Integrated intelligent gas station operation data information collection method and system

Abstract

The present application relates to the technical field of business resource management, in particular to a kind of integrated intelligent gas station operation data information acquisition method and system, after obtaining the dispenser operation data of each month, according to the overall size of the dispenser operation data of each month, the discrete distribution condition and seasonal trend, the operation threshold adjustment characteristic value of each month is obtained;According to the overall fluctuation of the dispenser operation data of each month, the operation stability characteristic value of each month is obtained;The operation stability characteristic value is weighted to the operation threshold adjustment characteristic value, so that the adaptive optimization filtering threshold of each month obtained after combining the prior filtering threshold is more accurate, so that the effect of filtering processing of the dispenser operation data of each month combined with the optimization filtering threshold is better, that is, the accuracy of gas station operation data information acquisition is higher, so that the gas station business resource is more accurately and efficiently managed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of business resource management, and in particular to an integrated intelligent gas station operation data information collection method and system. BACKGROUND

[0002] A gas station is an important node of petroleum energy supply. With the development of information technology, intelligent gas station management applications are becoming more and more widespread, and therefore an efficient data collection method is needed for real-time monitoring of gas station operation data to timely detect abnormal operation data of the gas station and manage the business resources of the gas station according to the abnormal operation data. Since the operation of the gas station is mainly carried out by the fuel dispenser, the data to be monitored and collected is usually the fuel dispenser operation data of the gas station.

[0003] In the prior art, the collection and processing of gas station operation data uses a traditional data filtering technology based on sensors, specifically, a prior data threshold is used to filter and process all fuel dispenser operation data, and the time period of the fuel dispenser operation data less than the prior data threshold is marked and an early warning is issued. However, the operation of the gas station is affected by seasonality, resulting in certain differences in the overall fuel dispenser operation data of the gas station between the peak season and the off-season. At this time, using a fixed prior data threshold to filter and process all fuel dispenser operation data may result in a situation that is not consistent with the objective facts, i.e., there are too few abnormal fuel dispenser operation data in the peak season or too many abnormal fuel dispenser operation data in the off-season. That is, the method of filtering and processing all fuel dispenser operation data by using a prior data threshold in the prior art has low accuracy in collecting gas station operation data information. SUMMARY

[0004] In order to solve the technical problem of low accuracy in collecting gas station operation data information by using the method of filtering and processing all fuel dispenser operation data by using a prior data threshold in the prior art, the purpose of the present application is to provide an integrated intelligent gas station operation data information collection method and system, and the technical solution adopted is as follows:

[0005] The present application provides an integrated intelligent gas station operation data information collection method, which comprises:

[0006] In the operation process of the gas station, the fuel dispenser operation data of each sampling time period in each month is obtained;

[0007] According to the overall distribution and dispersion of the fuel dispenser operation data of each sampling time period in each month, an overall operation characteristic value of each month is obtained; and according to the overall operation characteristic value, the overall fuel dispenser operation data of each month, and the corresponding seasonal trend, an operation threshold adjustment characteristic value of each month is obtained;

[0008] According to the overall fluctuation of the fuel dispenser operation data of each month, an operation stability characteristic value of each month is obtained; and according to the operation threshold adjustment characteristic value, the operation stability characteristic value and the prior filtering threshold value, an optimized filtering threshold value of each month is obtained.

[0009] According to the optimized filtering threshold value, the gas station operation data information is collected.

[0010] Further, the method for obtaining the overall operation characteristic value comprises:

[0011] In all sampling time periods of each month, the ratio between the difference between the mean value and the minimum value of all fuel dispenser operation data and the range of all fuel dispenser operation data is taken as a reference operation characteristic value of each month;

[0012] The negative correlation mapping value of the variance of all fuel dispenser operation data of each month is taken as an operation characteristic value credibility of each month;

[0013] According to the reference operation characteristic value and the operation characteristic value credibility, an overall operation characteristic value of each month is obtained; the reference operation characteristic value and the operation characteristic value credibility are in positive correlation with the overall operation characteristic value.

[0014] Further, the method for obtaining the operation threshold adjustment characteristic value comprises:

[0015] The mean value of the maximum value and the minimum value of all fuel dispenser operation data of each month is taken as a standard reference value; and the positive correlation mapping value of the difference between the mean value of all fuel dispenser operation data of each month and the standard reference value is taken as an operation data standard deviation of each month.

[0016] According to the mean value of all fuel dispenser operation data of each month, the operation data standard deviation and the overall operation characteristic value, a reference adjustment characteristic value of each month is obtained; wherein the mean value of all fuel dispenser operation data of each month, the operation data standard deviation and the overall operation characteristic value are in positive correlation with the reference adjustment characteristic value.

