Loader endurance prediction method and device based on working conditions and electronic equipment

Abstract

The invention is suitable for the technical field of vehicles, and provides a working condition-based loader endurance prediction method and device and electronic equipment, and the method comprises the steps: obtaining the operation data of a loader under different working conditions, with different shovel-to-transport ratios under different working conditions; segmenting the operation data under different working conditions according to a preset duration value to obtain operation data of a plurality of time periods; in each time period, the total power consumption, the total working hours and the transportation distance of the time period are determined according to the operation data, the transportation distance is the distance that the loader walks by transporting materials once, and the endurance time corresponding to the time period is determined according to the total power consumption and the total working hours; determining an average endurance time value corresponding to the same freight distance and working condition time period; and according to the endurance time average value, obtaining an endurance prediction table of the loader. According to the invention, accurate and reliable endurance prediction information of the loader can be 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 working conditions. 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 the working conditions of loaders are more complex than the relatively simple working conditions of passenger cars, 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 operating conditions, to take into account the operating conditions of the loader 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 operating conditions, comprising:

[0006] Obtain the loader's operating data under different working conditions, as the shovel-to-carry ratio varies under different working conditions;

[0007] The operating data under different working conditions is divided according to a preset duration value to obtain operating data for multiple time periods;

[0008] In each time period, the total power consumption, total working hours, and transport distance are determined based on the operating data. The transport distance is the distance that the loader travels in one transport. The driving time corresponding to that time period is determined based on the total power consumption and the total working hours.

[0009] Determine the average driving time for the same transport distance and operating period;

[0010] Based on the average driving time, a driving range prediction table for the loader is obtained.

[0011] In conjunction with the first aspect, in one possible implementation of the first aspect, after segmenting the operating data under different working conditions according to a preset duration value to obtain operating data for multiple time periods, the method further includes:

[0012] Determine the ratio of the total working hours to the duration value for each time period;

[0013] Delete the time periods in which the ratio is less than a preset ratio threshold.

[0014] Combined with the first aspect, in a possible implementation manner of the first aspect, in each time period, determining the haulage distance of the time period according to the operation data includes:

[0015] Integrating the walking motor speed within this time period to obtain the driving mileage;

[0016] Determine the number of walking cycles of the loader within this time period;

[0017] Determine the ratio of the driving mileage and the number of walking cycles to obtain the haulage distance.

[0018] Combined with the first aspect, in a possible implementation manner of the first aspect, determining the number of walking cycles of the loader within this time period includes:

[0019] Within this time period, if the speed of the loader is greater than the preset upper speed threshold at the first moment and less than the preset lower speed threshold at the subsequent second moment, then take the moment when the speed is zero before the first moment to the moment when the speed is zero after the second moment as a walking cycle period; wherein, the preset upper speed threshold is greater than zero, and the preset lower speed threshold is less than zero;

[0020] Determine the number of walking cycles within this time period according to the number of walking cycle periods within this time period.

[0021] Combined with the first aspect, in a possible implementation manner of the first aspect, determining the corresponding endurance time of this time period according to the total power consumption and the total working hours includes:

[0022] Determine the corresponding endurance time of this time period according to h = k * C / (E / T1);

[0023] Wherein, 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.

[0024] Combined with the first aspect, in a possible implementation manner of the first aspect, the method further includes:

[0025] In each time period, determine the number of loading cycles of the time period according to the operation data;

[0026] Determine the ratio of the number of loading cycles and the total working hours to obtain the output of this time period;

[0027] Determine the average value of the outputs corresponding to the time periods with the same haulage distance and working conditions;

[0028] Obtain the output prediction table of the loader according to the average value of the outputs.

[0029] In conjunction with the first aspect, in one possible implementation of the first aspect, determining the number of loading cycles for each time period based on the operational data includes:

[0030] During this period, if the torque of the loader 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 taken as a loading cycle; wherein, the fifth moment is the moment when the torque is greater than the preset upper torque threshold, and both the preset upper torque threshold and the preset lower torque threshold are greater than zero.

