Logistics order real-time business volume monitoring method and device

Abstract

The invention relates to the technical field of logistics order monitoring, and discloses a logistics order real-time business volume monitoring method. Comprising the steps of collecting order information data in real time, and meanwhile, collecting logistics transportation related basic data; obtaining operation demand information and management key information of a logistics enterprise, and setting business volume monitoring index information according to the operation demand information and the management key information; processing and analyzing the order information data collected in real time according to the set business volume monitoring index information through a big data processing technology, and generating analysis result information; generating visual chart information according to the analysis result information; the method has the advantages that the order business volume condition is accurately mastered in real time, abnormal fluctuation of the business volume is found in time and effective response is made, so that the logistics operation efficiency and the service quality are improved, and the competitiveness of enterprises in the market is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics order monitoring, and in particular to a method and device for real-time business volume monitoring of logistics orders. Background Art

[0002] The number of logistics orders is huge and constantly changing. Accurate monitoring of the real-time business volume of logistics orders is extremely critical for logistics companies to rationally allocate resources, optimize transportation plans, improve service quality and reduce costs. Traditional logistics order business volume monitoring methods often have problems such as untimely data updates, limited monitoring scope, and insufficient analysis accuracy, which are difficult to meet the increasingly complex and efficient logistics operation needs. For example, some logistics companies only rely on manual regular statistics of order data, which not only consumes a lot of manpower and time, but also is prone to errors and omissions in the process of data collection and collation, resulting in inaccurate judgment of business volume, which in turn affects subsequent logistics decisions. In addition, although some existing monitoring systems can achieve a certain degree of automated data collection, they lack effective algorithms and models in data processing and analysis, and cannot deeply explore the business rules and trends behind the order data, making it difficult to provide forward-looking and targeted decision support.

[0003] Therefore, a method and device for real-time business volume monitoring of logistics orders are provided to solve the above problems. Summary of the invention

[0004] The main purpose of the present invention is to solve the problem that although the monitoring system in the prior art can achieve a certain degree of automated data collection, it lacks effective algorithms and models in data processing and analysis, cannot deeply explore the business rules and trends behind the order data, and is difficult to provide forward-looking and targeted decision support.

[0005] A first aspect of the present invention provides a method for monitoring the real-time business volume of logistics orders, the method comprising: Collect order information data in real time, and collect basic data related to logistics and transportation; Obtain the operational demand information and management key information of the logistics enterprise, and set the business volume monitoring indicator information based on the operational demand information and management key information; Through big data processing technology, the order information data collected in real time is processed and analyzed according to the set business volume monitoring indicator information to generate analysis result information; Generate visual chart information based on the analysis result information.

[0006] Furthermore, the real-time collection of order information data and the collection of basic data related to logistics and transportation include: Collect order information data in real time, including order generation time information, order shipping location information, order delivery location information, order weight information, order volume information and order commodity type information; At the same time, basic data related to logistics and transportation is collected, and the basic data related to logistics includes transportation vehicle location information, warehouse inventory information and logistics personnel work status information; Generate interactive pages based on order information data and basic data related to logistics and transportation.

[0007] Furthermore, the order information data collected in real time is processed and analyzed by big data processing technology according to the set business volume monitoring indicator information, and the analysis result information generated includes: The DBSCAN density clustering algorithm is used to cluster the order delivery locations and delivery locations, and the concentration and distribution patterns of order business volumes in different regions are analyzed and generated; The Apriori association rule algorithm is used to mine the relationship between order product types and order weight and volume; Determine the intrinsic relationship between product combinations and logistics resource requirements through frequent item set mining; The order generation time series is analyzed through the ARIMA autoregressive moving average model to predict the short-term and medium-term trends of order business volume.

[0008] Furthermore, generating an interactive page based on order information data and logistics and transportation related basic data specifically includes: Generate a business volume interactive interface based on order information data and basic data related to logistics and transportation; A real-time business volume display page is obtained, wherein the real-time business volume display page includes achievement rate information, actual achievement information, and expected achievement information.

[0009] Furthermore, it also includes: obtaining an interactive page for collection volume details, wherein the interactive page for collection volume details includes an organization selection unit, a date selection unit, a collection volume trend chart unit, a sequence ranking unit and a details list unit.

[0010] Furthermore, it also includes: obtaining a delivery volume details interactive page, wherein the delivery volume details interactive page includes an agency selection unit, a date selection unit, a delivery volume trend chart unit, a sequence ranking unit and a details list unit.

