Data monitoring method and device, electronic equipment and medium

Abstract

The invention relates to a data monitoring method and device, electronic equipment and a medium, and the method comprises the steps: receiving a data monitoring request which carries monitoring link information and monitoring index information; the monitoring link information is used for specifying to monitor at least one target link in the whole link process of the target scene; obtaining link data and link association data of the target link; the link association data represents data having an association relationship with the link data in the whole link process; and for the link data and the link association data, performing data monitoring based on the monitoring index information to obtain a monitoring result. As the link data and the link associated data can provide an association condition under a full-link perspective, a monitoring result under a global perspective can be obtained in a process of performing data monitoring on the link data and the link associated data based on the monitoring index information, and the improvement of reference and promotion effects on actual services is facilitated.

Description

Technical Field

[0001] The present disclosure relates to the field of operation and maintenance technology, and in particular to a method, device, electronic equipment and medium for data monitoring. Background Art

[0002] For various real-life scenarios, there may be multiple complex processes that need to be processed in sequence, which are usually divided into multiple processing links according to the order of processing. These processing links perform data processing based on their respective business systems.

[0003] In the process of realizing the concept of the present disclosure, the inventor found that the following technical problems exist in the related technology: In the related technology, most of them are for each business system to carry out separate data monitoring and operation and maintenance, the information given by the scattered point monitoring method belongs to the information island, when a business system is monitored to be abnormal, the abnormality of the business system is prompted, lacking the perspective of data monitoring and operation and maintenance in the whole scene, the operation and maintenance results are low. For example, taking the transaction scenario for enterprises (to B) as an example, the buyer (for example, the purchasing staff) usually completes the order in the order system (which can be the order system provided by the platform or the order system provided by the seller); according to the contract agreement, the seller performs the performance behaviors such as delivery and installation and enters the performance system; afterwards, according to the actual situation, the seller or the platform provides the corresponding supporting after-sales service; based on the contract agreement, the buyer settles and pays the relevant fees to the seller, etc. Generally speaking, since the transaction volume of to B business is generally large, the settlement payment is not completed at one time. This transaction scenario involves multiple independent systems such as order system, performance system, payment system, etc. The monitoring and operation and maintenance of these independent systems cannot meet the monitoring and analysis needs from the perspective of the whole scene. Summary of the invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, embodiments of the present disclosure provide a method, device, electronic device and medium for data monitoring.

[0005] In the first aspect, an embodiment of the present disclosure provides a method for data monitoring. The method includes: receiving a data monitoring request, the data monitoring request carries: monitoring link information and monitoring indicator information; the monitoring link information is used to specify monitoring of at least one target link in the full link process of the target scenario; obtaining link data and link-related data of the target link; the link-related data represents data that has an association relationship with the link data in the full link process; for the link data and the link-related data, data monitoring is performed based on the monitoring indicator information to obtain a monitoring result.

[0006] According to an embodiment of the present disclosure, the monitoring indicator information includes: monitoring indicators and a target algorithm for calculating the monitoring indicators. For the link data and the link-related data, data monitoring is performed based on the monitoring indicator information to obtain monitoring results, including: determining the target field involved in the calculation according to the target algorithm; matching the target field in the link data and the link-related data to obtain the target value corresponding to the target field; substituting the target value into the target algorithm for calculation to obtain the monitoring result of the monitoring indicator.

[0007] According to an embodiment of the present disclosure, the above-mentioned target field includes: the first field in the above-mentioned link data and the second field in the above-mentioned link-associated data; when the above-mentioned first field and the above-mentioned second field are different fields and the above-mentioned first field is not associated with the above-mentioned second field, the above-mentioned target field is matched in the above-mentioned link data and the above-mentioned link-associated data to obtain the target value corresponding to the above-mentioned target field, including: matching the above-mentioned first field in the above-mentioned link data to obtain the first field value; matching the above-mentioned second field in the above-mentioned link-associated data to obtain the second field value; the above-mentioned first field value and the above-mentioned second field value are used as the above-mentioned target value. In the case where the first field and the second field are different fields and the first field is associated with the second field, the target field is matched in the link data and the link-associated data to obtain the target value corresponding to the target field, including: matching the first field in the link data to obtain the first field value; matching the second field in the link-associated data to obtain the second field value; verifying whether the first field value and the second field value are valid based on the association relationship between the first field and the second field; in the case where both the first field value and the second field value are valid, determining the first field value and the second field value as the target value; in the case where the first field value and the second field value are invalid, obtaining the first monitoring result of the associated field anomaly in the full-link data.

[0008] According to an embodiment of the present disclosure, the target field includes a third field, and the third field exists in both the link data and the link-related data. The target field is matched in the link data and the link-related data to obtain the target value corresponding to the target field, including: matching the third field in the link data to obtain a first matching value; matching the third field in the link-related data to obtain a second matching value; determining the relationship information of the link data and the link-related data for the third field; in the case where the relationship information indicates that the third field in the link data and the third field in the link-related data are the input and output of the same field, verifying whether the first matching value is consistent with the second matching value; in the case where the first matching value is consistent with the second matching value, determining the first matching value or the second matching value as the target value; in the case where the first matching value is inconsistent with the second matching value, obtaining a second monitoring result that the entire link data has an abnormal value of the same field.

[0009] According to an embodiment of the present disclosure, the target field is matched in the link data and the link-associated data to obtain the target value corresponding to the target field, and also includes: in a case where the relationship information indicates that the third field in the link data and the third field in the link-associated data are different processing stages of the same field, verifying whether the first matching value and the second matching value meet the corresponding stage value conditions; in a case where both the first matching value and the second matching value meet the corresponding stage value conditions, determining the matching value corresponding to the third field of the stage required by the target algorithm as the target value; in a case where the first matching value and the second matching value do not meet the corresponding stage value conditions, obtaining a third monitoring result that there is a common field stage value abnormality in the full-link data.

[0010] According to an embodiment of the present disclosure, the above data monitoring request also carries: abnormal configuration information, and the above abnormal configuration information includes: judgment conditions and prompt path information of abnormal indicators. The above method also includes: determining the target abnormal indicator with abnormality in the above monitoring results according to the above judgment conditions; generating abnormal information from the perspective of the whole link according to the above target abnormal indicator; determining the target prompt path corresponding to the above target abnormal indicator according to the above prompt path information; and prompting the above abnormal information based on the above target prompt path.

