Method and system for using oil in construction site

Abstract

The invention provides a construction site oil consumption method and system, relates to the technical field of commercial or financial data processing, and introduces an oil consumption demand fluctuation coefficient of a user side and a completion degree coefficient of a refueling vehicle by comprehensively analyzing historical oil consumption demand data and execution condition and state information of the refueling vehicle. Therefore, priority classification and screening of the refueling vehicles are realized. The fuel demand trend of a user can be effectively predicted, refueling vehicle scheduling can be optimized based on real-time data, resource waste is reduced, and refueling efficiency is improved; meanwhile, through analysis and calculation of the cloud data platform, the economic fluctuation coefficient of each refueling vehicle is brought into a decision-making process, and it is ensured that the finally selected refueling vehicle not only meets user requirements, but also can achieve the optimal economic effect; the intelligent level of oil management of the construction site is improved, more efficient resource allocation is achieved, and reliable guarantee is provided for smooth operation of the construction site.

Description

Technical Field

[0001] The present invention relates to the technical field of commercial or financial data processing, and in particular to a method and system for using oil on a construction site. Background Art

[0002] With the rapid development of urban construction and infrastructure projects, the scientific and intelligent level of construction site oil management has received increasing attention. Traditional construction site refueling methods often rely on manual experience and lack systematic monitoring and data analysis, resulting in resource waste and inefficiency in the refueling process. In recent years, with the rapid development of technologies such as the Internet of Things, big data, and cloud computing, construction site refueling management has gradually developed in the direction of automation and intelligence. Through the collection and analysis of historical data, related companies have begun to explore how to use data-driven decision-making to optimize the construction site refueling process, improve resource utilization efficiency and meet user needs. For example, using sensors to monitor oil status and implement precise refueling has become a trend in more and more construction sites;

[0003] In the prior art, the publication number is CN116862139A, and the name is a method, device and system for intelligent scheduling decision-making of a refueling truck; wherein the method includes using airport flight information, airport parking position information, refueling truck information and refueler information to plan the priority of flights to be refueled, and plan target refuelers and target refueling trucks that match the flights to be refueled respectively, to obtain task planning information; according to the task planning information, a corresponding refueling task is assigned to the target refueler, and the data information of the refueling task includes a refueling task number, the flight number of the flight to be refueled, the model of the flight to be refueled, the parking position of the flight to be refueled, the amount of refueling required for the flight to be refueled, the estimated remaining time to complete the refueling task and a recommended path.

[0004] However, the existing construction site refueling technology still has many shortcomings: First, although existing technical means can collect and upload refueling data, most systems lack in-depth analysis of historical oil demand and cannot accurately predict the fluctuation of oil demand on the user side; this lack of prediction directly affects the dispatch efficiency of refueling trucks, resulting in long waiting times and failure to meet construction needs in a timely manner; secondly, the existing technology often lacks an effective comprehensive evaluation mechanism for the status of different refueling vehicles and their performance in meeting user needs; it does not fully consider the actual available oil reserves, fuel consumption, driving time and other information of the refueling truck, resulting in a lack of sufficient scientific basis for decision-making in the refueling truck selection process; in addition, the existing technology is relatively simple in terms of priority classification, and fails to effectively distinguish the importance of different refueling trucks in meeting oil demand, further affecting the efficiency and economy of the entire refueling process; these shortcomings not only increase the operating costs of the construction site, but also cause delays in project progress;

[0005] The above information disclosed in the above Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The object of the present invention is to provide a method and system for using oil on construction sites to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] The method of using oil on construction site includes the following specific steps:

[0009] Step S1: In the previous monitoring period, collect historical fuel demand data of the user end in M ​​construction site refueling events, as well as the execution and status data of each refueling truck to complete these fuel demands, and upload these data to the cloud data platform for storage;

[0010] Step S2: in the refueling event of the current user end, determining the oil demand data including the amount of oil to be refueled, the maximum waiting time and the oil demand fluctuation index;

[0011] and determine the status data of each refueling truck, including the remaining oil reserves, the amount of oil consumed when arriving at the user's end area, and the predicted travel time;

[0012] The execution data includes the first error average value of the actual available oil reserves of each refueling truck after each refueling truck completes M times of user-end oil demand in the previous monitoring time period, and the second error average value of the actual driving time;

[0013] Step S3: In the cloud data platform, the historical oil demand data of the user end in the previous monitoring period is analyzed to calculate the oil demand fluctuation coefficient of the user end;

[0014] Step S4: in the cloud data platform, the execution data of each refueling truck in completing the user's oil demand in the previous monitoring period is obtained, and the completion coefficient is calculated based on the execution data;

[0015] Step S5: In the cloud data platform, the economic fluctuation coefficient of each refueling truck arriving at the user end area is calculated according to the amount of fuel consumed by each refueling truck when it arrives at the user end area from the current position and the predicted travel time;

[0016] Step S6: Classify the priorities of the refueling vehicles according to the oil demand data corresponding to the current user-side refueling event, wherein the refueling vehicles that meet the requirements of the amount of oil to be refueled and the maximum waiting time are classified into the first priority sequence, and the refueling vehicles that meet the requirements of the maximum waiting time among the remaining refueling vehicles are classified into the second priority sequence;

[0017] Step S7: among the several refueling vehicles determined in the first priority sequence, analyzing the corresponding oil demand fluctuation coefficient and completion coefficient to generate a first screening index for providing a screening strategy for the refueling vehicles;

[0018] Step S8: Among the several refueling vehicles determined by the second priority sequence, analysis is performed in combination with the corresponding oil demand fluctuation coefficient and completion coefficient to generate a second screening index for providing a screening strategy for the refueling vehicles;

[0019] Step S9: Determine the final selection of a refueling vehicle based on the economic fluctuation coefficient among the several refueling vehicles determined by the first screening index and the second screening index.

