Method, device, equipment and storage medium for generating delivery time portrait

Abstract

A method, device, equipment and storage medium for generating a time portrait of express delivery, wherein the generation method includes the steps of: acquiring and storing express delivery business data information, the express delivery business data information includes data information of each operation link from the generation of express delivery order to the end of express delivery; extracting time information from the express delivery business data information; performing correlation analysis and processing on the time information to generate a time portrait of express delivery. Through the time portrait of express delivery, the number of express parcels can be predicted, and intelligent scheduling and route planning of express delivery can be further realized; through the time portrait of express delivery, different shopping scenarios, shopping motivations, shopping experiences and shopping feedback related to consumer shopping time can be abstracted, and then the overall picture of consumer shopping time can be abstracted, and further express delivery can be realized. More comprehensive and intelligent services for shopping malls, merchants, consumers, e-commerce platforms, outlets, and couriers can be provided, and the efficiency of express delivery services can be improved, and the consumer experience can be improved.

Description

Technical Field

[0001] The present invention relates to the field of express delivery technology, and in particular to a method, a generating device, a device and a storage medium for generating an express delivery time portrait. Background Art

[0002] The rise of the express delivery industry has greatly facilitated online shopping for users. With the development of the express delivery industry, the following problems still exist in the industry:

[0003] 1. The business volume of the express delivery industry will be affected by the user's shopping frequency, shopping habits and holiday promotions. However, at present, there is no use of express delivery data information to mine consumers' shopping frequency and shopping habits, and it is impossible to analyze consumers through express delivery business insights;

[0004] 2. Consumers have personalized service requirements for pick-up time, delivery time, and delivery time, but the current express delivery industry's service improvement is limited and it is difficult to meet personalized service requirements;

[0005] 3. The operation of the express delivery industry is greatly affected by e-commerce activities, and the express delivery operation planning is relatively passive. Summary of the invention

[0006] In response to the defects existing in the express delivery industry, the present application provides a method, a generating device, a equipment and a storage medium for an express delivery time portrait. Through the express delivery time portrait, different shopping scenarios, shopping motivations, shopping experiences and shopping feedback related to consumers' shopping time can be abstracted, thereby abstracting the overall picture of consumers' shopping time.

[0007] The technical solution of the present invention is as follows:

[0008] The present invention provides a method for generating a delivery time portrait, comprising the steps of:

[0009] Acquire and store express delivery service data information, wherein the express delivery service data information includes data information of each operation link from express order generation to express delivery completion;

[0010] Extracting time information from the express service data information;

[0011] The time information is subjected to correlation analysis and processing to generate a delivery time portrait.

[0012] Further preferably, the obtaining and storing of express service data information specifically comprises the steps of:

[0013] Collect data information of each operation link in express delivery business;

[0014] Parsing the data information through a log analyzer, and converting the parsed data information into structured data;

[0015] The structured data is stored in a Kafka message queue.

[0016] Further preferably, before converting the parsed data information into structured data, the step of desensitizing the parsed data information is also included.

[0017] Further preferably, after parsing the data information, it is also included to determine whether the time information in the parsed data information is abnormal. If it is abnormal, it further includes correcting the abnormal time information.

[0018] Further preferably, the performing correlation analysis on the time information to generate a delivery time portrait comprises the steps of:

[0019] Matching the time information with preset time attribute information;

[0020] Create a delivery time profile with matching time information and preset time attribute information.

[0021] Further preferably, the preset time attribute information includes e-commerce holidays, Gregorian calendar holidays, lunar calendar holidays, custom holidays, and express delivery cycles.

[0022] Further preferably, the process of extracting the time information from the express business data information also includes extracting the ID information from the express business data information. Further, after generating the express time portrait, it also includes associating and mapping the ID information with the express time portrait.

[0023] The present invention also provides a device for generating a delivery time portrait, comprising:

[0024] An acquisition module is used to acquire and store express delivery service data information, wherein the express delivery service data information includes data information of each operation link from the generation of express delivery order to the completion of express delivery;

[0025] An extraction module, used to extract time information from the express service data information;

[0026] A generation module is used to perform correlation analysis on the time information to generate a delivery time portrait.

[0027] The present invention also provides an express delivery time portrait terminal device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the above-mentioned method for generating the express delivery time portrait.

[0028] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for generating the express delivery time portrait is implemented.

