Method, device, equipment and medium for dynamically optimizing transport load of mine car train

Abstract

This invention discloses a method, apparatus, equipment, and medium for dynamic optimization of transport load in a mining truck fleet. The method includes: assigning an initial transport load to each mining truck and having it travel along a preset operating route, while collecting real-time location and road condition information; dividing the operating route into segments and determining load limits; calculating the highest gearbox gear and actual traction force for each segment; determining the estimated speed for each segment based on a pre-stored three-dimensional mapping relationship; calculating the total length of effective segments and the distance ratio to the total length of the operating route; if the distance ratio is greater than or equal to a set ratio threshold, the load limit is used as the new initial transport load; otherwise, the load limit is adaptively lowered, and a new initial transport load is determined. This invention, through adaptive road condition calculation and iterative optimization of economic operating conditions, achieves the optimal load for each truck and updates iteratively for each truck, enabling the fleet to maintain efficient and economical operation while ensuring safety on inclines and declines, significantly improving overall operational efficiency.

Description

Technical Field

[0001] This invention relates to the field of engineering machinery technology, and in particular to a method, device, equipment and medium for dynamic optimization of transport load of a mining truck fleet. Background Technology

[0002] Mining transport vehicles are the core equipment for material transfer in open-pit mines, undertaking the critical task of transporting mined materials from the mining area to processing stations or spoil heaps. The rationality of their load-bearing configuration directly affects the overall transportation efficiency, operating costs, and operational safety of the mine. In actual operating scenarios, transport routes are generally characterized by continuous undulating slopes and complex road conditions. The power requirements, braking load, and driving efficiency of vehicles vary significantly on different road sections, such as uphill, downhill, and flat roads. To ensure the stable operation of the transportation system, the industry typically needs to strike a balance between vehicle safety load limits and transportation turnover efficiency. A reasonable load-bearing strategy not only reduces vehicle wear and fuel consumption but is also a crucial element in achieving intensive, safe, and green production in mines.

[0003] Existing load management systems for mining vehicles mostly rely on fixed rated loads or operator experience, failing to incorporate detailed segmented calculations based on continuous road condition data collected from actual vehicles. They also lack separate checks for uphill power constraints and downhill braking constraints, and cannot match load strategies with the engine's optimal economic operating conditions. Furthermore, existing solutions generally lack iterative load adjustment mechanisms based on speed-efficiency deviations, and cannot transmit individual vehicle optimization results to each vehicle to achieve continuous dynamic optimization at the fleet level. This leads to problems such as insufficient power, inadequate braking redundancy, and deviations from economic operating conditions, making it difficult to synergistically improve transportation efficiency, fuel economy, and driving safety. Overall, their intelligence and practicality are significantly lacking. Summary of the Invention

[0004] Based on this, the present invention provides a method, device, equipment and medium for dynamic optimization of transport load of mining truck fleet, in order to solve the problems of existing mining load strategies being singular, unable to dynamically adapt to route gradient, difficult to coordinate with engine economic operating conditions, and lacking a vehicle-by-vehicle iterative optimization mechanism.

[0005] In a first aspect, embodiments of the present invention provide a method for dynamic optimization of the transport load of a mining truck fleet, the method comprising: In response to the start command of the transportation operation, the target mining truck is selected from the mining truck fleet and an initial transportation load is assigned. The target mining truck is controlled to travel along the preset operation route. Location information and road condition information are collected in real time and bound together to form a full-line condition dataset. Based on the full road condition dataset, the operation route is divided into segments according to the preset slope threshold, and the segment information of each segment is recorded. Based on the road section information and the driving performance parameters preset by the target mining truck at the factory, the maximum load limit for uphill and downhill are calculated respectively, and the minimum value among the maximum load limit for downhill, maximum load limit for uphill and rated load of mining truck is selected as the current load limit. Call the factory-preset engine parameters of the target mining truck to determine the engine's optimal economic operating speed range corresponding to the lowest fuel consumption. Combined with the load limit, calculate the highest gearbox gear and the corresponding actual traction force for each section of the operation route. Based on the highest gearbox gear and the actual traction force, combined with the pre-stored three-dimensional mapping relationship between gear, traction force and vehicle speed, the estimated vehicle speed for each road segment is determined; wherein, the three-dimensional mapping relationship is pre-constructed through mining car bench tests or actual vehicle calibration tests. The total length of effective road segments in the statistical operation route whose estimated vehicle speed is within the efficient driving speed range is calculated, and the distance ratio to the total length of the operation route is calculated. If the distance ratio is greater than or equal to a set ratio threshold, the load limit is used as the new initial transport load of the next target mining truck. If the distance ratio is lower than the set ratio threshold, the load limit is adaptively lowered based on the deviation between the estimated speed and the efficient driving speed range of each road segment. The highest gearbox gear, actual traction force and estimated speed of each road segment are recalculated iteratively based on the lowered load limit until the distance ratio is higher than the set ratio threshold. The load limit after the iteration is then used as the new initial transport load of the next target mining truck.

[0006] Secondly, embodiments of the present invention also provide a dynamic optimization device for the transport load of a mining truck fleet, the device comprising: The road condition data acquisition module is used to respond to the transportation operation start command, select the target mining truck in the mining truck fleet and assign the initial transportation load, control the target mining truck to travel along the preset operation route, collect location information and road condition information in real time, and form a full road condition dataset after binding. The road segmentation module is used to divide the operation route into segments based on the full road condition dataset and according to a preset slope threshold, and to record the road segment information of each segment. The load limit determination module is used to calculate the maximum load limit for uphill and downhill based on road section information and the factory-preset driving performance parameters of the target mining truck, and select the minimum value among the maximum load limit for downhill, maximum load limit for uphill, and rated load of the mining truck as the current load limit. The engine economic speed determination module is used to call the factory-preset engine inherent parameters of the target mining truck, determine the engine's optimal economic operating speed range corresponding to the lowest fuel consumption, and calculate the highest gearbox gear and corresponding actual traction force for each section of the operation route in combination with the load limit. The estimated vehicle speed determination module is used to determine the estimated vehicle speed for each road segment based on the highest gearbox gear and the actual traction force, combined with the pre-stored three-dimensional mapping relationship between gear, traction force and vehicle speed; wherein, the three-dimensional mapping relationship is pre-constructed through mining car bench tests or actual vehicle calibration tests. The first load optimization module is used to count the total length of effective road segments in the operation route where the estimated vehicle speed is within the efficient driving speed range, and calculate the distance ratio with the total length of the operation route. If the distance ratio is greater than or equal to a set ratio threshold, the load limit is used as the new initial transport load of the next target mining truck. The second load optimization module is used to adaptively lower the load limit based on the deviation between the estimated speed and the efficient driving speed range of each road segment if the distance ratio is lower than the set ratio threshold. Based on the lowered load limit, the module re-iterates the highest gearbox gear, actual traction force and estimated speed of each road segment until the distance ratio is higher than the set ratio threshold. Then, the load limit after iteration is used as the new initial transport load of the next target mining truck.

