Abnormal logistics tracking method and device, terminal equipment and storage medium

Abstract

The invention is suitable for the technical field of logistics, and provides an abnormal logistics tracking method and device, terminal equipment and a storage medium, and the method comprises the steps: firstly obtaining the data information of one or more business objects, the data information comprises at least one of the generation time, the current business state and the operation action; obtaining an exception judgment rule corresponding to each business object; then matching the data information of each business object with a corresponding exception judgment rule to obtain a matching result; and finally, based on the matching result corresponding to each business object, identifying a target object. Therefore, matching operation is performed on the acquired data information of each business object in combination with the abnormity judgment rule, so that the abnormal target object in one or more business objects is determined in time, and the timeliness of abnormal logistics tracking is improved.

Description

Technical Field

[0001] The present application belongs to the field of logistics technology, and in particular, relates to an abnormal logistics tracking method, device, terminal equipment and storage medium. Background Art

[0002] Each express parcel has to go through multiple business links, from order placement to transportation transit, to delivery and receipt, as well as possible forwarding returns or user complaints. Different business links may lead to different abnormal situations.

[0003] In related technologies, in order to track and process abnormal logistics, each business link corresponds to a business system, and each business system provides corresponding business data screening functions, such as the delivery business system provides a delivery list query function. The operator responsible for each business link needs to actively screen and determine which express parcels have abnormal conditions based on the screening results. However, this abnormal logistics tracking method relies on manual screening and has poor timeliness. Summary of the invention

[0004] The embodiments of the present application provide an abnormal logistics tracking method, apparatus, terminal device and storage medium, which can solve the problem that the traditional abnormal logistics tracking method relies on manual screening and has poor timeliness.

[0005] A first aspect of an embodiment of the present application provides an abnormal logistics tracking method, comprising:

[0006] Acquire data information of one or more business objects, the data information including at least one of generation time, current business status and operation action; acquire an exception determination rule corresponding to each business object; match the data information of each business object with the corresponding exception determination rule to obtain a matching result; identify a target object based on the matching result corresponding to each business object.

[0007] In a possible implementation of the first aspect, before obtaining the abnormality determination rule corresponding to each business object, the abnormal logistics tracking method further includes:

[0008] In response to an operation instruction sent by a user through a visual editing interface, obtaining operation configuration information carried in the operation instruction;

[0009] updating at least one abnormality determination rule according to the operation configuration information;

[0010] The updated exception determination rules are stored in the database.

[0011] Optionally, in another possible implementation of the first aspect, the obtaining of an exception determination rule corresponding to each business object includes:

[0012] For each business object, determine whether there is a corresponding exception determination rule in the local cache;

[0013] If so, the corresponding exception determination rule is obtained from the local cache;

[0014] If not, determine whether there is a corresponding exception determination rule in the remote data service Redis (Remote Dictionary Server, remote data service) cache;

[0015] If so, the corresponding exception determination rule is obtained from the Redis cache.

[0016] Optionally, in yet another possible implementation of the first aspect, matching the data information of each business object with the corresponding exception determination rule to obtain a matching result includes:

[0017] According to the data information of the multi-rule business object and the corresponding abnormality determination rules, a plurality of abnormality determination tasks are formed, wherein the multi-rule business object corresponds to at least two abnormality determination rules;

[0018] The various anomaly determination tasks corresponding to the multi-rule business objects are executed in parallel to determine the matching results of the multi-rule business objects.

[0019] Optionally, in another possible implementation of the first aspect, after identifying the target object based on the matching result corresponding to each business object, the abnormal logistics tracking method further includes:

[0020] Generate abnormal reminder information;

[0021] Determine the abnormal reminder interval according to the abnormal judgment rules corresponding to the target object;

[0022] Based on the exception reminder interval, send exception reminder information to the business processing user.