[0029] The method, device, equipment and storage medium for generating a delivery time profile provided by the present invention have at least the following advantages:

[0030] 1) Through the express delivery time portrait, the number of express parcels can be predicted, and further express delivery intelligent scheduling and express delivery route planning can be realized.

[0031] 2) Through the express delivery time portrait, we can abstract the different shopping scenarios, shopping motivations, shopping experiences, and shopping feedback related to consumers' shopping time, and then abstract the overall picture of consumers' shopping time, and further realize more comprehensive and intelligent express delivery services for shopping malls, merchants, consumers, e-commerce platforms, outlets, and couriers.

[0032] 3) Improve the efficiency of express delivery services and enhance consumer experience.

[0033] 4) Provide data support for personalized express delivery products. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flow chart for express delivery;

[0035] Figure 2 A flow chart of the method for generating a time profile for express delivery;

[0036] Figure 3 Schematic diagram of the device for generating the express time portrait;

[0037] Figure 4 Schematic diagram of the express time portrait terminal equipment. DETAILED DESCRIPTION

[0038] The present invention will be further described in detail below through specific implementation modes in conjunction with the accompanying drawings.

[0039] In the era of big data, user information is flooding the Internet. Each specific information of the user is abstracted into a label, and these labels are used to concretize the user image, so as to provide users with targeted services. This is user portrait, and user portrait has been widely used in various fields. Looking at the basic characteristics of the express delivery industry, a complete express delivery process diagram is as follows: Figure 1As shown, no matter it is the collection link or the delivery link, each operation link in the whole process of express delivery has a specific operation time. Based on the concept of user portrait, this application creatively proposes the concept of express delivery time portrait, that is, the time information of each operation link of express delivery is abstracted into a label, and these labels are used to generate express delivery time portrait. The express delivery time portrait can solve the current problems in the express delivery industry. For example, through the express delivery time portrait, the number of express delivery items can be predicted, and the intelligent scheduling and express delivery route planning of express delivery can be further realized; the express delivery time portrait can also enhance the personalized express delivery product services of consumers, for example, meet the personalized service requirements of consumers for the collection time and the pickup time; the express delivery time portrait can also abstract different shopping scenes, shopping motivations, shopping experiences, and shopping feedback related to the consumer's shopping time, and then abstract the overall picture of the consumer's shopping time, and further realize the express delivery of more comprehensive and intelligent services to shopping malls, merchants, consumers, e-commerce platforms, outlets, and couriers. The specific implementation scheme of the express delivery time portrait proposed in this application is described below.

[0040] Embodiment 1:

[0041] This embodiment provides a method for generating a delivery time portrait, and its flow chart is as follows: Figure 2 As shown, the specific steps include the following steps.

[0042] S100: Acquire and store express delivery service data information.

[0043] Among them, express delivery business data information includes data information of each operation link from express order generation to express delivery completion; please continue to refer to Figure 1 In the express order generation link, the data information generated in this link includes, for example, the basic information of the sender, the basic information of the recipient, and the express order number; in the business outlet link, the data information generated in this link includes, for example, the basic information of the business outlet and the operation time information; and so on, corresponding data information is generated in each link of express delivery.

[0044] Each link of express delivery is equipped with corresponding operating equipment (such as a barcode scanner), through which the data information generated by the corresponding link is collected. Then the operating equipment sends the collected data to the background management system (such as a management platform) through the Flume data channel or the Kafka data channel. The background management system is used to collect data information from each link of express delivery so as to uniformly manage the express delivery business data information.

[0045] Further, obtaining and storing the express delivery service data information specifically includes the following steps:

[0046] S101: Obtain data information of each operation link in the express delivery business.

[0047] Specifically, based on the above-mentioned background management system to obtain express business data information, the background management system will collect data information of each link and manage and store it through association mapping. For example, the data information generated by each link from the generation of an order to the end of delivery of a certain express is associated and mapped and stored according to the express order number, so that all data information associated with the express order number can be retrieved later according to the express order number. This association mapping storage method greatly facilitates the subsequent viewing or search for required data information.

[0048] S102: Parse the data information and convert the parsed data information into structured data.

[0049] Since different express delivery links generate different data information, and different operating devices may generate different data formats, in order to facilitate the subsequent extraction of time information, this step parses the acquired data information and converts the parsed data information into structured data.

