Methods, devices, equipment, and storage media for time-segmented monitoring and early warning of distribution centers.

Abstract

A method, apparatus, equipment, and storage medium for time-sensitive segmented monitoring and early warning of distribution centers include: pre-establishing a layering mechanism for real-time data of the distribution center in a data warehouse, and setting the scope, data extraction path, and data processing method for each target data layer after layering; layering the target data to be statistically analyzed according to the layering mechanism to obtain the target data layers of corresponding numbers and the real-time target data corresponding to each target data layer; setting multiple time-sensitive monitoring modules for the target data layers, and calculating the time-sensitive information of the real-time target data in each time-sensitive monitoring module according to a preset time-sensitive report model based on the multiple time-sensitive monitoring modules; displaying the time-sensitive information of each time-sensitive monitoring module, and issuing corresponding early warning information according to a preset early warning strategy. This invention effectively standardizes and integrates all time-sensitive related indicators, while ensuring data accuracy and feasibility.

Description

Technical Field

[0001] This invention belongs to the field of logistics timeliness management technology, and particularly relates to a method, device, equipment and storage medium for timeliness segmentation monitoring and early warning in distribution centers. Background Technology

[0002] Currently, data timeliness monitoring systems in the logistics field primarily target all data across the entire network of orders. However, they fail to differentiate between data from distribution centers, applying different evaluation criteria to all data. This results in low accuracy and a lack of specificity in data timeliness monitoring, making it difficult to intuitively assess the results. For distribution center managers, leveraging data to support business decisions requires monitoring this data scattered across various business scenarios. Analyzing and presenting data through traditional reports not only consumes significant time and effort from data analysts but also only displays data from several hours or even days ago. This leads to slow data retrieval and low work efficiency. Both the accuracy and timeliness of the data need improvement. Summary of the Invention

[0003] To address the aforementioned issues, the present invention aims to provide a method, apparatus, device, and storage medium for segmented monitoring and early warning of timeliness in distribution centers. This method effectively integrates all timeliness-related indicators, enabling faster maintenance of data accuracy. When data issues arise, it eliminates the need to repair all data; repairs can begin from the problematic step, thereby helping managers make better business decisions and improve work efficiency.

[0004] To achieve the above objectives, the technical solution of the present invention is as follows:

[0005] A time-sensitive segmented monitoring and early warning method for distribution centers includes: pre-establishing a layered mechanism for real-time data of the distribution center in a data warehouse, and setting the scope, data extraction path, and data processing method for each target data layer after layering; layering the target data to be statistically analyzed according to the layered mechanism to complete the layering of the target data layers and the real-time target data corresponding to each target data layer; setting multiple time-sensitive monitoring modules for the target data layers, and calculating the time-sensitive information of the real-time target data in each time-sensitive monitoring module based on the multiple time-sensitive monitoring modules according to a preset time-sensitive report model; displaying the time-sensitive information of each time-sensitive monitoring module, and issuing corresponding early warning information according to a preset early warning strategy.

[0006] In one embodiment of the present invention, the step of calculating the timeliness information of the real-time target data in each timeliness monitoring module based on the plurality of timeliness monitoring modules according to a preset timeliness report model further includes: setting a timeliness report model according to business requirements, wherein the timeliness report model includes at least the set timeliness monitoring module information and the generation rule information of the order timeliness monitoring report, wherein the generation rule information further includes which field information of which target data layer the timeliness monitoring module obtains, and which calculation methods are used to update the corresponding field values ​​of the order timeliness monitoring report; the timeliness monitoring module obtains the real-time data in the corresponding field of the corresponding target data layer according to the timeliness report model, and updates the corresponding field values ​​of the order timeliness monitoring report according to a preset calculation method; and calculates the timeliness information in the order timeliness monitoring report of the order layer.

[0007] In one embodiment of the present invention, after calculating the timeliness information of the real-time target data in each timeliness monitoring module based on the multiple timeliness monitoring modules according to the preset timeliness report model, the method further includes: using the originating point-entry sorting module, the entry-exit sorting module, the exit sorting module, the exit sorting-entry-end sorting module, the entry-exit sorting-exit-end sorting module, and the exit sorting-end delivery module in parallel to generate order timeliness monitoring reports corresponding to each order, forming multi-segment timeliness report model data of the order layer; establishing corresponding layer timeliness monitoring reports for the order timeliness monitoring reports according to the site layer or the sorting center layer, setting the timeliness statistical dimensions of the corresponding layer, and generating timeliness report model data of different layers and different granularities; receiving the user's timeliness monitoring request, obtaining the corresponding weights of the user, and providing timeliness report model data of different granularities of the layer corresponding to the weights.

[0008] In one embodiment of the present invention, after issuing the corresponding early warning information according to the preset early warning strategy, the method further includes: when receiving a request from other servers or users to obtain time-series monitoring data of the distribution center, performing legality authentication on the request; after successful authentication, processing the time-series information of the real-time target data in each time-series monitoring module as privacy data; obtaining the privacy data through the key management system for encrypted transmission; the key management system generating at least one key data in different periods, wherein the key data includes at least: a content key CEK and a key encryption key KEK.

