Oil distribution equalization method and system for intelligent oil distribution tank

Abstract

The application discloses an oil quantity distribution equalization method and system of an intelligent oil distribution tank, relates to the technical field of oil quantity distribution regulation, and comprises the following steps: predicting oil quantity demand according to a vehicle operation scheme of a target vehicle, establishing an oil quantity demand curve, obtaining a cavity state characteristic sequence, regulating oil supply for the intelligent oil distribution tank, obtaining a first space of oil supply regulation, performing equalization calculation optimization, obtaining a second space of oil supply regulation, performing scheduling evaluation analysis optimization on the second space of oil supply regulation, obtaining a third space of oil supply regulation, performing multiple reproduction joint optimization, determining an oil supply regulation optimization result, and performing oil supply management and control. The application solves the technical problems that the oil quantity distribution of the intelligent oil distribution tank is unbalanced, oil supply scheduling is unreasonable, the efficiency of oil supply management and control of the target vehicle is low, and the reliability is poor in the prior art, achieves the equalization of the oil quantity distribution of the intelligent oil distribution tank and the rationality of oil supply scheduling, and improves the technical effects of the efficiency and reliability of the oil supply management and control of the target vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil distribution regulation, in particular to an oil distribution balancing method and system of an intelligent oil distribution tank. BACKGROUND

[0002] In a vehicle power system, the intelligent oil distribution tank is a core component of multi-cavity oil supply, and the balancing of its oil distribution directly affects the engine operation stability, fuel utilization efficiency and vehicle endurance. The existing oil supply regulation of the oil distribution tank relies on preset thresholds or single-cavity oil quantity feedback for simple distribution, and it is difficult to dynamically adapt to the oil quantity demand according to the real-time operation scheme of the vehicle (such as load change, driving conditions, endurance demand, etc.). The imbalance phenomenon of some cavities with excessive oil quantity and other cavities with insufficient oil supply often occurs, resulting in lagging response of oil supply scheduling, serious energy waste, and even power interruption risk.

[0003] The existing technology has the technical problem of unbalanced oil distribution of the intelligent oil distribution tank, and unreasonable oil supply scheduling leads to low and poor reliability of the target vehicle oil supply control efficiency. SUMMARY

[0004] The present application provides an oil distribution balancing method and system of an intelligent oil distribution tank, which is used to solve the technical problem of unbalanced oil distribution of the intelligent oil distribution tank in the prior art, and unreasonable oil supply scheduling leads to low and poor reliability of the target vehicle oil supply control efficiency.

[0005] In view of the above problems, the present application provides an oil distribution balancing method and system of an intelligent oil distribution tank.

[0006] In a first aspect of the present application, an oil distribution balancing method of an intelligent oil distribution tank is provided, which comprises:

[0007] According to the vehicle operation scheme of the target vehicle, the oil quantity demand is predicted, and the oil quantity demand curve is established. The intelligent oil distribution tank of the target vehicle is sensed in real time to obtain a sequence of state characteristics of each cavity, and the intelligent oil distribution tank is provided with a plurality of oil tank cavities. Based on the sequence of state characteristics of each cavity, the intelligent oil distribution tank is adjusted according to the oil quantity demand curve to obtain a first space of oil supply adjustment. According to the oil distribution balancing threshold, the first space of oil supply adjustment is balanced and optimized to obtain a second space of oil supply adjustment. Based on the vehicle operation scheme, the second space of oil supply adjustment is evaluated and analyzed according to the oil supply scheduling evaluation model to obtain a third space of oil supply adjustment. Based on the oil distribution balancing threshold and the oil supply scheduling evaluation model, the third space of oil supply adjustment is subjected to multiple reproduction joint optimization to determine the optimization result of oil supply adjustment, and the target vehicle is supplied with oil in combination with the intelligent oil distribution tank.

[0008] In a second aspect of the present application, an oil quantity distribution balancing system of an intelligent oil distribution tank is provided, and the system comprises:

[0009] An oil quantity demand curve establishing module is configured to predict oil quantity demand based on a vehicle operation scheme of a target vehicle and establish an oil quantity demand curve; a real-time sensing module is configured to perform real-time sensing on an intelligent oil distribution tank of the target vehicle and obtain a sequence of state features of each cavity, the intelligent oil distribution tank being provided with a plurality of tank cavities; an oil supply adjusting module is configured to perform oil supply adjustment on the intelligent oil distribution tank based on the sequence of state features of each cavity and the oil quantity demand curve, and obtain a first space of oil supply adjustment; a balancing calculation and optimization module is configured to perform balancing calculation and optimization on the first space of oil supply adjustment based on an oil supply distribution balancing threshold, and obtain a second space of oil supply adjustment; a scheduling evaluation and analysis optimization module is configured to perform scheduling evaluation and analysis optimization on the second space of oil supply adjustment based on an oil supply scheduling evaluation model and the vehicle operation scheme, and obtain a third space of oil supply adjustment; and an oil supply adjustment optimization result determining module is configured to perform multiple reproduction joint optimization based on the third space of oil supply adjustment, the oil supply distribution balancing threshold and the oil supply scheduling evaluation model, determine an oil supply adjustment optimization result, and perform oil supply management and control on the target vehicle in combination with the intelligent oil distribution tank.

[0010] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0011] The oil quantity demand curve is established based on the vehicle operation scheme of the target vehicle; the intelligent oil distribution tank of the target vehicle is sensed in real time to obtain a sequence of state features of each cavity; the intelligent oil distribution tank is adjusted for oil supply based on the oil quantity demand curve to obtain a first space of oil supply adjustment; the first space of oil supply adjustment is calculated and optimized for balancing based on an oil supply distribution balancing threshold to obtain a second space of oil supply adjustment; the second space of oil supply adjustment is evaluated and analyzed for scheduling optimization based on an oil supply scheduling evaluation model to obtain a third space of oil supply adjustment; multiple reproduction joint optimization is performed based on the third space of oil supply adjustment to determine an oil supply adjustment optimization result, and oil supply management and control are performed. The technical effect of achieving the balancing of oil quantity distribution and the rationality of oil supply scheduling of the intelligent oil distribution tank is achieved, and the efficiency and reliability of oil supply management and control of the target vehicle are improved. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0013] Figure 1A schematic diagram of the fuel distribution balancing method for an intelligent fuel tank provided in an embodiment of this application;

[0014] Figure 2 This is a schematic diagram of the oil quantity distribution and balancing system of the intelligent oil distribution tank provided in the embodiments of this application.

[0015] Figure labeling: Module 10 for establishing oil demand curve, Module 20 for real-time sensing, Module 30 for oil supply regulation, Module 40 for equilibrium calculation and optimization, Module 50 for scheduling evaluation and analysis optimization, and Module 60 for determining the optimization result of oil supply regulation. Detailed Implementation

[0016] This application provides a method and system for balancing fuel distribution in an intelligent fuel tank, which addresses the technical problems of uneven fuel distribution and unreasonable fuel supply scheduling in existing intelligent fuel tanks, resulting in low efficiency and poor reliability in fuel supply control of target vehicles.

