Automatic website monitoring processing method, device and equipment and medium

Abstract

The invention provides a website automatic monitoring processing method, device and equipment and a medium. The method comprises the following steps: collecting package information and scanning information based on a website automatic sorting line database; preprocessing the collected package information and scanning information to obtain preprocessed package information and scanning information; carrying out statistical analysis, identification and statistics on bay data on the basis of the pre-processed package collection information and the pre-processed scanning information; the floating lattice means that the name of the next station of the package is inconsistent with the name of the scanning station corresponding to the next scanning node. According to the system and the method, a big data analysis and visualization technology is utilized to automatically identify and count the floating case data, and a visual report is provided, so that network management personnel are helped to quickly know the floating case condition and carry out data-driven decision making.

Description

Technical Field

[0001] The present invention relates to the field of network point monitoring technology, and in particular to a network point automatic monitoring processing method, device, equipment and medium. Background Art

[0002] While the automated sorting lines at outlets improve sorting efficiency, they also face the problem of parcel drift, that is, the actual destination of the parcel does not match the package collection information, resulting in misclassification and misdelivery, affecting express delivery timeliness and customer experience. Existing drift monitoring mainly relies on manual experience judgment, lacks data analysis and visualization tools, and is difficult to quantify the drift problem and develop effective solutions. Summary of the invention

[0003] The main purpose of the present invention is to solve the problem that the floating grid monitoring in the prior art mainly relies on manual experience judgment, lacks data analysis and visualization tools, and is difficult to quantify the floating grid.

[0004] A first aspect of the present invention provides a method for automatically monitoring a network point, comprising: Collect package information and scanning information based on the database of the network point automated sorting line; Preprocessing the collected package collection information and scanning information to obtain preprocessed package collection information and scanning information; Perform statistical analysis based on pre-processed packet information and scanning information, identify and count floating grid data; The floating grid means that the name of the next station in the collection package is inconsistent with the name of the scanning station corresponding to the next scanning node.

[0005] Optionally, in a first implementation of the first aspect of the present invention, before collecting the package collection information and scanning information based on the network point automatic sorting line database, it also includes: Obtain package collection and scanning information based on scanners and sensors on the automated sorting line at the outlets; The acquired package information and scanning information are stored in a database.

[0006] Optionally, in a second implementation of the first aspect of the present invention, the package collection information includes: parcel number, package collection large package number, package collection equipment number, platform number, package collection time, package collection site, package collection site name, package collection site to which the distribution belongs, package collection site to which the distribution belongs, the name of the distribution, the next site, the name of the next site, the distribution to which the next site belongs, and the name of the distribution to which the next site belongs; The scanning information includes: receipt time, scanning time corresponding to the next scanning node, scanning site corresponding to the next scanning node, scanning site name corresponding to the next scanning node, distribution to which the scanning site corresponding to the next scanning node belongs, and distribution name to which the scanning site corresponding to the next scanning node belongs.

[0007] Optionally, in a third implementation manner of the first aspect of the present invention, the preprocessing of the collected package information and scanning information includes: Cleaning the collected package information and scanning information, including: removing abnormal data, removing attribution relationship data and removing large package data, to obtain cleaned package information and scanning information; The abnormal data to be eliminated includes: eliminating the scan record of picking up the package after the package is assembled, eliminating the virtual M logistics record, eliminating the wrong package collection record, eliminating the invalid package label record, and eliminating the data of the next stop being the aviation scan record; The removing of the attribution relationship data includes: removing the data having the attribution relationship between the collection package site and the next scanning node; The removal of large package data includes: removing large packages containing scanned records of small items above the preset ticket; The cleaned package collection information and the scanned information are associated, including: associating the cleaned package collection information and the scanned information based on the package number and the timestamp to obtain the associated package collection information and the scanned information.

[0008] Optionally, in a fourth implementation of the first aspect of the present invention, the performing statistical analysis based on the preprocessed packet information and scanning information, identifying and counting the floating frame data, includes: Extract key information based on the preprocessed package information and scan information, including: the name of the next station of the package and the name of the scan station corresponding to the next scan node; The extracted next station name of the collection package is compared with the scanning station name corresponding to the next scanning node. When the next station name of the collection package and the scanning station name corresponding to the next scanning node are inconsistent, it is marked as a floating grid.