[0009] In one embodiment of the present invention, issuing corresponding early warning information according to a preset early warning strategy further includes: setting an early warning range for timeliness data, wherein the early warning range includes at least one node in the logistics network, including a station or distribution center, and the timeliness data is used to characterize the processing time of logistics objects within the early warning range; acquiring historical multi-segment timeliness data of historical logistics objects processed within the early warning range recorded by the node, and a first multi-segment timeliness data of a logistics object from entering the early warning range to leaving the early warning range; determining the current multi-segment timeliness data of the early warning range based on the historical multi-segment timeliness data, the first multi-segment timeliness data, and a preset logistics attenuation coefficient, wherein the logistics attenuation coefficient is used to characterize the frequency of timeliness changes within the early warning range; and dynamically generating early warning information corresponding to the early warning range when the current timeliness data meets a preset threshold.

[0010] In one embodiment of the present invention, the step of completing the target data layering and the real-time target data corresponding to each target data layer according to the layering mechanism further includes: setting up a synchronization module in the order server, the transportation information server, and the scanning server respectively, and periodically updating the data in the servers to the corresponding order data layer, transportation information layer, and scanning information layer respectively, so as to obtain the real-time target data corresponding to each target data layer.

[0011] In one embodiment of the present invention, obtaining the target data layer of the corresponding layer number and the real-time target data corresponding to each target data layer includes: determining the target data to be processed in the server; obtaining the data push requirement information of the target data; wherein the data push requirement information is information pre-configured based on the data push requirement template; and performing corresponding data push processing on the target data based on the data push requirement information to send the target data to its corresponding target data layer.

[0012] Based on the same concept, the present invention also provides a timeliness segmentation monitoring and early warning device for a distribution center, comprising: a data layering module, used to pre-establish several layering mechanisms for real-time data of the distribution center in a data warehouse, and to set the scope corresponding to each target data layer after layering, the data extraction path of the scope, and the data processing method; a data acquisition module, used to complete the layering of the target data to be statistically analyzed according to the layering mechanism and the real-time target data corresponding to each target data layer; a timeliness monitoring module, used to set multiple timeliness monitoring modules for the target data layer, and to calculate the timeliness information of the real-time target data in each timeliness monitoring module according to a preset timeliness report model based on the multiple timeliness monitoring modules; and an early warning module, used to display the timeliness information of each timeliness monitoring module and issue corresponding early warning information according to a preset early warning strategy.

[0013] Based on the same concept, the present invention also provides a computer device, characterized in that it includes: a memory for storing a processing program; and a processor for executing the processing program, wherein the processor executes the above-mentioned time-segmented monitoring and early warning method for distribution centers.

[0014] Based on the same concept, the present invention also provides a readable storage medium, characterized in that the readable storage medium stores a processing program, which, when executed by a processor, implements the above-described method for time-segmented monitoring and early warning of distribution centers.

[0015] Compared with the prior art, the advantages of this invention after adopting the above technical solution are as follows:

[0016] 1. Before conducting timely data analysis, the target data to be analyzed can be utilized from the existing server. This data can be stratified, with each stratum having its own scope. Further processing and analysis of the stratified data is then performed. If data issues arise, it's not necessary to repair all data; only the problematic step or data layer needs to be addressed, improving the efficiency of timely data analysis. Furthermore, when problems occur in the original or underlying data, corresponding warnings or actions can be provided during the separate processing of each stratified level. This isolation of issues during timely data analysis further prevents problems from arising.

[0017] 2. Existing data timeliness analysis cannot achieve a relatively standardized approach, thus posing challenges when quantifying timeliness analysis data. Our applicant, addressing the issue from a technical perspective, found that simply establishing a single analytical standard is insufficient. The core issue lies in the massive volume and constant updating of logistics or order data, requiring data timeliness analysis algorithms to achieve a certain level of computational responsiveness. Our company utilizes existing teams to maintain data from order servers, transportation information servers, and scanning servers. Our technical staff only need to directly leverage this data to construct the corresponding order data layer, transportation information layer, and scanning information layer of our data warehouse. Furthermore, the synchronization module preserves the timeliness and accuracy of these layered data, saving significant processing time.

[0018] 3. This invention sets up a timeliness report model according to business needs. The timeliness report model includes at least the set timeliness monitoring module information and the generation rule information for order timeliness monitoring reports. The generation rule information further includes which target data layers and which fields of information the timeliness monitoring module acquires, and which calculation methods are used to update the corresponding field values ​​of the order timeliness monitoring reports. That is, if business needs require changes to the timeliness rules, the timeliness report model can be directly modified. The timeliness monitoring module generates the timeliness information in the order timeliness monitoring reports in real time according to the timeliness report model, which has high processing efficiency, strong real-time performance, and automatic generation capability.

[0019] 4. The data for time-sensitive segmented monitoring at the distribution center is divided into underlying data: order time-sensitive monitoring reports. Based on this, corresponding time-sensitive monitoring reports are generated at the site level or distribution center level. Users can access the system to obtain their corresponding rights and receive time-sensitive report model data of different granularities corresponding to those rights and granularities. This hierarchical management also takes into account the different content displayed to different users.

