CN116450356B - Cross-border logistics management method based on cloud management and control - Google Patents
Cross-border logistics management method based on cloud management and control Download PDFInfo
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Abstract
The invention relates to the field of logistics system background management, in particular to a cross-border logistics management method based on cloud management.
Description
Technical Field
The invention relates to the field of logistics system background management, in particular to a cross-border logistics management method based on cloud management and control.
Background
With the continuous development of the internet technology and the electronic commerce technology, people live in a cross-border electronic commerce platform and walk in, and of course, the development of electronic commerce is necessarily accompanied with the development of logistics, and various cross-border logistics management platforms are generated;
for example, chinese patent publication No.: CN110490508A, a cross-border logistics transportation management system and a control method thereof, wherein the system comprises a database module, a front-end module and a background module; the database module is developed based on MySQL and is used for storing total order information, logistics information and account information; the front-end module based on HTML development comprises a plurality of functional units, is used for acquiring an operation instruction in the authority range through a visual interface and realizes the operation on a database through the operation instruction; a background module developed based on PHP, which is used for associating the database module with the front-end module and managing the front-end module; and the database module, the front-end module and the background module establish a cross-border logistics transportation management system through a B/S structure. The system has complete service functions, good safety, strong stability and expansibility and small occupied memory. The invention can be widely applied to the field of cross-border logistics transportation management.
However, the prior art has the following problems,
due to the particularity of the cross-border logistics, the background data volume of the related cross-border logistics management platform is huge, and the fluctuation condition of the processed data volume is large, so that challenges are brought to the data processing technology;
in the prior art, the problem that the fluctuation amount of the logistics order data of the cross-border logistics management platform is large and overload is easy to occur to the system is not considered, and the problem that the configuration server or the computing power resource is not adjusted in advance according to the updating trend of the current logistics order data amount so as to reduce the background computing resource waste or the overload is solved.
Disclosure of Invention
The invention provides a cross-border logistics management method based on cloud management and control, which aims to solve the problems that a configuration server or an algorithm resource is not adjusted in advance according to the update trend of the current logistics order data volume in the prior art so as to reduce background operation resource waste or overload, and comprises the following steps:
step S1, a data acquisition module is set to acquire updated logistics order data of each data updating port of a cloud logistics platform;
s2, constructing an update quantity fluctuation curve corresponding to a data update port, and determining the average slope of the update quantity fluctuation curve, wherein the update quantity fluctuation curve is constructed based on the data update quantity of the data update port within a preset time length;
step S3, determining the data updating trend of each data updating port based on the average slope of the updating quantity fluctuation curve;
step S4, determining the number of servers connected with the data update port based on the data update trend of the data update port, wherein the step comprises the steps of acquiring the data update amount of the data update port when the data update port is judged to be in the first data update trend, judging whether the number of servers connected with the data update port needs to be adjusted based on the average fluctuation amplitude of the update amount of the data update amount in the preset duration, and adjusting the number of servers connected with the data update port to a corresponding value;
and determining an adjustment mode when the number of the data update ports connected with the server is adjusted based on the average slope of the update amount fluctuation curve when the data update ports are judged to be in the second data update state.
Further, in the step S2, an update amount fluctuation curve corresponding to the data update port is constructed and an average slope of the update amount fluctuation curve is determined, wherein,
acquiring data updating quantity of a data updating port in a preset duration tn, establishing a rectangular coordinate system by taking time as an x axis and the data updating quantity as a y axis, constructing an updating quantity fluctuation curve in the rectangular coordinate system, calculating the average slope K of the updating quantity fluctuation curve according to a formula (1),
in the formula (1), ki represents the slope of the update amount fluctuation curve when the lateral coordinate is i, i being an integer greater than 0.
Further, in the step S3, a data update trend of each data update port is determined based on an average slope of the update amount fluctuation curve, wherein
Comparing the average slope K of the update quantity fluctuation curve with a preset first slope comparison parameter K1 and a second slope comparison parameter K2, judging the data update trend of the data update port according to the comparison result,
under a first slope comparison result, judging that the data updating port is in a first data updating trend;
under the second slope comparison result, judging that the data updating port is in a second data updating trend;
the first slope comparison result is K1 < K2, and the second slope comparison result is K less than or equal to K1 or K more than or equal to K2.