[0007] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute a dynamic optimization method for the transport load of a mining truck fleet as described in any embodiment of the present invention.

[0008] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute and implement a dynamic optimization method for the transport load of a mining truck fleet as described in any embodiment of the present invention.

[0009] Fifthly, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements a dynamic optimization method for the transport load of a mining truck fleet as described in any embodiment of the present invention.

[0010] The technical solution of this invention, through road condition adaptive load calculation and economic condition closed-loop iteration, can automatically match a target load that satisfies both uphill power and downhill braking safety, and conforms to the engine's economic operating range, based on the actual slope constraints of the operating route. The optimized load is then iteratively transferred to subsequent mining trucks as the initial transport load, realizing dynamic optimization of the overall load strategy of the fleet. This fundamentally avoids the safety risks and inefficiency and high fuel consumption problems caused by heavy loads, enabling mining trucks to simultaneously meet the requirements of power safety, braking safety, and efficient and economical driving throughout the entire route, significantly improving the safety, economy, and overall operating efficiency of the mining truck fleet.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a method for dynamic optimization of the transport load of a mining truck fleet according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of another method for dynamic optimization of transport load of a mining truck fleet according to Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of a dynamic optimization device for the transport load of a mining truck fleet according to Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device for implementing a dynamic optimization method for the transport load of a mining truck fleet according to an embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Example 1 Figure 1 This is a flowchart of a dynamic load optimization method for a mining truck fleet according to Embodiment 1 of the present invention. This embodiment is applicable to situations where a mining truck fleet dynamically optimizes its initial load during material transportation. This method can be executed by a dynamic load optimization device for a mining truck fleet, which can be implemented in hardware and / or software. This device can be configured in a vehicle dispatching system. Figure 1 As shown, the method includes: S110: In response to the start command of transportation operation, select the target mining truck in the mining truck fleet and assign the initial transportation load, control the target mining truck to travel along the preset operation route, collect location information and road condition information in real time, and form a full road condition dataset after binding.

[0017] Mining area transportation routes are generally characterized by continuous slopes, undulating surfaces, and alternating long and short inclines. The constraints on vehicle power and braking vary greatly depending on the location. Traditional methods of setting loads based on manual experience or static drawings cannot match actual road conditions, easily leading to problems such as insufficient power uphill, overloaded braking downhill, or conservative loads resulting in low transportation efficiency. This embodiment, by assigning target mining trucks to actual driving with initial loads, collects continuous road condition data that precisely corresponds to spatial location. This ensures that subsequent load calculations perfectly match actual route conditions, achieving accurate adaptation of the load strategy to real road conditions.

[0018] Initial transport load refers to the initial trial load allocated by the vehicle dispatching system. This load is used to simultaneously carry out transport tasks while collecting road condition data, avoiding the time-consuming operations and wasted capacity caused by separate route exploration. Location information is bound to road condition information, and GPS / BeiDou positioning coordinates are matched one-to-one with road condition data such as slope and road surface condition according to timestamps, forming a structured data sequence with spatial index, so that positioning can determine road conditions.

[0019] S120. Based on the full road condition dataset, the operation route is divided into segments according to the preset slope threshold, and the segment information of each segment is recorded.

[0020] The original road conditions consist of high-density continuous sampling points. Direct calculation would lead to data redundancy, feature ambiguity, and an inability to segment and adapt to vehicle performance. Gradient is a core road condition factor determining the driving resistance, power requirements, and braking requirements of mining trucks. The mechanical constraints of uphill, flat, and downhill roads are completely different: uphill is limited by driving force, downhill by braking force, and flat roads prioritize economical operation. Segmenting the route using a gradient threshold essentially abstracts the complex continuous route into multiple road segment units with unique mechanical properties, enabling segment-by-segment differentiated calculations and avoiding the safety hazards and efficiency losses caused by a single load across the entire route.

[0021] S130. Based on the road section information and the factory-preset driving performance parameters of the target mining truck, calculate the maximum load limit for uphill and downhill respectively, and select the minimum value among the maximum load limit for downhill, maximum load limit for uphill, and rated load of the mining truck as the current load limit.

[0022] Mining cars are subject to three insurmountable constraints on their operating routes: insufficient driving force when going uphill can lead to stalling and slippage; excessive load when going downhill can cause insufficient braking force and loss of speed control; and the vehicle's own structure, frame, and tires have rated load limits. These three constraints are independent of each other, and exceeding any one of them will cause a safety accident. Therefore, the minimum value of the three must be taken as a unified load limit to ensure that the triple safety requirements of power, braking, and structure are met throughout the entire process.

[0023] The driving performance parameters are the inherent performance indicators calibrated by the manufacturer for the mining truck, mainly including the maximum driving force and maximum braking force of the entire vehicle, which directly determine the upper limit of safe load capacity when going uphill or downhill. The maximum load limit when going uphill is determined by the maximum driving force of the mining truck, which is the maximum permissible load mass that can overcome the gravity component and driving resistance when going uphill. The maximum load limit when going downhill is determined by the maximum braking force of the mining truck, which is the maximum permissible load mass that can be stably controlled during descent without brake fade or acceleration loss of control.

[0024] S140. Call the factory-preset engine parameters of the target mining truck to determine the engine's optimal economic operating speed range corresponding to the lowest fuel consumption. Combined with the load limit, calculate the highest gearbox gear and the corresponding actual traction force for each section of the operation route.