[0050] Structured data, also known as row data, is data that is logically expressed and implemented by a two-dimensional table structure. It strictly follows the data format and length specifications and is mainly stored and managed through a relational database. The general characteristics of structured data are: data is in rows, a row of data represents the information of an entity, and the attributes of each row of data are the same. It can be represented by data or a unified structure, such as numbers and symbols. The traditional relational data model and row data are stored in a database and can be represented by a two-dimensional table structure. Therefore, structured data is very convenient to query, which further greatly facilitates the extraction operation in step S200.

[0051] Since the express delivery business data information contains sensitive information of the recipient and / or sender (such as gender), further, before converting the parsed data information into structured data, it also includes the step of desensitizing the parsed data information. The sensitive information is anonymized by data desensitization technology to achieve the effect of protecting sensitive information. Among them, data desensitization technology is well known to those skilled in the art. For example, data desensitization is performed in an alternative manner, that is, the sensitive data in the source data is completely replaced by disguised data. Generally, the replacement data is irreversible to ensure safety. The specific process of data desensitization is not described in detail in this embodiment.

[0052] Since the present application is to realize the time profiling of express delivery, it is necessary to ensure that the time information extracted in step S200 is the correct time information. In actual applications, in various links of express delivery, there may be abnormal situations such as incorrect or missing time information generated by the operating equipment due to failure of the operating equipment. Therefore, after parsing the data information, it also includes judging whether the time information in the parsed data information is abnormal. If abnormal, it further includes correcting the abnormal time information.

[0053] Based on the characteristic of the express delivery process that the operation time sequence is chronological, this implementation corrects the abnormal time information through the chronological sequence of the express delivery links. For example, when the time information is missing in the data information fed back by a certain express delivery link, the time information of the express delivery link is determined through the previous link and the next link of the express delivery link. For another example, when it is determined that the time information fed back by a certain express delivery link obviously does not match the time information fed back by the previous link and the next link (for example, it is earlier than the time information of the previous link, or later than the time information of the next link), the time information of the express delivery link can also be corrected through the previous link and the next link of the express delivery link.

[0054] S103: Storing the structured data in the Kafka message queue.

[0055] Kafka is a distributed messaging system developed in the Scala language that is horizontally scalable and has high throughput.

[0056] S200: extracting time information from express delivery service data information.

[0057] By reading the structured data in the Kafka message queue, the time information in the structured data is further extracted.

[0058] S300: Perform correlation analysis on the time information to generate a delivery time portrait.

[0059] Perform correlation analysis on the time information to generate a delivery time profile, which includes the following steps:

[0060] S301: Match the time information with preset time attribute information.

[0061] Among them, the preset time attribute information includes e-commerce holidays, solar calendar holidays, lunar calendar holidays, custom holidays, express delivery cycles, etc. The specific preset time attribute information is described below.

[0062] E-commerce festivals, the E-commerce Festival was originally Singles' Day. Because of the various "Singles' Day" promotions held by well-known e-commerce companies, it was nicknamed "E-commerce Festival" by netizens. E-commerce festivals are festivals for e-commerce promotions to drive consumer activities. Currently, e-commerce festivals include: Double Eleven, Double Twelve, 618 (JD.com), 818 (Suning), the start of school season, graduation season, spring new products, autumn new products, autumn and winter new products, etc.

[0063] Gregorian holidays refer to holidays corresponding to the Gregorian calendar, for example, New Year's Day, Valentine's Day, April Fools' Day, Labor Day, Mother's Day, Father's Day, etc.

[0064] Lunar festivals refer to festivals corresponding to the lunar calendar, for example, the Spring Festival, Lantern Festival, Dragon Boat Festival, Mid-Autumn Festival, etc.

[0065] Customized holidays are holidays defined based on natural time and the special needs of consumers, including, for example, payday, repayment day, anniversary day, membership day, work and rest day, etc.

[0066] The express delivery cycle refers to the entire process of express delivery from order generation to the end of delivery. Therefore, the express delivery cycle is equal to the order generation time (order time) minus the delivery end time (receipt time).

[0067] The above-mentioned e-commerce holidays, solar calendar holidays, and lunar calendar holidays are obtained based on known holiday definitions, while the above-mentioned custom holidays and express delivery cycles are obtained through a large amount of data statistics. For example, the express delivery cycle can be obtained by statistically analyzing historical data through statistical methods such as mode and average.