[0023] Optionally, in another possible implementation of the first aspect, the above-mentioned exception determination rule includes a timeout determination rule, and the above-mentioned matching of the data information of each business object with the corresponding exception determination rule to obtain a matching result includes:

[0024] Determine the current execution duration of the business object based on the generation time of the business object and the current time;

[0025] Based on the current execution time, determine whether the data information of the business object matches the timeout determination rule to obtain a matching result.

[0026] Optionally, in yet another possible implementation of the first aspect, the above-mentioned exception determination rule includes an abnormal event determination rule, and the above-mentioned matching of the data information of each business object with the corresponding exception determination rule to obtain a matching result includes:

[0027] According to the abnormal event determination rules and the operation actions corresponding to the business objects, determine the abnormal business status corresponding to the operation actions;

[0028] According to the abnormal business status and the current business status of the business object, it is judged whether the data information of the business object matches the abnormal event determination rules to obtain the matching result.

[0029] A second aspect of an embodiment of the present application provides an abnormal logistics tracking device, comprising:

[0030] An information acquisition module, used to acquire data information of one or more business objects, the data information including at least one of generation time, current business status and operation action;

[0031] A rule acquisition module is used to obtain the exception determination rules corresponding to each business object;

[0032] A rule matching module is used to match the data information of each business object with the corresponding exception determination rule to obtain a matching result;

[0033] The object identification module is used to identify the target object based on the matching result corresponding to each business object.

[0034] A third aspect of an embodiment of the present application provides a terminal device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the abnormal logistics tracking method of the first aspect when executing the computer program.

[0035] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the abnormal logistics tracking method of the first aspect is implemented.

[0036] A fifth aspect of an embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device executes the abnormal logistics tracking method of the first aspect.

[0037] Compared with the prior art, the embodiments of the present application have the following beneficial effects: the embodiments of the present application provide a method, apparatus, terminal device and storage medium for tracking abnormal logistics, including: first, obtaining data information of one or more business objects, the data information including at least one of generation time, current business status and operation action; then obtaining the abnormality determination rule corresponding to each business object; then matching the data information of each business object with the corresponding abnormality determination rule to obtain a matching result; finally, identifying the target object based on the matching result corresponding to each business object. Thus, by matching the data information of each business object obtained in combination with the abnormality determination rule, the target object with abnormality in one or more business objects is timely determined, thereby improving the timeliness of abnormal logistics tracking. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 It is a flowchart of an abnormal logistics tracking method provided in Example 1 of the present application;

[0040] Figure 2 This is a schematic diagram of a process for obtaining data information provided by an embodiment of the present application;

[0041] Figure 3 This is a schematic diagram of an interface for constructing an abnormality determination rule provided in an embodiment of the present application;

[0042] Figure 4 It is a flowchart of an abnormality determination rule update provided by an embodiment of the present application;

[0043] Figure 5 This is a flow chart of obtaining an abnormality determination rule provided in an embodiment of the present application;

[0044] Figure 6 It is a flowchart of parallel matching of multiple rule business objects provided in an embodiment of the present application;

[0045] Figure 7 It is a structural schematic diagram of an abnormal logistics tracking device provided in Example 2 of the present application;

[0046] Figure 8 It is a structural diagram of a terminal device provided in Example 3 of the present application. DETAILED DESCRIPTION

[0047] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0048] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0049] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0050] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0051] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0052] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0053] It should be understood that the size of the serial numbers of the steps in this embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0054] In related technologies, in order to track and process abnormal logistics, each business link corresponds to a business system, and each business system provides corresponding business data screening functions, such as the delivery business system provides a delivery list query function. The operator responsible for each business link needs to actively screen and determine which express parcels have abnormal conditions based on the screening results. However, this abnormal logistics tracking method relies on manual screening and has poor timeliness.

[0055] In view of this, the embodiments of the present application provide an abnormal logistics tracking method, apparatus, terminal device and storage medium, which match the data information of each business object obtained by combining abnormal judgment rules, thereby timely determining the target objects with abnormalities in one or more business objects, thereby improving the timeliness of abnormal logistics tracking.

