CN108596709B - Real-time pressure monitoring system for take-out orders - Google Patents
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Abstract
The invention discloses a real-time pressure monitoring system for take-out orders, which comprises a mobile client, an order center, a data center and a service platform. According to the invention, the city operation manager can directly find out specific orders and merchants in the corresponding pressure grid through the pressure monitoring graph, and allocate riders according to the rider load rate near the pressure grid to enable the transport capacity to reach the optimal configuration; after the operation manager selects the responsible city and region, the pressure states under different dimensions are shown from the three dimensions of a merchant, a rider and an order, and according to the real-time situation of the pressure grid: the order quantity is large, the number of riders is small, and the transportation capacity needs to be allocated to the area to provide corresponding support; high pressure orders are more (not sent out for a long time) and need to match the appropriate rider order taking.
Description
Technical Field
The invention belongs to the technical field of network information, and particularly relates to a real-time pressure monitoring system for take-out orders.
Background
As an instant logistics platform, on one hand, the instant logistics platform provides on-time, extremely-fast and reliable delivery service for users, and hopes to rapidly dispatch orders and avoid the accumulation of a large number of orders; on the other hand, as a 3 hundred million blue collar professional development platform, the system provides opportunities for the income, professional development and promotion of riders and helps the riders to realize a better life.
In the take-out industry, most orders are concentrated in two peak periods of noon and evening, so that the accumulation of a large number of orders is avoided, on one hand, the real-time state of the orders is controlled more accurately, and the reading stability is kept when a large number of orders are written in the peak period; on the other hand, the transportation capacity is more scientifically allocated and the whole situation is collected, and finally the distribution efficiency of the system is improved.
In order to more accurately control the real-time state of an order, only the latest state of the order is concerned and kept, a record is hopefully inserted in a covering mode, the subsequent state (for example, the order transferring state is the record with the same order number in the storage) of the same order (for example, the order transferring state is completed after the order is dispatched) covers the precursor state, the currently and commonly used relational database cannot insert a new record in a native covering mode, if the purpose is to be achieved, the original record needs to be deleted firstly, then the new record is inserted, and the stored reading and writing pressure is greatly increased after the original record is deleted frequently in a peak period; or the original record is marked to be invalid and then a new record is inserted, so that a large amount of redundant records are caused, and the development difficulty is increased.
In order to improve the distribution efficiency of the system, a pressure monitoring system needs to be introduced; the current pressure monitoring is shown in the form of thermodynamic diagrams, the pressure is reflected by the shade degree of colors, the pressure can be sensed from the visual sense in a general way, the pressure cannot be sensed in a grid unit, and the real quantity, the detail and the pressure value of an unpopulated order, the pressure of a merchant and the load condition of a nearby rider cannot be reflected quantitatively.
Disclosure of Invention
In view of the above, the present invention provides a real-time pressure monitoring system for take-out orders, which enables a city operation manager to more quickly view a grid with a higher order pressure, find out specific orders in the grid and allocate riders according to the orders and merchants, thereby achieving balance of transportation resources and optimizing the transportation.
A real-time pressure monitoring system for take-out orders comprises a mobile client, an order center, a data center and a service platform; wherein: the mobile client is used for generating an order of a user and sending the order to an order center; the order center is used for receiving orders and writing the orders into Mysql (relational database management system); the data center is used for monitoring logs in Mysql, packaging corresponding information into Kafka (a high-throughput distributed publish-subscribe message system) type messages, and writing the Kafka type messages into ScylladB (high-throughput, low-delay and excellent-performance column storage NoSQL (non-relational database), which can be inserted into new records in a covering mode in a native manner); the service platform performs gridding division on urban areas by reading order data in the ScylLADB for a period of time, counts high-pressure orders, high-pressure merchants and rider loads in each grid block through the order data, calculates grid pressure values, and then sends the information to an operation manager and a user mobile client for display.
Further, the order center is composed of Java EE server clusters and is responsible for order warehousing.
Further, the data center comprises a DTS cluster, a Storm cluster, a Kafka cluster, a Spark Streaming cluster, and a ScylladB cluster; wherein: the DTS cluster monitors the change of a Binlog (binary log file) in Mysql by a DTS (Data Transformation Service, Data Transformation Service in SQL); the Storm (an open source distributed real-time computing system, often called as a streaming computing framework) cluster is used for monitoring DTS messages in the DTS cluster, and further encapsulating the DTS messages into Kafka messages to be written into the Kafka cluster; the Spark Streaming (fast general compute engine designed for large-scale data processing) cluster is used to read Kafka messages from Kafka cluster and write them in overlay mode into the ScyllaDB cluster (the same order has multiple states, records in the next batch, overlay the state of the previous batch).
