Intelligent logistics process management method and system

Abstract

The invention is suitable for the technical field of logistics management, and provides an intelligent logistics process management method and system. The method comprises the following steps: determining a plurality of logistics key nodes and a plurality of process risk types; carrying out data acquisition processing; obtaining logistics feedback data, and generating whole-process management data; carrying out risk matching and statistics; and carrying out calculation and comparison on risk importance values, selecting a plurality of serious risk types, and carrying out optimization management early warning. A plurality of logistics key nodes and a plurality of flow risk types can be determined, data acquisition processing is carried out on the plurality of logistics key nodes, logistics feedback data is acquired, risk matching, statistics and risk important value calculation are carried out, a plurality of serious risk types are selected, and optimization management early warning is carried out. Therefore, continuous monitoring, analysis and optimized early warning management of the logistics process are realized, the dynamic adaptability and intelligent level of the logistics process are improved, and improvement of logistics efficiency, reduction of cost and enhancement of market competitiveness can be promoted.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of logistics management, and particularly relates to a process management method and system for intelligent logistics. BACKGROUND

[0002] Logistics management is a management activity that, in the process of social reproduction, according to the law of the flow of material entities, applies the basic principles and scientific methods of management to plan, organize, direct, coordinate, control and supervise logistics activities, so as to achieve the best coordination and cooperation of logistics activities, thereby reducing logistics costs and improving logistics efficiency and economic benefits.

[0003] Logistics management involves the physical flow of goods from the supply to the receiving site, including transportation, warehousing, loading and unloading, handling, packaging, distribution, information processing and other links.

[0004] In logistics management, process management is particularly important. In the prior art, logistics processes generally exhibit a highly solidified and static characteristic, and their operation mostly relies on a pre-set fixed framework, with only limited and manual adjustments by the execution personnel according to experience, lacking a pre-warning management mechanism capable of continuously monitoring, analyzing and optimizing logistics processes, which limits the dynamic adaptability and intelligent level of logistics processes, resulting in the inability to automatically and accurately identify potential risks and take corresponding measures in time, which seriously hinders the improvement of logistics efficiency, the reduction of costs and the enhancement of market competitiveness. SUMMARY

[0005] The purpose of the embodiments of the present application is to provide a process management method and system for intelligent logistics, aiming to solve the technical problems existing in the prior art mentioned in the background.

[0006] The embodiments of the present application are implemented as follows: A process management method for intelligent logistics, the method specifically comprising the following steps: Obtaining a basic logistics process, performing key analysis on the basic logistics process, determining a plurality of logistics key nodes, and performing risk identification to determine a plurality of process risk types; Based on a plurality of the logistics key nodes, performing key inspection and supplement on the basic logistics process to generate a complete logistics process, and based on cloud computing technology, performing data collection and processing on a plurality of the logistics key nodes to generate optimized collection data; After completing the complete logistics process, obtaining logistics feedback data, integrating the logistics feedback data and the optimized collection data to generate whole-process management data; Obtaining a logistics cargo quantity, based on a plurality of the process risk types, performing risk matching and statistics on the logistics cargo quantity and the whole-process management data to generate risk probability data; According to the risk probability data, risk importance values of a plurality of the process risk types are calculated and compared, a plurality of serious risk types are selected, and early warning of optimization management is performed.

[0007] As a further limitation of the technical scheme of the embodiment of the application, the acquisition of the basic logistics process, the key analysis of the basic logistics process, the determination of a plurality of logistics key nodes, and the risk identification determine a plurality of process risk types, and specifically include the following steps: Acquire the basic logistics process; Identify the logistics state of the basic logistics process, and determine a plurality of logistics key nodes with logistics state changes; Acquire the historical logistics data corresponding to the basic logistics process; Based on a plurality of the logistics key nodes, perform abnormal analysis on the historical logistics data, and extract a plurality of node abnormal data; Identify the risk of a plurality of the node abnormal data, and determine a plurality of process risk types.

[0008] As a further limitation of the technical scheme of the embodiment of the application, based on a plurality of the logistics key nodes, the key inspection and supplement of the basic logistics process are performed to generate a complete logistics process, and based on cloud computing technology, data collection and processing of a plurality of the logistics key nodes are performed to generate optimized collection data, specifically including the following steps: According to a plurality of the process risk types, match a plurality of node risk types corresponding to a plurality of the logistics key nodes; According to a plurality of the node risk types, perform key inspection and optimization on a plurality of logistics key nodes of the basic logistics process to generate a plurality of key optimization sub-processes; According to a plurality of the key optimization sub-processes, supplement and optimize the basic logistics process to generate a complete logistics process; Collect data of a plurality of the logistics key nodes to obtain key collection data; Based on cloud computing technology, perform cloud storage and verification optimization on the key collection data to generate optimized collection data.