[0017] The reference adjustment characteristic values of each month and a preset number of months before each month are arranged in time sequence and fitted by the least square method to obtain an operation threshold adjustment characteristic value of each month.

[0018] Further, the calculation formula of the operation stability characteristic value comprises:

[0019] ;

[0020] wherein, is the operation stability characteristic value of the i-th month. is the operation stability characteristic value of the i-th month. is the number of refueling machine operation data of the first month; is the number of refueling machine operation data of the first month; is the number of refueling machine operation data of the first month; is the number of refueling machine operation data of the first month; is the number of refueling machine operation data of the first month; is the number of refueling machine operation data of the first month; is the number of refueling machine operation data of the first month; is the number of refueling machine operation data of the first month; is the number of refueling machine operation data of the first month; is the absolute value symbol; is the minimum value selection function.

[0021] Further, the method for obtaining the optimized filtering threshold value comprises:

[0022] obtaining a threshold adjustment value of each month according to the operation stability characteristic value and the operation threshold adjustment characteristic value; the operation stability characteristic value and the operation threshold adjustment characteristic value are positively correlated with the threshold adjustment value;

[0023] obtaining a prior filtering threshold value of all refueling machine operation data; taking the sum of the prior filtering threshold value and the threshold adjustment value as the optimized filtering threshold value of each month.

[0024] Further, the method for collecting refueling station operation data information according to the optimized filtering threshold value comprises:

[0025] after transmitting all refueling machine operation data of each month into the refueling station operation database, when there is refueling machine operation data less than the optimized filtering threshold value in all refueling machine operation data of each month, performing operation data warning and recording the sampling time period corresponding to the refueling machine operation data less than the optimized filtering threshold value as an abnormal sampling time period.

[0026] Further, the method for obtaining the overall operation characteristic value of each month according to the reference running characteristic value and the running characteristic value credibility comprises:

[0027] taking the normalized value of the product between the reference running characteristic value and the running characteristic value credibility as the overall operation characteristic value of each month.

[0028] Further, the method for obtaining the reference adjustment characteristic value comprises:

[0029] taking the product of the mean value of all refueling machine operation data of each month, the operation data standard deviation and the overall operation characteristic value as the reference adjustment characteristic value of each month.

[0030] Further, the method for obtaining the threshold adjustment value comprises:

[0031] The product of the positive correlation mapping value of the operation stability characteristic value and the operation threshold adjustment characteristic value is taken as a threshold adjustment value of each month.

[0032] The application further provides an integrated intelligent gas station operation data information acquisition system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of any one of the integrated intelligent gas station operation data information acquisition methods when executing the computer program.

[0033] The application has the following beneficial effects:

[0034] The reason why the accuracy of the gas station operation data information acquisition is low is that the operation of the gas station is affected by the season, resulting in a certain difference between the overall operation data of the gas station in the peak season and the off-season, and the use of the fixed prior data threshold value may result in too few abnormal operation data of the gas station in the peak season or too much abnormal operation data of the gas station in the off-season, which is inconsistent with the objective fact; therefore, different threshold values can be set according to different operation conditions to filter the operation data of the gas station. The operation of the gas station is related to the traffic flow, and the traffic flow is larger in the time period with more holidays or more convenient travel, and vice versa; and considering that the time period of the operation examination of the gas station is usually a month or a quarter, the application analyzes the operation data in the unit of month to better pay attention to the seasonal change trend of the operation data of the gas station, that is, different filtering threshold values are set according to the operation condition of each month to acquire the operation data information of the gas station.

[0035] For the seasonality of the gas station, the main embodiment is that the operation status corresponding to different time periods is different, for example, the operation is smooth in the working season, the operation is relatively hot in the season with holiday concentration or the tourism season; these situations will be reflected in the overall data of the operation data, for example, when the overall gas dispenser operation data corresponding to the month is larger, it means that the operation status of the gas station in the month is better, it is more likely to belong to the operation peak season or the tourism season, the corresponding seasonal factor is larger, and the overall operation characteristic value is larger. Further, when the gas dispenser operation data of each month is more discrete, it means that the change of the corresponding gas dispenser operation data is larger, that is, the change of the operation status is more intense, thereby affecting the credibility of the seasonal trend, and indirectly affecting the size of the overall operation characteristic value, and the corresponding overall operation characteristic value is smaller. Therefore, according to the overall distribution and dispersion of the gas dispenser operation data of each sampling time period in each month, the overall operation characteristic value of each month is obtained; the larger the overall operation characteristic value is, the more the overall gas dispenser operation data corresponding to the month conforms to the seasonal trend, and the greater the necessity of adjusting the threshold value is, so that the calculation of the operation threshold adjustment characteristic value is further carried out on the basis of the overall operation characteristic value.

