Business full-link monitoring method, equipment and media

Abstract

The present invention provides a full-link business monitoring method, device and medium, relating to the field of data processing technology. After determining that a demand side sends a business monitoring demand, a first business attribute corresponding to the corresponding business is extracted; based on the first business attribute, a corresponding first link in a database is traversed, and all link nodes in the first link are classified and counted to obtain a first active node and a first passive node; a business monitoring scenario corresponding to the business is extracted, and the first active node and the first passive node are screened based on the scenario attributes of the business monitoring scenario to obtain a second active node and a second passive node; the second active node and the second passive node are combined in sequence to obtain a total link for monitoring, so that full-link business monitoring can be achieved according to actual business scenarios.

Description

Technical Field

[0001] The present invention relates to data processing technology, and in particular to a full-link business monitoring method, equipment, and medium. Background Art

[0002] In the power grid marketing business, complex operations such as equipment transportation and fault repair often involve the coordination of multiple links. For example, the transformer replacement business must go through link nodes such as "warehouse pickup, long-distance transportation, temporary storage at the transfer station, and on-site installation." The execution efficiency and data accuracy of each node directly affect the reliability of the business.

[0003] Traditional monitoring methods rely on static link templates, such as node monitoring in fixed transportation routes. They cannot dynamically adapt to path changes in actual business scenarios, such as sudden traffic control leading to route adjustments, changes in equipment status, or differences in personnel operations such as on-site QR code scanning confirmation delays. These methods lead to delayed monitoring data and incomplete node coverage, making it difficult to detect abnormal breakpoints in the link in real time. Full-link monitoring of the business cannot be achieved, affecting business execution results.

[0004] Therefore, how to achieve full-link business monitoring based on actual business scenarios has become an urgent problem that needs to be solved. Summary of the Invention

[0005] The present invention provides a full-link business monitoring method, device and medium, which can realize full-link business monitoring according to actual business scenarios.

[0006] A first aspect of the present invention provides a method for monitoring a full service link, comprising:

[0007] After determining that the demand side sends a business monitoring demand, extract the first business attribute corresponding to the corresponding business;

[0008] Based on the first service attribute, the corresponding first link in the database is traversed, and all link nodes in the first link are classified and counted to obtain a first active node and a first passive node;

[0009] Extracting a business monitoring scenario corresponding to the business, screening the first active node and the first passive node based on the scenario attributes of the business monitoring scenario, and obtaining a second active node and a second passive node;

[0010] The second active node and the second passive node are combined in sequence to obtain a total link for monitoring.

[0011] Optionally, in a possible implementation of the first aspect, extracting the first service attribute corresponding to the corresponding service after determining that the demand side sends the service monitoring demand includes:

[0012] After determining that the demand side sends a business monitoring requirement to the server, extract the target business within the business monitoring requirement;

[0013] A first business attribute corresponding to a target business is determined, where each business has a preset business attribute, and the business attribute has no relevance to a business monitoring scenario of the entity.

[0014] Optionally, in a possible implementation of the first aspect, traversing the corresponding first link in the database based on the first service attribute and classifying and counting all link nodes in the first link to obtain the first active node and the first passive node includes:

[0015] Traversing all links corresponding to the first service attribute in the database to obtain a first link;

[0016] Traversing each link node in the first link in sequence, and dividing the link nodes into first active nodes and first passive nodes according to the first device and the first method for extracting information from the link nodes;

[0017] The union of the path point label, the personnel label, and the equipment label corresponding to each node in the first link is counted to obtain a first label set.

[0018] Optionally, in a possible implementation of the first aspect, dividing the link node into a first active node and a first passive node according to the first device and the first method for extracting information from the link node includes:

[0019] If it is determined that the first device in the link node does not have an input module, and the corresponding first mode is to execute program automatic extraction, then the corresponding link node is used as a first active node;

[0020] If it is determined that the first device in the link node has an input module, and the corresponding first mode is extraction after corresponding triggering, the corresponding link node is used as a first passive node.

[0021] Optionally, in a possible implementation of the first aspect, extracting a service monitoring scenario corresponding to the service, screening the first active node and the first passive node based on a scenario attribute of the service monitoring scenario, and obtaining the second active node and the second passive node includes:

[0022] Identify the physical path points, physical personnel, and physical equipment in the business monitoring scenario, and generate corresponding path point tags, personnel tags, and equipment tags;

[0023] The path point labels, personnel labels and equipment labels are compared with the first active node and the first passive node to determine the retained second active node and the second passive node, and a node statistics table is generated after the nodes are classified.

