Loader endurance prediction method and device based on operation mode and electronic equipment

Abstract

The invention is suitable for the technical field of vehicles, and provides a loader endurance prediction method and device based on an operation mode and electronic equipment, and the method comprises the steps: obtaining the operation data of a loader during operation, segmenting the operation data according to a preset duration value, and obtaining the operation data of a plurality of time periods; in each time period, determining the total power consumption, the total working hours, the freight distance and the expressway proportion of the time period according to the operation data, and determining the endurance time corresponding to the time period according to the total power consumption and the total working hours; according to the freight distance, the high-speed ratio and the corresponding endurance time of each time period, determining an average endurance time value corresponding to the same freight distance and high-speed ratio time period; and according to the endurance time average values corresponding to the same freight distance and high-speed ratio time periods, a freight distance-high-speed ratio endurance prediction table of the loader is obtained. According to the method, the working mode difference when the user uses the loader can be considered, and accurate and reliable loader endurance prediction information is provided.

Description

Technical Field

[0001] This invention belongs to the field of vehicle technology, and in particular relates to a method, device and electronic equipment for predicting the range of a loader based on its operating mode. Background Technology

[0002] Loaders, as a type of engineering machinery with high operating efficiency, are widely used in various construction projects, mainly for loading and transporting bulk materials.

[0003] When purchasing loaders, users are most concerned about range. However, the answer to this question is vague. In related technologies, the defined range of a product is a broad range, mainly relying on marketing personnel to estimate the approximate range based on the user's existing fuel consumption of a pure gasoline loader. Because loaders are large machines, their operating methods have a significant impact on range. Therefore, estimating the range of a loader based on experience is not accurate enough and cannot provide users with accurate and reliable range information. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a loader endurance prediction method, device and electronic device based on operation mode, so as to take into account the differences in the operation mode when users use loaders and provide accurate and reliable loader endurance prediction information.

[0005] A first aspect of this invention provides a method for predicting the range of a loader based on its operating mode, comprising:

[0006] The loader's operating data during operation is acquired, and the operating data is divided into multiple time periods according to a preset duration value.

[0007] In each time period, the total power consumption, total working hours, transportation distance, and high-speed ratio of the time period are determined based on the operating data, and the corresponding driving time is determined based on the total power consumption and the total working hours.

[0008] Based on the distance, percentage of high-speed traffic, and corresponding driving time for each time period, determine the average driving time for the same distance and percentage of high-speed traffic during each time period.

[0009] Based on the average driving time corresponding to the same transport distance and high-speed ratio period, the driving distance-high-speed ratio driving range prediction table of the loader is obtained.

[0010] In conjunction with the first aspect, in one possible implementation of the first aspect, determining the high-speed percentage of each time period based on the operational data includes:

[0011] The duration during which the vehicle speed exceeds a preset speed threshold is determined, thus obtaining the first duration;

[0012] Determine the duration of the forward vehicle speed within this period to obtain the second duration;

[0013] Calculate the ratio of the first duration to the second duration to obtain the high-speed occupancy ratio of this period.

[0014] Combined with the first aspect, in a possible implementation manner of the first aspect, the haul distance is the distance traveled by the loader for one material transportation;

[0015] Within each period, determining the haul distance of the period according to the operation data includes:

[0016] Integrate the walking motor speed within this period to obtain the driving mileage;

[0017] Determine the number of walking cycles of the loader within this period;

[0018] Determine the ratio of the driving mileage to the number of walking cycles to obtain the haul distance.

[0019] Combined with the first aspect, in a possible implementation manner of the first aspect, determining the corresponding endurance time for this period according to the total power consumption and the total working hours includes:

[0020] Determine the corresponding endurance time for this period according to h = k * C / (E / T1);

[0021] Where, h is the endurance time; k is a preset coefficient, where 0 < k < 1; C is the total power of the battery pack; E is the total power consumption; T1 is the total working hours.

[0022] Combined with the first aspect, in a possible implementation manner of the first aspect, determining the average value of the endurance time corresponding to the periods with the same haul distance and high-speed occupancy ratio according to the haul distance, high-speed occupancy ratio, and corresponding endurance time of each period includes:

[0023] Obtain a preset haul distance reference sequence and a high-speed occupancy ratio reference sequence;

[0024] Correct the haul distance of each period to the haul distance closest to it in the haul distance reference sequence;

[0025] Correct the high-speed occupancy ratio of each period to the high-speed occupancy ratio closest to it in the high-speed occupancy ratio reference sequence;

[0026] Based on the corrected haul distance and high-speed occupancy ratio of each period, and the corresponding endurance time of each period, determine the average value of the endurance time corresponding to the periods with the same haul distance and high-speed occupancy ratio.

[0027] · Combined with the first aspect, in a possible implementation manner of the first aspect, the method further includes:

[0028] In each time period, the number of loading cycles for that period is determined based on the operational data;

[0029] The output for that period is obtained based on the ratio of the number of loading cycles to the total working hours.