[0025] Engine inherent parameters refer to universal characteristic parameters measured by the manufacturer through bench testing, including torque characteristics, fuel consumption characteristics, and speed range. These are fixed parameters for the vehicle and do not change with road conditions or usage. The optimal economic operating speed range refers to the range of speeds where the engine has the lowest specific fuel consumption and the highest operating efficiency; it represents the optimal operating range for long-term economical vehicle operation. The highest gearbox gear refers to the gear with the lowest gear ratio and the highest potential speed while meeting the current road traction requirements; it represents the optimal balance between power and economy. Actual traction force is the real output traction force determined by the engine's economic speed and the gear ratio, used to meet the road resistance requirements and provide input for subsequent vehicle speed estimation.

[0026] Fuel costs account for a very high proportion of the operating costs of mining vehicles, and the engine's fuel consumption rate is lowest only within the optimal economic operating speed range. Under the premise of meeting load limits, by calculating the highest gearbox gear that can be used on each road section, the engine can be made to operate in the economic range as much as possible, while reducing transmission losses and improving driving efficiency; avoiding the high fuel consumption and high wear caused by low gear and high speed, and also preventing insufficient power reserve caused by high gear and low speed.

[0027] S150. Based on the highest gearbox gear and the actual traction force, and combined with the pre-stored three-dimensional mapping relationship between gear, traction force and vehicle speed, determine the estimated vehicle speed corresponding to each road segment; wherein, the three-dimensional mapping relationship is pre-constructed through mining car bench test or actual vehicle calibration test.

[0028] Gear position and traction force only reflect the power matching status and cannot directly measure transportation efficiency. Vehicle speed is a direct indicator of whether the mining truck is in the efficient operating range, and it is determined by the coupling of gear position, traction force, and load, making it impossible to calculate simply. Through experimentally calibrated three-dimensional mapping relationships, the power matching results are transformed into quantifiable predicted vehicle speeds, providing a calculable indicator for subsequently evaluating the overall efficiency of load-bearing strategies.

[0029] S160. Calculate the total length of effective road segments in the operation route whose estimated speed is within the efficient driving speed range, and calculate the distance ratio to the total length of the operation route. If the distance ratio is greater than or equal to a set ratio threshold, then the load limit is used as the new initial transport load for the next target mining truck.

[0030] Using the proportion of efficient road sections as an overall efficiency evaluation index can avoid the impact of individual short road section anomalies on the overall judgment. When the proportion meets the standard, it means that the current load balances safety and efficiency and can be directly used as the initial load of subsequent mining trucks, so as to realize the unified transmission of the fleet's load strategy.

[0031] S170. If the distance ratio is lower than the set ratio threshold, the load limit is adaptively lowered according to the deviation between the estimated speed and the efficient driving speed range of each road segment. Based on the lowered load limit, the highest gearbox gear, actual traction force and estimated speed of each road segment are recalculated iteratively until the distance ratio is higher than the set ratio threshold. The load limit after iteration is then used as the new initial transport load of the next target mining truck.

[0032] The insufficient proportion of high-efficiency road sections is essentially due to the current excessive load, resulting in high driving resistance and the need to use lower gears, leading to lower speeds and deviations from the high-efficiency range. Adaptively adjusting the load to reduce speed deviation lowers driving resistance, allowing the vehicle to shift to higher gears and increase speed, returning to the high-efficiency range. Iterative calculations form a closed-loop optimization, gradually making small adjustments to approach the optimal solution, avoiding large adjustments that would waste capacity or result in insufficient adjustment. Ultimately, the optimal load that balances safety, fuel consumption, and efficiency is obtained and passed on to subsequent mining truck use.

[0033] In this embodiment of the invention, through adaptive load calculation based on road conditions and closed-loop iteration of economic operating conditions, the target load can be automatically matched according to the actual gradient constraints of the operating route. This load satisfies both uphill power and downhill braking safety, and also conforms to the engine's economic operating range. The optimized load is then iteratively transferred to subsequent mining trucks as the initial transport load, realizing dynamic optimization of the overall load strategy of the fleet. This fundamentally avoids the safety risks and inefficient high fuel consumption problems caused by heavy loads, enabling mining trucks to simultaneously meet the requirements of power safety, braking safety, and efficient and economical driving throughout the entire route. This significantly improves the safety, economy, and overall operating efficiency of the mining truck fleet.

[0034] Optionally, in response to a transportation operation start command, a target mining truck is selected from the fleet of mining trucks awaiting departure and an initial transportation load is assigned. The target mining truck is then controlled to travel along a preset operation route, and its location and road condition information are collected in real time. After binding, a full-line condition dataset is formed, which may include: Upon receiving a request to start a transportation operation for a fleet of mining trucks, the system selects a target mining truck from the fleet of mine trucks waiting to depart, assigns an initial transport load, and then controls the target mining truck to automatically travel along a preset operation route. The on-board positioning device and inertial measurement unit of the target mining truck are activated simultaneously to collect the location information and road condition information of the operation route in real time at a preset high frequency. After real-time location information and corresponding road condition information are bound together to form associated data, the associated data is then processed by noise reduction and deduplication, and then integrated into a full-line road condition dataset.

[0035] "Mine cars ready to depart" refers to vehicles in the mine car fleet that are ready to perform transportation tasks at any time, and is within the legal scope of target mine cars selected by the system. Target mine cars are those selected from the vehicles ready to depart to perform the current round of transportation and road condition data collection tasks. Their selection method can be random, location-based, or sequentially determined according to the scheduling strategy; this embodiment does not restrict specific selection rules. The onboard positioning device is used to acquire real-time spatial location information such as longitude, latitude, and altitude of the vehicle, providing an accurate spatial index for road condition data. The inertial measurement unit is used to collect information such as vehicle driving attitude, slope, and acceleration, and is the core sensor for acquiring road condition characteristics. The raw collected data is discrete and asynchronous sensor output; direct use will result in problems such as location and road condition mismatch, noise interference, and redundant duplicate data, affecting the accuracy and efficiency of subsequent calculations. By binding the location and road conditions at the same time to the timestamp, a one-to-one structured association data is formed. Then, after noise reduction to remove abnormal jitter values ​​and deduplication to reduce redundant data, a clean, well-organized full-line condition dataset that can be directly used for road segment division is finally formed, ensuring the reliability and consistency of subsequent load optimization calculations.

[0036] Furthermore, based on the full road condition dataset, the work route is divided into segments according to a preset slope threshold, and the segment information for each segment is recorded, which may include: The slope information in the road condition data is extracted by calling the full road condition dataset. According to the preset uphill slope threshold and downhill slope threshold, the operation route is divided into continuous uphill sections, downhill sections and flat road sections according to the road section type. Based on the location information and road condition information binding information centrally recorded in the entire road condition dataset, the length and average slope of each road segment are calculated as the road segment information corresponding to the current road segment.