[0036] For the gas dispenser operation data of each month, the larger the corresponding overall gas dispenser operation data is, the better the operation status of the gas station in the corresponding month is, and it is more likely to belong to the operation peak season, so as to avoid the case that the abnormal gas dispenser operation data is too small in the operation peak season, it is necessary to adjust the filtering threshold value of the month; on the contrary, the smaller the overall gas dispenser operation data corresponding to the month is, the better the operation status of the gas station in the corresponding month is, and it is more likely to belong to the operation off-season, so as to avoid the case that the abnormal gas dispenser operation data is too much in the operation off-season, it is also necessary to adjust the filtering threshold value of the month; further, considering that the operation peak season or the operation off-season can last for a longer time, the corresponding influence has a certain persistence, in order to make the calculated operation threshold adjustment characteristic value more accurate, the seasonal trend of the overall gas dispenser operation data of each month can be analyzed; therefore, according to the overall operation characteristic value, the overall gas dispenser operation data of each month and the corresponding seasonal trend, the operation threshold adjustment characteristic value of each month is obtained.

[0037] Furthermore, considering that stronger fluctuations in the overall fuel dispenser operation data within a given month indicate greater instability, this reduces the reliability of the operation threshold adjustment feature value obtained from analyzing the overall fuel dispenser operation data. Therefore, it is necessary to further correct the operation threshold adjustment feature value based on the fluctuations of the fuel dispenser operation data each month to obtain a more accurate threshold correction amount. This invention obtains a monthly operation stability feature value based on the overall fluctuations of the fuel dispenser operation data each month; this stability feature value is then used to correct the operation threshold adjustment feature value, making the subsequent filtering threshold more accurate. Since combining the operation stability feature value and the operation threshold adjustment feature value yields a more accurate threshold adjustment amount, this invention obtains a more accurate optimized filtering threshold for each month based on the operation threshold adjustment feature value, the operation stability feature value, and the prior filtering threshold. This results in better filtering of the monthly fuel dispenser operation data using the optimized filtering threshold, leading to higher accuracy in the collection of gas station operation data and more accurate and efficient management of gas station operating resources. Attached Figure Description

[0038] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 The flowchart illustrates an integrated intelligent gas station operation data information collection method according to an embodiment of the present invention. Detailed Implementation

[0040] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an integrated intelligent gas station operation data information collection method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0042] Specifically, the application provides a method and system for collecting operation data information of an integrated intelligent gas station.

[0043] Please refer to Figure 1 which shows a flow chart of a method for collecting operation data information of an integrated intelligent gas station according to an embodiment of the application, the method comprising:

[0044] Step S1: obtaining the dispenser operation data of each sampling time period in each month during the operation of the gas station.

[0045] The embodiment of the application aims to provide a method and system for collecting operation data information of an integrated intelligent gas station, which is used for analyzing the dispenser operation data of each month during the operation of the gas station, obtaining the optimized filtering threshold of each month, and then performing adaptive data information collection on the dispenser operation data of each month according to the optimized filtering threshold.

[0046] In the embodiment of the application, the dispenser operation data of each month during the operation of the gas station is obtained, specifically: the dispenser operation data in the embodiment of the application is the fueling amount, and the implementer can also use other data as the dispenser operation data, for example, the sales amount collected by a price meter. The fueling amount of each sampling time period is collected in real time by a flow meter on the dispenser, and the sampling time period is set to one day in the embodiment of the application, that is, the fueling amount of each day in 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 other than the sampling month, for example, each week, each quarter, etc., which will not be described further herein.

[0047] Step S2: obtaining the overall operation characteristic value of each month according to the overall distribution and dispersion of the dispenser operation data of each sampling time period in each month, and obtaining the operation threshold adjustment characteristic value of each month according to the overall operation characteristic value, the overall dispenser operation data of each month and the corresponding seasonal trend.