[0031] The number of loading cycles within a given time period is determined based on the number of loading cycles within that time period.

[0032] A second aspect of the present invention provides a loader range prediction device based on operating conditions, comprising:

[0033] The acquisition module is used to acquire the loader's operating data under different working conditions, and the shovel-to-carry ratio is different under different working conditions;

[0034] The preprocessing module is used to segment the operating data under different working conditions according to a preset duration value to obtain operating data for multiple time periods;

[0035] The processing module is used to determine the total power consumption, total working hours and transport distance of each time period based on the operating data, wherein the transport distance is the distance traveled by the loader in one transport, and to determine the driving time corresponding to that time period based on the total power consumption and the total working hours.

[0036] The determination module is used to determine the average driving time corresponding to the same transport distance and working period; based on the average driving time, the driving time prediction table of the loader is obtained.

[0037] In conjunction with the second aspect, in one possible implementation of the second aspect, after the operating data under different working conditions is segmented according to a preset duration value to obtain operating data for multiple time periods, the preprocessing module is further used for:

[0038] Determine the ratio of the total working hours to the duration value for each time period;

[0039] Delete the time periods in which the ratio is less than a preset ratio threshold.

[0040] In conjunction with the second aspect, in one possible implementation of the second aspect, the processing module is specifically used for:

[0041] The travel distance is obtained by integrating the rotational speed of the walking motor during this period.

[0042] Determine the number of walking cycles of the loader during this period;

[0043] Determine the ratio of the driving mileage to the number of walking cycles to obtain the haul distance.

[0044] Combined with the second aspect, in a possible implementation manner of the second aspect, the processing module is specifically configured to:

[0045] During this period, if the rotation speed of the loader is greater than the preset upper rotation speed threshold at the first moment and less than the preset lower rotation speed threshold at the subsequent second moment, then the time when the rotation speed is zero before the first moment to the time when the rotation speed is zero after the second moment is used as a walking cycle period; wherein, the preset upper rotation speed threshold is greater than zero, and the preset lower rotation speed threshold is less than zero;

[0046] Determine the number of walking cycles during this period according to the number of walking cycle periods during this period.

[0047] Combined with the second aspect, in a possible implementation manner of the second aspect, the processing module is specifically configured to:

[0048] Determine the corresponding endurance time for this period according to h = k*C / (E / T1);

[0049] Wherein, 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.

[0050] Combined with the second aspect, in a possible implementation manner of the second aspect, the determining module is further configured to:

[0051] In each period, determine the number of loading cycles of the period according to the operation data;

[0052] Determine the ratio of the number of loading cycles to the total working hours to obtain the output of this period;

[0053] Determine the average value of the outputs corresponding to the same haul distance and working condition periods;

[0054] Obtain the output prediction table of the loader according to the average value of the outputs.

[0055] Combined with the second aspect, in a possible implementation manner of the second aspect, the processing module is specifically configured to:

[0056] During this period, if the torque of the loader 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 taken as a loading cycle; wherein, the fifth moment is the moment when the torque is greater than the preset upper torque threshold, and both the preset upper torque threshold and the preset lower torque threshold are greater than zero.

[0057] The number of loading cycles within a given time period is determined based on the number of loading cycles within that time period.

[0058] 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.

[0059] 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.

[0060] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows:

[0061] This invention addresses the problem of complex operating conditions and difficulty in accurately estimating the range of loaders. It differentiates operating conditions by using a shovel-to-load ratio and segments operational data across a large sample of operating conditions. Within each time period, the total power consumption, total working hours, and haul distance are determined based on the operational data, and the corresponding range is analyzed. By calculating the average range for the same haul distance and operating conditions within a given time period, the large sample size allows for a relatively accurate prediction of the range for that specific haul distance and operating conditions. Furthermore, a range prediction table for loaders is generated based on the range under different haul distances and / or operating conditions. This range prediction table provides users with a clear understanding of the loader's range under various haul distances and operating conditions, offering guidance for selecting and correctly operating loaders. Attached Figure Description

[0062] 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.