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0018] Example 1, as Figure 1 As shown, this application provides a method for balancing fuel distribution in an intelligent fuel tank, the method comprising:

[0019] Step S100: Based on the vehicle operation plan of the target vehicle, predict the fuel demand and establish a fuel demand curve.

[0020] Specifically, the vehicle operation plan (such as driving route, driving time, load, etc.) of the target vehicle is analyzed to predict the fuel demand of the vehicle at different times, thereby obtaining a fuel demand set containing the fuel demand of each time period; then, a fuel demand coordinate system is constructed with time as the horizontal axis and fuel demand as the vertical axis, and the time and corresponding fuel demand data in the fuel demand set are substituted into the coordinate system for curve fitting, finally generating a fuel demand curve that can reflect the fuel demand change pattern of the vehicle throughout the entire operation process.

[0021] Step S200: Real-time sensing of the intelligent fuel tank of the target vehicle to obtain the state feature sequence of each cavity. The intelligent fuel tank has multiple fuel tank cavities.

[0022] Specifically, by deploying multi-dimensional monitoring devices (such as liquid level sensors, pressure sensors, etc.) on the multiple tank cavities of the intelligent oil distribution tank, the real-time state of each cavity is monitored, and monitoring data including the current oil quantity, liquid level change rate, internal pressure, etc. is collected to form a cavity monitoring set; abnormal data (such as liquid level values exceeding the normal range, pressure mutation values, etc.) in the cavity monitoring set are removed and standardized, and the processed data is sorted by time sequence or cavity number to generate a cavity state feature sequence that can fully reflect the real-time running state of each tank cavity.

[0023] Step S300: Based on the cavity state feature sequence, the oil supply adjustment of the intelligent oil distribution tank is adjusted according to the oil quantity demand curve to obtain a first space of oil supply adjustment.

[0024] Specifically, based on the cavity state feature sequence (containing real-time oil quantity, liquid level change, pressure, etc. of each tank cavity), combined with the established oil quantity demand curve (reflecting the oil quantity demand change of the vehicle at different times), an oil supply adjustment strategy for the intelligent oil distribution tank is formulated, including the oil distribution ratio between cavities, the timing of oil supply start and stop, oil transfer rate, etc. All adjustment strategies that match the current state of each cavity and the oil quantity demand curve are integrated to form a set containing multiple basic oil supply adjustment schemes, i.e. a first space of oil supply adjustment.

[0025] Step S400: According to the oil supply distribution balance threshold, the first space of oil supply adjustment is balanced and optimized to obtain a second space of oil supply adjustment.

[0026] Specifically, each oil supply adjustment scheme (denoted as S scheme, S is a positive integer) is extracted from the first space of oil supply adjustment, based on the cavity state feature sequence, the oil quantity change of multiple tank cavities after the implementation of the scheme is simulated, and the cavity oil quantity change curve is fitted. At multiple times, the cavity oil quantity is read to form a cavity oil quantity sequence at each time, and the standard deviation (first oil quantity standard deviation) and mean (first oil quantity mean) of the first time cavity oil quantity sequence are calculated. The ratio of the two is the first oil quantity balance degree and is included in the oil quantity balance S sequence. The difference between 1 and each oil quantity balance degree in the sequence is calculated to obtain the oil supply balance S sequence, and the mean of the sequence is taken to obtain the S oil supply distribution balance coefficient. If the coefficient is greater than or equal to the oil supply distribution balance threshold, the S scheme is added to the second space of oil supply adjustment, and the balanced and optimized calculation of the first space of oil supply adjustment is completed.

[0027] Step S500: Based on the vehicle operation scheme, the second space of oil supply adjustment is evaluated and analyzed for optimization according to the oil supply scheduling evaluation model to obtain a third space of oil supply adjustment.

[0028] Specifically, based on the vehicle operation scheme of the target vehicle, each fuel supply adjustment scheme in the fuel supply adjustment second space is simulated for the fuel supply process on the target vehicle, respectively, to obtain a plurality of corresponding fuel supply scheduling simulation data; the fuel supply scheduling simulation data is input into a fuel supply scheduling evaluation model (the model includes fuel supply continuity, fuel supply safety, fuel supply loss, and other fuel supply scheduling evaluation multi-indexes), and a plurality of fuel supply scheduling evaluation sequences are output; a fuel supply scheduling evaluation constraint is constructed according to the above fuel supply scheduling evaluation multi-indexes, and the plurality of fuel supply scheduling evaluation sequences are checked through the constraint to obtain a plurality of evaluation checking results; the fuel supply adjustment scheme meeting the constraint requirement is selected according to the evaluation checking result, and the schemes are integrated to form a fuel supply adjustment third space.

[0029] Step S600: based on the fuel supply distribution balance threshold and the fuel supply scheduling evaluation model, a plurality of reproduction joint optimizations are performed according to the fuel supply adjustment third space, a fuel supply adjustment optimization result is determined, and the target vehicle is controlled for fuel supply in combination with the intelligent oil distribution tank.

[0030] Specifically, based on the fuel supply distribution balance threshold and the fuel supply scheduling evaluation model, the weight distribution is performed according to the fuel supply scheduling evaluation multi-indexes to establish a fuel supply adjustment reproduction value analysis model, the model is used to analyze and reproduce the reproduction value of each fuel supply adjustment scheme in the fuel supply adjustment third space, the fuel supply adjustment reproduction optimization first domain is selected in combination with the fuel supply distribution balance threshold and the fuel supply scheduling evaluation model, the fuel supply adjustment reproduction optimization Hth domain is obtained through a predetermined reproduction optimization number (H times) in this way, the fuel supply adjustment third space and each reproduction optimization domain are integrated into a fuel supply adjustment fourth space; then the schemes in the fourth space are sorted according to the fuel supply continuity, safety, and loss in a predetermined number, a fuel supply adjustment optimization first set, a second set, and a third set are obtained, and the three sets are intersected to generate a fuel supply adjustment optimization result; finally, the target vehicle is controlled for fuel supply through the intelligent oil distribution tank according to the result.

[0031] In one possible implementation manner, step S100 further includes:

[0032] Step S110: oil demand prediction is performed on the vehicle operation scheme to obtain an oil demand set.

[0033] Step S120: an oil demand coordinate system is constructed with time as the horizontal coordinate axis and demand oil quantity as the vertical coordinate axis, and a curve fitting is performed on the oil demand set according to the oil demand coordinate system to generate the oil demand curve.

[0034] Specifically, the random forest regression algorithm is used to realize the following steps: first, feature variables are extracted from the vehicle operation scheme, including the slope, length, expected speed, load weight, weather conditions of the driving section, and the actual fuel consumption under the same or similar working conditions in history is taken as the label data to build the training data set; then the random forest regression model is trained, and the overfitting risk is reduced through the ensemble learning of multiple decision trees, and the model output is the predicted fuel consumption in different time periods; finally, the prediction results of the model for each time period of the current vehicle operation scheme are sorted into the fuel demand set. This algorithm can effectively handle the interaction of multiple features and improve the accuracy of fuel demand prediction.