[0024] Optionally, in a possible implementation of the first aspect, the determining of the retained second active nodes and second passive nodes, and generating a node statistics table after classifying the nodes, includes:

[0025] retaining the first active node and the first passive node corresponding to the waypoint label, the personnel label, and the equipment label as the second active node and the second passive node;

[0026] The deleted first active node and the first passive node are used as the third active node and the third passive node;

[0027] After the second active node and the second passive node are verified twice, statistics are collected on all nodes and filled into the corresponding areas in the statistics table.

[0028] Optionally, in a possible implementation of the first aspect, after the second active node and the second passive node are verified twice, counting all nodes and filling the corresponding areas in the statistical table includes:

[0029] Counting the physical path points, physical personnel, and physical devices of the business monitoring scenarios corresponding to the second active node and the second passive node to obtain a second tag set;

[0030] Comparing the first label set with the second label set, determining labels that exist in the first label set but do not exist in the second label set to obtain a difference label set;

[0031] Count all nodes and difference labels and fill them into the statistics table.

[0032] Optionally, in a possible implementation of the first aspect, combining the second active node and the second passive node in sequence to obtain a total link for monitoring includes:

[0033] Determine the temporal sequence of physical path points, physical personnel, and physical equipment in business monitoring scenarios;

[0034] Extracting the earliest time sequence of the labels corresponding to the second active node and the second passive node as the combined time sequence;

[0035] All the second active nodes and the second passive nodes are combined based on the combined timing to obtain a total link for monitoring.

[0036] Optionally, in a possible implementation of the first aspect, combining all the second active nodes and the second passive nodes based on the combined timing to obtain a total link for monitoring includes:

[0037] If it is determined that the timing phase difference value of any second active node and / or second passive node is less than or equal to a preset value, the second active node and / or the second passive node are treated as a parallel group;

[0038] After the parallel groups are set in parallel, they are respectively connected to the second active nodes and / or second passive nodes before and after to obtain a total link in a mixed connection state.

[0039] Optionally, in a possible implementation of the first aspect, generating a node statistics table after classifying the nodes includes:

[0040] Determine the number of the third active node and the third passive node in the statistical table to obtain the number of missing nodes;

[0041] Determine the number of waypoint labels, personnel labels, and equipment labels in each third active node and third passive node in the statistical table to obtain the number of missing first labels;

[0042] Determine the number of difference label sets in the statistical table to obtain the number of second label missing;

[0043] The coefficient of the total link to be filled is calculated based on the number of missing nodes, the number of missing first labels, and the number of missing second labels and is filled into the node statistics table.

[0044] Optionally, in a possible implementation of the first aspect, obtaining the to-be-filled coefficient of the total link based on the comprehensive calculation of the number of missing nodes, the number of missing first labels, and the number of missing second labels includes:

[0045] The number of missing nodes, the number of missing first labels, and the number of missing second labels are compared with a preset constant value and then added to obtain the coefficient to be filled.

[0046] A second aspect of the present invention provides a full-link service monitoring device, comprising:

[0047] A judgment module, configured to extract a first business attribute corresponding to a corresponding business after judging that a demand side sends a business monitoring demand;

[0048] A traversal module, configured to traverse the corresponding first link in the database based on the first service attribute, and classify and count all link nodes in the first link to obtain a first active node and a first passive node;

[0049] An extraction module is used to extract a business monitoring scenario corresponding to the business, and screen the first active node and the first passive node based on the scenario attributes of the business monitoring scenario to obtain a second active node and a second passive node;

[0050] The combining module is used to combine the second active node and the second passive node in sequence to obtain a total link for monitoring.

[0051] According to a third aspect of the present invention, a storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the method of the first aspect of the present invention and various possible designs of the first aspect.

[0052] The beneficial effects of the present invention are as follows:

[0053] 1. The present invention can realize full-link monitoring of business according to actual business scenarios. First of all, the present invention can realize dynamic generation of monitoring links and improve adaptability through link traversal and scene label screening driven by business attributes. Among them, the database is traversed according to the business attributes of the demand side, and the historical link template is extracted. The scene label is generated based on the entity elements of the current business monitoring scene. By comparing the label set of the historical link node with the scene label, irrelevant nodes are eliminated and matching nodes are retained. Finally, a total link that fits the actual business is generated. The irrelevant nodes in the historical link can be automatically filtered, and only the active nodes and passive nodes related to the business are retained, so that the monitoring range accurately covers the current business path, improves the efficiency of link construction, and avoids monitoring redundancy or omissions caused by traditional static templates.

