A method and medium for quantifying operating characteristic data of mobile machinery

Abstract

The embodiments of this specification disclose a method and medium for quantifying operational characteristic data of mobile machinery, relating to the field of mobile machinery optimization technology and addressing the problem of constrained performance optimization results in traditional applications. The method includes: obtaining data information of the mobile machinery to be optimized based on a preset pressure transmitter and a CAN bus; constructing a quantitative model corresponding to each operational characteristic to be quantified based on associated information corresponding to the operational characteristic to be quantified; wherein the operational characteristic to be quantified includes: operating condition characteristics, driving mode characteristics, and power system operating characteristics; using each quantitative model and the data information, quantitatively evaluating the operational characteristic to be quantified of the mobile machinery to be optimized to obtain a quantitative evaluation result; determining optimized control parameters of the mobile machinery to be optimized based on the quantitative evaluation result, and dynamically adjusting the mobile machinery to be optimized based on the optimized control parameters.

Description

Technical Field

[0001] This specification relates to the technical field of mobile machinery optimization, and in particular to a method and medium for quantifying operational characteristic data of a mobile machinery. Background Art

[0002] Mobile machinery is a type of mechanical equipment capable of autonomous movement and performing specific tasks. In practical applications, it is widely used in numerous fields, including construction, transportation, and agriculture. Common construction machinery, such as excavators, loaders, bulldozers, and cranes, flexibly maneuver around construction sites to perform tasks such as earthmoving and material handling. These machines utilize power drive systems and work with corresponding working devices to achieve various operational objectives. As core equipment in the field of construction machinery, research on the operational characteristics of mobile machinery is of great significance for equipment optimization, efficiency improvement, and energy consumption reduction.

[0003] Current research on the operational characteristics of mobile machinery in real-world application scenarios is primarily based on observation and statistics of small samples over short timescales. On the one hand, due to research costs and testing conditions, it is difficult to obtain a large number of mobile machinery samples, resulting in a lack of data diversity. On the other hand, the limited timescale cannot cover complex and changing construction environments, such as seasonal and day-night operating conditions, resulting in atypical operational data. Furthermore, the lag in traditional testing and analysis methods results in insufficient data collection accuracy and a single analysis dimension, making it difficult to capture dynamic changes in equipment operation. This makes it difficult for data obtained using these traditional methods to truly reflect the full operational picture of mobile machinery in various real-world application scenarios, making it difficult to accurately quantify and characterize the operational characteristics of mobile machinery. Furthermore, the conclusions drawn lack representativeness and universality, which restricts the optimization of mobile machinery performance. Summary of the Invention

[0004] In order to solve the above technical problems, one or more embodiments of this specification provide a method and medium for quantifying operational characteristic data of a mobile machinery.

[0005] One or more embodiments of this specification adopt the following technical solutions:

[0006] One or more embodiments of this specification provide a method for quantifying operational characteristic data of a mobile machinery, the method comprising:

[0007] Based on the preset pressure transmitter and CAN bus, obtain the data information of the mobile machinery to be optimized;

[0008] Based on the correlation information corresponding to the operating characteristics to be quantified, a quantification model corresponding to each of the operating characteristics to be quantified is constructed; wherein the operating characteristics to be quantified include: operating condition characteristics, driving mode characteristics, and power system operating characteristics;

[0009] Performing a quantitative evaluation on the quantified operating characteristics of the mobile machinery to be optimized using the quantitative models and the data information to obtain a quantitative evaluation result;

[0010] The optimized control parameters of the mobile machine to be optimized are determined according to the quantitative evaluation results, so as to dynamically adjust the mobile machine to be optimized based on the optimized control parameters.

[0011] Optionally, in one or more embodiments of the present specification, the method for quantifying operational characteristic data of a mobile machinery is characterized in that, before obtaining data information of the mobile machinery to be optimized based on a preset pressure transmitter and a CAN bus, the method further comprises:

[0012] determining a pilot system position of the mobile machine to be optimized based on the mechanical structure of the mobile machine to be optimized;

[0013] Obtaining the pilot system pressure range corresponding to each pilot system position, and determining the pressure transmitter type of the preset pressure transmitter; wherein the pressure transmitter type includes: low pressure range type and high pressure range type;

[0014] Determining the layout position of each preset pressure transmitter based on the physical layout requirements corresponding to each pilot system position and the pressure transmitter type corresponding to each pilot system position, and implementing the layout of the preset pressure transmitter;

[0015] The preset pressure transmitters are connected based on an acquisition board, so that the acquisition board can obtain the analog information output by the preset pressure transmitter, and read the digital information of the CAN bus based on the CAN communication function of the acquisition board.

[0016] Optionally, in one or more embodiments of this specification, obtaining data information of the fluid machinery to be optimized based on a preset pressure transmitter and a CAN bus specifically includes:

[0017] Acquiring initial analog information of a pilot system in the mobile machinery to be optimized based on a preset pressure transmitter, and acquiring digital information of the mobile machinery to be optimized on the CAN bus;

[0018] Performing time domain alignment on the digital quantity information read from the CAN bus and the initial analog quantity information;

[0019] The aligned initial analog information and the digital information are subjected to data preprocessing to obtain data information of the pilot system in the mobile machinery to be optimized; wherein the data preprocessing includes: noise reduction processing, filtering processing, and normalization processing.

