Shovel loading control method and device based on working condition recognition, medium and product

Abstract

The invention discloses a shovel loading control method and device based on working condition recognition, a medium and a product, and the method comprises the steps that when a loader works, the initial height of a movable arm and the first shovel loading weight of each time of shovel loading are collected in real time; on the basis of the data of multiple operations, the weight and granularity of the materials are judged to recognize the attribute category of the materials, and a matched first optimized shovel loading control parameter is called from a preset shovel loading control parameter database according to the attribute category of the materials; controlling the loader to continue working according to the parameters, collecting a second shoveling weight and working time of each subsequent operation, and calculating an average working efficiency value of the loader according to the second shoveling weight and the working time; if the value is lower than the set efficiency threshold value, the first optimized shovel loading control parameter is finely adjusted to obtain a second optimized shovel loading control parameter, and the loader is controlled to continue working according to the second optimized shovel loading control parameter. And the working efficiency, the equipment adaptability and the overall intelligent level of the loader under different working conditions are improved.

Description

Technical Field

[0001] This invention relates to the field of auxiliary loading technology, and in particular to a loading control method, equipment, medium and product based on working condition identification. Background Technology

[0002] With the accelerated advancement of intelligent upgrades in construction machinery, the loading efficiency and operational intelligence level of loaders have become key indicators of their core competitiveness. Loading operations involve variable factors such as materials, slopes, and road conditions, and their efficiency and smoothness directly impact production costs, equipment lifespan, and operator experience. However, traditional control modes based on fixed parameters are ill-suited to complex and unstructured real-world operating environments, leading to increasingly prominent issues such as efficiency fluctuations, equipment wear and tear, and operator fatigue. Therefore, achieving intelligent loader operation has become an urgent industry need.

[0003] In existing technologies, optimization for loading operations mainly relies on two types of methods: one is to preset fixed control parameters based on typical working conditions. This method lacks the ability to perceive the actual material properties and working environment, and any change in working conditions leads to decreased efficiency and equipment wear; the other is to attempt to make local adjustments by adding sensors or using simple rules, but these methods often remain at a single-point optimization level and lack overall optimization. Both of these technologies are difficult to cope with complex, time-varying working conditions and cannot meet the current demand for highly adaptable, self-learning, and collaborative intelligent loading control. Summary of the Invention

[0004] This invention provides a loading control method, equipment, medium, and product based on working condition identification to achieve adaptive optimization and intelligent control of loading operations, thereby improving efficiency, safety, and comfort.

[0005] According to one aspect of the present invention, a loading control method based on working condition identification is provided, the method comprising: During the loader's loading operation within the loading area, the initial boom height and the first loading weight are collected in real time for each loading operation. Based on the initial boom height and first load weight corresponding to multiple loading operations, the weight and particle size of the loaded material are determined, and the attribute category of the loaded material is identified based on the determination results. In the preset loading control parameter database, obtain the first optimized loading control parameter that matches the attribute category of the loading material; The loader is controlled to continue performing loading operations according to the first optimized loading control parameters, and the second loading weight and operation time corresponding to each loading operation are collected. The average operating efficiency of the loader is calculated based on the weight of each second shovel operation and the time of each operation corresponding to the multiple shovel operations that are performed. When the average operating efficiency value is lower than the set efficiency threshold, the first optimized loading control parameter is finely adjusted to obtain the second optimized loading control parameter, and the loader is controlled to continue to perform loading operation according to the second optimized loading control parameter.

[0006] According to another aspect of the present invention, a loading control device based on working condition identification is provided, the device comprising: The data acquisition module is used to collect the initial boom height and the first load weight in real time for each loading operation during the loading operation of the loader in the loading area. The identification module is used to determine the weight and particle size of the material being loaded based on the initial boom height and first loading weight corresponding to multiple loading operations, and to identify the attribute category of the material being loaded based on the determination results. The parameter acquisition module is used to retrieve the first optimized loading control parameter that matches the attribute category of the loading material from the preset loading control parameter database; The continuous operation module is used to control the loader to continue to perform loading operations according to the first optimized loading control parameters, and to collect the second loading weight and operation time corresponding to each loading operation. The efficiency value calculation module is used to calculate the average operating efficiency value of the loader based on the second loading weight and the operation time corresponding to the multiple loading operations that are continued to be performed. The parameter optimization module is used to fine-tune the first optimized loading control parameters to obtain the second optimized loading control parameters when the average operating efficiency value is lower than the set efficiency threshold, and control the loader to continue to perform loading operations according to the second optimized loading control parameters.

[0007] According to another aspect of the present invention, an electronic device is provided, 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 that can be executed by the at least one processor, which enables the at least one processor to perform a loading control method based on working condition identification as described in any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement a shovel loading control method based on working condition identification as described in any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer program product is also provided, including a computer program / instructions that, when executed by a processor, implement the steps of the method as described in any embodiment of the present invention.