[0054] 2. The present invention can classify nodes to improve monitoring accuracy. Specifically, the present invention can classify link nodes into active nodes and passive nodes based on device characteristics and data collection methods, achieving differentiated management of monitoring strategies. At the same time, by counting indicators such as the number of missing nodes and the number of missing labels, a coefficient to be filled is generated to quantify link integrity, avoiding abnormal missed detections due to incomplete data collection, thereby improving the accuracy of full-link monitoring.

[0055] 3. The present invention can realize dynamic sorting and parallel processing of node combinations according to the timing logic of the business scenario, ensuring that the monitoring link conforms to the actual execution order. Specifically, the present invention can obtain the combined timing by extracting the earliest timing of the node label, sequentially connect each node according to the combined timing, and calculate the timing difference value corresponding to each node. When the timing difference value is less than the preset value, it is set as a parallel group to indicate synchronous execution for monitoring. At the same time, it is easy to solve the problem of fault location lag caused by timing confusion. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A flowchart of a full-link business monitoring method provided by the present invention;

[0057] Figure 2 A schematic diagram of a parallel group link connection provided by the present invention;

[0058] Figure 3 This is a structural diagram of a full-link business monitoring device provided by the present invention. DETAILED DESCRIPTION

[0059] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0060] like Figure 1 The present invention provides a flow chart of a method for monitoring a full service link, the method comprising:

[0061] S1, extracting a first service attribute corresponding to a corresponding service after determining that a service monitoring demand is sent by a demand side.

[0062] It should be noted that the marketing business in the power grid has corresponding starting and ending locations. Therefore, the corresponding business link can be determined according to business needs so that the business link can be fully monitored subsequently. For example, when a transformer in a certain place has an abnormality, it may need to be replaced or repaired. Therefore, it is necessary to select a transformer of the corresponding model and transport it to the location. Among them, due to the different transmission locations and temporary storage points during transportation of different businesses, the attributes of the corresponding nodes are inevitably inconsistent. Therefore, the nodes can be adjusted according to the actual link to improve the accuracy of link detection.

[0063] It can be understood that the demand side is the information terminal of the management personnel who execute the business, the business monitoring demand is the demand information for comprehensive monitoring of the business process, and the first business attribute is the type corresponding to the business, such as a maintenance work order or a replacement work order, etc., that is, when the corresponding business is maintenance, the corresponding first business attribute maintenance work order can be extracted, and when the corresponding business is replacement, the corresponding first business attribute replacement work order can be extracted, so that the link monitoring can be performed subsequently according to the first business attribute.

[0064] In some embodiments, a specific implementation of step S1 (the step of extracting the first service attribute corresponding to the corresponding service after determining that the demand side sends a service monitoring demand) includes:

[0065] S11, after determining that the demand side sends a business monitoring requirement to the server, extract the target business in the business monitoring requirement.

[0066] It can be understood that when it is determined that the server has received the business monitoring requirements from the demand side, the information of the target business contained in the business monitoring requirements can be extracted. For example, when the transformer needs to be transported, the corresponding transportation location, specifications, models and quantity of the transported transformer and other business information can be extracted.

[0067] It is not difficult to understand that by extracting the target business, it is convenient to make a preliminary judgment on the complexity of the business. For example, cross-provincial transportation requires multi-node and full-link monitoring.

[0068] S12: Determine a first business attribute corresponding to the target business. Each business has a preset business attribute, and the business attribute has no relevance to the business monitoring scenario of the entity.

[0069] It is understandable that the corresponding first business attributes can be determined based on the extracted target business. Since the business attributes are set based on the historical data of the same business, for example, transporting item 1 from place A to place B involves relevant temporary storage node information, transportation methods and other information. Therefore, the preset business attributes may be different from the current business monitoring scenario, that is, they are not related to the entity's business monitoring scenario, and corresponding adjustments may be required later, but an initial monitoring method and direction can be provided to facilitate the subsequent determination of the monitoring link and realize monitoring of the entire business link.

[0070] S2: traverse the corresponding first link in the database based on the first service attribute, classify and count all link nodes in the first link to obtain a first active node and a first passive node.

[0071] It should be noted that the first link is obtained by traversing the database according to the first business attribute. For example, when the current business is to repair transformers, the links of historical transformer repair in the database can be traversed to determine the node attributes of the link corresponding to the current business, so as to facilitate subsequent link monitoring. In addition, since some business links require people to passively obtain business information, some related equipment directly sends it to the server actively. For example, in the process of transporting equipment, there is a positioning device on the transport vehicle, and the positioning device can actively send the corresponding positioning information in real time. Then, the corresponding business nodes can be divided by attributes for subsequent classification monitoring.

