Buried point data processing method and device, computer equipment and storage medium

Abstract

The invention discloses a burying point data processing method and device, computer equipment and a storage medium, and the method comprises the steps: dynamically configuring a full-amount rule through a rule configuration module, and storing the full-amount rule when the full-amount rule passes the approval; if the preset rule acquisition condition is met, acquiring the currently stored newest full-quantity rule through a dynamic data distribution module; if the newest full-amount rule is not consistent with the local shunting rule, the newest full-amount rule data is pulled in a full-amount mode; performing rule construction on changed data in the newest full-amount rule data to obtain a newly added rule; and compiling the newly added rule to obtain a new shunting rule, and performing shunting processing on the burying point data based on the new shunting rule. Through the mode, the time and cost consumption caused by rule conversion is reduced, the decision-making time of an enterprise can be greatly reduced, the data processing efficiency and accuracy are improved, the business is finally optimized, and the competitive capacity and profitability of the enterprise are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, computer equipment and storage medium for processing buried point data. Background Art

[0002] Currently, the real-time data distribution of tracking points is mainly carried out through fixed logic and one or more identifiers. Common distribution identifiers include project ID, event, OS, etc.

[0003] However, since fixed logic is used and data diversion is dependent on specific identifiers (such as project ID, events, operating systems, etc.), this means that whenever an identifier changes, the corresponding diversion code needs to be modified. This makes code maintenance relatively complex and difficult. And whenever a new tracking point needs to be added, the corresponding diversion code needs to be redeveloped, which will add additional development work and further increase development costs. In addition, the new diversion code has a long launch cycle and requires the R&D team to access it, which will cause the system to respond to new changes too long, thereby reducing the system's flexibility and ability to respond quickly. In addition, the failure to respond to tracking point changes in a timely manner may cause the tracking point diversion function to be abnormal, thus affecting the quality of the data and subsequent analysis and decision-making based on these data. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment and storage medium for processing buried point data in order to solve at least one problem existing in the above-mentioned prior art.

[0005] In a first aspect, a method for processing buried point data is provided, comprising:

[0006] Dynamically configure the full set of rules through the rule configuration module, and store the full set of rules when the full set of rules is approved;

[0007] If the preset rule acquisition conditions are met, the dynamic data diversion module is used to obtain the latest full set of rules currently stored;

[0008] If the latest full rule is inconsistent with the local diversion rule, the latest full rule data is fully pulled;

[0009] Perform rule construction on the changed data in the latest full rule data to obtain newly added rules;

[0010] The newly added rules are compiled to obtain new diversion rules, so as to perform diversion processing on the buried point data based on the new diversion rules.

[0011] In one embodiment, if the latest full rule is inconsistent with the local diversion rule, the latest full rule data is fully pulled, including:

[0012] Obtain the version number corresponding to the latest full rule and the version number corresponding to the local diversion rule;

[0013] Compare the version number corresponding to the latest full rule and the version number corresponding to the local diversion rule;

[0014] If the comparison results are inconsistent, it means that the latest full rule has been updated relative to the local diversion rule.

[0015] In one embodiment, the dynamic configuration of the full amount rule includes:

[0016] Receive the diversion nested structure condition array and corresponding data processor from the front end;

[0017] Verifying the diversion nested structure condition array and the data processor;

[0018] When the verification is passed, the diversion nested structure conditions are converted one by one into rules recognized by the rule engine, and merged with the data processor, and the public configuration is supplemented to obtain the latest full rules.

[0019] In one embodiment, when the full amount of rules is approved, before storing the full amount of rules, the process includes:

[0020] Load the latest full set of rules into the local rule engine for testing, and simulate the rule running environment during the testing process;

[0021] When the test passes, the latest full set of rules will be approved.

[0022] In one embodiment, if the preset rule acquisition condition is met, obtaining the latest full set of currently stored rules includes:

[0023] When each pod node is started, it will pull a copy of the latest full set of rules from the storage, and each pod node will initialize a rule engine instance.