[0030] Based on the transport distance, output, and corresponding endurance time for each time period, determine the average endurance time for the same transport distance and output time periods;

[0031] Based on the average driving time corresponding to the same transport distance and production period, a driving distance-production driving time prediction table for the loader is obtained.

[0032] In conjunction with the first aspect, in one possible implementation of the first aspect, determining the average flight time corresponding to the same flight distance and output for each time period based on the transport distance, output, and corresponding flight time for each time period includes:

[0033] Obtain the preset transport distance reference sequence and production reference sequence;

[0034] The transport distance for each time period is corrected to the closest transport distance in the transport distance reference sequence;

[0035] The output for each time period is adjusted to the output closest to the output reference sequence;

[0036] Based on the corrected transport distance and output for each time period, and the corresponding endurance time for each time period, the average endurance time for time periods with the same transport distance and output is determined.

[0037] A second aspect of the present invention provides a loader endurance prediction device based on operating mode, comprising:

[0038] The acquisition module is used to acquire the operating data of the loader during operation, and to divide the operating data into multiple time periods according to a preset duration value.

[0039] The processing module is used to determine the total power consumption, total working hours, transportation distance and high-speed ratio of each time period based on the operating data, and to determine the corresponding battery life for that time period based on the total power consumption and the total working hours.

[0040] The determination module is used to determine the average driving time for the same driving distance and high-speed ratio time periods based on the driving distance, high-speed ratio and corresponding driving time for each time period.

[0041] The generation module is used to obtain the loader's distance-high-speed ratio range prediction table based on the average range time corresponding to the same transport distance and high-speed ratio period.

[0042] In combination with the second aspect, in a possible implementation manner of the second aspect, the processing module is specifically configured to:

[0043] Determine the duration during which the vehicle speed is greater than a preset vehicle speed threshold within this period, and obtain the first duration;

[0044] Determine the duration of the forward vehicle speed within this period, and obtain the second duration;

[0045] Calculate the ratio of the first duration to the second duration to obtain the high-speed occupancy ratio of this period.

[0046] In combination with the second aspect, in a possible implementation manner of the second aspect, the haul distance is the distance traveled by the loader for one material transportation;

[0047] The processing module is specifically configured to:

[0048] Integrate the rotational speed of the traveling motor within this period to obtain the driving mileage;

[0049] Determine the number of walking cycles of the loader within this period;

[0050] Determine the ratio of the driving mileage to the number of walking cycles to obtain the haul distance.

[0051] In combination with the second aspect, in a possible implementation manner of the second aspect, the processing module is specifically configured to:

[0052] Determine the corresponding endurance time for this period according to h = k * C / (E / T1);

[0053] Where h is the endurance time; k is a preset coefficient, 0 < k < 1; C is the total power of the battery pack; E is the total power consumption; T1 is the total working hours.

[0054] In combination with the second aspect, in a possible implementation manner of the second aspect, the determining module is specifically configured to:

[0055] Obtain a preset haul distance reference sequence and a high-speed occupancy ratio reference sequence;

[0056] Correct the haul distance of each period to the haul distance closest to that in the haul distance reference sequence;

[0057] Correct the high-speed occupancy ratio of each period to the high-speed occupancy ratio closest to that in the high-speed occupancy ratio reference sequence;

[0058] Based on the corrected haul distance and high-speed occupancy ratio of each period, and the corresponding endurance time of each period, determine the average value of the endurance time corresponding to the periods with the same haul distance and high-speed occupancy ratio. <00C0119>

[0059] In combination with the second aspect, in a possible implementation manner of the second aspect, the generating module is further configured to:

[0060] In each time period, the number of loading cycles for that period is determined based on the operational data;

[0061] The output for that period is obtained based on the ratio of the number of loading cycles to the total working hours.

[0062] Based on the transport distance, output, and corresponding endurance time for each time period, determine the average endurance time for the same transport distance and output time periods;

[0063] Based on the average driving time corresponding to the same transport distance and production period, a driving distance-production driving time prediction table for the loader is obtained.

[0064] In conjunction with the second aspect, in one possible implementation of the second aspect, the generation module is further specifically used for:

[0065] Obtain the preset transport distance reference sequence and production reference sequence;

[0066] The transport distance for each time period is corrected to the closest transport distance in the transport distance reference sequence;

[0067] The output for each time period is adjusted to the output closest to the output reference sequence;

[0068] Based on the corrected transport distance and output for each time period, and the corresponding endurance time for each time period, the average endurance time for time periods with the same transport distance and output is determined.

[0069] A third aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect or any implementation thereof.

[0070] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any implementation thereof.