[0020] A system for using oil on a construction site, the system being used to execute the method for using oil on a construction site, comprising:

[0021] Data collection and upload module: used to collect historical fuel demand data of the user end in M ​​construction site refueling events during the previous monitoring period, as well as the execution and status data of each refueling truck to complete these fuel demands, and upload these data to the cloud data platform for storage;

[0022] Data determination module: used to determine the fuel demand data including the amount of fuel to be refueled, the maximum waiting time and the fuel demand fluctuation index in the current user-side refueling event;

[0023] and determine the status data of each refueling truck, including the remaining oil reserves, the amount of oil consumed when arriving at the user's end area, and the predicted travel time;

[0024] The execution data includes the first error average value of the actual available oil reserves of each refueling truck after each refueling truck completes M times of user-end oil demand in the previous monitoring time period, and the second error average value of the actual driving time;

[0025] Oil demand fluctuation coefficient generation module: used to analyze the historical oil demand data of the user end in the previous monitoring period in the cloud data platform to calculate the oil demand fluctuation coefficient of the user end;

[0026] Completion coefficient generation module: used to obtain the execution data of each refueling truck in completing the user's fuel demand in the previous monitoring period in the cloud data platform, and calculate the completion coefficient based on the execution data;

[0027] Economic fluctuation coefficient generation module: In the cloud data platform, the economic fluctuation coefficient of each refueling truck reaching the user end area is calculated based on the amount of fuel consumed by each refueling truck when it reaches the user end area from the current location and the predicted driving time;

[0028] Priority sequence classification module: used to classify the priorities of various refueling vehicles according to the oil demand data corresponding to the current user-side refueling event, wherein the refueling vehicles that meet the requirements of the amount of oil to be refueled and the maximum waiting time are classified into the first priority sequence, and the refueling vehicles that meet the requirements of the maximum waiting time among the remaining refueling vehicles are classified into the second priority sequence;

[0029] A first screening index generating module: used to analyze a number of refueling vehicles determined in the first priority sequence in combination with corresponding oil demand fluctuation coefficients and completion coefficients, and generate a first screening index for providing a screening strategy for the refueling vehicles;

[0030] A second screening index generating module: used to analyze a number of refueling vehicles determined in the second priority sequence in combination with corresponding oil demand fluctuation coefficients and completion coefficients, and generate a second screening index for providing a screening strategy for the refueling vehicles;

[0031] The final selection module is used to determine the final selection of a refueling vehicle based on the economic fluctuation coefficient among the several refueling vehicles determined by the first screening index and the second screening index.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows: by comprehensively analyzing historical oil demand data, the execution status and status information of refueling trucks, the user-side oil demand fluctuation coefficient and the refueling truck completion coefficient are introduced, thereby realizing the priority classification and screening of refueling trucks; it can not only effectively predict the user's oil demand trend, but also optimize the refueling truck scheduling based on real-time data, reduce resource waste, and improve refueling efficiency; at the same time, through the analysis and calculation of the cloud data platform, the economic fluctuation coefficient of each refueling truck is incorporated into the decision-making process to ensure that the final selected refueling truck not only meets user needs, but also achieves the best economic effect; it improves the level of intelligence of construction site oil management, realizes more efficient resource allocation, and provides reliable guarantee for the smooth progress of the construction site. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0034] Figure 2 This is a block diagram of the overall system module of the present invention. DETAILED DESCRIPTION

[0035] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0036] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0037] Embodiment 1:

[0038] See also Figure 1 , the present invention provides a technical solution:

[0039] The method of using oil on construction site includes the following specific steps:

[0040] Step S1: In the previous monitoring period, collect historical fuel demand data of the user end in M ​​construction site refueling events, as well as the execution and status data of each refueling truck to complete these fuel demands, and upload these data to the cloud data platform for storage;

[0041] Step S2: in the refueling event of the current user end, determining the oil demand data including the amount of oil to be refueled, the maximum waiting time and the oil demand fluctuation index;

[0042] and determine the status data of each refueling truck, including the remaining oil reserves, the amount of oil consumed when arriving at the user's end area, and the predicted travel time;

[0043] The execution data includes the first error average value of the actual available oil reserves of each refueling truck after each refueling truck completes M times of user-end oil demand in the previous monitoring time period, and the second error average value of the actual driving time;

[0044] Step S3: In the cloud data platform, the historical oil demand data of the user end in the previous monitoring period is analyzed to calculate the oil demand fluctuation coefficient of the user end;

[0045] Step S4: in the cloud data platform, the execution data of each refueling truck in completing the user's oil demand in the previous monitoring period is obtained, and the completion coefficient is calculated based on the execution data;

[0046] Step S5: In the cloud data platform, the economic fluctuation coefficient of each refueling truck arriving at the user end area is calculated according to the amount of fuel consumed by each refueling truck when it arrives at the user end area from the current position and the predicted travel time;