[0035] A two-dimensional fuel demand coordinate system is established with time parameters (such as minutes, hours) as the horizontal coordinate axis and the demand fuel (such as liters) corresponding to the time as the vertical coordinate axis. Then each data point (containing time information and corresponding demand fuel) in the fuel demand set is accurately mapped into the coordinate system, and then a curve fitting method (least squares fitting) is used to fit these discrete data points to obtain a curve that can smoothly reflect the trend of fuel demand change with time, i.e. the fuel demand curve.

[0036] In one possible implementation, step S200 further includes:

[0037] Step S210: Multi-dimensional monitoring of the plurality of tank cavities is performed to obtain a plurality of cavity monitoring sets.

[0038] Step S220: Data cleaning is performed on the plurality of cavity monitoring sets to generate the plurality of cavity state feature sequences.

[0039] Specifically, for the plurality of tank cavities of the intelligent oil distribution tank, corresponding monitoring equipment (such as fuel level sensors, pressure sensors, temperature sensors, etc.) is deployed to monitor multiple dimensions of each cavity, including real-time fuel level, oil level, internal oil pressure, oil temperature, and flow of the oil supply pipeline; parameter data is continuously collected at a set time interval (such as every second or every minute), and each parameter data of each cavity at different monitoring times is sorted in chronological order to form a data set containing time stamp and corresponding parameter value, i.e. a plurality of cavity monitoring sets.

[0040] For the centralized raw data of each cavity monitoring, first, abnormal value detection is performed, and by setting a reasonable threshold range (such as upper and lower limits of oil quantity, normal range of pressure, etc.), jump values and values beyond the range caused by sensor failure, transmission interference, etc. are identified and excluded; for missing values that occur during data collection, linear interpolation or adjacent time value filling method is used for supplement; then the processed monitoring parameters (such as oil quantity, pressure, temperature, etc.) are standardized, and data of different orders of magnitude are converted to a unified interval; finally, the parameters of each cavity are integrated in time sequence to form a cavity state feature sequence that can clearly reflect the real-time state change of the cavity.

[0041] In one possible implementation manner, step S400 further includes:

[0042] Step S410: extracting the first oil supply adjustment scheme according to the oil supply adjustment first space.

[0043] Step S420: based on the cavity state feature sequence, fitting the oil quantity change of the plurality of tank cavities according to the first S scheme of oil supply adjustment to obtain the oil quantity change curve of each cavity.

[0044] Step S430: performing multi-time oil quantity balancing calculation according to the oil quantity change curve of each cavity to obtain the Sth sequence of oil quantity balancing.

[0045] Step S440: calculating the difference between 1 and each oil quantity balancing degree in the Sth sequence of oil quantity balancing to obtain the Sth sequence of oil supply balancing, and calculating the mean value of the Sth sequence of oil supply balancing to obtain the Sth oil supply distribution balancing coefficient.

[0046] Step S450: if the Sth oil supply distribution balancing coefficient is greater than or equal to the oil supply distribution balancing threshold, adding the first S scheme of oil supply adjustment to the second space of oil supply adjustment.

[0047] Specifically, from the obtained first space of oil supply adjustment, each oil supply adjustment scheme contained therein is extracted in sequence one by one, and each extracted scheme is sequentially marked as the first S scheme of oil supply adjustment, wherein S is a positive integer, used to distinguish different oil supply adjustment schemes, and provide a basis for subsequent balancing calculation optimization for each scheme.

[0048] According to the state data such as the real-time oil quantity and the liquid level change rate of each tank cavity recorded in the cavity state feature sequence, and according to the adjustment rules such as the oil quantity distribution proportion, the transmission start time and the flow control parameter of each cavity in the Sth oil supply adjustment scheme, the time sequence fitting algorithm (least square method) is used to simulate and calculate the oil quantity value of each tank cavity at different time nodes, the oil quantity data at each time point obtained by calculation is taken as the fitting point, the curve fitting tool (polynomial fitting) is used to generate the curve with time as the horizontal axis and oil quantity as the vertical axis, and finally the oil quantity dynamic change trajectory of each tank cavity under the scheme, that is, the cavity oil quantity change curve, is obtained.

[0049] For the obtained cavity oil quantity change curve, a plurality of key time nodes (such as every fixed time interval or important time of oil supply scheduling) are selected, the oil quantity data of all tank cavities at each node is read to form a cavity oil quantity sequence at each time; taking the cavity oil quantity sequence at the first time as an example, the standard deviation (i.e. the first oil quantity standard deviation) and the mean value (i.e. the first oil quantity mean value) of the sequence are calculated, the ratio of the first oil quantity standard deviation to the first oil quantity mean value is determined as the first oil quantity balance degree, and the first oil quantity balance degree is added to the oil quantity balance S sequence; the cavity oil quantity sequences at other times are processed in this way, the oil quantity balance degree corresponding to each time is calculated, and finally the oil quantity balance S sequence containing a plurality of time oil quantity balance degrees is integrated and formed.

[0050] For the obtained oil quantity balance S sequence, the difference between 1 and each oil quantity balance degree in the sequence is calculated in sequence, for example, if an oil quantity balance degree in the sequence is 0.15, then 1-0.15=0.85 is calculated, and all such differences are integrated to form the oil supply balance S sequence; then the arithmetic average of all values in the oil supply balance S sequence is calculated, and the average value obtained is the Sth oil supply distribution balance coefficient, which is used to measure the comprehensive performance of the Sth oil supply adjustment scheme in the oil quantity distribution balance.

[0051] The Sth oil supply distribution balance coefficient calculated is compared with the preset oil supply distribution balance threshold value, if the coefficient is greater than or equal to the oil supply distribution balance threshold value, it indicates that the Sth oil supply adjustment scheme has reached the set standard in the balance of oil quantity distribution, and meets the basic requirement of entering the next stage of optimization, then the Sth oil supply adjustment scheme is included in the second oil supply adjustment space as one of the optional schemes for subsequent scheduling evaluation and analysis optimization.

[0052] In one possible implementation, step S430 further includes:

[0053] Step S431: Multi-time oil quantity reading is performed on the cavity oil quantity change curve to obtain a cavity oil quantity sequence at each time.

[0054] Step S432: Extracting a first time cavity oil quantity sequence from the cavity oil quantity sequences at each time, and calculating the standard deviation of the first time cavity oil quantity sequence to obtain a first oil quantity standard deviation.

[0055] Step S433: Calculating the mean value of the first time cavity oil quantity sequence to obtain a first oil quantity mean value.

[0056] Step S434: Taking the ratio of the first oil quantity standard deviation and the first oil quantity mean value as a first oil quantity balance degree, and adding the first oil quantity balance degree to the oil quantity balance sequence.

[0057] Specifically, for the obtained oil quantity change curve of each oil tank cavity, a plurality of time points (such as different fuel supply stage time points, fixed time interval points, etc.) are selected, and the oil quantity value of each oil tank cavity at each time point is read respectively. The real-time oil quantity data of all oil tank cavities at the same time point is arranged into a group of data according to the cavity number or a predetermined order to form a cavity oil quantity sequence corresponding to the time point, and finally a plurality of time points and their respective corresponding real-time oil quantity of all cavities at each time are obtained.

