Integrated intelligent gas station operation data information acquisition method and system

Abstract

The invention relates to the technical field of operation resource management, in particular to an integrated intelligent gas station operation data information acquisition method and system.The method comprises the steps that after refueling machine operation data of each month are obtained, the operation data of each month are acquired according to the overall size, discrete distribution condition and seasonal trend of the refueling machine operation data of each month; obtaining an operation threshold adjustment characteristic value of each month; according to the overall fluctuation condition of the oiling machine operation data of each month, an operation stability characteristic value of each month is obtained; the operation threshold adjustment characteristic value is weighted by taking the operation stable characteristic value as a weight, so that the self-adaptive optimization filtering threshold value of each month obtained by combining the prior filtering threshold value is more accurate, and the effect of filtering the oiling machine operation data of each month by combining the optimization filtering threshold value is better; and the accuracy of gas station operation data information acquisition is higher, so that the gas station operation resources can be managed more accurately and efficiently.

Description

Technical Field

[0001] The present invention relates to the technical field of business resource management, and particularly relates to an integrated intelligent gas station operation data information collection method and system. Background Art

[0002] Gas stations are important nodes in the supply of petroleum energy. With the development of information technology, the application of intelligent gas station management is becoming more and more extensive. Therefore, an efficient data collection method is needed to monitor the operation data of gas stations in real time, so as to timely detect abnormal operation data of gas stations and manage the business resources of gas stations according to the abnormal operation data. Since the operation of gas stations mainly relies on fuel dispensers, the data that needs to be monitored and collected is usually the operation data of fuel dispensers in gas stations.

[0003] In the prior art, the traditional sensor-based data filtering technology is used for the collection and processing of gas station operation data. Specifically, the prior data threshold is used to filter all the operation data of fuel dispensers, and the time period of the operation data of fuel dispensers less than the prior data threshold is marked and a warning is issued. However, the operation of gas stations is affected by seasons, resulting in certain differences in the overall operation data of fuel dispensers in gas stations between peak operation seasons and off-peak operation seasons. At this time, using a fixed prior data threshold to filter all the operation data of fuel dispensers may result in a situation that does not conform to the objective facts, such as too few abnormal operation data of fuel dispensers in peak operation seasons or too many abnormal operation data of fuel dispensers in off-peak operation seasons. That is, the method of filtering all the operation data of fuel dispensers by the prior data threshold in the prior art has a low accuracy in collecting gas station operation data information. Summary of the Invention

[0004] In order to solve the technical problem that the method of filtering all the operation data of fuel dispensers by the prior data threshold in the prior art has a low accuracy in collecting gas station operation data information, the purpose of the present invention is to provide an integrated intelligent gas station operation data information collection method and system, and the specific technical solutions adopted are as follows: The present invention provides an integrated intelligent gas station operation data information collection method, and the method includes: During the operation of the gas station, obtain the operation data of the fuel dispenser for each sampling time period under each month; According to the overall distribution and discrete situation of the operation data of the fuel dispenser for each sampling time period in each month, obtain the overall operation characteristic value of each month; according to the overall operation characteristic value, the overall operation data of the fuel dispenser for each month, and the corresponding seasonal trend, obtain the operation threshold adjustment characteristic value of each month; Based on the overall fluctuation of the monthly operation data of fuel dispensers, obtain the monthly operation stability eigenvalue; adjust the eigenvalue, operation stability eigenvalue, and prior filtering threshold according to the operation threshold to obtain the monthly optimized filtering threshold; Collect the gas station operation data information according to the optimized filtering threshold.

[0005] Furthermore, the method for obtaining the overall operation eigenvalue includes: In all sampling time periods of each month, take the ratio of the difference between the mean and the minimum value of all fuel dispenser operation data to the range of all fuel dispenser operation data as the monthly reference operation eigenvalue; Take the negative correlation mapping value of the variance of all fuel dispenser operation data of each month as the credibility of the operation eigenvalue of each month; Obtain the overall operation eigenvalue of each month according to the reference operation eigenvalue and the credibility of the operation eigenvalue; both the reference operation eigenvalue and the credibility of the operation eigenvalue are positively correlated with the overall operation eigenvalue.

[0006] Furthermore, the method for obtaining the operation threshold adjustment eigenvalue includes: Take the mean of the maximum and minimum values of all fuel dispenser operation data of each month as the standard reference value; take the positive correlation mapping value of the difference between the mean of all fuel dispenser operation data of each month and the standard reference value as the standard deviation of the operation data of each month; Obtain the reference adjustment eigenvalue of each month according to the mean of all fuel dispenser operation data of each month, the standard deviation of the operation data, and the overall operation eigenvalue; among them, the mean of all fuel dispenser operation data of each month, the standard deviation of the operation data, and the overall operation eigenvalue are all positively correlated with the reference adjustment eigenvalue; Arrange the reference adjustment eigenvalues of each month and the preset number of months before each month in chronological order and perform fitting by the least squares method to obtain the operation threshold adjustment eigenvalue of each month.

