Energy efficiency optimization method, device and equipment for engineering machinery

Abstract

The embodiment of the invention provides an energy efficiency optimization method, device and equipment for engineering machinery. The method comprises the steps that firstly, according to configuration parameters, operation parameters, working condition data, environment data and operation data, obtained in a preset period, of multiple pieces of engineering mechanical equipment, the multiple pieces of engineering mechanical equipment are matched and classified, at least one set of equipment is obtained, and each set of equipment comprises at least two pieces of engineering mechanical equipment; all the engineering mechanical equipment are matched in the equipment dimension, the personnel operation level dimension and the working environment dimension; then, for each group of equipment, obtaining an energy efficiency value of each engineering mechanical equipment in the group according to the operation parameters and working condition data of each engineering mechanical equipment in the group; and finally, optimizing the energy efficiency of other engineering mechanical equipment in the group according to the configuration parameter of the target equipment with the highest energy efficiency value of each engineering mechanical equipment in the group. Through the method, the manual parameter adjustment cost is reduced, and the overall energy efficiency of the equipment is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of equipment energy efficiency optimization, and in particular to an energy efficiency optimization method, device and equipment of engineering machinery. BACKGROUND

[0002] Engineering machinery is an important part of the equipment industry, and its energy efficiency level not only relates to the operation economy of a single device, but also directly affects the energy utilization efficiency and carbon emission level of the entire mining area and even the industry. With the expansion of mining operation scale and the rise of energy costs, the energy efficiency of engineering machinery under high load and complex terrain conditions is increasingly prominent. Without effective energy efficiency optimization means, it is easy to lead to fuel waste, low operation efficiency and accelerated equipment wear and tear. Therefore, it is of great significance to carry out energy efficiency optimization research on engineering machinery under complex working conditions in the mining area, and to build a dynamic energy efficiency management mechanism that can adapt to different device types, operation scenarios and operation habits, in order to reduce operation cost, prolong equipment life, improve resource utilization and promote high-quality development of the equipment industry.

[0003] In related technologies, the energy efficiency optimization method of engineering machinery mainly collects the running parameters of a single engineering machinery in real time through a vehicle-mounted sensor. This engineering machinery energy efficiency optimization method cannot adapt to different mining area environments or differences in operating personnel in a dynamic manner, and the energy efficiency optimization effect is limited. SUMMARY

[0004] The embodiments of the present application provide an energy efficiency optimization method, device and equipment of engineering machinery, to solve the problem in the prior art that it is difficult to adapt to different mining area environments or differences in operating personnel in a dynamic manner, and the energy efficiency optimization effect is limited.

[0005] In a first aspect, the embodiments of the present application provide an energy efficiency optimization method of engineering machinery, comprising:

[0006] According to the configuration parameters, running parameters, working condition data, environment data and operation data of a plurality of engineering machinery devices obtained within a preset period, the plurality of engineering machinery devices are matched and classified to obtain at least one group of devices, each group of devices including at least two engineering machinery devices, and each engineering machinery device is matched in the device dimension, the personnel operation level dimension and the working environment dimension;

[0007] For each group of devices, according to the running parameters and working condition data of each engineering machinery device in the group, the energy efficiency value of each engineering machinery device in the group is obtained.

[0008] According to the configuration parameters of the target device with the highest energy efficiency value of each engineering machinery device in the group, the energy efficiency of other engineering machinery devices in the group is optimized.

[0009] In one possible implementation, optimizing the energy efficiency of other construction machinery equipment within the group based on the configuration parameters of the target equipment with the highest energy efficiency value for each piece of construction machinery equipment in the group includes:

[0010] For each group of equipment other than the target equipment, verify whether the configuration parameters of the target equipment exceed the safe range of the parameters of the other engineering machinery equipment.

[0011] When the configuration parameters of the target device do not exceed the safe range of the parameters of the other engineering machinery equipment, the configuration parameters of the target device are sent to the other engineering machinery equipment in the group.

[0012] In one possible implementation, optimizing the energy efficiency of other construction machinery equipment within the group based on the configuration parameters of the target equipment with the highest energy efficiency value for each piece of construction machinery equipment in the group includes:

[0013] For the other engineering machinery equipment in each group of equipment besides the target equipment, the preset proportion of the configuration parameters of the target equipment is sent to the other engineering machinery equipment in the group, wherein the preset proportion is between 60% and 100%.

[0014] In one possible implementation, the method further includes:

[0015] Real-time acquisition of operating parameters for each piece of engineering machinery within the group after energy efficiency optimization;

[0016] If the deviation of the operating parameters of any construction machinery equipment is detected to be greater than a preset threshold, a parameter rollback instruction is issued to instruct the construction machinery equipment to roll back the configuration parameters to the state before modification.

[0017] In one possible implementation, other engineering machinery equipment that needs to be optimized within each group includes engineering machinery equipment whose energy efficiency value differs from the energy efficiency value of the target equipment within the group by a preset energy efficiency deviation.

[0018] In one possible implementation, obtaining the energy efficiency value of each piece of construction machinery in the group based on the operating parameters and working condition data of each piece of construction machinery in the group includes:

[0019] For each piece of construction machinery within the group, the initial energy efficiency value is calculated using the energy efficiency calculation formula based on the operating parameters and working condition data of the construction machinery.

[0020] Based on the environmental data of the engineering machinery equipment, the initial energy efficiency value is adjusted to obtain the energy efficiency value of the engineering machinery equipment.

[0021] In one possible implementation, the energy efficiency calculation formula is: the ratio of workload to fuel consumption multiplied by a working condition correction factor; wherein the working condition correction factor is calculated based on the weights of multiple working conditions that affect energy efficiency.

