CN114971096B - Intelligent liquefied gas distribution management system based on data analysis of Internet of things - Google Patents
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Abstract
The invention relates to the technical field of intelligent liquefied gas distribution, in particular to an intelligent liquefied gas distribution management system based on data analysis of the Internet of things. It includes an information acquisition unit. According to the invention, a liquefied gas use frequency storage unit stores time information of liquefied gas used by each customer, a use amount simulation and inference unit predicts the time of liquefied gas distribution required by the customer in different time periods according to the time information of liquefied gas used by each customer, a customer use time prediction unit predicts the time of liquefied gas distribution required by the customer in different time periods according to the time spent by the customer in the same amount of liquefied gas in different time periods, and a distribution time analysis unit analyzes the predicted time information and analyzes the time of liquefied gas distribution required by each customer in a liquefied gas distribution range in the same time period, so that the distribution date of each customer in the liquefied gas distribution range can be obtained in advance, distribution preparation can be made in time for the need of time, and the liquefied gas distribution efficiency can be improved.
Description
Technical Field
The invention relates to the technical field of intelligent liquefied gas distribution, in particular to an intelligent liquefied gas distribution management system based on data analysis of the Internet of things.
Background
With the development of the petrochemical industry, liquefied gas has been increasingly regarded as a chemical basic material and a novel fuel, and the liquefied gas is a colorless volatile liquid obtained by pressurizing, cooling and liquefying natural gas or petroleum in an oil refinery, is used as a fuel, and has widely entered the living field of people due to high heat value, no smoke and dust and no carbon residue, and is convenient to operate and use.
Liquefied gas is at the delivery in-process, need rely on liquefied gas tank to save, after the user used up the liquefied gas of splendid attire in the liquefied gas tank, the empty liquefied gas tank will be retrieved to the delivery company, after the splendid attire was full of liquefied gas, redistribute liquefied gas tank to corresponding user, and present delivery mode is mostly to use up the back through the user and remind the delivery company to deliver, because the liquefied gas volume that the delivery company need carry out the delivery every day is huge, in the delivery in-process, only rely on using the user to remind, the not enough condition in stock appears very easily, lead to delivery efficiency greatly reduced, so need promptly now based on thing networking data analysis's intelligent liquefied gas delivery management system.
Disclosure of Invention
The invention aims to provide an intelligent liquefied gas distribution management system based on data analysis of the Internet of things, so as to solve the problems in the background technology.
In order to achieve the purpose, the intelligent liquefied gas distribution management system based on data analysis of the internet of things comprises an information acquisition unit, wherein the output end of the information acquisition unit is connected with a liquefied gas use frequency storage unit, the liquefied gas use frequency storage unit is used for storing time information of liquefied gas used by each customer, the output end of the liquefied gas use frequency storage unit is connected with a customer use time prediction unit, the output end of the liquefied gas use frequency storage unit is further connected with a use amount simulation and inference unit, the use amount simulation and inference unit is used for inferring the time spent by the customer on using the same amount of liquefied gas in different time periods according to the time information of liquefied gas used by each customer, the output end of the use amount simulation and inference unit is connected with the input end of the customer use time prediction unit, the customer use time prediction unit predicts the time required for liquefied gas distribution by the customer in different time periods according to the time spent on using the same amount of liquefied gas by the customer in different time periods, and the output end of the use amount simulation and inference unit is connected with a distribution time analysis unit.
As a further improvement of the technical scheme, the liquefied gas usage frequency storage unit comprises a gas usage amount storage module used by different customers per month, and the output ends of the gas usage amount storage modules used by different customers per month are connected with different customer monthly distribution time point pre-storage modules.
As a further improvement of the technical solution, the output end of the delivery time analysis unit is connected with a delivery route planning unit, and the delivery route planning unit formulates a suitable delivery route according to delivery times of liquefied gases used by different customers.
As a further improvement of the technical solution, the delivery route planning unit includes modules for delivering time by different customers in the same time period, the output ends of the modules for delivering time by different customers in the same time period are connected with modules for determining residence points of different customers, the modules for determining residence points of different customers are used for determining residence points for delivering, and the output ends of the modules for determining residence points of different customers are connected with a matching route simulation module.
As a further improvement of the technical solution, an input end of the matching route simulation module is connected with an emergency delivery point determination module, and the emergency delivery point determination module is used for determining customers who urgently need liquefied gas and matching the customers with corresponding address information.
As a further improvement of the technical scheme, the output end of the distribution time analysis unit is connected with a distribution quantity pre-storage unit, and the distribution quantity pre-storage unit pre-stores the liquefied gas quantity to be distributed in advance according to the distribution time analysis result.