[0071] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows:

[0072] This invention addresses the problem that the runtime of loaders is difficult to accurately estimate due to the influence of operating methods. It acquires a large amount of operational data from loaders during operation and segments it. For each time period, the total power consumption, total working hours, transport distance, and high-speed ratio are determined based on the operational data. The runtime for each time period can be determined based on the total power consumption and total working hours. The operating methods for each time period can be distinguished based on the transport distance and high-speed ratio. Furthermore, based on the average runtime corresponding to time periods with the same transport distance and high-speed ratio, a transport distance-high-speed ratio runtime prediction table for loaders is generated. This runtime prediction table can guide users to improve their operating methods to increase runtime, such as by changing speed and transport distance. Users can also use this runtime prediction table to accurately select loaders that meet their specific operating methods. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0074] Figure 1 This is a schematic diagram illustrating an application scenario of the loader endurance prediction method based on operating mode provided in an embodiment of the present invention.

[0075] Figure 2 This is a schematic diagram of the implementation process of the loader range prediction method based on operating mode provided in this embodiment of the invention. Figure 1 ;

[0076] Figure 3 This is a schematic diagram of the loader speed and torque provided in an embodiment of the present invention;

[0077] Figure 4 This is the distance-high-speed ratio range prediction table provided in the embodiments of the present invention;

[0078] Figure 5 This is a schematic diagram of the implementation process of the loader range prediction method based on operating mode provided in this embodiment of the invention. Figure 2 ;

[0079] Figure 6 This is a distance-output endurance time prediction table provided in an embodiment of the present invention;

[0080] Figure 7 This is a schematic diagram of the loader endurance prediction device based on the working mode provided in an embodiment of the present invention;

[0081] Figure 8 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0082] The present application will be described more clearly below with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the function of the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all fall within the protection scope of the present application.

[0083] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0084] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0085] In the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0086] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0087] Furthermore, the term "multiple" mentioned in the embodiments of this application should be interpreted as two or more.

[0088] Different users operate loaders in different scenarios, employing different working methods. For example, loader operation modes include shoveling and stacking. Under the same working conditions, the haul distance and hauling speed may differ, resulting in significant variations in the loader's range. Therefore, relying on experience to estimate the range of new energy loaders is not accurate enough. However, by utilizing the loader's extensive bus data, including battery current, voltage, motor speed, and torque, the loader's operating mode can be identified based on this data, enabling accurate range prediction under different operating modes.

[0089] Figure 1 This is a schematic diagram illustrating an application scenario of the loader range prediction method shown in this embodiment. Figure 1 In this embodiment, the loader uses various sensors to detect battery data, speed data, torque data, etc., during operation and sends them to the vehicle controller, which then uploads this construction machinery data to a server.

[0090] in:

[0091] Rotational speed data can be measured by rotational speed sensors, commonly including magnetoelectric wheel speed sensors and Hall effect wheel speed sensors.

[0092] Battery data can be detected by current and voltage sensors located at the battery.

[0093] Torque data can be directly measured from the drive system using a torque sensor to obtain a torque signal. A torque sensor, also known as a torque meter, is used to detect torsional torque on various rotating or non-rotating mechanical components. It converts the physical change in torque into a precise electrical signal. For vehicles, higher torque results in better acceleration, stronger climbing ability, and greater load-bearing capacity.

[0094] These sensor data can be transmitted to the vehicle controller via the CAN bus. The vehicle controller is the control center for normal vehicle operation and the core component of the vehicle control system. It is the main control component for functions such as normal vehicle operation, regenerative braking energy recovery, fault diagnosis and handling, and vehicle status monitoring. The vehicle controller collects driving information such as accelerator pedal signals, brake pedal signals, and gear switch signals. It also receives data from the motor controller and battery management system on the CAN bus, and analyzes and judges this information in conjunction with the vehicle control strategy to extract the driver's driving intentions and vehicle operating status information. Finally, it issues commands via the CAN bus to control the operation of various component controllers to ensure normal vehicle operation. Therefore, various vehicle sensors typically send data to the vehicle controller.

[0095] Here, the vehicle controller can also transmit this sensor data to the vehicle networking big data platform on the server via a wireless network. Therefore, the server stores a large number of data samples, which can be used to perform predictive analysis of the loader's range.

[0096] The following is combined with Figure 1 Application scenarios, refer to Figure 2 This application describes a loader range prediction method based on operating mode, according to exemplary embodiments thereof. It should be noted that the above application scenarios are shown only to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited in any way. Rather, the embodiments of this application can be applied to any applicable scenario.

[0097] The following is combined with Figure 2 The method of this embodiment will be described in detail below:

[0098] Step S201: Obtain the operating data of the loader during operation, and divide the operating data into multiple time periods according to a preset duration value.

[0099] In this embodiment, the loader's operational data during operation can be downloaded directly from the server's vehicle network big data platform. Here, "loader operation" primarily refers to the loader's working conditions, not scenarios such as the loader being stationary or traveling long distances. Loader operation is generally cyclical, and the work cycle differs depending on the working condition. Typical working conditions include stacking and loading. For example, in stacking, bulk materials need to be piled together, and each work cycle requires three actions: unloaded forward movement, shoveling, and unloaded reverse movement. In loading, five actions are required: unloaded forward movement, shoveling, loaded reverse movement, loaded forward movement, unloading, and unloaded reverse movement. Of course, this embodiment is not limited to these two working conditions; other working conditions will not be listed here.