[0009] Optionally, in a fifth implementation of the first aspect of the present invention, the method further comprises: calculating a floating grid rate based on counting the floating grid votes; Floating grid rate = number of floating grid tickets / total number of tickets on the automated sorting line of the outlet; Set a floating frame rate threshold. When the floating frame rate exceeds the floating frame rate threshold, an early warning is triggered and an optimization strategy is provided.

[0010] Optionally, in a sixth implementation of the first aspect of the present invention, the method further includes: displaying the analysis result in a chart form; Among them, the analysis results include the number of floating grid votes and the floating grid rate; The chart forms include bar charts, line charts, pie charts and heat maps.

[0011] A second aspect of the present invention provides a network point automatic monitoring processing device, comprising: Data acquisition module, used to collect package information and scanning information based on the network point automatic sorting line database; A preprocessing module is used to preprocess the collected package information and scanning information to obtain preprocessed package information and scanning information; Identification and statistics module, used to perform statistical analysis based on pre-processed packet information and scanning information, and to identify and count floating grid data; The floating grid means that the name of the next station in the collection package is inconsistent with the name of the scanning station corresponding to the next scanning node.

[0012] Optionally, in a first implementation of the second aspect of the present invention, the device further includes: a data acquisition module; collecting packaging information and scanning information based on scanners and sensors on the automated sorting line of the outlets; and storing the acquired packaging information and scanning information in a database.

[0013] Optionally, in a second implementation of the second aspect of the present invention, the package collection information includes: parcel number, package collection large package number, package collection equipment number, platform number, package collection time, package collection site, package collection site name, package collection site to which the distribution belongs, package collection site to which the distribution name belongs, package collection next site, package collection next site name, package collection next site to which the distribution belongs, and package collection next site to which the distribution name belongs; The scanning information includes: receipt time, scanning time corresponding to the next scanning node, scanning site corresponding to the next scanning node, scanning site name corresponding to the next scanning node, distribution to which the scanning site corresponding to the next scanning node belongs, and distribution name to which the scanning site corresponding to the next scanning node belongs.

[0014] Optionally, in a third implementation of the second aspect of the present invention, the preprocessing module includes: The cleaning submodule is used to clean the collected package information and scanning information, including: removing abnormal data, removing attribution relationship data and removing large package data, and obtaining cleaned package information and scanning information; The abnormal data to be eliminated includes: eliminating the scan record of picking up the package after the package is assembled, eliminating the virtual M logistics record, eliminating the wrong package collection record, eliminating the invalid package label record, and eliminating the data of the next stop being the aviation scan record; The removing of the attribution relationship data includes: removing the data having the attribution relationship between the collection package site and the next scanning node; The removal of large package data includes: removing large packages containing scanned records of small items above the preset ticket; The association submodule is used to associate the cleaned package collection information and the scanning information, including: associating the cleaned package collection information and the scanning information based on the package number and the timestamp to obtain the associated package collection information and the scanning information.

[0015] Optionally, in a fourth implementation of the second aspect of the present invention, the identification statistics module includes: The key information extraction submodule is used to extract key information based on the preprocessed package information and scanning information, including: the name of the next station of the package and the name of the scanning station corresponding to the next scanning node; The comparison submodule is used to compare the extracted next station name of the collection package with the scanning station name corresponding to the next scanning node. When the next station name of the collection package and the scanning station name corresponding to the next scanning node are inconsistent, it is marked as a floating grid.

[0016] Optionally, in a fifth implementation of the second aspect of the present invention, the device further comprises: an early warning module, configured to calculate a floating grid rate based on the number of floating grid tickets; Floating grid rate = number of floating grid tickets / total number of tickets on the automated sorting line of the outlet; Set a floating frame rate threshold. When the floating frame rate exceeds the floating frame rate threshold, an early warning is triggered and an optimization strategy is provided.