[0037] The overall mining route is continuously undulating, and the force characteristics, safety constraints, and driving strategies of mining cars differ fundamentally at different slopes: uphill is mainly limited by driving force, downhill by braking force, and flat roads prioritize economical operation. By extracting the overall slope information and segmenting the route using both uphill and downhill thresholds, the continuously changing route can be decomposed into segmented units with relatively consistent mechanical properties. This lays the foundation for subsequent calculation of load limits and matching of power gears for each segment, enabling refined control.

[0038] Simply classifying road segments is insufficient to support subsequent load and speed calculations; each segment also requires quantitative description. Single-point slope values ​​are susceptible to sensor fluctuations and local road surface undulations, leading to calculation distortion if used directly. Furthermore, segment lengths must be recorded to determine the proportion of efficient segments and assess overall transportation efficiency in subsequent steps. Calculating segment lengths through location binding relationships and averaging the slopes of all sampling points within a segment yields more stable and representative overall road condition characteristics, avoiding interference from anomalous single-point data and improving the reliability of load limits, power matching, and speed prediction.

[0039] Example 2 Figure 2 This is a flowchart of another intelligent method for determining the target load of a mining truck fleet provided in Embodiment 2 of the present invention. This embodiment is a refinement based on Embodiment 1, specifically as follows: Figure 2 As shown, the method includes: S210: In response to the start command of transportation operation, select the target mine car in the mine car fleet and assign the initial transportation load, control the target mine car to travel along the preset operation route, collect location information and road condition information in real time, and form a full road condition dataset after binding.

[0040] S220. Based on the full road condition dataset, the operation route is divided into segments according to the preset slope threshold, and the segment information of each segment is recorded.

[0041] S230. Based on the road section information, select all uphill and downhill sections in the work route, and based on the average gradient of each uphill and downhill section, determine the key uphill section with the steepest gradient and the key downhill section with the steepest gradient respectively.

[0042] Not all slopes along the entire operating route constrain the load capacity; only the steepest and most demanding sections determine the vehicle's safe load limit. Calculating load limits for each slope individually would result in computational redundancy and fail to capture the core safety constraints. Conversely, directly using the average slope would ignore the most dangerous sections, creating safety hazards. Therefore, this implementation method selects the critical uphill and downhill sections with the steepest gradients from all slopes, using the most stringent operating conditions as the basis for load constraints across the entire route, simplifying the calculation logic while ensuring safety.

[0043] The road segment information includes the segment type, length, and average gradient of each section, which is the basic data for selecting critical slopes. Critical uphill segments refer to the uphill segments with the steepest average gradient in the work route. These segments place the highest demands on vehicle driving force and represent the most stringent constraints on uphill loads. Critical downhill segments refer to the downhill segments with the steepest average gradient in the work route. These segments place the highest demands on vehicle braking force and represent the most stringent constraints on downhill loads.

[0044] S240. Obtain the factory-preset maximum driving force of the target mining truck, and combine it with the average gradient of the key uphill section to calculate the first maximum permissible load required for the target mining truck to overcome the uphill gravity component, which is used as the maximum uphill load limit.

[0045] When a mining truck goes uphill, it needs to overcome both the resistance to movement and the gravitational force caused by the slope. The greater the load, the greater the gravitational force, and the higher the demand for driving force. If the load exceeds the vehicle's power limit, there will be a risk of insufficient power to climb the slope, excessively low speed, or even the truck rolling away. This embodiment uses the vehicle's factory-calibrated maximum driving force as the upper limit, combined with the steepest uphill slope, to calculate the maximum load that the vehicle can safely pass, thus obtaining an uphill load limit constrained only by power performance, ensuring safe uphill passage along the entire route from a power perspective.

[0046] The maximum driving force of the vehicle refers to the maximum effective traction force transmitted from the vehicle's power system to the wheels via the transmission system. It is determined by the engine, gearbox, and final drive ratio, and is an inherent parameter of the mining car at the factory. The uphill gravity component refers to the component of the mining car's load acting downwards along the slope; it is the main additional resistance when going uphill and is positively correlated with both load and slope. The first maximum permissible load refers to the maximum load that can be safely passed through the steepest uphill section without exceeding the maximum driving force; that is, the maximum uphill load limit.

[0047] S250. Obtain the maximum braking force preset by the factory for the target mining car. Combined with the average gradient of the key downhill section, calculate the second maximum allowable load required for the target mining car to overcome the gravity component of the downhill section. Use this as the maximum load limit for the downhill section. Select the minimum value among the maximum load limit for the downhill section, the maximum load limit for the uphill section, and the rated load of the mining car as the current load limit.

[0048] When a mining car descends a slope, the gravitational force generated by its load causes the vehicle to continuously accelerate, which must be counteracted by the braking system. If the load is too large, the braking force will be unable to effectively control the speed, leading to loss of speed control, brake fade, or even brake failure. This embodiment uses the vehicle's maximum braking force as the safety upper limit, and combines it with the steepest downhill slope to calculate the maximum permissible load that can be stably controlled, thus obtaining a downhill load limit constrained only by braking performance. This ensures reliable downhill driving throughout the entire route from a braking safety perspective.

[0049] S260: Call the factory-preset engine parameters of the target mining truck to determine the engine's optimal economic operating speed range corresponding to the lowest fuel consumption. Combined with the load limit, calculate the highest gearbox gear and the corresponding actual traction force for each section of the work route.

[0050] Optionally, the system can call the factory-preset engine parameters of the target mining truck to determine the optimal economic operating speed range of the engine corresponding to the lowest fuel consumption. Combined with the load limit, it can calculate the highest gearbox gear and the corresponding actual traction force for each section of the operating route, which may include: Call the engine's pre-set inherent parameters from the factory of the target mining truck, and based on the engine's inherent parameters, query and determine the engine's optimal economic operating speed range corresponding to the lowest fuel consumption and the engine's output torque corresponding to the range; For each road segment, the total mass of the vehicle is determined based on the load limit and the empty weight of the mine car. Based on the total mass of the vehicle and the average gradient of the current road segment, the minimum traction force required for the target mine car to travel on the current road segment is calculated. Based on the engine output torque and the inherent parameters of the transmission, the gear output traction force provided by each gear of the transmission is calculated respectively. The output traction force of each gear is compared with the minimum traction force of the current road segment. The gear that has the highest output traction force and the highest gear value is determined as the highest gear for the current road segment. Based on the highest gearbox gear, engine output torque, and inherent parameters of the gearbox determined for the current road segment, the actual traction force corresponding to the current road segment is calculated.

