connected in different ways.

[0033] In some implementations, the visual recognition system 208 uses manual operator input and / or computer vision methods to determine the association between the machine and the truck. GPS tracking and / or proximity detection between the machine and the transport vehicle can also be used to identify the machine. System 204 uses computer processor 212 to process the recognition data to accurately track which machines are loading specific transport vehicles. Communication module 216 enables data exchange between the machine, operator, and cloud server using a wireless network. Module 216 transmits load data, machine location, and loading status updates in real time to coordinate loading operations. When approaching a weight limit, alarm generation module 220 generates a warning and can generate signals or instructions to automatically adjust the material delivery rate.

[0034] Memory 224 can store critical operational data, including tare weights, calibration parameters, and historical loading information for different transport vehicle types. System 204 uses electronic display screen 228 to display comprehensive load information, enabling operators to monitor loading progress and receive alerts. First machine 236 (e.g., a cold milling machine) incorporates a first load measurement system 240, which uses force sensor 268 and speed monitoring on the conveying system to determine the material delivery rate. Second machine 244 utilizes a second load measurement system 248, using a boom angle sensor and hydraulic monitoring to measure bucket load.

[0035] System 204 processes first load data 252 from the cold milling machine's conveyor-based measurement system and second load data 256 from the machine's bucket weight measurement system. This data is combined to calculate the cumulative total weight and determine the remaining capacity of the transport vehicle. Conveyor belt 260 conveys material from the cold milling machine simultaneously, monitored by multiple sensor systems. Hydraulic motor 264 drives the conveyor belt, measuring its output power and pressure differential to help determine the material conveying status. Force sensor 268 measures the downward force on the material on the conveyor belt, while speed sensor 272 monitors the belt speed for mass flow rate calculation.

[0036] System 204 can use network 280 to send alarms within information 276 to computer server 284 or mobile device 288. Network 280 is capable of exchanging data using various communication protocols. Direct data links such as Ethernet connections and Connected Local Area Networks (CAN) can be used for local machine-to-machine communication. For wireless connectivity, network 280 can use radio, satellite, cellular networks, Bluetooth, WiFi, infrared communication, and / or other short- and long-range communications using communication module 216. The network infrastructure allows for rapid data exchange to coordinate loading operations while maintaining low latency for critical real-time monitoring functions. Multiple wireless protocols can be used simultaneously to ensure reliable data transmission on the site, where computer server 284 processes information received from various machines and sensors via network 280.

[0037] System 204 or computer server 284 can implement scheduling algorithms to coordinate loading sequences based on machine location, load capacity, and transport vehicle requirements. Mobile device 288 provides operators with real-time visibility of the loading process using a user interface that displays current fill levels, remaining capacity, and loading sequence details.

[0038] The short-range wireless module 292 enables close-range detection between the machine and the transport vehicle, while facilitating direct machine-to-machine communication of load data. The GPS module 296 provides accurate position tracking to support loading sequence optimization and machine-to-truck association determination. The load monitoring system 204 implements an accuracy determination algorithm, evaluating measurement accuracy based on sensor input and operating conditions. System 204 applies appropriate accuracy tolerances to load measurements from the first load measurement system 240 and the second load measurement system 248, adjusting calculations to maintain reliable weight determination.

[0039] Computer processor 212 executes a scheduling algorithm to maintain a dynamic loading sequence that takes into account machine positions tracked via GPS module 296, current load capacity, and transport vehicle requirements. Processor 212 determines estimated completion times by analyzing the mass flow rate, bucket capacity, and cycle time of each machine. When approaching weight limits, alarm generation module 220 generates visual and audible warnings using electronic display screen 228. Module 220 can coordinate with communication module 216 to transmit alarms to mobile device 288 and automatically adjust the material delivery rate using control of hydraulic motor 264.

[0040] System 204 integrates conveyor belt 260 measurement, bucket weight data, and precision control algorithms to achieve comprehensive load monitoring. Memory 224 retains historical loading data, while computer server 284 processes real-time sensor inputs to optimize loading efficiency and prevent overloading. Network 280 facilitates data exchange between the first machine 236, the second machine 244, and computer server 284. Network 280 can transmit first load data 252 and second load data 256 to achieve real-time monitoring of the cumulative total weight of all loading sources.