[0020] Optionally, in one or more embodiments of the present specification, a method for quantifying mobile machinery operation characteristic data is characterized in that, based on associated information corresponding to the operation characteristics to be quantified, a quantification model corresponding to each of the operation characteristics to be quantified is constructed, specifically comprising:

[0021] If it is determined that the operating characteristic to be quantified is an operating condition characteristic, the accumulated time during which the mobile machine to be optimized is in the operating state is set as the total operating time of the mobile machine to be optimized;

[0022] The ratio of the total working time of the mobile machinery to be optimized to the operating time under each operating condition is used as a quantitative model corresponding to the operating condition characteristics; wherein, the operating conditions include: idle condition, traveling condition, and working condition; the idle condition includes: low idle condition corresponding to the idle gear and high idle condition corresponding to the non-idle gear.

[0023] Optionally, in one or more embodiments of this specification, constructing a quantization model corresponding to each of the operating features to be quantified based on the associated information corresponding to the operating features to be quantified specifically includes:

[0024] If it is determined that the operating characteristic to be quantified is a driving mode characteristic, the accumulated time that the mobile machinery to be optimized is in operation is set as the total working time of the mobile machinery to be optimized;

[0025] Obtaining a driving mode corresponding to the driving gear of the mobile machinery to be optimized; wherein the driving mode includes: low idle mode, fine operation mode, normal operation mode, and heavy load operation mode;

[0026] The ratio of the total working time of the mobile machine to be optimized to the operating time in each driving mode is used as a quantitative model corresponding to the driving mode feature.

[0027] Optionally, in one or more embodiments of this specification, constructing a quantization model corresponding to each of the operating features to be quantified based on the associated information corresponding to the operating features to be quantified specifically includes:

[0028] If it is determined that the operating characteristic to be quantified is a power system operating characteristic, the accumulated time that the mobile machinery to be optimized is in operation is set as the total working time of the mobile machinery to be optimized;

[0029] Obtaining the average engine load based on a preset load formula, and determining the time for each engine load range segment based on a preset time formula;

[0030] Determine the time proportion of the load percentage within the specified range segment based on the ratio of the cumulative time of the engine's specified load range segment to the cumulative time of each engine range segment;

[0031] Determine the average fuel consumption of the engine at each driving gear according to the preset engine fuel consumption formula;

[0032] Determine the pressure status of the hydraulic pump by counting the proportion of the hydraulic pump's working time in different pressure ranges;

[0033] The time ratio of the load percentage being within a specified range, the average fuel consumption and the hydraulic pump pressure state are used as a quantitative model corresponding to the operating characteristics of the power system.

[0034] Optionally, in one or more embodiments of this specification, the preset load formula is:

[0035] , ;in, Indicates the average load percentage of the engine in gear i; Indicates the load percentage of the engine in the i-th gear; Represents the time set when the engine is in the i-th gear, where i is the engine's operating gear. The idle mode gears include I1 and I2; the fine operation mode gears include F2 and F1; the normal operation mode gears include G4, G3, G2, and G1; and the heavy load operation mode gears include H and P.

[0036] The preset time formula is:

[0037] , ;in, To satisfy E(t)=1, the engine is running and The time during which the engine load percentage is in the kth range segment;

[0038] The preset engine fuel consumption formula is:

[0039] ;in, It represents the time proportion that the load percentage is in the kth range segment, and j represents the engine gear.

[0040] Optionally, in one or more embodiments of the present specification, quantitatively evaluating the quantified operating characteristics of the mobile machinery to be optimized using the quantitative models and the data information to obtain quantitative evaluation results specifically includes:

[0041] Substitute the characteristic data corresponding to the secondary pressure information and the driving information obtained from the preset CAN bus into the corresponding quantitative model;

[0042] Performing quantitative evaluation on the to-be-quantified operating characteristics of the mobile machinery to be optimized based on the quantitative model, and obtaining evaluation results corresponding to each of the to-be-quantified operating characteristics;

[0043] The evaluation results are summarized to obtain a quantitative evaluation result of the mobile machinery to be optimized.

[0044] Optionally, in one or more embodiments of this specification, determining the optimized control parameters of the mobile machinery to be optimized according to the quantitative evaluation results, so as to dynamically adjust the mobile machinery to be optimized based on the optimized control parameters, specifically includes:

[0045] Determining, based on the quantitative evaluation results, the operating characteristics to be quantified corresponding to the optimized control parameters of the mobile machinery to be optimized;

[0046] If it is determined that the optimized control parameter corresponds to the operating condition characteristic, adaptively adjusting the operating condition characteristic of the mobile machinery to be optimized based on the preset time corresponding to each of the operating condition characteristics;

[0047] If it is determined that the optimized control parameters do not correspond to the operating condition characteristics, the design of the mobile machinery of the next stage to be optimized is guided based on the matching of the optimized control parameters.

[0048] One or more embodiments of this specification provide a non-volatile storage medium storing computer-executable instructions, wherein the computer-executable instructions can execute any of the above-described methods.