[0063] Figure 1This is a schematic diagram illustrating an application scenario of the loader endurance prediction method based on working conditions provided in this embodiment of the invention.

[0064] Figure 2 This is a schematic diagram of the implementation process of the loader range prediction method based on working conditions provided in this embodiment of the invention. Figure 1 ;

[0065] Figure 3 This is a schematic diagram of the rotational speed and torque provided in an embodiment of the present invention;

[0066] Figure 4 This is a schematic diagram of the implementation process of the loader range prediction method based on working conditions provided in this embodiment of the invention. Figure 2 ;

[0067] Figure 5 This is a schematic diagram of the loader endurance prediction device based on working conditions provided in an embodiment of the present invention;

[0068] Figure 6 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] Furthermore, the term "multiple" mentioned in the embodiments of this application should be interpreted as two or more.

[0075] Because loaders operate under more complex conditions than passenger cars, which have relatively simpler operating conditions, 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 conditions can be identified based on this data, enabling accurate range prediction under different operating conditions.

[0076] Figure 1 This is a schematic diagram illustrating an application scenario of the loader range prediction method based on operating conditions, as shown in this embodiment. Figure 1 In this embodiment, the loader uses various sensors to detect battery data, speed data, torque data, etc., under different working conditions, and sends them to the vehicle controller. The vehicle controller then uploads this construction machinery data to the server.

[0077] in:

[0078] Rotational speed data can be measured by rotational speed sensors, commonly including magnetoelectric wheel speed sensors and Hall effect wheel speed sensors.

[0079] Battery data can be detected by current and voltage sensors located at the battery.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] The following is combined with Figure 1 Application scenarios, refer to Figure 2 This application describes a condition-based loader range prediction method 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.

[0084] The following is combined with Figure 2 The method of this embodiment will be described in detail below:

[0085] Step S201: Obtain the operating data of the loader under different working conditions.

[0086] Here, you can directly download the operational data for different operating conditions from the vehicle networking big data platform on the server.

[0087] The operating conditions mentioned here mainly refer to the working conditions of the loader. Typical operating conditions include stacking and loading. Of course, this embodiment is not limited to these two operating conditions, and other operating conditions will not be listed in this application.

[0088] Loaders typically operate in cyclical patterns, with the cycle varying depending on the specific working conditions. For instance, in a stockpiling operation, where bulk materials need to be stacked together, each cycle requires three actions: unloaded forward movement, shoveling, and unloaded reverse movement. In a loading operation, however, five actions are required: unloaded forward movement, shoveling, loaded reverse movement, loaded forward movement, unloading, and unloaded reverse movement.

[0089] Loading-to-carrying ratio: The ratio of the number of loading operations to the number of material transport operations within the same time period.

[0090] For example, in the stockpiling scenario described above, a complete operation requires three actions: unloaded forward movement, shoveling, and unloaded reverse movement. This involves one forward / reverse movement and one shoveling action, resulting in a shoveling-to-carry ratio of 1. In the loading scenario, however, a complete operation requires five actions: unloaded forward movement, shoveling, loaded reverse movement, loaded forward movement, unloading, and unloaded reverse movement. This involves two forward / reverse movements and one shoveling action, resulting in a shoveling-to-carry ratio of 0.5. Other scenarios are not listed here.

[0091] It is evident that the scraper-to-carry ratio varies depending on the operating conditions. Therefore, the scraper-to-carry ratio (or the transport-to-scraper ratio) can be used to distinguish between different operating conditions.

[0092] Step S202: Divide the operating data under different working conditions according to the preset duration value to obtain operating data for multiple time periods.

[0093] In this embodiment, the running data is divided according to a preset duration value T, and the size of T can be set by the user.

[0094] In some embodiments, the loader can directly upload the current operating conditions and operational data to the vehicle-to-everything (V2X) big data platform, which then directly stores the operational data and corresponding operating conditions. Alternatively, the loader only uploads the operational data to the V2X big data platform, which pre-analyzes the operating conditions using a specific algorithm. In this case, the operational data obtained from the V2X big data platform on the server has already been divided into operating conditions, and each segmented time period corresponds to a specific operating condition.