[0011] Furthermore, it also includes: pushing visualization chart information to the client at preset time intervals, and the visualization chart information includes data content from the current time to the previous 7 days.

[0012] A second aspect of the present invention provides a real-time business volume monitoring device for logistics orders, comprising: Data collection module, used to collect order information data in real time, and also collect basic data related to logistics and transportation; An indicator information setting unit is used to obtain the operation demand information and management key information of the logistics enterprise, and to set the business volume monitoring indicator information according to the operation demand information and management key information; A processing and analysis unit is used to process and analyze the order information data collected in real time according to the set business volume monitoring indicator information through big data processing technology, and generate analysis result information; The visualization chart generating unit is used to generate visualization chart information according to the analysis result information.

[0013] A third aspect of the present invention provides an electronic device, the electronic device comprising a memory and at least one processor, the memory storing instructions; The at least one processor calls the instructions in the memory so that the electronic device executes each step of the method for real-time business volume monitoring of logistics orders as described above.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed by a processor, implement the various steps of the method for real-time business volume monitoring of logistics orders as described above.

[0015] The present invention sets a series of business volume monitoring indicators according to the operational needs and management priorities of logistics companies, such as the total number of orders, the distribution of order volume in different regions, the proportion of order volume of different commodity types, the order weight / volume distribution, and the order processing time distribution; uses big data processing technology to process and analyze the collected real-time order data according to the set business volume indicators; can grasp the order business volume in real time and accurately, promptly discover abnormal fluctuations in business volume and make effective responses, thereby improving logistics operation efficiency and service quality, and enhancing the competitiveness of enterprises in the market. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a method for real-time business volume monitoring of logistics orders provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a real-time logistics order business volume monitoring device provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] An embodiment of the present invention provides a real-time business volume monitoring method for logistics orders, including real-time collection of order information data, and at the same time collecting basic data related to logistics transportation; obtaining operational demand information and management focus information of the logistics enterprise, and setting business volume monitoring indicator information according to the operational demand information and management focus information; processing and analyzing the order information data collected in real time according to the set business volume monitoring indicator information through big data processing technology to generate analysis result information; generating visual chart information according to the analysis result information. The main purpose of the present invention is to solve the problem that although the monitoring system in the prior art can achieve a certain degree of automated data collection, it lacks effective algorithms and models in data processing and analysis, cannot deeply explore the business laws and trends behind the order data, and is difficult to provide forward-looking and targeted decision support.

[0018] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 The first embodiment of the method for real-time business volume monitoring of logistics orders provided in this embodiment includes: Collect order information data in real time, and collect basic data related to logistics and transportation; Specifically, they include: Collect order information data in real time, including order generation time information, order shipping location information, order delivery location information, order weight information, order volume information and order commodity type information; At the same time, basic data related to logistics and transportation is collected, and the basic data related to logistics includes transportation vehicle location information, warehouse inventory information and logistics personnel work status information; Generate interactive pages based on order information data and basic data related to logistics and transportation; Obtain the operational demand information and management key information of the logistics enterprise, and set the business volume monitoring indicator information based on the operational demand information and management key information; Through big data processing technology, the order information data collected in real time is processed and analyzed according to the set business volume monitoring indicator information to generate analysis result information; Specifically, they include: The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) density clustering algorithm is used to cluster the order delivery locations and delivery locations, and analyze the concentration and distribution patterns of order business volume in different regions. The algorithm can automatically identify high-density and low-density areas in the data, effectively handle irregularly shaped clusters, and has good robustness to noisy data. The Apriori (Autoregressive Integrated Moving Average) association rule algorithm is used to mine the relationship between order product types and order weight and volume, so as to better allocate transportation resources; Determine the intrinsic relationship between product combinations and logistics resource requirements through frequent item set mining; The order generation time series is analyzed through the ARIMA autoregressive moving average model to predict the short-term and medium-term trends of order business volume; this model has good performance and high prediction accuracy in processing the linear relationship of time series data; Generate visual chart information based on the analysis result information.