[0049] At least one of the above technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects:

[0050] By acquiring data from the mobile machinery to be optimized through pre-installed pressure transmitters and the CAN bus, the operational characteristics of the mobile machinery to be optimized can be reflected in real time, providing accurate data support for equipment operating characteristic analysis. A quantitative model is constructed based on correlation information of operating conditions, driving modes, power systems, and other characteristics. This accurately maps the inherent connection between pressure signals and equipment operating status, and covers multi-dimensional operating characteristics, breaking through the limitations of single-parameter analysis. By integrating secondary pressure information with driving information, the quantitative model transforms abstract operating characteristics into quantifiable indicators, improving the accuracy of the assessment. Based on the quantitative assessment results, the optimized control parameters of the mobile machinery to be optimized are determined, and then dynamically adjusted according to the optimized control parameters, ensuring that the equipment always maintains the optimal operating state and adapts to complex operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some of the embodiments described in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without inventive work. In the drawings:

[0052] Figure 1 A schematic flow chart of a method for quantifying operational characteristic data of mobile machinery provided in an embodiment of this specification;

[0053] Figure 2 A schematic diagram of collecting analog information provided in the embodiments of this specification;

[0054] Figure 3 A schematic diagram of the structure of a non-volatile storage medium provided in an embodiment of this specification. DETAILED DESCRIPTION

[0055] The embodiments of this specification provide a method and medium for quantifying operational characteristic data of a mobile machinery.

[0056] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this specification without creative work should fall within the scope of protection of this specification.

[0057] like Figure 1 As shown in FIG, the embodiment of this specification provides a structural diagram of a method for quantifying the operating characteristic data of a mobile machinery. Figure 1 It can be seen that in one or more embodiments of this specification, a method for quantifying operational characteristic data of a mobile machinery specifically includes:

[0058] S101: Based on the preset pressure transmitter and the CAN bus, data information of the flow machinery to be optimized is obtained.

[0059] Currently, manual on-site monitoring of mobile machinery is the most primitive way to identify operational characteristics. This method not only consumes a lot of manpower and financial resources, but the intervention of human subjective factors makes the results less credible. The mobile machinery operational feature recognition technology based on computer vision has achieved remarkable results in some scenarios, but it still faces many challenges in practical applications. Existing research mainly focuses on the basic working condition analysis of mobile machinery, and lacks comprehensive and refined identification of operational characteristics. In addition, the current observation and statistics are based on small samples on a short time scale, which is limited by the number of measured objects, time scale, testing and analysis methods. There are certain limitations on the credibility of the data and the representativeness of the conclusions. Therefore, if Figure 2 As shown, in the embodiments of this specification, data information of the mobile machinery to be optimized is collected based on the preset pressure transmitter and the CAN bus. Furthermore, in one or more embodiments of this specification, before obtaining the data information of the mobile machinery to be optimized based on the preset pressure transmitter and the CAN bus, the method also includes the following process:

[0060] According to the mechanical structure of the mobile machinery to be optimized, determine the pilot system position of the mobile machinery to be optimized. Then obtain the pilot system pressure range corresponding to each pilot system position, and determine the pressure transmitter type of the preset pressure transmitter. Among them, the transformer type includes: low pressure range type and high pressure range type. For example: the pressure signal from the hydraulic pump outlet requires a high pressure range type pressure transmitter, and the secondary pilot pressure from the handle / foot pedal requires a low pressure range type pressure transmitter. For example, refer to Figure 2 The high-pressure range type pressure transmitter shown corresponds to the area where the main control valve is located, where ARM1 is the 2-arm hydraulic cylinder motion control link, ARM2 is the arm hydraulic cylinder hydraulic flow confluence control link in the arm hydraulic cylinder motion control link, BM1 is the 2-boom hydraulic cylinder motion control link, BM2 is the boom hydraulic cylinder hydraulic flow confluence control link in the boom hydraulic cylinder motion control link, SW is the rotary hydraulic motor control link, BKT is the bucket hydraulic cylinder motion control link, TR is the right travel hydraulic motor motion control link, TL is the left travel hydraulic motor motion control link, TR-ST is the auxiliary function control link, and X1 is the attachment hydraulic drive oil circuit control link. Then, according to the physical layout requirements corresponding to each pilot system position and the pressure transmitter type corresponding to each pilot system position, determine the layout position of each preset pressure transmitter to realize the layout of the preset pressure transmitter. Then, as shown in Figure 2The example shows an acquisition board connected to each pre-set pressure transmitter, allowing the board to capture analog output from the transmitters and read digital information from the CAN bus using the acquisition board's CAN communication capabilities. Also noteworthy is the CAN bus information on the driving gear, engine speed, load percentage, and fuel consumption rate, which provides a foundation for subsequent analysis of driving mode characteristics and powertrain operating characteristics.

[0061] Furthermore, the acquisition board includes analog quantity acquisition function, digital quantity acquisition function, serial communication function, CAN communication function, Ethernet communication function and the like.