[0010] The technical solution of this invention involves real-time acquisition of the initial boom height and first loading weight corresponding to each loading operation during the loading process of the loader within the loading area; based on the initial boom height and first loading weight corresponding to multiple loading operations, the weight and particle size of the loading material are determined, and the attribute category of the loading material is identified according to the determination results; a first optimized loading control parameter matching the attribute category of the loading material is obtained from a preset loading control parameter database; the loader is controlled to continue performing the loading operation according to the first optimized loading control parameter, and the data of each subsequent loading operation is collected. The loading operation is divided into two parts: the second loading weight and the operation time corresponding to each loading operation; based on the second loading weight and the operation time corresponding to each of the multiple loading operations, the average operation efficiency value of the loader is calculated; when the average operation efficiency value is lower than the set efficiency threshold, the first optimized loading control parameter is finely adjusted to obtain the second optimized loading control parameter, and the loader is controlled to continue to perform loading operations according to the second optimized loading control parameter. This solves the problem that traditional fixed parameter control cannot adapt to actual complex working conditions, resulting in low operation efficiency and large equipment wear, and achieves the beneficial effect of improving the intelligence level and continuous operation efficiency of loading operations.

[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 loading control method based on working condition identification according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of another loading control method based on working condition identification provided in Embodiment 2 of the present invention; Figure 3 This is a flowchart of another loading control method based on working condition identification provided in Embodiment 3 of the present invention; Figure 4This is a schematic diagram of a loading control device based on working condition identification according to Embodiment 4 of the present invention; Figure 5 This is a schematic diagram of the structure of an electronic device that implements a loading control method based on working condition identification 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. 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 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 loading control method based on working condition identification provided in Embodiment 1 of the present invention. This embodiment is applicable to loaders that need to perform intelligent loading operation control to adapt to complex and changing working conditions. The method can be executed by a loading control device based on working condition identification. This device can be implemented in hardware and / or software and is generally configured in electronic devices.

[0017] Correspondingly, such as Figure 1 As shown, the method includes: S110. During the loading operation performed by the loader in the loading area, the initial boom height and the first loading weight corresponding to each loading operation are collected in real time.

[0018] The initial boom height can be understood as the vertical height of the boom hinge point relative to the ground or machine body at the moment when the bucket is about to insert into the material pile for digging at the beginning of each loading operation cycle of the loader.

[0019] In this embodiment, when the loader operates in the designated loading area, it acquires key data for each loading action in real time through its onboard sensors. Specifically, it records the boom height at the moment the bucket inserts into the material pile, i.e., the initial boom height, and simultaneously measures the weight of the material scooped in, i.e., the first loading weight, using a weighing device. This data forms the basis for subsequent operational condition analysis and judgment.

[0020] S120. Based on the initial boom height and first loading weight corresponding to multiple loading operations, the weight and particle size of the loading material are judged, and the attribute category of the loading material is identified based on the judgment results.

[0021] Particle size can be understood as the size and fineness of material particles. The document categorizes materials into three types based on particle size: fine particles, medium particles, and large particles. Materials of different particle sizes exhibit different flowability, bulk density, and resistance characteristics during loading.

[0022] In this embodiment, the characteristics of the material being loaded are analyzed based on multiple sets of initial boom height and first-load weight data collected from consecutive loading operations. First, the density or weight category of the material is determined based on the ratio between the actual load weight and the loader's rated load capacity for each operation. Second, the rate of change is calculated by comparing the differences in load weight obtained when operating at different initial boom heights, thereby determining the particle size category of the material. By combining the results of these weight and particle size determinations, the overall attribute category of the material being processed can be identified.

[0023] S130. Obtain the first optimized loading control parameter that matches the attribute category of the loading material from the preset loading control parameter database.

[0024] In this embodiment, after identifying the material's attribute category, a pre-established and stored loading control parameter database is queried. This database contains optimal control parameter combinations obtained through preliminary experiments for different material attribute categories. Based on the identified specific material attribute category, a set of control parameters that perfectly matches it is searched and retrieved from this database. This set of parameters is called the first optimized loading control parameters and will serve as the initial control benchmark for the current operation.

[0025] S140. Control the loader to continue performing the loading operation according to the first optimized loading control parameters, and collect the second loading weight and operation time corresponding to each loading operation.

[0026] In this embodiment, the loader continues to perform subsequent loading operations based on the first optimized loading control parameters obtained from the call. During this process, new data generated in these subsequent operations are continuously collected and recorded, including the actual weight of material obtained in each loading operation, i.e., the second loading weight, and the operation time consumed to complete the loading action. This newly collected data is used to evaluate the actual operational effectiveness of the currently used parameters.

[0027] S150. Calculate the average operating efficiency value of the loader based on the weight of each second shovel operation and the time of each operation corresponding to the multiple shovel operations that are continued.

[0028] In this embodiment, to quantify the evaluation of operational effectiveness, the average operational efficiency value of the loader over a recent period is calculated based on a series of collected second-load weights and their corresponding operational times. This efficiency value typically reflects the total amount of material loaded per unit time and is a core indicator for measuring the superiority of control parameters and the efficiency of the operation.

[0029] S160. When the average operating efficiency value is lower than the set efficiency threshold, the first optimized loading control parameter is finely adjusted to obtain the second optimized loading control parameter, and the loader is controlled to continue to perform loading operation according to the second optimized loading control parameter.

[0030] In this embodiment, the calculated average operating efficiency value is compared with a pre-set efficiency threshold. If the average operating efficiency value is lower than the threshold, it indicates that the currently used first optimized loading control parameters may not be optimal and are not fully utilizing the equipment's performance. At this point, a parameter optimization process is triggered, making minor adjustments and corrections to the first optimized loading control parameters based on a preset optimization algorithm, thereby generating a new set of optimized control parameters, namely the second optimized loading control parameters. Subsequently, the loader will continue to perform loading operations based on these newly generated second optimized loading control parameters, thus achieving parameter self-iteration and continuous improvement of operational performance.