[0024] In one embodiment, the step of constructing rules for the changed data in the latest full rule data to obtain newly added rules includes:

[0025] Loading the changed data in the latest full amount of rule data into the memory through the rule engine loading module;

[0026] The rule file is used to construct rules for the changed data in the latest full rule data to obtain the newly added rules.

[0027] In one embodiment, the rule configuration module is deployed on a common machine, and the dynamic data diversion module is deployed in a container orchestration engine.

[0028] In a second aspect, a device for processing buried data is provided, comprising:

[0029] A rule configuration module is used to dynamically configure the full set of rules, and when the full set of rules is approved, the full set of rules is stored;

[0030] The dynamic data distribution module is used to:

[0031] If the preset rule acquisition conditions are met, the latest full set of rules currently stored are obtained;

[0032] If the latest full rule is inconsistent with the local diversion rule, the latest full rule data is fully pulled;

[0033] Perform rule construction on the changed data in the latest full rule data to obtain newly added rules;

[0034] The newly added rules are compiled to obtain new diversion rules, so as to perform diversion processing on the buried point data based on the new diversion rules.

[0035] In a third aspect, a computer device is provided, comprising a memory, a processor, and computer-readable instructions stored in the memory and running on the processor, wherein the processor implements the method for processing buried point data as described above when executing the computer-readable instructions.

[0036] In a fourth aspect, a readable storage medium is provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the method for processing buried point data as described above is implemented.

[0037] The above-mentioned point-of-sale data processing method, device, computer equipment and storage medium, the method implementation includes: dynamically configuring full rules through a rule configuration module, and storing the full rules when the full rules are approved; if the preset rule acquisition conditions are met, the latest full rules currently stored are obtained through a dynamic data diversion module; if the latest full rules are inconsistent with the local diversion rules, the latest full rule data is pulled in full; the change data in the latest full rule data is constructed according to the rules to obtain new rules; the new rules are compiled to obtain new diversion rules, and the point-of-sale data is diverted based on the new diversion rules. In the embodiment of the present application, the system can better adapt to the changing business needs by dynamically adjusting the rules, reducing the time and cost consumption caused by rule conversion. It enables the operation and maintenance personnel to make appropriate adjustments according to the real-time business needs and system operation conditions, making the system more efficient and reducing the cost of manual intervention. Dynamically configured point-of-sale data processing can respond to market changes in real time and provide accurate and real-time data to decision makers, thereby seizing the initiative in market competition. Through the above methods, the decision-making time of enterprises can be greatly reduced, the efficiency and accuracy of data processing can be improved, and ultimately the business can be optimized and the competitiveness and profitability of the enterprise can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0039] Figure 1 It is a flow chart of a method for processing buried point data in one embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram of an application environment of a method for processing buried point data in an embodiment of the present invention. Figure 1 ;

[0041] Figure 3 This is a schematic diagram of an application environment of a method for processing buried point data in an embodiment of the present invention. Figure 2 ;

[0042] Figure 4 It is a structural schematic diagram of a buried point data processing device in one embodiment of the present invention;

[0043] Figure 5 is a schematic diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] In one embodiment, if Figure 1 , Figure 2 As shown, a method for processing buried point data is provided, comprising the following steps:

[0046] In step S110, the full amount of rules are dynamically configured through the rule configuration module, and when the full amount of rules are approved, the full amount of rules are stored;

[0047] In one embodiment of the present application, a rule configuration module and a dynamic data diversion module can be set. The rule configuration module can not only create queries, update and delete diversion rules, but also support the combination of complex rules, priority settings, condition associations, etc., to meet complex business needs. Specifically, a rule configuration interface can be provided, and users can create queries, update and delete diversion rules in the rule configuration interface. Then the newly created diversion rules can be tested. After the test passes, the diversion rules can be approved. If approved, the diversion rules can be sent to Redis for storage for subsequent calls. Among them, Redis is a key-value storage system, which is often used for data caching, transfer and storage.