Further, the DTS message records a database name, a table name, and values of data rows in the table, and the Storm cluster extracts row records in the order table according to the configuration of the database name and the table name, and then writes the row records into the Kafka message of the corresponding topic.
Further, the service platform performs gridding division on the urban area by the block size of 1km × 1km, and then calculates the grid pressure value of each grid block according to the following procedures:
(1) for any grid block, counting the number N of unpopulated orders entering a dispatching time window in the grid block, and further calculating to obtain
Is an upward rounding function;
(2) calculating the average dispatching time length L of the orders which are generated in the last 10 minutes in the grid block and enter the dispatching time window at the current moment;
(3) calculating V2: if V1<60 seconds, then
If V1 is greater than or equal to 60 seconds, then
(4) The result obtained by V1V 2 is compared with 1, and the smaller value is taken as the grid pressure value of the grid block.
Further, the service platform endows each grid block with the same color and transparency, namely, the grid pressure value is divided into four intervals: [0, 0.25], (0.25, 0.5], (0.5, 0.75], (0.75, 1], the transparency has four grades and sequentially corresponds to the four intervals from shallow to deep, and the transparency corresponding to the grid block is determined according to the interval where the grid pressure value is located.
Furthermore, the mobile client is provided with a corresponding APP, and the APP is an application program which is constructed by adopting a read Native technology, namely based on Javascript and read and adopts a consistent development technology and runs on an iOS platform and an Android platform, so that the development cost is greatly saved; in the development process, an application program does not need to be continuously and repeatedly constructed, the browsing effect is achieved, the app can be automatically updated as long as one line of js codes is updated, and the development efficiency is greatly improved. The fact Native reduces the updating frequency of the version, and if the version is not the Native code update, the buddy file can be updated in a hot updating mode without downloading the whole APP installation.
The present invention is intended to maintain only the most current status of orders in storage and to maintain read stability during peak heavy writes, while not only subjectively sensing load conditions by the user, but also allowing the user to obtain detailed data as a decision basis to achieve pressure balance (supply and demand pressure ratios). The pressure balance of the instant logistics can be calculated to specific points, including the pressure value of each store, the rider and the order, then the whole transport capacity market is adjusted by means of the pressure value, the order pressure is comprehensively considered by a system dispatching decision, the transport capacity is dispatched by the system, and finally supply and demand balance is achieved.
The method converts two-dimensional longitude and latitude into character strings by introducing the GeoHash, each character string represents a certain rectangular area grid, all points (longitude and latitude coordinates) in the rectangular area have the same GeoHash character string, the longer the character string is, the more accurate the represented range is (7-bit GeoHash can be adopted), a map is divided into a plurality of rectangular area grids with the same size, the grids are taken as the basis to calculate the specific pressure value, and a user allocates a rider according to the actual pressure value of each grid.
In addition, the invention uses Spark Streaming task to synchronize the real-time status of the order (including order taking, order transferring, order dispatching, completing, etc.) to the ScylLADB, obtains the order list in the rectangular area grid by using the order dispatching time as the ordering rule (orders earlier in the forward order, i.e. push order, should be dispatched first) according to the conditions of city, channel and the area concerned by city operation, and then calculates the order pressure value of the grid according to the conditions of the order dispatching status, total order quantity, merchant, etc. of each order in the order list. The real-time position information of the rider is uploaded by the APP end of the rider, the Spark Streaming task synchronizes the real-time position information of the rider to the elastic search, and the urban operation manager allocates the transport capacity according to the load condition of the rider in the grid area.
According to the invention, the city operation manager can directly find out specific orders and merchants in the corresponding pressure grid through the pressure monitoring graph, and allocate riders according to the rider load rate near the pressure grid to enable the transport capacity to reach the optimal configuration; after the operation manager selects the responsible city and region, the pressure states under different dimensions are shown from the three dimensions of a merchant, a rider and an order, and according to the real-time situation of the pressure grid: the order quantity is large, the number of riders is small, and the transportation capacity needs to be allocated to the area to provide corresponding support; high pressure orders are more (not sent out for a long time) and need to match the appropriate rider order taking.
Therefore, the invention can help an operation manager to better master real-time transport capacity, thereby achieving the purpose of relieving regional pressure; the monitoring system of the invention has the following advantages: 1. urban operation can intuitively monitor the specific grid order pressure; 2. quickly locating the merchants with high-pressure orders through the grid; 3. looking at nearby rider load rates at the high pressure grid; 4. the rider with low urban operation allocation load rate drives to the high-pressure grid block to balance the transport capacity, so that the optimal allocation of the transport capacity is achieved.
Drawings
Fig. 1 is a schematic structural diagram of a pressure monitoring system according to the present invention.
Detailed Description
In order to more specifically describe the present invention, the following detailed description is provided for the technical solution of the present invention with reference to the accompanying drawings and the specific embodiments.