[0007] Furthermore, the calculation formula of the operation stability eigenvalue includes: ; Among them, is the operation stability eigenvalue of the th month; is the number of fuel dispenser operation data of the th month; is the th fuel dispenser operation data of the th month; The minimum value of the operation data of the fuel dispenser for [number of] months; is the maximum value of the operation data of the fuel dispenser for [number of] months; is the absolute value symbol; is the minimum value selection function.

[0008] Furthermore, the method for obtaining the optimized filtering threshold includes: Based on the operation stability eigenvalue and the operation threshold adjustment eigenvalue, obtain the threshold adjustment value for each month; both the operation stability eigenvalue and the operation threshold adjustment eigenvalue have a positive correlation with the threshold adjustment value; Obtain the prior filtering threshold of all the operation data of the fuel dispenser; use the sum value of the prior filtering threshold and the threshold adjustment value as the optimized filtering threshold for each month.

[0009] Furthermore, the method for collecting the operation data information of the gas station according to the optimized filtering threshold includes: After transmitting all the operation data of the fuel dispenser for each month into the operation database of the gas station, when there is operation data of the fuel dispenser less than the optimized filtering threshold among all the operation data of the fuel dispenser for each month, give an operation data warning and record the sampling time period corresponding to the operation data of the fuel dispenser less than the optimized filtering threshold as the abnormal sampling time period.

[0010] Furthermore, the method for obtaining the overall operation eigenvalue for each month according to the reference operation eigenvalue and the credibility of the operation eigenvalue includes: Use the normalized value of the product between the reference operation eigenvalue and the credibility of the operation eigenvalue as the overall operation eigenvalue for each month.

[0011] Furthermore, the method for obtaining the reference adjustment eigenvalue includes: Use the mean value of all the operation data of the fuel dispenser for each month, the product of the standard deviation of the operation data and the overall operation eigenvalue as the reference adjustment eigenvalue for each month.

[0012] Furthermore, the method for obtaining the threshold adjustment value includes: Use the product of the positive correlation mapping value of the operation stability eigenvalue and the operation threshold adjustment eigenvalue as the threshold adjustment value for each month.

[0013] The present invention also proposes an integrated intelligent gas station operation data information collection system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of any one of the integrated intelligent gas station operation data information collection methods.

[0014] The present invention has the following beneficial effects: The method of filtering all the fuel dispenser operation data by a fixed prior data threshold has a low accuracy in the collection of gas station operation data information because the gas station operation situation is affected by seasonality, resulting in a certain difference in the overall fuel dispenser operation data of the gas station in the peak season and the off-season. The use of a fixed prior data threshold may result in too little abnormal fuel dispenser operation data in the peak season or too much abnormal fuel dispenser operation data in the off-season, which is inconsistent with the objective facts. Therefore, different thresholds can be set for different operation conditions to filter the fuel dispenser operation data. The operation of a gas station is related to the traffic volume. The traffic volume is larger in the time period with more festivals or more convenient travel, while the traffic volume is smaller in the time period with fewer festivals or inconvenient travel. Considering that the time period for gas station operation assessment is usually monthly or quarterly, the present invention analyzes it in months to better pay attention to the seasonal trend of fuel dispenser operation data, that is, different filtering thresholds are set according to the operation conditions of each month to collect gas station operation data information.

[0015] As for the seasonality of gas stations, it is mainly reflected in the different operating conditions corresponding to different time periods, such as the slow operation in the working season, and the hot operation corresponding to the holiday season or the tourist season; these situations will be reflected in the overall data of the operation data. For example, when the overall operation data of the gas station in the corresponding month is larger, it means that the operation condition of the gas station in that month is better, and it is more likely to belong to the peak operation season or the tourist season. The larger the corresponding seasonal factor, the larger the overall operation characteristic value. Further considering that the more discrete the gas station operation data of each month is, the greater the change of the corresponding gas station operation data is, that is, the more drastic the change of the operation condition is, thus affecting the credibility of its seasonal trend, indirectly affecting the size of the overall operation characteristic value, and the corresponding overall operation characteristic value is smaller. Therefore, the present invention obtains the overall operation characteristic value of each month according to the overall distribution and discreteness of the gas station operation data in each sampling time period in each month; the larger the overall operation characteristic value, the more consistent the overall operation data of the corresponding month is with the seasonal trend, and the greater the necessity of adjusting the threshold is, so the operation threshold adjustment characteristic value can be calculated based on the overall operation characteristic value.