[0100] In some embodiments, the loader can directly upload the current working conditions and operational data to the vehicle-to-everything (V2X) big data platform, which then directly stores the operational data and corresponding working conditions. Alternatively, the loader only uploads the operational data to the V2X big data platform, which analyzes the working conditions using a specific algorithm. In this case, the operational data obtained from the server's V2X big data platform has already been categorized into working conditions; obtaining the operational data under these working conditions yields the operational data for the current operation.

[0101] In this embodiment, the running data can be divided according to a preset duration value T, and the size of T can be set by the user.

[0102] Since loaders do not operate continuously, the acquired operational data also includes data from non-operational periods. Therefore, after segmentation, the total working hours for each time period can be calculated, and then the ratio of the total working hours to the duration value for each time period can be calculated. Time periods with a ratio less than a preset threshold are deleted. For example, the ratio here can be set to 50%, meaning that the working time of each time period is greater than 1 / 2T and is valid. By filtering valid time periods, the accuracy and reliability of subsequent calculation results can be improved.

[0103] In step S202, for each time period, the total power consumption, total working hours, transport distance and high-speed ratio of the time period are determined based on the operating data, and the corresponding driving time is determined based on the total power consumption and total working hours.

[0104] Total power consumption: The total power consumption can be obtained by integrating the product of the battery current and voltage over a period of time.

[0105] Total working hours: Loaders do not work continuously; they may be stationary or moving at times. Total working hours refer to the working time, excluding the time spent stationary or moving. Whether the vehicle is in a working state can be determined by parameters such as the loader's rotational speed, torque, and travel speed. This embodiment will not elaborate on the specific steps.

[0106] Transport distance: The distance a loader travels in one transport operation. For example, in stockpiling operations, where bulk materials need to be piled up, each work cycle requires three actions: unloaded forward movement, shoveling, and unloaded reverse movement. The transport distance is the sum of the distances covered in one unloaded forward movement and one unloaded reverse movement. In shoveling operations, five actions are required: unloaded forward movement, shoveling, loaded reverse movement, loaded forward movement, unloading, and unloaded reverse movement. The transport distance is the sum of the distance covered in one unloaded forward movement and one loaded reverse movement, or vice versa. The specific calculation method for transport distance is not limited here.

[0107] For example, the transport distance can be calculated as follows:

[0108] The travel distance is obtained by integrating the rotational speed of the walking motor during this period.

[0109] Determine the number of travel cycles of the loader during this period;

[0110] The distance traveled is obtained by determining the ratio of the mileage traveled to the number of travel cycles.

[0111] Among them, a walking cycle can be a forward and backward process of the loader. From the above introduction of the loader, it can be known that the operation of the loader is generally a cyclic operation. No matter which working condition, it at least includes the steps of forward movement, material shoveling, and backward movement. For example, for the stacking working condition, it is necessary to stack the bulk materials together. Each operation cycle requires three actions: moving forward without load, shoveling materials, and moving backward without load. For the loading working condition, it requires five actions: moving forward without load, shoveling materials, moving backward with load, moving forward with load, discharging materials, and moving backward without load. These steps are continuously cycled. The process of one forward movement plus one backward movement is a walking cycle of the loader. Refer to Figure 3 As shown by the first curve in Figure 3 , for the walking motor, when the loader starts to move forward, the speed starts from zero and continuously increases. Then the loader stops in front of the material, and the speed decreases to zero. During this process, the speed is positive. Similarly, when the loader starts to move backward, the speed starts from zero and continuously increases. Then the loader stops at a specific position, and the speed decreases to zero. During this process, the speed is negative. Therefore, by analyzing the speed, the forward and backward situations of the loader can be judged, and then the walking cycle can be divided. That is, within this time period, if the speed of the loader is greater than the preset upper speed threshold S1 at the first moment and less than the preset lower speed threshold S2 at the second moment later, then the moment when the speed is zero before the first moment to the moment when the speed is zero after the second moment is taken as a walking cycle; where the preset upper speed threshold is greater than zero, and the preset lower speed threshold is less than zero. According to the number of walking cycles within this time period, the number of walking cycles within this time period can be determined.

[0112] High-speed ratio: Determine the duration when the vehicle speed is greater than the preset vehicle speed threshold within this time period to obtain the first duration; determine the duration of the forward vehicle speed within this time period to obtain the second duration; calculate the ratio of the first duration and the second duration to obtain the high-speed ratio of this time period. The value of this vehicle speed threshold is usually greater than the maximum vehicle speed during reverse driving. Therefore, the duration when the vehicle speed is greater than the preset vehicle speed threshold is also the forward vehicle speed. For the loader, high-speed driving is an important factor that reduces the endurance time. The greater the high-speed ratio, the shorter the endurance time. Therefore, in this embodiment, the high-speed ratio is used to describe the characteristics of the user's operation mode.