[0022] The step of adjusting the initial energy efficiency value based on the environmental data of the construction machinery equipment to obtain the energy efficiency value of the construction machinery equipment includes:

[0023] If the environmental data indicates that the current working condition is frozen soil, the weight of the hydraulic stability working condition is increased, a new working condition correction factor is recalculated, and the initial energy efficiency value is corrected according to the new working condition correction factor to obtain the corrected energy efficiency value.

[0024] If the environmental data indicates that the current operating condition is rainy, the weight of the anti-skid operation economic condition is increased, a new operating condition correction factor is recalculated, and the initial energy efficiency value is corrected according to the new operating condition correction factor to obtain the corrected energy efficiency value.

[0025] In one possible implementation, the step of matching and classifying the multiple engineering machinery equipment based on the obtained configuration parameters, operating parameters, working condition data, environmental data, and operational data of the mining area to obtain at least one group of equipment includes:

[0026] Based on the configuration parameters, operating parameters, working condition data, environmental data, and operation data of the multiple engineering machinery equipment, as well as the preset matching rules for each dimension in the equipment dimension, personnel operation level dimension, and working environment dimension, a pre-acquired multi-dimensional matching algorithm is used to match and classify the multiple engineering machinery equipment to obtain the at least one group of equipment.

[0027] Among them, the matching rules for equipment include matching tonnage and / or model, and matching hydraulic system version;

[0028] The matching rules for personnel operation level include: the difference between operation intensity indices is within a preset range;

[0029] The matching rules for the work environment dimension include: the slope distribution similarity of the work site is greater than the preset similarity, and the soil hardness difference of the work site is less than the preset hardness.

[0030] Secondly, embodiments of this application provide an energy efficiency optimization device for engineering machinery, comprising:

[0031] The processing module is used to match and classify the multiple construction machinery equipment according to the configuration parameters, operating parameters, working condition data, environmental data and operation data of multiple construction machinery equipment obtained within a preset period, to obtain at least one group of equipment. Each group of equipment includes at least two construction machinery equipment, and each construction machinery equipment is matched in terms of equipment dimension, personnel operation level dimension and working environment dimension.

[0032] The acquisition module is used to acquire the energy efficiency value of each piece of construction machinery in the group based on the operating parameters and working condition data of each piece of construction machinery in the group.

[0033] The optimization module is used to optimize the energy efficiency of other construction machinery equipment in the group based on the configuration parameters of the target equipment with the highest energy efficiency value for each piece of construction machinery equipment in the group.

[0034] In one possible implementation, the optimization module is specifically used for:

[0035] For each group of equipment other than the target equipment, verify whether the configuration parameters of the target equipment exceed the safe range of the parameters of the other engineering machinery equipment.

[0036] When the configuration parameters of the target device do not exceed the safe range of the parameters of the other engineering machinery equipment, the configuration parameters of the target device are sent to the other engineering machinery equipment in the group.

[0037] In one possible implementation, the optimization module is specifically used for:

[0038] For the other engineering machinery equipment in each group of equipment besides the target equipment, the preset proportion of the configuration parameters of the target equipment is sent to the other engineering machinery equipment in the group, wherein the preset proportion is between 60% and 100%.

[0039] In one possible implementation, the energy efficiency optimization device for the process machinery further includes a control module, the control module being configured to:

[0040] Real-time acquisition of operating parameters for each piece of engineering machinery within the group after energy efficiency optimization;

[0041] If the deviation of the operating parameters of any construction machinery equipment is detected to be greater than a preset threshold, a parameter rollback instruction is issued to instruct the construction machinery equipment to roll back the configuration parameters to the state before modification.

[0042] In one possible implementation, other engineering machinery equipment that needs to be optimized within each group includes engineering machinery equipment whose energy efficiency value differs from the energy efficiency value of the target equipment within the group by a preset energy efficiency deviation.

[0043] In one possible implementation, the acquisition module is configured to:

[0044] For each piece of construction machinery within the group, the initial energy efficiency value is calculated using the energy efficiency calculation formula based on the operating parameters and working condition data of the construction machinery.

[0045] Based on the environmental data of the engineering machinery equipment, the initial energy efficiency value is adjusted to obtain the energy efficiency value of the engineering machinery equipment.

[0046] In one possible implementation, the energy efficiency calculation formula is: the ratio of workload to fuel consumption multiplied by a working condition correction factor; wherein the working condition correction factor is calculated based on the weights of multiple working conditions that affect energy efficiency.

[0047] The acquisition module is specifically used for:

[0048] If the environmental data indicates that the current working condition is frozen soil, the weight of the hydraulic stability working condition is increased, a new working condition correction factor is recalculated, and the initial energy efficiency value is corrected according to the new working condition correction factor to obtain the corrected energy efficiency value.

[0049] If the environmental data indicates that the current operating condition is rainy, the weight of the anti-skid operation economic condition is increased, a new operating condition correction factor is recalculated, and the initial energy efficiency value is corrected according to the new operating condition correction factor to obtain the corrected energy efficiency value.

[0050] In one possible implementation, the processing module is specifically used for:

[0051] Based on the configuration parameters, operating parameters, working condition data, environmental data, and operation data of the multiple engineering machinery equipment, as well as the preset matching rules for each dimension in the equipment dimension, personnel operation level dimension, and working environment dimension, a pre-acquired multi-dimensional matching algorithm is used to match and classify the multiple engineering machinery equipment to obtain the at least one group of equipment.

[0052] Among them, the matching rules for equipment include matching tonnage and / or model, and matching hydraulic system version;

[0053] The matching rules for personnel operation level include: the difference between operation intensity indices is within a preset range;

[0054] The matching rules for the work environment dimension include: the slope distribution similarity of the work site is greater than the preset similarity, and the soil hardness difference of the work site is less than the preset hardness.