[0047] Step S6: Classify the priorities of the refueling vehicles according to the oil demand data corresponding to the current user-side refueling event, wherein the refueling vehicles that meet the requirements of the amount of oil to be refueled and the maximum waiting time are classified into the first priority sequence, and the refueling vehicles that meet the requirements of the maximum waiting time among the remaining refueling vehicles are classified into the second priority sequence;

[0048] Step S7: among the several refueling vehicles determined in the first priority sequence, analyzing the corresponding oil demand fluctuation coefficient and completion coefficient to generate a first screening index for providing a screening strategy for the refueling vehicles;

[0049] Step S8: Among the several refueling vehicles determined by the second priority sequence, analysis is performed in combination with the corresponding oil demand fluctuation coefficient and completion coefficient to generate a second screening index for providing a screening strategy for the refueling vehicles;

[0050] Step S9: Determine the final selection of a refueling vehicle based on the economic fluctuation coefficient among the several refueling vehicles determined by the first screening index and the second screening index.

[0051] Further explanation:

[0052] The previous monitoring period is set to T; in this embodiment, T is the past week or month as the data collection period;

[0053] Mark the construction site refueling events of the current user end in the previous monitoring period to form an event sequence set {1,2,…,M}, where j represents the jth construction site refueling event in the event sequence set, and M represents the total number of construction site refueling events of the user end, j∈{1,2,…,M};

[0054] Mark each refueling truck to form a number sequence set of refueling trucks {1,2,…,N}, where i represents the i-th refueling truck in the number sequence set, N represents the total number of refueling trucks, i∈{1,2,…,N};

[0055] In the construction site refueling event at the current user end, the oil demand data including the amount of oil to be refueled, the maximum waiting time and the oil demand fluctuation index are recorded as DJy, ZDt and YBd respectively;

[0056] The remaining oil reserves, the oil consumption and the predicted driving time when reaching the user end area, including the corresponding status data of each refueling truck, are recorded as ;

[0057] The fuel consumption and predicted travel time of each refueling truck when it arrives at the user's end area are described in detail as follows:

[0058] Driving distance acquisition:

[0059] Use a GPS system or map service to obtain the travel distance of the refueling truck from the departure point to the user's end area;

[0060] Fuel consumption (L / km) is determined as follows:

[0061] Actual test: Conduct actual fuel consumption test on the refueling vehicle and record the fuel consumption under different driving conditions;

[0062] Provided by the manufacturer: Refer to the fuel consumption data provided by the fuel truck manufacturer;

[0063] Multiply the distance traveled by the fuel consumption to get the amount of fuel consumed.

[0064] For the calculation of predicted travel time:

[0065]

[0066] in, is the average speed of the i-th refueling truck;

[0067] For the average driving speed, the average driving speed is estimated based on historical data of vehicles or actual traffic conditions.

[0068] The first error mean value calculation formula for defining the actual available oil reserves is as follows:

[0069]

[0070] in It represents the first error average value after the i-th refueling truck completes M times of user-side oil demand in the previous monitoring period, represents the actual available oil reserves corresponding to the jth construction site refueling event of the i-th refueling truck in the previous monitoring period, represents the remaining oil reserves corresponding to the jth construction site refueling event of the i-th refueling truck in the previous monitoring period;

[0071] The second error average calculation formula that defines the actual travel time is as follows:

[0072]

[0073] in It represents the second error average value after the i-th refueling truck completes M times of user-side oil demand in the previous monitoring period, represents the actual driving time corresponding to the jth construction site refueling event of the i-th refueling truck in the previous monitoring period, It represents the predicted driving time corresponding to the jth construction site refueling event of the i-th refueling truck in the previous monitoring time period.

[0074] Further explanation: calculating the oil demand fluctuation coefficient at the user end includes:

[0075] On the cloud data platform, obtain the historical oil demand data in the previous monitoring period;

[0076] The amount of fuel to be refilled is the amount of fuel required for each refueling event (in liters)

[0077] The maximum waiting time is the longest time that users expect in each refueling event (unit: minutes);

[0078] The oil demand fluctuation index is the degree of fluctuation of oil demand;

[0079] Store this data in a structured database for subsequent real-time analysis and processing;

[0080] Use statistical analysis methods to process historical oil demand data and calculate the oil demand fluctuation coefficient;

[0081] The calculation formula for defining the oil demand fluctuation index is as follows:

[0082]

[0083] in, Indicates the maximum amount of oil to be added during the previous monitoring period;

[0084] Indicates the minimum amount of oil to be added during the previous monitoring period;

[0085] is the mean of the historical amount of oil to be refilled;

[0086] The value range of YBd is [0, +∞). When YBd is closer to 0, the user's oil demand is more stable; when the value of YBd is larger, the user's oil demand changes more.

[0087] The mean and standard deviation of historical oil demand data are respectively and , the calculation formula is as follows:

[0088]

[0089]

[0090] The oil demand fluctuation coefficient is defined as the standard deviation With the mean The ratio of is:

[0091]

[0092] Normalize with respect to CV using the following formula:

[0093]

[0094] is the standardized oil demand fluctuation coefficient, with a value range of (0,1); CV is the original oil demand fluctuation coefficient, is the minimum value of CV in the data set; is the maximum value of CV in the data set;

[0095] when The closer the value is to 0, the more stable the user's oil demand is;

[0096] when The closer the value is to 1, the greater the fluctuation of oil demand at the user end and the more unstable it is.