[0095] In other embodiments, the operating data obtained from the vehicle network big data platform of the server does not distinguish between operating conditions, so the operating condition needs to be determined for each segmented time period.

[0096] Here is a method for judging working conditions using the scraping-to-carry ratio:

[0097] (1) Obtain the speed and torque of the loader's travel motor during the specified time period from the operating data.

[0098] (2) Determine the number of travel cycles of the loader during this period, including: if the loader's rotational speed is greater than a preset upper threshold speed at the first moment during this period (e.g., ... Figure 3 S1 in the middle), and at the second time thereafter it is less than the preset speed threshold (e.g. Figure 3 In S2), the time from the moment when the rotational speed is zero before the first moment to the moment when the rotational speed is zero after the second moment is taken as a walking cycle; wherein, the preset upper threshold of rotational speed is greater than zero, and the preset lower threshold of rotational speed is less than zero; the number of walking cycles in this period is determined according to the number of walking cycle periods in this period.

[0099] In this embodiment, a travel cycle can be a forward and backward movement of the loader. As described above, the operation of a loader is generally cyclical. Regardless of the working condition, it includes at least the steps of forward movement, shoveling, and backward movement. For example, in a stockpiling operation, where bulk materials need to be piled up, each work cycle requires three actions: unloaded forward movement, shoveling, and unloaded backward movement. In a loading operation, it requires five actions: unloaded forward movement, shoveling, loaded backward movement, loaded forward movement, unloading, and unloaded backward movement. These steps are continuously repeated. One forward movement plus one backward movement constitutes one travel cycle of the loader.

[0100] For the travel motor, when the loader starts moving forward, the speed starts from zero, continuously increases, and then stops before reaching the material, at which point the speed decreases to zero. During this process, the speed is positive. Similarly, when the loader starts moving backward, the speed starts from zero, continuously increases, and then stops at a specific position, at which point the speed decreases to zero. During this process, the speed is negative. Therefore, by analyzing the speed, the forward and backward movement of the loader can be determined, and thus the travel cycle can be defined. For example, the travel cycle can be found in [reference needed]. Figure 3 The first curve in the diagram is shown.

[0101] (3) In each time period, the number of loading cycles in the time period is determined based on the operating data, including: if the torque of the loader 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 moment when the torque is zero before the third moment to the moment when the torque is zero before the fifth moment is taken as a loading cycle; wherein, the fifth moment is the moment when the torque is greater than the preset upper torque threshold next time, and both the preset upper torque threshold and the preset lower torque threshold are greater than zero; the number of loading cycles in the time period is determined based on the number of loading cycle periods in the time period.

[0102] See Figure 3 As shown, 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 must be 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 is detected to exceed Y1, and then the loader reverses, the torque becomes less than Y, then it is considered that a shoveling action has been performed. Furthermore, the time from when the torque is zero before shoveling to the time when the torque is zero before the next shoveling is considered as a shoveling cycle.

[0103] For example, Figure 3For the loading operation condition: The loader starts to move forward, and the torque increases from 0. When approaching the material, the loader decelerates, and the torque has a small decrease. When the loader scoops up the material, the torque rapidly increases and exceeds Y1. When the loader moves backward, the torque decreases to less than Y. When the loader moves forward again, the torque increases. When the loader discharges the material, the torque decreases. When the loader moves backward, the torque becomes negative and finally becomes zero. The entire loading cycle can be detected based on the torque change.

[0104] The process of the stacking operation condition is similar, and this embodiment will not be described in detail.

[0105] The ratio of the number of loading cycles to the number of walking cycles within the same time is the loading and hauling ratio. The loading and hauling ratio of the stacking operation condition is 1, and the loading and hauling ratio of the loading operation condition is 0.5, thereby distinguishing the two operation conditions.

[0106] Step S203, in each time period, determine the total power consumption, total working hours, and hauling distance of the time period according to the operation data. The hauling distance is the distance traveled by the loader for one material transportation. According to the total power consumption and total working hours, determine the corresponding endurance time of the time period.