[0016] For the operation data of fuel dispensers for each month, the larger the corresponding overall operation data of fuel dispensers, the better the operation situation of the gas station in the corresponding month, and the more likely it belongs to the peak operation season. Therefore, in order to avoid the situation of too little abnormal operation data of fuel dispensers in the peak operation season, it is necessary to adjust the filtering threshold for that month; on the contrary, the smaller the overall operation data of fuel dispensers in the corresponding month, the better the operation situation of the gas station in the corresponding month, and the more likely it belongs to the off-peak operation season. Therefore, in order to avoid the situation of too much abnormal operation data of fuel dispensers in the off-peak operation season, it is also necessary to adjust the filtering threshold for that month; further considering that the peak operation season or the off-peak operation season may last for a longer time and the corresponding impact has a certain persistence, in order to make the calculated operation threshold adjustment eigenvalue more accurate, the seasonal trend of the overall operation data of fuel dispensers for each month can be analyzed; therefore, the present invention obtains the operation threshold adjustment eigenvalue for each month according to the overall operation eigenvalue, the overall operation data of fuel dispensers for each month, and the corresponding seasonal trend.

[0017] Further considering that when the volatility of the overall operation data of fuel dispensers in the corresponding month is stronger, it indicates that the operation data of fuel dispensers in that month is more unstable, which will lead to a decrease in the credibility of the operation threshold adjustment eigenvalue obtained by analyzing the overall operation data of fuel dispensers. Therefore, further, it is necessary to correct the operation threshold adjustment eigenvalue in combination with the fluctuation situation of the operation data of fuel dispensers for each month, so as to obtain a more accurate threshold correction amount. Therefore, the present invention obtains the operation stability eigenvalue for each month according to the overall fluctuation situation of the operation data of fuel dispensers for each month; thus, the operation threshold adjustment eigenvalue is corrected by the operation stability eigenvalue, making the subsequent obtained filtering threshold more accurate. Since combining the operation stability eigenvalue and the operation threshold adjustment eigenvalue can obtain a more accurate threshold adjustment amount, the present invention obtains a more accurate optimized filtering threshold for each month according to the operation threshold adjustment eigenvalue, the operation stability eigenvalue, and the prior filtering threshold, making the effect of filtering the operation data of fuel dispensers for each month in combination with the optimized filtering threshold better, that is, making the accuracy of collecting gas station operation data information higher, and thus managing the operation resources of gas stations more accurately and efficiently. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1A flowchart of a method for collecting integrated intelligent gas station operation data information provided by an embodiment of the present invention. Detailed implementation manners

[0020] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features and effects of a method and system for collecting integrated intelligent gas station operation data information according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0022] The following specifically describes the specific solutions of a method and system for collecting integrated intelligent gas station operation data information provided by the present invention with reference to the accompanying drawings.

[0023] Please refer to Figure 1 , which shows a flowchart of a method for collecting integrated intelligent gas station operation data information provided by an embodiment of the present invention. The method includes: Step S1: During the operation of the gas station, obtain the operation data of the fuel dispenser for each sampling time period under each month.

[0024] The embodiments of the present invention aim to provide a method and system for collecting integrated intelligent gas station operation data information, which are used to analyze the operation data of the fuel dispenser for each month during the operation of the gas station to obtain an optimized filtering threshold for each month, so as to perform adaptive data information collection on the operation data of each fuel dispenser according to the optimized filtering threshold.

[0025] In the embodiments of the present invention, during the operation of the gas station, the operation data of the fuel dispenser for each sampling time period under each month is obtained. Specifically: the operation data of the fuel dispenser in the embodiments of the present invention uses the fuel volume. Implementers can also use other data as the operation data of the fuel dispenser, such as the sales amount collected by the meter. The fuel volume for each sampling time period is collected in real time through the flow meter on the fuel dispenser, and the sampling time period in the embodiments of the present invention is set to one day, that is, the fuel volume for each day under each month is obtained. It should be noted that the implementer can adjust the length of the sampling time period according to the specific implementation environment, and the implementer can also analyze other unit time periods outside the sampling month according to the specific implementation environment, such as weekly, quarterly, etc., which will not be further elaborated here.

[0026] Step S2: According to the overall distribution and discreteness of the gas station operation data in each sampling time period of each month, the overall operation characteristic value of each month is obtained; according to the overall operation characteristic value, the overall gas station operation data of each month and the corresponding seasonal trend, the operation threshold adjustment characteristic value of each month is obtained.