[0048] The reason why the method of filtering all refueling machine operation data by a fixed prior data threshold has low accuracy of refueling station operation data information collection is that the refueling station operation is affected by seasonality, resulting in that the refueling machine operation data of the refueling station as a whole has certain differences in the operation peak season and the operation off-season, and the fixed prior data threshold may result in too few abnormal refueling machine operation data in the operation peak season or too much abnormal refueling machine operation data in the operation off-season, which does not conform to the objective fact; therefore, different threshold values can be set according to different operation conditions to filter the refueling machine operation data. The refueling station operation is related to the traffic flow, and the traffic flow is larger in time periods with more holidays or more convenient travel, and vice versa; and considering that the time period of the refueling station operation assessment is usually a month or a quarter, the application analyzes the data in units of months to better focus on the seasonal variation trend of the refueling machine operation data, that is, different filter threshold values are set according to the operation condition of each month to collect the refueling station operation data information.

[0049] For the seasonality of the refueling station, the operation condition corresponding to different time periods is different, for example, the operation is smooth in the working season, and the operation is hot in the holiday-concentrated season or the tourism season; these situations will be reflected in the overall operation data, for example, when the overall refueling machine operation data corresponding to the month is larger, it means that the operation condition of the refueling station in the month is better, and it is more likely to belong to the operation peak season or the tourism season, and the corresponding seasonal factor is larger, and the overall operation characteristic value is also larger. Further, when the refueling machine operation data of each month is more discrete, it means that the change of the corresponding refueling machine operation data is larger, that is, the change of the operation condition is more intense, thereby affecting the credibility of the seasonal trend, and indirectly affecting the size of the overall operation characteristic value. Therefore, the embodiment of the application obtains the overall operation characteristic value of each month according to the overall distribution and dispersion of the refueling machine operation data of each sampling time period in each month. The larger the overall operation characteristic value is, the more the refueling machine operation data of the corresponding month conforms to the seasonal trend, and the greater the necessity of adjusting the threshold value is, so that the calculation of the subsequent operation threshold adjustment characteristic value can be further based on the overall operation characteristic value.

[0050] Preferably, the method for obtaining the overall operation characteristic value comprises:

[0051] For each month's refueling machine operation data, the greater the month's refueling machine operation data as a whole, the greater the mean of all the refueling machine operation data corresponding, and therefore in each month's all sampling time period, the difference between the mean and the minimum of all the refueling machine operation data is taken as the ratio between the range of all the refueling machine operation data, as the reference running characteristic value of each month. Since the greater the refueling machine operation data as a whole, the better the gas station operation condition of the month, the more likely to belong to the operation season or the tourist season, the greater the corresponding seasonal factor, and the greater the overall operation characteristic value, therefore the size of the reference running characteristic value is positively correlated with the overall operation data.

[0052] Since the more dispersed the refueling machine operation data of each month, the lower the reliability of the corresponding seasonal trend, and the smaller the overall operation characteristic value; and the variance can represent the dispersion fluctuation of a group of data, and the greater the variance, the greater the dispersion; therefore the embodiment of the present application takes the negative correlation mapping value of the variance of all the refueling machine operation data of each month as the running characteristic value reliability of each month, and the greater the corresponding running reference reliability, the smaller the dispersion of the refueling machine operation data of the month, and the greater the overall operation characteristic value.

[0053] Further, according to the relationship between the reference running characteristic value, the running characteristic value reliability and the overall operation characteristic value, the overall operation characteristic value of each month is obtained according to the reference running characteristic value and the running characteristic value reliability; the reference running characteristic value and the running characteristic value reliability are positively correlated with the overall operation characteristic value. Preferably, the method for obtaining the overall operation characteristic value of each month according to the reference running characteristic value and the running characteristic value reliability comprises:

[0054] Since the greater the reference running characteristic value and the greater the running characteristic value reliability, the greater the corresponding overall operation characteristic value, and the running characteristic value reliability represents the reliability of the overall operation characteristic value, therefore the overall operation characteristic value can be weighted by the running characteristic value reliability, and the embodiment of the present application takes the normalized value of the product between the reference running characteristic value and the running characteristic value reliability as the overall operation characteristic value of each month. In the embodiment of the present application, the normalization method adopts linear normalization, and the implementer can adjust the normalization method according to the specific implementation environment, which will not be described further herein.