[0113] Endurance time: The endurance time corresponding to this time period can be determined according to h = k*C / (E / T1); where h is the endurance time; k is a preset coefficient, 0 < k < 1, and the typical value is 80%; C is the total power of the battery pack; E is the total power consumption; T1 is the total working hours.

[0114] Step S203, according to the transportation distance, high-speed ratio and corresponding endurance time of each time period, determine the average value of the endurance time corresponding to the time periods with the same transportation distance and high-speed ratio.

[0115] In this embodiment, for time periods with the same distance and high-speed ratio, the average of their driving time is calculated to obtain the driving time under that distance and high-speed ratio.

[0116] The ideal scenario is that the transport distance and the proportion of high-speed travel are exactly the same. However, considering that it is difficult to achieve the exact same transport distance and high-speed travel proportion under various operating scenarios, the average driving time corresponding to the same transport distance and high-speed travel proportion is determined by the following method:

[0117] Obtain the preset transport distance reference sequence and high-speed ratio reference sequence;

[0118] The transport distance for each time period is corrected to the closest transport distance in the transport distance reference sequence;

[0119] The high-speed percentage for each time period is corrected to the highest high-speed percentage in the high-speed percentage reference sequence;

[0120] Based on the corrected travel distance and highway ratio for each time period, and the corresponding driving time for each time period, the average driving time for the same travel distance and highway ratio is determined.

[0121] That is, by setting a series of typical values ​​for transport distance and highway ratio, and modifying the transport distance and highway ratio to the closest typical values, similar transport distances and highway ratios can be considered the same. For example, the typical values ​​for transport distance can be set to 10, 20, 30, 40, 50..., and the typical values ​​for highway ratio can be set to 0.1, 0.2, 0.3, 0.4, 0.5...

[0122] Step S204: Based on the average driving time corresponding to the same transport distance and high-speed ratio period, obtain the loader's transport distance-high-speed ratio driving range prediction table.

[0123] See Figure 4 As shown in the chart, the horizontal values ​​represent transport distance, and the vertical values ​​represent the percentage of high-speed travel. The color depth of the dots in the chart indicates the range of endurance, and the size of the dots represents the sample size. The larger the sample size, the higher the accuracy of the endurance prediction. This chart allows users to intuitively see the endurance for each transport distance and high-speed percentage. When selecting a loader, users can determine whether the loader's endurance meets their needs based on their usual transport distance and high-speed percentage. Users can also adjust their operating methods based on the endurance for different transport distances and high-speed percentages, for example, by shortening the transport distance and reducing the high-speed percentage to extend the endurance.

[0124] This invention addresses the problem that the runtime of loaders is difficult to accurately estimate due to the influence of operating methods. It acquires a large amount of operational data from loaders during operation and segments it. For each time period, the total power consumption, total working hours, transport distance, and high-speed ratio are determined based on the operational data. The runtime for each time period can be determined based on the total power consumption and total working hours. The operating methods for each time period can be distinguished based on the transport distance and high-speed ratio. Furthermore, based on the average runtime corresponding to time periods with the same transport distance and high-speed ratio, a transport distance-high-speed ratio runtime prediction table for loaders is generated. This runtime prediction table can guide users to improve their operating methods to increase runtime, such as by changing speed and transport distance. Users can also use this runtime prediction table to accurately select loaders that meet their specific operating methods.

[0125] according to Figure 4 The distance-high-speed-percentage range prediction table shows that the higher the percentage of high-speed travel, the shorter the driving time. However, customers and even sales personnel often don't fully understand the concept of high-speed percentage, leading to inaccurate range predictions. Therefore, this embodiment can also convert this model for easier understanding. Specifically, in conjunction with... Figure 5 As shown, this embodiment will be described in detail.

[0126] Step S501: Obtain the operating data of the loader during operation, and divide the operating data into multiple time periods according to a preset duration value.

[0127] For details on how to implement this step, please refer to [link / reference]. Figure 2 The descriptions in the embodiments will not be repeated in this embodiment.

[0128] In step S502, for each time period, the total power consumption, total working hours, transport distance and output of the time period are determined based on the operating data, and the corresponding endurance time for that time period is determined based on the total power consumption and total working hours.

[0129] In this embodiment, the output can be calculated in the following way:

[0130] The number of loading cycles for each time period is determined based on operational data.

[0131] The output for that period is obtained by the ratio of the number of loading cycles to the total working hours.