Further, in the step S4, the method further includes obtaining an average fluctuation width of the update amount of the data update amount within a preset duration, where,
calculating the average fluctuation amplitude D of the update amount of the data update amount within the preset time period tn according to a formula (2),
in the formula (2), E (i+1) represents the data update amount of the data update port at the i+1 th time, E (i) represents the data update amount of the data update port at the i th time, and i is an integer greater than 0.
Further, in the step S4, it is determined whether the number of the data update port connection servers needs to be adjusted based on the average fluctuation range of the update amount of the data update amount within the preset duration, wherein,
comparing the updated average fluctuation amplitude D with a preset first fluctuation amplitude comparison parameter D1,
under the first fluctuation amplitude comparison condition, judging the quantity of the data updating port connection servers to be adjusted;
under the second fluctuation amplitude comparison condition, judging that the quantity of the data updating port connection servers does not need to be adjusted;
the first fluctuation amplitude comparison condition is D & gtD 1, and the second fluctuation amplitude comparison condition is D & ltoreq.D1.
Further, in the step S4, the method further includes determining an adjustment mode when the number of the data update port connection servers needs to be adjusted based on the average fluctuation amplitude of the update amount of the data update amount within the preset duration, where,
comparing the average fluctuation amplitude D of the updated quantity with a preset average fluctuation amplitude D2 of a second updated quantity, wherein D2 is more than D1,
the first adjustment mode is to adjust the number of the data update port connection servers to a first number P1 according to a preset first number adjustment parameter P1, and set p1=p0+p1;
the second adjustment mode is to adjust the number of the data update port connection servers to a second number P2 according to a preset second number adjustment parameter P2, and set p2=p0+p2;
the first adjustment mode needs to satisfy D < D2, the second adjustment mode needs to satisfy D > D2, and P0 represents the number of servers currently connected with the data update port, and P1 < P2.
Further, in the step S4, an adjustment manner when the number of the data update port connection servers is adjusted is determined based on the average slope of the update amount fluctuation curve, wherein,
comparing the average slope K of the update fluctuation curve with a preset slope comparison parameter K0, wherein K0 is more than K2, p4 is more than p3,
the third adjustment mode is to adjust the number of the data update port connection servers to a third number value P3 according to a preset third number adjustment parameter P3, and set p3=p0+p3;
the fourth adjustment mode is to adjust the number of the data update port connection servers to a fourth number value P4 according to a preset third number adjustment parameter P4, and set p4=p0+p4;
the fifth adjustment mode is to adjust the number of the data update port connection servers to a fifth number value P5 according to a preset third number adjustment parameter P4, and set p5=p0-P3;
the sixth adjustment manner is to adjust the number of the data update port connection servers to a sixth number value P6 according to a preset third number adjustment parameter P4, and set p6=p0-P4.
Further, the third adjustment mode needs to satisfy K & gt0 and |K| & lt K0, the fourth adjustment mode needs to satisfy K & lt0 and |K| & gtor equal to K0, the fifth adjustment mode needs to satisfy K & lt0 and |K| & gtor equal to K0, and the sixth adjustment mode needs to satisfy K & lt0 and |K| & gtor equal to K0.
Further, in the step S4, before adjusting the number of servers connected to the data update port, the method further includes comparing the adjusted number of servers connected to the total number of servers, determining whether to send out overload warning information according to the comparison result,
and if the number of the servers connected after adjustment is larger than the total number of the servers, judging that overload warning information needs to be sent out.