[0051] Mining transport vehicles operate continuously for long periods, and fuel consumption accounts for a high proportion of operating costs. Engines achieve their lowest fuel consumption per unit power only when operating within a specific speed range. To achieve optimal economy while meeting power demands, it's necessary to first determine the optimal economic operating range from the engine's inherent characteristics and lock in a stable output torque within that range. This serves as the benchmark for subsequent gear matching and traction calculations, avoiding excessive fuel consumption and low efficiency due to arbitrary speed selection. Engine inherent parameters refer to the universal characteristic parameters calibrated by the manufacturer through bench testing, including vehicle-fixed parameters such as fuel consumption characteristic curves, torque characteristic curves, and speed ranges, which do not change with road conditions or load. The optimal economic operating speed range is the stable operating speed range where the engine's overall thermal efficiency is highest and fuel consumption is lowest, representing the optimal operating range for long-term mining truck operation. Engine output torque is the effective torque the engine can output within the economic speed range, serving as the direct input for power transmission and traction calculations.

[0052] Whether a mining truck can travel safely on a certain section of track depends first on meeting the minimum traction requirement of that section. This requirement is determined by both the total weight of the vehicle and the gradient of the track: the greater the load and the steeper the gradient, the higher the required traction force. First, calculate the total mass of the vehicle, then combine this with the average gradient to calculate the driving resistance, thus obtaining the minimum traction force required for the vehicle to just overcome the resistance and travel stably. This provides a clear threshold condition for subsequent gear selection.

[0053] Different gears in a transmission correspond to different gear ratios, which directly determine the engine torque amplification ratio and the traction force at the wheels. Lower gears have higher gear ratios, resulting in stronger traction but lower vehicle speed and higher engine speed; higher gears have the opposite effect. To find the optimal gear that balances power and efficiency, it's necessary to first calculate the output traction force offered by all gears, creating a comparable list of power capabilities. Under the premise of meeting minimum traction requirements, higher gears mean higher transmission efficiency, lower engine speed, and better fuel economy. Selecting too low a gear will lead to higher engine speed and increased fuel consumption; selecting too high a gear will result in insufficient power.

[0054] The highest gear in the transmission refers to the highest available gear with the smallest transmission ratio, optimal efficiency, and best economy while meeting the power requirements of the road segment. Once the highest gear is determined, the actual output traction force of the vehicle is also determined. This traction force satisfies the driving needs of the road segment while operating within the economical speed range, representing a stable and reliable actual output value.

[0055] S270. Based on the highest gearbox gear and the actual traction force, and combined with the pre-stored three-dimensional mapping relationship between gear, traction force and vehicle speed, determine the estimated vehicle speed corresponding to each road segment; wherein, the three-dimensional mapping relationship is pre-constructed through mining car bench test or actual vehicle calibration test.

[0056] S280. Calculate the total length of effective road segments in the operation route whose estimated speed is within the efficient driving speed range, and calculate the distance ratio to the total length of the operation route. If the distance ratio is greater than or equal to a set ratio threshold, then the load limit is used as the new initial transport load for the next target mining truck.

[0057] S290. If the distance ratio is lower than the set ratio threshold, the load limit is adaptively lowered according to the deviation between the estimated speed and the efficient driving speed range of each road segment. Based on the lowered load limit, the highest gearbox gear, actual traction force and estimated speed of each road segment are recalculated iteratively until the distance ratio is higher than the set ratio threshold. The load limit after iteration is then used as the new initial transport load of the next target mining truck.

[0058] Furthermore, based on the deviation between the estimated vehicle speed and the efficient driving speed range for each road segment, the load limit can be adaptively lowered, which may include: Extract all road segments that meet the condition that the estimated vehicle speed is lower than the lower limit of the efficient driving speed range, and take them as speed deviation road segments. Calculate the difference between the estimated vehicle speed and the lower limit of the efficient driving speed range for each speed deviation road segment to obtain the corresponding speed deviation value for each speed deviation road segment. Based on the length of each road segment with speed deviation, the weighted average of the speed deviation value of each deviation segment and the corresponding road segment length is calculated to obtain the average speed deviation of the operation route. Based on the average vehicle speed deviation, load limit, and lower limit of the efficient driving speed range, the reduction amount of the load limit is calculated according to a preset proportional formula. The load limit is adjusted by the reduction amount to obtain a new load limit. If the new load limit is not lower than the preset minimum allowable load, the adaptive reduction of the load limit is completed. If the new load limit is lower than the preset minimum allowable load of the mine car, the preset minimum allowable load of the mine car will be used as the new load limit, and the load limit will be adjusted accordingly.

[0059] When the estimated speed of a mining truck is lower than the minimum efficient operating speed, it indicates that the current load is too high, forcing the truck to operate at low gears and low speeds, resulting in low transportation efficiency. To quantify this inefficiency, all inefficient road sections need to be identified first, and the speed difference between each section and the efficient range needs to be calculated. This provides a quantifiable basis for subsequent overall evaluation and load adjustment. Different road section lengths have different impacts on overall transportation efficiency. Even if the deviation is large in short sections, the impact on overall efficiency is limited, while a slight deviation in long sections can significantly reduce overall efficiency. Therefore, a weighted average is performed using road section length as the weight to obtain an average speed deviation that truly reflects the overall inefficiency of the entire route, making subsequent load adjustments more consistent with actual operational impacts.

[0060] There is a stable correlation between vehicle speed deviation and load deviation; the larger the deviation, the greater the need to reduce the load. Through a preset proportional formula, an adaptive adjustment can be achieved: larger deviations require larger reductions, and smaller deviations require smaller reductions. This avoids the arbitrariness of manual experience-based adjustments and ensures that the load correction precisely matches the actual level of inefficiency.