[0020] See also Figure 1 The second embodiment of the method for real-time business volume monitoring of logistics orders provided in this embodiment includes: Collect order information data in real time, and collect basic data related to logistics and transportation; Specifically, they include: Collect order information data in real time, including order generation time information, order shipping location information, order delivery location information, order weight information, order volume information and order commodity type information; At the same time, basic data related to logistics and transportation is collected, and the basic data related to logistics includes transportation vehicle location information, warehouse inventory information and logistics personnel work status information; Generate interactive pages based on order information data and basic data related to logistics and transportation; Obtain the operational demand information and management key information of the logistics enterprise, and set the business volume monitoring indicator information based on the operational demand information and management key information; Through big data processing technology, the order information data collected in real time is processed and analyzed according to the set business volume monitoring indicator information to generate analysis result information; Specifically, they include: The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) density clustering algorithm is used to cluster the order delivery locations and delivery locations, and analyze the concentration and distribution patterns of order business volume in different regions. The algorithm can automatically identify high-density and low-density areas in the data, effectively handle irregularly shaped clusters, and has good robustness to noisy data. The Apriori (Autoregressive Integrated Moving Average) association rule algorithm is used to mine the relationship between order product types and order weight and volume, so as to better allocate transportation resources; Determine the intrinsic relationship between product combinations and logistics resource requirements through frequent item set mining; The order generation time series is analyzed through the ARIMA autoregressive moving average model to predict the short-term and medium-term trends of order business volume; this model has good performance and high prediction accuracy in processing the linear relationship of time series data; Generate visual chart information based on the analysis result information.

[0021] It also includes: pushing visual chart information to the client at preset time intervals, wherein the visual chart information includes data content from the current time to the previous 7 days. For example, the visual chart information is pushed to the client every 1 hour, and the 7-day data is recalculated each time to push the latest data for 7 days.

[0022] See also Figure 1 The third embodiment of the method for real-time business volume monitoring of logistics orders provided in this embodiment includes: Collect order information data in real time, and collect basic data related to logistics and transportation; Specifically, they include: Collect order information data in real time, including order generation time information, order shipping location information, order delivery location information, order weight information, order volume information and order commodity type information; At the same time, basic data related to logistics and transportation is collected, and the basic data related to logistics includes transportation vehicle location information, warehouse inventory information and logistics personnel work status information; Generate an interactive page based on order information data and basic data related to logistics and transportation; obtain a real-time business volume display page, which includes achievement rate information, actual achievement information and expected achievement information.

[0023] It also includes: obtaining a collection volume details interactive page, wherein the collection volume details interactive page includes an institution selection unit, a date selection unit, a trend chart unit, a sequence ranking unit, and a details list unit; It also includes: obtaining a delivery volume details interactive page, wherein the delivery volume details interactive page includes an agency selection unit, a date selection unit, a trend chart unit, a sequence ranking unit, and a details list unit; Obtain the operational demand information and management key information of the logistics enterprise, and set the business volume monitoring indicator information based on the operational demand information and management key information; Through big data processing technology, the order information data collected in real time is processed and analyzed according to the set business volume monitoring indicator information to generate analysis result information; Specifically, they include: The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) density clustering algorithm is used to cluster the order delivery locations and delivery locations, and analyze the concentration and distribution patterns of order business volume in different regions. The algorithm can automatically identify high-density and low-density areas in the data, effectively handle irregularly shaped clusters, and has good robustness to noisy data. The Apriori (Autoregressive Integrated Moving Average) association rule algorithm is used to mine the relationship between order product types and order weight and volume, so as to better allocate transportation resources; Determine the intrinsic relationship between product combinations and logistics resource requirements through frequent item set mining; The order generation time series is analyzed through the ARIMA autoregressive moving average model to predict the short-term and medium-term trends of order business volume; this model has good performance and high prediction accuracy in processing the linear relationship of time series data; Generate visual chart information based on the analysis result information.

[0024] It also includes: pushing visual chart information to the client at preset time intervals, wherein the visual chart information includes data content from the current time to the previous 7 days. For example, the visual chart information is pushed to the client every 1 hour, and the 7-day data is recalculated each time to push the latest data for 7 days.

[0025] See also Figure 1 The fourth embodiment of the method for real-time business volume monitoring of logistics orders provided in this embodiment includes: Collect order information data in real time, and collect basic data related to logistics and transportation; Specifically, they include: Collect order information data in real time, including order generation time information, order shipping location information, order delivery location information, order weight information, order volume information and order commodity type information; At the same time, basic data related to logistics and transportation is collected, and the basic data related to logistics includes transportation vehicle location information, warehouse inventory information and logistics personnel work status information; Generate an interactive page based on order information data and basic data related to logistics and transportation; obtain a real-time business volume display page, which includes achievement rate information, actual achievement information and information to be achieved. The achievement rate information is in percentage format with 2 decimal places; the unit of the actual achievement information is the actual business volume value of the current login role architecture, and the information to be achieved is the business volume value that should be achieved by the current login role architecture.