[0009] As a further limitation of the technical scheme of the embodiment of the application, after the complete logistics process is completed, acquire logistics feedback data, integrate the logistics feedback data and the optimized collection data to generate whole-process management data, specifically including the following steps: Determine a feedback receiving period; Acquire logistics feedback data within the feedback receiving period after the complete logistics process is completed; Integrate the logistics feedback data and the optimized collection data to generate whole-process management data.

[0010] As a further limitation of the technical scheme of the embodiment of the application, the obtaining of the logistics cargo quantity, the risk matching and statistics of the logistics cargo quantity and the whole-process management data based on the plurality of process risk types, and the generation of risk probability data specifically include the following steps: Identifying and extracting process abnormal data in the whole-process management data; Based on the plurality of process risk types, the process abnormal data is risk matched, and the risk type quantity of the plurality of process risk types is recorded; Obtaining the logistics cargo quantity; According to the logistics cargo quantity and the plurality of risk type quantities, the risk probability statistics of the plurality of process risk types are performed, and risk probability data is generated.

[0011] As a further limitation of the technical scheme of the embodiment of the application, the calculation and comparison of the risk importance value of the plurality of process risk types according to the risk probability data, the selection of the plurality of serious risk types, and the optimization management warning specifically include the following steps: According to the risk probability data, the risk importance value of the plurality of process risk types is calculated; The plurality of risk importance values are compared with a preset importance threshold value, and the plurality of serious risk values are selected from the plurality of risk importance values; According to the plurality of serious risk values, the plurality of serious risk types are selected from the plurality of process risk types; According to the plurality of serious risk types, the plurality of serious risk process intervals are selected from the complete logistics process; The plurality of serious risk process intervals are optimized and warned.

[0012] As a further limitation of the technical scheme of the embodiment of the application, the calculation formula of the risk importance value of the plurality of process risk types is: ; wherein, represents the risk importance value of the i-th process risk type, represents the risk probability of the i-th process risk type, represents the risk probability of the i-th process risk type.

[0013] A process management system of intelligent logistics, the system comprises a process key analysis module, an inspection supplement acquisition module, a data comprehensive arrangement module, a risk identification statistics module and an optimization management warning module, wherein: ​​​A process key analysis module is configured to obtain a basic logistics process, perform key analysis on the basic logistics process, determine a plurality of logistics key nodes, and perform risk identification to determine a plurality of process risk types. A test supplementary collection module is configured to perform key test supplementation on the basic logistics process based on the plurality of logistics key nodes, generate a complete logistics process, and perform data collection and processing on the plurality of logistics key nodes based on cloud computing technology to generate optimized collection data. A data comprehensive arrangement module is configured to obtain logistics feedback data after the complete logistics process is completed, comprehensively arrange the logistics feedback data and the optimized collection data, and generate whole-process management data. A risk identification and statistics module is configured to obtain a logistics cargo quantity, perform risk matching and statistics on the logistics cargo quantity and the whole-process management data based on the plurality of process risk types, and generate risk probability data. An optimization management and early warning module is configured to calculate and compare risk importance values of the plurality of process risk types according to the risk probability data, select a plurality of serious risk types, and perform optimization management and early warning.

[0014] As a further limitation of the technical scheme of the embodiment of the application, the process key analysis module specifically includes: A process acquisition unit is configured to obtain a basic logistics process. A state identification unit is configured to perform logistics state identification on the basic logistics process to determine a plurality of logistics key nodes with logistics state changes. A historical data acquisition unit is configured to obtain historical logistics data corresponding to the basic logistics process. An abnormality analysis unit is configured to perform abnormality analysis on the historical logistics data based on the plurality of logistics key nodes to extract a plurality of node abnormality data. A risk identification unit is configured to perform risk identification on the plurality of node abnormality data to determine a plurality of process risk types.

[0015] As a further limitation of the technical scheme of the embodiment of the application, the optimization management and early warning module specifically includes: A risk importance value calculation unit is configured to calculate risk importance values of the plurality of process risk types according to the risk probability data. A threshold comparison unit is configured to compare the plurality of risk importance values with a preset importance threshold value, and select a plurality of serious risk values from the plurality of risk importance values. A risk type selection unit is configured to select a plurality of serious risk types from the plurality of process risk types according to the plurality of serious risk values. a risk interval selection unit configured to select a plurality of serious risk process intervals from the complete logistics process according to a plurality of the serious risk types; a management early warning unit configured to perform optimized management early warning on the plurality of the serious risk process intervals.