[0056] In order to illustrate the technical solution of the present application, specific embodiments are provided below.

[0057] Reference Figure 1 , shows a flow chart of an abnormal logistics tracking method provided in Example 1 of the present application. Figure 1 As shown, the abnormal logistics tracking method may include the following steps:

[0058] Step 101: Obtain data information of one or more business objects.

[0059] Among them, business objects refer to the different links or tasks involved in the logistics process, which may involve different operations, processing and management. Business objects can be front-end collection business, end-end delivery business, return business, etc.

[0060] In the embodiment of the present application, the data information of one or more business objects may be generated by multiple express business systems, such as a collection business system, a transportation transit business system, a delivery business system, etc. These express business systems together constitute a complete express business process, covering all links from collection to delivery. Each express business system plays a specific role in the entire process to ensure that express parcels can be processed and delivered smoothly and efficiently.

[0061] It should be noted that the data information of each business object may include the express order number, business object, current business status, time attribute, operation action and other information, among which the time attribute may be generation time, pickup time, collection time, delivery time, receipt time, etc.; the operation action may be return scanning, etc. For example, the collection business system generates a piece of data information with the business object being the front-end collection business at 8:00 a.m. on June 29, 2023. The data information of the front-end collection business may specifically be {express order number: 11001, business object: front-end collection business, current business status: not picked up, generation time: 2023 / 06 / 29 08:00:00}. For another example, the delivery business system generates a piece of data information with the business object being the delivery business at 8:00 a.m. on June 30, 2023. The data information of the delivery business may specifically be {express order number: 13001, operation action: delivery scanning, generation time: 2023 / 06 / 30 08:00:00}.

[0062] For example, refer to Figure 2 , is a flow chart of data information acquisition. Figure 2 As shown, the data transmission service can be used to monitor various express delivery business systems ( Figure 2 Only the terminal delivery business system and the return business system are shown in the figure) The binlog (binary log) of the MySQL database, where the MySQL binlog is a log file that records database change operations. These binlogs are then parsed to obtain the data information of the corresponding business objects. The data information of these business objects is then put into multiple message queues of the message middleware service, so that the exception judgment service cluster can receive the data information in these message queues in parallel, and then execute subsequent steps. Among them, during business peaks, the amount of data information surges, the data transmission service can dynamically increase the data transmission queue to push to the message middleware, and the exception judgment service cluster can also dynamically expand the capacity and increase the exception judgment service nodes to improve the exception judgment processing capabilities of the business object data information.

[0063] Step 102: Obtain the exception determination rule corresponding to each business object.

[0064] In an embodiment of the present application, the express service system to which the business object belongs can be determined, and then the exception determination rule included in the express service system can be obtained, and the exception determination rule is the exception determination rule corresponding to the business object. Each business object corresponds to at least one exception determination rule, and the exception determination rule is set according to the actual needs of each express service system and business object. For example, in order to prevent the business operation time from being too long and causing a decrease in customer satisfaction, a timeout determination rule can be set to ensure that the business is carried out efficiently.

[0065] Furthermore, the express delivery business environment often changes, and new exception determination rules may appear or the existing exception determination rules may need to be adjusted. Therefore, as a possible implementation method of the embodiment of the present application, before the above step 102, the operation configuration information carried in the operation instruction can be obtained in response to the operation instruction sent by the user through the visual editing interface; at least one exception determination rule is updated according to the operation configuration information; and the updated exception determination rule is stored in the database. Specifically, each exception determination rule can be abstracted into a rule factor, which is the minimum granularity of the exception determination rule and is used to construct the exception determination rule. In actual application, the exception determination rule can be updated in response to the user's operation instruction on the rule factor.