[0027] The method of filtering all the fuel dispenser operation data by a fixed prior data threshold has a low accuracy in the collection of gas station operation data information because the gas station operation situation is affected by seasonality, resulting in a certain difference in the overall fuel dispenser operation data of the gas station in the peak season and the off-season. The use of a fixed prior data threshold may result in too little abnormal fuel dispenser operation data in the peak season or too much abnormal fuel dispenser operation data in the off-season, which is inconsistent with the objective facts. Therefore, different thresholds can be set for different operation conditions to filter the fuel dispenser operation data. The operation of a gas station is related to the traffic volume. The traffic volume is larger in the time period with more festivals or more convenient travel, while the traffic volume is smaller in the time period with fewer festivals or inconvenient travel. Considering that the time period for gas station operation assessment is usually monthly or quarterly, the present invention analyzes it in months to better pay attention to the seasonal trend of fuel dispenser operation data, that is, different filtering thresholds are set according to the operation conditions of each month to collect gas station operation data information.

[0028] As for the seasonality of gas stations, it is mainly reflected in the different operating conditions corresponding to different time periods, such as the slow operation in the working season, and the hot operation corresponding to the holiday season or the tourist season; these situations will be reflected in the overall data of the operation data. For example, when the overall operation data of the gas station in the corresponding month is larger, it means that the operation condition of the gas station in that month is better, and it is more likely to belong to the peak operation season or the tourist season. The larger the corresponding seasonal factor, the larger the overall operation characteristic value. Further considering that the more discrete the gas station operation data of each month is, the greater the change of the corresponding gas station operation data is, that is, the more drastic the change of the operation condition is, which affects the credibility of its seasonal trend and indirectly affects the size of the overall operation characteristic value. Therefore, the embodiment of the present invention obtains the overall operation characteristic value of each month according to the overall distribution and discreteness of the gas station operation data in each sampling time period in each month. The larger the overall operation characteristic value is, the more the gas station operation data of the corresponding month is in line with the seasonal trend, and the greater the necessity of adjusting the threshold is, so the subsequent operation threshold adjustment characteristic value can be calculated based on the overall operation characteristic value.

[0029] Preferably, the method for obtaining the overall operation characteristic value includes: For the operation data of fuel dispensers each month, when the overall operation data of the fuel dispensers in that month is larger, the average value of all the operation data of the fuel dispensers is relatively larger. Therefore, in all sampling time periods of each month, the ratio of the difference between the average value and the minimum value of all the operation data of the fuel dispensers to the range of all the operation data of the fuel dispensers is used as the reference operation characteristic value for each month. Since the larger the overall operation data of the fuel dispensers, the better the operation status of the gas station in that month, the more likely it belongs to the peak operation season or the tourist season, the greater the corresponding seasonal factor, and the larger the overall operation characteristic value. Therefore, the size of the reference operation characteristic value is positively correlated with the overall operation data.

[0030] Since the more discrete the operation data of the fuel dispensers is each month, the lower the credibility of the corresponding seasonal trend, and the smaller the overall operation characteristic value; and the variance can characterize the discrete fluctuation of a set of data, the larger the corresponding variance, the greater the degree of dispersion. Therefore, in the embodiments of the present invention, the negative correlation mapping value of the variance of all the operation data of the fuel dispensers each month is used as the credibility of the operation characteristic value for each month. The greater the corresponding operation reference credibility, the smaller the degree of dispersion of the operation data of the fuel dispensers in that month, and the larger the corresponding overall operation characteristic value.

[0031] Furthermore, in combination with the relationship between the reference operation characteristic value, the credibility of the operation characteristic value and the overall operation characteristic value, the overall operation characteristic value for each month is obtained according to the reference operation characteristic value and the credibility of the operation characteristic value. Both the reference operation characteristic value and the credibility of the operation characteristic value are positively correlated with the overall operation characteristic value. Preferably, the method for obtaining the overall operation characteristic value for each month according to the reference operation characteristic value and the credibility of the operation characteristic value includes: Since the larger the reference operation characteristic value and the larger the credibility of the operation characteristic value, the larger the corresponding overall operation characteristic value, and the credibility of the operation characteristic value characterizes the credibility degree of the overall operation characteristic value, the overall operation characteristic value can be weighted by the credibility of the operation characteristic value. In the embodiments of the present invention, the normalized value of the product between the reference operation characteristic value and the credibility of the operation characteristic value is used as the overall operation characteristic value for each month. In the embodiments of the present invention, the normalization method adopts linear normalization, and the implementer can adjust the normalization method according to the specific implementation environment, and no further elaboration is made here.