[0016] Compared with the prior art, the present application has the following beneficial effects: The embodiments of the present application determine a plurality of logistics key nodes and a plurality of process risk types, perform data collection and processing, obtain logistics feedback data, generate complete process management data, perform risk matching and statistics, calculate and compare risk importance values, select a plurality of serious risk types, and perform optimized management early warning. The plurality of logistics key nodes and the plurality of process risk types are determined, data collection and processing are performed on the plurality of logistics key nodes, logistics feedback data is obtained, risk matching, statistics, and risk importance value calculation are performed, a plurality of serious risk types are selected, and optimized management early warning is performed, so as to realize continuous monitoring, analysis, and optimized early warning management of the logistics process, improve the dynamic adaptability and intelligent level of the logistics process, and promote the improvement of logistics efficiency, the reduction of cost, and the enhancement of market competitiveness. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 a flow chart of a process management method of intelligent logistics provided by the embodiments of the present application is shown; Figure 2 a flow chart of determining a plurality of logistics key nodes and a plurality of process risk types in the method provided by the embodiments of the present application is shown; Figure 3 a flow chart of performing key inspection supplement and data collection and processing in the method provided by the embodiments of the present application is shown; Figure 4 a flow chart of generating complete process management data in the method provided by the embodiments of the present application is shown; Figure 5 a flow chart of performing risk matching and statistics in the method provided by the embodiments of the present application is shown; Figure 6 a flow chart of performing optimized management early warning in the method provided by the embodiments of the present application is shown; Figure 7 an application architecture diagram of a process management system of intelligent logistics provided by the embodiments of the present application is shown; Figure 8 a structure block diagram of a process key analysis module in the system provided by the embodiments of the present application is shown; Figure 9 a structure block diagram of an optimized management early warning module in the system provided by the embodiments of the present application is shown. DETAILED DESCRIPTION

[0018] In order to make the objectives, technical solutions and advantages of the present application clearer, further detailed description will be given below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and not to limit the present application.

[0019] It can be understood that in logistics management, process management is particularly important. In the prior art, the logistics process generally presents a highly solidified and static characteristic, and its operation mostly depends on a preset fixed framework, and only limited and manual adjustment is made by the execution personnel according to experience, lacking a pre-warning management mechanism capable of continuously monitoring, analyzing and optimizing the logistics process, which limits the dynamic adaptability and intelligent level of the logistics process, and leads to the inability to automatically and accurately identify potential risks and take corresponding processing in time, thereby seriously hindering the improvement of logistics efficiency, the reduction of cost and the enhancement of market competitiveness.

[0020] To solve the above problems, the embodiment of the present application discloses a process management method and system for intelligent logistics, which acquires a basic logistics process, performs key analysis on the basic logistics process, determines a plurality of logistics key nodes, performs risk identification, and determines a plurality of process risk types; based on the plurality of logistics key nodes, performs key inspection and supplement on the basic logistics process, generates a complete logistics process, and based on cloud computing technology, performs data collection and processing on the plurality of logistics key nodes, and generates optimized collection data; after completing the complete logistics process, acquires logistics feedback data, comprehensively processes the logistics feedback data and the optimized collection data, and generates whole-process management data; acquires the quantity of logistics goods, based on the plurality of process risk types, performs risk matching and statistics on the quantity of logistics goods and the whole-process management data, and generates risk probability data; according to the risk probability data, performs calculation and comparison of risk importance values on the plurality of process risk types, selects a plurality of serious risk types, and performs optimization management pre-warning. The plurality of logistics key nodes and the plurality of process risk types are determined, data collection and processing are performed on the plurality of logistics key nodes, logistics feedback data is acquired, risk matching, statistics and risk importance value calculation are performed, a plurality of serious risk types are selected, and optimization management pre-warning is performed, so as to realize continuous monitoring, analysis and optimization pre-warning management of the logistics process, improve the dynamic adaptability and intelligent level of the logistics process, and promote the improvement of logistics efficiency, the reduction of cost and the enhancement of market competitiveness.

[0021] Specifically, Figure 1 A flow chart of a process management method for intelligent logistics provided by the embodiment of the present application is shown.

[0022] In one preferred embodiment provided by the present application, a process management method for intelligent logistics, the method specifically comprises the following steps: In step S101, a basic logistics process is acquired, key analysis is performed on the basic logistics process, a plurality of logistics key nodes are determined, risk identification is performed, and a plurality of process risk types are determined.