[0055] In the embodiment of the present application, each month is taken in turn as the first month, the second month, the third month, the fourth month, the fifth month, the sixth month, the seventh month, the eighth month, the ninth month, the tenth month, the eleventh month and the twelfth month, and the overall operation characteristic value of each month is obtained according to the reference running characteristic value and the running characteristic value reliability of the corresponding month. The acquisition method of the overall operation characteristic value of the first month is represented in formula as:

[0056] ;

[0057] Wherein, the acquisition method of the overall operation characteristic value of the second month is represented in formula as: ​​a whole operation characteristic value of the month; a mean value of all fuel dispenser operation data in the month; a mean value of all fuel dispenser operation data in the month; a minimum value of fuel dispenser operation data in the month; a minimum value of fuel dispenser operation data in the month; a maximum value of fuel dispenser operation data in the month; a maximum value of fuel dispenser operation data in the month; a variance of all fuel dispenser operation data in the month; an exponential function with a natural constant as a base; a normalization function. a normalization function. a range of all fuel dispenser operation data in the month; a range of all fuel dispenser operation data in the month; a reference operation characteristic value of the month; a reference operation characteristic value of the month; a reference operation characteristic value of the month; a reference operation characteristic value of the month;

[0058] For each month of fuel dispenser operation data, the greater the corresponding whole fuel dispenser operation data, the better the gas station operation in the corresponding month, and the more likely it is to belong to the operation peak season, so as to avoid the case that there are too few abnormal fuel dispenser operation data in the operation peak season, the filtering threshold of the month needs to be adjusted; on the contrary, the smaller the corresponding whole fuel dispenser operation data, the better the gas station operation in the corresponding month, and the more likely it is to belong to the operation off-season, so as to avoid the case that there are too many abnormal fuel dispenser operation data in the operation off-season, the filtering threshold of the month also needs to be adjusted; further considering that the operation peak season or the operation off-season may last for a longer time, the corresponding influence has a certain persistence, in order to make the calculated operation threshold adjustment characteristic value more accurate, the seasonal trend of the whole fuel dispenser operation data of each month can be analyzed; therefore, according to the whole operation characteristic value, the whole fuel dispenser operation data of each month and the corresponding seasonal trend, the operation threshold adjustment characteristic value of each month is obtained.

[0059] Preferably, the operation threshold adjustment characteristic value acquisition method comprises:

[0060] The average of the maximum and minimum values ​​of all fuel dispenser operation data for each month is used as the standard reference value. The positive correlation mapping value between the average of all fuel dispenser operation data for each month and the standard reference value is used as the standard deviation of the operation data for each month. The standard reference value is the median of all fuel dispenser operation data for each month. When the average of all fuel dispenser operation data is greater than the standard reference value, it indicates that the overall operation data for that month is relatively high, and it is more likely to be a peak season. The corresponding filtering threshold should be higher, so the standard deviation of the operation data is greater than 0. In the subsequent calculation process, the prior filtering threshold is adjusted upward by a positive value greater than 0. Conversely, when the average of all fuel dispenser operation data is less than the standard reference value, it indicates that the overall operation data for that month is relatively low, and it is more likely to be a low season. The corresponding filtering threshold should be lower, so the standard deviation of the operation data is less than 0. In the subsequent calculation process, the prior filtering threshold is adjusted downward by a negative value less than 0.

[0061] The standard deviation of operational data is mainly used to correct the sign in subsequent calculations. When the standard deviation of operational data is greater than 0, the corresponding values ​​in subsequent calculations will all be greater than 0, thus adjusting the prior filtering threshold upwards. However, the overall size of the fuel dispenser operational data varies from month to month, so the degree of adjustment also varies. Considering that representing the degree of adjustment solely based on the standard deviation of operational data is not accurate enough, this embodiment of the invention uses the mean to represent the overall size of the fuel dispenser operational data for each month. This allows for the calculation of a more accurate reference adjustment feature value by combining the mean of the monthly fuel dispenser operational data with the standard deviation of operational data. Furthermore, since a larger overall operational feature value indicates that the corresponding month's fuel dispenser operational data more closely matches the seasonal trend, the necessity for adjusting the threshold is greater. Therefore, the operational threshold adjustment feature value can be further calculated based on the overall operational feature value.

[0062] Therefore, in this embodiment of the invention, a reference adjustment characteristic value is obtained for each month based on the mean, standard deviation, and overall operational characteristic value of all fuel dispenser operating data for each month; wherein, the mean, standard deviation, and overall operational characteristic value of all fuel dispenser operating data for each month are positively correlated with the reference adjustment characteristic value; preferably, the method for obtaining the reference adjustment characteristic value includes:

[0063] The average of all fuel dispenser operating data for each month, the standard deviation of the operating data, and the product of the overall operating characteristic value are used as the reference adjustment characteristic value for each month.