[0097] Further explanation: obtaining the execution data of each refueling truck in completing the user's fuel demand in the previous monitoring period, and calculating the completion coefficient based on the execution data, specifically including:

[0098] The completion coefficient is defined as the overall completion degree of the refueling vehicle in fulfilling the user's fuel demand. The calculation formula is:

[0099]

[0100] in, is the completion coefficient of the i-th refueling truck in the previous monitoring period; is the correction factor used to avoid The value is 0, and The value range of is selected between (0.1, 0.65); in this embodiment, The initial value of is set to 0.11;

[0101] is the first error average value after the i-th refueling truck completes M times of user-end oil demand in the previous monitoring period;

[0102] is the second error average value after the i-th refueling truck completes M times of user-end oil demand in the previous monitoring period;

[0103] set up The valid value range of is [0,P1];

[0104] when The closer the value is to 0, the worse the performance of the refueling truck in executing user needs;

[0105] when The closer the value is to P1, the better the performance of the refueling truck in fulfilling user needs. The specific values ​​of P1 and P2 are determined by the expert group through experimental data, which will not be elaborated here. Other value ranges are not within the protection scope of this embodiment and are therefore not described in detail.

[0106] Further explanation: Calculating the economic fluctuation coefficient of each refueling truck arriving at the user end area specifically includes:

[0107] The economic fluctuation coefficient corresponding to the i-th refueling truck when it arrives at the user end area is defined as:

[0108]

[0109] in, is the economic fluctuation coefficient corresponding to the i-th refueling truck arriving at the user end area from the current location;

[0110] is the amount of fuel consumed by the i-th refueling truck when it arrives at the user end area; the calculation formula is:

[0111]

[0112] Among them, Xd represents the driving distance to the user end area, is the fuel consumption of the i-th refueling truck;

[0113] is the predicted travel time of the i-th refueling truck, and the calculation formula is:

[0114]

[0115] in, is the average speed of the i-th refueling truck;

[0116] is a weighting factor used to adjust the calculation of the economic volatility coefficient. The value range is [0.1,10]; To ensure The valid range of is (P2, P3); Other value ranges of are not within the protection scope of the embodiments of the present application, and are therefore not described in detail;

[0117] According to the maximum waiting time ZDt determined by the current user end; ≤ZDt, The economic fluctuation coefficient corresponding to the minimum value is taken as the value of P2;

[0118] when The closer it is to P2, the better the economy of the refueling vehicle, the reasonable fuel consumption and the expected driving time.

[0119] when The closer it is to P3, the worse the economy of the refueling truck.

[0120] The specific values ​​of P2 and P3 are determined by the expert group through experimental data and will not be elaborated on here.

[0121] Further explanation: the acquisition of the first priority sequence and the second priority sequence specifically includes:

[0122] The number sequence set of refueling trucks {1,2,…,N} that meets The refueling trucks are screened out to form the first priority sequence {1,2,…,U1}, and u1∈{1,2,…,U1} is set, and u1 represents the u1th refueling truck in the first priority sequence, and U1 is the one that meets Total number of refueling trucks;

[0123] At the same time, the fuel trucks other than {1,2,…,U1} in the quantity sequence set {1,2,…,N} are screened and the fuel trucks that meet the requirements are The refueling trucks are screened out to form a second priority sequence {1,2,…,U2}, and u2∈{1,2,…,U2} is set, and u2 represents the u2th refueling truck in the second priority sequence, U2 is the total number of refueling trucks in the second priority sequence, and U2≤N-U1.

[0124] Further explanation, the calculation formula for the first screening index defining the first priority sequence is as follows:

[0125]

[0126] in, is the first screening index of the oil demand fluctuation coefficient of the u1th refueling vehicle in the first priority sequence relative to the user end; is the normalized oil demand fluctuation coefficient;

[0127] is the completion coefficient of the u1th refueling truck in fulfilling the user's fuel demand;

[0128] is a weight factor used to adjust the impact of the oil demand fluctuation coefficient and the completion coefficient on the first screening index; ,and The value range is in the range of (0, 1);

[0129] Set the first filter index The valid value range of is (0, P1]; the rest of the value range is not within the protection scope of this embodiment, so it is not described in detail; the initial judgment threshold of the first screening index is set to 0.5P1; and then the value range of the first screening index (0, P1] is divided into and ; The screening strategy for refueling trucks is as follows:

[0130] Based on The closer the value is to 0, the worse the performance of the refueling truck in executing user needs; The closer the value is to P1, the better the refueling truck performs in fulfilling user needs;

[0131] when The closer the value is to 0, the more stable the user's oil demand is;

[0132] when The closer the value is to 1, the greater the fluctuation of the oil demand at the user end is and the more unstable it is;

[0133] when When , it means that the performance of the u1-th refueling truck in meeting user needs is poor, and it is recommended to reduce the frequency of use of the refueling truck; based on the expert group system according to the experimental data analysis, when When setting , The setting of means that in the case of poor performance, the impact of the volatility coefficient is emphasized to better reflect the instability of demand;

[0134] The setting of makes the impact of completion on the screening index relatively weakened, reflecting that in a high volatility environment, refueling trucks with low completion should not be taken seriously;

[0135] for Impact:

[0136] like Approaching 1, the user's fuel demand fluctuates greatly and is unstable. At this time, even if the completion rate of the refueling truck is improved, the overall At a low level, it means that it is difficult for refueling trucks to maintain stable service efficiency in a frequently changing demand environment;

[0137] like The closer the value is to 0, the more stable the user demand is. At this time, it is also at a low level. It will still be low, indicating that refueling trucks cannot effectively meet the basic needs of users;