[0032] In the embodiments of the present invention, each month is sequentially used as the th month, then the method for obtaining the overall operation characteristic value of the th month is expressed in formula as: ; wherein, is the overall operation characteristic value of the th month; is the The mean of the operation data of all fuel dispensers in a month; is the minimum value of the operation data of the fuel dispenser in the th month; is the maximum value of the operation data of the fuel dispenser in the th month; is the variance of the operation data of all fuel dispensers in the th month; is the exponential function with the natural constant as the base; is the normalization function. is the range of the operation data of all fuel dispensers in the th month; is the reference operation characteristic value in the th month; is the credibility of the operation characteristic value in the th month.

[0033] For the operation data of the fuel dispenser in each month, the larger the corresponding overall operation data of the fuel dispenser, the better the operation of the gas station in the corresponding month, and the more likely it belongs to the peak operation season. Therefore, in order to avoid the situation of too little abnormal operation data of the fuel dispenser in the peak operation season, it is necessary to adjust the filtering threshold for that month; on the contrary, the smaller the overall operation data of the fuel dispenser in the corresponding month, the better the operation of the gas station in the corresponding month, and the more likely it belongs to the off-peak operation season. Therefore, in order to avoid the situation of too much abnormal operation data of the fuel dispenser in the off-peak operation season, it is also necessary to adjust the filtering threshold for that month; further considering that the peak operation season or the off-peak operation season may last for a longer time and the corresponding impact has a certain persistence, in order to make the calculated operation threshold adjustment characteristic value more accurate, the seasonal trend of the overall operation data of the fuel dispenser in each month can be analyzed; therefore, in the embodiments of the present invention, the operation threshold adjustment characteristic value for each month is obtained according to the overall operation characteristic value, the overall operation data of the fuel dispenser in each month, and the corresponding seasonal trend.

[0034] Preferably, the method for obtaining the operation threshold adjustment characteristic value includes: Take the mean of the maximum and minimum values in all the operation data of the fuel dispensers each month as the standard reference value; take the positive correlation mapping value of the difference between the mean of all the operation data of the fuel dispensers each month and the standard reference value as the standard deviation of the operation data for each month. The standard reference value is the median of all the operation data of the fuel dispensers each month. When the mean of all the operation data of the fuel dispensers is greater than the standard reference value, it indicates that the overall operation data of the fuel dispensers in that month is relatively large, and the possibility of being in the peak operation season is relatively high. The corresponding filtering threshold should be larger. Therefore, the standard deviation of the operation data corresponding to it is greater than 0. Combining the subsequent calculation process, the prior filtering threshold is adjusted upward by a positive value greater than 0. On the contrary, when the mean of all the operation data of the fuel dispensers is less than the standard reference value, it indicates that the overall operation data of the fuel dispensers in that month is relatively small, and the possibility of being in the off-peak operation season is relatively high. The corresponding filtering threshold should be smaller. Therefore, the standard deviation of the operation data corresponding to it is less than 0. Combining the subsequent calculation process, the prior filtering threshold is adjusted downward by a negative value less than 0.

[0035] The standard deviation of the operation data is mainly used to correct the signs in the subsequent calculation process. When the standard deviation of the operation data corresponding to it is greater than 0, then all the corresponding values in the subsequent calculation process are greater than 0, so as to adjust the prior filtering threshold upward. However, the overall sizes of the operation data of the fuel dispensers in different months are different, so the adjustment degrees are also different. Considering that it is not accurate enough to characterize the adjustment degree only based on the standard deviation of the operation data, the embodiments of the present invention use the mean value to characterize the overall size of the operation data of the fuel dispensers each month, so as to calculate a more accurate reference adjustment characteristic value by combining the mean value of the operation data of the fuel dispensers each month and the standard deviation of the operation data. Also, because the larger the overall operation characteristic value is, it indicates that the overall operation data of the fuel dispensers in the corresponding month conforms more to the seasonal trend, and the greater the necessity to adjust the threshold is. Therefore, further, the calculation of the operation threshold adjustment characteristic value can be carried out on the basis of the overall operation characteristic value.

[0036] Therefore, the embodiments of the present invention obtain the reference adjustment characteristic value for each month according to the mean value, the standard deviation of the operation data and the overall operation characteristic value of all the operation data of the fuel dispensers each month; among them, the mean value, the standard deviation of the operation data and the overall operation characteristic value of all the operation data of the fuel dispensers each month are all positively correlated with the reference adjustment characteristic value; preferably, the method for obtaining the reference adjustment characteristic value includes: Take the product of the mean value, the standard deviation of the operation data and the overall operation characteristic value of all the operation data of the fuel dispensers each month as the reference adjustment characteristic value for each month.