[0061] After adjusting the load capacity, the first step is to determine whether the adjusted load meets the minimum transportation requirements of the mine. If the adjusted load is still higher than the minimum allowable load set by the system, it indicates that the adjustment has achieved a reasonable balance between safety and efficiency, increasing vehicle speed while ensuring basic transportation capacity, and the new load limit can be directly adopted. If the load calculated based on the vehicle speed deviation is too low, falling below the minimum transportation requirements for mine operations, further adjustment would lose its practical transportation significance. Therefore, a lower limit protection mechanism is set up to force the minimum allowable load as the final load, avoiding the loss of economic efficiency in transportation tasks due to excessive pursuit of high vehicle speed, and achieving a final balance between safety, efficiency, and transportation capacity.

[0062] Optionally, the method may further include: After determining the new initial transport load of the next target mine car, control the next target mine car to travel along the preset operation route with the new initial transport load, and sequentially execute operations such as updating the entire road condition dataset, dividing the road segment, calculating the load limit, calculating the gear and traction force, determining the estimated speed, calculating the distance ratio, and updating the new initial transport load, until all mine cars in the mine car fleet have completed the transport operation.

[0063] In this embodiment, the new initial transport load obtained by the preceding mining truck through road condition data collection and iterative calculation is not only applicable to that truck itself, but also a general optimization result for the entire operating route and the entire mining truck fleet. Using the optimized new initial transport load directly as the initial transport load of the next mining truck allows subsequent trucks to operate on a more optimal load benchmark, ensuring both driving safety and economy, while improving the overall departure and transport efficiency of the fleet. Meanwhile, mine road conditions are not static and may change slightly with transport frequency, road surface compaction, weather, and other factors. Therefore, subsequent mining trucks cannot directly use a fixed load; they still need to re-complete road condition data collection and route segmentation based on the new initial transport load. Through iterative updates for each truck, the load strategy of each mining truck is aligned with the latest road conditions, while continuously optimizing the initial load benchmark, achieving overall dynamic optimization of the fleet.

[0064] Example 3 Figure 3 This is a schematic diagram of the structure of an intelligent device for determining the target load of a mining truck fleet, provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: The road condition data acquisition module 310 is used to respond to the transportation operation start command, select the target mine car in the mine car fleet and assign the initial transportation load, control the target mine car to travel along the preset operation route, collect location information and road condition information in real time, and form a full road condition dataset after binding. The road segmentation module 320 is used to divide the operation route into road segments based on the full road condition dataset and according to the preset slope threshold, and to record the road segment information of each road segment. The load limit determination module 330 is used to calculate the maximum load limit for uphill and downhill based on the road section information and the driving performance parameters preset by the target mining truck at the factory, and select the minimum value among the maximum load limit for downhill, maximum load limit for uphill and rated load of the mining truck as the current load limit. The engine economic speed determination module 340 is used to call the engine inherent parameters preset by the target mining truck at the factory, determine the engine's optimal economic operating speed range corresponding to the lowest fuel consumption, and calculate the highest gearbox gear and corresponding actual traction force for each section of the operation route in combination with the load limit. The estimated vehicle speed determination module 350 is used to determine the estimated vehicle speed for each road segment based on the highest gearbox gear and the actual traction force, combined with the pre-stored three-dimensional mapping relationship between gear, traction force and vehicle speed; wherein, the three-dimensional mapping relationship is pre-constructed through mining car bench test or actual vehicle calibration test. The first load optimization module 360 ​​is used to count the total length of effective road segments in the operation route where the estimated vehicle speed is within the efficient driving speed range, and calculate the distance ratio with the total length of the operation route. If the distance ratio is greater than or equal to a set ratio threshold, the load limit is used as the new initial transport load of the next target mining truck. The second load optimization module 370 is used to adaptively lower the load limit based on the deviation between the estimated speed and the efficient driving speed range of each road segment if the distance ratio is lower than the set ratio threshold. Based on the lowered load limit, the module re-iterates the highest gearbox gear, actual traction force and estimated speed of each road segment until the distance ratio is higher than the set ratio threshold. Then, the load limit after iteration is used as the new initial transport load of the next target mining truck.

[0065] In this embodiment of the invention, through adaptive load calculation based on road conditions and closed-loop iteration of economic operating conditions, the target load can be automatically matched according to the actual gradient constraints of the operating route. This load satisfies both uphill power and downhill braking safety, and also conforms to the engine's economic operating range. The optimized load is then iteratively transferred to subsequent mining trucks as the initial transport load, realizing dynamic optimization of the overall load strategy of the fleet. This fundamentally avoids the safety risks and inefficient high fuel consumption problems caused by heavy loads, enabling mining trucks to simultaneously meet the requirements of power safety, braking safety, and efficient and economical driving throughout the entire route. This significantly improves the safety, economy, and overall operating efficiency of the mining truck fleet.

[0066] Optionally, based on the above embodiments, the road condition data acquisition module 310 may include: The initial load allocation unit is used to select the target mine car from the mine cars waiting to depart in the mine car fleet after receiving a request to start a transportation operation for the mine car fleet, allocate the initial transportation load, and then control the target mine car to drive automatically along the preset operation route. The road condition data acquisition unit is used to simultaneously activate the on-board positioning device and inertial measurement unit mounted on the target mining truck to collect the location information and road condition information of the operation route in real time at a preset high frequency. The data association unit is used to bind real-time collected location information and corresponding road condition information to form associated data. After noise reduction and deduplication processing, the associated data is integrated into a full-line road condition dataset.

[0067] Optionally, based on the above embodiments, the road segment division module 320 may include: The road segment identification unit is used to extract slope information from the road condition data by calling the full road condition dataset. According to the preset uphill slope threshold and downhill slope threshold, the operation route is divided into continuous uphill road segments, downhill road segments and flat road segments according to the road segment type. The information binding unit is used to calculate the length and average slope of each road segment based on the location information and road condition information binding information centrally recorded in the full road condition data set, and to use this as the road segment information corresponding to the current road segment.

[0068] Optionally, based on the above embodiments, the load limit determination module 330 may include: The key slope screening unit is used to filter out all uphill and downhill sections in the operation route based on the road section information, and to determine the key uphill section with the largest slope and the key downhill section with the largest slope based on the average slope of each uphill and downhill section. The uphill load limit determination unit is used to obtain the maximum driving force of the target mining car preset at the factory, and combined with the average gradient of the key uphill section, calculate the first maximum allowable load required for the target mining car to overcome the uphill gravity component, which is used as the uphill maximum load limit. The downhill load limit determination unit is used to obtain the maximum braking force preset by the factory for the target mining car, and combined with the average gradient of the key downhill section, calculate the second maximum allowable load required for the target mining car to overcome the gravity component of the downhill section, which is used as the downhill maximum load limit.