As shown in fig. 1, the real-time pressure monitoring system for take-away orders of the present invention is composed of an order center, a service platform, a big data center, and a mobile app end; the order center consists of Java EE server clusters and is mainly responsible for warehousing orders; the big data center consists of Storm, Kafka, Spark Streaming and a ScylladB cluster and is mainly responsible for writing orders of the order center into the Scylladb cluster; the service platform is composed of Java EE server end clusters to inquire the ScylLADB, to inquire unpopulated orders, high pressure orders and to calculate the pressure value of the pressure grid; the mobile app end is divided into an android platform and an ios platform, receives an http restful request of a service platform, displays unpopulated orders and high-pressure orders and calculates pressure values of a pressure grid.
The specific process of the monitoring system for processing the high-pressure order is as follows:
(1) and after receiving the upstream order, the order center writes the upstream order into the Mysql database.
(2) The DTS cluster monitors the change of a binary log Binlog in the Mysql library.
(3) In the Storm task, DTS messages are monitored, database names, table names and data line values are recorded in the DTS messages, line records in an order table are fetched according to the configuration of the database names and the table names, and then each line record is written into a Kafka message of a certain topic.
(4) The Spark Streaming task reads the Kafka message in the topic and writes the message into the scyldb in an ap-pend mode (the same order has multiple states, and the record in the next batch overwrites the state of the previous batch).
(5) The service platform reads data of a period of time in the ScylladB for the partition key according to cities, regions and channels, and calculates the grid pressure value according to the order placing time and the order dispatching time of the order, and the realization process comprises the following steps: the method comprises the steps of area range division, pressure grid division, high-pressure order judgment, calculation of order pressure values of all pressure grids in an area according to the real-time state of an order, rendering of color depth differences according to the order pressure values of the pressure grids, real-time tracking of a rider and rider loads near the pressure grids, and increment real-time synchronization of data in Mysql and ScylladB.
Specifically, the service platform reads records in the ScylladB for a period of time, and calculates grid pressure values (the pressure grid has 1 color and 4 transparency displays (UI diagram is standard), the pressure values are divided into 4 sections of [0, 0.25], (0.25, 0.50], (0.50, 0.75], (0.75, 1], the transparency is from shallow to deep), high pressure orders (orders which are not yet dispatched 10 minutes after the order dispatching time), high pressure traders (merchants with more than one high pressure order), and rider loads (the number of orders accepted/the upper limit of orders accepted).
The grid pressure values are calculated as follows for a 1km × 1km large grid:
5.1 count the number of unpopulated orders that entered the time window for dispatch (i.e., the current time is greater than the time at which the order should be dispatched for the first time) in the grid (the grid the merchant is in), divided by 3 times 0.1, and then round up to a value of V1.
5.2 counting the orders generated in the grid (the grid where the merchant is located) in the previous 10 minutes, and entering the average dispatching duration L (in seconds) of the dispatching time window at the current moment, wherein the specific logic is the sum of the orders (dispatching time of the orders-dispatching time to be dispatched) meeting the condition, and dividing the sum by the total number of the orders meeting the condition.
5.3 calculate V2, if V1<60 seconds, L divided by 15 rounded up, then multiplied by 0.1, then added 1; if V1> is 60 seconds, L is rounded up by dividing by 300, then multiplied by 0.1, and then 1.8.
5.4 multiplying V1 by the value of V2 compared to 1 gives a smaller value recorded as the grid pressure value.
(6) After the AM selects the responsible city and region, the pressure states under different dimensions are shown from the three dimensions of a merchant, a rider and an order, and according to the real-time situation of the pressure grid: the order quantity is large, the number of riders is small, and the transportation capacity needs to be allocated to the area to provide corresponding support; high pressure orders are more (not sent out for a long time), and proper riders need to be matched to receive orders, so that the AM is helped to better grasp real-time transport capacity, and the purpose of relieving regional pressure is achieved.
The embodiments described above are presented to enable a person having ordinary skill in the art to make and use the invention. It will be readily apparent to those skilled in the art that various modifications to the above-described embodiments may be made, and the generic principles defined herein may be applied to other embodiments without the use of inventive faculty. Therefore, the present invention is not limited to the above embodiments, and those skilled in the art should make improvements and modifications to the present invention based on the disclosure of the present invention within the protection scope of the present invention.
Claims (7)
1. A real-time pressure monitoring system for take-away orders, comprising: the system comprises a mobile client, an order center, a data center and a service platform; wherein: the mobile client is used for generating an order of a user and sending the order to an order center; the order center is used for receiving an order and writing the order into Mysql; the data center is used for monitoring logs in Mysql, packaging corresponding information into Kafka-type information and writing the Kafka-type information into the ScylaDB; the service platform performs gridding division on urban areas by reading order data in the ScylLADB for a period of time, counts high-pressure orders, high-pressure merchants and rider loads in each grid block through the order data, calculates grid pressure values, and then sends the information to an operation manager and a user mobile client for display.