[0023] In the embodiment of the present application, by acquiring the basic logistics process, the logistics state of the basic logistics process is identified, a plurality of logistics key nodes with logistics state changes are determined from the basic logistics process, historical logistics data corresponding to the basic logistics process is acquired, abnormal analysis is performed on the historical logistics data, a plurality of node abnormal data corresponding to the plurality of logistics key nodes are extracted from the historical logistics data, risk identification is performed on the plurality of node abnormal data, corresponding risks caused by different abnormalities are determined, risk type classification is performed, and a plurality of process risk types are obtained.

[0024] It can be understood that the logistics key node is a process node with a logistics state change in the basic logistics process, including: motion state change (for example, logistics transfer changes a transportation vehicle, logistics distribution changes a transportation vehicle), packaging change (for example, logistics collection / inventory increases packaging, multi-cargo consolidation packaging), environmental change (for example, cold chain temperature change), and the like.

[0025] It can be understood that the node abnormal data is logistics abnormal data corresponding to the logistics key node extracted from the historical logistics data, including: cargo loss, cargo and order mismatch, transportation damage, transfer damage, cold chain temperature abnormality, distribution overtime, collection overtime, transportation overtime, and cargo contamination, and the like. In the embodiment of the present application, the node abnormal data is abnormal feedback obtained after the basic logistics process is completed, and data obtained by associating and matching the plurality of logistics key nodes.

[0026] It can be understood that the plurality of process risk types can include: improper collection, packaging damage, inaccurate temperature measurement, improper maintenance, improper personnel operation, improper planning, equipment failure, and the like.

[0027] Specifically, Figure 2 A flowchart for determining the plurality of logistics key nodes and the plurality of process risk types in the method provided by the embodiment of the present application is shown.

[0028] In the preferred embodiment provided by the present application, the acquiring the basic logistics process, performing key analysis on the basic logistics process, determining a plurality of logistics key nodes, and performing risk identification to determine a plurality of process risk types specifically includes the following steps: In step S1011, a basic logistics process is acquired.

[0029] In step S1012, logistics state identification is performed on the basic logistics process, and a plurality of logistics key nodes with logistics state changes are determined.

[0030] Step S1013, acquire the historical logistics data corresponding to the basic logistics process.

[0031] Step S1014, based on multiple logistics key nodes, perform anomaly analysis on the historical logistics data, and extract multiple node anomaly data.

[0032] Step S1015, risk identification is performed on the multiple node anomaly data, and multiple process risk types are determined.

[0033] Further, the intelligent logistics process management method further comprises the following steps: Step S102, based on multiple logistics key nodes, perform key inspection and supplement on the basic logistics process, generate a complete logistics process, and based on cloud computing technology, perform data collection and processing on multiple logistics key nodes, and generate optimized collection data.

[0034] In the embodiment of the application, according to multiple process risk types, multiple node risk types corresponding to multiple logistics key nodes are matched, and then according to multiple node risk types corresponding to each logistics key node, key inspection and optimization are performed, multiple key optimization sub-processes corresponding to multiple logistics key nodes are generated, and then multiple key optimization sub-processes are supplemented and optimized at multiple logistics key nodes of the basic logistics process, a complete logistics process is generated, and during logistics processing according to the complete logistics process, data collection is performed on multiple logistics key nodes to obtain key collection data, cloud storage is performed on the key collection data based on cloud computing technology, cloud computing analysis is performed on the key collection data, and repeated, useless and incomplete data in the key collection data are removed, and subsequent data with corrections are traced and optimized to generate optimized collection data.

[0035] It can be understood that each logistics key node has one or more node risk types, and each logistics key node corresponds to a key optimization sub-process.

[0036] It can be understood that the key optimization sub-process is a planning process for detecting multiple corresponding node risk types at the corresponding logistics key node, for example, performing cargo scanning detection on the process risk type of "improper collection", performing external shooting detection on the process risk type of "damaged packaging", and performing temperature detection on the process risk type of "inaccurate temperature measurement".

[0037] It can be understood that the trace optimization of the subsequent data with corrections is mainly for the temperature measurement data of the cold chain logistics. If the temperature measurement data is corrected in the subsequent, the previous temperature measurement data needs to be optimized according to the corrected error to ensure that the final temperature measurement data is correct.

[0038] Specifically, Figure 3 A flow chart of performing key inspection supplement and data collection and processing in the method provided by the embodiment of the application is shown.

[0039] In the preferred embodiment provided by the application, the key inspection supplement of the basic logistics process based on the plurality of logistics key nodes, the generation of the complete logistics process, and the data collection and processing of the plurality of logistics key nodes based on the cloud computing technology to generate the optimized collection data specifically include the following steps: Step S1021, according to the plurality of process risk types, matching the plurality of node risk types corresponding to the plurality of logistics key nodes.