[0107] Total power consumption: Integrate the product of the current and voltage of the battery within the time period to obtain the total power consumption.

[0108] Total working hours: The loader does not work continuously and may be in a stationary state for some time. The total working hours here refer to the working time, and the time in the stationary state is not included in the total working hours. It can be determined that the vehicle is in a stationary state through parameters such as the rotational speed, torque, and traveling speed of the loader. This embodiment will not elaborate on the specific steps.

[0109] Hauling distance: The distance traveled by the loader for one material transportation. For example, for the stacking operation condition, it is necessary to stack the bulk materials together, and each operation cycle requires three actions: moving forward空载, scooping up the material, and moving backward空载. The hauling distance is the sum of the distances of one forward空载 and one backward空载. For the loading operation condition, it requires five actions: moving forward空载, scooping up the material, moving backward负载, moving forward负载, discharging the material, and moving backward空载. The hauling distance is the sum of one forward空载 and one backward负载, or the sum of one forward负载 and one backward空载. Here, the specific calculation method of the hauling distance is not limited.

[0110] Exemplarily, the corresponding endurance time of the 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.

[0111] Step S204, determine the average value of the endurance times corresponding to the same hauling distance and operation condition time periods.

[0112] In this embodiment, for time periods with the same transport distance and operating conditions, the average of their driving time is calculated to obtain the driving time under that transport distance and operating conditions.

[0113] The "same distance" mentioned here ideally means exactly the same. However, considering that the distances calculated using the above formula are difficult to make completely identical across different time periods, a series of typical values ​​can be set. By modifying the distance to the closest typical value, similar distances can be considered the same. For example, typical distance values ​​include: 10, 15, 20, 25, 30... If there are two time periods with distances of 14 and 18, then distance 14 is modified to 15, and distance 18 is modified to 20.

[0114] Step S205: Based on the average endurance time, obtain the loader's endurance prediction table.

[0115] The driving time under different distances and operating conditions is used to construct a driving time prediction table, as shown in Table 1 (h1, h2, h3... are driving times):

[0116] Table 1. Range Prediction Table

[0117] Transport distance 10 15 20 25 30 35 40 ... Operating Condition 1 h1 h2 h3 ... ... ... ... ... Operating Condition 2 ... ... ... ... ... ... ... ...

[0118] This invention addresses the problem of complex operating conditions and difficulty in accurately estimating the range of loaders. It differentiates operating conditions by using a shovel-to-load ratio and segments operational data across a large sample of operating conditions. Within each time period, the total power consumption, total working hours, and haul distance are determined based on the operational data, and the corresponding range is analyzed. By calculating the average range for the same haul distance and operating conditions within a given time period, the large sample size allows for a relatively accurate prediction of the range for that specific haul distance and operating conditions. Furthermore, a range prediction table for loaders is generated based on the range under different haul distances and / or operating conditions. This range prediction table provides users with a clear understanding of the loader's range under various haul distances and operating conditions, offering guidance for selecting and correctly operating loaders.

[0119] Figure 3 This is a schematic diagram of another implementation process of the loader endurance prediction method based on working conditions provided in the embodiments of the present invention.

[0120] In this embodiment, considering that the operational data obtained from the vehicle networking big data platform on the server may contain a large amount of invalid data from non-operational conditions, data filtering processing was also performed. Simultaneously, a loader production forecast table was established to further guide users in understanding the loader's performance.

[0121] Figure 4 Examples include:

[0122] Step S401: Obtain the operating data of the loader under different working conditions.

[0123] 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.

[0124] Step S402: Divide the operating data under different working conditions according to the preset duration value to obtain operating data for multiple time periods.

[0125] 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.

[0126] Step S403: Determine the ratio of total working hours to duration for each time period; delete time periods with a ratio less than a preset threshold.

[0127] The ratio here can be set to 50%, meaning that the effective operation time in each time period is greater than 1 / 2T. By filtering out effective time periods, the accuracy and reliability of subsequent calculation results can be improved.