[0017] Optionally, in a sixth implementation of the second aspect of the present invention, the device further includes: a visualization module, configured to display the analysis result in a chart form; Among them, the analysis results include the number of floating grid votes and the floating grid rate; The chart forms include bar charts, line charts, pie charts and heat maps.

[0018] A third aspect of the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned network point automatic monitoring processing method are implemented.

[0019] A fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the above-mentioned network point automatic monitoring processing method when executed by the processor.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention uses big data analysis and visualization technology to automatically identify and count floating grid data and provide visualization reports to help branch managers quickly understand the floating grid situation and make data-driven decisions; 2. The present invention comprehensively monitors the flow of packages by associating package collection information and scanning information, timely discovers the risk of floating grids, and issues early warnings; 3. The present invention can more comprehensively grasp the parcel flow direction, timely discover and solve problems, and thus effectively reduce the floating rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 A first flow chart of the network point automatic monitoring processing method provided by an embodiment of the present invention.

[0022] Figure 2 A second flow chart of the network point automatic monitoring processing method provided by an embodiment of the present invention.

[0023] Figure 3 This is a third flow chart of the network point automatic monitoring processing method provided by an embodiment of the present invention.

[0024] Figure 4 This is a fourth flow chart of the network point automatic monitoring processing method provided by an embodiment of the present invention.

[0025] Figure 5 A schematic diagram of the structure of a network point automatic monitoring processing device provided by an embodiment of the present invention.

[0026] Figure 6 Another structural schematic diagram of the network point automatic monitoring processing device provided by an embodiment of the present invention.

[0027] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The embodiment of the present invention provides a method, device, equipment and medium for automatic monitoring and processing of network points, which collects package information and scanning information based on the network point automatic sorting line database; pre-processes the collected package information and scanning information, including data cleaning and data association processing, to obtain pre-processed package information and scanning information; statistical analysis is performed based on the pre-processed package information and scanning information, including: extracting key information, wherein the key information includes: the name of the next station of the package and the name of the scanning site corresponding to the next scanning node; comparing the extracted name of the next station of the package and the name of the scanning site corresponding to the next scanning node, when the name of the next station of the package and the name of the scanning site corresponding to the next scanning node are inconsistent, it is marked as a floating grid, thereby realizing the identification and statistics of floating grid data; the floating grid refers to the inconsistency between the name of the next station of the package and the name of the scanning site corresponding to the next scanning node. The present invention solves the problem that the floating grid monitoring in the prior art mainly relies on manual experience judgment, lacks data analysis and visualization tools, and is difficult to quantify the floating grid.

[0029] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 The first embodiment of the network point automatic monitoring processing method in the embodiment of the present invention includes: 101. Collect package information and scanning information based on the database of the network point automatic sorting line; In this embodiment, the package collection information includes: parcel order number, package collection package number, package collection equipment number, platform number, package collection time, package collection site, package collection site name, package collection site to which the distribution, package collection site to which the distribution name, package collection next site, package collection next site name, package collection next site to which the distribution, and package collection next site to which the distribution name; The scanning information includes: receipt time, scanning time corresponding to the next scanning node, scanning site corresponding to the next scanning node, scanning site name corresponding to the next scanning node, distribution to which the scanning site corresponding to the next scanning node belongs, and distribution name to which the scanning site corresponding to the next scanning node belongs.

[0031] 102. Preprocessing the collected package collection information and scanning information to obtain preprocessed package collection information and scanning information; In this embodiment, the collected package information and scanning information are cleaned, including: removing abnormal data, removing attribution relationship data and removing large package data, to obtain cleaned package information and scanning information; The abnormal data to be eliminated includes: eliminating the scan record of picking up the package after the package is assembled, eliminating the virtual M logistics record, eliminating the wrong package collection record, eliminating the invalid package label record, and eliminating the data of the next stop being the aviation scan record; The removing of the attribution relationship data includes: removing the data having the attribution relationship between the collection package site and the next scanning node; The removal of large package data includes: removing large packages containing scanned records of small items above the preset ticket; The cleaned package collection information and the scanned information are associated, including: associating the cleaned package collection information and the scanned information based on the package number and the timestamp to obtain the associated package collection information and the scanned information.