[0066] The visual editing interface is a user interface that allows users to intuitively create, edit and manage operation instructions in a graphical way without having to understand programming or technical details. The visual editing interface is designed to provide logistics business operators with an intuitive and easy-to-use way to configure and manage exception judgment rules to improve the efficiency of exception judgment rule updates.

[0067] For example, refer to Figure 3 , is a schematic diagram of an interface constructed by an abnormality judgment rule. Figure 3 As shown, the content in each box is a rule factor, and users can customize the exception judgment rules included in each express business system. In this way, the different exception judgment rule requirements of each express business system can be met, the scalability of the exception judgment rules can be improved, and the maintenance cost can be reduced.

[0068] Reference Figure 4 , is a flowchart of an abnormality determination rule update. Figure 4 As shown in the figure, when a user creates or updates an exception determination rule, the updated exception determination rule can be persistently stored in the database and Redis cache. Redis cache is an open source, high-performance memory data storage system that is widely used in cache, message queue, session storage and other scenarios. After the updated exception determination rule is stored in the database, the rule management service can be used to send the rule change event to the exception determination service, so that the exception determination service can sense the change of the exception determination rule, clear the exception rule cache, re-pull the latest exception determination rule and write it to the local cache to ensure the real-time performance of the exception determination rule.

[0069] Furthermore, due to abnormal conditions such as network jitter and server abnormality, a certain abnormality determination rule update may fail, so the abnormality determination rule can be synchronized and updated at a preset time interval, for example, the latest abnormality determination rule is synchronized to the local cache every five minutes. It should be understood that the preset time interval can be set in combination with actual application scenarios and requirements, and the embodiments of the present application do not limit this.

[0070] Correspondingly, in order to obtain the exception judgment rules corresponding to each business object, it can be obtained through a combination of local cache and Redis cache, that is, for each business object, determine whether there is a corresponding exception judgment rule in the local cache; if so, obtain the corresponding exception judgment rule from the local cache; if not, determine whether there is a corresponding exception judgment rule in the remote data service Redis cache; if so, obtain the corresponding exception judgment rule from the Redis cache.

[0071] Reference Figure 5 , is a flowchart of obtaining an abnormality determination rule. Figure 5 As shown in the figure, first check whether there is the latest exception determination rule in the local cache. If it exists in the local cache, it can be directly obtained from the local cache, avoiding the overhead of accessing remote data sources and improving the data acquisition speed. If there is no latest exception determination rule in the local cache, access the Redis cache and search for the exception determination rule in the Redis cache. If it exists, it can be directly obtained and loaded into the local cache. If it is not in the Redis cache, it is necessary to obtain the latest exception determination rule from the rule configuration service through the rule interface, and the exception determination rule can be loaded into the local cache and Redis cache for subsequent fast access. The advantage of local cache is extremely low latency and high concurrency, while Redis cache provides persistence, clustering and high availability. The combination of local cache and Redis cache can greatly reduce the number of visits to the backend data source and improve the efficiency of obtaining exception determination rules.

[0072] Step 103, matching the data information of each business object with the corresponding abnormality determination rule to obtain a matching result.

[0073] As a possible implementation, when a business object corresponds to at least two exception determination rules, the business object can be defined as a multi-rule business object. When the data information of the multi-rule business object and the corresponding exception determination rules are obtained, the data information of the multi-rule business object can be matched with the corresponding exception determination rules in turn to obtain the matching result of the multi-rule business object.

[0074] Furthermore, the matching efficiency of the multi-rule business object can be improved by matching the data information of the multi-rule business object with the corresponding abnormality determination rules in parallel. That is, as a possible implementation of the embodiment of the present application, the above step 103 may include:

[0075] According to the data information of the multi-rule business object and the corresponding abnormality determination rules, a plurality of abnormality determination tasks are formed, wherein the multi-rule business object corresponds to at least two abnormality determination rules;

[0076] The various anomaly determination tasks corresponding to the multi-rule business objects are executed in parallel to determine the matching results of the multi-rule business objects.