[0048] It should be noted that a unique data version number corresponding to the diversion rule can be generated according to the timestamp, and the data version number and the diversion rule are stored in correspondence.

[0049] It is understandable that the diversion rules integrate the diversion conditions and data processors input by the front end, as well as all the rule information after conversion, merging, and supplementing the public configuration. It is the latest rule version that has been verified, tested, and approved. The rules can be re-formulated and adjusted at any time according to needs. For example, marketing activities focus on new user groups, and high-spending potential users need special treatment during e-commerce promotions. At this time, specific diversion conditions need to be entered and the diversion rules need to be readjusted to achieve dynamic adjustment of the rules.

[0050] In step S120, if the preset rule acquisition condition is met, the latest full set of rules currently stored is acquired through the dynamic data diversion module;

[0051] Optionally, a time trigger strategy can be set. For example, a specific time period can be set daily or weekly, such as 2-3 am, when the business traffic is low, and the system automatically triggers the rule update operation. The dynamic data diversion module can actively pull the latest full rules from Redis to ensure that the data is processed according to the latest rules when a new day or a new week of business starts. Or you can set a business indicator threshold. For example, when the real-time traffic of the buried point is monitored to rise or fall sharply in a short period of time, exceeding the preset traffic threshold range, such as the traffic instantly soaring by more than 50% or dropping by less than 30%, the rule acquisition is automatically triggered. In this case, it is likely that there is an emergency in the business, such as a hot product suddenly becoming popular or encountering a public opinion crisis that leads to user loss. Obtaining the latest full rules in a timely manner can flexibly adjust the data flow, avoid overload or idle resources, and maintain system stability. Alternatively, when the pod node in the dynamic data diversion module is started, the latest full rules can be actively pulled from Redis to ensure that each pod node has exactly the same basic rule data in the initial state, and subsequent pod nodes only need to pay attention to changes in the rule version number to determine whether an update is needed, reducing the frequency of interaction with Redis, reducing network overhead and possible delays caused by frequent requests.

[0052] Among them, pod node refers to the smallest deployable and manageable computing unit running in the Kubernetes (K8S) cluster environment. The dynamic data diversion module may include multiple pod nodes, each of which encapsulates all the resources required to run the dynamic data diversion module, including the full set of rules pulled from Redis and the initialized rule engine instance. These pod nodes can be flexibly allocated to different physical machines or virtual machines in the cluster to run according to the K8S scheduling strategy.

[0053] It should be noted that when faced with changes in the size of real-time traffic at the tracking point, K8S can dynamically increase or decrease the number of Pod nodes according to pre-set expansion and contraction rules. For example, during peak traffic hours, more Pod nodes are automatically created to allow each node to share part of the traffic processing tasks to avoid performance bottlenecks due to overload of a single node; and when traffic is low, redundant Pod nodes are recycled to save system resources. This allows module B to ensure stability and efficient use of resources when processing data diversion, and each Pod node is the key basic unit to achieve this dynamic expansion and contraction and carry business logic.

[0054] In step S130, if the latest full rule is inconsistent with the local diversion rule, the latest full rule data is fully pulled;

[0055] In the embodiment of the present application, the local diversion rule refers to the latest full rule obtained last time. The latest full rule can be compared with the local diversion rule to determine whether they are completely consistent. If not, it means that the diversion rule has been updated. At this time, the complete set of rules can be retrieved from Redis. Redis, as the center for storing rules, stores the complete set of rules on which the entire system operates. When the rules change, in order to ensure that dynamic data diversion has the latest and most complete information, only the modified part cannot be obtained, because the rule system is holistic and relevant. For example, a rule about user behavior point diversion is adjusted. It may cooperate with other user attribute judgment rules and data flow destination rules. Updating only this one may destroy the coherence of the entire diversion logic. By pulling the entire data, the dynamic data diversion module can obtain complete content including newly added rules, modified rules, and unchanged but interrelated rules at one time, and use this complete rule set as the basis for subsequent loading, building, and compiling new diversion rules in memory, ensuring that data diversion processing strictly follows the latest and complete business specifications, avoiding problems such as erroneous diversion and data confusion caused by missing or inconsistent information, and maintaining stable system operation.