[0032] 103. Perform statistical analysis based on the pre-processed package information and scanning information, and identify and count the floating grid data; In this embodiment, key information is extracted based on the preprocessed package collection information and scanning information, including: the name of the next station of the package collection and the name of the scanning station corresponding to the next scanning node; The extracted next station name of the collection package is compared with the scanning station name corresponding to the next scanning node. When the next station name of the collection package and the scanning station name corresponding to the next scanning node are inconsistent, it is marked as a floating grid.

[0033] Among them, the floating grid means that the name of the next station in the collection package is inconsistent with the name of the scanning site corresponding to the next scanning node.

[0034] This embodiment uses big data analysis technology to automatically identify and count grid data, helping branch managers to quickly understand grid conditions; providing grid data details to facilitate branch managers to quickly locate and solve problems, thereby improving response speed.

[0035] See also Figure 2 The second embodiment of the network point automatic monitoring processing method in the embodiment of the present invention includes: 201. Acquire packaging information and scanning information based on scanners and sensors on the automated sorting line of the outlets; and store the acquired packaging information and scanning information in a database.

[0036] 202. Collecting package information and scanning information based on the database of the network point automatic sorting line; This embodiment comprehensively monitors the flow of packages by associating package collection information and scanning information.

[0037] 203. Clean and associate the collected package information and scanning information, perform statistical analysis based on the preprocessed package information and scanning information, and identify and count the floating grid data; 204. Calculate the floating rate based on the statistical floating votes; Floating grid rate = number of floating grid tickets / total number of tickets on the automated sorting line of the outlet; 205. Set a floating frame rate threshold. When the floating frame rate exceeds the floating frame rate threshold, an early warning is triggered and an optimization strategy is provided; This embodiment can detect the risk of floating grid in time and issue an early warning, thereby effectively reducing the floating grid rate.

[0038] 206. Display the analysis results in the form of charts; Among them, the analysis results include the number of floating grid votes and the floating grid rate; The chart forms include bar charts, line charts, pie charts and heat maps.

[0039] Among them, the floating grid means that the name of the next station in the collection package is inconsistent with the name of the scanning site corresponding to the next scanning node.

[0040] This embodiment displays the analysis results in the form of charts, which is convenient for branch managers to intuitively understand the floating grid situation.

[0041] This embodiment uses big data analysis and visualization technology to automatically identify and count grid data and provide visualization reports to help branch managers quickly understand grid conditions and make data-driven decisions. It converts manual experience judgment into data-driven judgment, thereby improving the accuracy and efficiency of monitoring.

[0042] See also Figure 3 The third embodiment of the network point automatic monitoring processing method in the embodiment of the present invention includes: 301. Collect and store the package collection information and scanning information on the automated sorting line of the outlets; Based on the scanners and sensors on the automated sorting lines at the outlets, the package collection and scanning information are acquired in real time; the acquired package collection and scanning information is stored in the database for subsequent processing and analysis; 302. Obtain the package collection information and scanning information on the automated sorting line of the outlets according to the requirements; In this embodiment, based on the database of the automated sorting line of the network point, the stored package collection information and scanning information are collected through query operations; the package collection information and scanning information are integrated using an association method to achieve comprehensive monitoring of the package flow.

[0043] 303. Preprocess and statistically analyze the acquired package collection information and scanning information on the automated sorting line of the outlets; In this embodiment, statistical analysis is performed on the pre-processed package collection information and scanning information; the floating grid data is identified and counted to evaluate abnormal conditions in the package flow process.

[0044] 304. Calculate the floating rate based on the number of floating votes obtained by statistics; Floating grid rate = number of floating grid tickets / total number of tickets on the outlet’s automated sorting line.

[0045] 305. Set the floating rate threshold. When the calculated floating rate exceeds the set threshold, the system will trigger an early warning. In this embodiment, the early warning mechanism may include notifying relevant personnel or systems and providing feasible optimization strategies to deal with the risk of drifting.