As a further improvement of the technical solution, the delivery volume prestoring unit includes a delivery date determining module, an output end of the delivery date determining module is connected with delivery volume determining modules corresponding to different dates, and an output end of the delivery volume determining module corresponding to different dates is connected with a liquefied gas delivery demand reserving module.
As a further improvement of the technical solution, the input end of the distribution time analysis unit is connected with a special date analysis unit.
As a further improvement of the technical solution, the special date analyzing unit adopts a special date analyzing algorithm, and an algorithm formula thereof is as follows:
wherein the content of the first and second substances,
in order to deliver the set of days of the month,
to
In order to deliver the various days of the month,
to deliver all of the special date sets of the current year,
to
In order to distribute each particular date of the year,
a representation map for determining whether a particular date exists for the delivery of the month.
Compared with the prior art, the invention has the beneficial effects that:
1. in the intelligent liquefied gas distribution management system based on the Internet of things data analysis, a liquefied gas use frequency storage unit stores time information of liquefied gas used by each customer, a use amount simulation and inference unit predicts the time of the liquefied gas distribution required by the customer in different time periods according to the time information of the liquefied gas used by each customer, a customer use time prediction unit predicts the time of the liquefied gas distribution required by the customer in different time periods according to the time spent by the customer in the same amount in different time periods, a distribution time analysis unit analyzes the predicted time information and analyzes the time of the liquefied gas distribution required by different customers in the same time period by each customer in a liquefied gas distribution range, so that the distribution date of each customer in the liquefied gas distribution range can be obtained in advance, distribution preparation is made in time, the need is made for time, and the liquefied gas distribution efficiency is improved.
2. In the intelligent liquefied gas distribution management system based on the data analysis of the internet of things, different customers use the gas quantity storage module every month to analyze previous distribution information to obtain the gas quantities used by the different customers every month and obtain the gas quantities used by the same customers in different months at the same time, the different customer monthly distribution time point pre-storage module analyzes according to the previous distribution information to obtain the distribution time points of the gas of the different customers every month and obtain the same customer monthly distribution time points in different months at the same time so as to predict in the later period.
3. In the intelligent liquefied gas distribution management system based on the data analysis of the internet of things, different customer distribution time modules in the same time period are used for determining the number of customers needing liquefied gas distribution in the same time period, distribution information is generated and is transmitted to different customer living point determining modules, the different customer living point determining modules determine different customer distribution living points, address determining information is generated and is transmitted to a matching route simulating module, and the matching route simulating module makes a proper route according to the address determining information to carry out liquefied gas distribution.
4. In the intelligent liquefied gas distribution management system based on the data analysis of the Internet of things, a distribution date determining module determines distribution date information and transmits the distribution date information to distribution amount determining modules corresponding to different dates, the distribution amount determining modules corresponding to different dates determine corresponding liquefied gas distribution amounts by combining the distribution date information, and a certain amount of liquefied gas is prestored in advance.
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is a flow chart of a liquefied gas use frequency storage unit according to the present invention;
FIG. 3 is a flow chart of a delivery time analysis unit of the present invention;
FIG. 4 is a flowchart of a delivery amount pre-storing unit according to the present invention.
The various reference numbers in the figures mean:
10. an information acquisition unit;
20. a liquefied gas use frequency storage unit; 210. different customers use the gas quantity storage module every month; 220. different customers distribute the time point pre-storing module every month;
30. a client usage time prediction unit;
40. a usage amount simulation and estimation unit;
50. a delivery time analysis unit;
60. a delivery route planning unit; 610. different clients in the same time period distribute the time module; 620. different customer living points determining module; 630. a matching route simulation module; 640. an emergency delivery point determination module;
70. a delivery amount prestoring unit; 710. a delivery date determination module; 720. the distribution amount determining module corresponds to different dates; 730. a liquefied gas distribution demand reservation module;
80. a special date analysis unit.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1 to 4, an intelligent liquefied gas distribution management system based on internet of things data analysis is provided, which includes an information acquisition unit 10, an output end of the information acquisition unit 10 is connected with a liquefied gas usage frequency storage unit 20, the liquefied gas usage frequency storage unit 20 is used for storing time information of liquefied gas usage by each customer, an output end of the liquefied gas usage frequency storage unit 20 is connected with a customer usage time prediction unit 30, an output end of the liquefied gas usage frequency storage unit 20 is further connected with a usage amount simulation inference unit 40, the usage amount simulation inference unit 40 is used for inferring time spent by the customer in using the same amount of liquefied gas in different time periods according to the time information of liquefied gas usage by each customer, an output end of the usage amount simulation inference unit 40 is connected with an input end of the customer usage time prediction unit 30, the customer usage time prediction unit 30 predicts time spent by the customer in using the same amount of liquefied gas in different time periods according to the time spent by the customer in using the same amount of liquefied gas in different time periods, and an output end of the usage amount simulation inference unit 40 is connected with a distribution time analysis unit 50.