[0072] It can be understood that by retrieving historical link templates through business attributes, link nodes are classified as active / passive based on device characteristics and data collection methods, so as to facilitate subsequent scenario screening and analysis and improve the accuracy of link monitoring.

[0073] Among them, the first link is the business link corresponding to the first business attribute, the link node is the structural connection node in the first link, the first active node is the node that actively sends information to the server, and the first passive node is the link node that passively obtains and sends.

[0074] In some embodiments, a specific implementation of step S2 (traversing the corresponding first link in the database based on the first service attribute and classifying and counting all link nodes in the first link to obtain the first active node and the first passive node) includes:

[0075] S21: Traverse all links corresponding to the first service attribute in the database to obtain a first link.

[0076] It is understandable that all links corresponding to different business attributes are pre-stored in the database. For example, for the transformer replacement business, the database stores links for transporting the transformer to various locations, and there can be multiple modes of transportation, such as air transportation, land transportation, and sea transportation, so that the first link can be traversed.

[0077] Through the above implementation, the present invention can avoid repeated link planning, so that the verified service path can be used to improve monitoring efficiency.

[0078] S22 , traverse each link node in the first link in sequence, and divide the link nodes into first active nodes and first passive nodes according to the first device and the first method for extracting information from the link nodes.

[0079] It can be understood that the first device is the device involved in the first business attribute, including cars, ships, GPS positioning devices, etc. as transportation methods, and the first method is the execution method corresponding to the business information, such as the device actively transmitting information or the human actively scanning to obtain information.

[0080] Through the above implementation, the present invention can distinguish corresponding link nodes, so as to perform targeted monitoring according to node attributes subsequently, thereby improving the accuracy of link monitoring.

[0081] In some embodiments, a specific implementation of step S22 (classifying the link node into a first active node and a first passive node according to the first device and the first method for extracting information from the link node) includes:

[0082] S221: If it is determined that the first device in the link node does not have an input module, and the corresponding first mode is to execute program automatic extraction, the corresponding link node is used as a first active node.

[0083] It is understandable that when the equipment of the link node (such as vehicle-mounted GPS, environmental sensor) does not have a manual input interface (such as a keyboard, touch screen), and the data is automatically collected at a preset time through a preset program (such as uploading location coordinates every 5 minutes), indicating that the node can continuously generate data without human intervention, the corresponding link node can be used as the first active node.

[0084] Among them, the input module is the module for information interaction between the device and people.

[0085] S222: If it is determined that the first device in the link node has an input module, and the corresponding first mode is extraction after corresponding triggering, the corresponding link node is used as a first passive node.

[0086] It is understandable that when a device has an input module and data collection relies on manual triggering (such as scanning a code to confirm arrival or manually entering quality inspection results), the node is a passive node.

[0087] S23, calculating the union of the path point label, the personnel label, and the device label corresponding to each node in the first link to obtain a first label set.

[0088] It can be understood that the path point label is the label corresponding to the positioning point of the equipment in the corresponding business, that is, the address label, the personnel label is the label of the personnel type that needs to be involved in the business process, such as maintenance personnel, installers and other label information, the equipment label is the label corresponding to the detection equipment that the business may encounter, such as detection equipment labels, including detectors, etc. The first label set is a collection of multiple labels corresponding to the node.

[0089] S3, extracting the business monitoring scenario corresponding to the business, screening the first active node and the first passive node based on the scenario attributes of the business monitoring scenario, and obtaining the second active node and the second passive node.

[0090] It is understandable that different businesses have corresponding business monitoring scenarios. For example, some businesses include multiple complex processes such as warehousing, verification, and outbound delivery, while some businesses only have transportation. Therefore, the corresponding monitoring scenarios are also different. Furthermore, the first active node and the first passive node can be screened according to the scenario attributes corresponding to the business monitoring scenario to obtain the link node that meets the current business monitoring scenario.

[0091] Among them, the scenario attribute is attribute information that meets the business monitoring scenario, the second active node is a node that satisfies the business monitoring scenario by screening the first active node, and the second passive node is a node that satisfies the current business monitoring scenario by screening the first passive node.

[0092] In some embodiments, a specific implementation of step S3 (extracting the service monitoring scenario corresponding to the service, screening the first active node and the first passive node based on the scenario attributes of the service monitoring scenario, and obtaining the second active node and the second passive node) includes:

[0093] S31, determining the physical path points, physical personnel and physical equipment in the business monitoring scenario, and generating corresponding path point tags, personnel tags and equipment tags.