[0011] According to an embodiment of the present disclosure, based on the above-mentioned target abnormality indicators, abnormal information from a full-link perspective is generated, including: determining the impact path of the above-mentioned target abnormality indicators in the full-link process of the above-mentioned target scenario; based on the above-mentioned impact path, abnormal information from a full-link perspective is generated.

[0012] According to an embodiment of the present disclosure, the above-mentioned data monitoring method also includes: receiving the handling information of the processing role on the above-mentioned exception information; and updating the exception handling status of the full-link perspective according to the above-mentioned handling information.

[0013] According to an embodiment of the present disclosure, the link data and link-related data of the above-mentioned target link are obtained, including: obtaining the system data of each link in the full-link process of the target scenario; integrating the data according to the association relationship between the system data of each link to obtain a full-link data table; the full-link data table is used to store related data entities and attributes in the same table; and querying the link data and link-related data corresponding to the above-mentioned target link in the full-link data table.

[0014] According to an embodiment of the present disclosure, the above-mentioned data monitoring request also carries a role identifier; the above-mentioned method also includes: determining the target authority corresponding to the above-mentioned role identifier based on the mapping relationship between the pre-configured role and the data authority; determining whether the above-mentioned data monitoring request is legal based on the above-mentioned target authority; when the above-mentioned data monitoring request is legal, performing the following steps: obtaining the link data and link-related data of the above-mentioned target link; wherein, querying the link data and link-related data corresponding to the above-mentioned target link in the above-mentioned full-link data table includes: querying the link data corresponding to the above-mentioned target link and the link-related data within the above-mentioned target authority in the above-mentioned full-link data table.

[0015] According to an embodiment of the present disclosure, the full-link process of the above-mentioned target scenario includes multiple links, and each link implements data processing based on an independent application system; wherein, the roles in the above-mentioned mapping relationship include at least one of the following: the user role and the operator role of the above-mentioned application system.

[0016] In the second aspect, an embodiment of the present disclosure provides a device for data monitoring. The device includes: a request receiving module, a data acquisition module and a monitoring module. The request receiving module is used to receive a data monitoring request, and the data monitoring request carries: monitoring link information and monitoring indicator information; the monitoring link information is used to specify monitoring of at least one target link in the full-link process of the target scene. The data acquisition module is used to obtain link data and link-related data of the target link; the link-related data represents data that has an association relationship with the link data in the full-link process. The monitoring module is used to perform data monitoring on the link data and the link-related data based on the monitoring indicator information to obtain a monitoring result.

[0017] In a third aspect, an embodiment of the present disclosure provides an electronic device. The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory is used to store computer programs; and the processor is used to implement the data monitoring method as described above when executing the program stored in the memory.

[0018] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the data monitoring method described above is implemented.

[0019] The above technical solution provided by the embodiments of the present disclosure has at least some or all of the following advantages:

[0020] When a data monitoring request is received, by obtaining the link data (such as the order data of one or more merchants) of at least one target link specified in the monitoring link information (such as the order system), and by obtaining the link-related data that has an association with the above-mentioned link data in the whole link process (such as the relevant data of one or more merchants corresponding to the fulfillment system and payment system), since the link data and the link-related data can provide the association situation from the perspective of the whole link, in the process of data monitoring of the link data and the link-related data based on the monitoring indicator information, the monitoring results from a global perspective can be obtained, which helps to improve the reference and promotion of actual business. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0023] Figure 1 The system architecture of the method and device for data monitoring applicable to the embodiments of the present disclosure is schematically shown;

[0024] Figure 2 The flowchart of the data monitoring method according to an embodiment of the present disclosure is schematically shown;

[0025] Figure 3 The detailed implementation flow chart of step S220 according to an embodiment of the present disclosure is schematically shown;

[0026] Figure 4 The detailed implementation flow chart of step S230 according to an embodiment of the present disclosure is schematically shown;

[0027] Figure 5 The following schematically shows a flow chart of a method for data monitoring according to another embodiment of the present disclosure;

[0028] Figure 6 The following schematically shows a flow chart of a method for data monitoring according to another embodiment of the present disclosure;

[0029] Figure 7 The following schematically shows a flowchart of an implementation of a subscription exception prompt according to an embodiment of the present disclosure;

[0030] Figure 8 The following schematically shows a flowchart of the implementation of a subscription task by a server according to an embodiment of the present disclosure;

[0031] Fig. 9 The following schematically shows an implementation flow chart of a message sending module in a server according to an embodiment of the present disclosure;

[0032] Fig.10 The following schematically shows an implementation flow chart of real-time order monitoring according to an embodiment of the present disclosure;

[0033] Fig.11 The following schematically shows an implementation flow chart of abnormality handling according to an embodiment of the present disclosure;

[0034] Fig.12 A structural block diagram schematically shows a data monitoring device according to an embodiment of the present disclosure; and

[0035] Fig.13 The structural block diagram of the electronic device provided by the embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0037] Figure 1 The system architecture of the method and device for data monitoring applicable to the embodiments of the present disclosure is schematically shown.

[0038] Reference Figure 1As shown, the system architecture 100 of the method and apparatus for data monitoring applicable to the embodiment of the present disclosure includes: a terminal device 110 and a server 120. The terminal device 110 and the server 120 can communicate with each other based on a network, and the network can be of various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0039] In some implementation scenarios, a data monitoring application or an operation and maintenance application is installed on the terminal device 110. The data monitoring application or the operation and maintenance application can provide a data monitoring service from a full-link perspective for multiple business systems. For example, these business systems are Figure 1 The example application systems M1, M2 and M3 have authorized data usage rights to the operation and maintenance applications. These application systems M1 to M3 have business relevance in the target scenario. For example, in the sales business scenario for enterprises, application system M1 is the order system, application system M2 is the fulfillment system, and application system M3 is the payment system. The server 120 is used to provide service support for the above-mentioned data monitoring applications or operation and maintenance applications. Based on the authorized access rights, the server 120 can extract corresponding system data from the database of application system M1, the database of application system M2 and the database of application system M3.

[0040] In other implementation scenarios, monitoring and operation and maintenance functions are provided in the form of cloud services. Users (who may be users of various role types, such as regional operations personnel, business managers, account managers, program managers, sales personnel, risk control personnel, telemarketing personnel, customer service personnel, research and development personnel, etc.) can enjoy customized monitoring and operation and maintenance functions by logging into the cloud operation and maintenance service account on the terminal device; the server 120 provides cloud services corresponding to the monitoring and operation and maintenance functions, and different accounts correspond to their own role identifiers. Different types of roles can customize the monitoring indicators of specific links (with configuration constraints on access rights) in the full-link process of the target scenario.