[0037] In the embodiments of the present invention, the method for obtaining the reference adjustment characteristic value in the th month is expressed in formula as: ; Wherein, is the reference adjustment eigenvalue for the th month; is the mean value of all fuel dispenser operation data in the th month; is the minimum value of the fuel dispenser operation data in the th month; is the maximum value of the fuel dispenser operation data in the th month; is the overall operation eigenvalue for the th month; is the standard reference value for the th month; is the standard deviation of the operation data in the th month. In the embodiment of the present invention, the range of the fuel dispenser operation data in the th month is used as the denominator for positive correlation mapping and normalization, which can ensure that the absolute value of the corresponding standard deviation of the operation data is always less than 1, making the threshold adjustment value calculated subsequently more in line with the actual situation. And the implementer can also perform positive correlation mapping through maximization, which will not be further elaborated here. Perform positive correlation mapping through maximization, which will not be further elaborated here.

[0038] Considering that the peak or off-peak season of operation may last for a longer time, and the corresponding influence has a certain persistence. For example, the peak season of operation may last for a quarter, resulting in seasonal trends in several consecutive months. Therefore, if the changes in the reference adjustment eigenvalues corresponding to consecutive months are combined, the calculated threshold adjustment amount can be combined with the seasonal trend, making the subsequent calculated filtering threshold more accurate. Therefore, in the embodiment of the present invention, the reference adjustment eigenvalues of each month and the preset number of months before each month are arranged in chronological order and then fitted by the least squares method to obtain the operation threshold adjustment eigenvalue of each month. In the embodiment of the present invention, the preset number is set to 12, that is, the reference adjustment amounts of the 12 months before each month are arranged in chronological order and then fitted by the least squares method, and the fitted value of each month is used as the corresponding operation threshold adjustment eigenvalue.

[0039] Step S3: Obtain the operation stability eigenvalue of each month according to the overall fluctuation of the fuel dispenser operation data of each month; obtain the optimized filtering threshold of each month according to the operation threshold adjustment eigenvalue, the operation stability eigenvalue, and the prior filtering threshold.

[0040] Furthermore, considering that when the volatility of the overall fuel dispenser operation data in the corresponding month is stronger, it indicates that the fuel dispenser operation data in that month is more unstable, which will lead to a decrease in the credibility of the operation threshold adjustment eigenvalue obtained by analyzing the overall fuel dispenser operation data. Therefore, it is further necessary to correct the operation threshold adjustment eigenvalue in combination with the fluctuation of the fuel dispenser operation data in each month, so as to obtain a more accurate threshold correction amount. Therefore, in the embodiment of the present invention, the operation stability eigenvalue of each month is obtained according to the overall fluctuation of the fuel dispenser operation data in each month, so as to correct the operation threshold adjustment eigenvalue through the operation stability eigenvalue, making the subsequent obtained filtering threshold more accurate.

[0041] Preferably, the calculation formula of the operation stability eigenvalue includes: ; Wherein, is the operation stability eigenvalue of the th month; is the number of fuel dispenser operation data in the th month; is the th fuel dispenser operation data in the th month; is the minimum value of the fuel dispenser operation data in the th month; is the maximum value of the fuel dispenser operation data in the th month; is the absolute value symbol; is the minimum value selection function. The maximum value and the minimum value of the fuel dispenser operation data in each month are the two value boundaries of the fuel dispenser operation data corresponding to each month. The closer the fuel dispenser operation data is to the value boundary, the more discrete the distribution of the fuel dispenser operation data is, and the closer it is to the data amplitude; Therefore can characterize the degree of proximity of each fuel dispenser operation data to the value boundary, that is, the degree of proximity to the data amplitude; considering that when the volatility of a set of data is stronger, the corresponding data distribution is chaotic and discrete, and the overall data is closer to the data amplitude. Therefore, when all the corresponding to each month are larger, it indicates that the overall data of that month is farther from the numerical boundary, the distribution is more concentrated, and the corresponding operation stability eigenvalue is larger. And taking The purpose of using it as the denominator is to enable the operating stability eigenvalue with better robustness to be obtained for months with different value ranges. It should be noted that the embodiments of the present invention only analyze months in which the maximum value of the fuel dispenser operation data is different from the minimum value of the fuel dispenser operation data. When the maximum value of the corresponding fuel dispenser operation data is the same as the minimum value of the fuel dispenser operation data, it indicates that the fuel dispenser operation data for that month is absolutely stable, and all the corresponding fuel dispenser operation data can be collected, and no further elaboration will be made hereinafter.

[0042] The purpose of calculating the operating stability eigenvalue is to correct the operating threshold adjustment eigenvalue to calculate a more accurate threshold correction amount. Therefore, by combining the prior filtering threshold based on the operating threshold adjustment eigenvalue and the operating stability eigenvalue, the adaptive filtering threshold for each month can be obtained. The embodiments of the present invention obtain the optimized filtering threshold for each month, that is, the adaptive filtering threshold for each month, according to the operating threshold adjustment eigenvalue, the operating stability eigenvalue, and the prior filtering threshold.