[0056] In step S140, rule construction is performed on the changed data in the latest full rule data to obtain a newly added rule;

[0057] Optionally, the change data in the latest full rule data can be loaded into memory through a rule engine, such as Drools, by loading a module. The rules can be rebuilt based on the change data loaded into memory with the help of a rule file. The rule file usually contains predefined templates, formats, and logic descriptions, which can organize scattered change data into a structured form that the rule engine can understand and execute, in preparation for compilation.

[0058] Understandably, if the new diversion rule fails to be rebuilt, the process will throw an exception, which may require manual intervention or further error handling. If the construction is successful, the session can be obtained, and then the rule judgment is executed in the order of the session, and finally the buried traffic is processed.

[0059] In step S150, the newly added rules are compiled to obtain new diversion rules, so as to perform diversion processing on the buried point data based on the new diversion rules.

[0060] In an embodiment of the present application, the rule engine compilation module can be used to compile the constructed new rules. First, the business rules described in natural language in the rule file can be converted into semantic representations that can be understood by the rule engine. Then, a rule execution logic tree is constructed. Based on the previously sorted rules and parsed semantics, an execution logic tree is constructed, which clearly shows the hierarchical relationship and execution order of the rules. The constructed rule execution logic tree is optimized to remove redundant branches, simplify complex conditional judgments, and improve execution efficiency. In addition, a rehearsal test is performed to simulate the process of real buried point data flowing through the newly added rules to see if the expected diversion effect can be achieved. If the diversion effect can be achieved, the compilation is completed. After the compilation is completed, new diversion rules are generated in the memory. When the subsequent dynamic data diversion module processes the buried point data, it can accurately classify the data according to these new rules and guide the flow to different processing paths, ensuring that the entire data processing process closely follows the rule changes and maintains the system stable and efficient operation.

[0061] like Figure 3 As shown in the figure, after configuring the full amount of rules in the rule configuration module, they can be sent to redis for storage, and then the dynamic data processing module can pull the latest full amount of rules from redis, rebuild and compile the newly added rules, obtain new diversion rules, and process and divert the buried point data based on the new diversion rules. For example, data of different types or conforming to different rules can be distributed to multiple data processors below, such as data processor A, data processor B, data processor C, etc.

[0062] It should be noted that in order to avoid high machine load caused by frequent creation and destruction of sessions, StatelessKieSession is used and the session is resident in memory. By using StatelessKieSession and resident in memory, the machine load is significantly reduced, and the waste of CPU resources and memory jitter caused by frequent creation and destruction of sessions are reduced. A stable rule engine operating environment helps reduce the probability of system failures.

[0063] The embodiment of the present application provides a method for processing point data, including: dynamically configuring full rules through a rule configuration module, storing the full rules when the full rules are approved; if the preset rule acquisition conditions are met, obtaining the latest full rules currently stored through a dynamic data diversion module; if the latest full rules are inconsistent with the local diversion rules, the latest full rule data is fully pulled; rule construction is performed on the change data in the latest full rule data to obtain new rules; the new rules are compiled to obtain new diversion rules, and the point data is diverted based on the new diversion rules. In the embodiment of the present application, the system can better adapt to the changing business needs by dynamically adjusting the rules, reducing the time and cost consumption caused by rule conversion. It enables operation and maintenance personnel to make appropriate adjustments according to real-time business needs and system operation conditions, making the system more efficient and reducing the cost of manual intervention. Dynamically configured point data processing can respond to market changes in real time and provide accurate and real-time data to decision makers, thereby seizing the initiative in market competition. Through the above methods, the decision-making time of enterprises can be greatly reduced, the efficiency and accuracy of data processing can be improved, and ultimately the business can be optimized and the competitiveness and profitability of the enterprise can be improved.