[0041] The short-range wireless module 292 automatically detects when the machine is within communication range to achieve machine-to-truck association. Module 292 can exchange load data between the first load measurement system 240 and the second load measurement system 248 to coordinate loading operations. In some embodiments, the vision recognition system 208 processes input from the operator 232 to maintain accurate tracking of machine-to-truck association. System 204 displays this information using an electronic display screen 228 while simultaneously recording the loading sequence and fill level in memory 224. The communication module 216 implements multiple wireless protocols to ensure reliable data transmission on the site. Module 216 can coordinate with the alarm generation module 220 to deliver warning and control signals while maintaining low latency for critical real-time monitoring functions.

[0042] Computer server 284 and / or system 204 execute load prediction algorithms that analyze historical mass flow rate and bucket weight data to optimize loading sequence. For example, server 284 adjusts the sequence in real time based on load measurements, while coordinating a reduction in material delivery rate when approaching weight limits. Integrated system 200 enables construction operations to improve loading efficiency while using precise monitoring of cumulative loads from all loading sources to ensure compliance with weight limits. The combination of conveyor belt 260 measurements, bucket weight data, and precision control algorithms provides comprehensive load monitoring capabilities.

[0043] Figure 3 This is a flowchart illustrating an example process for real-time load monitoring according to some aspects of the present technology. In some embodiments, the process is described by reference... Figure 2The system 204 is shown and described in more detail for execution. (See reference) Figure 6 The computer system 600, shown and described in more detail in other embodiments, performs some or all of the steps of this process. Similarly, embodiments may include different and / or additional steps, or these steps may be performed in a different order.

[0044] At point 304, the computer system receives first load data from the first load measurement system of the first machine. Figure 2 Example load data 252, example first load measurement system 240, and example first machine 236 are shown. The first machine may be a cold milling machine that uses a conveyor-based measurement system that combines force and speed sensors to monitor material transport using a conveyor belt. Figure 1 An example cold milling machine 104 and an example conveyor belt 152 are shown. Initial load data can be obtained via an integrated sensor system that measures the material flow. For example, a force sensor mounted on the roller assembly measures the downward force of the material on the conveyor belt, while a speed sensor monitors the belt speed for mass flow rate calculation. Example sensors 268 and 272 are shown. Figure 2 As shown in the image.

[0045] The communication module can use a short-range wireless module to perform wireless machine-to-machine communication to receive payload data, enabling direct data exchange between machines. Figure 2 Example communication module 216 and example short-range wireless module 292 are shown. A computer system can use cellular network communication and / or server communication using a computer server to transmit payload information over a network. Figure 1 Example computer server 132 is shown. Wireless signals enable real-time data transmission, while an integrated communication protocol between the machine and computer devices is used to maintain continuous monitoring of material delivery. Figure 1 An example computer device 148 is shown. When the conveyor is not transporting material, the computer system can automatically recalibrate measurements to maintain accuracy during operation. Initial load data can be sent to a computer processor, which combines conveyor measurements with accuracy tolerances. The processor can analyze mass flow rate, conveyor speed, and force measurements to determine the real-time load loaded into the transport vehicle.

[0046] At point 308, the computer system receives the second load from the second load measurement system of the second machine. The second machine can be a support machine, such as a wheel loader, excavator, tractor, bulldozer, small tracked loader, or skid steer loader, used to assist in material handling operations. Examples of support machines are 112, 116, and 120. Figure 1As shown. The supporting machine has a second load measurement system that uses a boom angle sensor and hydraulic cylinder pressure monitoring to determine the bucket load, thereby enabling accurate measurement of the loaded material. The second load can include the bucket load measured using the boom angle sensor and hydraulic cylinder pressure monitoring. Even when operating on a slope, weight calculations that take into account the ground inclination and hydraulic cylinder pressure differences can be performed for accurate measurement. The second load data is received using a communication module and sent to a computer processor, which applies accuracy tolerances and combines the bucket weight data with known characteristics of the transport vehicle to determine the transport vehicle's fill level and remaining capacity.