[0077] Reference Figure 6 , is a flowchart of parallel matching of multiple rule business objects. Figure 6 As shown, the data information of the multi-rule business object and each corresponding exception judgment rule are combined to form multiple exception judgment tasks, such as rule 1 matching task, rule 2 matching task and rule 3 matching task, etc., and then the multi-threaded concurrent execution method is used, and each thread executes a rule matching task, which greatly improves the matching efficiency of the multi-rule business object.

[0078] In a possible implementation of the present application, in order to prevent the business operation time from being too long, which may lead to a decrease in customer satisfaction, a timeout determination rule may be set to ensure that the business is carried out efficiently. Specifically, the current execution duration corresponding to the business object may be determined based on the generation time and current time of the business object; based on the current execution duration, it is determined whether the data information of the business object matches the timeout determination rule to obtain a matching result. Taking the collection service as an example, it is usually necessary to complete the collection within 24 hours after the customer places an order, that is, the collection service corresponds to a timeout determination rule, and the duration threshold is 24 hours. The generation time included in the data information of the collection service is added to the duration threshold and recorded as the delay time. If the system status of the collection service is still "not picked up" at the delay time, it is determined that the matching result of the collection service and the timeout determination rule is a successful match.

[0079] In another possible implementation of the present application, in order to prevent the operation action of the express parcel from not matching the actual business status, an abnormal event determination rule can also be set to ensure that the business can proceed smoothly. Specifically, the abnormal business status corresponding to the operation action can be determined according to the abnormal event determination rule and the operation action corresponding to the business object; according to the abnormal business status and the current business status of the business object, it is determined whether the data information of the business object matches the abnormal event determination rule to obtain a matching result. Taking the business object as a return service as an example, assuming that the user temporarily abandons the mailing and issues a return interception instruction, the current business status included in the data information of the return service should be return interception. At this time, if the operation action corresponding to the return service data is delivery scanning, it means that the express parcel has been delivered, that is, the return service has an abnormality. Therefore, when the operation action corresponding to the return service is delivery scanning, the abnormal business status corresponding to the delivery scanning includes return interception. If the current business status of the return service is also return interception, it is determined that the return service matches the abnormal event determination rule. The matching result is successful.

[0080] Step 104: Identify the target object based on the matching result corresponding to each business object.

[0081] In the embodiment of the present application, if the matching result corresponding to a certain business object is a successful match, it is considered that there is an abnormality in the business object, and the business object is the target object.

[0082] It should be noted that after the business object is determined as the target object, the data information of the target object can be persistently stored using MYCAT sub-libraries and sub-tables to ensure that a large amount of abnormal data is not lost.

[0083] Furthermore, the data information of each business object corresponds to a business processing user, and the exception judgment rule has a corresponding exception level, such as warning, exception, problem, etc. After determining a target object, a corresponding exception follow-up task can be generated and an exception reminder message can be sent to the business processing user, for example, by sending an email or IM (Instant Messaging) to the business processing user of the exception follow-up task. In order to ensure that urgent abnormal data is not ignored and is processed in a timely manner, the exception reminder interval can be determined according to the exception level corresponding to the exception judgment rule matched by the target object; based on the exception reminder interval, a reminder message is sent to the business processing user. That is, as a possible implementation method of an embodiment of the present application, after the above step 104, an exception reminder message can be generated; according to the exception judgment rule corresponding to the target object, the exception reminder interval is determined; based on the exception reminder interval, an exception reminder message is sent to the business processing user.

[0084] For example, assuming that the abnormal level is a problem, you can remind 3 times, once every 5 minutes, once every 10 minutes, and once every 30 minutes. It should be understood that the higher the abnormal level, the shorter the abnormal reminder interval can be, and the higher the abnormal reminder frequency can be.