In a specific use, the information collecting unit 10 collects information of liquefied gas used by each customer in a liquefied gas distribution range, generates collected information, and transmits the collected information to the liquefied gas use frequency storage unit 20, the liquefied gas use frequency storage unit 20 stores time information of liquefied gas used by each customer, generates stored information, and transmits the stored information to the customer use time prediction unit 30, and transmits the stored information to the use amount simulation and estimation unit 40, the use amount simulation and estimation unit 40 estimates time spent by the customer in using the same amount of liquefied gas in different time periods, which represents time spent by the same customer in using the same amount of liquefied gas in different time periods, according to the time spent by the customer in using the same amount of liquefied gas in different time periods, the customer use time prediction unit 30 predicts time required by the customer in different time periods in the same time period, that the customer may use the last distributed liquefied gas in the day, generates predicted time information, transmits the predicted time information to the liquefied gas distribution time analysis unit 50, and analyzes the time required by the customer in the same time period, and analyzes the time distribution information of the customer in the distribution range, thereby obtaining the time required by the customer, and analyzing unit 50.
In addition, the liquefied gas usage frequency storage unit 20 includes different customer monthly usage gas amount storage modules 210, different customer monthly usage gas amount storage modules 210 are connected to the output end of the different customer monthly usage gas amount storage module 210, different customers monthly usage gas amount storage modules 210 analyze previous distribution information to obtain the gas amounts used by different customers monthly and obtain the gas amounts used by the same customer in different months, the different customer monthly usage gas amount storage modules 220 analyze the previous distribution information to obtain the distribution time points of the gas of different customers monthly and obtain the distribution time points of the same customer in different months, so as to predict the later period.
Further, the output end of the delivery time analysis unit 50 is connected with a delivery route planning unit 60, the delivery route planning unit 60 formulates a proper delivery route according to the delivery time of liquefied gas used by different customers, and the delivery route planning unit 60 plans the route according to the delivery time of liquefied gas used by different customers, so as to ensure that the liquefied gas delivery work of different customers is reasonably arranged in the shortest time and improve the delivery efficiency.
Still further, the distribution route planning unit 60 includes a same time period and different customer distribution time module 610, an output end of the same time period and different customer distribution time module 610 is connected with a different customer living point determining module 620, the different customer living point determining module 620 is used for determining different customer distribution living points, an output end of the different customer living point determining module 620 is connected with a matching route simulating module 630, the same time period and different customer distribution time module 610 is used for determining how many customers need to perform liquefied gas distribution in the same time period to generate distribution information and transmit the distribution information to the different customer living point determining module 620, the different customer living point determining module 620 determines different customer distribution living points to generate address determination information and transmit the address determination information to the matching route simulating module 630, and the matching route simulating module 630 makes a suitable route for liquefied gas distribution according to the address determination information.
Specifically, the input end of the matching route simulation module 630 is connected with an emergency delivery point determination module 640, the emergency delivery point determination module 640 is used for determining customers who need liquefied gas urgently and matching the corresponding address information, in the process of route planning, the matching route simulation module 630 determines customers who need liquefied gas urgently through the emergency delivery point determination module 640, matches the corresponding address information to generate emergency delivery address information, and transmits the emergency delivery address information to the matching route simulation module 630, and the matching route simulation module 630 reforms delivery addresses according to the emergency delivery address information to ensure that the customers who need liquefied gas urgently can receive liquefied gas supply in the shortest time.
In addition, the output end of the distribution time analysis unit 50 is connected with a distribution amount pre-storage unit 70, the distribution amount pre-storage unit 70 pre-stores the liquefied gas amount to be distributed in advance according to the distribution time analysis result, during specific use, each customer in the liquefied gas distribution range is analyzed by the distribution time analysis unit 50 in the same time period, the liquefied gas distribution time is needed by different customers, analysis information is generated, the analysis information is transmitted to the distribution amount pre-storage unit 70, the distribution amount pre-storage unit 70 combines the analysis information result, the liquefied gas amount to be distributed is pre-stored in advance, and the liquefied gas distribution amount is ensured to be sufficient.
