A service verification method, system and device

Abstract

The present application belongs to the technical field of data analysis, in order to solve the problems of lag, resource waste, poor real-time performance and the like existing in the existing business verification, a business verification method, system and device are proposed, a dynamic rule dependency graph is constructed by combining business feature data with rules related to the current business scene in the rule library; based on the rule dependency analysis result, combined with user historical behavior data, the operation to be executed subsequently and the required preloaded verification rules are predicted through a behavior prediction model, based on the rule preloading priority queue, the necessary verification rule scripts and the preloaded verification rule scripts of the current business scene are subjected to syntax analysis and compilation to obtain target code, and the target code is executed according to the rule execution priority sequence in combination with the business feature data to perform verification. Through early prediction and on-demand loading of verification logic, the business processing time is greatly shortened, and the waste of memory and network resources is reduced.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field related to data analysis, and particularly relates to a business verification method, system and device. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] The existing system generally adopts a verification rule full loading mechanism, all verification rules are loaded at one time during system initialization, including verification logic that is not needed in the current business scenario; at the same time, a static verification combination method is adopted, and the fixed verification rule set cannot adapt to the dynamic changing business scenario requirements, resulting in serious lack of system flexibility. These two technical defects together cause the problem of high delay response in the business operation process, especially when the complex verification logic is temporarily loaded during the submission operation, causing obvious interaction lag phenomenon, which seriously affects the processing efficiency of the key business.

[0004] The technical root of the above problem lies in the static architecture design of the traditional system, the verification rule is strongly coupled with the core business, and the dynamic loading capability is lacking. The existing scheme is limited by the early technology stack, neither modularization splitting nor supporting the dynamic import mechanism of modern ES6, resulting in the necessity of full loading of all verification rules. The deeper reason lies in the disconnection between business understanding and technical implementation: the traditional system regards verification as an independent link and fails to identify the continuity characteristics of the operation such as the link relationship of bill uploading→ expense classification→ budget checking, resulting in the inability to predictively load the required verification rules. In terms of performance optimization, excessive reliance on hardware upgrade and neglect of algorithm improvement, without distinguishing the priority of blocking / non-blocking verification, all verification is executed synchronously, causing delay of the key operation path. This design thinking has been unable to meet the requirements of real-time response and flexible configuration of modern business requirements.

[0005] In summary, how to solve the problems of lag, delay and poor real-time performance in the existing business verification process is a problem to be solved at present. SUMMARY

[0006] In order to overcome the above-mentioned deficiencies of the prior art, the present application provides a business verification method, system and device, which greatly shortens the business processing time by predicting and loading verification logic in advance, and reduces the waste of memory and network resources.

[0007] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme:

[0008] In a first aspect, the present application provides a business verification method, comprising:

[0009] Collecting current business key parameters and encapsulating to obtain business feature data;

[0010] According to the business feature data, the verification rules are abstracted as nodes in the graph and the dependency relationship between the rules is abstracted as edges in the graph according to the rule definition related to the current business scene in the rule library, a dynamic rule dependency graph is constructed, graph analysis is performed on the dynamic rule dependency graph, and a minimum rule set and a rule execution priority sequence under the current business scene are obtained;

[0011] Based on the rule dependency analysis result, the user historical behavior data is combined, analysis is performed through a behavior prediction model, the subsequent operation and the required preloaded verification rule are predicted, and a rule preloading priority queue is generated based on the prediction result;

[0012] Based on the rule preloading priority queue, syntax analysis and compilation are performed on the necessary verification rule script and the preloaded verification rule script of the current business scene to obtain target code, and the target code is executed according to the rule execution priority sequence to perform verification in combination with the business feature data.

[0013] In a second aspect, the present application provides a business verification system, comprising:

[0014] The acquisition module is configured to acquire and encapsulate current business key parameters to obtain business feature data;

[0015] The graph construction module is configured to abstract the verification rules as nodes in the graph and the dependency relationship between the rules as edges in the graph according to the business feature data in combination with the rule definition related to the current business scene in the rule library, construct a dynamic rule dependency graph, perform graph analysis on the dynamic rule dependency graph, and obtain a minimum rule set and a rule execution priority sequence under the current business scene;

[0016] The prediction module is configured to perform analysis through a behavior prediction model based on the rule dependency analysis result in combination with the user historical behavior data, predict the subsequent operation and the required preloaded verification rule, and generate a rule preloading priority queue based on the prediction result;

[0017] The verification module is configured to perform syntax analysis and compilation on the necessary verification rule script and the preloaded verification rule script of the current business scene to obtain target code based on the rule preloading priority queue, and execute the target code according to the rule execution priority sequence to perform verification in combination with the business feature data.

[0018] In a third aspect, the present application provides an electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, when the computer instructions are run by the processor, the method of the first aspect is completed.