[0055] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0056] The memory stores computer-executed instructions;

[0057] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0058] The energy efficiency optimization method, apparatus, and equipment for construction machinery provided in this application firstly, based on the configuration parameters, operating parameters, working condition data, environmental data, and operational data of multiple construction machinery devices acquired within a preset period, the multiple construction machinery devices are matched and classified to obtain at least one group of devices. Each group of devices includes at least two construction machinery devices, and each construction machinery device is matched in terms of equipment dimension, personnel operation level dimension, and working environment dimension. This method ensures that the devices within the same group maintain basic consistency in equipment dimension, personnel operation level dimension, and working environment dimension, thereby effectively eliminating the influence of equipment heterogeneity, differences in operation level, or external environmental interference in subsequent analysis. The impact of disturbances lays a reliable foundation for accurate horizontal comparison of energy efficiency under the same benchmark. Then, for each group of equipment, the energy efficiency value of each piece of engineering machinery in the group is obtained based on the operating parameters and working condition data of each piece of engineering machinery in the group, so that the energy efficiency performance of each piece of engineering machinery in the group can be objectively quantified. Finally, based on the configuration parameters of the target equipment with the highest energy efficiency value in each piece of engineering machinery in the group, the energy efficiency of other engineering machinery in the group is optimized. This realizes the efficient transmission from individual excellent practices to group energy efficiency improvement, effectively overcomes the drawbacks of poor adaptability of static rules, and significantly improves the overall energy efficiency management level and dynamic optimization capability of the mining area equipment group. Attached Figure Description

[0059] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0060] Figure 1 A schematic diagram of the structure of the energy efficiency optimization system for engineering machinery provided in the embodiments of this application;

[0061] Figure 2 A flowchart illustrating the energy efficiency optimization method for construction machinery provided in this application embodiment. Figure 1 ;

[0062] Figure 3 A flowchart illustrating the energy efficiency optimization method for construction machinery provided in this application embodiment. Figure 2 ;

[0063] Figure 4a A horizontal comparison of the engineering machinery and equipment provided in the embodiments of this application. Figure 1 ;

[0064] Figure 4b A horizontal comparison of the engineering machinery and equipment provided in the embodiments of this application. Figure 2 ;

[0065] Figure 5 A schematic diagram of the structure of the energy efficiency optimization device for engineering machinery provided in the embodiments of this application;

[0066] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0067] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0068] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0069] Construction machinery is a crucial component of the equipment manufacturing industry. Its energy efficiency not only affects the economics of individual units but also directly impacts the energy utilization efficiency and carbon emission levels of the entire mining area and even the entire industry. With the expansion of mining operations and rising energy costs, the energy efficiency of construction machinery under high loads and complex terrain conditions is becoming increasingly prominent. Without effective energy efficiency optimization methods, fuel waste, low operational efficiency, and accelerated equipment wear are likely to occur. Therefore, conducting energy efficiency optimization research on construction machinery under complex mining conditions and constructing a dynamic energy efficiency management mechanism that can adapt to different equipment types, operating scenarios, and operating habits is of great significance for reducing operating costs, extending equipment lifespan, improving resource utilization, and promoting the high-quality development of the equipment manufacturing industry.

[0070] In existing technologies, energy efficiency optimization methods for construction machinery mainly involve real-time collection of operating parameters for individual machines using onboard sensors, such as fuel consumption, engine speed, and load rate. Subsequently, analysis algorithms deployed locally or in the cloud are used to assess the equipment's energy efficiency status and generate optimized operating suggestions based on preset rules. Meanwhile, some solutions employ standardized parameter configurations for multiple machines, attempting to achieve standardized energy efficiency management.

[0071] However, existing energy efficiency optimization methods for engineering machinery lack horizontal collaborative comparisons between equipment in their single-machine focused mode, making it impossible to identify the optimal operating mode within a group through data mining. Secondly, optimization suggestions using static rule bases are difficult to adapt to the dynamically changing working environment of mining areas, such as unstructured scenarios like geological structure migration and sudden weather changes. Finally, standardized parameter configurations completely ignore key variables such as differences in equipment models, operator skill levels, and regional environmental characteristics, resulting in a general lack of adaptability in the practical application of optimization measures.

[0072] Based on this, this application proposes an energy efficiency optimization method for engineering machinery. Given the large variety of equipment models and significant differences in operators and environmental conditions, single-machine optimization and static rules cannot uncover the optimal operating mode within a group, nor can they generalize to dynamic operating conditions. The inventors devised a method to first precisely match and group equipment based on its configuration, operation and operating conditions, environment, and operational behavior across equipment, personnel, and environment dimensions, ensuring the comparability of samples within each group. Then, real-time / historical operating data within the group is used to calculate the energy efficiency of each piece of equipment. Finally, the operating parameters of the best-performing piece of equipment in the current actual environment (the target equipment) are used as the real-time optimization benchmark, enabling the optimization strategy to adapt to continuous changes in geological conditions, weather, and other environmental factors. This method retains the advantages of direct transfer based on best practices while addressing the adaptability issues caused by model differences, operating habits, and complex geographical environments through multi-dimensional matching and online adaptive mechanisms, thereby achieving dynamic energy efficiency improvement at the group level.

[0073] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0074] Figure 1 This is a schematic diagram of the structure of the energy efficiency optimization system for engineering machinery provided in the embodiments of this application, as shown below. Figure 1 As shown, the system includes: a data acquisition layer, a data middleware layer, an energy efficiency analysis layer, and a parameter synchronization layer.