[0094] It can be understood that the physical path points are the path points in the actual business monitoring scenario, the physical personnel are the business personnel in the actual business monitoring scenario, such as transport drivers, installers, etc., and the physical equipment is the equipment required in the actual business monitoring scenario, such as transport trucks, etc.

[0095] It is not difficult to understand that by extracting the labels of these entities, such as "Location A", "Transport Driver", "Truck"), it is possible to accurately locate the link nodes related to the current scenario and exclude inapplicable elements in the historical links, such as sea transportation nodes are invalid for land transportation scenarios, so as to subsequently generate targeted monitoring links.

[0096] S32, comparing the path point label, the personnel label and the equipment label with the first active node and the first passive node, determining the second active node and the second passive node to be retained, and generating a node statistics table after classifying the nodes.

[0097] It can be understood that historical link nodes are filtered through label comparison, scene-related nodes are retained and statistical reports are generated to facilitate subsequent monitoring resource allocation and optimize link monitoring plans. That is, nodes that match the label can be retained as the second active node or the second passive node, and unmatched nodes can be deleted.

[0098] The node statistics table is a table that performs comprehensive statistics on the determined nodes.

[0099] It should be noted that the first active node and the first passive node are designed with corresponding equipment and information acquisition methods. Therefore, the waypoint label, personnel label and equipment label can be compared with the corresponding node to complete the screening. For example, when the address in the waypoint label is connected to the land, there is no need to use sea transportation, and the nodes related to sea transportation can be deleted.

[0100] It is not difficult to understand that the statistical table clearly presents the monitoring range and node distribution, eliminates interference from irrelevant nodes, and improves link monitoring efficiency.

[0101] In some embodiments, a specific implementation of step S32 (determining the retained second active nodes and second passive nodes, and generating a node statistics table after classifying the nodes) includes:

[0102] S321 , retaining the first active node and the first passive node corresponding to the waypoint tag, the personnel tag, and the device tag as the second active node and the second passive node.

[0103] It can be understood that historical link nodes are filtered based on scene labels, and monitoring nodes that are strongly related to the current business are retained. Path point labels, personnel labels, and equipment labels directly define the scope and objects of current business monitoring. Through label matching, only nodes whose locations belong to the current business path points, whose node operation roles are within the scope of business personnel, and whose equipment meets the current business needs are retained.

[0104] Through the above implementation, the present invention can eliminate redundant nodes in the historical link, concentrate monitoring resources on the current business key links, ensure that all retained nodes match the actual business process, and avoid disconnection between monitoring logic and execution.

[0105] S322: Use the deleted first active node and first passive node as the third active node and third passive node.

[0106] It can be understood that the third active node is the deleted first active node that is irrelevant to the current business monitoring scenario, and the third passive node is the deleted first passive node that is irrelevant to the current business monitoring scenario.

[0107] It should be noted that the deleted first active node and the first passive node can be information nodes that are not needed in the current business monitoring scenario, or they can be information nodes that do not meet the standards in the current business monitoring scenario. For example, some businesses require temperature and humidity detection equipment in the transport vehicle or storage space, but the actual equipment is not equipped with the corresponding testing device, then the node can be deleted, or some warehouses have cameras, and some warehouses do not have cameras, so the corresponding image information cannot be extracted, and the corresponding scene node will also be deleted.

[0108] S323: After the second active node and the second passive node are verified twice, statistics are collected on all nodes and filled into corresponding areas in the statistics table.

[0109] It can be understood that the validity of the retained nodes is ensured through secondary verification, and the node information is visualized in the form of a statistical table to improve the reliability of the monitoring plan, that is, all relevant nodes are counted in the statistical table.

[0110] In some embodiments, the specific implementation of step S323 (after performing secondary verification on the second active node and the second passive node, counting all nodes and filling the corresponding areas in the statistical table) includes:

[0111] S3231 , counting the physical path points, physical personnel, and physical equipment of the business monitoring scene corresponding to the second active node and the second passive node to obtain a second tag set.

[0112] It is understandable that the information actually involved in this business is counted to obtain the second tag set so as to perform subsequent information comparison with the first tag set.

[0113] Among them, the second tag set is a set of information tags corresponding to physical path points, physical personnel and physical equipment involved in actual business.

[0114] S3232: Compare the first tag set and the second tag set to determine tags that exist in the first tag set but not in the second tag set to obtain a difference tag set.