[0058] From the obtained cavity oil quantity sequence at each time, the cavity oil quantity sequence corresponding to any one time is selected as the first time cavity oil quantity sequence, which contains the real-time oil quantity data of the plurality of oil tank cavities of the intelligent oil separation tank at the time. Then, according to the calculation method of the standard deviation in statistics, all oil quantity data in the first time cavity oil quantity sequence are calculated, that is, the difference between each oil quantity data in the sequence and the average value of the sequence is first calculated, then the average value of the squares of these differences is calculated, and finally the square root is taken. The result obtained is the first oil quantity standard deviation, which is used to reflect the dispersion degree of the oil quantity data of each cavity at the time.

[0059] For the extracted first time cavity oil quantity sequence (containing the real-time oil quantity data of the plurality of oil tank cavities of the intelligent oil separation tank at the time), the sum of the real-time oil quantity values of all cavities in the sequence is calculated, and then the sum is divided by the number of cavities. The result obtained by the arithmetic mean calculation is the first oil quantity mean value, which is used to reflect the average level of the oil quantity of each oil tank cavity at the time.

[0060] The first oil quantity standard deviation obtained is divided by the first oil quantity mean value obtained, and the ratio obtained is defined as the first oil quantity balance degree. The balance degree is used to quantify the balance degree of the oil quantity distribution among the plurality of oil tank cavities at the first time. Then, the calculated first oil quantity balance degree is included in the oil quantity balance sequence as a component of the sequence, providing basic data for the calculation of the subsequent fuel supply distribution balance coefficient.

[0061] In one possible implementation, step S500 further comprises:

[0062] Step S510: Based on the vehicle operation scheme, simulate fuel supply for the target vehicle according to each fuel supply adjustment scheme in the fuel supply adjustment second space respectively to obtain a plurality of fuel supply scheduling simulation data.

[0063] Step S520: Input the plurality of fuel supply scheduling simulation data into the fuel supply scheduling evaluation model to obtain a plurality of fuel supply scheduling evaluation sequences, wherein the fuel supply scheduling evaluation model comprises a fuel supply scheduling evaluation multi-index, and the fuel supply scheduling evaluation multi-index comprises fuel supply continuity, fuel supply safety and fuel supply loss.

[0064] Step S530: According to the fuel supply scheduling evaluation multi-index, construct a fuel supply scheduling evaluation constraint, and verify the plurality of fuel supply scheduling evaluation sequences according to the fuel supply scheduling evaluation constraint to obtain a plurality of evaluation verification results.

[0065] Step S540: According to the plurality of evaluation verification results, screen the fuel supply adjustment second space to generate a fuel supply adjustment third space.

[0066] Specifically, a simulation environment is built relying on the driving route, working condition load, and predicted duration and other parameters contained in the vehicle operation scheme; for each fuel supply adjustment scheme in the fuel supply adjustment second space, the fuel tank cavity fuel supply sequence, flow control parameter, and valve switching logic set by the scheme are input into the simulation environment; the dynamic process of fuel supply of each cavity according to the scheme is simulated by simulation software under different driving stages (such as starting, accelerating, constant speed, and decelerating) of the target vehicle, and data such as fuel supply pressure, flow stability, cavity residual oil amount at each time, fuel supply interruption frequency and duration, and oil transfer loss amount are recorded in real time; the complete recorded data corresponding to each scheme is integrated into a set of fuel supply scheduling simulation data, and finally a plurality of fuel supply scheduling simulation data corresponding to all schemes are obtained.

[0067] A fuel supply scheduling evaluation model is constructed, which adopts a random forest algorithm, takes fuel supply continuity, fuel supply safety, and fuel supply loss as output indexes, selects fuel supply interruption frequency, longest duration of single interruption, pressure abnormal fluctuation frequency, overpressure duration, oil evaporation amount, and transmission leakage amount in the simulation fuel supply process as input features, trains the model through historical fuel supply data, so that the model can output evaluation coefficients (coefficient range is 0-1, and the closer to 1, the better the performance) of the corresponding three indexes according to the input feature data. Subsequently, the obtained plurality of fuel supply scheduling simulation data are input into the random forest model, the model analyzes and calculates the features in each simulation data, outputs a fuel supply scheduling evaluation sequence containing fuel supply continuity coefficient, fuel supply safety coefficient, and fuel supply loss coefficient, and finally obtains a plurality of fuel supply scheduling evaluation sequences corresponding to each fuel supply adjustment scheme.

[0068] According to the oil supply scheduling evaluation multi-index (oil supply continuity, oil supply safety, and oil supply loss), combined with the operation requirements of the target vehicle and the performance standards of the oil supply system, specific constraint conditions are set for each index, for example, the oil supply continuity needs to ensure that the interruption time does not exceed the preset threshold, the oil supply safety needs to ensure that there is no risk of overpressure or leakage, and the oil supply loss needs to be controlled within a certain proportion, thereby constructing a complete oil supply scheduling evaluation constraint; then, the index coefficients contained in each sequence in the multiple oil supply scheduling evaluation sequences are compared with the corresponding constraint conditions one by one, and it is judged whether each sequence meets all the constraint conditions. If all the constraint conditions are met, the evaluation check result is "satisfy", if any one of the constraint conditions is not met, the evaluation check result is "not satisfy", and finally multiple evaluation check results corresponding to the multiple oil supply scheduling evaluation sequences are obtained.

[0069] According to the obtained multiple evaluation check results, all the oil supply regulation schemes in the second oil supply regulation space are screened, and those corresponding evaluation check results that meet the oil supply scheduling evaluation constraint are selected. The schemes that meet the conditions are integrated together to form a new third oil supply regulation space, so as to ensure that the schemes in the space can meet the preset evaluation standard in terms of oil supply scheduling.

[0070] In a possible implementation manner, step S600 further includes:

[0071] Step S610: based on the oil supply distribution balance threshold and the oil supply scheduling evaluation model, multiple reproduction optimizations are performed according to the third oil supply regulation space, and a fourth oil supply regulation space is established.

[0072] Step S620: according to a predetermined number, oil supply continuity ordering optimization is performed on the fourth oil supply regulation space to establish a first oil supply regulation optimization set.

[0073] Step S630: according to the predetermined number, oil supply safety ordering optimization is performed on the fourth oil supply regulation space to establish a second oil supply regulation optimization set.

[0074] Step S640: according to the predetermined number, oil supply loss ordering optimization is performed on the fourth oil supply regulation space to establish a third oil supply regulation optimization set.

[0075] Step S650: an intersection analysis is performed on the first oil supply regulation optimization set, the second oil supply regulation optimization set, and the third oil supply regulation optimization set to generate the oil supply regulation optimization result.

[0076] Specifically, genetic algorithm is used for multiple reproduction optimization. First, the weight distribution of multiple indexes (oil supply continuity, oil supply safety, and oil supply loss) is used to construct the fitness function (i.e., the oil supply adjustment reproduction value analysis model) according to the oil supply scheduling evaluation, and the schemes in the third space of oil supply adjustment are taken as the initial population. The schemes with higher fitness are selected by the selection operator, the oil supply parameters (such as allocation ratio and scheduling opportunity) of the selected schemes are crossed by the crossover operator, and part of the parameters are randomly adjusted by the mutation operator to generate new schemes to form the first reproduction group of oil supply adjustment. The first reproduction group is checked for balance according to the oil supply distribution balance threshold, and the schemes meeting the standard are retained to form the first reproduction optimization group, which is then evaluated and screened by the oil supply scheduling evaluation model to obtain the first reproduction optimization domain of oil supply adjustment. The above reproduction and screening process is repeated H times to obtain the Hth domain. Finally, the initial third space of oil supply adjustment and each reproduction optimization domain are integrated to establish the fourth space of oil supply adjustment.