[0138] for Impact:

[0139] like Approaching 0, the tanker truck performs poorly in executing user needs and cannot complete the scheduled task, resulting in reduce;

[0140] like Approaching P1, but At a high level, in this case, although the tanker trucks were able to complete the task, the overall performance was still poor due to the sharp fluctuations in demand;

[0141] when When it falls within this range, it is recommended to reduce the frequency of use of the refueling truck, and the frequency of use is reduced by 10% of the current value each time. This value is adjusted according to the experimental data of the expert group system and is not limited; especially when and If both perform poorly, you should consider replacing or optimizing resource allocation;

[0142] when When , it means that the u1-th refueling truck performs well in meeting user needs, and it is recommended to increase the frequency of use of this refueling truck. Based on the expert group system based on the experimental data analysis, when When setting , The setting of indicates that in good performance, completion is more important despite smaller demand fluctuations;

[0143] The setting of indicates that in good performance, the influence of completion is emphasized, reflecting the efficient execution of the refueling truck in a stable environment;

[0144] for Impact:

[0145] like Approaching 0, the user's oil demand is relatively stable, which provides a good service environment for refueling trucks; in this case, even if fluctuates, as long as its value is not at a low level, It will also remain at a high level;

[0146] like Approaching 1, despite large fluctuations in demand, refueling vehicles can effectively cope with these fluctuations, and can still maintain a high ;

[0147] for Impact:

[0148] like Approaching P1, the tanker truck performs well in executing user needs and can meet the needs quickly and accurately, which will directly improve The value of

[0149] like is a low level value, but Become more acceptable: If the user demand fluctuation is within 0.1, the overall performance of the refueling vehicle is still considered good;

[0150] when When it falls within this range, it is recommended to increase the frequency of use of the refueling vehicle; especially is a low level value and When the value is high, the utilization efficiency of resources will be maximized.

[0151] The low level of the above content means that the current corresponding valid value is less than 20%, and the high level means that the corresponding valid value is more than 80%;

[0152] For example The low level is ; For high level, , the rest is not elaborated.

[0153] Further explanation, the calculation formula of the second screening index defining the second priority sequence is as follows:

[0154]

[0155] in, is the second screening index of the oil demand fluctuation coefficient of the u2th refueling vehicle in the second priority sequence relative to the user end; is the normalized oil demand fluctuation coefficient;

[0156] is the completion coefficient of the u2th refueling truck in fulfilling the user's fuel demand;

[0157] is a weighting factor used to adjust the impact of the oil demand fluctuation coefficient and the completion coefficient on the second screening index; ,and The value range is in the range of (0, 1);

[0158] Set the second filter index The valid value range is (0, P1]; the rest of the value range is not within the protection scope of this embodiment; the initial judgment threshold of the second screening index is also set to 0.5P1; and then the value interval for the screening strategy is determined in the value range of the second screening index (0, P1] as ;

[0159] Only when When , it means that the u2th refueling truck performs well in meeting user needs. At this time, it is necessary to screen out a refueling truck that matches the current u2th refueling truck in the second priority sequence to form a refueling truck combination; the matching strategy of the refueling truck combination is: add the remaining oil reserves of the two refueling trucks to the corresponding first error average value to obtain the final real-time superimposed oil reserves, and the final real-time superimposed oil reserves need to be greater than the amount of oil to be filled at the user end. At the same time, it is necessary to limit the difference between the real-time superimposed oil reserves and the amount of oil to be filled to within 10% of the amount of oil to be filled, and the difference is expressed as DJy×0.1; if it cannot be satisfied, then re-search for any other two refueling truck combinations in the second priority sequence for analysis;

[0160] What needs to be determined is that of the two fuel trucks in the combination, at least one of them must meet That's it;

[0161] Furthermore, if two other refueling truck combinations in the second priority sequence are re-analyzed and still no two refueling trucks that meet the requirements cannot be found, then from any two refueling truck combinations exceeding DJy×0.1, the two refueling trucks with the smallest value exceeding DJy×0.1 are selected as the refueling truck combination to be screened.

[0162] Further explanation: The final selection of the refueling vehicle is determined based on the economic fluctuation coefficient, including:

[0163] Will comply with The corresponding refueling trucks are selected and the economic fluctuation coefficients corresponding to these refueling trucks when they arrive at the user end area are calculated. ;

[0164] Will comply with The corresponding refueling trucks are selected and the corresponding refueling truck combination is determined, and the economic fluctuation coefficient corresponding to these refueling trucks when they arrive at the user end area is calculated. ;

[0165] The economic fluctuation coefficient for fuel trucks is:

[0166] When the economic fluctuation coefficient is closer to P2, it means that the economy of the refueling vehicle is better, the fuel consumption is reasonable and the driving time is in line with expectations;

[0167] When the economic fluctuation coefficient approaches P3, it means that the economy of the refueling vehicle is worse;

[0168] The economic fluctuation coefficient The corresponding refueling truck closest to P2 is given priority as the final refueling truck selection;

[0169] Economic Fluctuation Coefficient The corresponding refueling truck closest to P2 is selected as the alternative refueling truck.

[0170] If the user terminal does not provide a matching For a refueling truck, the initial judgment threshold of the first screening index is reduced by K1 percentage value; and the reduced initial judgment threshold is used by the user terminal in the next construction site refueling event; in this embodiment, the initial value of K1 percentage value is set to 3%; K1 can be adjusted according to the experimental data of the expert group and is not limited.