[0041] The terminal device 110 may be any electronic device with a display screen, including but not limited to a smart phone, a tablet computer, a laptop computer, a smart watch, a smart bracelet, a smart robot, a desktop computer, a smart car, and the like.

[0042] Reference Figure 1As shown, the terminal device 110 has a configuration module, and the user can configure personalized monitoring link information and monitoring indicator information through the configuration module; the abnormal configuration information can also be configured, and the above-mentioned abnormal configuration information includes the judgment conditions and prompt path information of the abnormal indicators. The server 120 will save the information configured by the user through the configuration module in the cache or database of the server; and execute the data monitoring method provided by the embodiment of the present disclosure according to the data monitoring request of the terminal device 110 to obtain the monitoring results. The server 120 can return the monitoring results to the terminal device 110, and the monitoring result presentation module of the terminal device 110 will perform a visual display of the monitoring results. The data monitoring device provided by the embodiment of the present disclosure may include the server 120 in the above-mentioned system architecture 100.

[0043] In the embodiments of the present disclosure, the monitoring indicator may be the original data in the link data and the link-related data; or data obtained by time series statistics or calculation of the original data; an abnormal indicator refers to a monitoring indicator whose value reaches a certain condition and is considered abnormal.

[0044] A first exemplary embodiment of the present disclosure provides a data monitoring method.

[0045] Figure 2 The flowchart of a data monitoring method according to an embodiment of the present disclosure is schematically shown.

[0046] Reference Figure 2 As shown, the data monitoring method provided by the embodiment of the present disclosure includes the following steps: S210, S220 and S230. The method of this embodiment can be executed by the server 120 in the system architecture 100.

[0047] In step S210, a data monitoring request is received, and the data monitoring request carries: monitoring link information and monitoring indicator information; the monitoring link information is used to specify monitoring of at least one target link in the full-link process of the target scenario.

[0048] The target scenario may include, but is not limited to, various types of business scenarios, operation and maintenance scenarios, data processing scenarios, etc. For example, a sales scenario for an enterprise is used as an example.

[0049] Taking multiple business systems corresponding to the target scenario as an example, the full-link process refers to the flow process of each business link involved in the target scenario. Since the sales scenario for enterprises has the characteristics of long cycle, large order amount, relatively complex service and settlement process,

[0050] In the related technologies, if an abnormality occurs during the operation, in order to ensure the timely progress of the order delivery, reconciliation and invoicing, and collection of payment, a specific role (such as a transport manager) is often required to rely on experience to check various business systems to locate the cause of the abnormality, which is inefficient. In order to improve operational efficiency and avoid potential risks in advance, the data monitoring method and device provided by the embodiments of the present disclosure can monitor and provide abnormal prompts for each link of the entire transaction chain; such as the links involving product order placement, delivery, after-sales, invoicing, reconciliation, etc.

[0051] The target link here refers to one or more links specified in the monitoring link information to be monitored. The server 120 can respond to data monitoring requests initiated by different role identifiers, and can support monitoring of any link, multiple links, or even all links in the full link process of the target scene.

[0052] In some embodiments, each link is divided according to an independent business system; the full-link process of the above target scenario includes multiple links, and each link implements data processing based on an independent application system. The business systems of the above multiple links cover the functions of order, fulfillment, after-sales, and payment settlement. For example, Figure 1 As shown, in the enterprise-oriented sales scenario, multiple links are divided based on the order system, fulfillment system and payment system: order link, fulfillment link and payment settlement link.

[0053] In step S220, the link data and link-related data of the target link are obtained; the link-related data represent data that have an association relationship with the link data in the whole link process.

[0054] The business relevance and data relevance between each link will be reflected in the correlation between the data of each link. By obtaining the link data of the target link (such as the data corresponding to the order system of one or some merchants) and the link-related data associated with the link data (such as the relevant data corresponding to the fulfillment system and payment system of one or some merchants), the data correlation situation from the perspective of the whole link can be provided.

[0055] For example, a merchant X1 placed an order for two sets of a large equipment E1 during the order process, but the order failed. From the seller's perspective, the order system will show that the order request for merchant X1 resulted in an order failure. The reason for the order failure may be due to data in the order process or other processes. For example, the relevant data in the order process shows that the inventory of large equipment E1 is insufficient, it is not available for sale in the area where merchant X1 is located, and the large equipment E1 is in a limited purchase state and only one piece can be purchased, etc., which will lead to the failure of the order. For example, the relevant data in the performance process shows that the status of the sales contract between merchant X1 and the seller for large equipment E1 is that the contract has not been signed, the contract configuration is wrong, the contract price does not match the order price, etc., which will also lead to the failure of the order. For another example, relevant data in the payment settlement link show that another ongoing order of merchant X1 has not yet made the balance payment in accordance with the proportion agreed in the contract. For example, another ongoing order is to purchase another smart device E0 from the current seller and the amount is large (for example, in the millions or tens of millions). 60% of the balance of the order has not yet been paid. From a risk control perspective, the new order request of merchant X1 will result in an order failure.

[0056] Figure 3 The detailed implementation flow chart of step S220 according to an embodiment of the present disclosure is schematically shown.

[0057] In some embodiments, reference Figure 3 As shown, in the above step S220, obtaining the link data and link association data of the above target link includes the following steps: S310, S320 and S330.

[0058] In step S310, system data of each link in the full-link process of the target scenario is obtained.

[0059] For example, in some embodiments, each link implements data processing based on an independent application system; the business systems of the above multiple links cover the functions of order, performance, after-sales, and payment settlement; in the above step S310, the system data of each link in the full-link process of the target scenario is obtained, including: extracting the system data of the application system corresponding to each link in the above full-link process into the target database or target data warehouse. For example, extract the system data of each link into a data warehouse, such as extracting it into a hive table (hive is a data warehouse tool based on the distributed computing and storage architecture Hadoop, used for data extraction, transformation, and loading). The above system data can cover business data, and can also cover the dimensions of system operation logs.

[0060] In step S320, data integration is performed according to the association relationship between the system data of each link to obtain a full-link data table. The full-link data table is used to store related data entities and attributes in the same table.

[0061] The business relevance and data relevance between various links will be reflected in the correlation between the data in each link. The above correlation includes but is not limited to: process correlation between business links, correlation between data generation relationships, etc. The correlation between system data can be based on the same data belonging object, the correlation between business, the calculation relationship or conditional relationship between data, etc.