[0046] 306. Visualize the statistical analysis results; In this embodiment, the analysis results are presented in the form of charts to facilitate understanding and monitoring.

[0047] Use a variety of chart types (such as bar charts, line charts, pie charts, etc.) to present the changing trends of the number of votes and the rate of votes.

[0048] This embodiment can timely discover and identify potential risks of parcel drift, and provide effective early warning and optimization solutions, thereby effectively reducing the parcel drift rate and improving the efficiency and accuracy of package processing.

[0049] See also Figure 4 The fourth embodiment of the network point automatic monitoring processing method in the embodiment of the present invention includes: 401. Acquire package collection information and scanning information based on scanners and sensors on the automated sorting line of the outlets; store the acquired package collection information and scanning information in a database; 402. Collecting package information and scanning information based on the database of the network point automatic sorting line; 403. Clean and associate the collected package information and scanning information, perform statistical analysis based on the preprocessed package information and scanning information, and identify and count the floating grid data; 404. Calculate the floating rate based on the statistical floating votes; Floating grid rate = number of floating grid tickets / total number of tickets on the automated sorting line of the outlet; 405. Collect and organize historical data on piaoge, including the number of piaoge tickets, occurrence time, sorting routes, package types and other information; identify the high-incidence periods and causes of piaoge time through data analysis, and build a piaoge risk model; 406. Based on historical data, apply statistical analysis and machine learning algorithms (such as time series analysis, regression analysis, decision trees, etc.) to predict future trends; In this embodiment, the prediction model should consider factors such as seasonality, periodicity, and emergencies to enhance accuracy.

[0050] 407. Set up a trend warning mechanism to generate timely warning information based on the forecast results; In this embodiment, the warning information is notified to relevant personnel through the system to remind them of the impending risk of floating grid.

[0051] 408. Take corresponding preventive measures according to the predicted drifting trend; In this embodiment, the preventive measures include: Adjust sorting routes: optimize the sorting process and re-plan the location of sorting stations to shorten processing time.

[0052] Suspend sorting operations: When the risk is particularly high, suspend sorting operations in a timely manner to ensure the accuracy of package handling.

[0053] Increase human resources: During periods of heavy traffic or peak hours, add material handling personnel to reduce sorting pressure.

[0054] Establish a comprehensive Piaoge emergency plan, including the following: Emergency response process: Detailed processing steps and responsible persons to ensure rapid response when a Piaoge incident occurs.

[0055] Personnel training: Provide emergency response training to relevant employees to familiarize them with the contents of the plan and operating manual.

[0056] Resource allocation: Allocate emergency resources in advance, such as additional sorting equipment, temporary storage space, etc.

[0057] Once a floating grid incident occurs, quickly activate the emergency plan and implement emergency response.

[0058] This embodiment can effectively enhance the ability to prevent the risk of piracy, ensure that corresponding prevention strategies are formulated during high-risk periods, and be able to respond quickly when piracy events occur, reduce losses and maintain operational efficiency.

[0059] The above describes the automatic monitoring processing method of the network point in the embodiment of the present invention. The following describes the automatic monitoring processing device of the network point in the embodiment of the present invention. Figure 5 In one embodiment of the present invention, an automatic monitoring and processing device for a network point includes: The data collection module 501 is used to collect the packaging information and scanning information, and store the collected packaging information and scanning information in the network point automatic sorting line database; In this embodiment, the data collection module 501 includes: collecting package information and scanning information based on scanners and sensors on the automated sorting line of the outlets; The package collection information includes: parcel number, package collection package number, package collection equipment number, platform number, package collection time, package collection site, package collection site name, package collection site to which the distribution, package collection site to which the distribution name, package collection next site, package collection next site name, package collection next site to which the distribution and the distribution name of the next package collection site; The scanning information includes: the receipt time, the scanning time corresponding to the next scanning node, the scanning site corresponding to the next scanning node, the scanning site name corresponding to the next scanning node, the distribution to which the scanning site corresponding to the next scanning node belongs, and the distribution name to which the scanning site corresponding to the next scanning node belongs.