[0026] It also includes: obtaining a collection volume details interactive page, wherein the collection volume details interactive page includes an institution selection unit, a date selection unit, a trend chart unit, a sequence ranking unit, and a details list unit; The organization selection unit is used to select a site. The site displays the data permissions of the current user role. The permissions of the current user role are displayed by default. The headquarters can view the data of the entire network and all regions, the region can view the data of the region and its subordinate sites, the province can view the data of the province and its subordinate sites, and the distribution can view the distribution and subordinate outlets data; headquarters-region-province-distribution (first level)-first level outlet, the lowest query is the first level outlet; Note: the lowest query of data is the first level outlet; The date selection unit is used to display and select dates. The date displays today's date by default, showing month and day. Click to enter the date selection item, and you can choose yesterday, this month (the natural month where the current date is located, counting from the 1st to yesterday. If today is the 1st, press to return to today's date), and last month (from the 1st of last month to the end of last month). The custom option allows you to select the date end, but the date difference cannot exceed 30 days. When returning to the upper menu interface, the date display format is 'month day-month day'. For example, if you select yesterday, it will display 12 / 26-12 / 26.

[0027] The trend chart unit is used to display a trend chart, which includes the volume of parcels collected and yesterday's value. The volume of parcels collected is the statistics of the volume of parcels collected at the current query time, which can be queried by day or time period; yesterday's value is to query yesterday's data by the current day. If historical data is queried, yesterday's value will not be modified; trend chart: query by day, display the data of the past 7 days, and click on the pop-up box of a single day to display the business volume; if the time period is queried, the time period data is displayed, and a maximum of 1 month of data is displayed. For data queries greater than 7 days, the horizontal axis only displays the start time and the end time; and click on the pop-up box of a single day to display the business volume; The order ranking unit is used to arrange the detail page in natural order by default, supports ranking and order switching, and the ranking is sorted in the current positive order and displayed in a list; The horizontal table header of the detail list unit includes serial number, province, minimum value, target value, daily cargo volume, daily achievement rate, daily ranking, daily average cargo volume, monthly achievement rate and cumulative ranking in sequence; Provinces and regions: displayed by headquarters, regions, and provincial headquarters by regions; (1) The headquarters can click on a single region to view provincial data and all six regions can be clicked to query regional / provincial / distribution / outlet data; institutions in yellow font can be clicked, but institutions in black font cannot be clicked; (2) The district manager can view the data of the entire network, but can only click on the provincial / district / outlet data of the region to which he belongs; he can click on the yellow font of the institution, but not the black font of the institution; (3) Provincial headquarters can view the data of the entire network and can only click on the distribution / outlet data of the province to which they belong; (4) You can view the data of the distribution by clicking on the distribution and outlet data to which you belong; Minimum value: the value is taken from the report platform, retaining one decimal place; Target value: value from the report platform, retaining 1 decimal place; Daily cargo volume (T): value from the report platform, retaining 1 decimal place; Daily achievement rate: percentage format, retain 1 decimal place, value logic: achievement rate = actual achievement / should be achieved; value reporting platform; Today's ranking: value is an integer; Average daily cargo volume (T): value obtained from the report platform, retaining 1 decimal place; Monthly achievement rate: percentage format, retain 1 decimal place, value logic: achievement rate = actual achievement / should be achieved; value reporting platform; Cumulative ranking: the value is an integer; It also includes: obtaining a delivery volume details interactive page, wherein the delivery volume details interactive page includes an agency selection unit, a date selection unit, a trend chart unit, a sequence ranking unit, and a details list unit; The organization selection unit is used to select a site. The site displays the data permissions of the current user role. The permissions of the current user role are displayed by default. The headquarters can view the data of the entire network and all regions, the region can view the data of the region and its subordinate sites, the province can view the data of the province and its subordinate sites, and the distribution can view the distribution and subordinate outlets data; headquarters-region-province-distribution (first level)-first level outlet, the lowest query is the first level outlet; Note: the lowest query of data is the first level outlet; The date selection unit is used to display and select dates. The date displays today's date by default, showing month and day. Click to enter the date selection item, and you can choose yesterday, this month (the natural month where the current date is located, counting from the 1st to yesterday. If today is the 1st, press to return to today's date), and last month (from the 1st of last month to the end of last month). The custom option allows you to select the date end, but the date difference cannot exceed 30 days. When returning to the upper menu interface, the date display format is 'month day-month day'. For example, if you select yesterday, it will display 12 / 26-12 / 26.