[0132] The number of loading cycles can be calculated in the following way:

[0133] If the loader's torque is greater than the preset upper torque threshold at the third moment and less than the preset lower torque threshold at the fourth moment, then the time from the moment before the third moment when the torque is zero to the moment before the fifth moment when the torque is zero is considered as one loading cycle. The fifth moment is the moment when the torque is greater than the preset upper torque threshold, and both the preset upper and lower torque thresholds are greater than zero. The number of loading cycles in this period is determined based on the number of loading cycles in this period.

[0134] See Figure 3 As shown in the second curve, in this embodiment, a preset upper torque threshold Y1 and a preset lower torque threshold Y can be set. The preset upper torque threshold Y1 is higher than the upper limit of the torque provided by the travel motor for acceleration. When the loader starts moving forward, the torque increases from 0. If no material is being shoveled, the torque will not exceed Y1. If the torque exceeds Y1 and then the loader reverses, the torque becomes less than Y, then it is considered that a shoveling action has been performed. Furthermore, a shoveling cycle is defined as the time from when the torque is zero before shoveling to the time when the torque is zero before the next shoveling. For example, Figure 3 For the shoveling operation: The loader starts moving forward, and the torque increases from 0; upon reaching the material, the loader decelerates, and the torque decreases slightly; the loader shovels the material, and the torque quickly increases to exceed Y1; the loader reverses, and the torque decreases to less than Y; the loader moves forward again, and the torque increases; the loader unloads the material, and the torque decreases; the loader reverses, and the torque becomes negative, eventually becoming zero. The entire shoveling cycle can be detected by monitoring the torque changes. The process for the stockpiling operation is similar, and will not be detailed in this embodiment.

[0135] The number of loading cycles within a time period is the number of loading cycles within that time period. The ratio of the number of loading cycles to the total working hours is the output of that time period.

[0136] In addition, the ratio of the number of loading cycles to the number of travel cycles within the same time period is the loading-transport ratio. Based on the loading-transport ratio, different operating conditions can be distinguished. For example, the loading-transport ratio for stockpiling conditions is 1, while the loading-transport ratio for loading conditions is 0.5.

[0137] Step S503: Determine the average flight time for the same transport distance and output time period based on the transport distance, output and corresponding flight time for each time period.

[0138] In this embodiment, for time periods with the same transport distance and output, the average of their endurance time is calculated to obtain the endurance time under that transport distance and output.

[0139] and Figure 2The implementation examples are similar, and the transport distance and output mentioned here are the same, ideally exactly the same. However, considering that it is difficult to make the transport distance and output exactly the same in various operating scenarios, the average endurance time corresponding to the same transport distance and output time period is determined by the following method:

[0140] Obtain the preset transport distance reference sequence and production reference sequence;

[0141] The transport distance for each time period is corrected to the closest transport distance in the transport distance reference sequence;

[0142] The output for each time period is adjusted to the output closest to the output reference sequence;

[0143] Based on the corrected transport distance and output for each time period, and the corresponding endurance time for each time period, the average endurance time for time periods with the same transport distance and output is determined.

[0144] That is, by setting a series of typical values ​​for transport distance and output, and modifying the transport distance and output to the closest typical values, similar values ​​and outputs can be considered the same. For example, the typical values ​​for transport distance can be set to 10, 20, 30, 40, 50..., and the typical values ​​for output can be set to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110...

[0145] Step S504: Based on the average driving time corresponding to the same transport distance and production period, obtain the transport distance-production driving time prediction table for the loader.

[0146] See Figure 6 As shown in the chart, the horizontal values ​​represent transport distance, and the vertical values ​​represent output. The color depth of the dots in the chart indicates the runtime, and the size of the dots represents the sample size; the larger the sample size, the higher the accuracy of the runtime prediction. This chart allows users to intuitively see the runtime for each transport distance and the percentage of high-speed operations. Under the same transport distance conditions, higher output results in shorter runtime; conversely, under the same output conditions, longer transport distances result in shorter runtime. This guides users in selecting and correctly operating loaders.

[0147] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0148] Figure 7 This is a schematic diagram of the loader endurance prediction device 70 based on the working mode provided in an embodiment of the present invention, including:

[0149] An acquisition module 71 is configured to acquire the operation data of the loader during operation and segment the operation data according to a preset duration value to obtain the operation data of multiple periods.

[0150] A processing module 72 is configured to determine the total power consumption, total working hours, transportation distance, and high-speed ratio of each period according to the operation data during each period, and determine the corresponding endurance time of the period according to the total power consumption and total working hours.

[0151] A determination module 73 is configured to determine the average value of the endurance time corresponding to the periods with the same transportation distance and high-speed ratio according to the transportation distance, high-speed ratio, and corresponding endurance time of each period.

[0152] A generation module 74 is configured to obtain the transportation distance-high-speed ratio endurance prediction table of the loader according to the average value of the endurance time corresponding to the periods with the same transportation distance and high-speed ratio.

