A fuel consumption optimization method, system, storage medium and electronic device

Abstract

The application provides a fuel consumption optimization method, system, storage medium and electronic equipment, and relates to the technical field of diesel generator fuel consumption control. The method collects and processes environmental information and working condition information through a preset sensing device. A preset function corresponding to a preset combustion model is fitted according to the working condition information, so that the parameters of the preset function are adjusted according to fitting data and the environmental information, and a target function is determined. The target control parameter is determined through an optimization algorithm according to the working condition information and the environmental information with the target function as a constraint, so that the real-time fuel consumption of the diesel generator set is controlled according to the target control parameter. The application solves the problem that there is no diesel generator set fuel consumption optimization method based on complex working condition dynamic optimization of fuel utilization and combustion state in the prior art.

Description

Technical Field

[0001] This invention relates to the field of diesel generator fuel consumption control technology, and in particular to a fuel consumption optimization method, system, storage medium, and electronic device. Background Technology

[0002] A diesel generator set is an industrial power supply equipment that uses a diesel engine as its core power source and integrates key components such as a generator and a control panel. Its working principle is to convert thermal energy into mechanical energy through diesel combustion, and then further convert mechanical energy into electrical energy, thereby realizing power output.

[0003] These types of equipment have a wide range of applications, including emergency power supply during sudden grid failures, backup power in industrial production, and core power supply in remote areas such as polar expeditions and wilderness areas without grid coverage. Current diesel generator sets typically employ a fixed "load-speed" mode and a simple fuel injection adjustment strategy. This involves adjusting the fuel injection quantity and intake air volume based on the power generation demand, monitoring parameters such as base load, engine speed, and fuel pressure to adapt to different operating requirements.

[0004] However, in remote areas without power grid coverage or in harsh environments such as low or high temperatures, equipment often requires continuous power supply for extended periods. This places higher demands on the precise control of fuel consumption and the effective guarantee of combustion efficiency, requiring dynamic optimization of fuel utilization and combustion status based on complex operating conditions. However, existing control methods lack specific adaptation to the characteristics of equipment in extreme environments, making it difficult to achieve precise control of fuel consumption. They can only meet basic power supply needs under conventional scenarios such as power grid failures, and cannot adapt to the long-term, high-efficiency, and low-emission operation requirements in extreme environments. Summary of the Invention

[0005] Based on this, the purpose of the present invention is to provide a fuel consumption optimization method, system, storage medium, and electronic device, aiming to solve the problem in the prior art of lacking a fuel consumption optimization method for diesel generator sets that dynamically optimizes fuel utilization and combustion state based on complex operating conditions.

[0006] A fuel consumption optimization method according to an embodiment of the present invention includes:

[0007] Environmental and operating condition information is collected and processed through pre-set sensing devices;

[0008] The preset function corresponding to the preset combustion model is fitted according to the operating condition information, and the parameters of the preset function are adjusted according to the fitted data and the environmental information to determine the target function.

[0009] Using the objective function as a constraint, the target control parameters are determined through an optimization algorithm based on the operating condition information and the environmental information, so as to control the real-time fuel consumption of the diesel generator set according to the target control parameters.

[0010] In addition, the fuel consumption optimization method according to the above embodiments of the present invention may also have the following additional technical features:

[0011] Furthermore, the step of fitting a preset function corresponding to a preset combustion model based on the operating condition information, and then adjusting the parameters of the preset function based on the fitted data and the environmental information to determine the target function includes:

[0012] Based on the aforementioned operating condition information, determine the actual mass combustion fraction curve and the corresponding real-time combustion initiation angle and real-time combustion duration;

[0013] Based on the environmental information, the real-time combustion initiation angle and the real-time combustion duration are corrected to determine the target combustion initiation angle and the target combustion duration.

[0014] Substitute the target combustion initiation angle and the target combustion duration into the preset function, and fit the theoretical mass combustion fraction curve predicted by the preset combustion model with the actual mass combustion fraction curve to determine the target combustion efficiency coefficient and the target combustion morphology index.

[0015] The target combustion efficiency coefficient, the target combustion morphology index, the target combustion initiation angle, and the target combustion duration are substituted into the preset function to determine the target function;

[0016] The preset function is:

[0017]

[0018] in, The mass fraction of the fuel already burned. The combustion efficiency coefficient, This is the current crankshaft angle. The combustion initiation angle, The duration of combustion, This is the combustion state index.