[0040] Step S1022, according to the plurality of node risk types, performing key inspection optimization on the plurality of logistics key nodes of the basic logistics process to generate a plurality of key optimization sub-processes.

[0041] Step S1023, according to the plurality of key optimization sub-processes, performing optimization supplement on the basic logistics process to generate a complete logistics process.

[0042] Step S1024, collecting data of the plurality of logistics key nodes to obtain key collection data.

[0043] Step S1025, based on the cloud computing technology, performing cloud storage and verification optimization on the key collection data to generate optimized collection data.

[0044] Further, the process management method of the intelligent logistics further includes the following steps: Step S103, after completing the complete logistics process, obtaining logistics feedback data, and comprehensively processing the logistics feedback data and the optimized collection data to generate whole-process management data.

[0045] In the embodiment of the application, a feedback receiving period is determined, the record and arrangement of customer feedback are performed in the feedback receiving period after the completion of the complete logistics process, the logistics feedback data is generated, and then the logistics feedback data and the optimized collection data are comprehensively processed to generate the whole-process management data of the complete logistics process.

[0046] Specifically, Figure 4 A flow chart of generating whole-process management data in the method provided by the embodiment of the application is shown.

[0047] In the preferred embodiment provided by the application, after completing the complete logistics process, obtaining logistics feedback data, and comprehensively processing the logistics feedback data and the optimized collection data to generate whole-process management data specifically include the following steps: Step S1031, determining a feedback receiving period.

[0048] Step S1032, obtaining logistics feedback data in a feedback receiving period after completing the complete logistics process.

[0049] Step S1033, generating whole-process management data by comprehensively processing the logistics feedback data and the optimized collection data.

[0050] Further, the process management method of intelligent logistics further includes the following steps: Step S104, obtaining a logistics cargo quantity, performing risk matching and statistics on the logistics cargo quantity and the whole-process management data based on a plurality of process risk types, and generating risk probability data.

[0051] In the embodiment of the present application, the whole-process management data is analyzed and identified, process abnormal data is extracted, and the process abnormal data is matched with risks based on a plurality of process risk types. The number of risk types of the plurality of process risk types is recorded, and the logistics cargo quantity of the complete logistics process is obtained. The number of risk types corresponding to each process risk type is divided by the logistics cargo quantity to obtain the risk probability corresponding to each process risk type. The risk probabilities corresponding to the plurality of process risk types are comprehensively arranged to generate risk probability data, thereby realizing risk probability statistics of the plurality of process risk types.

[0052] Specifically, Figure 5 A flowchart of risk matching and statistics in the method provided by the embodiment of the present application is shown.

[0053] In the preferred embodiment provided by the present application, the obtaining of the logistics cargo quantity, the risk matching and statistics on the logistics cargo quantity and the whole-process management data based on a plurality of process risk types, and the generation of risk probability data specifically include the following steps: Step S1041, identifying and extracting process abnormal data in the whole-process management data.

[0054] Step S1042, performing risk matching on the process abnormal data based on a plurality of process risk types, and recording the number of risk types of the plurality of process risk types.

[0055] Step S1043, obtaining a logistics cargo quantity.

[0056] Step S1044, performing risk probability statistics on a plurality of process risk types according to the logistics cargo quantity and the number of risk types, and generating risk probability data.

[0057] Further, the process management method of intelligent logistics further includes the following steps: Step S105, according to the risk probability data, calculating and comparing risk importance values of multiple process risk types, selecting multiple serious risk types, and performing optimization management warning.

[0058] In the embodiment of the present application, according to the risk probability data, risk importance values of multiple process risk types are calculated, and the multiple risk importance values are compared with a preset importance threshold value, and risk importance values greater than the importance threshold value are marked as serious risk values, so that multiple serious risk values are screened from the multiple risk importance values, and multiple serious risk types corresponding to the multiple serious risk values are selected from the multiple process risk types, according to the multiple serious risk types, multiple serious risk process intervals are selected from the complete logistics process, and then optimization management warning is performed on the multiple serious risk process intervals. Specifically, the calculation formula of the risk importance value of the multiple process risk types is: ; wherein, represents the risk importance value of the i th process risk type, represents the risk probability of the i th process risk type, represents the risk probability of the i th process risk type.

[0059] It can be understood that the serious risk process interval is a process interval between two logistics key nodes in the complete logistics process, and has one or more serious risk types.

[0060] Specifically, Figure 6 FIG. 4 shows a flowchart of the optimization management warning in the method provided by the embodiment of the present application.

[0061] In the preferred embodiment provided by the present application, the calculating and comparing risk importance values of multiple process risk types according to the risk probability data, selecting multiple serious risk types, and performing optimization management warning specifically includes the following steps: Step S1051, according to the risk probability data, calculating risk importance values of multiple process risk types.