Further, the distribution volume prestoring unit 70 includes a distribution date determining module 710, an output end of the distribution date determining module 710 is connected with a distribution volume determining module 720 corresponding to different dates, an output end of the distribution volume determining module 720 corresponding to different dates is connected with a liquefied gas distribution demand predetermining module 730, the distribution date determining module 710 determines distribution date information and transmits the distribution date information to the distribution volume determining module 720 corresponding to different dates, and the distribution volume determining module 720 corresponding to different dates determines the corresponding liquefied gas distribution volume by combining the distribution date information, and prestores a certain amount of liquefied gas in advance.
Still further, the input end of the distribution time analysis unit 50 is connected with a special date analysis unit 80, the special date analysis unit 80 determines the special date of the month, because the festival of each month is different, and the cooking amount is greatly increased when the festival is the liquefied gas usage amount, when the festival number of a month is more, the usage amount predicted before is changed, so the festival number corresponding to each month needs to be determined in advance, the corresponding distribution time needs to be predicted again, and the prediction accuracy is improved.
Further, the special date analyzing unit 80 employs a special date analyzing algorithm whose formula is as follows:
wherein the content of the first and second substances,
in order to deliver the set of days of the month,
to
In order to deliver the various days of the month,
in order to deliver all the special date sets of the current year,
to
In order to distribute each particular date of the year,
a representation map for determining whether a particular date exists for the delivery of the month.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (1)
1. Intelligence liquefied gas delivery management system based on thing networking data analysis, including information acquisition unit (10), its characterized in that: the output end of the information acquisition unit (10) is connected with a liquefied gas use frequency storage unit (20), the liquefied gas use frequency storage unit (20) is used for storing time information of liquefied gas used by each customer, the output end of the liquefied gas use frequency storage unit (20) is connected with a customer use time prediction unit (30), the output end of the liquefied gas use frequency storage unit (20) is also connected with a use amount simulation and inference unit (40), the use amount simulation and inference unit (40) is used for inferring the time spent by the customer on using the same amount of liquefied gas in different time periods according to the time information of liquefied gas used by each customer, the output end of the use amount simulation and inference unit (40) is connected with the input end of the customer use time prediction unit (30), the customer use time prediction unit (30) predicts the time required for distribution by the customer in different time periods according to the time spent on using the same amount of liquefied gas by the customer in different time periods, and the output end of the use amount simulation and inference unit (40) is connected with a distribution time analysis unit (50);
the liquefied gas use frequency storage unit (20) comprises different customers monthly use gas quantity storage modules (210), and the output ends of the different customers monthly use gas quantity storage modules (210) are connected with different customers monthly distribution time point pre-storage modules (220);
the output end of the delivery time analysis unit (50) is connected with a delivery route planning unit (60), and the delivery route planning unit (60) formulates a proper delivery route according to the delivery time of liquefied gas used by different customers;
the distribution route planning unit (60) comprises a same-time-period different-customer distribution time module (610), wherein the output end of the same-time-period different-customer distribution time module (610) is connected with a different-customer living point determining module (620), the different-customer living point determining module (620) is used for determining different-customer distribution living points, and the output end of the different-customer living point determining module (620) is connected with a matching route simulating module (630);
the input end of the matching route simulation module (630) is connected with an emergency distribution point determination module (640), and the emergency distribution point determination module (640) is used for determining customers needing liquefied gas and matching the customers with corresponding address information;
the output end of the distribution time analysis unit (50) is connected with a distribution quantity pre-storage unit (70), and the distribution quantity pre-storage unit (70) pre-stores the liquefied gas quantity to be distributed in advance according to the distribution time analysis result;
the delivery volume prestoring unit (70) comprises a delivery date determining module (710), the output end of the delivery date determining module (710) is connected with delivery volume determining modules (720) corresponding to different dates, and the output end of the delivery volume determining module (720) corresponding to different dates is connected with a liquefied gas delivery demand reserving module (730);
the input end of the distribution time analysis unit (50) is connected with a special date analysis unit (80);
the special date analysis unit (80) adopts a special date analysis algorithm, and the algorithm formula is as follows:
wherein A is a set of days in the current month of delivery, a 1 To a n For distribution of days of the month, B for distribution of all special date sets of the year, B 1 To b n A → B represents a map for determining whether or not a special date exists in the delivery month for delivering each special date of the current year, wherein the special date is used for indicating a date on which the usage of liquefied gas is increased due to an increase in meal size;
the special date analyzing unit (80) is used for determining the special date of each month in advance, and then predicting the corresponding delivery time again, so that the prediction accuracy is improved.
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| JP5947422B1 (en) * | 2015-04-30 | 2016-07-06 | 日本瓦斯株式会社 | Prioritization method for delivery leveling |
| CN112132519A (en) * | 2020-09-30 | 2020-12-25 | 瓶安用气(杭州)物联网科技有限公司 | Bottled gas cross network system based on thing networking big data |
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