[0047] The memory can store tare weight, volumetric capacity, and weight limits for different types of transport vehicles. Figure 2 Example memory 224 is shown. The computer system can also receive tare weight data via an electronic display using manual operator input or automatically using wireless signals from the transport vehicles. The computer system determines the remaining capacity by subtracting the cumulative total weight and tare weight from a predetermined weight limit. The computer system can continuously update the determination of the remaining capacity as additional materials are loaded, enabling real-time monitoring of the fill level. The computer system can use an electronic display to show the remaining capacity while tracking multiple transport vehicles over time. An alarm generation module can use the remaining capacity to coordinate a reduction in the material delivery rate and generate warnings when approaching capacity limits.

[0048] At point 312, the computer system determines the machine's identity (e.g., using...). Figure 2 The visual recognition system 208). When using manual operator input, the operator can select or input machine recognition using an electronic display, thus allowing for a clear association between a specific machine and the loaded transport vehicle. The computer system can store these manual associations in memory for tracking loading operations. For GPS-based recognition, ( Figure 2 The GPS module 296 can generate signals indicating the geographical locations of the first machine, the second machine, and the transport vehicle. A computer processor can use algorithms to analyze the location signals to determine which machines are near a specific transport vehicle. The computer system can continuously track the relative positions between the machines to maintain accurate loading correlation. In some implementations, proximity detection uses a short-range wireless module to automatically detect when a machine moves within the communication range of the transport vehicle. The computer system can establish direct wireless connections between nearby machines to enable payload data sharing and loading coordination.

[0049] At point 316, the computer system combines first load data (e.g., a conveyor-based measurement system from a cold milling machine) with second load data (e.g., a bucket weight measurement from a support machine) to determine the cumulative gross weight in real time. For example, mass flow rate measurements from the conveyor belt and separate bucket load data can be used, while applying appropriate accuracy tolerances to maintain reliable weight determination. The computer system can track the accumulated material delivery based on data from the conveyor system, combining it with boom angle and hydraulic measurements from bucket loading operations. The cumulative gross weight calculation takes into account the conveyor mass flow rate, bucket load measurements, and transport vehicle parameters including tare weight and remaining capacity. While the computer system processes real-time sensor inputs using algorithms to determine the cumulative gross weight from all loading sources, memory can retain historical loading data. This integrated measurement method enables monitoring of the cumulative load while automatically compensating for operating conditions using continuous calibration. The computer system can display the real-time cumulative gross weight on an electronic display screen and generate an alarm using an alarm generation module when approaching a predetermined weight limit.

[0050] At point 320, the computer system compares the total accumulated weight with predetermined weight limits stored in memory to prevent transport vehicles from being overloaded beyond required capacity limits. For example, the computer system retrieves weight limits from stored transport vehicle parameters, including road restrictions, fleet operator restrictions, and manufacturer specifications. Figure 2 The alarm generation module 220 continuously monitors the comparison between the current cumulative total weight and a predetermined weight limit to enable automatic control response. When the cumulative total weight approaches the weight limit, the computer system coordinates a reduction in the material delivery rate on the loader, while simultaneously generating visual and audible warnings using an electronic display screen. In this implementation, the computer system tracks the remaining capacity of the transport vehicle by comparing the cumulative total weight with a predetermined weight limit and the tare weight of the transport vehicle. This allows for real-time monitoring of the fill level, while ensuring compliance with weight limits through precise tracking of the cumulative load from all loading sources.

[0051] At point 324, when the cumulative total weight approaches a predetermined weight limit, the computer system generates visual and audible warnings (e.g., via an electronic display). The computer system can provide tiered warnings to the operator by generating progressively escalating alarms that change sound patterns and light intensity as the fill level increases. A communication module can transmit the alarm to the mobile device while automatically adjusting the material delivery rate using hydraulic motor control when approaching the weight limit. The computer system can coordinate a reduction in the conveyor speed and bucket load target on one or more loaders to prevent exceeding capacity limits. The alarm enables the operator to improve truck loading efficiency while using automated monitoring and control responses to ensure compliance with weight limits. The warning device can provide visual and / or audible notifications that are detectable by the machine operator and other personnel to coordinate loading operations. The computer system can automatically track alarm history in memory while maintaining real-time monitoring of remaining capacity using continuous load measurements.