[0128] Step S404: In each time period, determine the total power consumption, total working hours, and transport distance based on the operating data. The transport distance is the distance the loader travels in one transport. Determine the driving time corresponding to that time period based on the total power consumption and total working hours. Determine the average driving time corresponding to the same transport distance and working conditions. Obtain the driving time prediction table of the loader based on the average driving time.

[0129] For each time period, the transport distance can be calculated as follows:

[0130] The travel distance is obtained by integrating the rotational speed of the walking motor during this period.

[0131] Determine the number of travel cycles of the loader during this period;

[0132] The distance traveled is obtained by determining the ratio of the mileage traveled to the number of travel cycles.

[0133] The determination of the number of travel cycles of the loader within the specified time period includes: if the loader's rotational speed is greater than a preset upper threshold at a first moment and less than a preset lower threshold at a second moment, then the time from when the rotational speed is zero before the first moment to when the rotational speed is zero after the second moment is considered as one travel cycle; wherein the preset upper threshold is greater than zero and the preset lower threshold is less than zero; the number of travel cycles within the specified time period is determined based on the number of travel cycle periods within that time period. For detailed explanation, please refer to the description in step S202, which will not be repeated in this embodiment.

[0134] Step S405: In each time period, determine the number of loading cycles for that time period based on the operating data; determine the ratio of the number of loading cycles to the total working hours to obtain the output for that time period; determine the average output for the same transport distance and working conditions; and obtain the output prediction table for the loader based on the average output.

[0135] Similar to the endurance forecast table, the output under different transport distances and operating conditions constitutes the output forecast table, as shown in Table 2 (Y1, Y2, Y3... represent output):

[0136] Table 2 Production Forecast Table

[0137] Transport distance 10 15 20 25 30 35 40 ... Operating Condition 1 Y1 Y2 Y3 ... ... ... ... ... Operating Condition 2 ... ... ... ... ... ... ... ...

[0138] In each time period, the number of loading cycles for that period is determined based on operational data. This includes: if the loader's torque is greater than a preset upper torque threshold at the third moment and less than a preset lower torque threshold at the fourth moment, then the time from when the torque is zero before the third moment to when the torque is zero before the fifth moment is considered as one loading cycle. The fifth moment is the next time 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 within that time period is determined based on the number of loading cycles within that period. For detailed explanation, please refer to the description in step S202; this embodiment will not repeat it further.

[0139] In this embodiment, based on the range prediction table and output prediction table, users can be accurately guided to purchase loaders that meet their needs. Furthermore, if a user's range or output is lower than the standard data under the same material, working conditions, and transport distance, the energy consumption ratio of the travel motor to the hydraulic motor can be used to determine if there are any abnormalities in the user's operation. If the ratio is high, the user is advised to improve their operation, thus guiding them to operate the loader correctly. This embodiment obtains a large number of working condition samples, analyzes the characteristics of different working conditions, and uses an algorithm that has undergone engineering verification to ensure the accuracy of range and output predictions.

[0140] 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.

[0141] Figure 5 This is a schematic diagram of the structure of the loader endurance prediction device 50 based on working conditions provided in an embodiment of the present invention. The device includes:

[0142] The acquisition module 51 is used to acquire the operating data of the loader under different working conditions, and the shovel-to-carry ratio is different under different working conditions.

[0143] A preprocessing module 52, configured to segment the operation data under different working conditions according to a preset duration value, so as to obtain operation data for multiple time periods.

[0144] A processing module 53, configured to determine the total power consumption, total working hours, and travel distance of each time period according to the operation data. The travel distance is the distance traveled by the loader for one material transportation. According to the total power consumption and total working hours, the corresponding endurance time of this time period is determined.

[0145] A determination module 54, configured to determine the average value of the endurance time corresponding to time periods with the same travel distance and working conditions; according to the average value of the endurance time, an endurance prediction table of the loader is obtained.

[0146] As a possible implementation manner, after segmenting the operation data under different working conditions according to the preset duration value to obtain operation data for multiple time periods, the preprocessing module 52 is further configured to:

[0147] Determine the ratio of the total working hours of each time period to the duration value;

[0148] Delete the time periods whose ratios are less than a preset ratio threshold.