[0115] It is understandable that when the information required in the standard business requirements is not available in the actual business link, that is, the relevant business conditions are actually missing. For example, when the equipment needs to be transported and monitored during the business execution, the transport vehicle needs to have a real-time camera device, but the corresponding transport vehicle in the second label set does not have a label corresponding to the camera device, indicating that the corresponding camera device is missing in the actual business execution. Then, the information that is required in the business but missing in the actual execution can be counted to obtain a set of difference labels, such as a set including vehicle-mounted cameras and temperature and humidity testing devices, so that subsequent statistics can be performed to prompt the equipment to be completed and realize full-link monitoring.

[0116] It is easy to understand that the difference label set is a conditional label set that is missing from the second label set relative to the first label set.

[0117] S3233: Count all nodes and difference label sets and fill them into a statistics table.

[0118] It is understandable that node information and label differences are integrated into the statistical table to form a comprehensive report that includes the current situation and potential problems. Through integration, personnel can intuitively understand the integrity and optimization direction of the monitoring link.

[0119] In some embodiments, the specific implementation of step S32 (generating a node statistics table after classifying the nodes) includes:

[0120] S324: Determine the number of third active nodes and third passive nodes in the statistical table to obtain the number of missing nodes.

[0121] It can be understood that the number of missing nodes is the number of the third active nodes and the third passive nodes in the statistical table, which represents the number of missing conditions in the actual business. For example, when the transport vehicle is missing an on-board camera device and a temperature and humidity detection device, the corresponding number of nodes can be accumulated and counted so that the corresponding coefficients to be filled can be calculated later, which is convenient for supplementary prompts for the business and improves monitoring efficiency.

[0122] S325 , determining the number of path point tags, personnel tags, and equipment tags in each third active node and third passive node in the statistical table to obtain the number of missing first tags.

[0123] It can be understood that each third node contains specific tags. By counting the number of these tags, for example, if one personnel tag is missing, the specific missing business element can be located, and the tag fields of the third node can be traversed to count the tags in groups by tag type (path point / personnel / equipment).

[0124] The first label missing quantity is the number of missing information corresponding to each node.

[0125] S326: Determine the number of difference label sets in the statistical table to obtain the second label missing number.

[0126] It can be understood that the number of second label missing is the number of difference label sets, reflecting the mismatch between historical experience and the current scenario.

[0127] S327 , based on the number of missing nodes, the number of missing first labels, and the number of missing second labels, a coefficient of the total link to be filled is calculated and filled into the node statistics table.

[0128] It can be understood that the three types of missing indicators are integrated through weighted calculation to generate the to-be-filled coefficients of the total link, so as to facilitate subsequent link optimization.

[0129] The coefficient to be filled is a coefficient value reflecting the link missing condition.

[0130] In some embodiments, a specific implementation of step S327 (the method of obtaining the to-be-filled coefficient of the total link based on the number of missing nodes, the number of missing first labels, and the number of missing second labels) includes:

[0131] S3271: Compare the number of missing nodes, the number of missing first labels, and the number of missing second labels with a preset constant value respectively, and then add them together to obtain a coefficient to be filled.

[0132] It can be understood that the coefficient to be filled is a value obtained by calculating the ratio of the number of missing nodes, the number of missing first labels, and the number of missing second labels to a preset constant value and then adding them together.

[0133] For example, when the preset constant value is 1, the number of missing nodes is 2, the number of missing first labels is 3, and the number of missing second labels is 1, then the coefficient to be filled is 6.

[0134] S4, combining the second active node and the second passive node in sequence to obtain a total link for monitoring.

[0135] It is understandable that in the business execution process, each step has a corresponding time sequence. Therefore, according to the timing constraints of the business scenario, the screened active and passive nodes can be connected in series in the correct order to build a monitoring link that conforms to the business execution logic and ensure visibility of the entire process.

[0136] In some embodiments, a specific implementation of step S4 (combining the second active node and the second passive node in sequence to obtain a total link for monitoring) includes:

[0137] S41, determining the temporal sequence of physical path points, physical personnel, and physical equipment in the business monitoring scenario.

[0138] It can be understood that the time sequence of physical path points, physical personnel and physical equipment in the business monitoring scenario is determined according to the business execution process. For example, the starting point-transfer point-destination point has a certain time sequence, and the personnel and equipment involved have corresponding time, thereby obtaining a time sequence, which is convenient for the subsequent combination and arrangement of the corresponding nodes to obtain the corresponding monitoring total link.

[0139] The temporal order is an order based on time.

[0140] S42 , extracting the earliest time sequence of the tags corresponding to the second active node and the second passive node as a combined time sequence.