The invention provides a cross-border logistics management system, which comprises:
the data acquisition module comprises a plurality of data recording units connected with the data updating port and is used for acquiring data updating quantity corresponding to the logistics order data updated by the data updating port;
the server module comprises a plurality of connectable servers, and each server establishes a connection protocol with each data updating port so that each server processes data transmitted by each data updating port;
the data processing module comprises a data analysis unit, a first operation unit and a second operation unit which are connected with each other;
the data analysis unit is connected with the data acquisition module and used for acquiring the data update quantity of the data update port in a preset duration, constructing an update quantity fluctuation curve based on the data update quantity, and judging the data update trend of the data update port based on the average slope of the update quantity fluctuation curve;
the first operation unit is connected with the data acquisition module and the server module and is used for acquiring the data updating quantity of the data updating port when the data analysis unit judges the first data updating state, and judging whether the quantity of the data updating port connected with the server needs to be adjusted or not based on the average fluctuation amplitude of the updating quantity of the data updating quantity within the preset duration;
the second operation unit is connected with the data acquisition module and the server module and is used for determining an adjustment mode when the number of the data update port connection servers is adjusted based on the average slope of the update quantity fluctuation curve when the data analysis unit judges the second data update state.
Compared with the prior art, the method and the device have the advantages that the data updating quantity corresponding to the logistics order data updated by the cloud logistics platform is obtained through the data obtaining module, the updating quantity fluctuation curve is constructed according to the data updating quantity, the data updating trend of the data updating port is judged based on the average slope of the updating quantity fluctuation curve, the number of the data updating port connection servers is adjusted based on the average fluctuation amplitude of the updating quantity of the data updating quantity within the preset duration in the first data updating trend, and the number of the data updating port connection servers is adjusted based on the average slope of the updating quantity fluctuation curve in the second data updating trend, so that the operation resource waste is reduced, and the overload risk of the servers is reduced.
In particular, the invention constructs an update quantity fluctuation curve by acquiring the data update quantity of the data update port within the preset time length, and represents the data change trend of the data update quantity by calculating the average slope of the update quantity fluctuation curve, wherein the data update quantity is stable fluctuation under the first data change trend, and the data update quantity is rising fluctuation or falling fluctuation under the second data change trend.
In particular, in step S4 of the present invention, an average fluctuation range of the update amount is obtained, and whether the number of the port connection servers needs to be adjusted is determined based on the average fluctuation range of the update amount, in practical situations, because the fluctuation of the data update amount of the logistics order data of the cross-border logistics transaction platform is large, the data update amount is always in a stable fluctuation state, in this state, whether the number of the port connection servers needs to be adjusted is determined based on the magnitude of the fluctuation range, and when the fluctuation range is large, the number of the port connection servers needs to be appropriately adjusted, so that the temporary overload of the server module caused by the fluctuation is avoided, and the risk of overload and paralysis of the server module is reduced.
In particular, in the step S4 of the present invention, the adjustment manner when the number of the data update port connection servers is adjusted is determined based on the average slope of the update amount fluctuation curve, in practical situations, because the phenomenon that the data update amount of the logistics order data of the cross-border logistics trading platform rises or falls within a period of time is common due to market factors, the rising or falling trend of the data update amount of the data update port can be detected based on the average slope of the update amount fluctuation curve, especially when the fluctuation of the data update amount is not large, but the obvious rising trend appears, the number of the data update port connection servers can be adjusted in advance, so that the risk that the load appears in the system when the subsequent data is out of blowout is reduced, and likewise, the number of the data update port connection servers can be reduced in advance when the data update amount of the data update port falls, so as to save operation resources.
Drawings
FIG. 1 is a cross-border logistics management method based on cloud management and control step diagram of an embodiment of the invention;
FIG. 2 is a graph showing the fluctuation of the update amount according to the embodiment of the present invention;
FIG. 3 is a schematic diagram of a cross-border logistics management system according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a data processing module according to an embodiment of the invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Please refer to fig. 1, which is a step diagram of a cross-border logistics management method based on cloud management, the cross-border logistics management method based on cloud management of the present invention includes:
step S1, a data acquisition module is set to acquire updated logistics order data of each data updating port of a cloud logistics platform;
s2, constructing an update quantity fluctuation curve corresponding to a data update port, and determining the average slope of the update quantity fluctuation curve, wherein the update quantity fluctuation curve is constructed based on the data update quantity of the data update port within a preset time length;
step S3, determining the data updating trend of each data updating port based on the average slope of the updating quantity fluctuation curve;
step S4, determining the number of servers connected with the data update port based on the data update trend of the data update port, wherein the step comprises the steps of acquiring the data update amount of the data update port when the data update port is judged to be in the first data update trend, judging whether the number of servers connected with the data update port needs to be adjusted based on the average fluctuation amplitude of the update amount of the data update amount in the preset duration, and adjusting the number of servers connected with the data update port to a corresponding value;
and determining an adjustment mode when the number of the data update ports connected with the server is adjusted based on the average slope of the update amount fluctuation curve when the data update ports are judged to be in the second data update state.