[0077] A predetermined number K is set, and the oil supply continuity of each oil supply adjustment scheme in the fourth space of oil supply adjustment is evaluated. During the evaluation, the total interruption time, the longest single interruption time, and the interruption frequency per unit time are extracted as three core indexes, and the weighted calculation is performed according to the weights of 50%, 30%, and 20% respectively (for example, the total interruption time of a certain scheme corresponds to 80 points, the longest single interruption time corresponds to 90 points, and the interruption frequency corresponds to 85 points, and the weighted score is 80 x 50% + 90 x 30% + 85 x 20% = 84 points). The weighted score is the comprehensive score of oil supply continuity, and the score range is 0-100 points. The higher the score, the better the continuity. Subsequently, all schemes are sorted in descending order according to the comprehensive score, and the top K schemes are selected to form the first set of oil supply adjustment optimization.

[0078] A predetermined number K is set, and the oil supply safety of each oil supply adjustment scheme in the fourth space of oil supply adjustment is evaluated. During the evaluation, the time length of pressure exceeding the safety range, the number of leakage warnings, and the number of explosion-proof device triggers are selected as three core indexes, and the quantitative calculation is performed according to the weights of 0%, 35%, and 25% respectively (for example, the time length of pressure exceeding the range of a certain scheme corresponds to 90 points, the number of leakage warnings corresponds to 85 points, and the number of explosion-proof triggers corresponds to 95 points, and the weighted score is 90 x 40% + 85 x 35% + 95 x 25% = 89.5 points). The weighted score is the comprehensive score of oil supply safety, and the score range is 0-100 points. The higher the score, the better the safety. Subsequently, all schemes are sorted in descending order according to the comprehensive score, and the top K schemes are selected to form the second set of oil supply adjustment optimization.

[0079] A predetermined number K is set, and each fuel supply adjustment scheme in the fourth space of the fuel supply adjustment is first evaluated for fuel supply loss. In the evaluation, three core indicators, namely the amount of oil evaporation loss, the amount of leakage loss in the transmission process, and the amount of residual oil after the end of fuel supply, are selected, and the actual values of the indicators are compared with the preset maximum acceptable loss value to convert into a single score of 0-100 points (the smaller the loss amount, the higher the score). Then, the comprehensive score is calculated according to the weights of 40% for evaporation loss, 35% for leakage loss, and 25% for residual oil (for example, a certain scheme has an evaporation loss score of 90 points, a leakage loss score of 85 points, and a residual oil score of 80 points, and the comprehensive score is 90*40%+85*35%+80*25%=85.75 points). Then, all the schemes are ranked from high to low according to the comprehensive score (i.e., from low to high in terms of fuel supply loss), and the top K schemes are selected to form the third set of fuel supply adjustment optimization.

[0080] First, the schemes in the three sets are listed with unique identifiers (such as scheme numbers), and then the intersection of the three sets is calculated using set operation algorithms, i.e., the schemes that appear in the top K in the fuel supply continuity optimization, the top K in the fuel supply safety optimization, and the top K in the fuel supply loss optimization are selected. These schemes together constitute the fuel supply adjustment optimization result, ensuring that they are at the optimal level in the three key indicators.

[0081] In one possible implementation, step S610 further includes:

[0082] Step S611: According to the fuel supply scheduling evaluation multi-index, the weight distribution is allocated, and a fuel supply adjustment breeding value analysis model is established.

[0083] Step S612: According to the fuel supply distribution balance threshold, the fuel supply scheduling evaluation model, and the fuel supply adjustment breeding value analysis model, the third space of the fuel supply adjustment is bred and optimized to obtain a first domain of the fuel supply adjustment breeding optimization.

[0084] Step S613: According to the fuel supply distribution balance threshold, the fuel supply scheduling evaluation model, and the fuel supply adjustment breeding value analysis model, the first domain of the fuel supply adjustment breeding optimization is further bred and optimized to obtain an Hth domain of the fuel supply adjustment breeding optimization, where H is a predetermined number of breeding optimization.

[0085] Step S614: The third space of the fuel supply adjustment, the first domain of the fuel supply adjustment breeding optimization,..., and the Hth domain of the fuel supply adjustment breeding optimization are added to the fourth space of the fuel supply adjustment.

[0086] Specifically, according to the oil supply scheduling, the multiple indexes (oil supply continuity, oil supply safety, and oil supply loss) are evaluated to assign weights, the weight of oil supply continuity is set as a, the weight of oil supply safety is set as b, and the weight of oil supply loss is set as c (a+b+c=1), and an oil supply adjustment breeding value analysis model is constructed according to the formula "oil supply adjustment breeding value coefficient = oil supply continuity weight × oil supply continuity coefficient + oil supply safety weight × oil supply safety coefficient - oil supply loss weight × oil supply loss coefficient", which can calculate the breeding value coefficient of each oil supply adjustment scheme through the index coefficients and their corresponding weights, so as to serve as a quantitative evaluation basis for the breeding value of the scheme.

[0087] The genetic algorithm is adopted, the scheme of adjusting the third space of oil supply is taken as the initial population, the fitness (i.e. the breeding value coefficient, the formula is "oil supply continuity weight × continuity coefficient + oil supply safety weight × safety coefficient - oil supply loss weight × loss coefficient") of each scheme is calculated through the oil supply adjustment breeding value analysis model, part of the schemes are selected as parents according to the fitness, the offspring schemes are generated through single-point crossover (such as exchanging the oil supply time period distribution parameters of different schemes) and random mutation (such as adjusting the cavity oil supply proportion in a small range), to form the first breeding population of oil supply adjustment; then the first breeding population is screened according to the oil distribution balance threshold, the schemes whose oil distribution balance coefficients meet the standard are retained to form the first breeding optimization population; finally, the schemes of the population are input into the oil supply scheduling evaluation model, the schemes that meet the standard are integrated into the first domain of breeding optimization of oil supply adjustment through the multi-index (continuity, safety, and loss) constraint verification.

[0088] The same genetic algorithm process as that for obtaining the first domain of breeding optimization of oil supply adjustment is adopted, the first domain of breeding optimization of oil supply adjustment is taken as the initial population, the breeding value coefficients (i.e. the fitness) of the schemes in the domain are calculated through the oil supply adjustment breeding value analysis model, and the parent schemes are selected according to the coefficients; the crossover operation (such as exchanging the cavity oil supply switching logic parameters of different schemes) and the mutation operation (such as randomly adjusting the fluctuation range parameters of the oil supply flow) are performed on the parents to generate a new scheme population; the new scheme population is screened for balance according to the oil distribution balance threshold, and the schemes whose oil distribution balance coefficients meet the standard are retained; the screened schemes are evaluated and verified through the oil supply scheduling evaluation model according to the multiple indexes (oil supply continuity, safety, and loss), and the schemes that meet the standard form the second domain of breeding optimization of oil supply adjustment; the above algorithm process is repeated until the predetermined H times of breeding optimization are completed, and finally the Hth domain of breeding optimization of oil supply adjustment is obtained.