[0171] Embodiment 2:

[0172] See also Figure 2 , a system for using oil on a construction site, the system being used to perform the method for using oil on a construction site, comprising:

[0173] Data collection and upload module: used to collect historical fuel demand data of the user end in M ​​construction site refueling events during the previous monitoring period, as well as the execution and status data of each refueling truck to complete these fuel demands, and upload these data to the cloud data platform for storage;

[0174] Data determination module: used to determine the fuel demand data including the amount of fuel to be refueled, the maximum waiting time and the fuel demand fluctuation index in the current user-side refueling event;

[0175] and determine the status data of each refueling truck, including the remaining oil reserves, the amount of oil consumed when arriving at the user's end area, and the predicted travel time;

[0176] The execution data includes the first error average value of the actual available oil reserves of each refueling truck after each refueling truck completes M times of user-end oil demand in the previous monitoring time period, and the second error average value of the actual driving time;

[0177] Oil demand fluctuation coefficient generation module: used to analyze the historical oil demand data of the user end in the previous monitoring period in the cloud data platform to calculate the oil demand fluctuation coefficient of the user end;

[0178] Completion coefficient generation module: used to obtain the execution data of each refueling truck in completing the user's fuel demand in the previous monitoring period in the cloud data platform, and calculate the completion coefficient based on the execution data;

[0179] Economic fluctuation coefficient generation module: In the cloud data platform, the economic fluctuation coefficient of each refueling truck reaching the user end area is calculated based on the amount of fuel consumed by each refueling truck when it reaches the user end area from the current location and the predicted driving time;

[0180] Priority sequence classification module: used to classify the priorities of various refueling vehicles according to the oil demand data corresponding to the current user-side refueling event, wherein the refueling vehicles that meet the requirements of the amount of oil to be refueled and the maximum waiting time are classified into the first priority sequence, and the refueling vehicles that meet the requirements of the maximum waiting time among the remaining refueling vehicles are classified into the second priority sequence;

[0181] A first screening index generating module: used to analyze a number of refueling vehicles determined in the first priority sequence in combination with corresponding oil demand fluctuation coefficients and completion coefficients, and generate a first screening index for providing a screening strategy for the refueling vehicles;

[0182] A second screening index generating module: used to analyze a number of refueling vehicles determined in the second priority sequence in combination with corresponding oil demand fluctuation coefficients and completion coefficients, and generate a second screening index for providing a screening strategy for the refueling vehicles;

[0183] The final selection module is used to determine the final selection of a refueling vehicle based on the economic fluctuation coefficient among the several refueling vehicles determined by the first screening index and the second screening index.

[0184] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0185] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0186] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0187] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for using oil on a construction site, characterized in that: The specific steps include: Step S1: In the previous monitoring period, collect historical fuel demand data of the user end in M ​​construction site refueling events, as well as the execution and status data of each refueling truck to complete these fuel demands, and upload these data to the cloud data platform for storage; Step S2: in the refueling event of the current user end, determining the oil demand data including the amount of oil to be refueled, the maximum waiting time and the oil demand fluctuation index; and determine the status data of each refueling truck, including the remaining oil reserves, the amount of oil consumed when arriving at the user's end area, and the predicted travel time; The execution data includes the first error average value of the actual available oil reserves of each refueling truck after each refueling truck completes M times of user-end oil demand in the previous monitoring time period, and the second error average value of the actual driving time; Step S3: In the cloud data platform, the historical oil demand data of the user end in the previous monitoring period is analyzed to calculate the oil demand fluctuation coefficient of the user end; Step S4: in the cloud data platform, the execution data of each refueling truck in completing the user's oil demand in the previous monitoring period is obtained, and the completion coefficient is calculated based on the execution data; Step S5: In the cloud data platform, the economic fluctuation coefficient of each refueling truck arriving at the user end area is calculated according to the amount of fuel consumed by each refueling truck when it arrives at the user end area from the current position and the predicted travel time; Step S6: Classify the priorities of the refueling vehicles according to the oil demand data corresponding to the current user-side refueling event, wherein the refueling vehicles that meet both the requirements of the amount of oil to be refueled and the maximum waiting time are classified into a first priority sequence, and the refueling vehicles that meet the requirements of the maximum waiting time among the remaining refueling vehicles are classified into a second priority sequence; Step S7: among the several refueling vehicles determined in the first priority sequence, analyzing the corresponding oil demand fluctuation coefficient and completion coefficient, and generating a first screening index for providing a screening strategy for the corresponding refueling vehicles; Step S8: Among the several refueling vehicles determined by the second priority sequence, analyzing the corresponding oil demand fluctuation coefficient and completion coefficient, and generating a second screening index for providing a screening strategy for the corresponding refueling vehicles; Step S9: Determine the final selection of a refueling vehicle based on the economic fluctuation coefficient among the several refueling vehicles determined by the first screening index and the second screening index.