Specifically, referring to fig. 2, which is a schematic diagram of an update amount fluctuation curve according to an embodiment of the invention, in the step S2, an update amount fluctuation curve corresponding to a data update port is constructed and an average slope of the update amount fluctuation curve is determined, wherein,
acquiring data updating quantity of a data updating port in a preset duration tn, establishing a rectangular coordinate system by taking time as an x axis and the data updating quantity as a y axis, constructing an updating quantity fluctuation curve in the rectangular coordinate system, calculating the average slope K of the updating quantity fluctuation curve according to a formula (1),
in the formula (1), ki represents the slope of the update amount fluctuation curve when the transverse coordinate is i, i is an integer greater than 0, and when the preset duration tn is set, those skilled in the art should understand that the preset duration tn should not be too short, otherwise, the statistical obtained data update amount may not be characterized, and tn is greater than 1h in this embodiment.
Specifically, when the update amount fluctuation curve is constructed in this embodiment, the data update amount in the minimum time interval is recorded at intervals of a minimum time interval, the data update amount is used as a y-axis coordinate, the time corresponding to the minimum time interval is determined as an x-axis coordinate, further coordinate points are determined, a plurality of coordinate points are constructed in the rectangular coordinate system, and the coordinate points are connected by a smooth curve to obtain the update amount fluctuation curve.
Specifically, in the step S3, the data update trend of each data update port is determined based on the average slope of the update amount fluctuation curve, wherein
Comparing the average slope K of the update quantity fluctuation curve with a preset first slope comparison parameter K1 and a second slope comparison parameter K2, judging the data update trend of the data update port according to the comparison result,
under a first slope comparison result, judging that the data updating port is in a first data updating trend;
under the second slope comparison result, judging that the data updating port is in a second data updating trend;
the first slope comparison result is K1 < K2, and the second slope comparison result is K less than or equal to K1 or K more than or equal to K2.
Specifically, when the first slope comparison parameter K1 and the second slope comparison parameter K2 are determined, the data update amount of the interface within the preset time tn needs to be counted for multiple times in advance, an update amount fluctuation curve is built based on the data update amount to calculate an average slope K, the average slope K obtained by counting for multiple times is obtained, the counted number of times is larger than the minimum sample size of normal distribution, the average slope K is used as a random variable, a probability density function is built, a normal distribution curve is correspondingly built based on the built probability density function, 75% probability interval of the normal distribution curve is determined, the average slope corresponding to the upper interval limit of the 75% probability interval is determined to be the second slope comparison parameter K2, and the average slope corresponding to the lower interval limit of the 75% probability interval is determined to be the first slope comparison parameter K1.
It should be understood by those skilled in the art that in normal distribution, the data characterization is better in the interval of 1 sigma to 2 sigma, namely 68% probability interval to 95% probability interval, and sigma is the standard deviation in normal distribution, therefore, the present invention selects 75% probability interval to determine the first slope comparison parameter K1 and the second slope comparison parameter K2 for improving the accuracy of data processing.
Specifically, the invention constructs an update quantity fluctuation curve by acquiring the data update quantity of the data update port within the preset time length, and represents the data change trend of the data update quantity by calculating the average slope of the update quantity fluctuation curve, wherein the data update quantity is stable fluctuation under the first data change trend, and the data update quantity is rising fluctuation or falling fluctuation under the second data change trend.