[0031] The technical solution of this invention involves real-time acquisition of the initial boom height and first loading weight corresponding to each loading operation during the loading process of the loader within the loading area; based on the initial boom height and first loading weight corresponding to multiple loading operations, the weight and particle size of the loading material are determined, and the attribute category of the loading material is identified according to the determination results; a first optimized loading control parameter matching the attribute category of the loading material is obtained from a preset loading control parameter database; the loader is controlled to continue performing the loading operation according to the first optimized loading control parameter, and the data of each subsequent loading operation is collected. The loading operation is divided into two parts: the second loading weight and the operation time corresponding to each loading operation; based on the second loading weight and the operation time corresponding to each of the multiple loading operations, the average operation efficiency value of the loader is calculated; when the average operation efficiency value is lower than the set efficiency threshold, the first optimized loading control parameter is finely adjusted to obtain the second optimized loading control parameter, and the loader is controlled to continue to perform loading operations according to the second optimized loading control parameter. This solves the problem that traditional fixed parameter control cannot adapt to actual complex working conditions, resulting in low operation efficiency and large equipment wear, and achieves the beneficial effect of improving the intelligence level and continuous operation efficiency of loading operations.

[0032] Example 2 Figure 2 This is a flowchart of another loading control method based on working condition identification provided in Embodiment 2 of the present invention. This embodiment is an optimization based on the above embodiments. Specifically, the operation of "judging the weight and particle size of the loading material according to the initial boom height and the first loading weight corresponding to multiple loading operations" has been refined.

[0033] Correspondingly, such as Figure 2 As shown, the method includes: S210. During the loading operation performed by the loader in the loading area, the initial boom height and the first loading weight corresponding to each loading operation are collected in real time.

[0034] S220. Calculate the weight ratio between the weight of each first shovel and the preset rated load.

[0035] In this embodiment, the weight of the first load collected for each instance is mathematically compared with a pre-set rated load value representing the loader's maximum loading capacity. Specifically, the actual load weight for each instance is divided by the rated load to calculate a "weight ratio." This ratio is a crucial intermediate parameter that quantifies the proportional relationship between the actual load of a single load and the equipment's theoretical maximum capacity, providing direct numerical data for subsequent determination of the material's density.

[0036] S230. Based on the comparison results between each weight ratio and a preset range of weight ratios, the weight category of the material is determined, and the weight determination result is obtained.

[0037] In this embodiment, after calculating the weight ratio, it is compared with several preset numerical ranges. These preset ranges are pre-defined and correspond to different material densities or weight categories. For example, when the calculated ratio falls into the "smaller" numerical range, the material is identified as a "lighter material"; if the ratio falls into the "medium" numerical range, it is identified as a "medium-density material"; and if the ratio falls into the "larger" numerical range, it is identified as a "higher-density material". Through this comparison with fixed threshold ranges, the method can automatically classify the weight attributes of the material and output a clear weight classification result.

[0038] S240. Based on the first loading weight corresponding to each boom's initial height, analyze the weight difference at different boom initial heights to obtain the weight difference rate between boom-off-ground height operation and boom-close-to-ground operation.

[0039] In this embodiment, to determine the particle size of the material, the relationship between the initial boom height and the first loading weight is specifically analyzed. Specifically, data collected under two typical operating postures are selected: one is the loading weight when the boom is at a high position (i.e., "off-the-ground operation"), and the other is the loading weight when the boom is at a very low position (i.e., "ground-level operation"). By calculating the difference between the loading weights obtained at these two different initial heights, and further converting it into a relative "difference rate" (e.g., dividing the difference by a certain baseline value), a quantitative index reflecting the degree to which the material loadability is affected by the operating posture is obtained.

[0040] S250. Based on the comparison results between the weight difference rate and multiple preset weight difference rate intervals, determine the particle size category of the material and obtain the particle size judgment result.

[0041] In this embodiment, after obtaining the weight difference rate, it is compared with another set of preset numerical intervals for particle size classification. These preset intervals correspond to different material particle size categories. For example, if the calculated weight difference rate is very small, falling into a lower threshold interval, it indicates that the weight of the material does not change much when scooped at different depths, usually corresponding to "fine-particle material" with good flowability and easy filling; if the difference rate is in a medium range, it may be identified as "medium-particle material"; if the difference rate is large, exceeding a higher threshold, it may be identified as "large-particle material" that is loose and difficult to fill. Through this comparison process, the final output is the judgment of the material particle size attribute, i.e., the "particle size judgment result".

[0042] S260. Based on the judgment result, identify the attribute category of the shovel-loaded material.

[0043] S270. Obtain the first optimized loading control parameter that matches the attribute category of the loading material from the preset loading control parameter database.

[0044] S280: Control the loader to continue performing the loading operation according to the first optimized loading control parameters, and collect the second loading weight and operation time corresponding to each loading operation.

[0045] S290. Calculate the average operating efficiency value of the loader based on the weight of each second shovel operation and the time of each operation corresponding to the multiple shovel operations that are continued.

[0046] Optionally, based on the above embodiments, calculating the loader's average operating efficiency value according to the second loading weight and the operating time corresponding to each of the multiple loading operations performed may include: Calculate the efficiency value of a single effective loading operation based on the second loading weight and operation time corresponding to each loading operation that continues to be performed; The average operating efficiency of the loader is calculated based on the efficiency values ​​of multiple single effective loading operations corresponding to the multiple loading operations that are performed.