[0062] Because statistical analysis is performed on a large number of vehicles, it is performed online, using a cloud platform. Furthermore, the size of the vehicle's data stream is related to the acquisition frequency, which in turn is related to the completeness of the signal representation. Excessive data streams can significantly strain upload throughput to the cloud platform. Therefore, to maximize the representation of signal characteristics, the data processing terminal is placed on the acquisition board. Furthermore, the acquisition board uploads the analysis results to the cloud platform via Ethernet communication. Storing large amounts of data locally, while processing results are stored on the cloud platform, creates a win-win situation.

[0063] Going a step further, the cloud platform performs visual analysis.

[0064] This approach, when acquiring analog information from the pilot system within the mobile machinery being optimized using pre-set pressure transmitters, distinguishes between high- and low-pressure signal sources and selects pressure transmitters with appropriate ranges. This avoids signal distortion or sensor damage caused by range mismatches, ensuring that the collected data truly reflects the system status. Furthermore, connecting each pre-set pressure transmitter via the CAN bus facilitates integration of pressure signals with operating condition data such as engine speed and load percentage, forming a foundation for multi-dimensional correlation analysis. This integration overcomes the limitation of a single pressure signal that cannot reflect motion information.

[0065] Specifically, in one or more embodiments of this specification, based on a preset pressure transmitter and a CAN bus, obtaining data information of the mobile machinery to be optimized specifically includes:

[0066] Because multiple pre-installed pressure transmitters collect analog information at different locations, while other operating data is read via the CAN bus, time domain alignment is required to avoid time domain discrepancies. To ensure temporal consistency across all signals, in the embodiments of this specification, the pre-installed pressure transmitters collect initial analog information from the pilot system of the mobile machine to be optimized, and digital information from the mobile machine to be optimized is read via the CAN bus. The digital information read from the CAN bus is then time-aligned with the initial analog information. For example, different data frames on the CAN bus have different communication frequencies. For example, the data frame ID F004 representing speed and torque percentage has an update frequency of 50 Hz. For example, the data frame ID F003 representing load percentage has an update frequency of 25 Hz. If the acquisition frequency of the pre-installed pressure transmitter is greater than the CAN bus frequency, the CAN bus frequency must be interpolated to achieve the same frequency, thereby achieving alignment. Time domain alignment of the data information can avoid analysis errors caused by time misalignment. The aligned initial analog information and digital information are then pre-processed to obtain data information about the pilot system of the mobile machine to be optimized. Among them, it should be noted that data preprocessing includes: noise reduction processing, filtering processing, normalization processing, etc. In this process, noise reduction processing can remove invalid signals caused by environmental interference and equipment noise during data acquisition, thereby improving signal purity. Filtering processing, by setting appropriate filters, filters out interference signals in a specific frequency range and retains valid pressure signal components. Normalization processing uniformly maps the signal to a specific range, eliminating the differences in amplitude of signals collected by different pressure transmitters, facilitating subsequent unified analysis and processing. By preprocessing the signal, the final analog information and other data obtained can be made more accurate and reliable, laying a solid data foundation for the quantitative evaluation and optimized control of mobile machinery operating characteristics.

[0067] S102: Based on the correlation information corresponding to the operating characteristics to be quantified, construct a quantification model corresponding to each of the operating characteristics to be quantified; wherein the operating characteristics to be quantified include: operating condition characteristics, driving mode characteristics, and power system operating characteristics.

[0068] Due to limitations on the number of measured objects, timescales, and testing and analysis methods, the credibility of the data and the representativeness of the conclusions are limited. Therefore, in order to accurately quantify and characterize the operating characteristics of mobile machinery, in the embodiments of this application, a quantitative model corresponding to each operating characteristic to be quantified is constructed based on the associated information corresponding to the operating characteristic to be quantified. It should be noted that the operating characteristics to be quantified include: operating condition characteristics, driving mode characteristics, and power system operating characteristics. In other words, it is necessary to determine the quantitative model corresponding to each operating condition characteristic, driving mode characteristic, and power system operating characteristic.

[0069] Specifically, in one or more embodiments of this specification, based on the association information corresponding to the operating features to be quantified, a quantization model corresponding to each operating feature to be quantified is constructed, specifically including:

[0070] If it is determined that the operating characteristic to be quantified is an operating condition characteristic, then the cumulative time that the mobile machinery to be optimized is in the operating state is set as the total working time of the mobile machinery to be optimized. Therefore, the ratio of the total working time of the mobile machinery to be optimized to the operating time under each operating condition is used as the quantitative model corresponding to the operating condition characteristic. Among them, the operating conditions include: idle conditions, walking conditions, and working conditions; the idle conditions include: low idle conditions corresponding to the idle gear and high idle conditions corresponding to the non-idle gear. Specifically, the operating condition characteristics mainly include: idle conditions, walking conditions, and working conditions. In order to analyze the operating condition characteristics and driving action characteristics of mobile machinery, 12 pressure characteristic variables are defined for the secondary pilot pressure combined with the action information, as shown in Table 1 below, secondary pressure characteristic variables of the mobile machinery handle / foot pilot system. In the table The subscript 1 indicates the action name number. The subscript in represents the name of the secondary pilot pressure variable, and its meaning corresponds to the action name.