Specifically, in step S4, the method further includes obtaining an average fluctuation width of the update amount of the data update amount within a preset time period, wherein,
calculating the average fluctuation amplitude D of the update amount of the data update amount within the preset time period tn according to a formula (2),
in the formula (2), E (i+1) represents the data update amount of the data update port at the i+1 th time, E (i) represents the data update amount of the data update port at the i th time, and i is an integer greater than 0.
Specifically, in the step S4, it is determined whether the number of the data update port connection servers needs to be adjusted based on the average fluctuation range of the update amount of the data update amount within the preset time period, wherein,
comparing the updated average fluctuation amplitude D with a preset first fluctuation amplitude comparison parameter D1,
under the first fluctuation amplitude comparison condition, judging the quantity of the data updating port connection servers to be adjusted;
under the second fluctuation amplitude comparison condition, judging that the quantity of the data updating port connection servers does not need to be adjusted;
the first fluctuation amplitude comparison condition is D & gtD 1, and the second fluctuation amplitude comparison condition is D & ltoreq.D1.
Specifically, in step S4, the method further includes determining an adjustment manner when the number of connection servers of the data update port needs to be adjusted based on the average fluctuation amplitude of the update amount of the data update amount within the preset duration, where,
comparing the average fluctuation amplitude D of the updated quantity with a preset average fluctuation amplitude D2 of a second updated quantity, wherein D2 is more than D1,
the first adjustment mode is to adjust the number of the data update port connection servers to a first number P1 according to a preset first number adjustment parameter P1, and set p1=p0+p1;
the second adjustment mode is to adjust the number of the data update port connection servers to a second number P2 according to a preset second number adjustment parameter P2, and set p2=p0+p2;
the first adjustment mode needs to satisfy D < D2, the second adjustment mode needs to satisfy D > D2, and P0 represents the number of servers currently connected with the data update port, and P1 < P2.
Specifically, when the first fluctuation amplitude comparison parameter D1 and the second fluctuation amplitude comparison parameter D2 are determined, the data update amount of the data update interface within the preset time period tn needs to be counted for multiple times in advance, the update amount average fluctuation amplitude is determined based on the data update amount, the update amount average fluctuation amplitude D obtained by counting for multiple times is obtained, the count number needs to be larger than the normal distribution minimum sample size, the update amount average fluctuation amplitude is used as a random variable, a probability density function is constructed, a normal distribution curve is correspondingly constructed based on the constructed probability density function, a 75% probability interval of the normal distribution curve is determined, the update amount average fluctuation amplitude corresponding to the interval upper limit of the 75% probability interval is determined to be the second fluctuation amplitude comparison parameter D2, and the update amount average fluctuation amplitude corresponding to the interval lower limit of the 75% probability interval is determined to be the first fluctuation amplitude comparison parameter D1.
Specifically, in step S4 of the present invention, the average fluctuation range of the update amount is obtained, and whether the number of the port connection servers needs to be adjusted is determined based on the average fluctuation range of the update amount, in practical situations, because the fluctuation of the data update amount of the logistics order data of the cross-border logistics transaction platform is large, the data update amount is always in a stable fluctuation state, in this state, whether the number of the port connection servers needs to be adjusted is determined based on the magnitude of the fluctuation range, and when the fluctuation range is large, the number of the port connection servers needs to be appropriately adjusted, so that the temporary overload of the server module caused by the fluctuation is avoided, and the risk of overload and paralysis of the server module is reduced.
Specifically, in the step S4, an adjustment manner when the number of the data update port connection servers is adjusted is determined based on the average slope of the update amount fluctuation curve, wherein,
comparing the average slope K of the update fluctuation curve with a preset slope comparison parameter K0, wherein K0 is more than K2, p4 is more than p3,
the third adjustment mode is to adjust the number of the data update port connection servers to a third number value P3 according to a preset third number adjustment parameter P3, and set p3=p0+p3;
the fourth adjustment mode is to adjust the number of the data update port connection servers to a fourth number value P4 according to a preset third number adjustment parameter P4, and set p4=p0+p4;
the fifth adjustment mode is to adjust the number of the data update port connection servers to a fifth number value P5 according to a preset third number adjustment parameter P4, and set p5=p0-P3;
the sixth adjustment manner is to adjust the number of the data update port connection servers to a sixth number value P6 according to a preset third number adjustment parameter P4, and set p6=p0-P4.