[0062] For example, the order system extracts order data of multiple corporate users in chronological order; the data extracted from the fulfillment system includes the transportation and installation status of the goods corresponding to the sales goods or contract number; the data extracted from the payment system includes: the completed payment amount of the order, the proportion of the payment amount compared to the contract amount, and other data.

[0063] Taking the data of a corporate user corresponding to multiple business systems as an example, we can understand the process of associating all the extracted system data. From the seller's perspective, the various application systems in the sales scenario, the merchant X1 (buyer role) in the order system corresponds to two order data, order data D1 and D2. Order data D1 is for the purchase of 1 large equipment E0, and order data D2 is for the purchase of 2 large equipment E1. In the fulfillment system, it includes: the delivery status of the order data (such as shipped, unshipped, out of the warehouse, etc.), the in-transit status (such as displaying specific logistics flow information, etc.), whether the goods have been received, installation information (installed by the seller, installed by the buyer or a third party, etc., whether it is installed according to the product instructions, etc.); for example, the fulfillment system includes the following data: large equipment E0 is in the fulfillment status T1 of having arrived and being installed by the seller; there is no fulfillment data for large equipment E1. In the payment system, the payment information F1 for order data D1 is that 40% of the payment has been paid and the 60% balance has not yet been paid; there is no payment data for large equipment E2. Then, in the process of data integration, based on the association between merchant identifiers, the association between device identifiers or contract identifiers, and the association between order numbers, the order data D1, fulfillment status T1, and payment information F1 of the merchant X1 can be integrated into the same data wide table. This data wide table is the full-link data table 121. Figure 1 Shown in the dashed box.

[0064] It should be noted that the data in the full-link data table will also be dynamically updated over time; it can be updated in real time or offline. For some data with high timeliness requirements, real-time updates are used for extraction; for some data with relatively low timeliness requirements, data is extracted periodically (every preset period of time) by synchronizing data from the application system.

[0065] A wide table is a table structure in a database or data warehouse that stores multiple related data entities and attributes in the same table for easier query and analysis.

[0066] Wide tables use horizontal splicing to merge related data from multiple tables into one wide table. This eliminates join operations in queries, helps simplify query statements and improves query efficiency. At the same time, the above full-link data table can also provide a more intuitive and clear data structure.

[0067] In step S330, the link data and link association data corresponding to the above-mentioned target link are queried in the above-mentioned full-link data table.

[0068] In an embodiment including steps S310 to S330, by constructing a full-link data table in the form of a wide table, the link data and link-related data corresponding to the target link can be quickly queried in the data structure of a table; and the constructed query statements are relatively simple, and there is no need to construct complex query statements like facing multiple separate data tables; in addition, the constructed full-link data table integrates the most complete data fields, and different types of roles can customize the monitoring indicators of specific links (with configuration constraints on access rights) in the full-link process of the target scenario, and the server can adapt and respond when faced with customized monitoring requests from various roles.

[0069] In step S230, data monitoring is performed on the link data and the link-related data based on the monitoring indicator information to obtain a monitoring result.

[0070] In the embodiments of the present disclosure, the monitoring indicator may be the original data in the link data and the link-related data; or data obtained by time series statistics or calculation of the original data; an abnormal indicator refers to a monitoring indicator whose value reaches a certain condition and is considered abnormal.

[0071] For example, some monitoring indicators are calculated through the status change time, such as the status retention time of user data, the remaining time for deleting an order, the delivery time, the in-transit time, the time for billing but not invoicing, the outstanding amount and other monitoring indicators; some monitoring indicators are calculated through the data of multiple existing fields. For example, the monitoring indicator of user credibility needs to be obtained through weighted calculation of payment information (for example, whether the payment amount and payment ratio are paid according to the progress agreed in the contract) and order data (for example, the size of the order amount).

[0072] Figure 8 The implementation flow chart of the server executing the subscription task according to the embodiment of the present disclosure is schematically shown.

[0073] Reference Figure 8 As shown, in some embodiments, different roles can obtain monitoring results from the server based on the subscription task. For the server, the subscription task is implemented based on a distributed task engine (such as Easy-job or other scheduling middleware). The specific process is as follows:

[0074] By accessing and using a distributed job scheduling engine (such as easy-job), user subscription tasks are scanned regularly, where distributed locks can be used to achieve mutual exclusive access to shared resources (such as subscription data);

[0075] According to the subscription conditions and the current subscriber data permissions, dynamic permission parameters are encapsulated and converted into SQL statements, and the data under the corresponding SQL is dynamically loaded;

[0076] Convert data into spreadsheet files according to system configuration and upload to the cloud;

[0077] Save the file download link of the cloud storage to the database, and send a message notification to the message notification component in the form of a message queue (MQ);

[0078] Based on the message sending module of the server 120, the monitoring results or abnormal prompt information are uniformly sent.

[0079] In an embodiment including the above steps S210 to S230, when a data monitoring request is received, by obtaining link data (such as order data of one or more merchants) of at least one target link specified in the monitoring link information (such as an order system), and by obtaining link-related data that has an association with the above link data in the whole link process (such as relevant data of one or more merchants corresponding to the fulfillment system and payment system), since the link data and link-related data can provide the association situation from the perspective of the whole link, in the process of data monitoring of the link data and link-related data based on the monitoring indicator information, the monitoring results from a global perspective can be obtained.

[0080] Figure 4 The detailed implementation flowchart of step S230 according to an embodiment of the present disclosure is schematically shown.

[0081] According to an embodiment of the present disclosure, the monitoring indicator information includes: a monitoring indicator and a target algorithm for calculating and obtaining the monitoring indicator.

[0082] Reference Figure 4 As shown, in the above step S230, data monitoring is performed on the above link data and the above link-related data based on the above monitoring indicator information to obtain a monitoring result, including the following steps: S410, S420 and S430.

[0083] In step S410, the target field involved in the operation is determined according to the target algorithm.

[0084] In step S420, the target field is matched in the link data and the link association data to obtain a target value corresponding to the target field.

[0085] In step S430, the target value is substituted into the target algorithm for calculation to obtain the monitoring result of the monitoring indicator.

[0086] In some embodiments, the target field determined in step S410 only includes fields in the link data, or only includes fields in the link-related data, and the matched target value can be substituted into the target algorithm to calculate the monitoring result.

[0087] In some embodiments, the target field includes: a first field in the link data and a second field in the link association data.

[0088] In the case where the first field and the second field are different fields and the first field is not associated with the second field, in the step S420, matching the target field in the link data and the link association data to obtain a target value corresponding to the target field includes:

[0089] Matching the first field in the link data to obtain a first field value;

[0090] Matching the second field in the link association data to obtain a second field value;

[0091] Among them, the above-mentioned first field value and the above-mentioned second field value serve as the above-mentioned target value.