[0089] All the fuel supply adjustment schemes in the third space and all the fuel supply adjustment schemes in the first domain, the second domain, and the Hth domain obtained through the breeding optimization are integrated into a same set, which is the fourth space, thereby realizing the collection of the effective schemes generated by the breeding optimization in each stage and providing a comprehensive scheme basis for the subsequent sorting optimization.

[0090] In one possible implementation manner, the step S612 further includes:

[0091] Step S6121: performing breeding value analysis on each fuel supply adjustment scheme in the third space according to the fuel supply adjustment breeding value analysis model, to obtain a fuel supply adjustment breeding value set.

[0092] Step S6122: breeding the third space according to the fuel supply adjustment breeding value set, to obtain a first breeding group of fuel supply adjustment.

[0093] Step S6123: performing balanced calculation optimization on the first breeding group of fuel supply adjustment according to the fuel supply distribution balance threshold, to obtain a first breeding optimization group.

[0094] Step S6124: performing scheduling evaluation analysis optimization on the first breeding optimization group according to the fuel supply scheduling evaluation model, to obtain a first domain of fuel supply adjustment breeding optimization.

[0095] Specifically, the fuel supply adjustment breeding value analysis model is used to perform breeding value analysis on each fuel supply adjustment scheme included in the third space. During the analysis, the performance coefficients of each scheme on the multiple indexes (fuel supply continuity, fuel supply safety, and fuel supply loss) are calculated in combination with the weight distribution of the indexes in the model, and then the breeding value coefficients of each scheme are obtained through a preset calculation formula (fuel supply continuity weight x continuity coefficient + fuel supply safety weight x safety coefficient - fuel supply loss weight x loss coefficient). The breeding value coefficients of all schemes are integrated to form a fuel supply adjustment breeding value set.

[0096] First, the breeding value coefficient of each scheme is determined according to the oil supply adjustment breeding value, for example, the coefficient is divided into high, medium and low three levels, and the corresponding breeding quantity ratio is 5:3:2; then the corresponding scheme is selected from the oil supply adjustment third space, for the high value coefficient scheme, multi-point crossover (such as exchanging cavity oil distribution parameters at different time periods) and adaptive mutation (adjusting mutation probability according to coefficient, the higher the coefficient, the lower the mutation probability) are adopted to generate 5 offspring, for the medium value coefficient scheme, single-point crossover and fixed probability mutation are adopted to generate 3 offspring, and for the low value coefficient scheme, simple crossover is adopted to generate 2 offspring; finally, all generated offspring schemes are integrated to form the oil supply adjustment first breeding group.

[0097] Each oil supply adjustment scheme in the oil supply adjustment first breeding group is traversed, the oil quantity change fitting of multiple oil tanks in each scheme is performed based on the cavity state feature sequence, and the oil quantity change curve of each cavity is obtained; the oil quantity data at multiple time points are read from the curves to form the cavity oil quantity sequence at each time point, the standard deviation and mean value of the cavity oil quantity sequence at each time point are calculated, the ratio of the standard deviation to the mean value is taken as the oil quantity balance degree at the time point, and the oil quantity balance sequence is composed; the oil supply balance sequence is calculated by the difference between 1 and each oil quantity balance degree in the sequence, and the mean value of the oil supply balance sequence is taken as the oil supply distribution balance coefficient of the scheme; the coefficient is compared with the oil supply distribution balance threshold value, and the scheme with a coefficient greater than or equal to the threshold value is retained, and the integrated scheme forms the breeding optimization first group.

[0098] Based on the vehicle operation scheme, the simulation oil supply operation is performed on each oil supply adjustment scheme in the breeding optimization first group, and the corresponding oil supply scheduling simulation data are collected, including the number of oil supply interruptions, the frequency of pressure abnormalities, the oil quantity loss, etc.; the data are input into the oil supply scheduling evaluation model, and the scores of each scheme in the oil supply continuity, the oil supply safety and the oil supply loss are calculated; the evaluation results of each scheme are verified according to the preset evaluation constraints (such as the oil supply continuity score being not less than 80 points, the oil supply safety being not less than 85 points, and the loss score being not less than 75 points), and the scheme meeting all the indicators is retained, and the integrated scheme forms the oil supply adjustment breeding optimization first domain.

[0099] In the embodiment two, based on the same inventive concept as the oil quantity distribution balance method of the intelligent oil distribution tank in the foregoing embodiments, as shown in Figure 2 The present application provides an oil quantity distribution balance system of an intelligent oil distribution tank, and the system and method embodiments in the present application are based on the same inventive concept. The system comprises:

[0100] The oil quantity demand curve establishment module 10 is used for predicting the oil quantity demand according to the vehicle operation scheme of the target vehicle, and establishing the oil quantity demand curve.

[0101] A real-time sensing module 20 is configured to perform real-time sensing on the intelligent split oil tank of the target vehicle to obtain a cavity state feature sequence, and the intelligent split oil tank is provided with a plurality of oil tank cavities.

[0102] A fuel supply adjustment module 30 is configured to perform fuel supply adjustment on the intelligent split oil tank according to the oil demand curve based on the cavity state feature sequence to obtain a fuel supply adjustment first space.

[0103] An equilibrium calculation optimization module 40 is configured to perform equilibrium calculation optimization on the fuel supply adjustment first space according to a fuel supply distribution equilibrium threshold to obtain a fuel supply adjustment second space.

[0104] A scheduling evaluation analysis optimization module 50 is configured to perform scheduling evaluation analysis optimization on the fuel supply adjustment second space according to a fuel supply scheduling evaluation model based on the vehicle operation scheme to obtain a fuel supply adjustment third space.

[0105] A fuel supply adjustment optimization result determination module 60 is configured to perform multiple reproduction joint optimization on the fuel supply adjustment third space based on the fuel supply distribution equilibrium threshold and the fuel supply scheduling evaluation model to determine a fuel supply adjustment optimization result, and to combine the intelligent split oil tank to perform fuel supply management and control on the target vehicle.

[0106] Further, the system is also configured to implement the following functions:

[0107] According to the fuel supply adjustment first space, a fuel supply adjustment Sth scheme is extracted, S is a positive integer; based on the cavity state feature sequence, an oil amount change fitting is performed on the plurality of oil tank cavities according to the fuel supply adjustment Sth scheme to obtain a cavity oil amount change curve; a multi-time oil amount equilibrium calculation is performed according to the cavity oil amount change curve to obtain an oil amount equilibrium Sth sequence; a difference calculation is performed on 1 and each oil amount equilibrium degree in the oil amount equilibrium Sth sequence to obtain a fuel supply equilibrium Sth sequence, and a mean value calculation is performed on the fuel supply equilibrium Sth sequence to obtain an Sth fuel supply distribution equilibrium coefficient; if the Sth fuel supply distribution equilibrium coefficient is greater than or equal to the fuel supply distribution equilibrium threshold, the fuel supply adjustment Sth scheme is added to the fuel supply adjustment second space.