Specifically, the slope comparison parameter K0 is determined based on a 75% probability interval obtained when the first slope comparison parameter K1 and the second slope comparison parameter K2 are determined, and an average slope corresponding to a midpoint of the interval of the 75% confidence interval is determined as the slope comparison parameter K0.
Specifically, in the prior art, when the number of servers is adjusted, typically manually, the specific adjustment ratio is often adjusted by a person skilled in the art through experience, and in this embodiment, P1, P2, P3, and P4 may be preset, where p1= 0.15P0, p2= 0.3P0, p3= 0.3P0 ×k/K0, and p4= 0.5P0 ×k/K0 are set.
Specifically, the third adjustment mode needs to satisfy K & gt0 and |K| & lt K0, the fourth adjustment mode needs to satisfy K & lt0 and |K| & gtor equal to K0, the fifth adjustment mode needs to satisfy K & lt0 and |K| & gtor equal to K0, and the sixth adjustment mode needs to satisfy K & lt0 and |K| & gtor equal to K0.
Specifically, in step S4 of the present invention, the adjustment manner when the number of the data update port connection servers is adjusted is determined based on the average slope of the update amount fluctuation curve, in practical situations, because the phenomenon that the data update amount of the logistics order data of the cross-border logistics trading platform rises or falls within a period of time is common, the rising or falling trend of the data update amount of the data update port can be detected based on the average slope of the update amount fluctuation curve, especially when the fluctuation of the data update amount is not large, but the obvious rising trend appears, the present invention can adjust the number of the data update port connection servers in advance, thereby reducing the risk that the load appears in the system when the subsequent data is out of blowout, and likewise, when the data update amount of the data update port falls, the number of the data update port connection servers can be reduced in advance, thereby saving the operation resources.
Specifically, in the step S4, before the number of servers connected to the data update port is adjusted, the method further includes comparing the adjusted number of servers connected to the total number of servers, determining whether to send out overload warning information according to the comparison result, wherein,
and if the number of the servers connected after adjustment is larger than the total number of the servers, judging that overload warning information needs to be sent out.
Specifically, referring to fig. 3 and fig. 4, which are schematic structural diagrams of a cross-border logistics management system based on cloud management and schematic structural diagrams of data processing modules according to an embodiment of the present invention, the present invention provides a cross-border logistics management system of cloud management using a cross-border logistics management method of cloud management, including:
the data acquisition module comprises a plurality of data recording units connected with the data updating port and is used for acquiring data updating quantity corresponding to the logistics order data updated by the data updating port;
the server module comprises a plurality of connectable servers, and each server establishes a connection protocol with each data updating port so that each server processes data transmitted by each data updating port;
the data processing module comprises a data analysis unit, a first operation unit and a second operation unit which are connected with each other;
the data analysis unit is connected with the data acquisition module and used for acquiring the data update quantity of the data update port in a preset duration, constructing an update quantity fluctuation curve based on the data update quantity, and judging the data update trend of the data update port based on the average slope of the update quantity fluctuation curve;
the first operation unit is connected with the data acquisition module and the server module and is used for acquiring the data updating quantity of the data updating port when the data analysis unit judges the first data updating state, and judging whether the quantity of the data updating port connected with the server needs to be adjusted or not based on the average fluctuation amplitude of the updating quantity of the data updating quantity within the preset duration;
the second operation unit is connected with the data acquisition module and the server module and is used for determining an adjustment mode when the number of the data update port connection servers is adjusted based on the average slope of the update quantity fluctuation curve when the data analysis unit judges the second data update state.
Specifically, the specific structure of the data acquisition module is not limited, and each data acquisition unit can be a logic component externally connected to the data update port, so that the function of recording the data volume can be realized.
The data updating port is a port for connecting the cloud logistics platform with the background server, which is the prior art and will not be described herein.