[0064] In one embodiment of the present application, if the latest full rule is inconsistent with the local diversion rule, the latest full rule data is fully pulled, including:

[0065] Obtain the version number corresponding to the latest full rule and the version number corresponding to the local diversion rule;

[0066] Compare the version number corresponding to the latest full rule and the version number corresponding to the local diversion rule;

[0067] If the comparison results are inconsistent, it means that the latest full rule has been updated relative to the local diversion rule.

[0068] Optionally, you can create query, update, and delete diversion rules in the rule configuration module. Then you can perform a rule test on the newly created full rules. After the test passes, you can approve the full rules. If approved, the diversion rules can be sent to Redis for storage for subsequent calls. And a unique data version number corresponding to the diversion rule can be generated based on the timestamp. And the data version number is stored in correspondence with the diversion rule. The dynamic data diversion module can extract the most recent diversion rule and the unique data version number corresponding to the latest diversion rule from Redis, and compare the version number with the version number corresponding to the local diversion rule. If the comparison results are inconsistent, and the version number of the new full rule is higher than the version number corresponding to the local diversion rule, it means that the diversion rule has been updated.

[0069] In an embodiment of the present application, the dynamic configuration of the full amount rule includes:

[0070] Receive the diversion nested structure condition array and corresponding data processor from the front end;

[0071] Verifying the diversion nested structure condition array and the data processor;

[0072] When the verification is passed, the diversion nested structure conditions are converted one by one into rules recognized by the rule engine, and merged with the data processor, and the public configuration is supplemented to obtain the latest full rules.

[0073] Optionally, front-end users, such as business personnel, can configure the diversion nested structure condition array and the corresponding data processor in the front-end interface based on information such as market dynamics and changes in user behavior. For example, during the e-commerce promotion period, in order to accurately push promotional information and optimize user shopping paths, front-end personnel may set conditions such as "users have browsed specific promotional product pages and purchased less than 3 times in the past week", and configure a special data processor to process the data of users who meet the conditions, such as pushing personalized coupons, recommending related products, etc. Then the diversion nested structure condition array configured on the front end can be verified, such as whether the diversion conditions are accurate, whether the data is comprehensive, etc., and the compatibility of the data processor and the condition array can be checked. Ensure that the data processor can correctly process the data that meets the conditions, and its input and output formats, data processing logic and condition settings match. When the verification passes, the diversion nested structure conditions are converted one by one into a rule format that the rule engine can recognize and execute. Taking the freight scenario as an example, if the front-end condition is "the user's car rental amount exceeds 500 yuan and the rental category is truck", the converted rule may be expressed as "when the user's transaction record shows that the single purchase amount is greater than 500, and the product category label is "truck"", so that the rule engine can accurately determine the data flow based on this.

[0074] Then, the converted rules are merged with the corresponding data processors to make them a whole. At the same time, common configurations are supplemented, such as general parameter settings, permission management, data format specifications, etc. This ensures that different rules remain consistent when processing time-related data, laying a solid foundation for the smooth execution of subsequent rules, and finally obtaining complete and latest full rules.

[0075] In an embodiment of the present application, when the full amount of rules is approved, before storing the full amount of rules, the process includes:

[0076] Load the latest full set of rules into the local rule engine for testing, and simulate the rule running environment during the testing process;

[0077] When the test passes, the latest full set of rules will be approved.

[0078] Optionally, the latest full set of generated rules is first loaded into the local rule engine for testing. Simulate the process of data flowing through the rules in real business scenarios. For example, the configuration of the local rule engine can be made as consistent as possible with the online formal operating environment, including memory allocation, thread pool settings, timeout parameters, etc. Then, business data can be collected, the business data can be constructed into test samples, and the test samples can be input into the local rule engine for multiple rounds. The diversion processing is performed through the latest diversion rules, and the diversion results are compared with the expected diversion results. The expected diversion results are pre-determined based on business needs and rule settings. If the comparison is consistent or the similarity is greater than the preset threshold, the test passes. At this time, you can enter the approval stage and approve it manually or automatically.