[0092] In some scenarios, it is often necessary to jointly monitor multiple related indicators across business systems. The scattered monitoring method used in related technologies can hardly provide this monitoring function; most of them are achieved by manually viewing and entering data across systems. The solution provided by this embodiment can meet the needs of joint monitoring, such as obtaining and calculating monitoring indicators across multiple business systems; when the target fields to be obtained include the first field in the link data and the second field in the link-related data, and the first field and the second field are different non-related fields, the cross-system monitoring indicator results can be obtained by substituting the first field value and the second field value into the target algorithm for calculation.

[0093] In the case where the first field and the second field are different fields and the first field is associated with the second field, in the step S420, matching the target field in the link data and the link association data to obtain a target value corresponding to the target field includes:

[0094] Matching the first field in the link data to obtain a first field value;

[0095] Matching the second field in the link association data to obtain a second field value;

[0096] Verify whether the first field value and the second field value are valid according to the association relationship between the first field and the second field;

[0097] In the case that both the first field value and the second field value are valid, determining the first field value and the second field value as the target value;

[0098] When the first field value and the second field value are invalid, a first monitoring result is obtained indicating that an abnormality exists in the associated field in the full-link data.

[0099] For example, the first field is the payment status field of the payment system, and the second field is the performance status field. The relationship between the two is, for example, the correspondence between the interval where the prepayment ratio is completed and the progress of the goods delivery. For example, the payment status field value is: 50% of the prepayment has been completed in accordance with the contract agreement; the performance status requirement of the second field is that the equipment is ready and the shipment or delivery is being arranged; if the second field value and the second field value meet the above correspondence, the first field value and the second field value are deemed valid. If the two do not meet the above correspondence, the first field value and the second field value are deemed invalid. By sending the first monitoring result of the abnormality of the associated field to the terminal device (front end), the abnormality display is performed based on the terminal device, which helps the business system or manual verification of the specific cause and performs corresponding abnormal handling.

[0100] There may be two reasons for the invalidity of the first field value and the second field value: one is that the actual business operation does not comply with the contract agreement, that is, the actual business operation is not carried out in accordance with the contract agreement, and the data recorded by the application system is in line with the actual situation. In this case, there is no need to deal with the abnormal data in the application system; the other is that there are errors or anomalies in the data in the application system. For example, the logistics personnel forget to update the logistics information in the fulfillment system, resulting in data lag in the fulfillment system; or there are data errors in the data transmission between the fulfillment system and the server 120 (this is an extremely small probability event, but it will also exist) or data synchronization lags, etc. In these cases, it is necessary to locate the specific cause and take corresponding measures.

[0101] In this embodiment, when the target fields to be obtained include the first field and the second field, and the first field and the second field are different associated fields, the validity of the first field value and the second field value is verified according to the association relationship between the first field and the second field. Not only can the cross-system monitoring indicator results be obtained, but also the logic of interactively verifying the validity of corresponding field values ​​based on the association relationship between multiple fields is set, which helps to prompt data errors or data anomalies that may exist in the data transmission process between the application system and the server 120, and the interaction process between the application system and some middleware or humans; it can even prompt behaviors that do not comply with contractual agreements in the actual business execution process.

[0102] According to an embodiment of the present disclosure, the target field includes a third field, and the third field exists in both the link data and the link association data.

[0103] In the step S420, matching the target field in the link data and the link association data to obtain a target value corresponding to the target field includes:

[0104] Matching the third field in the above link data to obtain a first matching value;

[0105] Matching the third field in the link association data to obtain a second matching value;

[0106] Determine the relationship information between the link data and the link association data for the third field;

[0107] In the case where the relationship information indicates that the third field in the link data and the third field in the link association data are input and output of the same field, verifying whether the first matching value is consistent with the second matching value;

[0108] When the first matching value is consistent with the second matching value, determining the first matching value or the second matching value as the target value;

[0109] When the first matching value is inconsistent with the second matching value, a second monitoring result is obtained that the entire link data has the same field value anomaly.

[0110] According to an embodiment of the present disclosure, matching the target field in the link data and the link association data to obtain a target value corresponding to the target field further includes:

[0111] In the case where the relationship information indicates that the third field in the link data and the third field in the link association data are different processing stages of the same field, verifying whether the first matching value and the second matching value meet the corresponding stage value conditions;

[0112] In the case where both the first matching value and the second matching value meet the corresponding stage value conditions, the matching value corresponding to the third field of the stage required by the target algorithm is determined as the target value;

[0113] When the first matching value and the second matching value do not meet the corresponding stage value conditions, a third monitoring result is obtained that there is a stage-by-stage value abnormality in the common field in the full-link data.

[0114] The specific examples here can refer to the aforementioned principle examples about the first field and the second field. The only difference is that the first field and the second field are understood as the same common fields, and the verification conditions of the two are changed accordingly. The cause of the exception is the same and will not be repeated here.

[0115] In this embodiment, when the target field to be obtained includes a third field, and the third field is a common field of the link data and the link-related data, the validity of the first matching value and the second matching value is verified through the relationship information of the link data and the link-related data for the third field. Not only can the cross-system monitoring indicator results be obtained, but also the logic for interactively verifying the validity of the corresponding field values ​​based on the relationship information between the common fields of the link data and the common fields of the link-related data is set, which helps to prompt data errors or data anomalies that may exist in the data transmission process between the application system and the server 120, and in the interaction process between the application system and some middleware or humans; it can even prompt behaviors that do not comply with contractual agreements in the actual business execution process.

[0116] Figure 5 The flowchart of a data monitoring method according to another embodiment of the present disclosure is schematically shown.

[0117] In some embodiments, the data monitoring request also carries a role identifier. Figure 5As shown, the data monitoring method includes the following steps in addition to the steps S210 to S230: S510 and S520. Steps S510 and S520 are performed after step S210.

[0118] In step S510, the target permission corresponding to the role identifier is determined according to the mapping relationship between the pre-configured roles and data permissions.

[0119] In step S520, it is determined whether the data monitoring request is legal based on the target authority.

[0120] In the case that the above data monitoring request is legal, the following step S220 is performed: obtaining the link data and link association data of the above target link.

[0121] Among them, in the above step 330, the link data and link-related data corresponding to the above target link are queried in the above full-link data table, including: querying the link data corresponding to the above target link and the link-related data within the above target authority in the above full-link data table.