[0020] 5. The warning range is dynamically adjusted based on historical time-of-delivery data, first multi-segment time-of-delivery data, and logistics attenuation coefficient. It can intelligently and accurately calculate the warning range dynamically based on the current multi-segment time-of-delivery data without manual adjustment. Especially when the data volume of the distribution center is very large, the warning range can be dynamically adjusted according to the current actual situation of each distribution center using a standard evaluation system, which is highly operable. Attached Figure Description

[0021] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, wherein:

[0022] Figure 1 This is a flowchart of the first embodiment of a time-segmented monitoring and early warning method for distribution centers according to the present invention;

[0023] Figure 2 This is a flowchart of the first embodiment of a time-segmented monitoring and early warning method for distribution centers according to the present invention;

[0024] Figure 3 This is a flowchart of the first embodiment of a time-segmented monitoring and early warning method for distribution centers according to the present invention;

[0025] Figure 4 This is a flowchart of the first embodiment of a time-segmented monitoring and early warning method for distribution centers according to the present invention;

[0026] Figure 5 This is a flowchart of the first embodiment of a time-segmented monitoring and early warning method for distribution centers according to the present invention;

[0027] Figure 6 This is a schematic diagram of a time-segmented monitoring and early warning device for a distribution center according to the present invention;

[0028] Figure 7 This is a schematic diagram of an embodiment of the computer device of the present invention. Detailed Implementation

[0029] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer from the following description and claims. It should be noted that the drawings are all in a very simplified form and use non-precise ratios, and are only used to facilitate and clarify the illustration of the embodiments of the present invention.

[0030] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0031] Example 1

[0032] Please see Figure 1 This is a flowchart of a time-segmented monitoring and early warning method for distribution centers. It includes:

[0033] S110: In the data warehouse, a layering mechanism is pre-established for the real-time data of the distribution center, and the scope, data extraction path and data processing method of each target data layer after layering are set.

[0034] S120: The target data to be statistically analyzed is divided into target data layers and real-time target data corresponding to each target data layer according to the layering mechanism;

[0035] S130: Set up multiple timeliness monitoring modules for the target data layers, and calculate the timeliness information of the real-time target data in each timeliness monitoring module according to a preset timeliness report model based on the multiple timeliness monitoring modules;

[0036] S140: Display the timeliness information of each timeliness monitoring module and issue corresponding early warning information according to the preset early warning strategy.

[0037] First, let's introduce steps S110-S120.

[0038] The applicant discovered that if the underlying real-time data is processed directly, the timeliness monitoring of the entire real-time data needs to be recalculated once the timeliness segmentation monitoring mechanism changes. The data in the data warehouse is updated in real time, making it impossible to monitor and recalculate, which is simply not feasible. This is the core reason why it is impossible to monitor data that is scattered across various business scenarios.

[0039] Therefore, this invention introduces a data warehouse layering technique in data warehouse construction. There are two possible implementation paths in this application: The first is to design the data warehouse layering ourselves, and define the scope corresponding to each target data layer after layering, the data extraction path of the scope, and the data processing method. The second is to utilize existing layering and the layered data in existing data warehouse construction.

[0040] Let's first introduce the second technical implementation scheme. After order data is generated, it is sent to the system's data warehouse through various interfaces. The current order server obtains the raw real-time data from the data warehouse, pre-cleans and performs secondary processing on the order-related data, and then organizes it into order data before saving it to the order server. Generally, the order server processes the real-time data according to a pre-set cycle and updates it to the order data layer. For example, the order number is used as the core data and saved to the user information corresponding to that order. After the data uploaded from the collection terminal of the transport vehicle enters the data warehouse, the transport information server similarly cleans and processes this data before storing it in the transport information layer. The data stored in the transport information layer includes, but is not limited to, origin station information, origin time, distribution center information, entry time, exit time, and route information. After the scanning terminal uploads the scanned information to the scanning server, it enters the data warehouse. The scanning server cleans and processes this data before storing it in the scanning information layer.

[0041] In establishing a data warehouse, this invention directly utilizes existing layered structures and the data after layering. For example, a synchronization module is set up in each of the order server, transportation information server, and scanning server to periodically update the data in these servers to the corresponding order data layer, transportation information layer, and scanning information layer. Taking the data in our data warehouse as an example, relevant teams already maintain the data on the order server, transportation information server, and scanning server. Our technical personnel only need to directly use this data to construct the corresponding order data layer, transportation information layer, and scanning information layer of this data warehouse. Moreover, the synchronization module can ensure the timeliness and accuracy of these layered data.

[0042] Without relevant data to utilize, one must manually define the scope of each target data layer after layering, the data extraction path for that scope, and the data processing method. The target data to be statistically analyzed is then layered according to the defined layering mechanism to obtain the corresponding number of target data layers and the real-time target data for each layer. Specifically, this includes defining which fields are included in the order data layer, transportation information layer, and scanning information layer, the data extraction path for these fields from the basic database, and how to perform cleaning and secondary processing. Each calculation can only be performed from the underlying basic database.