[0079] In an embodiment of the present application, if the preset rule acquisition condition is met, the latest full set of currently stored rules is acquired, including:

[0080] When each pod node is started, it will pull a copy of the latest full set of rules from the storage, and each pod node will initialize a rule engine instance.

[0081] Optionally, when the system is started or expanded, a new Pod node is created and the startup process begins. The Pod node actively initiates a connection request to the storage Redis based on the preset configuration information. After successfully connecting, the Pod node queries Redis for the version number of the currently stored diversion rule, and compares it with the version number recorded locally (if it is empty, it defaults to the initial version). If the versions are inconsistent, it means that the rules have been updated, and the Pod node pulls the full amount of rule data; if the versions are consistent, the existing local rule copies can be used directly to save resources and time.

[0082] Among them, pod node refers to the smallest deployable and manageable computing unit running in the Kubernetes (K8S) cluster environment. The dynamic data diversion module may include multiple pod nodes, each of which encapsulates all the resources required to run the dynamic data diversion module, including the full set of rules pulled from Redis and the initialized rule engine instance. These pod nodes can be flexibly allocated to different physical machines or virtual machines in the cluster to run according to the K8S scheduling strategy.

[0083] In an embodiment of the present application, the step of constructing rules for the changed data in the latest full rule data to obtain newly added rules includes:

[0084] Loading the changed data in the latest full amount of rule data into the memory through the rule engine loading module;

[0085] The rule file is used to construct rules for the changed data in the latest full rule data to obtain the newly added rules.

[0086] Optionally, the change data in the latest full rule data can be loaded into the memory through the rule engine loading module. And the rules can be rebuilt based on the change data loaded into the memory with the help of the rule file. Among them, the rule file usually contains predefined templates, formats and logical descriptions, which can organize scattered change data into a structured form that the rule engine can understand and execute, in preparation for compilation.

[0087] For example, in an e-commerce scenario, when business adjustments lead to rule changes, the specific processing process of the rule file to organize the scattered change data into a structured form that the rule engine can understand is: according to the business field, set a specific classification identifier for the new rule. Then you can introduce key data, such as user membership level and recent consumption frequency in e-commerce scenarios, or core judgment data such as corporate tax credit level and cash flow status in financial credit scenarios. Define the rule name and set the trigger conditions. For example, in the e-commerce scenario, it is accurate to "user membership level is greater than level 3 and the consumption frequency in the past month exceeds 5 times" to determine when to start the rule to process the data. Finally, it is necessary to determine the data flow operation. Specify where the data will flow after the conditions are met, such as "directing the data of this type of user to an exclusive path for high-end discounts."

[0088] In the embodiment of the present application, the system can be better adapted to the changing business needs by dynamically adjusting the rules, reducing the time and cost consumption caused by rule conversion. It enables the operation and maintenance personnel to make appropriate adjustments according to the real-time business needs and system operation conditions, making the operation of the system more efficient and reducing the cost of manual intervention. Dynamically configured point-of-sale data processing can respond to market changes in real time and provide accurate and real-time data to decision makers, thereby seizing the initiative in market competition. Through the above-mentioned methods, the decision-making time of the enterprise can be greatly reduced, the data processing efficiency and accuracy can be improved, and the business can be finally optimized to improve the competitiveness and profitability of the enterprise. In addition, by isolating the business rule configuration module and the dynamic data diversion module, that is, deploying them in different systems respectively, the independence of each business line can be guaranteed, and the mutual influence between businesses can be reduced to a certain extent, and the stability of the entire system can be improved. It can flexibly handle complex business scenarios, such as data diversion according to various conditions such as time, geographic location, and user portraits, improve the overall data processing efficiency, enable faster response to business needs, and provide more accurate data support.