[0075] The data acquisition layer collects equipment parameters through vehicle-mounted sensors and edge gateways; the data middleware layer stores and cleans the data, establishing multi-dimensional labels such as "equipment model / tonnage - operator skill level - geographical range"; the energy efficiency analysis layer calculates energy efficiency values ​​based on preset rules or machine learning models, generating optimal configuration parameters (such as handle signal-valve core curve, handle signal-main pump current curve, slewing priority, etc.); the parameter synchronization layer pushes the configuration to the target device through the cloud, Wireless Fidelity (WiFi) network, or 5G network. Therefore, the energy efficiency optimization method for construction machinery provided in this application embodiment is applied to a server or cloud that can collect data from multiple devices and perform complex calculations.

[0076] Figure 2 A flowchart illustrating the energy efficiency optimization method for construction machinery provided in this application embodiment. Figure 1 ;like Figure 2 As shown, the method includes:

[0077] S201. Based on the configuration parameters, operating parameters, working condition data, environmental data, and operation data of multiple construction machinery equipment obtained within a preset period, match and classify the multiple construction machinery equipment to obtain at least one set of equipment.

[0078] The preset cycle can be a week, a month, or a quarter, etc., and this application embodiment does not impose specific limitations here; each group of equipment includes at least two engineering machinery devices, and each engineering machinery device is matched in terms of equipment dimension, personnel operation level dimension, and working environment dimension; configuration parameters may include handle signal-valve core curve, handle signal-main pump current curve, slewing priority, etc.; operating parameters may include position, model / tonnage, engine power curve, hydraulic system pressure, stick action frequency, handle signal, attitude angle, etc., and handle signal and attitude angle can be used to calculate the operator's driving level; working condition data may include fuel consumption, workload, work object, etc.

[0079] In one feasible approach, based on the configuration parameters, operating parameters, working condition data, environmental data, and operational data of multiple engineering machinery equipment, as well as the preset matching rules for each dimension in the equipment dimension, personnel operation level dimension, and working environment dimension, a pre-acquired multi-dimensional matching algorithm is used to match and classify the multiple engineering machinery equipment to obtain at least one set of equipment.

[0080] The matching rules for equipment include matching tonnage and / or model, and matching hydraulic system version; the matching rules for personnel operation level include: the difference between operation intensity indices is within a preset range; the matching rules for work environment include: the slope distribution similarity of the work site is greater than the preset similarity, and the soil hardness difference of the work site is less than the preset hardness.

[0081] It should be understood that, in this embodiment, the server or cloud system first obtains multi-source data from multiple construction machinery devices within a preset period (e.g., one week) from the data platform, including configuration parameters, operating parameters, working condition data, environmental data, and operational data. Based on this multi-dimensional data, the system calls a pre-trained multi-dimensional matching algorithm to perform multi-level matching and classification of the devices, ensuring that each group of devices has a high degree of consistency in performance, operating habits, and working environment, providing a foundation for subsequent energy efficiency comparison and optimal strategy discovery.

[0082] To ensure consistency in physical performance and system architecture among equipment and avoid energy efficiency differences caused by different generations or system configurations, structural parameters such as equipment model, tonnage, and hydraulic system version are used as the main matching features at the equipment level. Matching rules are set, such as tonnage error not exceeding 5%, same model category, and consistent hydraulic system version, and algorithms such as Euclidean distance or cosine similarity are used to calculate the parameter matching degree between equipment. When the equipment-level matching degree exceeds a preset threshold, it is considered to meet the equipment-level matching conditions.

[0083] In terms of operator skill level, the intensity index of each operator is calculated by analyzing characteristic data such as handle signals, valve core response curves, posture angle changes, and stick movement frequency. Specifically, an operational behavior model can be constructed by statistically analyzing features such as the rate of change of operational signals per unit time, extreme frequency, and reaction delay. Then, the intensity index differences for operators corresponding to different equipment are compared. When the difference is lower than a preset range (e.g., ±10%), the dimension is considered to be matched.

[0084] In terms of the working environment, slope distribution characteristics and soil hardness characteristics are extracted based on the geographical information and sensor data of the equipment's operating site. By calculating the similarity of slope distribution (e.g., histogram cross-validation) and comparing soil hardness differences, when the similarity is higher than a preset threshold (e.g., 0.8) and the hardness difference is lower than a preset value (e.g., 5 MPa), the working environments of the two pieces of equipment are considered to be matched. This ensures the consistency of the grouped equipment under operating resistance and terrain load conditions, making energy efficiency analysis more comparable.

[0085] Understandably, through the aforementioned refined multi-dimensional matching process, this embodiment can accurately construct the optimal grouping for benchmarking analysis from a mixed group of equipment. This fundamentally eliminates the interference caused by external variables such as the age of the equipment model, the operator's habits (rough or meticulous), and the harsh or favorable working environment on energy efficiency assessment, enabling subsequent algorithms to have higher accuracy and stability in identifying high-energy-efficiency operating modes, abnormal energy-consuming equipment, and differentiated operating strategies.

[0086] S202. For each group of equipment, obtain the energy efficiency value of each piece of engineering machinery in the group based on the operating parameters and working condition data of each piece of engineering machinery in the group.

[0087] It should be noted that this application does not cover the specific process of calculating the energy efficiency value of construction machinery based on operating parameters and working condition data. Figure 3 The embodiments are described in detail, but will not be elaborated upon in this embodiment.

[0088] Understandably, after the equipment is grouped, the energy efficiency value of each piece of construction machinery within each group is calculated based on operating parameters and working condition data. This establishes a quantifiable energy efficiency evaluation system for each group, enabling energy efficiency comparisons across equipment and working conditions. The energy efficiency value characterizes the effective workload per unit of energy consumption and is a crucial indicator reflecting the economic efficiency and operational effectiveness of the equipment.