[0122] For example, for some roles, in addition to being able to access the link data of the target link to which the role has access rights, they can only access part of the data in the link-related data, and these roles do not have access to other data in the link-related data. The mapping relationship between specific roles and data permissions can be pre-configured through the configuration module of the terminal device 110, and can be configured accordingly by the platform administrator of the operation and maintenance application or the operation and maintenance cloud service according to the requirements of the target scenario. For example, in some embodiments, the roles in the above-mentioned mapping relationship include at least one of the following: the user role and the operator role of the above-mentioned application system.

[0123] Figure 6 The following schematically shows a flow chart of a data monitoring method according to another embodiment of the present disclosure. Figure 7 The implementation flow chart of subscription exception prompt according to an embodiment of the present disclosure is schematically shown.

[0124] According to the embodiments of the present disclosure, referring to Figure 7 As shown, the above data monitoring request carries not only monitoring link information and monitoring indicator information, but also abnormal configuration information. The above abnormal configuration information includes: abnormal indicator judgment conditions and prompt path information. Figure 7 The monitoring point configuration process (user interaction with terminal device 110) is used as an example. The judgment conditions of abnormal indicators (which can also be described as abnormal judgment rules) can be user-defined judgment conditions or some general judgment conditions; accordingly, the server 120 generates configuration table information or updates configuration table information based on the user's abnormal configuration information.

[0125] In addition to the above steps S210 to S230, or including steps S210 to S230, S510 and S520, the above data monitoring method further includes the following steps: S610, S620, S630 and S640. Figure 6 Steps S610 to S640 are shown in FIG. Step S610 is performed after step S230.

[0126] In step S610, according to the above determination conditions, the target abnormality index having an abnormality is determined in the above monitoring results.

[0127] Monitoring indicators include, but are not limited to: order information, large-scale transportation status, small and medium-sized transportation status, platform performance status, seller performance status, service (such as price guarantee service, after-sales service, etc.) performance status, settlement links, settlement results, settlement efficiency, invoice status, etc.

[0128] Target abnormal indicators include but are not limited to: order failure, order suspension, large orders, delayed invoices, etc.

[0129] Reference Figure 7 As shown, the offline subscription exception prompt is implemented by means of scheduled tasks + dynamic structured language query (SQL) + database. The specific process is as follows: the user interacts with the front end to configure the monitoring point, and generates a configuration table on the server; then the worker thread (worker) corresponding to the scheduled task on the server calculates the monitoring indicators according to the configured exception judgment rules, and sends the data that meets the exception judgment rules to a unified exception prompt message pipeline; the message processing module on the server performs different logical processing for different messages based on the pre-configured strategy mode, and uniformly records the processing process or sends messages.

[0130] In step S620, abnormal information from a full-link perspective is generated based on the above target abnormality indicators.

[0131] In some embodiments, in the above step S620, abnormal information from a full-link perspective is generated based on the above-mentioned target abnormality indicator, including: determining the impact path of the above-mentioned target abnormality indicator in the full-link process of the above-mentioned target scenario; and generating abnormal information from a full-link perspective based on the above-mentioned impact path.

[0132] In step S630, a target prompt path corresponding to the target abnormality indicator is determined according to the prompt path information.

[0133] In step S640, based on the target prompting approach, the abnormal information is prompted.

[0134] Fig. 9 The implementation flow chart of the message sending module in the server according to the embodiment of the present disclosure is schematically shown.

[0135] Combination Figure 7 and Fig. 9 As shown, the message sending module sends the message according to the target prompt path, such as using email, text message or other communication tools.

[0136] In the terminal device corresponding to the server, monitoring modules corresponding to various application systems may be presented, including but not limited to: order monitoring module, contract performance monitoring module, after-sales monitoring module, operation settlement monitoring module, etc. The message sending module is a unified component serving the above-mentioned monitoring modules, and may be implemented based on message queue middleware (e.g., JMQ middleware) + strategy mode.

[0137] The specific implementation process includes:

[0138] Use the message queue middleware to send messages uniformly. For high availability, save them in the database first.

[0139] Wait for data to be saved successfully before distributing messages;

[0140] If data saving fails, the message will be retried;

[0141] In case of failure to send a message after successful saving, the scheduled task worker thread is used to make up for the failure, that is, the failed message is sent again based on the corresponding prompt path to ensure successful message sending.

[0142] In this embodiment, for an anomaly occurring in a certain link, other indicators affected on the impact path of the anomaly will also be prompted as an anomaly; at the same time, after locating the target abnormal indicator, not only the abnormal situation of the link where the target abnormal indicator is located is considered, but the corresponding abnormal information is generated from the perspective of the entire link, which helps to improve the reference of the operation and maintenance results to the actual business and promote the effectiveness.

[0143] For the scenario of sales to enterprises, the reasons for abnormal data monitoring indicators may include anomalies that may occur during the order placement, fulfillment, reconciliation and settlement processes, such as unsaleable, purchase restrictions, out of stock, insufficient inventory, unsigned contract, contract configuration errors, contract price mismatch, abnormal payment status of other orders from the same user, etc.; fulfillment anomalies such as refusal to accept, false delivery by merchants, overdue delivery, and order closing time limit during the fulfillment process; and reconciliation issues such as lost invoices, inconsistent accounts receivable and invoices, payment periods, and approval timeouts during the reconciliation process.

[0144] Fig.10 The implementation flow chart of real-time order monitoring according to an embodiment of the present disclosure is schematically shown.

[0145] Reference Fig.10 As shown in the figure, monitoring for near real-time scenarios is achieved by monitoring data status change messages + rule engine. The specific process is as follows:

[0146] Business data status message queue triggers execution;

[0147] The chain of responsibility pattern processes messages. The chain of responsibility pattern is a behavioral design pattern that allows requests to be sent along a chain of handlers. After receiving a request, each handler can process the request or pass it to the next handler in the chain.

[0148] The calculation engine loads rule information and context data;

[0149] The calculation engine executes the exception judgment rule expression, generates an exception information record according to the expression calculation result and saves the exception record to the database;

[0150] Send exception information record message notification to the message queue;

[0151] The message sending module consumes the exception information in the message queue and sends the message. For the specific sending process, please refer to Figure 7 and Fig. 9 Description.

[0152] According to some embodiments of the present disclosure, the data monitoring method further includes: receiving processing information of the processing role on the abnormal information; and updating the abnormal processing status of the full link perspective according to the processing information. Figure 7 As shown, the message processing module of the server uses the pre-configured strategy mode to perform different logical processing on different messages and uniformly records the processing process. The messages processed by the message processing module can be entered into the historical message database or sent to the message sending module.