[0060] Data acquisition module 502, used to collect package information and scanning information based on the network point automatic sorting line database; The preprocessing module 503 is used to preprocess the collected package collection information and scanning information to obtain preprocessed package collection information and scanning information; In this embodiment, the preprocessing module 503 includes: The cleaning submodule 5031 is used to clean the collected package information and scanning information, including: removing abnormal data, removing attribution relationship data and removing large package data, to obtain cleaned package information and scanning information; The abnormal data to be eliminated includes: eliminating the scan record of picking up the package after the package is assembled, eliminating the virtual M logistics record, eliminating the wrong package collection record, eliminating the invalid package label record, and eliminating the data of the next stop being the aviation scan record; The removing of the attribution relationship data includes: removing the data having the attribution relationship between the collection package site and the next scanning node; The removal of large package data includes: removing large packages containing scanned records of small items above the preset ticket; The association submodule 5032 is used to associate the cleaned package collection information and the scan information, including: associating the cleaned package collection information and the scan information based on the package number and the timestamp to obtain the associated package collection information and the scan information.

[0061] The identification and statistics module 504 is used to perform statistical analysis based on the pre-processed packet information and scanning information, and to identify and count the floating grid data; In this embodiment, the identification statistics module 504 includes: The key information extraction submodule 5041 is used to extract key information based on the preprocessed package collection information and scanning information, including: the name of the next station of the package collection and the name of the scanning station corresponding to the next scanning node; The comparison submodule 5042 is used to compare the extracted next station name of the collection package with the scanning site name corresponding to the next scanning node. When the next station name of the collection package and the scanning site name corresponding to the next scanning node are inconsistent, it is marked as a floating grid.

[0062] The early warning module 505 is used to calculate the floating rate based on the number of floating votes; set a floating rate threshold, and when the floating rate exceeds the floating rate threshold, trigger an early warning and provide an optimization strategy; In this embodiment, the floating grid rate = the number of floating grid tickets / the total number of tickets on the automated sorting line of the outlet.

[0063] A visualization module 506 is used to display the analysis results in the form of charts; In this embodiment, the analysis results include the number of floating grid tickets and the floating grid rate; The chart forms include bar charts, line charts, pie charts and heat maps.

[0064] See also Figure 6 Another embodiment of the network point automatic monitoring processing device in the embodiment of the present invention includes: The data acquisition module 601 is used to obtain the packaging information and scanning information based on the scanner and sensor on the automatic sorting line of the network point; and store the obtained packaging information and scanning information in the database.

[0065] Data acquisition module 602, used to collect package collection information and scanning information based on the network point automatic sorting line database; The identification and statistics module 603 is used to clean and associate the collected packet information and scanning information, perform statistical analysis based on the preprocessed packet information and scanning information, and identify and count the floating grid data; The floating rate calculation module 604 is used to calculate the floating rate based on the statistical floating votes; Floating grid rate = number of floating grid tickets / total number of tickets on the automated sorting line of the outlet; The piaoge risk model building module 605 is used to collect and organize historical piaoge data, including the number of piaoge tickets, occurrence time, sorting route, package type and other information; through data analysis, identify the high-incidence period and reasons of piaoge time, and build a piaoge risk model; The trend prediction module 606 is used to predict the future trend of the grid based on historical data by applying statistical analysis and machine learning algorithms (such as time series analysis, regression analysis, decision tree, etc.).

[0066] In this embodiment, the prediction model should consider factors such as seasonality, periodicity, and emergencies to enhance accuracy.

[0067] The early warning module 607 is used to set up an early warning mechanism for the trend of the floating grid and generate timely early warning information based on the prediction results.

[0068] In this embodiment, the warning information is notified to relevant personnel through the system to remind them of the impending risk of floating grid.

[0069] The prevention module 608 is used to take corresponding preventive measures according to the predicted drifting trend.

[0070] In this embodiment, the preventive measures include: Adjust sorting routes: optimize the sorting process and re-plan the location of sorting stations to shorten processing time.