[0062] Step S1052, comparing multiple risk importance values with a preset importance threshold value, and selecting multiple serious risk values from the multiple risk importance values.

[0063] Step S1053, according to the multiple serious risk values, selecting multiple serious risk types from the multiple process risk types.

[0064] Step S1054, according to the multiple serious risk types, selecting multiple serious risk process intervals from the complete logistics process. ​​​

[0065] Step S1055, the multiple serious risk process intervals are optimized and managed to give early warning.

[0066] Further, Figure 7 An application architecture diagram of the process management system of the intelligent logistics provided by the embodiment of the present application is shown.

[0067] In another preferred embodiment of the present application, a process management system of intelligent logistics comprises: The process key analysis module 101 is configured to acquire a basic logistics process, perform key analysis on the basic logistics process, determine multiple logistics key nodes, and perform risk identification to determine multiple process risk types.

[0068] In the embodiment of the present application, the process key analysis module 101 acquires the basic logistics process, performs logistics state identification on the basic logistics process, determines multiple logistics key nodes with logistics state changes from the basic logistics process, acquires historical logistics data corresponding to the basic logistics process, performs abnormality analysis on the historical logistics data, extracts multiple node abnormality data corresponding to the multiple logistics key nodes from the historical logistics data, further performs risk identification on the multiple node abnormality data, determines corresponding risks caused by different abnormalities, performs risk type division, and obtains multiple process risk types.

[0069] Specifically, Figure 8 A structural block diagram of the process key analysis module 101 in the system provided by the embodiment of the present application is shown.

[0070] In the preferred embodiment of the present application, the process key analysis module 101 specifically comprises: The process acquisition unit 1011 is configured to acquire a basic logistics process.

[0071] The state identification unit 1012 is configured to perform logistics state identification on the basic logistics process to determine multiple logistics key nodes with logistics state changes.

[0072] The historical data acquisition unit 1013 is configured to acquire historical logistics data corresponding to the basic logistics process.

[0073] The abnormality analysis unit 1014 is configured to perform abnormality analysis on the historical logistics data based on the multiple logistics key nodes to extract multiple node abnormality data.

[0074] The risk identification unit 1015 is configured to perform risk identification on the multiple node abnormality data to determine multiple process risk types.

[0075] Further, the process management system of intelligent logistics further comprises: The inspection supplement acquisition module 102 is used for supplementing key inspection based on the plurality of logistics key nodes to the basic logistics process, generating a complete logistics process, and performing data acquisition and processing on the plurality of logistics key nodes based on cloud computing technology, and generating optimized acquisition data.

[0076] In the embodiment of the present application, the inspection supplement acquisition module 102 matches a plurality of node risk types corresponding to a plurality of logistics key nodes according to a plurality of process risk types, and then performs key inspection optimization according to the plurality of node risk types corresponding to each logistics key node, generates a plurality of key optimization sub-processes corresponding to the plurality of logistics key nodes, and then supplements the plurality of key optimization sub-processes at the plurality of logistics key nodes of the basic logistics process to generate a complete logistics process. In the process of performing logistics processing according to the complete logistics process, data acquisition is performed on the plurality of logistics key nodes to obtain key acquisition data. Based on cloud computing technology, the key acquisition data is cloud-stored and cloud-computed and analyzed, and repeated, useless and incomplete data in the key acquisition data are removed. Subsequent data with corrections are traced and optimized to generate optimized acquisition data.

[0077] The data comprehensive arrangement module 103 is used for obtaining logistics feedback data after the complete logistics process is completed, and generating whole-process management data by comprehensively arranging the logistics feedback data and the optimized acquisition data.

[0078] In the embodiment of the present application, the data comprehensive arrangement module 103 determines a feedback receiving period, records and arranges customer feedback in the feedback receiving period after the complete logistics process is completed to generate logistics feedback data, and then comprehensively arranges the logistics feedback data and the optimized acquisition data to generate whole-process management data of the complete logistics process.

[0079] The risk identification and statistics module 104 is used for obtaining a logistics cargo quantity, performing risk matching and statistics on the logistics cargo quantity and the whole-process management data based on a plurality of process risk types, and generating risk probability data.

[0080] In the embodiment of the present application, the risk identification and statistics module 104 performs identification and analysis on the whole-process management data, extracts process abnormal data, performs risk matching on the process abnormal data based on a plurality of process risk types, records a risk type quantity of the plurality of process risk types, obtains a logistics cargo quantity of the complete logistics process, divides the risk type quantity corresponding to each process risk type by the logistics cargo quantity to obtain a risk probability corresponding to each process risk type, and comprehensively arranges the risk probabilities corresponding to the plurality of process risk types to generate risk probability data, thereby realizing risk probability statistics of the plurality of process risk types.