[0153] Fig.11 The flowchart of implementing the abnormality handling according to the embodiment of the present disclosure is schematically shown.

[0154] Reference Fig.11 As shown in the figure, during the process of exception handling between the terminal device and the server, the server's three-party contract performance exception reporting task (job) is sent to the corresponding subscription role through the middleware (for example, including a message sending module); the risk order real-time warning data queue (MQ) is sent to the corresponding subscription role through the middleware; the risk account reporting task is also sent to the corresponding subscription role through the message middleware. Fig.11After the monitoring results and abnormal information sent by the server are displayed, the processing roles (such as operation personnel, sales personnel, customer service personnel, etc.) will handle the corresponding abnormalities on the web side. Figure 8 The examples include handling of third-party performance exceptions, risky orders, risky accounts or risky customers.

[0155] In some embodiments, the middleware includes: database middleware and big data analysis management module, for example, based on JED database middleware (distributed database middleware deployed in a full container, suitable for scenarios of massive data transaction processing, supporting dynamic online expansion, automatic backup and recovery, etc.) and analysis management module for column storage database to build the above middleware. The above analysis management model for column storage data is, for example, Clikhouse management system, referred to as CK, which can perform big data analysis.

[0156] Reference Figure 8 As shown in the figure, exception handling is based on the state machine shared by each monitoring module to transfer data. The specific process includes:

[0157] After the exception information is reported to the middleware, the corresponding status is pending, which is regarded as the default status;

[0158] After the processing role receives the above exception information, it is deemed to be accepted and the status is changed to processing;

[0159] After the processing role handles the abnormal information and gives feedback, the status is changed to pending confirmation;

[0160] Finally, the disposal result is determined manually or by the operation and maintenance cloud service or system, and the status is updated to completed if confirmed.

[0161] A second exemplary embodiment of the present disclosure provides a data monitoring device.

[0162] Fig.12 The structure block diagram of the data monitoring device according to the embodiment of the present disclosure is schematically shown.

[0163] Reference Fig.12 As shown, the data monitoring device 1200 includes: a request receiving module 1201, a data acquisition module 1202 and a monitoring module 1203. It can be understood that the data monitoring device 1200 in this embodiment can be a server, and the monitoring module of the server is used as the actual execution subject of the background to provide service support for data storage and data calculation for each monitoring module included in the front end.

[0164] The request receiving module 1201 is used to receive a data monitoring request, which carries: monitoring link information and monitoring indicator information; the monitoring link information is used to specify monitoring of at least one target link in the full-link process of the target scenario.

[0165] The data acquisition module 1202 is used to acquire the link data and link-related data of the target link; the link-related data represents data that has an association relationship with the link data in the whole link process.

[0166] The monitoring module 1203 is used to perform data monitoring on the link data and the link-related data based on the monitoring indicator information to obtain monitoring results.

[0167] According to an embodiment of the present disclosure, the above data monitoring request also carries: abnormal configuration information, and the above abnormal configuration information includes: determination conditions of abnormal indicators and prompt path information.

[0168] The above-mentioned device 1200 also includes: an abnormality determination module and a message sending module.

[0169] The above-mentioned abnormality determination module is used to determine the target abnormality indicator where the abnormality occurs in the above-mentioned monitoring results according to the above-mentioned judgment conditions; and generate abnormal information from the perspective of the whole link according to the above-mentioned target abnormality indicator.

[0170] The message sending module is used to determine the target prompt path corresponding to the target abnormal indicator according to the prompt path information; based on the target prompt path, prompt the abnormal information. Figure 7 and Fig. 9 Description.

[0171] According to an embodiment of the present disclosure, the above-mentioned device 1200 further includes: a disposal information receiving module and a status updating module.

[0172] The above-mentioned disposal information receiving module is used to receive the disposal information of the processing role on the above-mentioned abnormal information.

[0173] The above-mentioned status update module is used to update the exception handling status of the full-link perspective according to the above-mentioned handling information.

[0174] According to an embodiment of the present disclosure, the above-mentioned data monitoring request also carries a role identifier; the above-mentioned device 1200 also includes: an authority determination module and a request legitimacy determination module.

[0175] The permission determination module is used to determine the target permission corresponding to the role identifier according to the mapping relationship between the pre-configured roles and data permissions.

[0176] The request legitimacy determination module is used to determine whether the data monitoring request is legal according to the target authority. If the data monitoring request is legal, the steps are performed to obtain the link data and link-related data of the target link. In the above-mentioned full-link data table, the link data and link-related data corresponding to the target link are searched, including: the link data corresponding to the target link and the link-related data within the target authority are searched in the full-link data table.

[0177] The data monitoring method and device provided by the embodiment of the present disclosure can perform data integration calculation through offline calculation, real-time calculation or a combination of the two to obtain a full-link data table, which can be stored in a cache, for example; and through unified data authority management, the integrated basic data is opened to users of different roles. For example, the full-link data table is synchronized to the database of the server through the big data push tool, and the database is automatically authorized to different user roles through the enterprise-oriented organizational relationship authority (CRM). Different roles can customize the abnormal indicators, judgment conditions and prompt path information of abnormal scenarios; in some embodiments, it can be scheduled through distributed tasks and abnormal notifications can be made regularly; the scheme of real-time monitoring of data and prompting abnormalities can be through message monitoring events, and the monitoring indicators can be calculated in real time according to the pre-configured judgment conditions, and the abnormal information can be prompted to the corresponding personnel; in addition, the recording and status transfer of the handling process of abnormal information can be unified. In some embodiments, abnormal information under the full-link perspective can be generated according to the abnormal indicators to realize the monitoring of the full-link perspective; according to the handling information of the above abnormal information by the processing role, the abnormal handling status of the full-link perspective is updated to realize the presentation of operation and maintenance information from the full-link perspective.

[0178] Any number of the functional modules included in the above-mentioned device 1200 can be combined in one module, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. At least one of the functional modules included in the device 1200 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, at least one of the functional modules included in the device 1200 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be performed.

[0179] A third exemplary embodiment of the present disclosure provides an electronic device.

[0180] Fig.13 The structural block diagram of the electronic device provided by the embodiment of the present disclosure is schematically shown.

[0181] Reference Fig.13 As shown, the electronic device 1300 provided by the embodiment of the present disclosure includes a processor 1301, a communication interface 1302, a memory 1303 and a communication bus 1304, wherein the processor 1301, the communication interface 1302 and the memory 1303 communicate with each other through the communication bus 1304; the memory 1303 is used to store computer programs; the processor 1301 is used to implement the data monitoring method as described above when executing the program stored in the memory.