[0085] Furthermore, the business processing user receives the exception reminder information, checks and handles this exception follow-up task, and can also set the next follow-up time based on the actual situation. When the next follow-up time arrives, the system will remind the user to follow up and handle the exception follow-up task again. When the business processing user cannot solve a certain exception follow-up task and needs assistance from other outlets, a work order can be generated for other outlets to assist in the investigation. For example, if the express delivery is delayed and timed out, a reminder work order can be sent to the delivery outlet.

[0086] In a possible implementation, data on the processing of abnormal follow-up tasks can be regularly counted, and then data on the timely processing volume / rate, overdue processing volume / rate, and resolution volume / rate of abnormal follow-up tasks can be regularly counted based on the dimensions of the follow-up person and the follow-up outlets. Then, data on the timely processing volume / rate, overdue processing volume / rate, and resolution volume / rate of the auxiliary investigation work orders can be counted based on the dimensions of the assistant investigator and the auxiliary investigation outlets. Finally, the above data can be summarized to generate corresponding incentive information and penalty information. In this way, the efficiency of abnormal logistics tracking and processing can be further improved.

[0087] The abnormal logistics tracking method disclosed in the above embodiment of the present application first obtains data information of one or more business objects, and the data information includes at least one of the generation time, current business status and operation action; then obtains the abnormal judgment rule corresponding to each business object; then matches the data information of each business object with the corresponding abnormal judgment rule to obtain a matching result; finally, based on the matching result corresponding to each business object, identifies the target object. Thus, by matching the data information of each business object obtained in combination with the abnormal judgment rule, the target object with abnormalities in one or more business objects is determined in time, thereby improving the timeliness of abnormal logistics tracking. The present application also improves the matching efficiency of multi-rule business objects by matching the data information of multi-rule business objects with the corresponding abnormal judgment rules in parallel. The abnormal judgment rule construction method provided in the embodiment of the present application is applicable to all express business systems, without the need for each express business system to develop an abnormal judgment function separately. The embodiment of the present application can also uniformly process the data information of each business object in all express business systems to avoid missing abnormal data follow-up.

[0088] See also Figure 7 , shows a schematic diagram of the structure of an abnormal logistics tracking device provided in Example 2 of the present application. For the sake of ease of explanation, only the parts related to the example of the present application are shown.

[0089] The abnormal logistics tracking device may specifically include the following modules:

[0090] The information acquisition module 701 is used to acquire data information of one or more business objects, where the data information includes at least one of a generation time, a current business status, and an operation action.

[0091] The rule acquisition module 702 is used to acquire the exception determination rule corresponding to each business object.

[0092] The rule matching module 703 is used to match the data information of each business object with the corresponding abnormality determination rule to obtain a matching result.

[0093] The object identification module 704 is used to identify the target object based on the matching result corresponding to each business object.

[0094] The abnormal logistics tracking device disclosed in the above embodiment of the present application first obtains data information of one or more business objects, and the data information includes at least one of the generation time, the current business status and the operation action. Then, the abnormality determination rule corresponding to each business object is obtained. Then, the data information of each business object is matched with the corresponding abnormality determination rule to obtain a matching result. Finally, based on the matching result corresponding to each business object, the target object is identified. Thus, by combining the abnormality determination rule with the data information of each business object obtained, the target object with abnormality in one or more business objects is timely determined, thereby improving the timeliness of abnormal logistics tracking.

[0095] In a possible implementation of the second embodiment of the present application, the abnormal logistics tracking device may further include the following modules:

[0096] The first acquisition module is used to respond to an operation instruction sent by a user through a visual editing interface and acquire operation configuration information carried in the operation instruction.

[0097] The first updating module is used to update at least one abnormality determination rule according to the operation configuration information.

[0098] The first storage module is used to store the updated abnormality determination rules in the database.

[0099] Furthermore, in another possible implementation of the second embodiment of the present application, the rule acquisition module 702 may specifically include the following submodules:

[0100] The first judgment submodule is used to judge whether there is a corresponding exception judgment rule in the local cache for each business object.