[0081] The optimization management early warning module 105 is configured to calculate and compare risk importance values of the plurality of process risk types according to the risk probability data, select a plurality of serious risk types, and perform optimization management early warning.

[0082] In the embodiment of the present application, the optimization management early warning module 105 calculates risk importance values of the plurality of process risk types according to the risk probability data, compares the plurality of risk importance values with a preset importance threshold value, marks risk importance values greater than the importance threshold value as serious risk values, selects a plurality of serious risk values from the plurality of risk importance values, selects serious risk types corresponding to the plurality of serious risk values from the plurality of process risk types, selects a plurality of serious risk process intervals from the complete logistics process according to the plurality of serious risk types, and performs optimization management early warning on the plurality of serious risk process intervals. Specifically, the calculation formula of the risk importance value of the plurality of process risk types is as follows: ; wherein, represents the risk importance value of the i th process risk type, represents the risk probability of the i th process risk type, represents the risk probability of the i th process risk type.

[0083] Specifically, Figure 9 FIG. 5 shows a structural block diagram of the optimization management early warning module 105 in the system provided by the embodiment of the present application.

[0084] In the preferred embodiment provided by the present application, the optimization management early warning module 105 specifically includes: The risk importance value calculation unit 1051 is configured to calculate risk importance values of the plurality of process risk types according to the risk probability data.

[0085] The threshold value comparison unit 1052 is configured to compare the plurality of risk importance values with a preset importance threshold value, and select a plurality of serious risk values from the plurality of risk importance values.

[0086] The risk type selection unit 1053 is configured to select a plurality of serious risk types from the plurality of process risk types according to the plurality of serious risk values.

[0087] The risk interval selection unit 1054 is configured to select a plurality of serious risk process intervals from the complete logistics process according to the plurality of serious risk types.

[0088] The management early warning unit 1055 is configured to perform optimization management early warning on the plurality of serious risk process intervals.

[0089] ​​​It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in a certain order following the arrows, the steps are not necessarily executed in the order following the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in order, and the steps can be executed in other orders. Moreover, at least some of the steps in the embodiments can include a plurality of sub-steps or a plurality of stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the sub-steps or stages is not necessarily sequential, but can be round-robin or alternately executed with other steps or sub-steps or stages of other steps.

[0090] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments of the methods. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0091] The above-mentioned embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be noted that, for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the scope of protection of the patent of the present application should be subject to the appended claims.

Claims

1. A process management method of intelligent logistics, characterized by, The method specifically comprises the following steps: Obtaining a basic logistics flow, performing key analysis on the basic logistics flow, determining a plurality of logistics key nodes, and performing risk identification to determine a plurality of process risk types; Based on a plurality of the logistics key nodes, performing key inspection and supplement on the basic logistics flow to generate a complete logistics flow, and based on cloud computing technology, performing data collection and processing on a plurality of the logistics key nodes to generate optimized collection data; After completing the complete logistics flow, obtaining logistics feedback data, and integrating the logistics feedback data and the optimized collection data to generate whole-process management data; Obtaining the quantity of logistics goods, based on a plurality of the process risk types, performing risk matching and statistics on the quantity of logistics goods and the whole-process management data to generate risk probability data; According to the risk probability data, calculating and comparing the risk importance values of a plurality of the process risk types, selecting a plurality of serious risk types, and performing optimization management warning.

2. The intelligent logistics process management method according to claim 1, characterized by, The obtaining of the basic logistics flow, the key analysis on the basic logistics flow, the determination of a plurality of logistics key nodes, and the risk identification to determine a plurality of process risk types specifically comprises the following steps: Obtaining a basic logistics flow; Performing logistics state identification on the basic logistics flow to determine a plurality of logistics key nodes with logistics state changes; Obtaining historical logistics data corresponding to the basic logistics flow; Based on a plurality of the logistics key nodes, performing abnormal analysis on the historical logistics data to extract a plurality of node abnormal data; Performing risk identification on a plurality of the node abnormal data to determine a plurality of process risk types.

3. The intelligent logistics process management method according to claim 1, wherein, The based on a plurality of the logistics key nodes, performing key inspection and supplement on the basic logistics flow to generate a complete logistics flow, and based on cloud computing technology, performing data collection and processing on a plurality of the logistics key nodes to generate optimized collection data specifically comprises the following steps: According to a plurality of the process risk types, matching a plurality of node risk types corresponding to a plurality of the logistics key nodes; According to a plurality of the node risk types, performing key inspection optimization on a plurality of logistics key nodes of the basic logistics flow to generate a plurality of key optimization sub-processes; According to a plurality of the key optimization sub-processes, performing optimization supplement on the basic logistics flow to generate a complete logistics flow; Collecting data of a plurality of the logistics key nodes to obtain key collection data; Based on cloud computing technology, performing cloud storage and verification optimization on the key collection data to generate optimized collection data.