[0019] The above one or more technical solutions have the following beneficial effects:

[0020] In the present application, by combining the service feature data with the rules related to the current service scenario in the rule library to define a dynamic rule dependency graph, the minimum rule set can be matched according to the real-time service scenario, avoiding the resource waste and response delay caused by loading all the verification rules; based on the rule dependency analysis result, combining the user historical behavior data, the subsequent operation and the required preloaded verification rules are obtained by analyzing and predicting through the behavior prediction model, and by the way of early prediction and loading, the operation can be directly executed at the time of submission, solving the lag caused by temporary loading, and at the same time, only the current and predicted rules are loaded, avoiding the resource waste caused by full loading; based on the rule preloading priority queue, the necessary verification rule scripts and the preloaded verification rule scripts of the current service scenario are parsed and compiled to obtain the target code, and the target code is executed according to the rule execution priority sequence to perform verification in combination with the service feature data. By predicting and loading the verification logic in advance, the business processing time is greatly shortened, and the memory and network resource waste is reduced. The present application compiles the new verification rule into the target code in real time for subsequent engine calling, without the need to restart the whole system or interrupt the current business operation, ensuring that the compliance adjustment takes effect immediately.

[0021] Advantages of the additional aspects of the present application will be partially given in the following description, partially will become obvious from the following description, or will be understood by the practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0022] The accompanying drawings, which form a part of the specification, are included to provide a further understanding of the application and are incorporated herein by reference. The illustrations are shown for the purpose of enabling those skilled in the art to implement the application and are not intended to limit the scope of the application.

[0023] Figure 1 The business verification method flowchart in the embodiment one of the present application. DETAILED DESCRIPTION

[0024] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0025] It should be noted that the terms used herein are only for the purpose of describing the specific embodiments, and are not intended to limit the exemplary embodiments according to the present application.

[0026] In the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0027] Embodiment one

[0028] The present embodiment discloses a business verification method, comprising:

[0029] Collect key parameters of the current business and encapsulate them to obtain business feature data;

[0030] Based on business characteristic data and combined with the rule definitions in the rule base that are relevant to the current business scenario, the validation rules are abstracted as nodes in the graph, and the dependencies between rules are abstracted as edges in the graph. A dynamic rule dependency graph is constructed, and graph analysis is performed on the dynamic rule dependency graph to obtain the minimum rule set and rule execution priority sequence under the current business scenario.

[0031] Based on the rule dependency analysis results and combined with user historical behavior data, the behavior prediction model is used to predict the subsequent operations and the required preloaded verification rules. A rule preload priority queue is generated based on the prediction results.

[0032] Based on the rule preloading priority queue, the necessary verification rule scripts and preloaded verification rule scripts for the current business scenario are parsed and compiled to obtain the target code. The target code is then executed according to the rule execution priority sequence based on the business feature data for verification.

[0033] This embodiment constructs a dynamic rule dependency graph by combining business feature data with rule definitions related to the current business scenario in the rule base. It can match the minimum rule set according to the real-time business scenario, avoiding resource waste and response delay caused by loading all validation rules. Based on the rule dependency analysis results and combined with user historical behavior data, the subsequent execution operations and required pre-loaded validation rules are analyzed and predicted through a behavior prediction model. By predicting and loading in advance, the rules can be executed directly upon submission, solving the lag caused by temporary loading. At the same time, loading only the rules required for the current and predicted operations also avoids the resource waste of full loading. Based on the rule pre-loading priority queue, the necessary validation rule scripts and pre-loaded validation rule scripts for the current business scenario are parsed and compiled to obtain target code. The target code is executed according to the rule execution priority sequence based on business feature data for validation. By compiling new validation rules into target code in real time for subsequent engine calls, the entire system does not need to be restarted or the current business operation is interrupted, ensuring that compliance adjustments take effect immediately.

[0034] The overall system architecture of this embodiment includes five main modules: a business feature awareness engine, a rule dependency analyzer, a predictive loading controller, a hot update compiler, and a validation rule execution engine. Based on this overall architecture, combined with... Figure 1 This embodiment provides a detailed description of a service verification method:

[0035] Step 1: Collect and encapsulate the key parameters of the current business to obtain business feature data.

[0036] As a specific implementation, the business feature perception engine serves as an input perception layer, responsible for collecting business key parameters and encapsulating them into JSON format to provide a business scenario portrait for subsequent implementation, which is the basis for dynamic adaptation. The business feature perception engine uses artificial intelligence technology to dynamically perceive business features and infer specific application scenarios, thereby providing a basis for subsequent decision-making.

[0037] Among them, the business key parameters include the stage such as the operation stage, the amount, and the type such as the business type.

[0038] The extracted amount feature is subjected to logarithmic classification judgment in this embodiment to determine the order of magnitude range of the amount, such as 10^3, 10^4, 10^5, etc. The logarithmic classification of the amount helps to divide the numerical range into discrete levels, facilitating subsequent decision-making. This judgment is represented by a diamond decision box, and different processing branches are entered according to whether the amount exceeds a certain threshold.

[0039] Step 2: According to the business feature data, combined with the rule definitions related to the current business scenario in the rule library, the verification rules are abstracted as nodes in the graph, and the dependency relationships between rules are abstracted as edges in the graph, a dynamic rule dependency graph is constructed, and the dynamic rule dependency graph is analyzed to obtain the minimum rule set and the rule execution priority sequence under the current business scenario.