[0071] Table 1. Secondary pressure characteristic variables of mobile mechanical handle / foot pilot system

[0072]

[0073] At this time, the total working time of the mobile machinery to be optimized is the cumulative time that the engine is turned on and in operation:

[0074] (1)

[0075] Where, is the total working hours of mobile machinery, h; E(t) represents the engine operating status, where 1 means the engine is running and 0 means the engine is shut down.

[0076] When the operating condition is idling, that is, the engine is running, and the left pilot handle, right pilot handle, and left and right walking pedals are not operating, the mobile machine is in the following state:

[0077] (2)

[0078] (3)

[0079] Where, is the cumulative idling operation time, h; A represents the time period when the engine is running and all pilot pressure signals are zero. Indicates the 12-way pilot pressure signals corresponding to the pilot handle and foot pedal. Indicates the percentage of total idling hours to total working hours.

[0080] When the operating condition is walking condition, that is, the engine is in running state, since the boarding mechanism generally does not move in the walking condition, when either the left or right walking pedal is operated, it is classified as the walking condition. At this time:

[0081] (4)

[0082] (5)

[0083] Where, is the cumulative running time of the traveling condition, h; B is the time period when the engine is running and the pilot pressure of any traveling condition is not zero. Indicates the percentage of total walking hours to total working hours.

[0084] When the operating condition is the working condition, that is, the engine is in operation, since the dismounting mechanism generally does not operate during mobile machinery operation, when the left and right walking pedal pilot pressure signals are both zero, and any handle operation signal is not zero, it is classified as the working condition:

[0085] (6)

[0086] (7)

[0087] Where, is the cumulative operating time of the working condition, h; C represents the time period when the engine is running and the pilot pressure of any travel channel is zero and the pressure signal of any handle channel is not zero. Indicates the percentage of total working hours in the operating condition to total working hours.

[0088] Specifically, in one or more embodiments of this specification, based on the association information corresponding to the operating features to be quantified, constructing a quantization model corresponding to each of the operating features to be quantified specifically includes:

[0089] If it is determined that the operating characteristic to be quantified is a driving mode characteristic, then the cumulative time that the mobile machinery to be optimized is in operation is set as the total working time of the mobile machinery to be optimized. Then, the driving mode corresponding to the driving gear of the mobile machinery to be optimized is obtained. It should be noted that the driving modes include: low idle mode, fine operation mode, normal operation mode, and heavy load operation mode. Then, the ratio of the total working time of the mobile machinery to be optimized to the operating time in each driving mode is used as the quantitative model corresponding to the driving mode characteristic. Specifically, the mobile machinery meets the working intensity requirements by setting different working gears. According to the driving mode, it can be divided into idle mode (I2, I1), fine operation mode (F2, F1), normal operation mode (G4, G3, G2, G1) and heavy load operation mode (H, P).

[0090] Idle mode usually refers to the process of starting or stopping a mobile machine or a short waiting time. Idle mode only includes I2 and I1, while the idle mode in the operating condition characteristics includes: idle mode and high idle conditions in other driving modes. When the driving mode is idle mode:

[0091] (8)

[0092] (9)

[0093] Where, is the accumulated idle mode operation time, h; I represents the time period during which the mobile machine operates in the idle mode gear. Indicates the percentage of total idle mode hours to total working hours; Indicates the driving gear status of the mobile machinery, 1 means it is in gear i, and 0 means it is not in gear i.

[0094] When the driving mode is the precision operation mode:

[0095] (10)

[0096] (11)

[0097] Where, is the cumulative running time of the fine operation mode, h; F represents the time period when the mobile machinery works in the fine operation mode gear. 。 Indicates the percentage of total working hours in fine work mode to total working hours.

[0098] When the driving mode is normal operation mode:

[0099] (12)

[0100] (13)

[0101] Where, is the accumulated normal operation mode running time, h; N represents the time period when the mobile machinery works in the normal operation mode gear. 。 Indicates the percentage of total working hours in normal working mode to total working hours.

[0102] When the driving mode is heavy load operation mode:

[0103] (14)

[0104] (15)

[0105] Where, is the cumulative running time in heavy-load operation mode, h; H represents the time period when the mobile machinery operates in the heavy-load operation mode gear. Indicates the percentage of total working hours in heavy-duty working mode.

[0106] Specifically, in one or more embodiments of this specification, based on the association information corresponding to the operating features to be quantified, a quantization model corresponding to each operating feature to be quantified is constructed, specifically including:

[0107] If it is determined that the operating characteristic to be quantified is a power system operating characteristic, then the cumulative time that the mobile machinery to be optimized is in operation is set as the total working time of the mobile machinery to be optimized. Then, the average load of the engine is obtained according to the preset load formula, and the time of each load range segment of the engine is determined according to the preset time formula. Then, based on the ratio of the cumulative time of the engine's specified load range segment to the cumulative time of each range segment of the engine, the time proportion that the load percentage is in the specified range segment is determined. Based on the preset engine fuel consumption formula, the average fuel consumption of the engine under each driving gear is determined. The pressure state of the hydraulic pump is determined by counting the proportion of the hydraulic pump's working time in different pressure ranges. Therefore, the time proportion of the load percentage in the specified range segment, the average fuel consumption and the pressure state of the hydraulic pump are used as the quantitative model corresponding to the power system operating characteristics. Specifically, the preset load formula is:

[0108] (16)

[0109] (17)

[0110] in, Indicates the average load percentage of the engine in gear i; Indicates the load percentage of the engine in the i-th gear; Represents the time set when the engine is in the i-th gear, where i is the engine's operating gear. The idle mode gears include I1 and I2; the fine operation mode gears include F2 and F1; the normal operation mode gears include G4, G3, G2, and G1; and the heavy load operation mode gears include H and P.