[0182] The fourth exemplary embodiment of the present disclosure further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the data monitoring method described above is implemented.

[0183] The computer-readable storage medium may be included in the device or apparatus described in the above embodiment; or it may exist independently without being assembled into the device or apparatus. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0184] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0185] It should be noted that the collection, collection, update, analysis, processing, use, transmission, storage and other aspects of user personal information involved in the technical solution provided by the embodiments of the present disclosure are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security and national security.

[0186] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0187] The foregoing is merely a specific embodiment of the present disclosure, which enables those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features applied herein.

Claims

1. A method for data monitoring, It is characterized in that include: Receive a data monitoring request, the data monitoring request carrying: monitoring link information and monitoring indicator information; The monitoring link information is used to specify monitoring of at least one target link in the full link process of the target scenario; Acquire link data and link association data of the target link; The link-related data represents data associated with the link data in the whole link process; For the link data and the link-related data, data monitoring is performed based on the monitoring indicator information to obtain a monitoring result.

2. The method according to claim 1, It is characterized in that The monitoring indicator information includes: a monitoring indicator and a target algorithm for calculating and obtaining the monitoring indicator; For the link data and the link-related data, data monitoring is performed based on the monitoring indicator information to obtain monitoring results, including: According to the target algorithm, determining the target field involved in the operation; Matching the target field in the link data and the link association data to obtain a target value corresponding to the target field; The target value is substituted into the target algorithm for calculation to obtain the monitoring result of the monitoring indicator.

3. The method according to claim 2, It is characterized in that The target field includes: a first field in the link data and a second field in the link association data; In the case where the first field and the second field are different fields and the first field is not associated with the second field, matching the target field in the link data and the link association data to obtain a target value corresponding to the target field includes: matching the first field in the link data to obtain a first field value; matching the second field in the link association data to obtain a second field value; using the first field value and the second field value as the target value; In the case where the first field and the second field are different fields and the first field is associated with the second field, the target field is matched in the link data and the link-associated data to obtain the target value corresponding to the target field, including: matching the first field in the link data to obtain the first field value; matching the second field in the link-associated data to obtain the second field value; verifying whether the first field value and the second field value are valid based on the association relationship between the first field and the second field; in the case where both the first field value and the second field value are valid, determining the first field value and the second field value as the target value; in the case where the first field value and the second field value are invalid, obtaining a first monitoring result of an abnormality in the associated fields in the entire link data.

4. The method according to claim 2, It is characterized in that The target field includes a third field, and the third field exists in both the link data and the link association data; Matching the target field in the link data and the link association data to obtain a target value corresponding to the target field includes: Matching a third field in the link data to obtain a first matching value; matching a third field in the link association data to obtain a second matching value; Determining relationship information between the link data and the link association data for a third field; In a case where the relationship information indicates that the third field in the link data and the third field in the link association data are input and output of the same field, verifying whether the first matching value is consistent with the second matching value; When the first matching value is consistent with the second matching value, determining the first matching value or the second matching value as the target value; When the first matching value is inconsistent with the second matching value, a second monitoring result is obtained that the entire link data has the same field value anomaly.

5. The method according to claim 4, It is characterized in that Matching the target field in the link data and the link association data to obtain a target value corresponding to the target field also includes: In a case where the relationship information indicates that the third field in the link data and the third field in the link association data are different processing stages of the same field, verifying whether the first matching value and the second matching value meet the corresponding stage value conditions; When both the first matching value and the second matching value meet the corresponding stage value conditions, the matching value corresponding to the third field of the stage required by the target algorithm is determined as the target value; When the first matching value and the second matching value do not meet the corresponding stage value conditions, a third monitoring result is obtained that there is a stage-by-stage value abnormality in the common field in the full-link data.

6. The method according to claim 1, It is characterized in that The data monitoring request also carries: abnormal configuration information, the abnormal configuration information includes: abnormal indicator determination conditions and prompt path information; The method further comprises: According to the determination condition, determining a target abnormality indicator having an abnormality in the monitoring result; Generate abnormal information from a full-link perspective based on the target abnormal indicator; Determining a target prompt path corresponding to the target abnormal indicator according to the prompt path information; Based on the target prompting approach, the abnormal information is prompted.

7. The method according to claim 6, It is characterized in that According to the target abnormality indicator, abnormal information from the perspective of the entire link is generated, including: Determine the impact path of the target abnormal indicator in the full-link process of the target scenario; According to the impact path, abnormal information from a full-link perspective is generated.

8. The method according to claim 6, It is characterized in that Also includes: Receive the processing information of the processing role on the abnormal information; According to the handling information, the exception handling status of the full-link perspective is updated.

9. The method according to any one of claims 1 to 8, It is characterized in that Acquiring link data and link association data of the target link, including: Obtain system data for each link in the entire process of the target scenario; Data integration is performed according to the association relationship between the system data of each link to obtain a full-link data table; the full-link data table is used to store related data entities and attributes in the same table; The link data and link-related data corresponding to the target link are queried in the full-link data table.

10. The method according to claim 9, It is characterized in that The data monitoring request also carries a role identifier; the method further includes: Determine the target permission corresponding to the role identifier according to the mapping relationship between the pre-configured roles and data permissions; Determining whether the data monitoring request is legal according to the target authority; In the case where the data monitoring request is legitimate, the following steps are performed: obtaining link data and link association data of the target link; Among them, querying the link data and link-related data corresponding to the target link in the full-link data table includes: querying the link data corresponding to the target link and the link-related data within the target authority in the full-link data table.

11. The method according to claim 10, It is characterized in that The full-link process of the target scenario includes multiple links, each of which implements data processing based on an independent application system; The roles in the mapping relationship include at least one of the following: a user role and an operator role of the application system.

12. A data monitoring device, It is characterized in that include: A request receiving module, used to receive a data monitoring request, wherein the data monitoring request carries: monitoring link information and monitoring indicator information; The monitoring link information is used to specify monitoring of at least one target link in the full link process of the target scenario; A data acquisition module, used to acquire link data and link-related data of the target link; The link-related data represents data associated with the link data in the whole link process; The monitoring module is used to perform data monitoring on the link data and the link-related data based on the monitoring indicator information to obtain monitoring results.

13. An electronic device, It is characterized in that It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, for implementing the method according to any one of claims 1 to 11 when executing a program stored in a memory.

14. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

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