[0089] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0090] In one embodiment, a buried point data processing device is provided, and the buried point data processing device corresponds to the buried point data processing method in the above embodiment. Figure 4 As shown, the buried point data processing device includes a rule configuration module 10 and a dynamic data diversion module 20. The detailed description of each functional module is as follows:

[0091] A rule configuration module 10 is used to dynamically configure a full set of rules, and when the full set of rules is approved, the full set of rules is stored;

[0092] The dynamic data distribution module 20 is used for:

[0093] If the preset rule acquisition conditions are met, the latest full set of rules currently stored are obtained;

[0094] If the latest full rule is inconsistent with the local diversion rule, the latest full rule data is fully pulled;

[0095] Perform rule construction on the changed data in the latest full rule data to obtain newly added rules;

[0096] The newly added rules are compiled to obtain new diversion rules, so as to perform diversion processing on the buried point data based on the new diversion rules.

[0097] In one embodiment of the present application, the dynamic data distribution module 20 is further used to:

[0098] Obtain the version number corresponding to the latest full rule and the version number corresponding to the local diversion rule;

[0099] Compare the version number corresponding to the latest full rule and the version number corresponding to the local diversion rule;

[0100] If the comparison results are inconsistent, it means that the latest full rule has been updated relative to the local diversion rule.

[0101] In an embodiment of the present application, the rule configuration module 10 is further used to:

[0102] Receive the diversion nested structure condition array and corresponding data processor from the front end;

[0103] Verifying the diversion nested structure condition array and the data processor;

[0104] When the verification is passed, the diversion nested structure conditions are converted one by one into rules recognized by the rule engine, and merged with the data processor, and the public configuration is supplemented to obtain the latest full rules.

[0105] In an embodiment of the present application, the rule configuration module 10 is further used to:

[0106] Load the latest full set of rules into the local rule engine for testing, and simulate the rule running environment during the testing process;

[0107] When the test passes, the latest full set of rules will be approved.

[0108] In one embodiment of the present application, the dynamic data distribution module 20 is further used to:

[0109] When each pod node is started, it will pull a copy of the latest full set of rules from the storage, and each pod node will initialize a rule engine instance.

[0110] In one embodiment of the present application, the dynamic data distribution module 20 is further used to:

[0111] Loading the changed data in the latest full amount of rule data into the memory through the rule engine loading module;

[0112] The rule file is used to construct rules for the changed data in the latest full rule data to obtain the newly added rules.

[0113] In one embodiment of the present application, the rule configuration module 10 is deployed on a common machine, and the dynamic data diversion module 20 is deployed in a container orchestration engine.

[0114] In the embodiment of the present application, the system can be better adapted to the changing business needs by dynamically adjusting the rules, reducing the time and cost consumption caused by rule conversion. It enables the operation and maintenance personnel to make appropriate adjustments according to the real-time business needs and system operation conditions, making the operation of the system more efficient and reducing the cost of manual intervention. Dynamically configured point-of-sale data processing can respond to market changes in real time and provide accurate and real-time data to decision makers, thereby seizing the initiative in market competition. Through the above-mentioned methods, the decision-making time of the enterprise can be greatly reduced, the data processing efficiency and accuracy can be improved, and the business can be finally optimized to improve the competitiveness and profitability of the enterprise. In addition, by isolating the business rule configuration module and the dynamic data diversion module, that is, deploying them in different systems respectively, the independence of each business line can be guaranteed, and the mutual influence between businesses can be reduced to a certain extent, and the stability of the entire system can be improved. It can flexibly handle complex business scenarios, such as data diversion according to various conditions such as time, geographic location, and user portraits, improve the overall data processing efficiency, enable faster response to business needs, and provide more accurate data support.