[0071] Suspend sorting operations: When the risk is particularly high, suspend sorting operations in a timely manner to ensure the accuracy of package handling.

[0072] Increase human resources: During periods of heavy traffic or peak hours, add material handling personnel to reduce sorting pressure.

[0073] Establish a comprehensive Piaoge emergency plan, including the following: Emergency response process: Detailed processing steps and responsible persons to ensure rapid response when a Piaoge incident occurs.

[0074] Personnel training: Provide emergency response training to relevant employees to familiarize them with the contents of the plan and operating manual.

[0075] Resource allocation: Allocate emergency resources in advance, such as additional sorting equipment, temporary storage space, etc.

[0076] Once a floating grid incident occurs, quickly activate the emergency plan and implement emergency response.

[0077] This embodiment can effectively enhance the ability to prevent the risk of piracy, ensure that corresponding prevention strategies are formulated during high-risk periods, and be able to respond quickly when piracy events occur, reduce losses and maintain operational efficiency.

[0078] In this embodiment, the floating grid means that the name of the next station in the collection package is inconsistent with the name of the scanning site corresponding to the next scanning node.

[0079] In this embodiment, from the perspective of reducing the floating rate, the sorting efficiency is improved. By timely identifying and solving the floating rate problem, the misclassification and misdelivery are reduced, the sorting efficiency is improved, and the parcel processing events are shortened, thereby improving the express delivery timeliness; reducing labor costs, reducing the floating rate can reduce the workload of manual investigation and processing, reduce labor costs, and improve the operating efficiency of outlets; reducing economic losses, reducing the floating rate can reduce the economic losses caused by misclassification and misdelivery, such as re-sorting, compensation to customers and other costs. From the perspective of improving customer satisfaction, the express delivery timeliness is improved, reducing the floating rate can shorten the parcel delivery time, improve the express delivery timeliness, and thus improve customer satisfaction. Reduce the complaint rate, reducing the floating rate can reduce customer complaints caused by misclassification and misdelivery, improve customer satisfaction, and enhance the corporate brand image. From the perspective of enhancing corporate competitiveness, the operating efficiency is improved, reducing the floating rate can improve the operating efficiency of outlets, reduce operating costs, and enhance corporate competitiveness. Improve service quality, reducing the floating rate can improve the quality of express service, enhance customer trust, enhance the corporate brand image, and thus enhance corporate competitiveness. From the perspective of data-driven decision-making, the sorting process is optimized. By analyzing the floating data, the main reasons for the floating can be found, and the sorting process is optimized, such as improving equipment, adjusting operating procedures, etc., so as to reduce the floating rate; the equipment performance is improved. By analyzing the floating data, the cause of equipment failure can be found, and timely maintenance and repair can be carried out to improve equipment performance and reduce the floating rate. From the perspective of preventing potential risks, the floating trend is predicted. Based on historical data, the future floating trend is predicted, potential risks are warned in advance, and preventive measures are formulated, such as adjusting the sorting path, suspending sorting operations, etc., to prevent floating. Develop emergency plans and establish emergency plans for floating. When a floating event occurs, it can respond quickly, handle it in time, and reduce losses.

[0080] above Figure 5-6 The network point automatic monitoring processing device in the embodiment of the present invention is described in detail from the perspective of modular functional entities, and the electronic device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0081] Figure 77 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device 700 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 710 (for example, one or more processors) and a memory 720, and one or more storage media 730 (for example, one or more mass storage devices) storing application programs 733 or data 732. Among them, the memory 720 and the storage medium 730 can be temporary storage or permanent storage. The program stored in the storage medium 730 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the electronic device 700. Furthermore, the processor 710 may be configured to communicate with the storage medium 730 to execute a series of instruction operations in the storage medium 730 on the electronic device 700.

[0082] The electronic device 700 may also include one or more power supplies 740, one or more wired or wireless network interfaces 750, one or more input and output interfaces 750, and / or one or more operating systems 731, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 7 The structure of the electronic device shown does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine some components, or arrange the components differently.