[0069] Optionally, based on the above embodiments, the engine economic speed determination module 340 may include: The output torque query unit is used to call the engine inherent parameters preset by the factory of the target mining truck, and based on the engine inherent parameters, query and determine the engine's optimal economic operating speed range corresponding to the lowest fuel consumption and the engine output torque corresponding to the range. The minimum traction force calculation unit is used to determine the total mass of the vehicle for each road segment based on the load limit and the empty weight of the mine car, and to calculate the minimum traction force required for the target mine car to travel on the current road segment based on the total mass of the vehicle and the average gradient of the current road segment. The output traction calculation unit is used to calculate the gear output traction provided by each gear of the transmission based on the engine output torque and the inherent parameters of the transmission. The highest gear determination unit is used to compare the output traction force of each gear with the minimum traction force of the current road segment, and determine the gear that has the highest gear ratio and output traction force greater than or equal to the minimum traction force of the road segment as the highest gear ratio of the current road segment. The actual traction force determination unit is used to calculate the actual traction force corresponding to the current road segment based on the highest gearbox gear, engine output torque, and inherent parameters of the gearbox determined for the current road segment.

[0070] Optionally, based on the above embodiments, the second load optimization module 370 may include: The vehicle speed deviation calculation unit is used to extract all road segments that meet the condition that the estimated vehicle speed is lower than the lower limit of the efficient driving speed range, as vehicle speed deviation road segments, and calculate the difference between the estimated vehicle speed and the lower limit of the efficient driving speed range for each vehicle speed deviation road segment to obtain the vehicle speed deviation value corresponding to each vehicle speed deviation road segment. The average speed deviation calculation unit is used to calculate the weighted average of the speed deviation value of each deviation road segment and the corresponding road segment length based on the road segment length of each deviation road segment, so as to obtain the average speed deviation of the operation route. The reduction calculation unit is used to calculate the reduction amount of the load limit based on the average vehicle speed deviation, the load limit, and the lower limit of the efficient driving speed range, according to a preset proportional formula. The first adjustment unit is used to adjust the load limit by the reduction amount to obtain a new load limit. If the new load limit is not lower than the preset minimum allowable load, the adaptive reduction of the load limit is completed. The second adjustment unit is used to adjust the load limit by taking the preset minimum allowable load of the mine car as the new load limit if the new load limit is lower than the preset minimum allowable load of the mine car.

[0071] Optionally, based on the above embodiments, it may also include: a load dynamic optimization module, used to control the next target mine car to travel along the preset operation route with the new initial transport load after determining the new initial transport load of the next target mine car, and sequentially execute operations such as updating the entire road condition dataset, dividing the road segment, calculating the load limit, calculating the gear and traction force, determining the estimated speed, calculating the distance ratio, and updating the new initial transport load, until all mine cars in the mine car fleet have completed the transport operation.

[0072] The mining truck fleet dynamic load optimization device provided in this embodiment of the invention can execute the mining truck fleet dynamic load optimization method provided in this embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0073] Example 4 Figure 4A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0074] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0075] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0076] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a method for dynamically optimizing the transport load of a mining truck fleet.

[0077] In some embodiments, a method for dynamically optimizing the transport load of a mining truck fleet can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for dynamically optimizing the transport load of a mining truck fleet described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform a method for dynamically optimizing the transport load of a mining truck fleet by any other suitable means (e.g., by means of firmware).

[0078] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0079] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0080] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0081] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0082] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0083] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0084] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0085] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for dynamic optimization of transport load in a mining truck fleet, characterized in that, The method includes: In response to the start command of the transportation operation, the target mining truck is selected from the mining truck fleet and an initial transportation load is assigned. The target mining truck is controlled to travel along the preset operation route. Location information and road condition information are collected in real time and bound together to form a full-line condition dataset. Based on the full road condition dataset, the operation route is divided into segments according to the preset slope threshold, and the segment information of each segment is recorded. Based on the road section information and the driving performance parameters preset by the target mining truck at the factory, the maximum load limit for uphill and downhill are calculated respectively, and the minimum value among the maximum load limit for downhill, maximum load limit for uphill and rated load of mining truck is selected as the current load limit. Call the factory-preset engine parameters of the target mining truck to determine the engine's optimal economic operating speed range corresponding to the lowest fuel consumption. Combined with the load limit, calculate the highest gearbox gear and the corresponding actual traction force for each section of the operation route. Based on the highest gearbox gear and the actual traction force, combined with the pre-stored three-dimensional mapping relationship between gear, traction force and vehicle speed, the estimated vehicle speed for each road segment is determined; wherein, the three-dimensional mapping relationship is pre-constructed through mining car bench tests or actual vehicle calibration tests. The total length of effective road segments in the statistical operation route whose estimated vehicle speed is within the efficient driving speed range is calculated, and the distance ratio to the total length of the operation route is calculated. If the distance ratio is greater than or equal to a set ratio threshold, the load limit is used as the new initial transport load of the next target mining truck. If the distance ratio is lower than the set ratio threshold, the load limit is adaptively lowered based on the deviation between the estimated speed and the efficient driving speed range of each road segment. The highest gearbox gear, actual traction force and estimated speed of each road segment are recalculated iteratively based on the lowered load limit until the distance ratio is higher than the set ratio threshold. The load limit after the iteration is then used as the new initial transport load of the next target mining truck.

2. The method according to claim 1, characterized in that, In response to the start command of the transportation operation, the system selects the target mining truck from the waiting mining trucks in the convoy and assigns an initial transportation load. It then controls the target mining truck to travel along the preset operation route, collecting real-time location and road condition information. This data, after being bound together, forms a full-line condition dataset, including: Upon receiving a request to start a transportation operation for a fleet of mining trucks, the system selects a target mining truck from the fleet of mine trucks waiting to depart, assigns an initial transport load, and then controls the target mining truck to automatically travel along a preset operation route. The on-board positioning device and inertial measurement unit of the target mining truck are activated simultaneously to collect the location information and road condition information of the operation route in real time at a preset high frequency. After binding the real-time collected location information with the corresponding road condition information to form associated data, the associated data is then processed by noise reduction and deduplication, and integrated into a full-line road condition dataset.