[0089] S203. Based on the configuration parameters of the target equipment with the highest energy efficiency value for each piece of engineering machinery in the group, optimize the energy efficiency of other engineering machinery in the group.

[0090] It should be understood that in this embodiment, the target device with the highest energy efficiency value in the group is first determined, and the configuration parameters corresponding to the target device are used as the baseline parameters. Then, the corresponding parameters of other devices with lower energy efficiency values ​​in the group are matched and recommended for adjustment. In this way, parameter migration optimization based on high energy efficiency samples in the same group can be achieved, so that other devices can obtain similar operating performance to high energy efficiency devices without changing the hardware structure, which greatly improves the overall energy efficiency level of the mining area.

[0091] The energy efficiency optimization method for construction machinery provided in this application firstly involves matching and classifying multiple construction machinery devices based on configuration parameters, operating parameters, working condition data, environmental data, and operational data obtained within a preset period. This results in at least one group of devices, with each group containing at least two pieces of construction machinery. The devices are matched in terms of equipment, operator skill level, and working environment. This method ensures that devices within the same group maintain basic consistency in these dimensions, effectively eliminating the effects of equipment heterogeneity, differences in operator skill level, or external environmental interference in subsequent analyses. The resulting impact laid a reliable foundation for accurate horizontal comparison of energy efficiency under the same benchmark. Then, for each group of equipment, the energy efficiency value of each piece of engineering machinery in the group was obtained based on the operating parameters and working condition data of each piece of engineering machinery in the group, so that the energy efficiency performance of each piece of engineering machinery in the group could be objectively quantified. Finally, based on the configuration parameters of the target equipment with the highest energy efficiency value in each piece of engineering machinery in the group, the energy efficiency of other engineering machinery in the group was optimized. This achieved efficient transmission from individual excellent practices to group energy efficiency improvement, effectively overcame the drawbacks of poor adaptability of static rules, and significantly improved the overall energy efficiency management level and dynamic optimization capability of the mining area equipment group.

[0092] It should be noted that when optimizing the energy efficiency of other construction machinery equipment within the group based on the configuration parameters of the target equipment, it is also necessary to verify the configuration parameters of the other construction machinery equipment within the group to ensure the safe operation of the construction machinery equipment. Specifically:

[0093] First, for each group of construction machinery equipment other than the target equipment, verify whether the configuration parameters of the target equipment exceed the safe range of the parameters of the other construction machinery equipment; then, if the configuration parameters of the target equipment do not exceed the safe range of the parameters of the other construction machinery equipment, distribute the configuration parameters of the target equipment to the other construction machinery equipment in the group.

[0094] It should be understood that when optimizing the energy efficiency of other construction machinery within a group based on the configuration parameters of the target equipment, strict safety verification must be performed before parameters are issued to avoid potential equipment overload or control anomalies caused by direct migration. This ensures the controllability and reliability of the optimization process. Specifically, for each group of construction machinery other than the target equipment, the configuration parameters of the target equipment are verified item by item to ensure they do not exceed the safe operating range of the equipment. Verification items may include core parameters such as maximum engine speed, rated hydraulic system pressure, maximum main pump flow rate, and control valve opening. When a parameter is detected to exceed the equipment's safe range, it is marked as an unmigratable parameter; if all parameters are within the safe range, the equipment is deemed safe to receive parameters from the target equipment.

[0095] In one feasible approach, for each group of engineering machinery equipment other than the target equipment, the preset proportion of the configuration parameters of the target equipment is distributed to the other engineering machinery equipment in the group.

[0096] The preset ratio is between 60% and 100%. Other engineering machinery equipment that needs to be optimized in each group includes engineering machinery equipment whose energy efficiency value is greater than the preset energy efficiency deviation compared to the energy efficiency value of the target equipment in the group.

[0097] Understandably, to further enhance the flexibility and security of optimization, in one optional implementation, instead of directly distributing all configuration parameters of the target device during parameter synchronization, a preset proportion (60%–100%) of parameters is selected for migration. This step-by-step, gradual approach reduces the impact of sudden system changes. Furthermore, the target for optimization is not all devices within the group; energy efficiency optimization is performed on engineering machinery equipment whose energy efficiency values ​​differ from the target devices' energy efficiency values ​​by a preset deviation. This ensures that computational resources are concentrated on low-energy-efficiency samples, improving optimization benefits and computational efficiency.

[0098] It should also be noted that after synchronizing the configuration parameters of the target equipment to other construction machinery, it is also necessary to monitor the operating parameters of the other construction machinery. Specifically:

[0099] First, the operating parameters of each piece of construction machinery within the group after energy efficiency optimization are obtained in real time. Then, if the deviation of the operating parameters of any piece of construction machinery is detected to be greater than the preset threshold, a parameter rollback instruction is issued to instruct the construction machinery to roll back the configuration parameters to the state before modification.

[0100] It should be understood that once the target equipment with the highest energy efficiency value within the group is identified, its configuration parameters are marked as baseline parameters and frozen for a preset period (e.g., one week or one work cycle) to prevent readjustment due to short-term fluctuations. Therefore, during the parameter freeze period, only the operating parameter data of other construction machinery within the group needs to be collected and monitored in real time, without needing to acquire the target equipment's configuration parameters in real time. This approach ensures the stability of the target equipment's parameters while reducing system computational overhead and improving the stability and safety of the energy efficiency optimization process for other construction machinery within the group.