[0108] Further, the system is also configured to implement the following functions:

[0109] The oil quantity change curve of each cavity is read at multiple times to obtain a cavity oil quantity sequence at each time; a first-time cavity oil quantity sequence is extracted from the cavity oil quantity sequence at each time, and a first oil quantity standard deviation is obtained by calculating the standard deviation of the first-time cavity oil quantity sequence; a first oil quantity mean value is obtained by calculating the mean value of the first-time cavity oil quantity sequence; a first oil quantity balance degree is recorded as the ratio of the first oil quantity standard deviation to the first oil quantity mean value, and the first oil quantity balance degree is added to the oil quantity balance S sequence.

[0110] Further, the system is also used to implement the following functions:

[0111] Based on the vehicle operation scheme, each oil supply adjustment scheme in the second oil supply adjustment space is simulated to supply oil to the target vehicle respectively to obtain a plurality of oil supply scheduling simulation data; the plurality of oil supply scheduling simulation data is input into the oil supply scheduling evaluation model to obtain a plurality of oil supply scheduling evaluation sequences, the oil supply scheduling evaluation model includes oil supply scheduling evaluation multi-indexes, the oil supply scheduling evaluation multi-indexes include oil supply continuity, oil supply safety, and oil supply loss; according to the oil supply scheduling evaluation multi-indexes, an oil supply scheduling evaluation constraint is constructed, and the plurality of oil supply scheduling evaluation sequences are verified according to the oil supply scheduling evaluation constraint to obtain a plurality of evaluation verification results; the oil supply adjustment third space is generated by screening the oil supply adjustment second space according to the plurality of evaluation verification results.

[0112] Further, the system is also used to implement the following functions:

[0113] Based on the oil supply distribution balance threshold and the oil supply scheduling evaluation model, the oil supply adjustment fourth space is established by performing multiple reproduction optimization according to the oil supply adjustment third space; the oil supply adjustment optimization first set is established by performing oil supply continuity ordering optimization on the oil supply adjustment fourth space according to a predetermined number; the oil supply adjustment optimization second set is established by performing oil supply safety ordering optimization on the oil supply adjustment fourth space according to the predetermined number; the oil supply adjustment optimization third set is established by performing oil supply loss ordering optimization on the oil supply adjustment fourth space according to the predetermined number; the oil supply adjustment optimization result is generated by performing intersection analysis on the oil supply adjustment optimization first set, the oil supply adjustment optimization second set, and the oil supply adjustment optimization third set.

[0114] Further, the system is also used to implement the following functions:

[0115] According to the oil supply scheduling evaluation multi-index weight distribution, an oil supply regulation breeding value analysis model is established; according to the oil supply distribution balance threshold, the oil supply scheduling evaluation model and the oil supply regulation breeding value analysis model, the oil supply regulation third space is bred and optimized to obtain an oil supply regulation breeding optimization first domain; according to the oil supply distribution balance threshold, the oil supply scheduling evaluation model and the oil supply regulation breeding value analysis model, the oil supply regulation breeding optimization first domain is continuously bred and optimized to obtain an oil supply regulation breeding optimization Hth domain, H being a predetermined breeding optimization number; the oil supply regulation third space, the oil supply regulation breeding optimization first domain, …, and the oil supply regulation breeding optimization Hth domain are added to the oil supply regulation fourth space.

[0116] Further, the system is also used to implement the following functions:

[0117] According to the oil supply regulation breeding value analysis model, the breeding value of each oil supply regulation scheme in the oil supply regulation third space is analyzed to obtain an oil supply regulation breeding value set; according to the oil supply regulation breeding value set, the oil supply regulation third space is bred to obtain an oil supply regulation first breeding group; according to the oil supply distribution balance threshold, the oil supply regulation first breeding group is balanced and calculated to obtain a breeding optimization first group; according to the oil supply scheduling evaluation model, the breeding optimization first group is scheduled and evaluated to obtain the oil supply regulation breeding optimization first domain.

[0118] Further, the system is also used to implement the following functions:

[0119] The oil demand set is obtained by predicting the oil demand of the vehicle operation scheme; an oil demand coordinate system is constructed by taking time as the horizontal coordinate axis and taking the demand oil as the vertical coordinate axis, and the oil demand curve is generated by curve fitting the oil demand set according to the oil demand coordinate system.

[0120] Further, the system is also used to implement the following functions:

[0121] The multi-dimensional monitoring of the plurality of oil tank cavities is performed to obtain a cavity monitoring set; the cavity monitoring set is data cleaned to generate the cavity state feature sequence.

[0122] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. Moreover, the above-mentioned specific embodiments of the present application are described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0123] The above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0124] The specification and drawings are only exemplary and illustrative of the present application and are to be considered within the scope of the present application. Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and equivalent technology, the present application is intended to include these modifications and variations.

Claims

1. An oil distribution equalization method for an intelligent oil distribution tank, characterized by, The method comprises: According to the vehicle operation scheme of the target vehicle, an oil demand curve is established; The intelligent oil distribution tank of the target vehicle is sensed in real time to obtain a cavity state feature sequence, and the intelligent oil distribution tank is provided with a plurality of oil tank cavities; Based on the cavity state feature sequence, the intelligent oil distribution tank is adjusted according to the oil demand curve to obtain a first space of oil supply adjustment; According to the oil distribution allocation balance threshold, the first space of oil supply adjustment is balanced and calculated to obtain a second space of oil supply adjustment; Based on the vehicle operation scheme, the second space of oil supply adjustment is evaluated and analyzed according to an oil supply scheduling evaluation model to obtain a third space of oil supply adjustment; Based on the oil distribution allocation balance threshold and the oil supply scheduling evaluation model, the third space of oil supply adjustment is subjected to multiple reproduction joint optimizations to determine an oil supply adjustment optimization result, and the target vehicle is controlled in combination with the intelligent oil distribution tank; According to the oil distribution allocation balance threshold, the first space of oil supply adjustment is balanced and calculated to obtain a second space of oil supply adjustment, which comprises: An Sth oil supply adjustment scheme is extracted from the first space of oil supply adjustment, S being a positive integer; Based on the cavity state feature sequence, the oil volume change of the plurality of oil tank cavities is fitted according to the Sth oil supply adjustment scheme to obtain a cavity oil volume change curve; The oil volume of the plurality of cavities is balanced and calculated at multiple time points according to the cavity oil volume change curve to obtain an Sth oil volume balance sequence; The difference between 1 and each oil volume balance degree in the Sth oil volume balance sequence is calculated to obtain an Sth oil supply balance sequence, and the Sth oil supply balance sequence is subjected to mean value calculation to obtain an Sth oil distribution allocation balance coefficient; If the Sth oil distribution allocation balance coefficient is greater than or equal to the oil distribution allocation balance threshold, the Sth oil supply adjustment scheme is added to the second space of oil supply adjustment.