[0141] It is understandable that each node tag has a first effective time in the business process. For example, the GPS positioning data of the active node first takes effect when loading is completed, and the code scanning confirmation of the passive node is triggered before departure. Extracting the earliest time sequence of these tags can ensure that the monitoring link is started in the order in which the business actually occurs.

[0142] The combined time sequence is the earliest time sequence of the tags corresponding to the second active node and the second passive node.

[0143] Through the above implementation, the earliest appearance time of the node label is used as a benchmark to determine the combination timing, thereby preventing the monitoring link from lagging behind the service execution.

[0144] S43: Combine all the second active nodes and the second passive nodes based on the combined timing to obtain a total link for monitoring.

[0145] It can be understood that the active / passive nodes are connected into a complete link based on the combined timing to achieve full-process monitoring of the business.

[0146] In some embodiments, a specific implementation of step S43 (combining all second active nodes and second passive nodes based on the combined timing to obtain a total link for monitoring) includes:

[0147] S431: If it is determined that the timing phase difference value of any second active node and / or second passive node is less than or equal to a preset value, the second active node and / or the second passive node are grouped as a parallel group.

[0148] It should be noted that in a business process, although some nodes belong to different entity elements (such as personnel and equipment), they can operate in parallel within the same time period.

[0149] For example: When a transport truck arrives at the transfer station (active node timing T1), the warehouse manager simultaneously scans the code for confirmation (passive node timing T2). If |T1-T2| ≤ the preset value (such as 10 minutes), the two can be regarded as parallel operations. By setting the timing difference threshold (such as ≤15 minutes), the active or passive nodes that meet the conditions are divided into parallel groups (such as {truck positioning node, warehouse manager scanning node}) to avoid monitoring delays caused by sequential execution.

[0150] The timing phase difference value is the interval difference between the times corresponding to different nodes, the preset value is a pre-set time interval, such as 15 minutes, and the parallel group is a parallel combination of nodes that meet the time interval.

[0151] S432: After the parallel groups are set in parallel, they are respectively connected to the second active nodes and / or second passive nodes before and after them to obtain a total link in a mixed connection state.

[0152] It is understandable that when a parallel group appears, e.g. Figure 2 As shown, the parallel group can be connected in series with the front and rear nodes to form a "hybrid connection" link, which can completely restore the timing logic and parallel characteristics of the business process, so as to accurately reflect the complex "serial and parallel" logic in the business, avoid the disconnection between the monitoring model and the actual situation, and realize full-link monitoring.

[0153] like Figure 3 FIG. 1 is a schematic diagram of the structure of a full-link service monitoring device provided by the present invention, which includes:

[0154] The judgment module is used to extract the first business attribute corresponding to the corresponding business after judging that the demand side sends the business monitoring demand.

[0155] The traversal module is used to traverse the corresponding first link in the database based on the first business attribute, and classify and count all link nodes in the first link to obtain a first active node and a first passive node.

[0156] The extraction module is used to extract the business monitoring scene corresponding to the business, and screen the first active node and the first passive node based on the scene attributes of the business monitoring scene to obtain the second active node and the second passive node.

[0157] The combining module is used to combine the second active node and the second passive node in sequence to obtain a total link for monitoring.

[0158] The present invention also provides a storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided in the various embodiments described above.

[0159] The storage medium may be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of a computer program from one location to another. A computer storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, a storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and the storage medium may be located in an application-specific integrated circuit (ASIC). In addition, the ASIC may be located in a user device. Of course, the processor and the storage medium may also exist as discrete components in a communication device. The storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0160] The present invention also provides a program product, which includes execution instructions stored in a storage medium. At least one processor of a device can read the execution instructions from the storage medium, and at least one processor executes the execution instructions so that the device implements the methods provided in the various embodiments described above.