[0101] Understandably, after synchronizing the configuration parameters of the target device to other devices, the optimized operating status is continuously monitored. Specifically, key operating parameters of the devices are collected in real time, such as engine load rate, hydraulic temperature, operating frequency, and fuel consumption, and their operating deviation values ​​are calculated. When the operating parameter deviation of any device exceeds a preset threshold (e.g., exceeding the original average by 15%), a parameter rollback command is immediately issued to instruct the device to restore its configuration parameters to the state before modification. Through this closed-loop control mechanism, potential abnormal operating conditions or safety hazards can be detected and prevented early, ensuring stable equipment operation.

[0102] For example, taking the three excavators in Group A as an example, Equipment 2 is the target equipment, and its hydraulic system control parameters and energy-saving curves show the best performance. The system first performs a safety check on Equipment 1 and Equipment 3, and finds that the upper limit of the main pump flow of Equipment 3 is lower than that of the target equipment. Therefore, this parameter is automatically masked, and only other safety parameters (approximately 80%) are migrated. Subsequently, the system synchronizes the optimized parameters to Equipment 1 and Equipment 3 and continuously monitors their operating data. After running for 30 minutes, it detects that the hydraulic system temperature deviation of Equipment 1 exceeds the threshold, and immediately triggers a rollback command to restore it to the original parameter configuration.

[0103] Figure 3 A flowchart illustrating the energy efficiency optimization method for construction machinery provided in this application embodiment. Figure 2 ,like Figure 3 As shown, in this embodiment... Figure 2 Based on the examples, the process of obtaining the energy efficiency value of each piece of construction machinery equipment in the group is described in detail. The method includes:

[0104] S301. For each piece of construction machinery within the group, the initial energy efficiency value is calculated using the energy efficiency calculation formula based on the operating parameters and working condition data of the construction machinery.

[0105] The energy efficiency calculation formula is: the ratio of workload to fuel consumption multiplied by the working condition correction factor; the working condition correction factor is calculated based on the weights of multiple working conditions that affect energy efficiency (such as digging resistance, slewing angle, lifting height, etc. in the work cycle).

[0106] It should be noted that the specific formula for calculating energy efficiency is as follows:

[0107]

[0108] in, This represents the initial energy efficiency value, where Q represents the amount of work completed by the equipment per unit time (e.g., excavation volume, loading tonnage, etc.), and F represents fuel consumption. This is the operating condition correction factor. This calculation formula comprehensively reflects the effective operating output corresponding to the unit fuel consumption of the equipment in actual operation, providing a basis for subsequent environmental corrections and energy efficiency comparisons.

[0109] For example, taking an excavator in a mining area as an example, its working volume is 120 m³ / h and its fuel consumption is 30 L / h, resulting in a working volume to fuel consumption ratio of 4.0. Based on the collected working condition data, the weights of the three main working conditions—hydraulic stability, anti-slip operation, and slope resistance—are determined to be 0.4, 0.3, and 0.3, respectively, with corresponding standardization coefficients of 0.95, 1.0, and 0.85. The working condition correction factor and the final initial energy efficiency value are then calculated as follows:

[0110]

[0111] S302. Based on the environmental data of the construction machinery and equipment, adjust the initial energy efficiency value to obtain the energy efficiency value of the construction machinery and equipment.

[0112] In one feasible approach, if environmental data indicates that the current operating condition is frozen soil, the weight of the hydraulic stability operating condition is increased, a new operating condition correction factor is recalculated, and the initial energy efficiency value is corrected according to the new operating condition correction factor to obtain the corrected energy efficiency value; if environmental data indicates that the current operating condition is rainy, the weight of the anti-skid operation economic operating condition is increased, a new operating condition correction factor is recalculated, and the initial energy efficiency value is corrected according to the new operating condition correction factor to obtain the corrected energy efficiency value.

[0113] In one feasible approach, if environmental data indicates that the current operating condition is frozen ground, the weight of the hydraulic stability condition is increased, for example, from 0.4 to 0.55, due to soil hardening and hydraulic system response delays, to reflect the greater impact of this factor on energy efficiency. The system then recalculates the new operating condition correction factor. Based on this, the initial energy efficiency value is corrected to obtain the energy efficiency value corrected for frozen soil.

[0114] If environmental data indicates a rainy operating condition, the weight of the economic efficiency of anti-slip maneuvers will be increased due to the increased slippage caused by wet surfaces. For example, the weight will be adjusted from 0.3 to 0.45, and the correction factor will be recalculated and the energy efficiency value will be adjusted accordingly. Through this adaptive weight adjustment mechanism, the energy efficiency calculation results can reflect dynamic environmental changes in real time.

[0115] Continuing with the example from step S301, if the operation is in a frozen soil environment, the weight of the hydraulic stability condition is increased from 0.4 to 0.55, while the weight of the anti-slip condition is appropriately reduced to 0.2. The correction factor is then recalculated as follows:

[0116] ;

[0117] Therefore, the corrected energy efficiency value E = 4.0 × 0.9275 = 3.71

[0118] This value is slightly higher than the energy efficiency under normal operating conditions, reflecting the system's ability to adapt to changes in the environment.

[0119] Understandably, by introducing an energy efficiency correction mechanism based on environmental data, dynamic adaptation to complex climate and geological conditions is achieved, significantly improving the accuracy and versatility of energy efficiency calculations. This method can maintain the comparability of energy efficiency assessments across different seasons and climatic conditions, providing real and reliable data support for subsequent energy efficiency optimization and strategy decision-making.