[0111] The preset time formula is:

[0112] (18)

[0113] (19)

[0114] The preset engine fuel consumption formula is:

[0115] (20)

[0116] Where, , j satisfies E(t)=1 and the engine is running and The time that the engine load percentage is in the kth range segment. It represents the time proportion that the load percentage is in the kth range segment, and j represents the engine gear.

[0117] The hydraulic pump pressure status is:

[0118] (twenty one)

[0119] (twenty two)

[0120] (twenty three)

[0121] Where, is the cumulative working time of hydraulic pump j in the pressure range [50(i-1), 50i)], h; , j means that E(t)=1 is satisfied within the time t set, that is, the engine is running and The time during which the pressure of hydraulic pump j at time t is in the range [50(i-1), 50i)]. J represents the hydraulic pump number, 1 for the left pump and 2 for the right pump; i represents the pressure index range, corresponding to different pressure intervals. It represents the ratio of the cumulative working time of hydraulic pump j in the pressure range [50(i-1), 50i)] to the total working time.

[0122] S103: Performing a quantitative evaluation on the quantified operating characteristics of the mobile machinery to be optimized using the quantitative models and the data information to obtain a quantitative evaluation result.

[0123] Based on the quantitative models obtained in step S102 and the secondary pressure information collected in step S101, a quantitative assessment is performed on the operational characteristics of the mobile machinery to be optimized, obtaining a quantitative assessment result. By quantifying the secondary pressure information, a quantitative assessment of each operational characteristic to be quantified is achieved. This objective assessment replaces the traditional approach of relying on engineer experience and judgment, enabling accurate quantification of energy consumption characteristics and efficiency indicators at each operating stage.

[0124] Specifically, in one or more embodiments of this specification, quantitative evaluation of the quantified operating characteristics of the mobile machinery to be optimized is performed using the quantitative models and the secondary pressure information to obtain the quantitative evaluation results, which specifically include:

[0125] Since secondary pressure information reflects the operating status of the pilot system, the characteristic data corresponding to this secondary pressure information, along with the pre-set driving information acquired via the CAN bus, are incorporated into the corresponding quantitative model. This provides data support based on both equipment operation and operation, providing a rich information foundation for quantitative evaluation. Based on the quantitative model derived from this process, the quantified operating characteristics of the mobile machinery to be optimized are quantitatively evaluated, yielding evaluation results corresponding to each quantified operating characteristic. These evaluation results are aggregated to obtain multi-dimensional quantitative evaluation results for the mobile machinery to be optimized. By combining the secondary pressure information reflecting the operating status of the pilot system with the driving information acquired via the CAN bus, a dual-dimensional data fusion of equipment operating status and operational intent is achieved. This multi-dimensional data support enables the quantitative evaluation to more comprehensively reflect the actual operating conditions of the equipment, avoiding the evaluation bias that can result from a single data source. Using a pre-set quantitative model to process the characteristic data, the evaluation process is standardized and automated. This model-based evaluation approach eliminates the subjectivity of human judgment and ensures greater repeatability and comparability of the evaluation results. At the same time, the model-based approach can quickly adapt to changes in different equipment or operating conditions, allowing evaluation to be completed simply by adjusting input parameters, significantly improving the method's versatility. Furthermore, a hierarchical approach, which independently evaluates each operational characteristic to be quantified and then aggregates the results into a multi-dimensional, comprehensive result, ensures the accuracy of the evaluation of each specific characteristic while providing a holistic understanding of the overall performance of the mobile machinery to be optimized.

[0126] S104: Determine the optimized control parameters of the mobile machine to be optimized according to the quantitative evaluation result, so as to dynamically adjust the mobile machine to be optimized based on the optimized control parameters.

[0127] After obtaining the quantitative evaluation results based on the above step S103, in order to achieve dynamic adjustment of the mobile machinery to be optimized, the optimized control parameters of the mobile machinery to be optimized will be determined according to the quantitative evaluation results in the embodiment of this specification, so as to dynamically adjust the mobile machinery to be optimized according to the optimized control parameters. The setting of traditional mechanical control parameters mainly relies on the experience of engineers and lacks data support. This process realizes the scientific and precise determination of control parameters through the precise mapping of quantitative evaluation results and equipment operation characteristics, so that the parameter adjustment has a clear standard orientation and can match the optimal control strategy according to the specific working conditions. In addition, this process realizes the dynamic adaptive optimization of control parameters, maximizing energy efficiency while ensuring system stability.