Specifically, the specific structure of the server module is not limited, and only the server module needs to respond to the service request and perform corresponding processing, in this embodiment, the server module may be a server node cluster, and the server refers to a server node in the server node cluster, so as to provide data processing calculation force for different interfaces, which is not described in detail herein in the prior art.
Specifically, the specific structure of the data processing module of the present invention is not limited, and the data processing module itself or each unit therein may be formed by using a logic component, where the logic component may be a field programmable logic component, a microprocessor, a processor used in a computer, etc., and will not be described herein.
Specifically, the specific form of the connection server is not limited in the present invention, and the communication interface of the server may be connected to each data update port by a protocol, or may be other forms, which are in the prior art and are not described herein.
Specifically, the specific content of the order data is not limited in the present invention, and it should be understood by those skilled in the art that the data content updated by the order data update interface may include a plurality of upstream and downstream data of a single order, and those skilled in the art may replace the order data with other data updated by the cloud end logistics platform, which does not affect the technical scheme of the present invention.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
Claims (6)
1. A cross-border logistics management method based on cloud management and control is characterized by comprising the following steps:
step S1, a data acquisition module is set to acquire updated logistics order data of each data updating port of a cloud logistics platform;
s2, constructing an update quantity fluctuation curve corresponding to a data update port, and determining the average slope of the update quantity fluctuation curve, wherein the update quantity fluctuation curve is constructed based on the data update quantity of the data update port within a preset time length;
step S3, determining the data updating trend of each data updating port based on the average slope of the updating quantity fluctuation curve;
step S4, determining the number of servers connected with the data update port based on the data update trend of the data update port, wherein the step comprises the steps of acquiring the data update amount of the data update port when the data update port is judged to be in the first data update trend, judging whether the number of servers connected with the data update port needs to be adjusted based on the average fluctuation amplitude of the update amount of the data update amount in the preset duration, and adjusting the number of servers connected with the data update port to a corresponding value;
when the data updating port is judged to be in the second data updating trend, determining an adjusting mode when the number of the data updating port connected servers is adjusted based on the average slope of the updating quantity fluctuation curve;
in the step S2, an update amount fluctuation curve corresponding to the data update port is constructed and an average slope of the update amount fluctuation curve is determined, wherein,
acquiring data updating quantity of a data updating port in a preset duration tn, establishing a rectangular coordinate system by taking time as an x axis and the data updating quantity as a y axis, constructing an updating quantity fluctuation curve in the rectangular coordinate system, calculating the average slope K of the updating quantity fluctuation curve according to a formula (1),
in the formula (1), ki represents the slope of the update amount fluctuation curve when the transverse coordinate is i, i being an integer greater than 0;
in the step S3, the data updating trend of each data updating port is determined based on the average slope of the updating amount fluctuation curve, wherein
Comparing the average slope K of the update quantity fluctuation curve with a preset first slope comparison parameter K1 and a second slope comparison parameter K2, judging the data update trend of the data update port according to the comparison result,
under a first slope comparison result, judging that the data updating port is in a first data updating trend;
under the second slope comparison result, judging that the data updating port is in a second data updating trend;
the first slope comparison result is K1 < K2, and the second slope comparison result is K1 or K2;
in step S4, the method further includes obtaining an average fluctuation amplitude of the update amount of the data update amount within a preset duration, where,
calculating the average fluctuation amplitude D of the update amount of the data update amount within the preset time period tn according to a formula (2),
in the formula (2), E (i+1) represents a data update amount of the data update port at the i+1 th time, E (i) represents a data update amount of the data update port at the i th time, and i is an integer greater than 0;
in the step S4, it is determined whether the number of the data update port connection servers needs to be adjusted based on the average fluctuation amplitude of the update amount of the data update amount within the preset time period, wherein,
comparing the updated average fluctuation amplitude D with a preset first fluctuation amplitude comparison parameter D1,
under the first fluctuation amplitude comparison condition, judging the quantity of the data updating port connection servers to be adjusted;
under the second fluctuation amplitude comparison condition, judging that the quantity of the data updating port connection servers does not need to be adjusted;
the first fluctuation amplitude comparison condition is D & gtD 1, and the second fluctuation amplitude comparison condition is D & ltoreq.D1.