[0028] The trend chart unit is used to display a trend chart, which includes the volume of parcels collected and yesterday's value. The volume of parcels collected is the statistics of the volume of parcels collected at the current query time, which can be queried by day or time period; yesterday's value is to query yesterday's data by the current day. If historical data is queried, yesterday's value will not be modified; trend chart: query by day, display the data of the past 7 days, and click on the pop-up box of a single day to display the business volume; if the time period is queried, the time period data is displayed, and a maximum of 1 month of data is displayed. For data queries greater than 7 days, the horizontal axis only displays the start time and the end time; and click on the pop-up box of a single day to display the business volume; The order ranking unit is used to arrange the detail page in natural order by default, supports ranking and order switching, and the ranking is sorted in the current positive order and displayed in a list; The horizontal table header of the detail list unit includes serial number, province, daily dispatched tickets, daily dispatched tonnage, monthly dispatched tickets and monthly dispatched tonnage in sequence; Provinces: headquarters, regions, and provincial headquarters are displayed by region. (1) The headquarters can click on a single region to view provincial data and all six regions can be clicked to query regional / provincial / distribution / outlet data; institutions in yellow font can be clicked, but institutions in black font cannot be clicked; (2) The district manager can view the data of the entire network, but can only click on the provincial / district / outlet data of the region to which he belongs; he can click on the yellow font of the institution, but not the black font of the institution; (3) Provincial headquarters can view the data of the entire network, and can only click on the distribution / outlet data of the province to which they belong; they can click on the yellow font of the institution, but not the black font of the institution; (4) For a distribution, you can view the data of the distribution. You can only click on the distribution and outlet data to which you belong. You can click on the yellow font of the institution, but not on the black font of the institution. Number of tickets to be distributed per day: the value is obtained from the report platform and the integer is retained; Daily tonnage to be dispatched: value from the report platform, retaining 1 decimal place; Monthly number of tickets to be distributed: the value is obtained from the report platform and the integer is retained; Monthly dispatchable tonnage: value from the report platform, retaining 1 decimal place; Obtain the operational demand information and management key information of the logistics enterprise, and set the business volume monitoring indicator information based on the operational demand information and management key information; Through big data processing technology, the order information data collected in real time is processed and analyzed according to the set business volume monitoring indicator information to generate analysis result information; Specifically, they include: The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) density clustering algorithm is used to cluster the order delivery locations and delivery locations, and analyze the concentration and distribution patterns of order business volume in different regions. The algorithm can automatically identify high-density and low-density areas in the data, effectively handle irregularly shaped clusters, and has good robustness to noisy data. The Apriori (Autoregressive Integrated Moving Average) association rule algorithm is used to mine the relationship between order product types and order weight and volume, so as to better allocate transportation resources; Determine the intrinsic relationship between product combinations and logistics resource requirements through frequent item set mining; The order generation time series is analyzed through the ARIMA autoregressive moving average model to predict the short-term and medium-term trends of order business volume; this model has good performance and high prediction accuracy in processing the linear relationship of time series data; Generate visual chart information based on the analysis result information.

[0029] It also includes: pushing visual chart information to the client at preset time intervals, wherein the visual chart information includes data content from the current time to the previous 7 days. For example, the visual chart information is pushed to the client every 1 hour, and the 7-day data is recalculated each time to push the latest data for 7 days.

[0030] The above describes the real-time business volume monitoring method for logistics orders in the embodiment of the present invention. The following describes the real-time business volume monitoring device for logistics orders in the embodiment of the present invention. Figure 2 The real-time business volume monitoring device for logistics orders in the embodiment of the present invention includes: The data collection module 201 is used to collect order information data in real time and collect basic data related to logistics and transportation; The indicator information setting unit 202 is used to obtain the operation demand information and management key information of the logistics enterprise, and set the business volume monitoring indicator information according to the operation demand information and management key information; The processing and analysis unit 203 is used to process and analyze the order information data collected in real time according to the set business volume monitoring indicator information through big data processing technology, and generate analysis result information; The visualization chart generating unit 204 is used to generate visualization chart information according to the analysis result information.

[0031] above Figure 2 The real-time business volume monitoring device for logistics orders in the embodiment of the present invention is described in detail from the perspective of modular functional entities, and the electronic device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0032] Figure 3 7 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device 700 may have relatively large differences due to different configurations or performances, and may include one or more processors 710 (for example, one or more processors) and a memory 720, and one or more storage media 730 (for example, one or more storage devices, including RAM\FLASH, etc.) storing application programs 733 or data 732. Among them, the memory 720 and the storage medium 730 may be temporary storage or permanent storage. The program stored in the storage medium 730 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the electronic device 700. Furthermore, the processor 710 may be configured to communicate with the storage medium 730 to execute a series of instruction operations in the storage medium 730 on the electronic device 700.