[0128] Specifically, in one or more embodiments of this specification, determining the optimized control parameters of the mobile machine to be optimized based on the quantitative evaluation results, and dynamically adjusting the mobile machine to be optimized based on the optimized control parameters, specifically includes the following process:

[0129] First, based on the quantitative evaluation results, the quantified operating characteristics corresponding to the optimized control parameters of the mobile machine to be optimized are determined. If the optimized control parameters are determined to correspond to the operating condition characteristics, the operating condition characteristics of the mobile machine to be optimized are adaptively adjusted based on the preset time corresponding to the operating condition characteristics. In other words, the operating condition characteristics provide guidance for idling, traveling, and working conditions. Because idling wastes energy, if the idle time is long, automatic idle control, particularly adaptive automatic idle control, can be used. For example, conventional automatic idle functions maintain high idle for n minutes, then automatically switch to low idle. If any action occurs, the high speed gear is restored. If no action continues for m minutes, the machine automatically shuts down. Conventional automatic idle functions have fixed idle times, independent of operating conditions. Our adaptive automatic idle control determines idle mode switching times based on actual operating scenarios. Each machine has its own optimal idle time, which is iterative and can be optimized in real time. This is achieved through interaction with the vehicle controller.

[0130] If it is determined that the optimized control parameters do not correspond to the operating conditions, then the design of the next stage of the mobile machinery to be optimized will be guided by matching the optimized control parameters, such as the best matching models of engines and hydraulic pumps under different scenarios, and the matching of the optimal operating points and scenarios.

[0131] In a feasible embodiment of the present specification, determining the optimized control parameters of the mobile machinery to be optimized according to the quantitative evaluation results, and dynamically adjusting the mobile machinery to be optimized based on the optimized control parameters, specifically includes:

[0132] The quantitative evaluation results are compared with the expected operating parameter range of the mobile machine to be optimized to determine the initial optimized parameter set. It is important to note that this initial optimized parameter set can be generated using a multi-objective optimization algorithm, ensuring that parameter adjustments remain within the safe operating boundaries of the equipment. Then, based on the structural characteristics and operating constraints of the mobile machine to be optimized, the optimal control parameter combinations corresponding to the initial optimized parameter set are screened. This fully considers the actual engineering requirements and hardware characteristics of the equipment, effectively eliminating parameter combinations that do not meet the actual engineering requirements and retaining feasible solutions that both meet the performance optimization objectives and are compatible with the hardware characteristics. The optimized control parameter combinations are then verified using an existing experimental platform and input into the controller, enabling the controller to dynamically adjust the mobile machine based on the verified optimized control parameter combinations. A pre-installed pressure transmitter collects the adjusted analog information and provides feedback, enabling closed-loop adjustment. The optimized control parameters are adjusted promptly based on this feedback information, ensuring that the mobile machine maintains optimal operating conditions.

[0133] like Figure 3 As shown in FIG, one or more embodiments of this specification provide a structural diagram of a non-volatile storage medium. Figure 3 It can be seen that in one or more embodiments of this specification, a non-volatile storage medium stores computer-executable instructions 301, and the computer-executable instructions 301 can: execute any of the methods described above.

[0134] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0135] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0136] The foregoing description is merely one or more embodiments of this specification and is not intended to limit this specification. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of one or more embodiments of this specification are intended to be within the scope of the claims of this specification.

Claims

1. A method for quantifying operational characteristic data of mobile machinery, characterized in that: The method comprises: Based on the preset pressure transmitter and CAN bus, obtain the data information of the mobile machinery to be optimized; Based on the correlation information corresponding to the operating characteristics to be quantified, a quantification model corresponding to each of the operating characteristics to be quantified is constructed; wherein the operating characteristics to be quantified include: operating condition characteristics, driving mode characteristics, and power system operating characteristics; Performing a quantitative evaluation on the quantified operating characteristics of the mobile machinery to be optimized using the quantitative models and the data information to obtain a quantitative evaluation result; determining an optimized control parameter of the mobile machine to be optimized according to the quantitative evaluation result, so as to dynamically adjust the mobile machine to be optimized based on the optimized control parameter; Based on the correlation information corresponding to the operating characteristics to be quantified, a quantization model corresponding to each of the operating characteristics to be quantified is constructed, specifically including: If it is determined that the operating characteristic to be quantified is an operating condition characteristic, the accumulated time during which the mobile machine to be optimized is in the operating state is set as the total operating time of the mobile machine to be optimized; The ratio of the total operating time of the mobile machinery to be optimized to the operating time under each operating condition is used as a quantitative model corresponding to the operating condition characteristics; wherein the operating conditions include: idle condition, traveling condition, and working condition; the idle condition includes: a low idle condition corresponding to an idle gear and a high idle condition corresponding to a non-idle gear; Based on the correlation information corresponding to the operating characteristics to be quantified, a quantization model corresponding to each of the operating characteristics to be quantified is constructed, specifically including: If it is determined that the operating characteristic to be quantified is a driving mode characteristic, the accumulated time that the mobile machinery to be optimized is in operation is set as the total working time of the mobile machinery to be optimized; Obtaining a driving mode corresponding to the driving gear of the mobile machinery to be optimized; wherein the driving mode includes: low idle mode, fine operation mode, normal operation mode, and heavy load operation mode; The ratio of the total working time of the mobile machine to be optimized to the operating time in each driving mode is used as a quantitative model corresponding to the driving mode characteristics; Based on the correlation information corresponding to the operating characteristics to be quantified, a quantization model corresponding to each of the operating characteristics to be quantified is constructed, specifically including: If it is determined that the operating characteristic to be quantified is a power system operating characteristic, the accumulated time that the mobile machinery to be optimized is in operation is set as the total working time of the mobile machinery to be optimized; Obtaining the average engine load based on a preset load formula, and determining the time for each engine load range segment based on a preset time formula; Determine the time proportion of the load percentage within the specified range segment based on the ratio of the cumulative time of the engine's specified load range segment to the cumulative time of each engine range segment; Determine the average fuel consumption of the engine at each driving gear according to the preset engine fuel consumption formula; Determine the pressure status of the hydraulic pump by counting the proportion of the hydraulic pump's working time in different pressure ranges; The time proportion of the load percentage being within a specified range, the average fuel consumption, and the hydraulic pump pressure state are used as a quantitative model corresponding to the operating characteristics of the power system.