[0101] The first execution submodule is used to obtain the corresponding exception determination rule from the local cache if yes.

[0102] The second execution submodule is used to determine whether there is a corresponding exception determination rule in the remote data service Redis cache if no.

[0103] The third execution submodule is used to obtain the corresponding exception judgment rule from the Redis cache if so.

[0104] Furthermore, in another possible implementation of the second embodiment of the present application, the rule matching module 703 may specifically include the following submodules:

[0105] The first constituting submodule is used to construct a plurality of abnormality determination tasks according to data information of the multi-rule business object and corresponding abnormality determination rules, wherein the multi-rule business object corresponds to at least two abnormality determination rules.

[0106] The fourth execution submodule is used to execute various abnormality determination tasks corresponding to the multi-rule business objects in parallel to determine the matching results of the multi-rule business objects.

[0107] Furthermore, in another possible implementation of the second embodiment of the present application, the abnormal logistics tracking device may further include the following modules:

[0108] The first generating module is used to generate abnormal reminder information.

[0109] The first determination module is used to determine the abnormality reminder interval according to the abnormality determination rule corresponding to the target object.

[0110] The first sending module is used to send abnormal reminder information to the business processing user based on the abnormal reminder interval.

[0111] Further, in another possible implementation of the second embodiment of the present application, the above-mentioned abnormality determination rule includes a timeout determination rule, and the above-mentioned rule matching module 703 may specifically include the following submodules:

[0112] The first determination submodule is used to determine the current execution duration corresponding to the business object according to the generation time of the business object and the current time.

[0113] The second judgment submodule is used to judge whether the data information of the business object matches the timeout judgment rule based on the current execution time, and obtain a matching result.

[0114] Furthermore, in another possible implementation of the second embodiment of the present application, the above-mentioned abnormality determination rule includes an abnormal event determination rule, and the above-mentioned rule matching module 703 may specifically include the following submodules:

[0115] The second determination submodule is used to determine the abnormal business state corresponding to the operation action according to the abnormal event determination rules and the operation action corresponding to the business object.

[0116] The third judgment submodule is used to judge whether the data information of the business object matches the abnormal event judgment rule according to the abnormal business state and the current business state of the business object, and obtain a matching result.

[0117] The abnormal logistics tracking device disclosed in the above embodiment of the present application improves the matching efficiency of the multi-rule business object by matching the data information of the multi-rule business object with the corresponding each abnormal judgment rule in parallel. The abnormal judgment rule construction method provided in the embodiment of the present application is applicable to all express business systems, without the need for each express business system to develop an abnormal judgment function separately. The embodiment of the present application can also uniformly process the data information of each business object in all express business systems to avoid missing abnormal data follow-up.

[0118] The abnormal logistics tracking device provided in the embodiment of the present application can be applied in the aforementioned method embodiment. For details, please refer to the description of the aforementioned method embodiment, which will not be repeated here.

[0119] Figure 8 Schematic diagram of the structure of the terminal device provided in the third embodiment of the present application. Figure 8 As shown, the terminal device 800 of this embodiment includes: at least one processor 810 ( Figure 8 Only one is shown in the figure) a processor, a memory 820, and a computer program 821 stored in the memory 820 and executable on the at least one processor 810, and when the processor 810 executes the computer program 821, the steps in the above-mentioned abnormal logistics tracking method embodiment are implemented.

[0120] The terminal device 800 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device may include, but is not limited to, a processor 810 and a memory 820. Those skilled in the art will appreciate that Figure 8 It is only an example of the terminal device 800 and does not constitute a limitation on the terminal device 800. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0121] The processor 810 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0122] In some embodiments, the memory 820 may be an internal storage unit of the terminal device 800, such as a hard disk or memory of the terminal device 800. In other embodiments, the memory 820 may also be an external storage device of the terminal device 800, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal device 800. Further, the memory 820 may also include both an internal storage unit of the terminal device 800 and an external storage device. The memory 820 is used to store an operating system, an application program, a boot loader (Boot Loader), data and other programs, such as the program code of the computer program, etc. The memory 820 may also be used to temporarily store data that has been output or is to be output.