[0043] However, the present invention offers the following advantages after data stratification and corresponding processing:

[0044] First: Clear data structure: Each data layer has its own scope, which makes it easier for us to locate and understand when using the table.

[0045] Second: Data lineage tracing: Simply put, we ultimately present the business with a business table that is directly used, but it has many sources. If a source table has a problem, we can quickly and accurately locate the problem and understand its scope of harm. This layering can achieve both rapid location and rapid modification.

[0046] Third: Reduce redundant development: Standardizing data layering and developing some common intermediate-layer data can greatly reduce redundant calculations. It simplifies complex problems. Breaking down a complex task into multiple steps, with each layer handling only a single step, makes it simpler and easier to understand. It also facilitates maintaining data accuracy; when data problems occur, it's not necessary to repair all the data, but only to start repairing the problematic step.

[0047] Fourth: It shields the anomalies in the original data and the impact on business operations, so that data needs to be re-integrated every time a business operation is modified.

[0048] The step of completing the target data layering and the corresponding real-time target data for each target data layer according to the layering mechanism further includes: setting up a synchronization module in the order server, transportation information server, and scanning server respectively, and periodically updating the data in these servers to the corresponding order data layer, transportation information layer, and scanning information layer, thereby obtaining the real-time target data corresponding to each target data layer.

[0049] Furthermore, obtaining the target data layer corresponding to the number of layers and the real-time target data corresponding to each target data layer includes: determining the target data currently to be processed in the server; obtaining the data push requirement information of the target data; wherein the data push requirement information is information pre-configured based on the data push requirement template; and performing corresponding data push processing on the target data based on the data push requirement information to send the target data to its corresponding target data layer.

[0050] This application addresses the problem of excessive development workload caused by the need for separate development for different data push requirements of various data tables in a database, by pre-creating a unified and universal data push requirement template. The data push requirement template integrates various attribute configuration items corresponding to the different data push requirements of each data table in the database. Specifically, it can integrate various attribute configuration items such as table name, field names for filtering data, query methods, and file generation rules (or file writing methods). In a practical application environment, the data push requirement template can be displayed to the user in the form of a configuration file. The user can then configure the values ​​of each attribute configuration item according to the actual push requirements of each data table in the database, thereby generating the required configuration files for each data table.

[0051] When developing and implementing the data push requirement template, the applicable scope of the template can be defined in advance according to the actual needs. For example, the template can be defined as applicable only to the data push requirement configuration of each data table in a certain database, or as applicable to the data push requirement configuration of each data table in multiple different databases. Based on this, the required attribute configuration items can be determined and integrated according to the defined applicable scope to finally form the required data push requirement template.

[0052] Next, let's introduce step S130. For example... Figure 2 As shown, the step of calculating the timeliness information of the real-time target data in each timeliness monitoring module based on the multiple timeliness monitoring modules according to the preset timeliness report model further includes:

[0053] S210: Set up a timeliness report model according to business requirements. The timeliness report model includes at least the set timeliness monitoring module information and the order timeliness monitoring report generation rule information. The generation rule information further includes which target data layers and which field information the timeliness monitoring module obtains, and which calculation methods are used to update the corresponding field values ​​of the order timeliness monitoring report.

[0054] S220: The timeliness monitoring module obtains the real-time data in the corresponding field of the corresponding target data layer according to the timeliness report model, and updates the corresponding field value of the order timeliness monitoring report according to the preset calculation method;

[0055] S230: Calculate the timeliness information in the order timeliness monitoring report of the order layer.

[0056] This is the core of the invention.

[0057] Set up a timeliness report model according to business needs. This timeliness report model should include at least: information on the set timeliness monitoring modules, the calculation methods for each timeliness monitoring module, and the storage path after calculation. Simply put, the timeliness monitoring modules are assumed to be multi-segmented, further including: originating point - inbound distribution module, inbound distribution - outbound distribution module, outbound distribution - inbound distribution module, inbound distribution - outbound distribution module, and outbound distribution - final delivery module.

[0058] The timeliness report model includes at least the generation of order timeliness monitoring reports, which is the data generation method at the lowest level—the order timeliness layer. Order timeliness monitoring reports include fields such as: "Order Number, Planned Days, Actual Days, Total Timeliness, Timeliness Achieved, Delayed Delivery from Outlet to Distribution Center, Delayed Origin Distribution Operation, Delayed Delivery from Distribution Center to Distribution Center, Delayed Last-Mile Distribution Operation, and Delayed Last-Mile Delivery." When the originating outlet-entry distribution center module receives and calculates the data, it directly generates a new order timeliness monitoring report. The order number data from the transportation information layer is directly written into the "Order Number" data of this order timeliness monitoring report. The data in the "Delayed Origin Distribution Operation" field is the time difference between the "Time Point of the First Distribution Operation" field and the "Origin Time" field in the transportation information layer. This represents the source (which data layer and field data it originates from), calculation method, storage path, and other storage attribute information for each field in the order timeliness monitoring report. This can be calculated using only one timeliness monitoring module (similar to a process). Considering the large volume of real-time data, this example can also configure multiple processes (such as the originating point-inbound sorting module, inbound-outbound sorting module, outbound-inbound-outbound sorting module, inbound-outbound-outbound module, and outbound-last delivery module) to perform parallel calculations, and update the corresponding fields in the order timeliness monitoring report with the calculated results. Setting the calculation field information for each module improves the overall parallel processing capability.