[0068] It should be noted that before generating the express delivery time portrait, it is necessary to classify and store the above-mentioned preset time attribute information in the early stage. For example, e-commerce festivals are classified into one category, Gregorian calendar festivals are classified into one category, lunar calendar festivals are classified into one category, custom festivals are classified into one category, and express delivery cycles are classified into one category. When storing, the above-mentioned preset time attribute information can be stored in one database by category, or different types of preset time attribute information can be stored in different databases respectively.

[0069] Furthermore, the format of the time information is year-month-day hour-minute-second, for example: 2020-08-1409:00:00.

[0070] The rules for the correspondence between time information and preset time attribute information are as follows:

[0071] 2020-06-18 00:00:00→2020-06-19 00:00:00→→On June 18;

[0072] 2020-10-01 00:00:00→2020-10-02 00:00:00→→National Day;

[0073] 2020-10-01 00:00:00→2020-10-02 00:00:00→→Mid-Autumn Festival;

[0074] 2020-10-01 00:00:00→2020-10-09 00:00:00→→National Day holiday.

[0075] Therefore, the time information can be matched with the preset time attribute information through the above rules. Furthermore, in order to improve the matching accuracy, for example, the autumn new products and the school season in the e-commerce festival cannot be correctly associated and matched only through the time information. Therefore, the item information in the express business data information can be further analyzed, the item attributes can be analyzed, and the label of the time information can be further determined whether it is the autumn new products or the school season.

[0076] S302: Create a delivery time profile of the matching time information and preset time attribute information.

[0077] For example, a corresponding express delivery time profile is established based on the time information of 2019-06-18 15:24:36 and the matching e-commerce festival 618.

[0078] In order to expand the application scope of the express time portrait, for example, the consumer's shopping habits are abstracted through the express time portrait, and it further includes extracting ID information from the express business data information. Specifically, the ID information in the structured data is extracted (the ID information may be the consumer's online shopping account). After the express time portrait is generated, the ID information is associated and mapped with the corresponding express time portrait. Therefore, the consumer's shopping frequency, shopping habits and other information can be abstracted through the express time portrait. Then, the consumer's shopping habits, shopping preferences and other information can be recommended to the e-commerce platform through the ID information.

[0079] The delivery time profile generation method provided in this embodiment can bring the following effects:

[0080] 1) Through the express delivery time portrait, the number of express parcels can be predicted, and further express delivery intelligent scheduling and express delivery route planning can be realized.

[0081] 2) Through the express delivery time portrait, we can abstract the different shopping scenarios, shopping motivations, shopping experiences, and shopping feedback related to consumers' shopping time, and then abstract the overall picture of consumers' shopping time, and further realize more comprehensive and intelligent express delivery services for shopping malls, merchants, consumers, e-commerce platforms, outlets, and couriers.

[0082] 3) Improve services and enhance consumer experience. For example, consumers can estimate the arrival time of express delivery based on the delivery cycle.

[0083] 4) Provide data support for personalized express delivery products.

[0084] 5) The express delivery cycle can be used to predict the number of express deliveries in advance. Furthermore, staff and vehicles can be arranged in advance, and the shift schedule can also be optimized in advance.

[0085] 6) The overall delivery efficiency and service can be improved through express delivery time profiling.

[0086] Embodiment 2:

[0087] Based on the first embodiment, this embodiment provides a device for generating a delivery time portrait, and its principle diagram is as follows: Figure 3 As shown, it includes an acquisition module 100, an extraction module 200 and a generation module 300.

[0088] The acquisition module 100 is used to acquire and store express business data information, which includes data information of each operation link from express order generation to express delivery completion.

[0089] The acquisition module 100 includes an acquisition unit 101, a parsing unit 102, a correction unit 103 and a storage unit 104, wherein the acquisition unit 101 is used to acquire data information of each operation link in the express delivery business; the parsing unit 102 is used to parse the data information and convert the parsed data information into structured data; the correction unit 103 is used to determine whether the time information in the parsed data information is abnormal, and if abnormal, further includes correcting the abnormal time information; the storage unit 104 is used to store the structured data in the Kafka message queue. For structured data and Kafka message queues, please refer to the description in Example 1, which will not be repeated in this embodiment.

[0090] The extraction module 200 is used to extract time information from the express service data information.