[0047] Generally, during the loading operation of the loader based on the first optimized loading control parameters, the weight of the material actually loaded in each completed loading action is recorded, i.e., the second loading weight. Simultaneously, the time taken from start to finish of that action is also recorded, i.e., the operation time. Using these two data points, by dividing the loading weight by the corresponding operation time, the amount of material loaded per unit time can be calculated. This calculation result is the single effective loading operation efficiency value, which directly reflects the instantaneous work efficiency of that specific operation process.

[0048] Generally, to objectively evaluate the overall performance of a loader over a continuous work cycle, it is necessary to comprehensively examine its performance across multiple operations. Therefore, the efficiency values ​​of each effective loading operation performed within that period are collected. By summing and averaging these values ​​representing the efficiency of each operation, the average operating efficiency value of the loader for that period can be obtained.

[0049] S2100: When the average operating efficiency value is lower than the set efficiency threshold, the first optimized loading control parameter is finely adjusted to obtain the second optimized loading control parameter, and the loader is controlled to continue to perform loading operation according to the second optimized loading control parameter.

[0050] Optionally, based on the above embodiments, the first optimized loading control parameters can be fine-tuned to obtain the second optimized loading control parameters, which may include: The first optimized loading control parameters are used as the initial fine-tuning parameters, and the fine-tuning parameters are adjusted based on the preset optimization algorithm to obtain the fine-tuned control parameters. Control the loader to continue performing at least one loading operation according to the fine-tuned control parameters, and obtain the fine-tuned operation efficiency value that matches the fine-tuned control parameters; If the adjusted operating efficiency value is greater than the average operating efficiency value of the loader, then the adjusted control parameter will be used as the second optimized loading control parameter. If the fine-tuned operating efficiency value is less than or equal to the average operating efficiency value, then the fine-tuned control parameter is used as the new parameter to be fine-tuned, and the operation of adjusting the parameter to be fine-tuned based on the preset optimization algorithm is returned to obtain the fine-tuned control parameter, until the second optimized loading control parameter is obtained.

[0051] Generally, when the average operating efficiency of a loader under the current loading control parameters fails to meet the target, a process of fine-tuning the control parameters is initiated. First, the currently used, optimized loading control parameters retrieved from the database are set as the initial objects to be optimized, i.e., the parameters to be fine-tuned. Then, using a preset mathematical optimization method, the values ​​of these parameters (such as those related to boom speed and bucket angle) are modified in a directional and minor manner, thereby generating a new, modified set of control commands—the fine-tuned control parameters.

[0052] Generally, to verify the effectiveness of these newly generated parameters, the loader is immediately instructed to perform at least one complete loading operation using these fine-tuned control parameters. After this or several specific operations are completed, the corresponding work efficiency value is calculated. This newly calculated efficiency value, because it is generated entirely under the new parameter settings, is called the "fine-tuned work efficiency value" and is used specifically to evaluate the actual effect of this parameter adjustment.

[0053] Generally, the fine-tuned operating efficiency value is directly compared with the previously calculated average operating efficiency value, which represents the average level of the old parameters. If the comparison shows that the new efficiency value is significantly higher than the old average efficiency value, this proves that the parameter fine-tuning was successful and effective. Then, this set of verified and effective fine-tuned control parameters will be officially adopted as the new optimization standard to guide loader operations for a period of time, which is called the "second optimized loading control parameters".

[0054] Generally, if the comparison reveals that the new "fine-tuned work efficiency value" does not exceed, or is even lower than or equal to, the original average work efficiency value, it indicates that the adjustment has not brought about improvement or the effect is unsatisfactory. In this case, the parameters will not be accepted. The method will reset this poorly performing fine-tuned control parameter as a new starting point, i.e., a new round of parameters to be fine-tuned, and then jump back to the previous process to perform another round of adjustments, testing, and comparisons based on the optimization algorithm, until a set of control parameters that can indeed improve work efficiency and is superior to the former is finally generated and verified, thus obtaining the final second optimized loading control parameters.

[0055] The technical solution of this invention involves real-time acquisition of the initial boom height and first loaded weight for each operation during loader loading, followed by subsequent analysis and processing. Specifically, this includes: calculating the weight ratio between each first loaded weight and a preset rated load, and determining the material weight category based on a comparison of this ratio with a preset range; simultaneously, analyzing and calculating the weight difference rate between boom-off-ground and ground-level operations based on the first loaded weights obtained at different boom initial heights, and determining the material particle size category based on a comparison of this difference rate with a preset range; combining the above weight and particle size determination results to accurately identify the attribute category of the loaded material; subsequently, retrieving the first optimized loading control parameters matching the attribute category from a preset loading control parameter database, and controlling the loader to continue operation according to these parameters, while simultaneously collecting... The system collects the second loading weight and operation time of subsequent operations; based on this subsequent data, it calculates the loader's average operating efficiency value. When this value is lower than the set efficiency threshold, the first optimized loading control parameters are fine-tuned to obtain the second optimized loading control parameters, and the loader continues to operate according to the new parameters. This solves the problems of low operating efficiency, increased equipment wear, and poor operating experience caused by the inability of traditional fixed parameter control mode to adapt to actual complex and variable working conditions. It achieves the beneficial effects of realizing intelligent and adaptive optimization of the entire loading operation process, significantly improving operating efficiency, equipment adaptability, and operating comfort. In particular, by comparing the loading weight with the rated load and combining it with the weight difference analysis under different boom heights, it realizes the automated and precise identification of the physical characteristics of the material, providing a key and reliable basis for subsequent accurate matching and optimization of control parameters.