[0153] As a possible implementation manner, the processing module 72 is specifically configured to:

[0154] Determine the duration during which the vehicle speed in this period is greater than a preset vehicle speed threshold to obtain a first duration;

[0155] Determine the duration of the forward vehicle speed in this period to obtain a second duration;

[0156] Calculate the ratio of the first duration to the second duration to obtain the high-speed ratio of this period. [[ID=NO]] [[ID=NO]]

[0157] As a possible implementation manner, the transportation distance is the distance traveled by the loader for one material transportation; the processing module 72 is specifically configured to:

[0158] Integrate the walking motor speed in this period to obtain the driving mileage;

[0159] Determine the number of walking cycles of the loader in this period;

[0160] Determine the ratio of the driving mileage to the number of walking cycles to obtain the transportation distance.

[0161] As a possible implementation manner, the processing module 72 is specifically configured to:

[0162] Determine the corresponding endurance time of this period according to h = k*C / (E / T1);

[0163] Where h is the endurance time; k is a preset coefficient, 0 < k < 1; C is the total power of the battery pack; E is the total power consumption; T1 is the total working hours.

[0164] As a possible implementation manner, the determination module 73 is specifically configured to:

[0165] Obtain a preset transportation distance reference sequence and high-speed ratio reference sequence;

[0166] The transport distance for each time period is corrected to the closest transport distance in the transport distance reference sequence;

[0167] The high-speed percentage for each time period is corrected to the highest high-speed percentage in the high-speed percentage reference sequence;

[0168] Based on the corrected travel distance and highway ratio for each time period, and the corresponding driving time for each time period, the average driving time for the same travel distance and highway ratio is determined.

[0169] As one possible implementation, generation module 74 is also used for:

[0170] The number of loading cycles for each time period is determined based on operational data.

[0171] The output for that period is obtained by the ratio of the number of loading cycles to the total working hours.

[0172] Based on the transport distance, output, and corresponding endurance time for each time period, determine the average endurance time for the same transport distance and output time periods;

[0173] Based on the average driving time corresponding to the same transport distance and production period, a driving time prediction table for the loader based on transport distance and production is obtained.

[0174] As one possible implementation, the generation module 74 is also specifically used for:

[0175] Obtain the preset transport distance reference sequence and production reference sequence;

[0176] The transport distance for each time period is corrected to the closest transport distance in the transport distance reference sequence;

[0177] The output for each time period is adjusted to the output closest to the output reference series;

[0178] Based on the corrected transport distance and output for each time period, and the corresponding endurance time for each time period, the average endurance time for time periods with the same transport distance and output is determined.

[0179] This invention addresses the problem that the runtime of loaders is difficult to accurately estimate due to the influence of operating methods. It acquires a large amount of operational data from loaders during operation and segments it. For each time period, the total power consumption, total working hours, transport distance, and high-speed ratio are determined based on the operational data. The runtime for each time period can be determined based on the total power consumption and total working hours. The operating methods for each time period can be distinguished based on the transport distance and high-speed ratio. Furthermore, based on the average runtime corresponding to time periods with the same transport distance and high-speed ratio, a transport distance-high-speed ratio runtime prediction table for loaders is generated. This runtime prediction table can guide users to improve their operating methods to increase runtime, such as by changing speed and transport distance. Users can also use this runtime prediction table to accurately select loaders that meet their specific operating methods.

[0180] Figure 8 This is a schematic diagram of an electronic device 80 provided in an embodiment of the present invention. For example... Figure 8 As shown, the electronic device 80 of this embodiment includes: a processor 81, a memory 82, and a computer program 83 stored in the memory 82 and executable on the processor 81, such as a loader endurance prediction program based on work mode. When the processor 81 executes the computer program 83, it implements the steps in the various embodiments of the loader endurance prediction method based on work mode described above, for example... Figure 3 Steps S301 to S304 are shown. Alternatively, when the processor 81 executes the computer program 83, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 7 The functions of modules 71 to 74 are shown.

[0181] For example, the computer program 83 can be divided into one or more modules / units, which are stored in the memory 82 and executed by the processor 81 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 83 in the electronic device 80. For example, the computer program 83 can be divided into an acquisition module 71, a processing module 72, a determination module 73, and a generation module 74 (a module in a virtual device), with the specific functions of each module as follows:

[0182] The acquisition module 71 is used to acquire the operating data of the loader during operation, and to divide the operating data into multiple time periods according to a preset duration value.

[0183] The processing module 72 is used to determine the total power consumption, total working hours, transportation distance and high-speed ratio of each time period based on the operating data, and to determine the corresponding driving time based on the total power consumption and total working hours.

[0184] The determination module 73 is used to determine the average driving time for the same driving distance and high-speed ratio period based on the driving distance, high-speed ratio and corresponding driving time for each time period.

[0185] The generation module 74 is used to obtain the loader's range prediction table based on the average range time corresponding to the same transport distance and high-speed ratio period.

[0186] The electronic device 80 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. The electronic device 80 may include, but is not limited to, a processor 81 and a memory 82. Those skilled in the art will understand that... Figure 8 This is merely an example of electronic device 80 and does not constitute a limitation on electronic device 80. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 80 may also include input / output devices, network access devices, buses, etc.