[0052] When the cumulative total weight approaches a predetermined weight limit, the computer system can automatically reduce the material delivery rate using coordinated control of the cold milling machine's hydraulic motors and conveyor belt speed. For example, the computer system monitors the mass flow rate from the cold milling machine's conveyor system and the load supporting the machine's bucket, and automatically reduces the delivery speed using hydraulic motor control when the fill level reaches a specified threshold.

[0053] At point 328, the computer system uses a network to transmit alarms and machine identification information to a computer device for real-time monitoring of loading operations. For example, the computer system sends machine identification determined using a visual recognition system, along with load alarms transmitted wirelessly, to coordinate loading activities. The computer device can process the transmitted data, including machine identification (e.g., proximity detection from a GPS module and / or a short-range wireless module). When approaching weight limits, the computer system can maintain a record of the machine's association with the truck while delivering alarms using a mobile device. This integrated communication method enables comprehensive tracking of loading operations by transmitting alarm notifications and machine identification information to the computer device. The alarm generation module can coordinate with the communication module to deliver warnings while maintaining an accurate record of which machines are loading specific transport vehicles.

[0054] Figure 4 This is a flowchart illustrating an example process for determining the cumulative total weight of a transport vehicle in real time, according to some aspects of the present technology. In some embodiments, the process is described by reference... Figure 2 The system 204 is shown and described in more detail for execution. (See reference) Figure 6 The computer system 600, shown and described in more detail in other embodiments, performs some or all of the steps of this process. Similarly, embodiments may include different and / or additional steps, or these steps may be performed in a different order.

[0055] At point 404, the computer system receives information about the material weight and conveyor belt speed from the load measurement system of the cold milling machine. The computer system can monitor material transport using the conveyor belt by processing force sensor data indicating the weight of the material on the belt and speed sensor data measuring the belt's linear speed.

[0056] At point 408, the computer system determines the mass flow rate, for example, by processing force sensor data indicating the weight of the material on the conveyor belt and speed sensor measurements of the belt speed. The computer system can determine the real-time mass flow rate by multiplying the sensed material weight by the conveyor belt speed, while applying a calibration factor during the transfer of material to the transport vehicle to maintain measurement accuracy.

[0057] At point 412, the computer system receives load data from the support machine, for example, via wireless signals. The load data describes the material loaded into the transport vehicle. For example, the computer system receives bucket load measurements from wheel loaders, excavators, mini track loaders, or skid steer loaders via integrated sensors that monitor boom angle and hydraulic pressure to determine the weight of the material during loading operations. The load data allows for real-time tracking of the load contribution of the support machine working in conjunction with a cold milling machine. The computer system can determine the accuracy level of the load measurement system through calibration and monitoring of sensor data quality, such as from force and speed sensors. For example, appropriate accuracy tolerances are applied to account for sensor calibration status, measurement variations, and environmental factors that may affect load measurements. When determining the cumulative total weight, the computer system incorporates these accuracy tolerances into the real-time weight determination to maintain measurement accuracy while taking into account known sensor accuracy limitations during material handling operations.

[0058] The computer system can use a vision recognition system and / or a GPS module to identify machines by tracking their position and proximity to the cold milling machine and its support machine. The computer system can also utilize wireless machine-to-machine communication using wireless signals and short-range wireless modules to establish machine identification and association during loading operations. The computer system can process location data and wireless communications to automatically identify machines operating together, while simultaneously enabling real-time coordination of load monitoring.

[0059] At point 416, the computer system determines the cumulative gross weight in real time, for example, by combining mass flow rate calculations from the cold milling machine conveyor system with load data received from the support machine measurement system. The computer system can continuously update the gross weight by integrating conveyor-based mass flow rate measurements with bucket load data, while maintaining real-time monitoring of the accumulated material being delivered to the transport vehicles. The computer system can track and record data from multiple transport vehicles, maintaining a record of each vehicle filled during the material loading operation. For example, the computer system stores the fill level data for each transport vehicle in memory, including the cumulative gross weight, remaining capacity, and loading completion time, to monitor extended operation cycles.