[0059] The above operations can generate the timeliness data for each order in the order timeliness layer.

[0060] like Figure 3 As shown, the process between steps S130 and S140 also includes:

[0061] S310: It adopts parallel processing of the originating point-entry sorting module, the entry sorting-exit sorting module, the exit sorting-entry end sorting module, the entry end sorting-exit end sorting module, and the exit end sorting-last delivery module to generate order timeliness monitoring reports corresponding to each order, forming a multi-segment timeliness report model data of the order layer.

[0062] S320: Establish corresponding timeliness monitoring reports for the aforementioned order timeliness monitoring reports according to the site layer or distribution center layer, set the timeliness statistical dimensions of the corresponding layer, and generate timeliness report model data of different layers and different granularities;

[0063] S330: Upon receiving a user's timeliness monitoring request, obtain the corresponding weights for the user, and provide timeliness report model data of different granularities at the corresponding weights.

[0064] The system generates timeliness reports by obtaining timeliness data for each order in the order timeliness layer through S130. This data can be aggregated, for example, by distribution center or station, and the granularity of aggregation can be set. For instance, it can aggregate delivery status based on the distribution center as the originating point, or by the destination center as the delivery point. Generally, monitoring permissions can be granted to users at distribution centers or stations. When they access the system, timeliness report models with different granularities corresponding to the attribute layer can be provided based on different permissions (such as permission design in attributes, demand level settings, etc.).

[0065] Detailed instructions for step S140.

[0066] like Figure 4 As shown, issuing corresponding early warning information according to the preset early warning strategy further includes:

[0067] S410: Set the warning range for timeliness data. The warning range shall include at least one node in the logistics network, including a station or distribution center. The timeliness data is used to characterize the processing time of the logistics object within the warning range.

[0068] S420: Obtain historical multi-segment time-efficiency data of historical logistics objects processed within the warning range recorded by the node, and first multi-segment time-efficiency data of a logistics object from entering the warning range to leaving the warning range;

[0069] S430: Based on the historical multi-segment time-efficiency data, the first multi-segment time-efficiency data, and the preset logistics attenuation coefficient, determine the current multi-segment time-efficiency data of the warning range, wherein the logistics attenuation coefficient is used to characterize the time-efficiency change frequency of the warning range;

[0070] S440: When the current time-sensitive data meets the set threshold, dynamically generate warning information for the corresponding warning range.

[0071] For example, for each node in the logistics network, based on historical timeliness data and the first multi-segment timeliness data, calculate the logistics attenuation coefficient of the node. This logistics node coefficient is used to characterize the timeliness change frequency within the warning range. For example, when the first multi-segment timeliness data of the relevant express deliveries at a certain node shows an extension, it will affect the logistics attenuation coefficient of this node. The system determines that the operation of this node has been affected recently, and according to a specific algorithm, it will extend the warning range of this node. When, at a certain future time period, the multi-segment timeliness data improves, it will also affect the logistics attenuation coefficient of this node, thereby shortening the warning range of this node. In this way, real-time dynamic prediction of the warning range for each node in the logistics network can be achieved. The nodes described here can be outlets or sorting centers in the logistics network, etc.

[0072] In addition, each timeliness module has a corresponding order delay rate, and a scoring strategy for the delay rate is formulated: for example, the delay rate for multi-segment timeliness in the case of responsibility determination for delays during the sending process; responsibility determination for delays during the sending / arrival process: ① The best in the past A1, the planned timeliness achievement rate B1, the difference is C1 = B1 - A1, the total number of votes is the number of votes with achieved timeliness, and the number of votes with unachieved timeliness; ② Delay rate: outlet → sorting center, origin sorting operation, sorting center → sorting center, terminal sorting operation.

[0073] For example, if the receiving time of express delivery 001 at the origin outlet is A1 and the arrival time at the first sorting center is B1, then the timeliness from the origin outlet to the first sorting center is C1 = B1 - A1. At this time, the standard timeliness value from the origin outlet corresponding to the vehicle line information of this express delivery to the first sorting center is Z1. Then, if C1 > Z1, it is determined that the timeliness of express delivery 001 is not achieved; if C1 < Z1, it is determined that the timeliness of express delivery 001 is achieved.

[0074] As Figure 5 shown, as a preferred embodiment of the present invention, it may further include:

[0075] S510: When receiving a request from other servers or users to obtain sorting center timeliness segment monitoring data, perform legality authentication on the request;

[0076] S520: After successful authentication, process the timeliness information of the real-time target data in each timeliness monitoring module as privacy data;

[0077] S530: Obtain the privacy data through the key management system for encrypted transmission; the key management system generates at least one type of key data in different cycles, where the key data at least includes: content encryption key CEK, key encryption key KEK.