[0064] In this embodiment of the invention, the first The method for obtaining the reference adjusted eigenvalues ​​for one month is expressed in the formula as follows:

[0065] ;

[0066] in, For the first Reference adjusted characteristic value for one month; For the first The average of all fuel dispenser operation data during the month; For the first Minimum monthly operating data for fuel dispensers; For the first The maximum value of fuel dispenser operation data for one month; For the first Overall operational characteristics for the month; For the first The standard reference value for one month; For the first The standard deviation of the monthly operating data, in this embodiment of the invention, is obtained through the first month. Using the range of monthly fuel dispenser operation data as the denominator for positive correlation mapping and normalization ensures that the absolute value of the corresponding standard deviation of the operation data is always less than 1. This makes the subsequently calculated threshold adjustment value more consistent with the actual situation, and implementers can also adjust the data accordingly. The positive correlation mapping is performed by maximization, which will not be elaborated further here.

[0067] Considering that peak or off-peak seasons may last longer, their impact can be persistent. For example, a peak season might last a quarter, resulting in a seasonal trend for several consecutive months. Therefore, combining the changes in reference adjustment feature values ​​for consecutive months allows the calculated threshold adjustment to incorporate seasonal trends, leading to more accurate filtering thresholds. Thus, this embodiment of the invention arranges the reference adjustment feature values ​​for each month and a preset number of months prior to each month in chronological order and fits them using the least squares method to obtain the operational threshold adjustment feature value for each month. In this embodiment, the preset number is set to 12, meaning the reference adjustment values ​​for the previous 12 months are arranged in chronological order and fitted using the least squares method, with the fitted value for each month serving as the corresponding operational threshold adjustment feature value.

[0068] Step S3: Based on the overall fluctuation of the fuel dispenser operation data each month, obtain the monthly operational stability feature value; adjust the feature value, operational stability feature value, and prior filtering threshold according to the operational threshold to obtain the monthly optimized filtering threshold.

[0069] Further, the stronger the fluctuation of the overall refueling machine operation data in the corresponding month, the more unstable the refueling machine operation data in the month, which reduces the reliability of the operation threshold adjustment characteristic value obtained by analyzing the overall refueling machine operation data, and therefore it is further needed to correct the operation threshold adjustment characteristic value in combination with the fluctuation of the refueling machine operation data of each month, so as to obtain a more accurate threshold correction amount. Therefore, according to the overall fluctuation of the refueling machine operation data of each month, the operation stability characteristic value of each month is obtained, so as to correct the operation threshold adjustment characteristic value by the operation stability characteristic value, so that the subsequent filtering threshold is more accurate.

[0070] Preferably, the calculation formula of the operation stability characteristic value comprises:

[0071] ;

[0072] Wherein, is the operation stability characteristic value of the m-th month; is the number of refueling machine operation data of the m-th month; is the m-th refueling machine operation data of the m-th month; is the minimum value of refueling machine operation data of the m-th month; is the maximum value of refueling machine operation data of the m-th month; is the absolute value symbol; is the minimum value selection function. The maximum value of refueling machine operation data and the minimum value of refueling machine operation data of each month are two value boundaries of the corresponding refueling machine operation data of each month. The closer the refueling machine operation data is to the value boundary, the more discrete the distribution of the refueling machine operation data is, and the closer to the data amplitude; Therefore can represent the closeness of each refueling machine operation data to the value boundary, that is, the closeness to the data amplitude; considering that the stronger the fluctuation of a group of data is, the more chaotic and discrete the distribution of the corresponding data is, and the overall data is closer to the data amplitude, therefore, the larger the overall is, the farther the overall data of the month is from the value boundary, the more concentrated the distribution is, and the larger the operation stability characteristic value is. And the is the operation stability characteristic value of the m-th month; is the number of refueling machine operation data of the m-th month; is the m-th refueling machine operation data of the m-th month;

[0073] is the minimum value of refueling machine operation data of the m-th month; is the maximum value of refueling machine operation data of the m-th month; is the absolute value symbol; The purpose of the denominator is to make the months with different value ranges obtain more robust operation stability characteristic values. It should be noted that the embodiment of the present application only analyzes the months with different maximum refueling machine operation data and minimum refueling machine operation data. When the corresponding maximum refueling machine operation data and minimum refueling machine operation data are the same, it indicates that the refueling machine operation data of the month is absolutely stable, and all the corresponding refueling machine operation data can be collected, and no further description is made subsequently.

[0074] The purpose of the operation stability characteristic value calculation is to correct the operation threshold adjustment characteristic value, so as to calculate a more accurate threshold correction value. Therefore, the adaptive filtering threshold of each month can be obtained by combining the operation threshold adjustment characteristic value and the operation stability characteristic value with the prior filtering threshold. According to the operation threshold adjustment characteristic value, the operation stability characteristic value and the prior filtering threshold, the embodiment of the present application obtains the optimized filtering threshold of each month, that is, the adaptive filtering threshold of each month.