[0083] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the network point automatic monitoring processing method.

[0084] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0085] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0086] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for automatically monitoring and processing a network point, characterized in that: include: Collect package information and scanning information based on the database of the network point automated sorting line; Preprocessing the collected package collection information and scanning information to obtain preprocessed package collection information and scanning information; Perform statistical analysis based on pre-processed packet information and scanning information, identify and count floating grid data; The floating grid means that the name of the next station in the collection package is inconsistent with the name of the scanning station corresponding to the next scanning node.

2. The method for automatically monitoring a network point according to claim 1, characterized in that: Before collecting the package collection information and scanning information based on the network point automatic sorting line database, it also includes: Obtain package collection and scanning information based on scanners and sensors on the automated sorting line at the outlets; The acquired package collection information and scanning information are stored in a database.

3. The method for automatically monitoring a network point according to claim 1, characterized in that: The package collection information includes: parcel number, package collection package number, package collection equipment number, platform number, package collection time, package collection site, package collection site name, package collection site to which the distribution, package collection site to which the distribution name, package collection next site, package collection next site name, package collection next site to which the distribution and the distribution name of the next site to which the distribution; The scanning information includes: receipt time, scanning time corresponding to the next scanning node, scanning site corresponding to the next scanning node, scanning site name corresponding to the next scanning node, distribution to which the scanning site corresponding to the next scanning node belongs, and distribution name to which the scanning site corresponding to the next scanning node belongs.

4. The method for automatically monitoring a network point according to claim 1, characterized in that: The preprocessing of the collected package information and scanning information includes: Cleaning the collected package information and scanning information, including: removing abnormal data, removing attribution relationship data and removing large package data, to obtain cleaned package information and scanning information; The abnormal data to be eliminated includes: eliminating the scan record of picking up the package after the package is assembled, eliminating the virtual M logistics record, eliminating the wrong package collection record, eliminating the invalid package label record, and eliminating the data of the next stop being the aviation scan record; The removing of the attribution relationship data includes: removing the data having the attribution relationship between the collection package site and the next scanning node; The removal of large package data includes: removing large packages containing scanned records of small items above the preset ticket; The cleaned package collection information and the scanned information are associated, including: associating the cleaned package collection information and the scanned information based on the package number and the timestamp to obtain the associated package collection information and the scanned information.

5. The method for automatically monitoring a network point according to claim 1, characterized in that: The statistical analysis based on the pre-processed package information and scanning information, identification and statistics of floating grid data, includes: Extract key information based on the preprocessed package information and scan information, including: the name of the next station of the package and the name of the scan station corresponding to the next scan node; The extracted next station name of the collection package is compared with the scanning station name corresponding to the next scanning node. When the next station name of the collection package and the scanning station name corresponding to the next scanning node are inconsistent, it is marked as a floating grid.

6. The method for automatically monitoring a network point according to claim 1, characterized in that: The method further comprises: calculating the floating rate based on the number of floating votes; Floating grid rate = number of floating grid tickets / total number of tickets on the automated sorting line of the outlet; Set the floating frame rate threshold. When the floating frame rate exceeds the floating frame rate threshold, an early warning is triggered and an optimization strategy is provided.

7. The method for automatically monitoring a network point according to claim 1, characterized in that: The method further comprises: presenting the analysis results in a graphical form; Among them, the analysis results include the number of floating grid votes and the floating grid rate; The chart forms include bar charts, line charts, pie charts and heat maps.

8. A network point automatic monitoring processing device, characterized in that: include: Data acquisition module, used to collect package information and scanning information based on the network point automatic sorting line database; A preprocessing module is used to preprocess the collected package information and scanning information to obtain preprocessed package information and scanning information; Identification and statistics module, used to perform statistical analysis based on pre-processed packet information and scanning information, and to identify and count floating grid data; The floating grid means that the name of the next station in the collection package is inconsistent with the name of the scanning station corresponding to the next scanning node.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the network point automatic monitoring processing method described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is executed by a processor, the steps of the network point automatic monitoring processing method described in any one of claims 1 to 7 are implemented.

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