[0033] The electronic device 700 may also include one or more power supplies 740, one or more input / output interfaces 750, and / or one or more operating systems 731, such as FreeRTOS, Android, etc. It will be appreciated by those skilled in the art that Figure 3 The structure of the electronic device shown does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine some components, or arrange the components differently.

[0034] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of a method for real-time business volume monitoring of logistics orders.

[0035] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0036] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, mobile device, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0037] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for real-time business volume monitoring of logistics orders, characterized in that: The logistics order real-time business volume monitoring method comprises: Collect order information data in real time, and collect basic data related to logistics and transportation; Obtain the operational demand information and management key information of the logistics enterprise, and set the business volume monitoring indicator information based on the operational demand information and management key information; Through big data processing technology, the order information data collected in real time is processed and analyzed according to the set business volume monitoring indicator information to generate analysis result information; Generate visual chart information based on the analysis result information.

2. The method for real-time business volume monitoring of logistics orders according to claim 1 is characterized in that: The real-time collection of order information data and the collection of basic data related to logistics and transportation include: Collect order information data in real time, including order generation time information, order shipping location information, order delivery location information, order weight information, order volume information and order commodity type information; At the same time, basic data related to logistics and transportation is collected, and the basic data related to logistics includes transportation vehicle location information, warehouse inventory information and logistics personnel work status information; Generate interactive pages based on order information data and basic data related to logistics and transportation.

3. The method for real-time business volume monitoring of logistics orders according to claim 2 is characterized in that: The order information data collected in real time is processed and analyzed by the big data processing technology according to the set business volume monitoring indicator information, and the analysis result information generated includes: The DBSCAN density clustering algorithm is used to cluster the order delivery locations and delivery locations, and the concentration and distribution patterns of order business volumes in different regions are analyzed and generated; The Apriori association rule algorithm is used to mine the relationship between order product types and order weight and volume; Determine the intrinsic relationship between product combinations and logistics resource requirements through frequent item set mining; The order generation time series is analyzed through the ARIMA autoregressive moving average model to predict the short-term and medium-term trends of order business volume.

4. The method for real-time business volume monitoring of logistics orders according to claim 2 is characterized in that: The generating of the interactive page according to the order information data and the basic data related to logistics and transportation specifically includes: Generate a business volume interactive interface based on order information data and basic data related to logistics and transportation; A real-time business volume display page is obtained, wherein the real-time business volume display page includes achievement rate information, actual achievement information, and expected achievement information.

5. The method for real-time business volume monitoring of logistics orders according to claim 4 is characterized in that: It also includes: obtaining a collection volume details interactive page, the collection volume details interactive page including an agency selection unit, a date selection unit, a collection volume trend chart unit, a sequence ranking unit and a details list unit.

6. The method for real-time business volume monitoring of logistics orders according to claim 4 is characterized in that: It also includes an interactive page for obtaining delivery volume details, and the delivery volume details interactive page includes an agency selection unit, a date selection unit, a delivery volume trend chart unit, a sequence ranking unit and a details list unit.

7. The method for real-time business volume monitoring of logistics orders according to claim 1, characterized in that: Also includes: The visualization chart information is pushed to the client at preset time intervals, and the visualization chart information includes data content from the current time to the previous 7 days.

8. A real-time business volume monitoring device for logistics orders, characterized in that: include: Data collection module, used to collect order information data in real time, and also collect basic data related to logistics and transportation; An indicator information setting unit is used to obtain the operation demand information and management key information of the logistics enterprise, and to set the business volume monitoring indicator information according to the operation demand information and management key information; A processing and analysis unit is used to process and analyze the order information data collected in real time according to the set business volume monitoring indicator information through big data processing technology, and generate analysis result information; The visualization chart generating unit is used to generate visualization chart information according to the analysis result information.

9. An electronic device, comprising a memory and at least one processor, wherein the memory stores instructions and data; The at least one processor calls the instructions and data in the memory so that the electronic device executes each step of the real-time business volume monitoring method for logistics orders as described in any one of claims 1-7.

10. A readable storage medium having instructions and data stored thereon, characterized in that: When the instructions are executed by the processor, the various steps of the method for real-time business volume monitoring of logistics orders as described in any one of claims 1-7 are implemented.

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