[0075] Preferably, the method for obtaining the optimized filtering threshold comprises:

[0076] According to the operation stability characteristic value and the operation threshold adjustment characteristic value, a threshold adjustment value of each month is obtained. The operation stability characteristic value and the operation threshold adjustment characteristic value are in a positive correlation with the threshold adjustment value. Preferably, the method for obtaining the threshold adjustment value comprises: taking the product of the positive correlation mapping value of the operation stability characteristic value and the operation threshold adjustment characteristic value as the threshold adjustment value of each month. Since the purpose of the operation stability characteristic value calculation is to correct the operation threshold adjustment characteristic value, the operation stability characteristic value is used as the weight of the operation threshold adjustment characteristic value for weighting, so as to obtain a more accurate threshold adjustment value. Since the operation threshold adjustment characteristic value can be negative, the corresponding threshold adjustment value can 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 refueling machine operation data is further obtained. The sum of the prior filtering threshold and the threshold adjustment value is taken as the optimized filtering threshold of each month. In the embodiment of the present application, the prior filtering threshold is set as the sum between one-eighth of the refueling machine operation data range of each year and the minimum refueling machine operation data of each year. The implementer can also set the prior filtering threshold by other methods, for example, select a fixed prior filtering threshold according to the experience value, and no further description is made herein.

[0077] In the embodiment of the present application, the method for obtaining the optimized filtering threshold of the first month is represented in the formula as:

[0078]

[0079] ​​​​the optimized filtering threshold value of the month, the operation stability characteristic value of the month; the prior filtering threshold value of the month; the operation threshold adjustment characteristic value of the month; the operation threshold adjustment characteristic value of the month; the operation threshold adjustment characteristic value of the month; the operation threshold adjustment characteristic value of the month; is an exponential function with a natural constant as a base. It should be noted that, in addition to the positive correlation mapping of the operation stability characteristic value, the implementer can also perform positive correlation mapping through other methods, such as a hyperbolic tangent function, and the purpose of this process is to make the data after the positive correlation mapping of the operation stability characteristic value conform to the value range of a weight, and also to make the calculated optimized filtering threshold value more robust, and further description is not repeated.

[0080] Step S4: collecting the gas station operation data information according to the optimized filtering threshold value.

[0081] After obtaining the optimized filtering threshold value of each month, further gas station operation data information collection is performed according to the method of filtering all dispenser operation data according to the threshold value in the prior art. The embodiment of the present application collects the gas station operation data information according to the optimized filtering threshold value.

[0082] Preferably, the method of collecting the gas station operation data information according to the optimized filtering threshold value comprises:

[0083] After transmitting all the dispenser operation data of each month into the gas station operation database, when there is dispenser operation data less than the optimized filtering threshold value in all the dispenser operation data of each month, operation data warning is performed, and the sampling time period corresponding to the dispenser operation data less than the optimized filtering threshold value is recorded as an abnormal sampling time period. In the embodiment of the present application, since the prior filtering threshold value is used to screen out too small dispenser operation data, the sampling time period of the dispenser operation data less than the optimized filtering threshold value is marked as abnormal; further, the operation condition of the abnormal sampling time period is analyzed in detail, for example, the monitoring corresponding to the abnormal sampling time period is retrieved, the cause of the abnormality is analyzed, and a decision is made, so as to more accurately and efficiently manage the gas station operation resources.

[0084] ​To sum up, after obtaining the monthly refueling machine operation data, the application obtains the operation threshold adjustment characteristic value of each month according to the overall size, discrete distribution and seasonal trend of the monthly refueling machine operation data, obtains the operation stability characteristic value of each month according to the overall fluctuation of the monthly refueling machine operation data, weights the operation threshold adjustment characteristic value by using the operation stability characteristic value as the weight, so that the adaptive optimized filtering threshold of each month obtained after combining the prior filtering threshold is more accurate, thereby making the filtering effect of the monthly refueling machine operation data combined with the optimized filtering threshold better, that is, making the accuracy of the gas station operation data information collection higher, and making the gas station operation resources more accurately and efficiently managed.

[0085] The application further provides an integrated intelligent gas station operation data information collection system, which comprises a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements the steps of any one of the integrated intelligent gas station operation data information collection methods when executing the computer program.

[0086] It should be noted that the above-mentioned sequence of the embodiments of the application 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 results. In some embodiments, multitasking and parallel processing are possible or can be advantageous.