2. The method for using oil on construction sites according to claim 1, characterized in that: Mark the construction site refueling events of the current user end in the previous monitoring period to form an event sequence set {1,2,…,M}, where j represents the jth construction site refueling event in the event sequence set, and M represents the total number of construction site refueling events of the user end, j∈{1,2,…,M}; Mark each refueling truck to form a number sequence set of refueling trucks {1,2,…,N}, where i represents the i-th refueling truck in the number sequence set, N represents the total number of refueling trucks, i∈{1,2,…,N}; The first error mean value calculation formula for defining the actual available oil reserves is as follows: in It represents the first error average value after the i-th refueling truck completes M times of user-side oil demand in the previous monitoring period, represents the actual available oil reserves corresponding to the jth construction site refueling event of the i-th refueling truck in the previous monitoring period, represents the remaining oil reserves corresponding to the jth construction site refueling event of the i-th refueling truck in the previous monitoring period; The second error average calculation formula that defines the actual travel time is as follows: in It represents the second error average value after the i-th refueling truck completes M times of user-side oil demand in the previous monitoring period, represents the actual driving time corresponding to the jth construction site refueling event of the i-th refueling truck in the previous monitoring period, It represents the predicted driving time corresponding to the jth construction site refueling event of the i-th refueling truck in the previous monitoring time period.

3. The method for using construction site oil according to claim 2, characterized in that: Calculate the oil demand fluctuation coefficient at the user end, including: The calculation formula for defining the oil demand fluctuation index is as follows: in, Indicates the maximum amount of oil to be added during the previous monitoring period; Indicates the minimum amount of oil to be added during the previous monitoring period; is the mean of the historical amount of oil to be refilled; The value range of YBd is [0, +∞). When YBd is closer to 0, the user's oil demand is more stable; when the value of YBd is larger, the user's oil demand changes more. The mean and standard deviation of historical oil demand data are respectively and , the calculation formula is as follows: The oil demand fluctuation coefficient is defined as the standard deviation With the mean The ratio of is: Normalize with respect to CV using the following formula: is the standardized oil demand fluctuation coefficient, which ranges from (0,1); CV is the original oil demand fluctuation coefficient, is the minimum value of CV in the data set; is the maximum value of CV in the data set; when The closer the value is to 0, the more stable the user's oil demand is; when The closer the value is to 1, the greater the fluctuation of oil demand at the user end and the more unstable it is.

4. The method for using oil at construction sites according to claim 3, characterized in that: Obtain the execution data of each refueling truck in completing the user's fuel demand in the previous monitoring period, and calculate the completion coefficient based on the execution data, including: The completion coefficient is defined as the overall completion degree of the refueling vehicle in fulfilling the user's fuel demand. The calculation formula is: in, is the completion coefficient of the i-th refueling truck in the previous monitoring period; is the correction factor used to avoid The value is 0, and The value range of is selected between (0.1, 0.65); is the first error average value after the i-th refueling truck completes M times of user-end oil demand in the previous monitoring period; is the second error average value after the i-th refueling truck completes M times of user-end oil demand in the previous monitoring period; set up The valid value range of is [0,P1]; when The closer the value is to 0, the worse the performance of the refueling truck in executing user needs; when The closer the value is to P1, the better the performance of the refueling truck in fulfilling user needs.

5. The method for using oil at construction sites according to claim 4, characterized in that: Calculate the economic fluctuation coefficient of each refueling truck arriving at the user end area, including: The economic fluctuation coefficient corresponding to the i-th refueling truck when it arrives at the user end area is defined as: in, is the economic fluctuation coefficient corresponding to the i-th refueling truck arriving at the user end area from the current location; is the amount of fuel consumed by the i-th refueling truck when it arrives at the user end area; the calculation formula is: Among them, Xd represents the driving distance to the user end area, is the fuel consumption of the i-th refueling truck; is the predicted travel time of the i-th refueling truck, and the calculation formula is: in, is the average speed of the i-th refueling truck; is a weighting factor used to adjust the calculation of the economic volatility coefficient. The value range is [0.1,10]; To ensure The valid range of is (P2, P3); According to the maximum waiting time ZDt determined by the current user end; ≤ZDt, The economic fluctuation coefficient corresponding to the minimum value is taken as the value of P2; when The closer it is to P2, the better the economy of the refueling vehicle, the reasonable fuel consumption and the expected driving time. when The closer it is to P3, the worse the economy of the refueling truck.

6. The method for using oil at construction sites according to claim 5, characterized in that: The first priority sequence and the acquisition of the second priority sequence specifically include: The number sequence set of refueling trucks {1,2,…,N} that meets The refueling trucks are screened out to form the first priority sequence {1,2,…,U1}, and u1∈{1,2,…,U1} is set, and u1 represents the u1th refueling truck in the first priority sequence, and U1 is the one that meets Total number of refueling trucks; At the same time, the fuel trucks other than {1,2,…,U1} in the quantity sequence set {1,2,…,N} are screened and the fuel trucks that meet the requirements are The refueling trucks are screened out to form a second priority sequence {1,2,…,U2}, and u2∈{1,2,…,U2} is set, and u2 represents the u2th refueling truck in the second priority sequence, U2 is the total number of refueling trucks in the second priority sequence, and U2≤N-U1.

7. The method for using oil at construction sites according to claim 6, characterized in that: The first screening index calculation formula that defines the first priority sequence is as follows: in, is the first screening index of the oil demand fluctuation coefficient of the u1th refueling vehicle in the first priority sequence relative to the user end; is the normalized oil demand fluctuation coefficient; is the completion coefficient of the u1th refueling truck in fulfilling the user's fuel demand; is a weight factor used to adjust the impact of the oil demand fluctuation coefficient and the completion coefficient on the first screening index; ,and The value range is in the range of (0, 1); Set the first filter index The effective value range of is (0, P1]; the initial judgment threshold of the first screening index is set to 0.5P1; and the value range of the first screening index (0, P1] is divided into and ; The screening strategy for refueling trucks is as follows: when When , it means that the performance of the u1-th refueling truck in meeting user needs is poor, and it is recommended to reduce the frequency of use of the refueling truck; when , it means that the u1-th refueling truck performs well in meeting user needs, and it is recommended to increase the frequency of use of this refueling truck.