[0078] Specifically, when another server requests to obtain time-series monitoring data from the distribution center, its legitimacy is authenticated according to a preset mechanism. After authentication, the time-series information from each monitoring module is grouped according to a certain bit length. For example, it can be grouped into groups of 8 bytes to obtain D1D2…Dn (if the data is not a multiple of 8, it is padded with specified PADDING data). The grouped current target data is then encrypted with a key to obtain the ciphertext of the current target data and sent to the requesting distribution center; the key data includes at least: the content key CEK and the key encryption key KEK.

[0079] Specifically:

[0080] 1) The result of XORing the first set of data D1 with the initialization vector I is encrypted with DES to obtain the first set of ciphertext C1 (initialization vector I is all zeros).

[0081] 2) The result of XORing the second set of data D2 with the encryption result C1 of the first set is then encrypted with DES to obtain the second set of ciphertext C2.

[0082] 3) The data thereafter are processed in the same way to obtain Cn.

[0083] 4) The encrypted result is obtained by connecting the results in the order C1C2C3…Cn. The server then sends the encrypted result to each distribution center.

[0084] The encrypted target data and the key are decrypted and merged, and then the target data is displayed through the distribution center terminal.

[0085] 1) First, group the data into groups of 8 bytes each to get C1C2C3…Cn.

[0086] 2) After decrypting the first set of data, XOR it with the initialization vector I to obtain the first set of plaintext D1 (Note: Decryption must be performed before XOR).

[0087] 3) After decrypting the second set of data C2, XOR it with the first set of ciphertext data to obtain the second set of data D2.

[0088] 4) And so on, until we get Dn.

[0089] 5) Connecting them in order as D1D2D3…Dn gives the decryption result.

[0090] Encrypting data during transmission ensures data security and system operational security.

[0091] Example 2

[0092] like Figure 6As shown, based on the same concept, the present invention also provides a timeliness segmentation monitoring and early warning device 600 for distribution centers. This device 600 includes: a data layering module 610, used to pre-establish several layering mechanisms for real-time data of the distribution center in a data warehouse, and to set the scope corresponding to each target data layer after layering, the data extraction path of the scope, and the data processing method; a data acquisition module 620, used to complete the layering of the target data to be statistically analyzed according to the layering mechanism and the real-time target data corresponding to each target data layer; a timeliness monitoring module 630, used to set multiple timeliness monitoring modules for the target data layers, and to calculate the timeliness information of the real-time target data in each timeliness monitoring module based on the multiple timeliness monitoring modules according to a preset timeliness report model; and an early warning module 640, used to display the timeliness information of each timeliness monitoring module and issue corresponding early warning information according to a preset early warning strategy.

[0093] Example 3

[0094] like Figure 7 As shown, based on the same concept, the present invention also provides a computer device 700, which may vary considerably due to different configurations or performance. It may include one or more central processing units (CPUs) 710 (e.g., one or more processors) and a memory 720, and one or more storage media 730 (e.g., one or more mass storage devices) storing application programs 733 or data 732. The memory 720 and storage media 730 may be temporary or persistent storage. The program stored in the storage media 730 may include one or more modules (not shown in the figure), each module including a series of instruction operations on the computer device 700. Furthermore, the processor 710 may be configured to communicate with the storage media 730 and execute the series of instruction operations in the storage media 730 on the computer device 700.

[0095] The computer device 700 may also include one or more power supplies 740, one or more wired or wireless network interfaces 750, one or more input / output interfaces 760, and / or one or more operating systems 731, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc.

[0096] Those skilled in the art will understand that Figure 7 The computer device structure shown does not constitute a limitation on the computer device and may include more or fewer components than shown, or combinations of certain components, or different component arrangements.

[0097] When the computer-readable instructions are executed by the processor, the processor performs the following steps: First, a layering mechanism is pre-established in the data warehouse to perform several layers on the real-time data of the distribution center, and the scope, data extraction path, and data processing method corresponding to each target data layer after layering are set; Second, the target data to be statistically analyzed is layered according to the layering mechanism to complete the layering of the target data layers and the real-time target data corresponding to each target data layer; Third, multiple timeliness monitoring modules are set up for the target data layers, and the timeliness information of the real-time target data in each timeliness monitoring module is calculated based on the multiple timeliness monitoring modules according to a preset timeliness report model; Fourth, the timeliness information of each timeliness monitoring module is displayed, and corresponding early warning information is issued according to a preset early warning strategy.

[0098] In one embodiment of the present invention, the step of calculating the timeliness information of the real-time target data in each timeliness monitoring module based on the plurality of timeliness monitoring modules according to a preset timeliness report model further includes: setting a timeliness report model according to business requirements, wherein the timeliness report model includes at least the set timeliness monitoring module information and the generation rule information of the order timeliness monitoring report, wherein the generation rule information further includes which field information of which target data layer the timeliness monitoring module obtains, and which calculation methods are used to update the corresponding field values ​​of the order timeliness monitoring report; the timeliness monitoring module obtains the real-time data in the corresponding field of the corresponding target data layer according to the timeliness report model, and updates the corresponding field values ​​of the order timeliness monitoring report according to a preset calculation method; and calculates the timeliness information in the order timeliness monitoring report of the order layer.