[0087] Each embodiment in the specification is described in a progressive manner, and the same and similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference 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, obtaining the dispenser operation data of each sampling time period in each month; According to the overall distribution and dispersion of the dispenser operation data of each sampling time period in each month, obtaining an overall operation characteristic value of each month; according to the overall operation characteristic value, the overall dispenser operation data of each month and the corresponding seasonal trend, obtaining an operation threshold adjustment characteristic value of each month; According to the overall fluctuation of the dispenser operation data of each month, obtaining an operation stability characteristic value of each month; according to the operation threshold adjustment characteristic value, the operation stability characteristic value and a prior filtering threshold value, obtaining an optimized filtering threshold value of each month; According to the optimized filtering threshold value, collecting gas station operation data information; The method for obtaining the optimized filtering threshold value comprises: According to the operation stability characteristic value and the operation threshold adjustment characteristic value, obtaining a threshold adjustment value of each month; the operation stability characteristic value and the operation threshold adjustment characteristic value are in a positive correlation with the threshold adjustment value; Obtaining a prior filtering threshold value of all dispenser operation data; taking the sum of the prior filtering threshold value and the threshold adjustment value as the optimized filtering threshold value of each month; The method for obtaining the threshold adjustment value comprises: Taking the product of the positive correlation mapping value of the operation stability characteristic value and the operation threshold adjustment characteristic value as the threshold adjustment value of each month.

2. The integrated intelligent gas station operation data information collection method of claim 1, wherein The method for obtaining the overall operation characteristic value comprises: In all sampling time periods of each month, taking the ratio between the difference between the mean value and the minimum value of all dispenser operation data and the range of all dispenser operation data as a reference running characteristic value of each month; Taking the negative correlation mapping value of the variance of all dispenser operation data of each month as a running characteristic value credibility of each month; According to the reference running characteristic value and the running characteristic value credibility, obtaining an overall operation characteristic value of each month; the reference running characteristic value and the running characteristic value credibility are in a positive correlation with the overall operation characteristic value.

3. The integrated intelligent gas station operation data information collection method of claim 1, wherein The method for obtaining the operation threshold adjustment characteristic value comprises: Taking the mean value of the maximum value and the minimum value of all dispenser operation data of each month as a standard reference value; taking the positive correlation mapping value of the difference between the mean value of all dispenser operation data of each month and the standard reference value as an operation data standard deviation of each month; According to the mean value of all dispenser operation data of each month, the operation data standard deviation and the overall operation characteristic value, obtaining a reference adjustment characteristic value of each month; wherein the mean value of all dispenser operation data of each month, the operation data standard deviation and the overall operation characteristic value are in a positive correlation with the reference adjustment characteristic value; Arranging the reference adjustment characteristic values of each month and a preset number of months before each month in time sequence and fitting them through the least square method to obtain an operation threshold adjustment characteristic value of each month.

4. The integrated intelligent gas station operation data information collection method of claim 1, wherein The calculation formula of the operation stability characteristic value comprises: wherein, is the operating stability characteristic value of the first month; is the operating stability characteristic value of the second month; is the operating stability characteristic value of the third month; is the number of refueling machine operating data of the first month; is the number of refueling machine operating data of the second month; is the number of refueling machine operating data of the third month; is the first refueling machine operating data of the first month; is the first refueling machine operating data of the second month; is the first refueling machine operating data minimum value of the first month; is the first refueling machine operating data maximum value of the first month; is the first refueling machine operating data maximum value of the second month; is the absolute value sign; is the minimum value selection function.

5. The integrated intelligent gas station operation data information collection method of claim 1, wherein, The method for collecting gas station operation data information according to the optimized filtering threshold value comprises: After all the monthly refueling machine operation data is transmitted into the gas station operation database, when there is refueling machine operation data less than the optimization filtering threshold value in all the monthly refueling machine operation data, operation data early warning is carried out and the sampling time period corresponding to the refueling machine operation data less than the optimization filtering threshold value is recorded as an abnormal sampling time period.

6. The integrated intelligent gas station operation data information collection method of claim 2, wherein 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 comprises: The normalized value of the product between the reference operation characteristic value and the operation characteristic value credibility is taken as the overall operation characteristic value of each month.

7. The integrated intelligent gas station operation data information collection method of claim 3, wherein, The method for obtaining the reference adjustment characteristic value comprises: The product of the mean value of all the monthly refueling machine operation data, the operation data standard deviation and the overall operation characteristic value is taken as the reference adjustment characteristic value of each month.

8. 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, The processor realizes the steps of the method according to any one of claims 1-7 when executing the computer program.

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