[0060] At point 420, when the computer system determines that the cumulative total weight is approaching a predetermined weight limit, it sends an alarm to the computer device, for example, via a communication module. The computer system can use wireless signals to transmit warning notifications, preventing overloading by instructing the operator to reduce the material delivery rate before exceeding the transport vehicle's capacity limit. The computer system can generate visual indicators and audible warnings using electronic displays and send alarms to the transport vehicle via wireless signals. This system uses visual displays, warning sounds, and wireless communication to provide coordinated alarms to notify the operator when approaching the weight limit during loading operations. This integrated alarm method enables real-time notification using multiple warning methods to prevent overloading.

[0061] Figure 5 This is a flowchart illustrating an example process for integrating load measurements from a cold milling machine and a support machine, according to some aspects of the present technology. In some embodiments, the process is described by reference... Figure 2 The system 204 is shown and described in more detail for execution. (See reference) Figure 6 The computer system 600, shown and described in more detail in other embodiments, performs some or all of the steps of this process. Similarly, embodiments may include different and / or additional steps, or these steps may be performed in a different order.

[0062] At point 504, the computer system stores the tare weight of the transport vehicle, which represents the empty weight of the vehicle before loading materials. The computer system maintains this stored tare weight value so that the remaining capacity of the transport vehicle and the cumulative total weight during the loading operation can be accurately determined by taking into account the vehicle's base weight. When the transport vehicle is connected to the computer system, the system can receive and store the tare weight using either manual operator input or automatic data exchange.

[0063] At point 508, the computer system receives load data, such as material weight measurements from sensors on the conveyor belt. The computer system can receive real-time weight and speed data using wireless signals to enable continuous monitoring of the material loaded into the transport vehicle. The computer system can process integrated sensor data to determine the material conveying rate while maintaining measurement accuracy during loading operations.

[0064] At point 512, the computer system receives load data regarding the weight of the material measured by integrated sensors on the second machine. For example, real-time weight measurements are received to enable continuous tracking of the loaded material. The computer system can determine the loading sequence by analyzing GPS location data of the transport vehicles and machines, while incorporating remaining load capacity calculations. For example, the computer system establishes machine identities and loading sequences between the cold milling machine and the support machine based on their relative positions and material conveying capabilities. The loading sequence can be maintained by sending machine-specific loading instructions, specifying coordinated target loads for each machine to ensure synchronized material conveying operations.

[0065] At point 516, the computer system determines the cumulative total weight in real time by combining first load data from the integrated measurement system of the supporting machine with second load data. The computer system can continuously integrate load measurements from both machines to maintain accurate real-time monitoring of the accumulated material being transported to the transport vehicle, thereby preventing overloading beyond predetermined limits. For example, the computer system determines the mass flow rate by analyzing conveyor belt speed data and combining material weight measurements from the conveying system of the cold milling machine. The computer system determines the estimated completion time by dividing the remaining load capacity by the determined mass flow rate of the cold milling machine, while simultaneously incorporating loading rate data from the supporting machine.

[0066] At point 520, the computer system determines the remaining capacity of the transport vehicle by subtracting the sum of the stored tare weight and the cumulative total weight from a predetermined weight limit. This remaining capacity determination is updated in real time as additional material is loaded. The computer system can update the loading sequence by monitoring real-time location changes using a GPS module and wireless signals for the cold milling machine, support machines, and transport vehicle. Based on the updated location data, the computer system dynamically adjusts machine coordination while incorporating remaining load capacity calculations to maintain loading efficiency. For example, the system processes bucket weight data in real time to optimize the loading sequence by adjusting target loads and machine sequencing to account for the actual amount of material being transported.

[0067] At point 524, when the remaining capacity approaches the threshold, the computer system generates an alarm. The computer system can determine the predicted load by analyzing real-time sensor data, thus determining, for example, the amount of material currently being transported on the conveyor belt. The computer system can maintain accurate real-time monitoring by combining the current conveyor load measurement with the weight of the material already transported and incorporating the predicted amount into the cumulative total weight.

[0068] Industrial applicability

[0069] The disclosed equipment and systems have broad applicability in various construction and infrastructure development scenarios where material loading and transportation operations are critical. In road construction and maintenance operations, the disclosed systems enable more precise coordination between cold milling machines removing existing pavement and support machinery such as wheel loaders and excavators for auxiliary material handling. These operations often involve loading multiple trucks simultaneously while adhering to strict Ministry of Transport weight limits, making accurate load monitoring essential. Mining operations represent another key application area where the system's ability to track material movement across different types of loading equipment provides significant value. The combination of conveyor-based measurements from primary extraction equipment and bucket load measurements from support machinery enables comprehensive monitoring of material extraction and transportation.