[0019] Furthermore, the step of correcting the real-time combustion initiation angle and the real-time combustion duration based on the environmental information to determine the target combustion initiation angle and the target combustion duration includes:

[0020] Based on the environmental information, the real-time combustion initiation angle and the real-time combustion duration are corrected using the combustion initiation angle correction equation and the combustion duration correction equation to determine the target combustion initiation angle and the target combustion duration.

[0021] The combustion initiation angle correction equation is:

[0022]

[0023] in, The starting angle for the target combustion. For real-time combustion initiation angle, This is the temperature compensation amount. For viscosity compensation, This is the drift compensation amount;

[0024] The corrected equation for the combustion duration is:

[0025]

[0026] in, For the target combustion duration, For real-time combustion duration, For temperature compensation factor, It is the viscosity compensation factor. This is the combustion state factor.

[0027] Furthermore, the step of determining the target control parameters using the objective function as a constraint and based on the operating condition information and the environmental information through an optimization algorithm includes:

[0028] Construct a multi-objective optimization function and determine the current working condition state based on the working condition information;

[0029] The target optimization algorithm is determined based on the operating conditions, and the target control parameters are determined by the target optimization algorithm based on the multi-objective optimization function, the operating conditions information, and the environmental information.

[0030] Furthermore, after determining the target control parameters using the target optimization algorithm based on the multi-objective optimization function, the operating condition information, and the environmental information, the process includes:

[0031] Based on the target control parameters, the operating condition information, and the environmental information, the fuel consumption index for a preset time period is predicted to determine the theoretical fuel index.

[0032] The actual fuel index for a preset time period is obtained and compared with the theoretical fuel index to determine the difference data, so as to adjust the multi-objective optimization function and the objective optimization algorithm according to the difference data.

[0033] Furthermore, after the steps of collecting and processing environmental and operational information through preset sensing devices, the process includes:

[0034] Determine the rate of change of the environmental information and the operating condition information, and determine whether the rate of change of the environmental information or the operating condition information exceeds a preset change threshold;

[0035] If so, based on the current operating condition information, the environmental information, and the historical optimal control parameters, temporary control parameters are generated within a preset time using a preset fast convergence optimization model, so as to control the real-time fuel consumption of the diesel generator set according to the temporary control parameters;

[0036] If not, perform the step of fitting a preset function corresponding to a preset combustion model based on the operating condition information, and adjusting the parameters of the preset function based on the fitted data and the environmental information to determine the target function.

[0037] Furthermore, the steps for collecting and processing environmental and operational information through preset sensing devices include:

[0038] Simultaneously acquire multi-source information collected by multiple sensors and align the multi-source information with timestamps, and filter and determine multi-source correction information by using corresponding methods according to information type;

[0039] The multi-source correction information is standardized and classified to determine operating condition information and environmental information.

[0040] Another object of the present invention is a fuel consumption optimization system, the system comprising:

[0041] The data acquisition module is used to collect and process environmental and operating condition information through preset sensing devices;

[0042] The function adjustment module is used to fit a preset function corresponding to a preset combustion model based on the operating condition information, and to adjust the parameters of the preset function based on the fitted data and the environmental information to determine the target function.

[0043] The parameter determination module is used to determine target control parameters based on the operating condition information and the environmental information using an optimization algorithm, with the objective function as a constraint, so as to control the real-time fuel consumption of the diesel generator set according to the target control parameters.

[0044] This invention collects real-time environmental and operating condition information, and uses this real-time information to fit and adjust a preset function corresponding to a preset combustion model to obtain an objective function corresponding to the current environment and operating state. In other words, the real-time fuel combustion model is determined through the objective function. Under the constraints of the objective function, an optimization algorithm is used to adjust various control parameters, determining parameters such as fuel utilization, combustion status, and combustion emissions under different control parameters, current environment, and operating state. This allows for the determination of optimal control parameters based on complex operating conditions and environments. Therefore, this invention solves the problem in the prior art of lacking a method for dynamically optimizing fuel utilization and combustion status in diesel generator sets under complex operating conditions. Attached Figure Description

[0045] Figure 1 This is a flowchart of the fuel consumption optimization method in the first embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of the results of the fuel consumption optimization system in the second embodiment of the present invention;

[0047] Figure 3 This is a schematic diagram of the structure of the electronic device in the third embodiment of the present invention;

[0048] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0049] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0051] Example 1

[0052] Please see Figure 1 The figure shows a fuel consumption optimization method in the first embodiment of the present invention, which specifically includes steps S01-S03.