[0149] As a possible implementation manner, the processing module 53 is specifically configured to:

[0150] Integrate the walking motor speed within this time period to obtain the driving mileage;

[0151] Determine the number of walking cycles of the loader within this time period;

[0152] Determine the ratio of the driving mileage to the number of walking cycles to obtain the travel distance.

[0153] As a possible implementation manner, the processing module 53 is specifically configured to:

[0154] Within this time period, if the speed of the loader is greater than a preset upper speed threshold at the first moment and less than a preset lower speed threshold at a subsequent second moment, then the time when the speed is zero before the first moment to the time when the speed is zero after the second moment is used as a walking cycle period; wherein, 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 cycle periods within this time period, the number of walking cycles within this time period is determined.

[0155] As a possible implementation manner, the processing module 53 is specifically configured to:

[0156] Determine the endurance time corresponding to this time period according to h = k * C / (E / T1);

[0157] Wherein, 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.

[0158] As one possible implementation, the determining module 54 is also used for:

[0159] The number of loading cycles for each time period is determined based on operational data.

[0160] Determine the ratio of the number of loading cycles to the total working hours to obtain the output for that period;

[0161] Determine the average output for the same transport distance and operating period;

[0162] Based on the average production output, a production forecast table for the loader is obtained.

[0163] As one possible implementation, processing module 53 is specifically used for:

[0164] 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.

[0165] This invention addresses the problem of complex operating conditions and difficulty in accurately estimating the range of loaders. It differentiates operating conditions by using a shovel-to-load ratio and segments operational data across a large sample of operating conditions. Within each time period, the total power consumption, total working hours, and haul distance are determined based on the operational data, and the corresponding range is analyzed. By calculating the average range for the same haul distance and operating conditions within a given time period, the large sample size allows for a relatively accurate prediction of the range for that specific haul distance and operating conditions. Furthermore, a range prediction table for loaders is generated based on the range under different haul distances and / or operating conditions. This range prediction table provides users with a clear understanding of the loader's range under various haul distances and operating conditions, offering guidance for selecting and correctly operating loaders.

[0166] Figure 6 This is a schematic diagram of an electronic device 60 provided in an embodiment of the present invention. Figure 6 As shown, the electronic device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61, such as a loader endurance prediction program based on operating conditions. When the processor 61 executes the computer program 63, it implements the steps in the various embodiments of the loader endurance prediction method based on operating conditions described above, for example... Figure 2Steps S201 to S205 are shown. Alternatively, when the processor 61 executes the computer program 63, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 5 The functions of modules 51 to 54 are shown.

[0167] For example, the computer program 63 can be divided into one or more modules / units, which are stored in the memory 62 and executed by the processor 61 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 63 in the electronic device 60. For example, the computer program 63 can be divided into an acquisition module 51, a preprocessing module 52, a processing module 53, and a determination module 54 (a module in a virtual device), with the specific functions of each module as follows:

[0168] The acquisition module 51 is used to acquire the operating data of the loader under different working conditions, and the shovel-to-carry ratio is different under different working conditions.

[0169] The preprocessing module 52 is used to segment the operating data under different working conditions according to a preset duration value to obtain operating data for multiple time periods.

[0170] The processing module 53 is used to determine the total power consumption, total working hours and haul distance for each time period based on the operating data. The haul distance is the distance that the loader travels in one haul. Based on the total power consumption and total working hours, the corresponding driving time for that time period is determined.

[0171] The determination module 54 is used to determine the average driving time corresponding to the same transport distance and working period; based on the average driving time, the driving time prediction table of the loader is obtained.

[0172] The electronic device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. The electronic device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 6 This is merely an example of electronic device 60 and does not constitute a limitation on electronic device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 60 may also include input / output devices, network access devices, buses, etc.

[0173] The processor 61 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.