[0056] Example 3 Figure 3 This is a flowchart of another loading control method based on working condition identification provided in Embodiment 3 of the present invention. This embodiment is an optimization based on the above embodiments. Specifically, a loading control method based on working condition identification is refined.

[0057] Correspondingly, such as Figure 3 As shown, the method includes: S310. During the loading operation performed by the loader in the loading area, the initial boom height and the first loading weight corresponding to each loading operation are collected in real time.

[0058] S320. Based on the initial boom height and first loading weight corresponding to multiple loading operations, the weight and particle size of the loading material are judged, and the attribute category of the loading material is identified based on the judgment results.

[0059] S330. Obtain the first optimized loading control parameter that matches the attribute category of the loading material from the preset loading control parameter database.

[0060] S340: Control the loader to continue performing loading operations according to the first optimized loading control parameters, and collect the second loading weight and operation time corresponding to each loading operation.

[0061] S350. Calculate the average operating efficiency value of the loader based on the weight of each second shovel operation and the time of each operation corresponding to the multiple shovel operations that are continued.

[0062] S360. When the average operating efficiency value is lower than the set efficiency threshold, the first optimized loading control parameter is finely adjusted to obtain the second optimized loading control parameter, and the loader is controlled to continue to perform loading operation according to the second optimized loading control parameter.

[0063] S370. The loader acceleration collected by the inertial measurement unit built into the loader is acquired in real time. The travel distance of the loader is calculated by integration based on the acceleration of each loader. The loading area is accurately located based on the travel distance calculation result.

[0064] In this embodiment, the inertial measurement unit built into the loader continuously collects acceleration data generated by the loader during operation. By performing mathematical integration on these acceleration data that change continuously over time, the distance traveled by the loader within any given time period can be accurately calculated. Combining the operation sequence with this distance information, the specific movement trajectory and position of the loader in the work site can be determined, thereby achieving accurate identification and positioning of the specific material pile or loading area currently being worked on, providing a spatial reference for subsequently distinguishing different work areas.

[0065] S380. After completing all loading operations for the material in the loading area, the average operating efficiency value of all loaders performing the loading operation in the loading area is collected through Internet of Things (IoT) technology.

[0066] In this embodiment, once all loading operations for all target materials are completed within a pre-defined loading area, a data aggregation and learning process is initiated. Through IoT technology, the average operational efficiency of all other loaders participating in the same loading area during the operation can be collected.

[0067] S390. Obtain the target loader corresponding to the maximum average operating efficiency value, and update the loading control parameter database according to the loading control parameters used by the target loader.

[0068] In this embodiment, after aggregating the average operating efficiency values ​​of all loaders within the same area, these values ​​are compared, and the highest value is selected as the maximum average operating efficiency value. Next, the specific loader that generated this optimal efficiency value is located, i.e., the target loader. The set of loading control parameters actually used by this target loader during its efficient operation, verified as highly efficient, is obtained. Finally, these efficient parameters from the optimal equipment, verified in practice, are added to or integrated into a preset loading control parameter database, thereby updating and enriching the database.

[0069] Furthermore, based on the above embodiments, a loading control method based on working condition identification may further include: After the loader completes each loading operation, the real-time road bump level and real-time road slope are acquired by the inertial measurement unit built into the loader. Based on the pre-established mapping relationship between bumpiness and speed limit rating, a real-time speed limit value is matched with the real-time road bumpiness and real-time road slope. Obtain the distance information of the global path formed by the loading end point, the reversal stop point, and the unloading stop point; Based on the distance information of the global path and the real-time speed limit value that matches the real-time road bumpiness and real-time road slope, the loader's travel speed between the loading end point and the unloading stop point is planned.

[0070] Generally, after a loader completes a shoveling operation and the material is loaded into the bucket, to ensure the safety and smoothness of subsequent travel, it immediately uses its built-in inertial measurement unit (IMU) to sense the actual condition of the road surface. The IMU can measure the acceleration and angular velocity changes of the vehicle body in three directions. By processing and analyzing this data in real time, it can calculate the unevenness of the road surface where the vehicle is currently located, i.e., the real-time road bumpiness, and the tilt angle of the road surface relative to the horizontal plane, i.e., the real-time road slope.

[0071] Generally, after obtaining real-time road surface roughness data, it is compared with a pre-defined mapping table. This table clearly defines the maximum permissible speed for different levels of roughness. For example, higher speeds are allowed for slightly rough roads, while very rough roads strictly limit speeds. By consulting this table, a maximum speed limit that must be observed and matches the current roughness level can be immediately determined; this value is the real-time speed limit. Road slope information is also taken into account; generally, the steeper the slope, the stricter the speed limit.

[0072] Generally, before planning the travel speed, it is necessary to clarify the spatial relationships between several key points that the loader will pass through in the complete sub-cycle of loading, transporting, and unloading. These key points include the position where the bucket begins to retract after closing (the loading end point), the transition position from retraction to forward (the retraction stop point), and the position where unloading is prepared (the unloading stop point). By integrating the travel trajectory using the inertial measurement unit, the straight-line or actual path distances between these points can be obtained. This information collectively constitutes the spatial constraints of distance information called the global path.