[0161] In the above-mentioned terminal or server embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), etc. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0162] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A full-link business monitoring method, characterized in that: include: After determining that the demand side sends a business monitoring demand, extracting the first business attribute corresponding to the corresponding business; Traversing the corresponding first link in the database based on the first service attribute, classifying and counting all link nodes in the first link to obtain a first active node and a first passive node, including: Traversing all links corresponding to the first service attribute in the database to obtain a first link; Traversing each link node in the first link in sequence, and dividing the link node into a first active node and a first passive node according to the first device and the first method for extracting information in the link node, including: If it is determined that the first device in the link node does not have an input module, and the corresponding first mode is to execute program automatic extraction, then the corresponding link node is used as a first active node; If it is determined that the first device in the link node has an input module, and the corresponding first mode is extraction after corresponding triggering, the corresponding link node is used as a first passive node; Counting the union of the path point label, the personnel label, and the device label corresponding to each node in the first link to obtain a first label set; Extracting a business monitoring scenario corresponding to the business, screening the first active node and the first passive node based on the scenario attributes of the business monitoring scenario, and obtaining a second active node and a second passive node, including: Identify the physical path points, physical personnel, and physical equipment in the business monitoring scenario, and generate corresponding path point tags, personnel tags, and equipment tags; Comparing the waypoint label, the personnel label, and the device label with the first active node and the first passive node to determine the retained second active node and the second passive node, and classifying the nodes to generate a node statistics table, including: retaining the first active node and the first passive node corresponding to the waypoint label, the personnel label, and the device label as the second active node and the second passive node; The second active node and the second passive node are combined in sequence to obtain a total link for monitoring.

2. The method according to claim 1, characterized in that The extracting the first service attribute corresponding to the corresponding service after determining that the demand side sends the service monitoring demand includes: After determining that the demand side sends a business monitoring requirement to the server, extract the target business within the business monitoring requirement; A first business attribute corresponding to a target business is determined, where each business has a preset business attribute, and the business attribute has no relevance to a business monitoring scenario of the entity.

3. The method according to claim 1, characterized in that The determining of the reserved second active nodes and second passive nodes, and generating a node statistics table after classifying the nodes, includes: The deleted first active node and the first passive node are used as the third active node and the third passive node; After the second active node and the second passive node are verified twice, statistics are collected on all nodes and filled into the corresponding areas in the statistics table.

4. The method according to claim 3, characterized in that After the second active node and the second passive node are verified twice, statistics are collected on all nodes and filled into corresponding areas in the statistics table, including: Counting the physical path points, physical personnel, and physical devices of the business monitoring scenarios corresponding to the second active node and the second passive node to obtain a second tag set; Comparing the first label set with the second label set, determining labels that exist in the first label set but do not exist in the second label set to obtain a difference label set; Count all nodes and difference labels and fill them into the statistics table.

5. The method according to claim 1, characterized in that The second active node and the second passive node are combined in sequence to obtain a total link for monitoring, including: Determine the temporal sequence of physical path points, physical personnel, and physical equipment in business monitoring scenarios; Extracting the earliest time sequence of the labels corresponding to the second active node and the second passive node as the combined time sequence; All the second active nodes and the second passive nodes are combined based on the combined timing to obtain a total link for monitoring.

6. The method according to claim 5, characterized in that The method of combining all the second active nodes and the second passive nodes based on the combined timing to obtain a total link for monitoring includes: If it is determined that the timing phase difference value of any second active node and / or second passive node is less than or equal to a preset value, the second active node and / or the second passive node are treated as a parallel group; After the parallel groups are set in parallel, they are respectively connected to the second active nodes and / or second passive nodes before and after to obtain a total link in a mixed connection state.

7. The method according to claim 3, characterized in that Generating a node statistics table after classifying the nodes includes: Determine the number of the third active node and the third passive node in the statistical table to obtain the number of missing nodes; Determine the number of waypoint labels, personnel labels, and equipment labels in each third active node and third passive node in the statistical table to obtain the number of missing first labels; Determine the number of difference label sets in the statistical table to obtain the number of second label missing; The coefficient of the total link to be filled is calculated based on the number of missing nodes, the number of missing first labels, and the number of missing second labels and is filled into the node statistics table.

8. The method according to claim 7, characterized in that The to-be-filled coefficient of the total link is obtained by comprehensive calculation based on the number of missing nodes, the number of missing first labels, and the number of missing second labels, including: The number of missing nodes, the number of missing first labels, and the number of missing second labels are compared with a preset constant value and then added to obtain the coefficient to be filled.

9. A service full-link monitoring device according to the service full-link monitoring method according to claim 1, characterized in that: include: A judgment module, configured to extract a first business attribute corresponding to a corresponding business after judging that a demand side sends a business monitoring demand; A traversal module, configured to traverse the corresponding first link in the database based on the first service attribute, and classify and count all link nodes in the first link to obtain a first active node and a first passive node; An extraction module is used to extract a business monitoring scenario corresponding to the business, and screen the first active node and the first passive node based on the scenario attributes of the business monitoring scenario to obtain a second active node and a second passive node; The combining module is used to combine the second active node and the second passive node in sequence to obtain a total link for monitoring.

10. A medium, characterized in that The medium stores a computer program, which is used to implement the method according to any one of claims 1 to 8 when executed by a processor.

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