8. The method for using oil at construction sites according to claim 7, characterized in that: The second screening index calculation formula that defines the second priority sequence is as follows: in, is the second screening index of the oil demand fluctuation coefficient of the u2th refueling vehicle in the second priority sequence relative to the user end; is the completion coefficient of the u2th refueling truck in fulfilling the user's fuel demand; is a weighting factor used to adjust the impact of the oil demand fluctuation coefficient and the completion coefficient on the second screening index; ,and The value range is in the range of (0, 1); Set the second filter index The effective value range of is (0, P1]; and the initial judgment threshold of the second screening index is set to 0.5P1; and then the value interval for the screening strategy is determined in the value range of the second screening index (0, P1] as ; Only when When , it means that the u2th refueling truck performs well in meeting user needs. At this time, it is necessary to select a refueling truck that matches the current u2th refueling truck in the second priority sequence to form a refueling truck combination; The matching strategy of the tanker truck combination is: add the remaining oil reserves of the two tanker trucks to the corresponding first error average value to obtain the final real-time superimposed oil reserves, and the final real-time superimposed oil reserves need to be greater than the amount of oil to be filled at the user end. At the same time, the difference between the real-time superimposed oil reserves and the amount of oil to be filled needs to be limited to within 10% of the amount of oil to be filled, and the difference is expressed as DJy×0.1; if it cannot be satisfied, then re-search for any other two tanker truck combinations in the second priority sequence for analysis; If two other refueling truck combinations in the second priority sequence are analyzed again and two refueling trucks that meet the requirements cannot be found, then among any two refueling truck combinations exceeding DJy×0.1, the two refueling trucks with the smallest values ​​exceeding DJy×0.1 are selected as the refueling truck combination to be screened.

9. The method for using oil at a construction site according to claim 8, characterized in that: The final choice of fuel truck is determined based on the economic fluctuation coefficient, including: Will comply with The corresponding refueling trucks are selected and the economic fluctuation coefficients corresponding to these refueling trucks when they arrive at the user end area are calculated. ; Will comply with The corresponding refueling trucks are selected and the corresponding refueling truck combination is determined, and the economic fluctuation coefficient corresponding to these refueling trucks when they arrive at the user end area is calculated. ; The economic fluctuation coefficient The corresponding refueling truck closest to P2 is given priority as the final refueling truck selection; Economic Fluctuation Coefficient The corresponding refueling truck closest to P2 is selected as the alternative refueling truck; If the user terminal does not provide a matching For a refueling truck, the initial judgment threshold of the first screening index is reduced by K1 percentage value, and the reduced initial judgment threshold is used by the user terminal for the next construction site refueling event.

10. A system for using oil on construction sites, characterized in that: The system is used to implement the method for using construction site oil according to any one of claims 1 to 9, comprising: Data collection and upload module: used to collect historical fuel demand data of the user end in M ​​construction site refueling events during the previous monitoring period, as well as the execution and status data of each refueling truck to complete these fuel demands, and upload these data to the cloud data platform for storage; Data determination module: used to determine the fuel demand data including the amount of fuel to be refueled, the maximum waiting time and the fuel demand fluctuation index in the current user-side refueling event; and determine the status data of each refueling truck, including the remaining oil reserves, the amount of oil consumed when arriving at the user's end area, and the predicted travel time; The execution data includes the first error average value of the actual available oil reserves of each refueling truck after each refueling truck completes M times of user-end oil demand in the previous monitoring time period, and the second error average value of the actual driving time; Oil demand fluctuation coefficient generation module: used to analyze the historical oil demand data of the user end in the previous monitoring period in the cloud data platform to calculate the oil demand fluctuation coefficient of the user end; Completion coefficient generation module: used to obtain the execution data of each refueling truck in completing the user's fuel demand in the previous monitoring period in the cloud data platform, and calculate the completion coefficient based on the execution data; Economic fluctuation coefficient generation module: In the cloud data platform, the economic fluctuation coefficient of each refueling truck reaching the user end area is calculated based on the amount of fuel consumed by each refueling truck when it reaches the user end area from the current location and the predicted driving time; Priority sequence classification module: used to classify the priorities of various refueling vehicles according to the oil demand data corresponding to the current user-side refueling event, wherein the refueling vehicles that meet the requirements of the amount of oil to be refueled and the maximum waiting time are classified into the first priority sequence, and the refueling vehicles that meet the requirements of the maximum waiting time among the remaining refueling vehicles are classified into the second priority sequence; A first screening index generating module: used to analyze a number of refueling vehicles determined in the first priority sequence in combination with corresponding oil demand fluctuation coefficients and completion coefficients, and generate a first screening index for providing a screening strategy for the refueling vehicles; A second screening index generating module: used to analyze a number of refueling vehicles determined in the second priority sequence in combination with corresponding oil demand fluctuation coefficients and completion coefficients, and generate a second screening index for providing a screening strategy for the refueling vehicles; The final selection module is used to determine the final selection of a refueling vehicle based on the economic fluctuation coefficient among the several refueling vehicles determined by the first screening index and the second screening index.

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