[0073] Generally, after simultaneously grasping the two core constraints of global path distance information and the real-time speed limits that must be adhered to, a comprehensive design of the speed curve is performed for the entire journey of the loader from the loading end point to the unloading stop point. This planning process needs to comprehensively consider the requirement to complete the journey within the given path length while ensuring that the maximum speed limit allowed by the current road conditions is not exceeded at any time. The final result is a set of suggested or mandatory speed variation schemes designed to ensure that the vehicle can safely, smoothly, and as efficiently as possible complete the transportation link from the material pile to the unloading point.

[0074] Optionally, based on the above embodiments, and based on the distance information of the global path and the real-time speed limit value that matches the real-time road bumpiness and real-time road slope, planning the loader's travel speed between the loading end point and the unloading stop point may include: During the process of the loader traveling from the reversing stop point to the unloading stop point, the remaining travel distance from the current position of the loader to the unloading stop point is determined based on the time when the boom begins to lift. Obtain the current loading weight and determine the lifting time required to raise the boom from the current height to the unloading height; Based on the remaining travel distance, the current loading weight, the lifting time, and the real-time speed limit, the optimal travel speed curve from the current position to the unloading stop point is calculated; The loader is controlled to travel according to the optimal travel speed curve so that the moment the boom is fully raised is synchronized with the moment the loader reaches the unloading stop point.

[0075] Generally, during the phase where the loader changes direction from its reverse stop point and begins to move towards the unloading stop point, a key signal is used to achieve precise coordination between travel and boom lifting: the moment the boom begins to lift. From this point onward, the loader's built-in positioning and ranging functions can calculate in real time the remaining distance between the loader's current location and the destination unloading stop point. This data, known as the remaining travel distance, is the basic spatial parameter for planning subsequent travel rhythm.

[0076] Generally, when planning speed, the loader's current load status must be considered. Therefore, the method obtains the actual weight of the material already loaded in the bucket, i.e., the current loading weight. Simultaneously, based on the performance of the boom hydraulic system, the current boom height, the required unloading height, and the current load weight, the complete time required to smoothly raise the boom from the initial lifting position to the unloading position can be accurately calculated; this time is called the lifting time. This time parameter is a key time constraint determining the vehicle's driving rhythm.

[0077] Generally, after determining the four core parameters—remaining travel distance, current loading weight, boom lifting time, and real-time speed limit determined by road conditions—a calculation process is initiated. The purpose of this calculation is to design an ideal curve of speed change over time from the current position to the unloading stop point, i.e., the optimal travel speed curve, while adhering to the speed limit. This curve needs to ensure that the travel time taken by the vehicle when following it perfectly matches the required lifting time of the boom, while also considering the smoothness of starting, cruising, and deceleration.

[0078] Generally, after calculating the optimal travel speed curve, it is translated into direct control commands for the loader's traveling mechanism. By controlling engine output, transmission gears, or the braking system, the loader is guided to travel strictly according to this preset speed curve. The ultimate goal is to achieve precise synchronization: at the exact moment the loader smoothly reaches the unloading stop point, the boom also completes its lifting action, stabilizing the bucket at the preset unloading height, thus completely avoiding situations where the vehicle waits for the boom or the boom waits for the vehicle, achieving smooth operational transitions.

[0079] The technical solution of this invention collects the initial boom height and first loading weight of each operation in real time during the loader's loading operation. Based on this, it identifies the material attribute category and calls the matching first optimized loading control parameters. The loader is controlled to continue working according to these parameters, and the subsequent second loading weight and operation time are collected to calculate the average operation efficiency value. When the efficiency is lower than the threshold, the parameters are fine-tuned and optimized. Furthermore, the loader's built-in inertial measurement unit collects acceleration data, and the travel distance is calculated by integration to accurately locate the loading area. After completing all operations in the area, the average operation efficiency value of all loaders in the same area is collected with the help of Internet of Things (IoT) technology. The loading control parameters used by the loader with the highest efficiency value are selected to update the loading control parameter database. This solves the problems that traditional fixed parameter control cannot adapt to complex and changing working conditions, and that single-machine learning has limitations and lags. It achieves the beneficial effects of realizing intelligent and adaptive optimization of the entire loading operation process, significantly improving the efficiency of single-machine operation and the comfort of operation. In particular, by combining precise positioning with the aggregation of group data and extraction of optimal experience from IoT, "experience sharing" between the fleet is realized, which can accelerate the adaptation and optimization process of the entire operating fleet to the material characteristics of specific areas.

[0080] Example 4 Figure 4 This is a schematic diagram of a loading control device based on working condition identification provided in Embodiment 4 of the present invention.

[0081] like Figure 4 As shown, the device includes: The data acquisition module 410 is used to collect the initial boom height and the first load weight in real time for each load operation during the loading operation performed by the loader in the loading area. The identification module 420 is used to determine the weight and particle size of the shoveled material based on the initial boom height and first shovel weight corresponding to multiple shoveling operations, and to identify the attribute category of the shoveled material based on the determination results. The parameter acquisition module 430 is used to acquire the first optimized loading control parameter that matches the attribute category of the loading material from the preset loading control parameter database. The continuous operation module 440 is used to control the loader to continue to perform loading operations according to the first optimized loading control parameters, and to collect the second loading weight and operation time corresponding to each loading operation. The efficiency value calculation module 450 is used to calculate the average operating efficiency value of the loader based on the second loading weight and the operation time corresponding to the multiple loading operations that are continued to be performed. The parameter optimization module 460 is used to fine-tune the first optimized loading control parameters to obtain the second optimized loading control parameters when the average operating efficiency value is lower than the set efficiency threshold, and control the loader to continue to perform loading operations according to the second optimized loading control parameters.