[0115] For the specific definition of the buried point data processing device, please refer to the definition of the buried point data processing method above, which will not be repeated here. Each module in the above-mentioned buried point data processing device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0116] In one embodiment, a computer device is provided. The computer device may be a terminal device, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a readable storage medium. The readable storage medium stores computer-readable instructions. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by the processor, a method for processing buried point data is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0117] In an embodiment of the present application, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the above-mentioned buried point data processing method are implemented.

[0118] In an embodiment of the application, a readable storage medium is provided, which stores computer-readable instructions. When the computer-readable instructions are executed by a processor, the steps of the above-mentioned buried point data processing method are implemented.

[0119] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through computer-readable instructions, and the computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When the computer-readable instructions are executed, they may include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0120] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0121] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for processing buried point data, characterized in that: The method comprises: Dynamically configure the full set of rules through the rule configuration module, and store the full set of rules when the full set of rules is approved; If the preset rule acquisition conditions are met, the latest full set of rules currently stored are obtained through the dynamic data diversion module; If the latest full rule is inconsistent with the local diversion rule, the latest full rule data is fully pulled; Perform rule construction on the changed data in the latest full rule data to obtain newly added rules; The newly added rules are compiled to obtain new diversion rules, so as to perform diversion processing on the buried point data based on the new diversion rules.

2. The method for processing buried point data according to claim 1, characterized in that: If the latest full rule is inconsistent with the local diversion rule, the latest full rule data is fully pulled, including: Obtain the version number corresponding to the latest full rule and the version number corresponding to the local diversion rule; Compare the version number corresponding to the latest full rule and the version number corresponding to the local diversion rule; If the comparison results are inconsistent, it means that the latest full rule has been updated relative to the local diversion rule.

3. The method for processing buried point data according to claim 1, characterized in that: The dynamic configuration full amount rule includes: Receive the diversion nested structure condition array and corresponding data processor from the front end; Verifying the diversion nested structure condition array and the data processor; When the verification is passed, the diversion nested structure conditions are converted one by one into rules recognized by the rule engine, and merged with the data processor, and the public configuration is supplemented to obtain the latest full rules.

4. The method for processing buried point data according to claim 3, characterized in that: When the full amount of rules is approved, before storing the full amount of rules, the method includes: Load the latest full set of rules into the local rule engine for testing, and simulate the rule running environment during the testing process; When the test passes, the latest full set of rules will be approved.

5. The method for processing buried point data according to claim 1, characterized in that: If the preset rule acquisition conditions are met, the latest full set of currently stored rules are obtained, including: When each pod node is started, it will pull a copy of the latest full set of rules from the storage, and each pod node will initialize a rule engine instance.

6. The method for processing buried point data according to claim 1, characterized in that: The step of constructing rules for the changed data in the latest full rule data to obtain newly added rules includes: Loading the changed data in the latest full amount of rule data into the memory through the rule engine loading module; The rule file is used to construct rules for the changed data in the latest full rule data to obtain the newly added rules.

7. The method for processing buried point data according to claim 1, characterized in that: The rule configuration module is deployed on a common machine, and the dynamic data diversion module is deployed in a container orchestration engine.

8. A buried point data processing device, characterized in that: The device comprises: A rule configuration module is used to dynamically configure the full set of rules, and when the full set of rules is approved, the full set of rules is stored; Dynamic data diversion module for: If the preset rule acquisition conditions are met, the latest full set of rules currently stored are obtained; If the latest full rule is inconsistent with the local diversion rule, the latest full rule data is fully pulled; Perform rule construction on the changed data in the latest full rule data to obtain newly added rules; The newly added rules are compiled to obtain new diversion rules, so as to perform diversion processing on the buried point data based on the new diversion rules.

9. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executed on the processor, characterized in that: When the processor executes the computer-readable instructions, the buried point data processing method as described in any one of claims 1 to 7 is implemented.

10. A readable storage medium having computer readable instructions stored thereon, characterized in that: When the computer-readable instructions are executed by a processor, the method for processing buried point data as described in any one of claims 1 to 7 is implemented.

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