[0043] Preferably, the method for obtaining the optimized filtering threshold includes: According to the operating stability eigenvalue and the operating threshold adjustment eigenvalue, the threshold adjustment value for each month is obtained; both the operating stability eigenvalue and the operating threshold adjustment eigenvalue are positively correlated with the threshold adjustment value; preferably, the method for obtaining the threshold adjustment value includes: taking the product of the positive correlation mapping value of the operating stability eigenvalue and the operating threshold adjustment eigenvalue as the threshold adjustment value for each month. Since the purpose of calculating the operating stability eigenvalue is to correct the operating threshold adjustment eigenvalue, the operating stability eigenvalue is used as the weight of the operating threshold adjustment eigenvalue for weighting to obtain a more accurate threshold adjustment value. Since the operating threshold adjustment eigenvalue may be negative, the corresponding threshold adjustment value may also be negative; when the threshold adjustment value is negative, it indicates that the calculated optimized filtering threshold should be smaller than the prior filtering threshold. Therefore, the prior filtering threshold of all fuel dispenser operation data is further obtained; the sum of the prior filtering threshold and the threshold adjustment value is used as the optimized filtering threshold for each month. In the embodiments of the present invention, the prior filtering threshold is set as the sum value between one-eighth of the range of the fuel dispenser operation data for each year and the minimum value of the fuel dispenser operation data for each year; the implementer can also set the prior filtering threshold by other methods, such as selecting a fixed prior filtering threshold according to empirical values, and no further elaboration will be made here.

[0044] In the embodiments of the present invention, the method for obtaining the optimized filtering threshold for the month is expressed in formula as: ; where is the optimized filtering threshold for the th month, is the The operation stability eigenvalue for months; is the prior filtering threshold for months; is the operation threshold adjustment eigenvalue for months; is the exponential function with the natural constant as the base. It should be noted that, in addition to performing a positive correlation mapping on the operation stability eigenvalue, the implementer can also perform a positive correlation mapping through other methods, such as the hyperbolic tangent function. The purpose of this process is that the data after the positive correlation mapping of the operation stability eigenvalue conforms to a value range of a weight, and it can also make the calculated optimized filtering threshold more robust. This will not be elaborated further here.

[0045] Step S4: Collect gas station operation data information according to the optimized filtering threshold.

[0046] After obtaining the optimized filtering threshold for each month, further collect gas station operation data information according to the method of filtering all fuel dispenser operation data according to the threshold in the prior art. In the embodiment of the present invention, the gas station operation data information is collected according to the optimized filtering threshold.

[0047] Preferably, the method for collecting gas station operation data information according to the optimized filtering threshold includes: After transmitting all the fuel dispenser operation data for each month into the gas station operation database, when there is fuel dispenser operation data less than the optimized filtering threshold in all the fuel dispenser operation data for each month, issue an operation data warning and record the sampling time period corresponding to the fuel dispenser operation data less than the optimized filtering threshold as an abnormal sampling time period. In the embodiment of the present invention, since the prior filtering threshold is used to screen out too small fuel dispenser operation data, the sampling time period of the fuel dispenser operation data less than the optimized filtering threshold is marked as abnormal; further, conduct a detailed analysis of the operation status during the abnormal sampling time period, such as retrieving the monitoring corresponding to the abnormal sampling time period, analyzing the cause of the abnormality, and making a decision to more accurately and efficiently manage the gas station operation resources.

[0048] In summary, after obtaining the operation data of the fuel dispenser for each month, the present invention obtains the operation threshold adjustment eigenvalue for each month according to the overall size, discrete distribution, and seasonal trend of the operation data of the fuel dispenser for each month; obtains the operation stability eigenvalue for each month according to the overall fluctuation of the operation data of the fuel dispenser for each month; weights the operation threshold adjustment eigenvalue with the operation stability eigenvalue, so that the combined prior filtering threshold can obtain a more accurate adaptive optimized filtering threshold for each month, thereby making the effect of filtering the operation data of the fuel dispenser for each month with the optimized filtering threshold better, that is, making the accuracy of the collection of the operation data information of the gas station higher, and enabling more accurate and efficient management of the operation resources of the gas station.

[0049] The present invention also proposes an integrated intelligent gas station operation data information collection system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any one of the integrated intelligent gas station operation data information collection methods are implemented.