[0053] S01 collects and processes environmental and operating condition information through preset sensing devices.

[0054] Specifically, multi-source information from multiple sensors is acquired synchronously, and the information is timestamped and aligned. Then, based on the information type, corresponding methods are used to filter and determine multi-source correction information. This correction information is standardized and classified to determine operating condition and environmental information. Specifically, through spatiotemporal alignment technology of multi-source sensor information, combined with a signal classification processing mechanism (i.e., differential filtering of cylinder pressure, speed, and temperature), the effectiveness of environmental and operating condition information is ensured from the data source, providing high signal-to-noise ratio input for upper-level decision-making.

[0055] Furthermore, after step S01, the fuel consumption optimization method in this application further includes determining the rate of change of the environmental information and the operating condition information, and judging whether the rate of change of the environmental information or the operating condition information exceeds a preset change threshold; if so, based on the current operating condition information, the environmental information, and historical optimal control parameters, temporary control parameters are generated within a preset time using a preset fast convergence optimization model, so as to control the real-time fuel consumption of the diesel generator set according to the temporary control parameters; if not, step S02 is executed. Specifically, for drastic changes in environmental or operating condition parameters, a multi-level response chain is constructed, including rate of change detection, historical parameter retrieval, and simplified optimization. By generating feasible control parameters in a short time through a fast convergence model, the risk of engine shutdown due to computational delays in traditional methods is avoided, providing safety redundancy for extreme operating conditions.

[0056] S02, fit the preset function corresponding to the preset combustion model according to the operating condition information, and adjust the parameters of the preset function according to the fitting data and the environmental information to determine the target function.

[0057] Specifically, based on the operating condition information, the actual mass combustion fraction curve and the corresponding real-time combustion initiation angle and real-time combustion duration are determined; based on the environmental information, the real-time combustion initiation angle and real-time combustion duration are corrected to determine the target combustion initiation angle and target combustion duration; the target combustion initiation angle and target combustion duration are substituted into the preset function, and the theoretical mass combustion fraction curve predicted by the preset combustion model and the actual mass combustion fraction curve are fitted to determine the target combustion efficiency coefficient and target combustion morphology index; the target combustion efficiency coefficient, the target combustion morphology index, the target combustion initiation angle, and the target combustion duration are substituted into the preset function to determine the target function; the preset function is:

[0058]

[0059] in, The mass fraction of the fuel already burned. The combustion efficiency coefficient, This is the current crankshaft angle. The combustion initiation angle, The duration of combustion, This refers to the combustion morphology index. Specifically, through function fitting techniques, the actual combustion curve is dynamically matched with the theoretical model. By incorporating combustion phase parameters (initiation angle / duration) corrected for environmental factors into the model, the accuracy of combustion state prediction under extreme conditions is significantly improved, laying the mathematical model foundation for the implementation of the optimization algorithm.

[0060] Furthermore, the step of correcting the real-time combustion initiation angle and the real-time combustion duration based on the environmental information to determine the target combustion initiation angle and the target combustion duration includes: correcting the real-time combustion initiation angle and the real-time combustion duration based on the environmental information using a combustion initiation angle correction equation and a combustion duration correction equation to determine the target combustion initiation angle and the target combustion duration;

[0061] The combustion initiation angle correction equation is:

[0062]

[0063] in, The starting angle for the target combustion. For real-time combustion initiation angle, This is the temperature compensation amount. For viscosity compensation, This is the drift compensation amount;

[0064] The corrected equation for the combustion duration is:

[0065]

[0066] in, For the target combustion duration, For real-time combustion duration, For temperature compensation factor, It is the viscosity compensation factor. This is a combustion state factor. Through multiphysics coupling, the influence of the environment on target parameters is determined, and corrections are made to achieve environmental compensation. It solves timing control problems such as low-temperature ignition delay and abnormal fuel atomization, ensuring sufficient accuracy of the ignition phase even in extreme environments. Furthermore, it dynamically adjusts the combustion window length according to environmental conditions to guarantee the accuracy of combustion duration. Finally, a dual compensation mechanism ensures combustion integrity under complex environments.

[0067] S03, using the objective function as a constraint, the target control parameters are determined through an optimization algorithm based on the operating condition information and the environmental information, so as to control the real-time fuel consumption of the diesel generator set according to the target control parameters.