3. The method according to claim 1, characterized in that, Based on the full road condition dataset, the work route is divided into segments according to a preset slope threshold, and the segment information for each segment is recorded, including: The slope information in the road condition data is extracted by calling the full road condition dataset. According to the preset uphill slope threshold and downhill slope threshold, the operation route is divided into continuous uphill sections, downhill sections and flat road sections according to the road section type. Based on the location information and road condition information binding information centrally recorded in the entire road condition dataset, the length and average slope of each road segment are calculated as the road segment information corresponding to the current road segment.

4. The method according to claim 1, characterized in that, Based on the road section information and the factory-preset driving performance parameters of the target mining trucks, the maximum load limits for uphill and downhill driving are calculated, including: Based on the road information, all uphill and downhill sections in the operation route are selected, and based on the average gradient of each uphill and downhill section, the key uphill section with the steepest gradient and the key downhill section with the steepest gradient are determined respectively. Obtain the maximum driving force of the target mining truck preset at the factory, and combine it with the average gradient of the key uphill section to calculate the first maximum allowable load that the target mining truck needs to overcome the gravity component of the uphill section, which is used as the maximum uphill load limit. Obtain the maximum braking force preset by the factory for the target mining truck, and combine it with the average gradient of the key downhill section to calculate the second maximum allowable load that the target mining truck needs to overcome the gravity component of the downhill section, which is used as the maximum load limit for the downhill section.

5. The method according to claim 1, characterized in that, By calling the factory-preset engine parameters of the target mining truck, the optimal economic operating speed range of the engine corresponding to the lowest fuel consumption is determined. Combined with the load limit, the highest gearbox gear and the corresponding actual traction force for each section of the operating route are calculated, including: Call the engine's pre-set inherent parameters from the factory of the target mining truck, and based on the engine's inherent parameters, query and determine the engine's optimal economic operating speed range corresponding to the lowest fuel consumption and the engine's output torque corresponding to the range; For each road segment, the total mass of the vehicle is determined based on the load limit and the empty weight of the mine car. Based on the total mass of the vehicle and the average gradient of the current road segment, the minimum traction force required for the target mine car to travel on the current road segment is calculated. Based on the engine output torque and the inherent parameters of the transmission, the gear output traction force provided by each gear of the transmission is calculated respectively. The output traction force of each gear is compared with the minimum traction force of the current road segment. The gear that has the highest output traction force and the highest gear value is determined as the highest gear for the current road segment. Based on the highest gearbox gear, engine output torque, and inherent parameters of the gearbox determined for the current road segment, the actual traction force corresponding to the current road segment is calculated.

6. The method according to claim 1, characterized in that, Based on the deviation between the estimated vehicle speed and the efficient driving speed range for each road section, the load limit is adaptively lowered, including: Extract all road segments that meet the condition that the estimated vehicle speed is lower than the lower limit of the efficient driving speed range, and take them as speed deviation road segments. Calculate the difference between the estimated vehicle speed and the lower limit of the efficient driving speed range for each speed deviation road segment to obtain the corresponding speed deviation value for each speed deviation road segment. Based on the length of each road segment with speed deviation, the weighted average of the speed deviation value of each deviation segment and the corresponding road segment length is calculated to obtain the average speed deviation of the operation route. Based on the average vehicle speed deviation, load limit, and lower limit of the efficient driving speed range, the reduction amount of the load limit is calculated according to a preset proportional formula. The load limit is adjusted by the reduction amount to obtain a new load limit. If the new load limit is not lower than the preset minimum allowable load, the adaptive reduction of the load limit is completed. If the new load limit is lower than the preset minimum allowable load of the mine car, the preset minimum allowable load of the mine car will be used as the new load limit, and the load limit will be adjusted accordingly.

7. The method according to claim 1, characterized in that, Also includes: After determining the new initial transport load of the next target mine car, control the next target mine car to travel along the preset operation route with the new initial transport load, and sequentially execute operations such as updating the entire road condition dataset, dividing the road segment, calculating the load limit, calculating the gear and traction force, determining the estimated speed, calculating the distance ratio, and updating the new initial transport load, until all mine cars in the mine car fleet have completed the transport operation.

8. A dynamic optimization device for the transport load of a mining truck fleet, characterized in that, The device includes: The road condition data acquisition module is used to respond to the transportation operation start command, select the target mining truck in the mining truck fleet and assign the initial transportation load, control the target mining truck to travel along the preset operation route, collect location information and road condition information in real time, and form a full road condition dataset after binding. The road segment division module is used to divide the operation route according to the road segment type based on the full road condition dataset and according to the preset slope threshold, assign an identifier to each segment and record the segment information; The load limit determination module is used to calculate the maximum load limit for uphill and downhill based on road section information and the factory-preset driving performance parameters of the target mining truck, and select the minimum value among the maximum load limit for downhill, maximum load limit for uphill, and rated load of the mining truck as the current load limit. The engine economic speed determination module is used to call the factory-preset engine inherent parameters of the target mining truck, determine the engine's optimal economic operating speed range corresponding to the lowest fuel consumption, and calculate the highest gearbox gear and corresponding actual traction force for each section of the operation route in combination with the load limit. The estimated vehicle speed determination module is used to determine the estimated vehicle speed for each road segment based on the highest gearbox gear and the actual traction force, combined with the pre-stored three-dimensional mapping relationship between gear, traction force and vehicle speed; wherein, the three-dimensional mapping relationship is pre-constructed through mining car bench tests or actual vehicle calibration tests. The first load optimization module is used to count the total length of effective road segments in the operation route where the estimated vehicle speed is within the efficient driving speed range, and calculate the distance ratio with the total length of the operation route. If the distance ratio is greater than or equal to a set ratio threshold, the load limit is used as the new initial transport load of the next target mining truck. The second load optimization module is used to adaptively lower the load limit based on the deviation between the estimated speed and the efficient driving speed range of each road segment if the distance ratio is lower than the set ratio threshold. Based on the lowered load limit, the module re-iterates the highest gearbox gear, actual traction force and estimated speed of each road segment until the distance ratio is higher than the set ratio threshold. Then, the load limit after iteration is used as the new initial transport load of the next target mining truck.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a method for dynamic optimization of transport load of a mining truck fleet according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute a method for dynamic optimization of the transport load of a mining truck fleet according to any one of claims 1-7.

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