[0082] The technical solution of this invention involves real-time acquisition of the initial boom height and first loading weight corresponding to each loading operation during the loading process of the loader within the loading area; based on the initial boom height and first loading weight corresponding to multiple loading operations, the weight and particle size of the loading material are determined, and the attribute category of the loading material is identified according to the determination results; a first optimized loading control parameter matching the attribute category of the loading material is obtained from a preset loading control parameter database; the loader is controlled to continue performing the loading operation according to the first optimized loading control parameter, and the data of each subsequent loading operation is collected. The loading operation is divided into two parts: the second loading weight and the operation time corresponding to each loading operation; based on the second loading weight and the operation time corresponding to each of the multiple loading operations, the average operation efficiency value of the loader is calculated; when the average operation efficiency value is lower than the set efficiency threshold, the first optimized loading control parameter is finely adjusted to obtain the second optimized loading control parameter, and the loader is controlled to continue to perform loading operations according to the second optimized loading control parameter. This solves the problem that traditional fixed parameter control cannot adapt to actual complex working conditions, resulting in low operation efficiency and large equipment wear, and achieves the beneficial effect of improving the intelligence level and continuous operation efficiency of loading operations.

[0083] Based on the above embodiments, the identification module 420 is specifically used for: Calculate the weight ratio of each first shovel load to the preset rated load; Based on the comparison results between each weight ratio and multiple preset weight ratio ranges, the material weight category is determined, and the weight determination result is obtained; Based on the first loading weight corresponding to each boom's initial height, the weight difference at different boom initial heights is analyzed to obtain the weight difference rate between boom-off-ground height operation and boom-close-to-ground operation. Based on the comparison results between the weight difference rate and multiple preset weight difference rate ranges, the particle size category of the material is determined, and the particle size judgment result is obtained.

[0084] Based on the above embodiments, the efficiency value calculation module 450 is specifically used for: Calculate the efficiency value of a single effective loading operation based on the second loading weight and operation time corresponding to each loading operation that continues to be performed; The average operating efficiency of the loader is calculated based on the efficiency values ​​of multiple single effective loading operations corresponding to the multiple loading operations that are performed.

[0085] Based on the above embodiments, the parameter optimization module 460 is specifically used for: The first optimized loading control parameters are used as the initial fine-tuning parameters, and the fine-tuning parameters are adjusted based on the preset optimization algorithm to obtain the fine-tuned control parameters. Control the loader to continue performing at least one loading operation according to the fine-tuned control parameters, and obtain the fine-tuned operation efficiency value that matches the fine-tuned control parameters; If the adjusted operating efficiency value is greater than the average operating efficiency value of the loader, then the adjusted control parameter will be used as the second optimized loading control parameter. If the fine-tuned operating efficiency value is less than or equal to the average operating efficiency value, then the fine-tuned control parameter is used as the new parameter to be fine-tuned, and the operation of adjusting the parameter to be fine-tuned based on the preset optimization algorithm is returned to obtain the fine-tuned control parameter, until the second optimized loading control parameter is obtained.

[0086] Furthermore, based on the above embodiments, a loading control device based on working condition recognition may further include: The module for locating the loading area is used to acquire the loader acceleration collected by the inertial measurement unit built into the loader in real time, calculate the loader's travel distance by integration based on the acceleration of each loader, and accurately locate the loading area based on the travel distance calculation result; The average operating efficiency value calculation module is used to collect the average operating efficiency value of all loaders that performed the loading operation in the loading area after completing all loading operations for the loading material in the loading area, through Internet of Things technology. The parameter database update module is used to obtain the target loader corresponding to the maximum average operating efficiency value, and update the loading control parameter database according to the loading control parameters used by the target loader.

[0087] Furthermore, based on the above embodiments, a loading control device based on working condition recognition may further include: The bumpiness and slope calculation module is used to acquire the real-time road bumpiness and real-time road slope collected by the inertial measurement unit built into the loader after the loader completes each loading operation. The speed limit calculation module is used to calculate a real-time speed limit value that matches the real-time road surface bumpiness and real-time road surface slope based on a pre-established mapping relationship between the bumpiness level and the speed limit rating. The global path construction module is used to obtain the distance information of the global path consisting of the loading end point, the reversal stop point, and the unloading stop point; The driving speed planning module is used to plan the driving speed of the loader between the loading end point and the unloading stop point based on the distance information of the global path and the real-time speed limit value that matches the real-time road bumpiness and real-time road slope.

[0088] Based on the above embodiments, the driving speed planning module is specifically used for During the process of the loader traveling from the reversing stop point to the unloading stop point, the remaining travel distance from the current position of the loader to the unloading stop point is determined based on the time when the boom begins to lift. Obtain the current loading weight and determine the lifting time required to raise the boom from the current height to the unloading height; Based on the remaining travel distance, the current loading weight, the lifting time, and the real-time speed limit, the optimal travel speed curve from the current position to the unloading stop point is calculated; The loader is controlled to travel according to the optimal travel speed curve so that the moment the boom is fully raised is synchronized with the moment the loader reaches the unloading stop point.

[0089] The loading control device based on working condition identification provided in the embodiments of the present invention can execute the loading control method based on working condition identification provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0090] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0091] Example 5 Figure 5 A schematic diagram of an electronic device 10 that 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 (such as 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.

[0092] like Figure 5As 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.

[0093] 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.