[0070] Furthermore, the disclosed method can be applied to large-scale demolition and site preparation projects where various types of materials must be efficiently removed and transported. In these cases, cold milling machines can operate in conjunction with excavators and wheel loaders to process and load materials into multiple transport vehicles. The disclosed system's ability to track load contributions from each machine while preventing overloading helps improve the overall material removal process. Infrastructure remediation projects such as airport runway refurbishment or large parking facility upgrades can benefit from the system's coordination capabilities. Due to tight operating windows and space constraints, these projects typically require precise timing and efficient material handling.

[0071] The benefits and advantages of the embodiments described herein include accurate tracking of cumulative loads from multiple loading sources while preventing transport vehicles from exceeding weight limits. By combining real-time load measurements from both the cold milling machine and the support machine, the disclosed system provides accurate total load estimates and uses real-time monitoring and alerts to prevent overloading that could result in fines due to road weight restrictions. The disclosed method delivers enhanced operational efficiency by enabling operators to improve truck loading efficiency while ensuring compliance with weight limits. In some cases, the disclosed method allows loading trucks to carry approximately 20% more capacity compared to conventional practices that result in underloading due to uncertainty. This reduces the number of trucks required to transport materials and improves milling efficiency through better coordination between machines.

[0072] Furthermore, continuous monitoring of the cumulative total weight across multiple machines enables real-time monitoring and control. The disclosed system generates an automatic alarm when approaching weight limits and automatically reduces the material conveying rate when approaching capacity. This allows operators to track remaining capacity and optimize loading sequences for maximum efficiency. The disclosed method provides flexible implementation options using standalone controllers, machine-to-machine communication, or servers.

[0073] Figure 6 This is a block diagram illustrating an example of a computer system 600 in which at least some of the operations described herein can be implemented. Components of the computer system 600 can be used for implementation. Figure 2 The systems shown are 204, 240, and 248.

[0074] As shown in the figure, a computer system 600 may include: one or more processors 602, main memory 606, non-volatile memory 610, network interface device 612, video display device 618, input / output device 620, control device 622 (e.g., keyboard and pointing device), a drive unit 624 including storage medium 626, and a signal generation device 620 communicatively connected to a bus 616. Bus 616 represents one or more physical buses and / or point-to-point connections connected via appropriate bridges, adapters, or controllers. For simplicity, ... Figure 6 Various common components (e.g., cache memory) are omitted. Instead, computer system 600 is intended to illustrate a hardware device on which components shown or described with respect to the examples in the accompanying drawings, as well as any other components described in this specification, may be implemented.

[0075] Computer system 600 can take any suitable physical form. For example, computer system 600 can have an architecture similar to that of a server computer, personal computer (PC), tablet computer, mobile phone, game console, music player, wearable electronic device, network-connected (“smart”) device (e.g., television or home assistant device), augmented reality / virtual reality system (e.g., head-mounted display), or any electronic device capable of executing a set of instructions specifying actions to be taken by computer system 600. In some embodiments, computer system 600 can be an embedded computer system, system-on-a-chip (SoC), single-board computer system (SBC), or a distributed system such as a computer system mesh network, or include one or more cloud components in one or more networks. Where appropriate, one or more computer systems 600 can operate in real-time, near real-time, or batch processing mode.

[0076] Network interface device 612 enables computer system 600 to exchange data with entities outside computer system 600 within network 614 using any communication protocols supported by computer system 600 and external entities. Examples of network interface device 612 include network adapter cards, wireless network interface cards, routers, access points, wireless routers, switches, multilayer switches, protocol converters, gateways, bridges, bridging routers, hubs, digital media receivers and / or repeaters, and all wireless elements mentioned herein.