[0091] The generation module 300 is used to perform correlation analysis on the time information and generate a delivery time portrait. Specifically, the generation module 300 includes a matching unit 301 and a generation unit 302, wherein the matching unit 301 is used to match the time information with the preset time attribute information; the generation unit 302 is used to establish a delivery time portrait of the matched time information and the preset time attribute information. For the preset time attribute information, please refer to the description in the first embodiment, which will not be repeated in this embodiment.

[0092] The delivery time profile generation device provided in this embodiment can bring the following effects:

[0093] 1) Through the express delivery time portrait, the number of express parcels can be predicted, and further express delivery intelligent scheduling and express delivery route planning can be realized.

[0094] 2) Through the express delivery time portrait, we can abstract the different shopping scenarios, shopping motivations, shopping experiences, and shopping feedback related to consumers' shopping time, and then abstract the overall picture of consumers' shopping time, and further realize more comprehensive and intelligent express delivery services for shopping malls, merchants, consumers, e-commerce platforms, outlets, and couriers.

[0095] 3) Improve services and enhance consumer experience. For example, consumers can estimate the arrival time of express delivery based on the delivery cycle.

[0096] 4) Provide data support for personalized express delivery products.

[0097] 5) The express delivery cycle can be used to predict the number of express deliveries in advance. Furthermore, staff and vehicles can be arranged in advance, and the shifts can be optimized in advance to improve delivery efficiency and service.

[0098] Embodiment three:

[0099] Based on the first and second embodiments, this embodiment provides a delivery time portrait terminal device, the schematic diagram of which is as follows: Figure 4 As shown, the terminal device 400 may be a tablet computer, a notebook computer or a desktop computer. The terminal device 400 may also be called a portable terminal, a laptop terminal, a desktop terminal or other names.

[0100] Typically, the terminal device 400 includes a processor 4001 and a memory 4002. The processor 4001 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 4001 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 4001 may also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in a standby state.

[0101] In some embodiments, the processor 4001 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 4001 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0102] The memory 4002 may include one or more computer-readable storage media, which may be non-transitory. The memory 4002 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 4002 is used to store at least one instruction, at least one program, code set or instruction set, which is used to be executed by the processor 4001 to implement the method for generating the express delivery time portrait provided in Example 1 of the present application.

[0103] Therefore, the device 400 of the present application executes the method for generating the delivery time portrait provided in the first embodiment through at least one instruction, at least one program, code set or instruction set, which has the following advantages:

[0104] 1) Through the express delivery time portrait, the number of express parcels can be predicted, and further express delivery intelligent scheduling and express delivery route planning can be realized.

[0105] 2) Through the express delivery time portrait, we can abstract the different shopping scenarios, shopping motivations, shopping experiences, and shopping feedback related to consumers' shopping time, and then abstract the overall picture of consumers' shopping time, and further realize more comprehensive and intelligent express delivery services for shopping malls, merchants, consumers, e-commerce platforms, outlets, and couriers.

[0106] 3) Improve services and enhance consumer experience. For example, consumers can estimate the arrival time of express delivery based on the delivery cycle.

[0107] 4) Provide data support for personalized express delivery products.

[0108] 5) The express delivery cycle can be used to predict the number of express deliveries in advance. Furthermore, staff and vehicles can be arranged in advance, and the shifts can be optimized in advance to improve delivery efficiency and service.

[0109] In some embodiments, the device 400 may further include: a peripheral device interface 4003 and at least one peripheral device. The processor 4001, the memory 4002 and the peripheral device interface 4003 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 4003 via a bus, a signal line or a circuit board.

[0110] Specifically in this embodiment, in order to realize the express delivery time portrait, the corresponding peripheral equipment includes a background management platform 4004 that interacts with information of each link of the express delivery. The background management platform 4004 can obtain data information of each link of the express delivery. Further, the processor 4001 can obtain the required express delivery business data information through the background management platform 4004.

[0111] 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. The computer-readable storage medium stores instructions, which, when executed on a computer, cause the computer to execute the method for generating a delivery time portrait in the first embodiment.

[0112] If the module in the second embodiment is implemented in the form of a software function module 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 for a computer device (which can be a personal computer, a server, or a 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 and other media that can store program codes.

[0113] The above specific examples are used to illustrate the present invention, which are only used to help understand the present invention and are not used to limit the present invention. For those skilled in the art to which the present invention belongs, some simple deductions, modifications or substitutions can be made according to the idea of ​​the present invention.