[0068] Specifically, a multi-objective optimization function is constructed, and the current operating state is determined based on the operating condition information. A target optimization algorithm is determined based on the operating state, and target control parameters are determined using the target optimization algorithm based on the multi-objective optimization function, the operating condition information, and the environmental information. In practical implementation, the adaptability of different optimization algorithms varies depending on the specific scenario and operating condition, requiring adjustments to the selection of the appropriate target optimization algorithm based on the actual situation. More specifically, the optimization algorithm may include particle swarm optimization, which is suitable for stages of drastic system state changes, such as cold start processes, sudden load changes, or initial operation without historical data. The optimization algorithm may include gradient descent, which is suitable for stable system operation stages, such as operating conditions with small speed fluctuations and load changes. The optimization algorithm may include case-based reasoning, which is suitable for system emergency states, such as sudden sensor linearity, transient process control overshoot, and other sudden situations, using historical cases for rapid emergency response.

[0069] Furthermore, after step S03, the fuel consumption optimization method in this application further includes predicting and determining the theoretical fuel consumption index for a preset time period based on the target control parameters, the operating condition information, and the environmental information; obtaining the actual fuel consumption index for the preset time period and comparing it with the theoretical fuel consumption index to determine the difference data, so as to adjust the multi-objective optimization function and the target optimization algorithm based on the difference data. Specifically, a reverse calibration channel for model parameters is established through the difference analysis between the theoretical fuel consumption index prediction and the actual operating data. When environmental degradation or equipment aging causes the combustion model to shift, this mechanism drives the system to autonomously update key parameters such as the combustion efficiency coefficient and morphology index, so that the optimization capability continues to improve over time and effectively combats system performance degradation.

[0070] In summary, the fuel consumption optimization method in the above embodiments of the present invention collects real-time environmental and operating condition information, and adjusts the preset function corresponding to the preset combustion model based on the real-time information to obtain the objective function corresponding to the current environment and operating state. That is, the real-time fuel combustion model is determined through the objective function. Under the constraint of the objective function, various control parameters are adjusted according to the optimization algorithm to determine parameters such as fuel utilization rate, combustion status, and combustion emissions under different control parameters, current environment, and operating state. This allows for the determination of optimal control parameters based on complex operating conditions and environments. Therefore, the present invention solves the problem in the prior art of lacking a fuel consumption optimization method for diesel generator sets that dynamically optimizes fuel utilization and combustion status based on complex operating conditions.

[0071] Example 2

[0072] Please see Figure 2The diagram shown is a structural block diagram of the fuel consumption optimization system proposed in the second embodiment of the present invention. The fuel consumption optimization system 200 includes: a data acquisition module 21, a function adjustment module 22, and a parameter determination module 23, wherein:

[0073] Data acquisition module 21 is used to collect and process environmental and operating condition information through preset sensing devices;

[0074] The function adjustment module 22 is used to fit a preset function corresponding to a preset combustion model according to the operating condition information, so as to adjust the parameters of the preset function according to the fitting data and the environmental information and thus determine the target function;

[0075] The parameter determination module 23 is used to determine the target control parameters based on the operating condition information and the environmental information using an optimization algorithm, with the objective function as a constraint, so as to control the real-time fuel consumption of the diesel generator set according to the target control parameters.

[0076] The functions or operation steps implemented by the above modules are largely the same as those in the above method embodiments, and will not be repeated here.

[0077] Example 3

[0078] In another aspect, the present invention also proposes an electronic device, please refer to [link to relevant documentation]. Figure 3 The diagram shown is a schematic diagram of an electronic device in the third embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, it implements the fuel consumption optimization method as described above.

[0079] In some embodiments, the processor 10 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip, used to run program code stored in memory 20 or process data, such as executing access restriction programs.

[0080] The memory 20 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 20 can include both internal and external storage units of the electronic device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or will be output.

[0081] It should be pointed out that, Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0082] This invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the fuel consumption optimization method described above.