[0099] In one embodiment of the present invention, after the timeliness information of the real-time target data in each timeliness monitoring module is calculated based on the multiple timeliness monitoring modules according to the preset timeliness report model, the method further includes: using the originating point-entry sorting module, the entry-exit sorting module, the exit sorting module, the exit sorting-entry-end sorting module, the entry-exit sorting-exit-end sorting module, and the exit-end delivery module to process in parallel, generating order timeliness monitoring reports corresponding to each order, forming multi-segment timeliness report model data of the order layer; establishing corresponding layer timeliness monitoring reports for the order timeliness monitoring reports according to the site layer or the sorting center layer, setting the timeliness statistical dimensions of the corresponding layer, and generating timeliness report model data of different layers and different granularities; receiving the user's timeliness monitoring request, obtaining the corresponding weights of the user, and providing timeliness report model data of different granularities of the layer corresponding to the weights.

[0100] In one embodiment of the present invention, after issuing the corresponding early warning information according to the preset early warning strategy, the method further includes: when receiving a request from other servers or users to obtain time-series monitoring data of the distribution center, performing legality authentication on the request; after successful authentication, processing the time-series information of the real-time target data in each time-series monitoring module as privacy data; obtaining the privacy data through the key management system for encrypted transmission; the key management system generating at least one key data in different periods, wherein the key data includes at least: a content key CEK and a key encryption key KEK.

[0101] In one embodiment of the present invention, issuing corresponding early warning information according to a preset early warning strategy further includes: setting an early warning range for timeliness data, wherein the early warning range includes at least one node in the logistics network, including a station or distribution center, and the timeliness data is used to characterize the processing time of logistics objects within the early warning range; acquiring historical multi-segment timeliness data of historical logistics objects processed within the early warning range recorded by the node, and a first multi-segment timeliness data of a logistics object from entering the early warning range to leaving the early warning range; determining the current multi-segment timeliness data of the early warning range based on the historical multi-segment timeliness data, the first multi-segment timeliness data, and a preset logistics attenuation coefficient, wherein the logistics attenuation coefficient is used to characterize the frequency of timeliness changes within the early warning range; and dynamically generating early warning information corresponding to the early warning range when the current timeliness data meets a preset threshold.

[0102] In one embodiment of the present invention, the step of completing the target data layering and the real-time target data corresponding to each target data layer according to the layering mechanism further includes: setting up a synchronization module in the order server, the transportation information server, and the scanning server respectively, and periodically updating the data in the servers to the corresponding order data layer, transportation information layer, and scanning information layer respectively, so as to obtain the real-time target data corresponding to each target data layer.

[0103] In one embodiment of the present invention, obtaining the target data layer with the corresponding number of layers and the real-time target data corresponding to each target data layer includes: determining the target data to be processed in the server; obtaining the data push requirement information of the target data; wherein the data push requirement information is information pre-configured based on the data push requirement template; and performing corresponding data push processing on the target data based on the data push requirement information to send the target data to its corresponding target data layer.

[0104] In one embodiment, a readable storage medium is provided, wherein when the computer-readable instructions are executed by one or more processors, the one or more processors perform the above-described steps, the specific steps of which will not be repeated here.

[0105] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0106] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0107] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring and early warning of time-critical segmented monitoring in a distribution center, characterized in that, The application relates to a time limit monitoring method and device for a logistics network. The application comprises the following steps: The target data to be statistically analyzed is divided into target data layers according to the hierarchical mechanism, and the scope corresponding to each target data layer, the data extraction path of the scope and the data processing mode of the scope are set; A plurality of time limit monitoring modules are set for the target data layers, time limit information of the real-time target data in each time limit monitoring module is calculated based on the plurality of time limit monitoring modules according to a preset time limit report model; further comprising: setting a time limit report model according to business requirements, the time limit report model at least comprises set time limit monitoring module information and order time limit monitoring report generation rule information, the generation rule information further comprises which field information of which target data layer is acquired by the time limit monitoring module, and which calculation mode is used to update the corresponding field value of the order time limit monitoring report; the time limit monitoring module acquires real-time data in the corresponding field of the corresponding target data layer according to the time limit report model, and updates the corresponding field value of the order time limit monitoring report according to the preset calculation mode; the time limit information in the order time limit monitoring report of the order layer is calculated; The order time limit monitoring report corresponding to each order is generated by parallel processing of the originating network point-in first distribution module, the in first distribution-out first distribution module, the out first distribution-in last distribution module, the in last distribution-out last distribution module and the out last distribution-end delivery module, and the multi-segment time limit report model data of the order layer is formed; the order time limit monitoring reports are set up into time limit monitoring reports of corresponding layers and the time limit statistical latitude of the corresponding layers is set up according to the site layer or the distribution center layer, and the time limit report model data of different layers and different granularities is generated; when a time limit monitoring request of a user is received, the right item corresponding to the user is obtained, and the time limit report model data of different layers and different granularities matched with the right item is provided; The time limit information of each time limit monitoring module is displayed, and corresponding early warning information is sent according to a preset early warning strategy; further comprising: setting an early warning range of time limit data, the early warning range at least comprising a node including a site or a distribution center in a logistics network, the time limit data being used for representing the processing time of the logistics object in the early warning range; acquiring historical multi-segment time limit data of historical logistics objects processed in the early warning range recorded by the node and first multi-segment time limit data of a logistics object from entering the early warning range to leaving the early warning range; determining current multi-segment time limit data of the early warning range according to the historical multi-segment time limit data, the first multi-segment time limit data and a preset logistics attenuation coefficient, wherein the logistics attenuation coefficient is used for representing the time limit change frequency of the early warning range; and dynamically generating early warning information of the corresponding early warning range when the current time limit data meets a set threshold.