[0120] Figure 4a A horizontal comparison of the engineering machinery and equipment provided in the embodiments of this application. Figure 1 ; Figure 4b A horizontal comparison of the engineering machinery and equipment provided in the embodiments of this application. Figure 2 ;exist Figure 4a To compare the energy efficiency of engineering machinery and equipment of the same tonnage and with similar operator skill levels in the same mining area, and to intuitively demonstrate the matching principle between equipment dimension and operator skill level in the embodiments of this application, a highly controlled comparison group was constructed by constraining the comparison range to a common geographical and operational environment, which is used to accurately identify the equipment with the best energy efficiency performance under the same external conditions. Figure 4b To compare the energy efficiency of engineering machinery with similar tonnage and operator skill levels within a radius of x kilometers (e.g., 10km) centered on a certain equipment.

[0121] Figure 4a and Figure 4b Together, these demonstrate that the embodiments of this application, under the premise of multi-dimensional matching, can perform both fine-grained benchmarking in fixed areas and flexible optimization within dynamic ranges. These two modes jointly ensure that a highly reliable energy efficiency optimization benchmark can be found for the target device group in different application scenarios.

[0122] Figure 5 This is a schematic diagram of the structure of the energy efficiency optimization device for engineering machinery provided in the embodiments of this application; as shown below. Figure 5 As shown, the device includes:

[0123] The processing module 501 is used to match and classify multiple construction machinery equipment according to the configuration parameters, operating parameters, working condition data, environmental data and operation data of multiple construction machinery equipment obtained within a preset period, to obtain at least one group of equipment, each group of equipment including at least two construction machinery equipment, and each construction machinery equipment is matched in terms of equipment dimension, personnel operation level dimension and working environment dimension.

[0124] The acquisition module 502 is used to acquire the energy efficiency value of each piece of construction machinery in the group based on the operating parameters and working condition data of each piece of construction machinery in the group.

[0125] The optimization module 503 is used to optimize the energy efficiency of other construction machinery equipment in the group based on the configuration parameters of the target equipment with the highest energy efficiency value for each piece of construction machinery equipment in the group.

[0126] In one possible implementation, the optimization module 503 is specifically used for:

[0127] For each group of equipment, except for the target equipment, verify whether the configuration parameters of the target equipment exceed the safe range of the parameters of the other engineering machinery equipment.

[0128] When the configuration parameters of the target equipment do not exceed the safe range of the parameters of other construction machinery equipment, the configuration parameters of the target equipment will be sent to other construction machinery equipment in the group.

[0129] In one possible implementation, the optimization module 503 is specifically used for:

[0130] For the other construction machinery equipment in each group of equipment besides the target equipment, the preset proportion parameters in the configuration parameters of the target equipment are sent to the other construction machinery equipment in the group. The preset proportion is between 60% and 100%.

[0131] In one possible implementation, the energy efficiency optimization device for the process machinery further includes a control module for:

[0132] Real-time acquisition of operating parameters for each piece of engineering machinery within the group after energy efficiency optimization;

[0133] If the deviation of the operating parameters of any construction machinery equipment is detected to be greater than the preset threshold, a parameter rollback instruction will be issued to instruct the construction machinery equipment to roll back the configuration parameters to the state before modification.

[0134] In one possible implementation, other engineering machinery equipment that needs to be optimized within each group includes engineering machinery equipment whose energy efficiency value differs from the energy efficiency value of the target equipment within the group by a preset energy efficiency deviation.

[0135] In one possible implementation, the acquisition module 502 is used for:

[0136] For each piece of construction machinery within the group, the initial energy efficiency value is calculated using the energy efficiency calculation formula based on the operating parameters and working condition data of the construction machinery.

[0137] Based on the environmental data of the construction machinery and equipment, the initial energy efficiency value is adjusted to obtain the energy efficiency value of the construction machinery and equipment.

[0138] In one possible implementation, the energy efficiency calculation formula is: the ratio of workload to fuel consumption multiplied by a working condition correction factor; wherein the working condition correction factor is calculated based on the weights of multiple working conditions that affect energy efficiency.

[0139] Module 502 is used for:

[0140] If environmental data indicates that the current working condition is frozen soil, the weight of the hydraulic stability working condition is increased, a new working condition correction factor is recalculated, and the initial energy efficiency value is corrected according to the new working condition correction factor to obtain the corrected energy efficiency value.

[0141] If environmental data indicates that the current operating condition is rainy, the weight of the anti-skid operation economic condition is increased, a new operating condition correction factor is recalculated, and the initial energy efficiency value is corrected according to the new operating condition correction factor to obtain the corrected energy efficiency value.

[0142] In one possible implementation, the processing module 501 is specifically used for:

[0143] Based on the configuration parameters, operating parameters, working condition data, environmental data, and operation data of multiple construction machinery equipment, as well as the preset matching rules for each dimension in the equipment dimension, personnel operation level dimension, and working environment dimension, a pre-acquired multi-dimensional matching algorithm is used to match and classify multiple construction machinery equipment to obtain at least one set of equipment.

[0144] Among them, the matching rules for equipment include matching tonnage and / or model, and matching hydraulic system version;

[0145] The matching rules for personnel operation level include: the difference between operation intensity indices is within a preset range;

[0146] The matching rules for the work environment dimension include: the slope distribution similarity of the work site is greater than the preset similarity, and the soil hardness difference of the work site is less than the preset hardness.

[0147] The energy efficiency optimization device for construction machinery provided in this application embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0148] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6 As shown, the electronic device 60 provided in this embodiment includes at least one processor 601 and a memory 602. Optionally, the device 60 further includes a communication component 603. The processor 601, memory 602, and communication component 603 are connected via a bus 604.

[0149] In a specific implementation, at least one processor 601 executes computer execution instructions stored in memory 602, causing at least one processor 601 to perform the above-described method.

[0150] The specific implementation process of processor 601 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0151] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0152] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0153] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0154] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0155] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0156] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0157] The division of units is merely a logical functional division; 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 indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0158] 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.