[0123] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0124] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0125] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0126] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0127] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0128] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0129] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0130] The present application implements all or part of the processes in the above-mentioned embodiment method, and may also be completed through a computer program product. When the computer program product runs on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0131] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application is described in detail with reference to the above-mentioned embodiments, a person skilled in the art should understand that the technical solutions described in the above-mentioned embodiments can still be modified, or some of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. An abnormal logistics tracking method, characterized in that: include: Acquire data information of one or more business objects, wherein the data information includes at least one of a generation time, a current business state, and an operation action; Obtaining an exception determination rule corresponding to each of the business objects; Matching the data information of each of the business objects with the corresponding exception determination rules to obtain a matching result; Based on the matching result corresponding to each of the business objects, a target object is identified.

2. The abnormal logistics tracking method according to claim 1, characterized in that: Before obtaining the abnormality determination rule corresponding to each of the business objects, the abnormal logistics tracking method further includes: In response to an operation instruction sent by a user through a visual editing interface, obtaining operation configuration information carried in the operation instruction; updating at least one of the abnormality determination rules according to the operation configuration information; The updated abnormality determination rules are stored in a database.

3. The abnormal logistics tracking method according to claim 1, characterized in that: The obtaining of the exception determination rule corresponding to each of the business objects includes: For each of the business objects, determining whether there is a corresponding exception determination rule in the local cache; If yes, obtaining the corresponding exception determination rule from the local cache; If not, determine whether there is a corresponding exception determination rule in the remote data service Redis cache; If so, the corresponding exception determination rule is obtained from the Redis cache.

4. The abnormal logistics tracking method according to claim 1, characterized in that: The matching of the data information of each business object with the corresponding exception determination rule to obtain a matching result includes: According to the data information of the multi-rule business object and the corresponding abnormality determination rules, a plurality of abnormality determination tasks are formed, wherein the multi-rule business object corresponds to at least two abnormality determination rules; The anomaly determination tasks corresponding to the multi-rule business objects are executed in parallel to determine the matching results of the multi-rule business objects.

5. The abnormal logistics tracking method according to claim 1, characterized in that: After identifying the target object based on the matching result corresponding to each of the business objects, the abnormal logistics tracking method further includes: Generate abnormal reminder information; Determining an abnormality reminder interval according to the abnormality determination rule corresponding to the target object; Based on the abnormal reminder interval, the abnormal reminder information is sent to the business processing user.

6. The abnormal logistics tracking method according to claim 1, characterized in that: The abnormality determination rule includes a timeout determination rule, and the data information of each business object is matched with the corresponding abnormality determination rule to obtain a matching result, including: Determine the current execution duration corresponding to the business object according to the generation time and current time of the business object; Based on the current execution duration, it is determined whether the data information of the business object matches the timeout determination rule to obtain the matching result.

7. The abnormal logistics tracking method according to claim 1, characterized in that: The abnormality determination rule includes an abnormal event determination rule, and the data information of each business object is matched with the corresponding abnormality determination rule to obtain a matching result, including: Determine the abnormal business state corresponding to the operation action according to the abnormal event determination rule and the operation action corresponding to the business object; According to the abnormal business status and the current business status of the business object, it is determined whether the data information of the business object matches the abnormal event determination rule to obtain the matching result.

8. An abnormal logistics tracking device, characterized in that: include: An information acquisition module, used to acquire data information of one or more business objects, wherein the data information includes at least one of a generation time, a current business status, and an operation action; A rule acquisition module, used to acquire the exception determination rule corresponding to each of the business objects; A rule matching module is used to match the data information of each business object with the corresponding exception determination rule to obtain a matching result; The object identification module is used to identify the target object based on the matching result corresponding to each of the business objects.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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