[0174] The memory 62 can be an internal storage unit of the electronic device 60, such as a hard disk or RAM of the electronic device 60. The memory 62 can also be an external storage device of the electronic device 60, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the electronic device 60. Furthermore, the memory 62 can include both internal and external storage units of the electronic device 60. The memory 62 is used to store the computer program and other programs and data required by the electronic device 60. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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 predicting the range of a loader based on operating conditions, characterized in that, Including: Obtain the operation data of the loader under different working conditions, and the loading and hauling ratios of different working conditions are different; Segment the operation data under different working conditions according to a preset duration value to obtain operation data for multiple time periods; In each time period, determine the total power consumption, total working hours, and hauling distance of the time period according to the operation data. The hauling distance is the distance traveled by the loader for one material transportation. According to the total power consumption and the total working hours, determine the corresponding endurance time of the time period; Determine the average value of the endurance times corresponding to time periods with the same hauling distance and working conditions; Obtain the endurance prediction table of the loader according to the average value of the endurance time; 2. The loader range prediction method based on operating conditions as described in claim 1, characterized in that, After segmenting the operation data under different working conditions according to the preset duration value to obtain operation data for multiple time periods, it further includes: Determine the ratio of the total working hours and the duration value of each time period; Delete the time periods with the ratio less than a preset ratio threshold; 3. The loader range prediction method based on operating conditions as described in claim 1, characterized in that, In each time period, determining the hauling distance of the time period according to the operation data includes: Integrate the walking motor speed within the time period to obtain the driving mileage; Determine the number of walking cycles of the loader within the time period; Determine the ratio of the driving mileage and the number of walking cycles to obtain the hauling distance; 4. The loader range prediction method based on operating conditions as described in claim 3, characterized in that, Determining the number of walking cycles of the loader within the time period includes: Within the time period, if the speed of the loader is greater than the preset upper speed threshold at the first moment and less than the preset lower speed threshold at the second moment after that, then take the moment when the speed is zero before the first moment to the moment when the speed is zero after the second moment as a walking cycle period; wherein, the preset upper speed threshold is greater than zero, and the preset lower speed threshold is less than zero; Determine the number of walking cycles within the time period according to the number of walking cycle periods within the time period; 5. The loader range prediction method based on operating conditions as described in claim 1, characterized in that, Determining the corresponding endurance time of the time period according to the total power consumption and the total working hours includes: Determine 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 < k < 1; C is the total power of the battery pack; E is the total power consumption; T1 is the total working hours; 6. The loader range prediction method based on operating conditions as described in any one of claims 1 to 5, characterized in that, The method further includes: In each time period, determine the number of loading cycles of the time period according to the operation data; Determine the ratio of the number of loading cycles and the total working hours to obtain the output of the time period; Determine the average value of the outputs corresponding to time periods with the same hauling distance and working conditions; Obtain the output prediction table of the loader according to the average value of the output; 7. The loader range prediction method based on operating conditions as described in claim 6, characterized in that, Determining the number of loading cycles of the time period according to the operation data in each time period includes: Within the time period, if the torque of the loader is greater than the preset upper torque threshold at the third moment and less than the preset lower torque threshold at the fourth moment after that, then take the moment when the torque is zero before the third moment to the moment when the torque is zero before the fifth moment as a loading cycle period; wherein, the fifth moment is the moment when the torque is greater than the preset upper torque threshold next time, and both the preset upper torque threshold and the preset lower torque threshold are greater than zero; Determine the number of loading cycles within the time period according to the number of loading cycle periods within the time period; 8. A loader range prediction device based on operating conditions, characterized in that, Including: The acquisition module is used to acquire the loader's operating data under different working conditions, and the shovel-to-carry ratio is different under different working conditions; The preprocessing module is used to segment the operating data under different working conditions according to a preset duration value to obtain operating data for multiple time periods; The processing module is used to determine the total power consumption, total working hours and transport distance of each time period based on the operating data, wherein the transport distance is the distance traveled by the loader in one transport, and to determine the driving time corresponding to that time period based on the total power consumption and the total working hours. The determination module is used to determine the average driving time corresponding to the same transport distance and working period; based on the average driving time, the driving time prediction table of the loader is obtained.

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, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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