[0159] In addition, 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.

[0160] If a function 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, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0161] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0162] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method of energy efficiency optimization of a working machine, characterized in that, The method comprises the following steps: According to the configuration parameters, operation parameters, working condition data, environmental data and operation data of the multiple engineering machinery devices obtained within the preset period, the multiple engineering machinery devices are matched and classified to obtain at least one group of devices, each group of devices comprising at least two engineering machinery devices, and each engineering machinery device matching in the device dimension, the personnel operation level dimension and the working environment dimension; For each group of devices, the energy efficiency value of each engineering machinery device in the group is obtained according to the operation parameters and working condition data of each engineering machinery device in the group; The energy efficiency of other engineering machinery devices in the group is optimized according to the configuration parameters of the target device with the highest energy efficiency value among the engineering machinery devices in the group.

2. The method of claim 1, wherein, The energy efficiency of other engineering machinery devices in the group is optimized according to the configuration parameters of the target device with the highest energy efficiency value among the engineering machinery devices in the group, comprising: For other engineering machinery devices in each group of devices except the target device, it is checked whether the configuration parameters of the target device exceed the parameter safety range of the other engineering machinery devices; When the configuration parameters of the target device do not exceed the parameter safety range of the other engineering machinery devices, the configuration parameters of the target device are issued to the other engineering machinery devices in the group.

3. The method of claim 1, wherein, The energy efficiency of other engineering machinery devices in the group is optimized according to the configuration parameters of the target device with the highest energy efficiency value among the engineering machinery devices in the group, comprising: For other engineering machinery devices in each group of devices except the target device, a preset proportion of parameters in the configuration parameters of the target device is issued to the other engineering machinery devices in the group, wherein the preset proportion is valued between 60% and 100%.

4. The method according to any one of claims 1 to 3, characterized in that, The method further comprises: Real-time acquisition of the operation parameters of each engineering machinery device in the group after energy efficiency optimization; If it is detected that the operation parameter deviation value of any engineering machinery device is greater than a preset threshold, a parameter rollback instruction is issued to indicate that the engineering machinery device rolls back the configuration parameters to the parameters before modification.

5. The method according to any one of claims 1 to 3, characterized in that, The other engineering machinery devices in each group that need to be optimized include engineering machinery devices with an energy efficiency value difference greater than a preset energy efficiency deviation from the energy efficiency value of the target device in the group.

6. The method according to any one of claims 1 to 3, characterized in that, The energy efficiency value of each engineering machinery device in the group is obtained according to the operation parameters and working condition data of each engineering machinery device in the group, comprising: For each engineering machinery device in the group, an energy efficiency initial value is calculated according to the operation parameters and working condition data of the engineering machinery device using an energy efficiency calculation formula; The energy efficiency initial value is adjusted according to the environmental data of the engineering machinery device to obtain the energy efficiency value of the engineering machinery device.

7. The method of claim 6, wherein, The energy efficiency calculation formula is the ratio of work quantity to oil consumption multiplied by a working condition correction factor, wherein the working condition correction factor is calculated according to the weights of multiple working conditions affecting energy efficiency; The energy efficiency initial value is adjusted according to the environmental data of the engineering machinery device to obtain the energy efficiency value of the engineering machinery device, comprising: If the environment data indicates that the current is in permafrost working condition, the weight of the hydraulic stability working condition is increased, a new working condition correction factor is recalculated, and the energy efficiency initial value is corrected according to the new working condition correction factor to obtain a corrected energy efficiency value; If the environment data indicates that the current is in rainy working condition, the weight of the anti-skid action economy working condition is increased, a new working condition correction factor is recalculated, and the energy efficiency initial value is corrected according to the new working condition correction factor to obtain a corrected energy efficiency value.

8. The method according to any one of claims 1 to 3, characterized in that, The plurality of engineering mechanical equipment is matched and classified according to the configuration parameters, operation parameters, working condition data, environment data and operation data of the plurality of engineering mechanical equipment in the mining area, and at least one group of equipment is obtained. According to the configuration parameters, operation parameters, working condition data, environment data and operation data of the plurality of engineering mechanical equipment, and the matching rules of each dimension of the equipment dimension, the personnel operation level dimension and the working environment dimension, the plurality of engineering mechanical equipment is matched and classified by using the multi-dimensional matching algorithm obtained in advance, and the at least one group of equipment is obtained. The matching rules of the equipment dimension include tonnage and / or model consistency, and hydraulic system version consistency. The matching rules of the personnel operation level dimension include that the difference between the operation intensity indexes is within a preset range. The matching rules of the working environment dimension include that the slope distribution similarity of the working ground is greater than a preset similarity, and the soil hardness difference of the working ground is less than a preset hardness.

9. An energy efficiency optimization device of a construction machine, characterized by, The method comprises: The processing module is configured to match and classify the plurality of engineering mechanical equipment according to the configuration parameters, operation parameters, working condition data, environment data and operation data of the plurality of engineering mechanical equipment obtained within a preset period, and obtain at least one group of equipment, each group of equipment including at least two engineering mechanical equipment, and each engineering mechanical equipment matching in the equipment dimension, the personnel operation level dimension and the working environment dimension; The obtaining module is configured to obtain the energy efficiency value of each engineering mechanical equipment in each group according to the operation parameters and working condition data of each engineering mechanical equipment in the group. The optimization module is configured to optimize the energy efficiency of other engineering mechanical equipment in the group according to the configuration parameters of the target equipment with the highest energy efficiency value.

10. An electronic device, comprising: The method comprises: The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory, so that the processor executes the method of any one of claims 1-8. The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory, so that the processor executes the method of any one of claims 1-8.

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