4. The intelligent logistics process management method according to claim 1, wherein, The after completing the complete logistics flow, obtaining logistics feedback data, and integrating the logistics feedback data and the optimized collection data to generate whole-process management data specifically comprises the following steps: Determining a feedback receiving period; Within the feedback receiving period after completing the complete logistics flow, obtaining logistics feedback data; Integrating the logistics feedback data and the optimized collection data to generate whole-process management data.

5. The intelligent logistics process management method according to claim 1, wherein, The obtaining of the quantity of logistics goods, based on a plurality of the process risk types, performing risk matching and statistics on the quantity of logistics goods and the whole-process management data to generate risk probability data specifically comprises the following steps: Identify and extract process exception data in the whole-process management data; Based on a plurality of process risk types, the process exception data is risk matched, and the risk type quantity of a plurality of process risk types is recorded; Obtain the quantity of logistics goods; According to the quantity of logistics goods and the quantity of a plurality of risk types, the risk probability of a plurality of process risk types is statistically calculated, and risk probability data is generated. 6.The intelligent logistics process management method of claim 1, wherein, The risk important value calculation and comparison of a plurality of process risk types according to the risk probability data, selecting a plurality of serious risk types, and performing optimization management warning specifically includes the following steps: According to the risk probability data, the risk important value of a plurality of process risk types is calculated; Compare a plurality of risk important values with a preset important threshold value, select a plurality of serious risk values from a plurality of risk important values; According to a plurality of serious risk values, a plurality of serious risk types are selected from a plurality of process risk types; According to a plurality of serious risk types, a plurality of serious risk process intervals are selected from the complete logistics process; Optimization management warning is performed on a plurality of serious risk process intervals.

7. The intelligent logistics process management method according to claim 6, wherein, The formula for calculating the risk importance value of multiple process risk types is as follows: ;in, Representing the Risk importance values ​​for each process risk type Representing the Risk probability of each process risk type Representing the The probability of risk for each process risk type.

8. A process management system for intelligent logistics, characterized by, The system comprises a process key analysis module, an inspection supplement acquisition module, a data comprehensive arrangement module, a risk identification statistical module and an optimization management warning module, wherein: The process key analysis module is used to obtain a basic logistics process, perform key analysis on the basic logistics process, determine a plurality of logistics key nodes, and perform risk identification to determine a plurality of process risk types; The inspection supplement acquisition module is used to perform key inspection supplement on the basic logistics process based on a plurality of logistics key nodes, generate a complete logistics process, and perform data acquisition processing on a plurality of logistics key nodes based on cloud computing technology to generate optimized acquisition data; The data comprehensive arrangement module is used to obtain logistics feedback data after the complete logistics process, and generate whole-process management data by comprehensively arranging the logistics feedback data and the optimized acquisition data; The risk identification statistical module is used to obtain the quantity of logistics goods, perform risk matching and statistics on the quantity of logistics goods and the whole-process management data based on a plurality of process risk types, and generate risk probability data; The optimization management warning module is used to calculate and compare the risk important value of a plurality of process risk types according to the risk probability data, select a plurality of serious risk types, and perform optimization management warning. 9.The intelligent logistics flow management system of claim 8, wherein, The process key analysis module specifically comprises: A process acquisition unit is used to acquire a basic logistics process; A state identification unit is used to identify the logistics state of the basic logistics process and determine a plurality of logistics key nodes with logistics state changes; A historical data acquisition unit is used to acquire historical logistics data corresponding to the basic logistics process; An abnormality analysis unit is used to perform abnormality analysis on the historical logistics data based on a plurality of logistics key nodes and extract a plurality of node abnormality data; A risk identification unit is used to perform risk identification on a plurality of node abnormality data and determine a plurality of process risk types. 10.The intelligent logistics flow management system of claim 8, wherein, The optimization management warning module specifically comprises: a risk importance value calculation unit configured to calculate risk importance values of the process risk types according to the risk probability data; a threshold comparison unit configured to compare the risk importance values with preset importance threshold values, and select serious risk values from the risk importance values; a risk type selection unit configured to select serious risk types from the process risk types according to the serious risk values; a risk interval selection unit configured to select serious risk process intervals from the complete logistics process according to the serious risk types; a management warning unit configured to perform optimized management warning on the serious risk process intervals.

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