[0094] Processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as performing a loading control method based on working condition identification as described in any embodiment of the present invention, i.e.: In some embodiments, a loading control method based on working condition identification, as described in any one of the embodiments of the present invention, 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 loading control method based on working condition identification as described above can be performed. Alternatively, in other embodiments, processor 11 can be configured by any other suitable means (e.g., by means of firmware) to perform the loading control method based on working condition identification as described in any one of the embodiments of the present invention.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. 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.

[0101] 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.

[0102] 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 loading control method based on working condition identification, characterized in that, The method includes: During the loading operation performed by the loader in the loading area, the initial boom height and the first loading weight are collected in real time for each loading operation. Based on the initial boom height and first load weight corresponding to multiple loading operations, the weight and particle size of the loaded material are determined, and the attribute category of the loaded material is identified based on the determination results. In the preset loading control parameter database, obtain the first optimized loading control parameter that matches the attribute category of the loading material; The loader is controlled to continue performing loading operations according to the first optimized loading control parameters, and the second loading weight and operation time corresponding to each loading operation are collected. The average operating efficiency of the loader is calculated based on the weight of each second shovel operation and the time of each operation corresponding to the multiple shovel operations that are performed. When the average operating efficiency value is lower than the set efficiency threshold, the first optimized loading control parameter is finely adjusted to obtain the second optimized loading control parameter, and the loader is controlled to continue to perform loading operation according to the second optimized loading control parameter.

2. The method according to claim 1, characterized in that, Based on the initial boom height and first load weight corresponding to multiple loading operations, the weight and particle size of the loaded material are determined, including: Calculate the weight ratio of each first shovel load to the preset rated load; Based on the comparison results between each weight ratio and multiple preset weight ratio ranges, the material weight category is determined, and the weight determination result is obtained; Based on the first loading weight corresponding to each boom's initial height, the weight difference at different boom initial heights is analyzed to obtain the weight difference rate between boom-off-ground height operation and boom-close-to-ground operation. Based on the comparison results between the weight difference rate and multiple preset weight difference rate ranges, the particle size category of the material is determined, and the particle size judgment result is obtained.

3. The method according to claim 1, characterized in that, The average operating efficiency of the loader is calculated based on the second load weight and the operation time corresponding to each of the multiple load operations performed, including: Calculate the efficiency value of a single effective loading operation based on the second loading weight and operation time corresponding to each loading operation that continues to be performed; The average operating efficiency of the loader is calculated based on the efficiency values ​​of multiple single effective loading operations corresponding to the multiple loading operations that are performed.

4. The method according to claim 3, characterized in that, The first optimized loading control parameters are fine-tuned to obtain the second optimized loading control parameters, including: The first optimized loading control parameters are used as the initial fine-tuning parameters, and the fine-tuning parameters are adjusted based on the preset optimization algorithm to obtain the fine-tuned control parameters. Control the loader to continue performing at least one loading operation according to the fine-tuned control parameters, and obtain the fine-tuned operation efficiency value that matches the fine-tuned control parameters; If the adjusted operating efficiency value is greater than the average operating efficiency value of the loader, then the adjusted control parameter will be used as the second optimized loading control parameter. If the fine-tuned operating efficiency value is less than or equal to the average operating efficiency value, then the fine-tuned control parameter is used as the new parameter to be fine-tuned, and the operation of adjusting the parameter to be fine-tuned based on the preset optimization algorithm is returned to obtain the fine-tuned control parameter, until the second optimized loading control parameter is obtained.

5. The method according to any one of claims 1-4, characterized in that, The method further includes: The loader's acceleration is acquired in real time by the inertial measurement unit built into the loader. The loader's travel distance is calculated by integration based on the acceleration of each loader. The loading area is then accurately located based on the travel distance calculation result. Furthermore, after completing all loading operations for the materials in the loading area, the average operating efficiency value of all loaders that performed the loading operation in the loading area is collected through Internet of Things technology. Obtain the target loader corresponding to the maximum average operating efficiency value, and update the loading control parameter database according to the loading control parameters used by the target loader.

6. The method according to any one of claims 1-4, characterized in that, The method further includes: After the loader completes each loading operation, the real-time road bump level and real-time road slope are acquired by the inertial measurement unit built into the loader. Based on the pre-established mapping relationship between bumpiness and speed limit rating, a real-time speed limit value is matched with the real-time road bumpiness and real-time road slope. Obtain the distance information of the global path formed by the loading end point, the reversal stop point, and the unloading stop point; Based on the distance information of the global path and the real-time speed limit value that matches the real-time road bumpiness and real-time road slope, the loader's travel speed between the loading end point and the unloading stop point is planned.

7. The method according to claim 6, characterized in that, Based on the distance information of the global path and the real-time speed limit value matched with the real-time road bumpiness and real-time road slope, the loader's travel speed between the loading end point and the unloading stop point is planned, including: During the process of the loader traveling from the reversing stop point to the unloading stop point, the remaining travel distance from the current position of the loader to the unloading stop point is determined based on the time when the boom begins to lift. Obtain the current loading weight and determine the lifting time required to raise the boom from the current height to the unloading height; Based on the remaining travel distance, the current loading weight, the lifting time, and the real-time speed limit, the optimal travel speed curve from the current position to the unloading stop point is calculated; The loader is controlled to travel according to the optimal travel speed curve so that the moment the boom is fully raised is synchronized with the moment the loader reaches the unloading stop point.

8. 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 that can be executed 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 the loading control method based on working condition identification as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the loading control method based on working condition identification as described in any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the loading control method based on working condition identification according to any one of claims 1-7.

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