[0050] It should be noted that: the above order of the embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0051] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. An integrated intelligent gas station operation data information collection method, characterized in that: The method comprises: During the operation of the gas station, obtain the gas station operation data for each sampling period of each month; According to the overall distribution and discreteness of the fuel dispenser operation data in each sampling period of each month, the overall operation characteristic value of each month is obtained; according to the overall operation characteristic value, the overall fuel dispenser operation data of each month and the corresponding seasonal trend, the operation threshold adjustment characteristic value of each month is obtained; According to the overall fluctuation of the fuel dispenser operation data of each month, the operation stability characteristic value of each month is obtained; according to the operation threshold adjustment characteristic value, the operation stability characteristic value and the prior filtering threshold, the optimized filtering threshold of each month is obtained; Gas station operation data information is collected according to the optimized filtering threshold.

2. The integrated intelligent gas station operation data information collection method according to claim 1 is characterized in that: The method for obtaining the overall operation characteristic value includes: In all sampling periods of each month, the difference between the mean and minimum value of all fuel dispenser operation data and the ratio between the range of all fuel dispenser operation data are used as the reference operation characteristic value of each month; The negative correlation mapping value of the variance of all the fuel dispenser operation data of each month is used as the credibility of the operation characteristic value of each month; According to the reference operating characteristic value and the operating characteristic value credibility, the overall operating characteristic value of each month is obtained; the reference operating characteristic value and the operating characteristic value credibility are both positively correlated with the overall operating characteristic value.

3. The integrated intelligent gas station operation data information collection method according to claim 1 is characterized in that: The method for obtaining the operation threshold adjustment characteristic value includes: The average of the maximum and minimum values ​​of all the fuel dispenser operation data of each month is used as the standard reference value; the positive correlation mapping value of the difference between the average of all the fuel dispenser operation data of each month and the standard reference value is used as the standard deviation of the operation data of each month; According to the mean of all the fuel dispenser operation data of each month, the standard deviation of the operation data and the overall operation characteristic value, a reference adjustment characteristic value of each month is obtained; wherein the mean of all the fuel dispenser operation data of each month, the standard deviation of the operation data and the overall operation characteristic value are all positively correlated with the reference adjustment characteristic value; The reference adjustment characteristic values ​​of each month and a preset number of months before each month are arranged in chronological order and fitted using the least squares method to obtain the operation threshold adjustment characteristic values ​​of each month.

4. The integrated intelligent gas station operation data information collection method according to claim 1 is characterized in that: The calculation formula of the operational stability characteristic value includes: ； in, For the Months of operational stability characteristic value; For the Months of fuel dispenser operation data; For the Month Operational data of fuel dispensers; For the Minimum value of fuel dispenser operation data for the month; For the The maximum value of the fuel dispenser operation data in the month; is the absolute value symbol; Choose the function for Minimum.

5. The integrated intelligent gas station operation data information collection method according to claim 1 is characterized in that: The method for obtaining the optimized filtering threshold comprises: According to the operational stability characteristic value and the operational threshold adjustment characteristic value, a threshold adjustment value for each month is obtained; the operational stability characteristic value and the operational threshold adjustment characteristic value are both positively correlated with the threshold adjustment value; Obtain a priori filtering thresholds for all fuel dispenser operation data; and use the sum of the priori filtering thresholds and the threshold adjustment value as the optimized filtering threshold for each month.

6. The integrated intelligent gas station operation data information collection method according to claim 1 is characterized in that: The method for collecting gas station operation data information according to the optimized filtering threshold comprises: After all the gas pump operation data of each month is stored into the gas station operation database, when there is gas pump operation data less than the optimized filtering threshold among all the gas pump operation data of each month, an operation data warning is issued and the sampling time period corresponding to the gas pump operation data less than the optimized filtering threshold is recorded as an abnormal sampling time period.

7. The integrated intelligent gas station operation data information collection method according to claim 2 is characterized in that: The method for obtaining the overall operation characteristic value of each month according to the reference operation characteristic value and the operation characteristic value credibility includes: The normalized value of the product of the reference operating characteristic value and the operating characteristic value credibility is used as the overall operating characteristic value for each month.

8. The integrated intelligent gas station operation data information collection method according to claim 3 is characterized in that: The method for obtaining the reference adjustment characteristic value includes: The mean of all the fuel dispenser operation data of each month, the product of the operation data standard deviation and the overall operation characteristic value are used as the reference adjustment characteristic value of each month.

9. The integrated intelligent gas station operation data information collection method according to claim 5 is characterized in that: The method for obtaining the threshold adjustment value includes: The product of the positive correlation mapping value of the operational stability characteristic value and the operational threshold adjustment characteristic value is used as the threshold adjustment value for each month.

10. An integrated intelligent gas station operation data information collection system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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