Claims

1. A method for generating a delivery time portrait, characterized in that: Includes steps: Acquire and store express delivery service data information, wherein the express delivery service data information includes data information of each operation link from express order generation to express delivery completion; Extracting time information from the express service data information; Performing correlation analysis on the time information to generate a delivery time profile; Based on the characteristics of the express delivery process with a time sequence of operations, the abnormal time information is corrected through the time sequence of the express delivery links. When the time information is missing in the data information fed back by a certain express delivery link, the time information of the express delivery link is determined through the previous link and the next link of the express delivery link. Alternatively, when it is determined that the time information fed back by a certain express delivery link obviously does not match the time information fed back by the previous link and the next link, the time information of the express delivery link is corrected through the previous link and the next link of the express delivery link. The obtaining and storing of express delivery service data information specifically comprises the following steps: Obtain data information of each operation link in express delivery business; Parsing the data information and converting the parsed data information into structured data; Storing the structured data in a Kafka message queue; Before converting the parsed data information into structured data, the method further includes performing data desensitization processing on the parsed data information; The step of performing correlation analysis on the time information to generate a delivery time portrait comprises the following steps: Matching the time information with preset time attribute information; Establish a delivery time profile with matching time information and preset time attribute information; The preset time attribute information includes e-commerce holidays, solar calendar holidays, lunar calendar holidays, custom holidays, and express delivery cycles; Before generating the express delivery time portrait, it is necessary to classify and store the above-mentioned preset time attribute information in the early stage, classify the e-commerce festivals into one category, classify the Gregorian calendar festivals into one category, classify the lunar calendar festivals into one category, classify the custom festivals into one category, and classify the express delivery cycle into one category. When storing, the above-mentioned preset time attribute information is stored in a database by category, or different types of preset time attribute information are stored in different databases.

2. The generation method according to claim 1, characterized in that: After parsing the data information, it is also included in determining whether the time information in the parsed data information is abnormal. If it is abnormal, it further includes correcting the abnormal time information.

3. The generation method according to claim 1, characterized in that: The process of extracting the time information from the express service data information also includes extracting the ID information from the express service data information. Further, after generating the express time portrait, it also includes associating and mapping the ID information with the express time portrait.

4. A device for generating a delivery time portrait, characterized in that: include: An acquisition module is used to acquire and store express delivery service data information, wherein the express delivery service data information includes data information of each operation link from the generation of express delivery order to the completion of express delivery; An extraction module, used to extract time information from the express service data information; A generation module, used to perform correlation analysis on the time information to generate a delivery time portrait; Based on the characteristics of the express delivery process with a time sequence of operations, the abnormal time information is corrected through the time sequence of the express delivery links. When the time information is missing in the data information fed back by a certain express delivery link, the time information of the express delivery link is determined through the previous link and the next link of the express delivery link. Alternatively, when it is determined that the time information fed back by a certain express delivery link obviously does not match the time information fed back by the previous link and the next link, the time information of the express delivery link is corrected through the previous link and the next link of the express delivery link. The obtaining and storing of express delivery service data information specifically comprises the following steps: Obtain data information of each operation link in express delivery business; Parsing the data information and converting the parsed data information into structured data; Storing the structured data in a Kafka message queue; Before converting the parsed data information into structured data, the method further includes performing data desensitization processing on the parsed data information; The step of performing correlation analysis on the time information to generate a delivery time portrait comprises the following steps: Matching the time information with preset time attribute information; Establish a delivery time profile with matching time information and preset time attribute information; The preset time attribute information includes e-commerce holidays, solar calendar holidays, lunar calendar holidays, custom holidays, and express delivery cycles; Before generating the express delivery time portrait, it is necessary to classify and store the above-mentioned preset time attribute information in the early stage, classify the e-commerce festivals into one category, classify the Gregorian calendar festivals into one category, classify the lunar calendar festivals into one category, classify the custom festivals into one category, and classify the express delivery cycle into one category. When storing, the above-mentioned preset time attribute information is stored in a database by category, or different types of preset time attribute information are stored in different databases.

5. A delivery time portrait terminal device, characterized in that: It includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method for generating an express delivery time portrait as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for generating a delivery time portrait according to any one of claims 1 to 3 is implemented.

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