2. A method for quantifying operational characteristic data of mobile machinery according to claim 1, characterized in that: Before obtaining data information of the mobile machinery to be optimized based on the preset pressure transmitter and the CAN bus, the method further includes: determining a pilot system position of the mobile machine to be optimized based on the mechanical structure of the mobile machine to be optimized; Obtaining the pilot system pressure range corresponding to each pilot system position, and determining the pressure transmitter type of the preset pressure transmitter; wherein the pressure transmitter type includes: pressure transmitters of different range types; Determining the layout position of each preset pressure transmitter based on the physical layout requirements corresponding to each pilot system position and the pressure transmitter type corresponding to each pilot system position, and implementing the layout of the preset pressure transmitter; The preset pressure transmitters are connected based on an acquisition board, so that the acquisition board can obtain the analog information output by the preset pressure transmitter, and read the digital information of the CAN bus based on the CAN communication function of the acquisition board.

3. A method for quantifying operational characteristic data of mobile machinery according to claim 2, characterized in that: Based on the preset pressure transmitter and CAN bus, the data information of the mobile machinery to be optimized is obtained, including: Acquiring initial analog information of a pilot system in the mobile machinery to be optimized based on a preset pressure transmitter, and acquiring digital information of the mobile machinery to be optimized on the CAN bus; Performing time domain alignment on the digital quantity information read from the CAN bus and the initial analog quantity information; The aligned initial analog information and the digital information are subjected to data preprocessing to obtain data information of the pilot system in the mobile machinery to be optimized; wherein the data preprocessing includes: noise reduction processing, filtering processing, and normalization processing.

4. The method for quantifying the operating characteristic data of mobile machinery according to claim 1, characterized in that: The preset load formula is: , ;in, Indicates the average load percentage of the engine in gear i; Indicates the load percentage of the engine in the i-th gear; Represents the time set when the engine is in the i-th gear, where i is the engine's operating gear. The idle mode gears include I1 and I2; the fine operation mode gears include F2 and F1; the normal operation mode gears include G4, G3, G2, and G1; and the heavy load operation mode gears include H and P. The preset time formula is: , ;in, To satisfy E(t)=1, the engine is running and the engine load percentage The time in the kth range segment; The preset engine fuel consumption formula is: ;in, It represents the time proportion that the load percentage is in the kth range segment, and j represents the engine gear.

5. The method for quantifying the operating characteristic data of mobile machinery according to claim 1, characterized in that: By using the quantitative models and the data information, a quantitative evaluation is performed on the quantified operating characteristics of the mobile machinery to be optimized to obtain a quantitative evaluation result, specifically including: Substitute the characteristic data corresponding to the secondary pressure information and the driving information obtained from the preset CAN bus into the corresponding quantitative model; Performing quantitative evaluation on the to-be-quantified operating characteristics of the mobile machinery to be optimized based on the quantitative model, and obtaining evaluation results corresponding to each of the to-be-quantified operating characteristics; The evaluation results are summarized to obtain a quantitative evaluation result of the mobile machinery to be optimized.

6. A method for quantifying operational characteristic data of mobile machinery according to claim 1, characterized in that: Determining the optimized control parameters of the mobile machinery to be optimized according to the quantitative evaluation results, so as to dynamically adjust the mobile machinery to be optimized based on the optimized control parameters, specifically includes: Determining, based on the quantitative evaluation results, the operating characteristics to be quantified corresponding to the optimized control parameters of the mobile machinery to be optimized; If it is determined that the optimized control parameter corresponds to the operating condition characteristic, adaptively adjusting the operating condition characteristic of the mobile machinery to be optimized based on the preset time corresponding to each of the operating condition characteristics; If it is determined that the optimized control parameters do not correspond to the operating condition characteristics, the design of the mobile machinery of the next stage to be optimized is guided based on the matching of the optimized control parameters.

7. A non-volatile storage medium storing computer-executable instructions, characterized in that: The computer executable instructions can execute the method according to any one of claims 1 to 6.

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