[0077] Memory (e.g., main memory 606, non-volatile memory 610, machine-readable medium 626) can be local, remote, or distributed. Although shown as a single medium, machine-readable medium 626 can include multiple media (e.g., centralized / distributed databases and / or associated caches and servers) storing one or more instruction sets 628. Machine-readable (storage) medium 626 can include any medium capable of storing, encoding, or carrying a set of instructions executed by computer system 600. Machine-readable medium 626 can be non-transitory or includes non-transitory means. In this context, non-transitory storage medium can include tangible means, meaning that the means has a concrete physical form, although the means can change its physical state. Thus, for example, "non-transitory" means that the means remains tangible despite changes in state.

[0078] Although implementations have been described in the context of a full-featured computing device, various examples can be distributed as program products in various forms. Examples of machine-readable storage media, machine-readable media, or computer-readable media include recordable media such as volatile and non-volatile memory devices 610, removable flash memory, hard disk drives, optical disks, and transmission media such as digital and analog communication links.

[0079] Generally, routines executed to implement the examples herein may be implemented as part of an operating system or a particular application, component, program, object, module, or sequence of instructions (collectively, a “computer program”). A computer program typically includes one or more instructions (e.g., instructions 604, 608, 628) set at different times in various memories and storage devices within a computing device. When read and executed by processor 602, the instructions cause computer system 600 to perform operations to execute elements relating to various aspects of the present invention.

Claims

1. A computer-implemented load monitoring system, comprising: At least one hardware processor; as well as At least one non-transitory memory storing instructions that, when executed by at least one hardware processor, enable the computer-implemented load monitoring system to: The first load data is received from the first load measurement system of the first machine, and the first machine measures the amount of first material loaded into the transport vehicle; The second load data is received from the second load measurement system of the second machine, which measures the amount of second material loaded into the transport vehicle. The identities of the first machine and the second machine are determined based on at least one of manual operator input, Global Positioning System (GPS) data, or proximity detection between the machine and the transport vehicle; The cumulative total weight of the transport vehicle is determined in real time based on the first load data and the second load data; The total accumulated weight is compared with a predetermined weight limit; An alarm is generated when the cumulative total weight approaches the predetermined weight limit; as well as Send the alarm or at least one of the identities of the first machine and the second machine to the computer device.

2. The computer-implemented load monitoring system according to claim 1, wherein the first machine comprises a cold milling machine, and the second machine comprises at least one of a wheel loader, excavator, bulldozer, tractor, small tracked loader, or skid steer loader.

3. The computer-implemented load monitoring system according to claim 1, wherein the first load measurement system comprises: Conveyor belt; A hydraulic motor configured to drive the conveyor belt; Force sensors are configured to measure the weight of materials on the conveyor belt; as well as A speed sensor is configured to measure the speed of the conveyor belt.

4. The computer-implemented load monitoring system according to claim 1, wherein the at least one hardware processor is configured to: Store the tare weight of the transport vehicle; and The remaining capacity is determined based on the tare weight, the cumulative total weight, and the predetermined weight limit.

5. The computer-implemented load monitoring system of claim 1, wherein the at least one hardware processor is configured to automatically reduce the material delivery rate when the cumulative total weight approaches the predetermined weight limit.

6. The computer-implemented load monitoring system of claim 1, wherein at least one of wireless machine-to-machine communication, cellular network communication, or server communication is used to receive at least one of the first load data or the second load data.

7. The computer-implemented load monitoring system of claim 1, wherein the predetermined weight limit is based on at least one of road weight limits or transport vehicle specifications.

8. A computer-implemented method for monitoring loads on multiple machines, comprising: Receive information from the load measurement system of the cold milling machine. The information includes the weight of the material on the conveyor belt of the cold milling machine and the speed of the conveyor belt; The mass flow rate of the material conveyed from the cold milling machine to the transport vehicle is determined based on the material weight and the speed. Receive load data from the support machine that is loading materials into the transport vehicle; The cumulative total weight of the transport vehicle is determined in real time based on the mass flow rate and the load data; and An alarm is sent to a computer device when the cumulative total weight approaches a predetermined weight limit to prevent the transport vehicle from being overloaded.

9. The computer-implemented method of claim 8, comprising determining the identities of the cold milling machine and the support machine using at least one of a visual recognition system, wireless machine-to-machine communication, cellular network communication, global positioning system (GPS) data, or short-range wireless communication.

10. The computer-implemented method of claim 8, wherein the alarm comprises at least one of a visual indicator, an audible warning, or a signal transmitted to the transport vehicle.

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