[0187] The processor 81 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0188] The memory 82 can be an internal storage unit of the electronic device 80, such as a hard disk or RAM of the electronic device 80. The memory 82 can also be an external storage device of the electronic device 80, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the electronic device 80. Furthermore, the memory 82 can include both internal and external storage units of the electronic device 80. The memory 82 is used to store the computer program and other programs and data required by the electronic device 80. The memory 82 can also be used to temporarily store data that has been output or will be output.

[0189] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0190] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0191] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0192] In the embodiments provided by this invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0193] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0194] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0195] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0196] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for work mode based loader endurance prediction, characterized in that, The method comprises: obtaining operation data of the loader during operation, and dividing the operation data according to a preset time length to obtain operation data of multiple time periods; in each time period, determining total power consumption, total working hours, distance and high-speed proportion of the time period according to the operation data, and determining the corresponding endurance time of the time period according to the total power consumption and the total working hours; determining the average value of the corresponding endurance time of the time period with the same distance and high-speed proportion according to the distance, high-speed proportion and corresponding endurance time of each time period; obtaining the distance-high-speed proportion endurance prediction table of the loader according to the average value of the corresponding endurance time of the time period with the same distance and high-speed proportion.

2. The job-mode-based loader endurance prediction method of claim 1, wherein, In each time period, the high-speed proportion of the time period is determined according to the operation data, comprising: determining the length of time when the vehicle speed is greater than a preset vehicle speed threshold in the time period to obtain a first length of time; determining the length of time when the forward vehicle speed in the time period to obtain a second length of time; calculating the ratio of the first length of time and the second length of time to obtain the high-speed proportion of the time period.

3. The job-mode-based loader endurance prediction method of claim 1, wherein, The distance is the distance walked by the loader during one material transportation; In each time period, the distance of the time period is determined according to the operation data, comprising: integrating the walking motor speed in the time period to obtain the driving distance; determining the walking cycle number of the loader in the time period; determining the ratio of the driving distance and the walking cycle number to obtain the distance.

4. The job-mode-based loader endurance prediction method of claim 1, wherein, The corresponding endurance time of the time period is determined according to the total power consumption and the total working hours, comprising: determining the corresponding endurance time of the time period according to h=k*C / (E / T1); wherein h is the endurance time; k is a preset coefficient, 0 5. The job-mode-based loader endurance prediction method of claim 1, wherein, The average value of the corresponding endurance time of the time period with the same distance and high-speed proportion is determined according to the distance, high-speed proportion and corresponding endurance time of each time period, comprising: obtaining a preset distance reference sequence and a high-speed proportion reference sequence; correcting the distance of each time period to the closest distance in the distance reference sequence; correcting the high-speed proportion of each time period to the closest high-speed proportion in the high-speed proportion reference sequence; determining the average value of the corresponding endurance time of the time period with the same distance and high-speed proportion based on the corrected distance and high-speed proportion of each time period and the corresponding endurance time of each time period.

6. The job-mode-based loader endurance prediction method according to any one of claims 1 to 5, characterized in that, The method further comprises: In each time period, the shovel loading cycle number of the time period is determined according to the operation data; obtaining the yield of the time period according to the ratio of the shovel loading cycle number and the total working hours; determining the average value of the corresponding endurance time of the time period with the same distance and yield according to the distance, yield and corresponding endurance time of each time period; obtaining the distance-yield endurance time prediction table of the loader according to the average value of the corresponding endurance time of the time period with the same distance and yield.

7. The job-mode-based loader endurance prediction method of claim 6, wherein, The average value of the corresponding endurance time of the time period with the same distance and yield is determined according to the distance, yield and corresponding endurance time of each time period, comprising: obtaining a preset distance reference sequence and a yield reference sequence; correcting the distance of each time period to the closest distance in the distance reference sequence; correcting the yield of each time period to the closest yield in the yield reference sequence; Determine the average value of the endurance time corresponding to the same distance and production time period based on the corrected distance and production of each time period and the endurance time corresponding to each time period.

8. A work mode based loader endurance prediction device, characterized by, Comprise: An acquisition module, configured to acquire running data of the loader during operation, and divide the running data according to a preset time length value to obtain running data of multiple time periods; A processing module, configured to determine total power consumption, total working hours, distance and high-speed proportion of each time period according to the running data, and determine the endurance time corresponding to the time period according to the total power consumption and the total working hours; A determination module, configured to determine the average value of the endurance time corresponding to the same distance and high-speed proportion time period according to the distance, high-speed proportion and corresponding endurance time of each time period; A generation module, configured to obtain the distance-high-speed proportion endurance prediction table of the loader according to the average value of the endurance time corresponding to the same distance and high-speed proportion time period.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the method in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to realize the steps of the method in any one of claims 1 to 7.

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