2. The oil amount distribution equalization method of the intelligent oil distribution tank according to claim 1, characterized by, The oil volume of the plurality of cavities is balanced and calculated at multiple time points according to the cavity oil volume change curve to obtain an Sth oil volume balance sequence, which comprises: The oil volume of the plurality of cavities is read at multiple time points to obtain a cavity oil volume sequence at each time point; A cavity oil volume sequence at a first time point is extracted from the cavity oil volume sequence at each time point, and a standard deviation of the cavity oil volume sequence at the first time point is calculated to obtain a first oil volume standard deviation; A mean value of the cavity oil volume sequence at the first time point is calculated to obtain a first oil volume mean value; The ratio of the first oil volume standard deviation to the first oil volume mean value is taken as a first oil volume balance degree, and the first oil volume balance degree is added to the Sth oil volume balance sequence.

3. The oil amount distribution equalization method of the intelligent oil distribution tank according to claim 1, characterized by, Based on the vehicle operation scheme, the second space of oil supply adjustment is evaluated and analyzed according to an oil supply scheduling evaluation model to obtain a third space of oil supply adjustment, which comprises: Based on the vehicle operation scheme, the target vehicle is simulated for oil supply according to each oil supply adjustment scheme in the second space of oil supply adjustment to obtain a plurality of oil supply scheduling simulation data; inputting the plurality of oil supply scheduling simulation data into the oil supply scheduling evaluation model to obtain a plurality of oil supply scheduling evaluation sequences, the oil supply scheduling evaluation model comprising oil supply scheduling evaluation multi-indexes, the oil supply scheduling evaluation multi-indexes comprising oil supply continuity, oil supply safety and oil supply loss; according to the oil supply scheduling evaluation multi-indexes, constructing oil supply scheduling evaluation constraints, and verifying the plurality of oil supply scheduling evaluation sequences according to the oil supply scheduling evaluation constraints to obtain a plurality of evaluation verification results; screening the oil supply regulation second space according to the plurality of evaluation verification results to generate the oil supply regulation third space.

4. The oil amount distribution equalization method of the intelligent oil distribution tank according to claim 1, characterized by, based on the oil supply distribution balance threshold and the oil supply scheduling evaluation model, performing multiple reproduction joint optimization according to the oil supply regulation third space to determine the oil supply regulation optimization result, comprising: based on the oil supply distribution balance threshold and the oil supply scheduling evaluation model, performing multiple reproduction optimization on the oil supply regulation third space to establish an oil supply regulation fourth space; performing oil supply continuity ordering optimization on the oil supply regulation fourth space according to a predetermined number to establish an oil supply regulation optimization first set; performing oil supply safety ordering optimization on the oil supply regulation fourth space according to the predetermined number to establish an oil supply regulation optimization second set; performing oil supply loss ordering optimization on the oil supply regulation fourth space according to the predetermined number to establish an oil supply regulation optimization third set; performing intersection analysis on the oil supply regulation optimization first set, the oil supply regulation optimization second set and the oil supply regulation optimization third set to generate the oil supply regulation optimization result.

5. The oil amount distribution equalization method of the intelligent oil distribution tank according to claim 4, characterized by, based on the oil supply distribution balance threshold and the oil supply scheduling evaluation model, performing multiple reproduction optimization according to the oil supply regulation third space to establish an oil supply regulation fourth space, comprising: performing weight distribution according to oil supply scheduling evaluation multi-indexes to establish an oil supply regulation reproduction value analysis model; performing reproduction optimization on the oil supply regulation third space according to the oil supply distribution balance threshold, the oil supply scheduling evaluation model and the oil supply regulation reproduction value analysis model to obtain an oil supply regulation reproduction optimization first domain; continuing to perform reproduction optimization on the oil supply regulation reproduction optimization first domain according to the oil supply distribution balance threshold, the oil supply scheduling evaluation model and the oil supply regulation reproduction value analysis model to obtain an oil supply regulation reproduction optimization Hth domain, H being a predetermined reproduction optimization number; adding the oil supply regulation third space, the oil supply regulation reproduction optimization first domain, …, the oil supply regulation reproduction optimization Hth domain to the oil supply regulation fourth space.

6. The oil amount distribution equalization method of the intelligent oil distribution tank according to claim 5, characterized by, performing reproduction optimization on the oil supply regulation third space according to the oil supply distribution balance threshold, the oil supply scheduling evaluation model and the oil supply regulation reproduction value analysis model to obtain an oil supply regulation reproduction optimization first domain, comprising: performing reproduction value analysis on each oil supply regulation scheme in the oil supply regulation third space according to the oil supply regulation reproduction value analysis model to obtain an oil supply regulation reproduction value set; performing reproduction on the oil supply regulation third space according to the oil supply regulation reproduction value set to obtain an oil supply regulation first reproduction group; According to the oil supply distribution balance threshold, the oil supply adjustment first breeding group is balanced and optimized to obtain a breeding optimization first group; According to the oil supply scheduling evaluation model, the breeding optimization first group is scheduled and evaluated to obtain the oil supply adjustment breeding optimization first domain.

7. The oil amount distribution equalization method of the intelligent oil distribution tank according to claim 1, characterized by, According to the vehicle operation scheme of the target vehicle, oil demand prediction is performed to establish an oil demand curve, including: According to the vehicle operation scheme, oil demand prediction is performed to obtain an oil demand set; Taking time as the horizontal coordinate axis and demand oil as the vertical coordinate axis, an oil demand coordinate system is constructed, and curve fitting is performed on the oil demand set according to the oil demand coordinate system to generate the oil demand curve.

8. The oil amount distribution equalization method of the intelligent oil distribution tank according to claim 1, characterized by, The intelligent oil distribution tank of the target vehicle is sensed in real time to obtain a cavity state feature sequence, including: The multiple oil tank cavities are multi-dimensionally monitored to obtain a cavity monitoring set; The cavity monitoring set is data cleaned to generate the cavity state feature sequence.

9. An oil distribution equalization system for an intelligent oil distribution tank, characterized by, The system is used to implement the oil distribution balance method of the intelligent oil distribution tank according to any one of claims 1-8, and the system includes: An oil demand curve establishment module is configured to perform oil demand prediction according to a vehicle operation scheme of a target vehicle to establish an oil demand curve; A real-time sensing module is configured to sense the intelligent oil distribution tank of the target vehicle in real time to obtain a cavity state feature sequence, and the intelligent oil distribution tank is provided with multiple oil tank cavities; An oil supply adjustment module is configured to perform oil supply adjustment on the intelligent oil distribution tank according to the oil demand curve based on the cavity state feature sequence to obtain an oil supply adjustment first space; A balanced calculation optimization module is configured to perform balanced calculation optimization on the oil supply adjustment first space according to an oil supply distribution balance threshold to obtain an oil supply adjustment second space; A scheduling evaluation analysis optimization module is configured to perform scheduling evaluation analysis optimization on the oil supply adjustment second space according to an oil supply scheduling evaluation model based on the vehicle operation scheme to obtain an oil supply adjustment third space; An oil supply adjustment optimization result determination module is configured to perform multiple breeding joint optimizations according to the oil supply adjustment third space based on the oil supply distribution balance threshold and the oil supply scheduling evaluation model to determine an oil supply adjustment optimization result, and to perform oil supply management and control on the target vehicle in combination with the intelligent oil distribution tank.

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