[0083] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0084] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0085] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0086] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0087] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for optimizing fuel consumption, characterized in that, The method for controlling fuel consumption of a diesel generator set includes: Environmental and operating condition information is collected and processed through pre-set sensing devices; The preset function corresponding to the preset combustion model is fitted according to the operating condition information, and the parameters of the preset function are adjusted according to the fitted data and the environmental information to determine the target function. Using the objective function as a constraint, the target control parameters are determined through an optimization algorithm based on the operating condition information and the environmental information, so as to control the real-time fuel consumption of the diesel generator set according to the target control parameters; The steps of fitting a preset function corresponding to a preset combustion model based on the operating condition information, and adjusting the parameters of the preset function based on the fitted data and the environmental information to determine the target function include: Based on the aforementioned operating condition information, determine the actual mass combustion fraction curve and the corresponding real-time combustion initiation angle and real-time combustion duration; Based on the environmental information, the real-time combustion initiation angle and the real-time combustion duration are corrected to determine the target combustion initiation angle and the target combustion duration. Substitute the target combustion initiation angle and the target combustion duration into the preset function, and fit the theoretical mass combustion fraction curve predicted by the preset combustion model with the actual mass combustion fraction curve to determine the target combustion efficiency coefficient and the target combustion morphology index. The target combustion efficiency coefficient, the target combustion morphology index, the target combustion initiation angle, and the target combustion duration are substituted into the preset function to determine the target function; The preset function is: in, The mass fraction of the fuel already burned. The combustion efficiency coefficient, This is the current crankshaft angle. The combustion initiation angle, The duration of combustion, It is the combustion state index; The steps for determining the target combustion initiation angle and target combustion duration by correcting the real-time combustion initiation angle and the real-time combustion duration based on the environmental information include: Based on the environmental information, the real-time combustion initiation angle and the real-time combustion duration are corrected using the combustion initiation angle correction equation and the combustion duration correction equation to determine the target combustion initiation angle and the target combustion duration. The combustion initiation angle correction equation is: in, The starting angle for the target combustion. For real-time combustion initiation angle, This is the temperature compensation amount. For viscosity compensation, This is the drift compensation amount; The corrected equation for the combustion duration is: in, For the target combustion duration, For real-time combustion duration, For temperature compensation factor, It is the viscosity compensation factor. This is the combustion state factor.

2. The fuel consumption optimization method according to claim 1, characterized in that, The steps of determining the target control parameters using the objective function as a constraint and based on the operating condition information and the environmental information through an optimization algorithm include: Construct a multi-objective optimization function and determine the current working condition state based on the working condition information; The target optimization algorithm is determined based on the operating conditions, and the target control parameters are determined by the target optimization algorithm based on the multi-objective optimization function, the operating conditions information, and the environmental information.

3. The fuel consumption optimization method according to claim 2, characterized in that, After the step of determining the target control parameters using the target optimization algorithm based on the multi-objective optimization function, the operating condition information, and the environmental information, the following steps are included: Based on the target control parameters, the operating condition information, and the environmental information, the fuel consumption index for a preset time period is predicted to determine the theoretical fuel index. The actual fuel index for a preset time period is obtained and compared with the theoretical fuel index to determine the difference data, so as to adjust the multi-objective optimization function and the objective optimization algorithm according to the difference data.

4. The fuel consumption optimization method according to claim 1, characterized in that, Following the steps of collecting and processing environmental and operational information through preset sensing devices, the following is included: Determine the rate of change of the environmental information and the operating condition information, and determine whether the rate of change of the environmental information or the operating condition information exceeds a preset change threshold; If so, based on the current operating condition information, the environmental information, and the historical optimal control parameters, temporary control parameters are generated within a preset time using a preset fast convergence optimization model, so as to control the real-time fuel consumption of the diesel generator set according to the temporary control parameters; If not, perform the step of fitting a preset function corresponding to a preset combustion model based on the operating condition information, and adjusting the parameters of the preset function based on the fitted data and the environmental information to determine the target function.

5. The fuel consumption optimization method according to claim 1, characterized in that, The steps for collecting and processing environmental and operational information using preset sensing devices include: Simultaneously acquire multi-source information collected by multiple sensors and align the multi-source information with timestamps, and filter and determine multi-source correction information by using corresponding methods according to information type; The multi-source correction information is standardized and classified to determine operating condition information and environmental information.

6. A fuel consumption optimization system, characterized in that, The system for implementing the fuel consumption optimization method as described in any one of claims 1 to 5 includes: The data acquisition module is used to collect and process environmental and operating condition information through preset sensing devices; The function adjustment module is used to fit a preset function corresponding to a preset combustion model based on the operating condition information, and to adjust the parameters of the preset function based on the fitted data and the environmental information to determine the target function. The parameter determination module is used to determine target control parameters based on the operating condition information and the environmental information using an optimization algorithm, with the objective function as a constraint, so as to control the real-time fuel consumption of the diesel generator set according to the target control parameters.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the fuel consumption optimization method as described in any one of claims 1 to 5.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the fuel consumption optimization method as described in any one of claims 1-5.

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