2. The cloud management and control-based cross-border flow management method according to claim 1, wherein in the step S4, the method further comprises determining an adjustment manner when the number of the data update port connection servers needs to be adjusted based on the average fluctuation amplitude of the update amount of the data update amount within a preset duration,
comparing the average fluctuation amplitude D of the updated quantity with a preset average fluctuation amplitude D2 of a second updated quantity, wherein D2 is more than D1,
the first adjustment mode is to adjust the number of the data update port connection servers to a first number P1 according to a preset first number adjustment parameter P1, and set p1=p0+p1;
the second adjustment mode is to adjust the number of the data update port connection servers to a second number P2 according to a preset second number adjustment parameter P2, and set p2=p0+p2;
the first adjustment mode needs to satisfy D < D2, the second adjustment mode needs to satisfy D > D2, and P0 represents the number of servers currently connected with the data update port, and P1 < P2.
3. The cloud-management-based cross-border flow management method according to claim 2, wherein in the step S4, an adjustment manner when the number of the data update port connection servers is adjusted is determined based on an average slope of the update amount fluctuation curve, wherein,
comparing the average slope K of the update fluctuation curve with a preset slope comparison parameter K0, wherein K0 is more than K2, p4 is more than p3,
the third adjustment mode is to adjust the number of the data update port connection servers to a third number value P3 according to a preset third number adjustment parameter P3, and set p3=p0+p3;
the fourth adjustment mode is to adjust the number of the data update port connection servers to a fourth number value P4 according to a preset third number adjustment parameter P4, and set p4=p0+p4;
the fifth adjustment mode is to adjust the number of the data update port connection servers to a fifth number value P5 according to a preset third number adjustment parameter P4, and set p5=p0-P3;
the sixth adjustment manner is to adjust the number of the data update port connection servers to a sixth number value P6 according to a preset third number adjustment parameter P4, and set p6=p0-P4.
4. The cloud management and control-based cross-border stream management method according to claim 3, wherein the third adjustment mode needs to satisfy K & gt0 and |K| & lt K0, the fourth adjustment mode needs to satisfy K & gt0 and |K| & gtor equal to K0, the fifth adjustment mode needs to satisfy K & gt0 and |K| & gtor equal to K0, and the sixth adjustment mode needs to satisfy K & gt0 and |K| & lt K0.
5. The cloud management and control-based cross-border flow management method according to claim 4, wherein in the step S4, before adjusting the number of servers connected to the data update port, the method further comprises comparing the adjusted number of servers with the total number of servers, and determining whether to send out overload warning information according to the comparison result, wherein,
and if the number of the servers connected after adjustment is larger than the total number of the servers, judging that overload warning information needs to be sent out.
6. A cross-border stream management system employing the method of any one of claims 1-5, comprising:
the data acquisition module comprises a plurality of data recording units connected with the data updating port and is used for acquiring data updating quantity corresponding to the logistics order data updated by the data updating port;
the server module comprises a plurality of connectable servers, and each server establishes a connection protocol with each data updating port so that each server processes data transmitted by each data updating port;
the data processing module comprises a data analysis unit, a first operation unit and a second operation unit which are connected with each other;
the data analysis unit is connected with the data acquisition module and used for acquiring the data update quantity of the data update port in a preset duration, constructing an update quantity fluctuation curve based on the data update quantity, and judging the data update trend of the data update port based on the average slope of the update quantity fluctuation curve;
the first operation unit is connected with the data acquisition module and the server module and is used for acquiring the data updating quantity of the data updating port when the data analysis unit judges the first data updating state, and judging whether the quantity of the data updating port connected with the server needs to be adjusted or not based on the average fluctuation amplitude of the updating quantity of the data updating quantity within the preset duration;
the second operation unit is connected with the data acquisition module and the server module and is used for determining an adjustment mode when the number of the data update port connection servers is adjusted based on the average slope of the update quantity fluctuation curve when the data analysis unit judges the second data update trend.
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