2. The real-time pressure monitoring system of claim 1, wherein: the order center is composed of Java EE server clusters and is responsible for order warehousing.
3. The real-time pressure monitoring system of claim 1, wherein: the data center comprises a DTS cluster, a Storm cluster, a Kafka cluster, a Spark Streaming cluster and a ScylladB cluster; wherein: the DTS cluster monitors the change of the Binlog in Mysql by a DTS technology; the Storm cluster is used for monitoring DTS messages in the DTS cluster, and further encapsulating the DTS messages into Kafka messages to be written into the Kafka cluster; the Spark Streaming cluster is used to read Kafka messages from Kafka clusters and write them in overlay mode into the ScyllaDB cluster.
4. The real-time pressure monitoring system of claim 3, wherein: the DTS message records a database name, a table name and a value of a data line in the table, the Storm cluster extracts the line record in the order table according to the configuration of the database name and the table name, and then writes the line record into the Kafka message of the corresponding subject.
5. The real-time pressure monitoring system of claim 1, wherein: the service platform carries out gridding division on an urban area according to the block size of 1km multiplied by 1km, and then the grid pressure value of each grid block is calculated according to the following flow:
(1) for any grid block, counting the number N of unpopulated orders entering a dispatching time window in the grid block, and further calculating to obtain
Is an upward rounding function;
(2) calculating the average dispatching time length L of the orders which are generated in the last 10 minutes in the grid block and enter the dispatching time window at the current moment;
(3) calculating V2: if V1<60 seconds, then
If V1 is greater than or equal to 60 seconds, then
(4) The result obtained by V1V 2 is compared with 1, and the smaller value is taken as the grid pressure value of the grid block.
6. The real-time pressure monitoring system of claim 1, wherein: the service platform endows each grid block with the same color and transparency, namely, the grid pressure value is divided into four intervals: [0, 0.25], (0.25, 0.5], (0.5, 0.75], (0.75, 1], the transparency has four grades and sequentially corresponds to the four intervals from shallow to deep, and the transparency corresponding to the grid block is determined according to the interval where the grid pressure value is located.
7. The real-time pressure monitoring system of claim 1, wherein: the mobile client is provided with a corresponding APP, and the APP is an application program which is constructed by adopting a real Native technology, namely based on Javascript and React and adopting a consistent development technology and runs on an iOS platform and an Android platform.
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| CN110020925A (en) * | 2019-04-15 | 2019-07-16 | 北京闪送科技有限公司 | Order processing method, apparatus, equipment and storage medium |
| CN112651681A (en) * | 2019-10-12 | 2021-04-13 | 沈思远 | Method and system for realizing management and screening of takeout orders by users based on computer equipment |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104851006A (en) * | 2015-05-21 | 2015-08-19 | 北京京东尚科信息技术有限公司 | Dispersing scope determination method and apparatus |
| CN105719109A (en) * | 2015-05-22 | 2016-06-29 | 北京小度信息科技有限公司 | Order monitoring method and device |
| CN106327287A (en) * | 2016-08-09 | 2017-01-11 | 北京创锐文化传媒有限公司 | Order processing method and device |
| CN106447470A (en) * | 2016-11-30 | 2017-02-22 | 北京小度信息科技有限公司 | Delivery order distribution method and delivery order distribution device |
| CN107092974A (en) * | 2016-11-29 | 2017-08-25 | 北京小度信息科技有限公司 | Dispense pressure prediction method and device |
| CN107093123A (en) * | 2017-01-12 | 2017-08-25 | 北京小度信息科技有限公司 | Data processing method and device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9708556B2 (en) * | 2013-04-12 | 2017-07-18 | Elwha Llc | Systems, methods, and apparatuses related to the use of gas clathrates |
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Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104851006A (en) * | 2015-05-21 | 2015-08-19 | 北京京东尚科信息技术有限公司 | Dispersing scope determination method and apparatus |
| CN105719109A (en) * | 2015-05-22 | 2016-06-29 | 北京小度信息科技有限公司 | Order monitoring method and device |
| CN106327287A (en) * | 2016-08-09 | 2017-01-11 | 北京创锐文化传媒有限公司 | Order processing method and device |
| CN107092974A (en) * | 2016-11-29 | 2017-08-25 | 北京小度信息科技有限公司 | Dispense pressure prediction method and device |
| CN106447470A (en) * | 2016-11-30 | 2017-02-22 | 北京小度信息科技有限公司 | Delivery order distribution method and delivery order distribution device |
| CN107093123A (en) * | 2017-01-12 | 2017-08-25 | 北京小度信息科技有限公司 | Data processing method and device |
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