2. The method of claim 1, wherein the time-critical segment is a time-critical segment of a call center. The application further comprises the following steps after the time limit information of each time limit monitoring module is displayed and corresponding early warning information is sent according to a preset early warning strategy: ​ When receiving a request for obtaining the time segment monitoring data of the distribution center from other servers or users, legitimacy authentication is performed on the request; After the authentication, the time information of the real-time target data in each time monitoring module is treated as privacy data; The privacy data is obtained through a key management system for encrypted transmission; the key management system generates at least one key data in different periods, wherein the key data at least includes: content encryption key (CEK) and key encryption key (KEK).

3. The time-segmented monitoring and early warning method for distribution centers as described in claim 1, characterized in that, The target data to be statistically analyzed is layered according to the hierarchical mechanism to complete the target data layer layering and the real-time target data corresponding to each target data layer further includes: A synchronization module is arranged in the order server, the transportation information server and the scanning server respectively, and the data in the servers is updated to the corresponding order data layer, transportation information layer and scanning information layer respectively to obtain the real-time target data corresponding to each target data layer.

4. The method of claim 1, wherein the time-critical segment is a time-critical segment of a call center. 5 The target data layer corresponding to the number of layers and the real-time target data corresponding to each target data layer include: Determining the target data currently to be processed in the server; Obtaining the data push demand information of the target data; wherein the data push demand information is information configured in advance based on a data push demand template; Based on the data push demand information, the target data is processed for corresponding data push to realize sending the target data to the corresponding target data layer.

5. A time-aging segment monitoring and early warning device for a distribution center, characterized by, It includes: A data layering module is used to pre-establish a plurality of hierarchical mechanisms for real-time data of the distribution center in the data warehouse, and set the scope corresponding to each target data layer after layering, the data extraction path of the scope and the data processing mode; A data acquisition module is used to complete the target data layer layering and the real-time target data corresponding to each target data layer according to the hierarchical mechanism for the target data to be statistically analyzed; A time monitoring module is used to set a plurality of time monitoring modules for the target data layer, calculate the time information of the real-time target data in each time monitoring module based on the plurality of time monitoring modules according to a preset time report model; the time report model is set according to business requirements, and at least includes the time monitoring module information and the generation rule information of the order time monitoring report, and the generation rule information further includes which target data layer and which field information are obtained by the time monitoring module, and how to update the corresponding field value of the order time monitoring report according to the calculation method; The time limit monitoring module obtains real-time data in corresponding fields of a corresponding target data layer according to the time limit report model, and updates corresponding field values of the order time limit monitoring report according to a pre-set calculation method; calculates time limit information in the order time limit monitoring report of the order layer; generates order time limit monitoring reports corresponding to each order by parallel processing of a starting network point-in first distribution module, an in first distribution-out first distribution module, an out first distribution-in last distribution module, an in last distribution-out last distribution module, and an out last distribution-end delivery module, and forms multi-segment time limit report model data of the order layer; establishes time limit monitoring reports of corresponding layers according to a site layer or a distribution center layer, sets time limit statistics latitude of the corresponding layer, and generates time limit report model data of different layers and different granularities; receives a time limit monitoring request of a user, obtains a right item corresponding to the user, and provides time limit report model data of different layers and different granularities matched with the right item; The early warning module is configured to display the time limit information of the time limit monitoring modules, and send corresponding early warning information according to a pre-set early warning strategy; set an early warning range of the time limit data, the early warning range at least including a node including a site or a distribution center in a logistics network, the time limit data being used to represent a processing time of the logistics object in the early warning range; obtain historical multi-segment time limit data of historical logistics objects processed in the early warning range recorded by the node, and first multi-segment time limit data of a logistics object from entering the early warning range to leaving the early warning range; determine current multi-segment time limit data of the early warning range according to the historical multi-segment time limit data, the first multi-segment time limit data, and a pre-set logistics attenuation coefficient, wherein the logistics attenuation coefficient is used to represent a time limit change frequency of the early warning range; and dynamically generate early warning information of the corresponding early warning range when the current time limit data meets a set threshold.

6. A computer device, comprising: Comprise: a memory for storing a processing program; a processor for executing the processing program to implement the distribution center time limit segmentation monitoring and early warning method according to any one of claims 1 to 4.

7. A readable storage medium, characterized by, The readable storage medium stores a processing program, and the processing program is executed by the processor to implement the distribution center time limit segmentation monitoring and early warning method according to any one of claims 1 to 4.

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