[0040] In this embodiment, the rule definitions in the rule library that match the current business scenario are obtained, each independent verification rule is abstracted as a node in the graph, the dependency relationships between rules are abstracted as edges in the graph, and a dynamic rule dependency graph is constructed. A graph traversal algorithm is used to analyze the dynamic rule dependency graph to filter out the minimum rule set. According to the minimum rule set, combined with the rule dependency relationship and the rule importance, a rule execution priority sequence is generated.

[0041] Specifically, according to the business feature data, a basic verification rule set suitable for the current business scenario is matched and generated from the rule library. For example, different business types and stages may correspond to different default verification rules. The basic verification set contains general rules that need to be executed regardless of the amount.

[0042] As an optional implementation, according to the result of the amount order judgment, parallel paths appear in the process. If the amount is in a lower order, such as less than or equal to 10^3, only the ordinary rules in the basic verification set are executed. The ordinary rules in the basic verification set are usually regular verification logic and can quickly complete the verification. If the amount exceeds a certain threshold, such as greater than 10^3, additional audit rules are executed in addition to the basic verification. Audit rules are usually more stringent or involve higher-level approval and are used to screen high-risk transactions. The additional rules and the ordinary rules are executed in parallel to ensure that all necessary verifications are covered in high-amount situations.

[0043] In the present embodiment, the rule execution priority sequence takes the rule execution engine as its action object. When the hot update compiler finishes compiling, the execution engine loads the bytecode, and the rule execution priority sequence determines the execution order of the current necessary verification rules, ensuring the efficiency and accuracy of the current business verification.

[0044] As a specific implementation, the rule dependency analyzer is responsible for analyzing the dependency relationship between verification rules and generating a dynamic rule dependency graph. The rule dependency analyzer receives business feature data from the business feature engine and constructs a rule dependency graph based on the rule definitions in the rule library, such as using a Neo4j graph database to store rules and their dependencies. Through graph analysis techniques, the rule dependency analyzer can calculate the minimum rule set, i.e., the minimum rule set that actually needs to be executed in the current business scenario, avoiding redundant verification. The rule dependency analyzer outputs GraphQL queries or structured dependency information for the predictive loading controller. The rule dependency analyzer supports the hot deployment of verification rules, and when the verification rules are updated, it automatically updates the dynamic rule dependency graph and notifies the relevant modules.

[0045] Specifically, after receiving the business feature data, the rule dependency analyzer filters out the candidate rule set related to the current business scenario from the rule library, and then constructs a dynamic rule dependency graph for the current scenario based on the pre / post dependency relationship between rules and data transmission requirements.

[0046] Through graph analysis techniques, the topology and node association of the dynamic rule dependency graph are calculated to obtain the minimum rule set and the rule execution priority sequence.

[0047] The minimum rule set is the minimum rule combination that meets all necessary verification requirements without redundancy in the current business scenario. The graph analysis calculation logic is as follows: taking the core verification target corresponding to the business feature data as the starting point, traversing the nodes and edges of the dynamic rule dependency graph, eliminating rules unrelated to the current scenario, and eliminating redundant rules that can be replaced by other rules or do not need to be executed repeatedly, finally obtaining a set containing only necessary verification rules.

[0048] The graph analysis technique generates a priority order for rule execution by the directional relationship of the edges in the dynamic rule dependency graph and the importance weight of the nodes, such as the amount of money and the reasonableness of the check directly affecting subsequent operations. The weight is higher than the note information check. For example, in the high-amount reimbursement scenario, the rule dependency analyzer identifies through the graph that the invoice information integrity check is a pre-requisite for the invoice authenticity verification, and both are core rules, so the invoice information integrity check is set as the highest priority, the invoice authenticity verification is set as the second highest priority, and the note information check is set as the low priority, ensuring that the key verification logic is executed first and reducing the waiting time.

[0049] Step 3: Based on the rule dependency analysis results, combined with user historical behavior data, the behavior prediction model is used for analysis to predict the subsequent operation and the required preloaded verification rules. Based on the prediction results, a rule preloading priority queue is generated.

[0050] In this embodiment, the behavior prediction model is used to analyze the user's past business operation links, historical business scenarios and matching records of corresponding verification rules, capture the user's operation habit rules, and combine the rule dependency analysis results of the current scenario to predict the subsequent operation and the required preloaded verification rules. The priority weight is set according to the rule importance and historical call frequency, the predicted preloaded verification rules are sorted according to the priority weight, and a rule preloading priority queue is generated.

[0051] In this embodiment, the rule importance is determined from the business characteristics, including business impact degree, compliance risk level and verification result relevance. The historical call frequency is determined according to the effective call number of a single rule in a statistical period, the daily call number and the peak call period frequency. The priority weight is set according to the rule importance and historical call frequency, the predicted preloaded verification rules are sorted, and a rule preloading priority queue is generated.

[0052] Specifically, on the basis of minimizing the rule set, the core basis for generating the rule preloading priority queue is the rule dependency relationship and the rule importance, which jointly determine the execution order of the verification rules.

[0053] For example, the behavior prediction model predicts that the next step is a save operation, and the verification rules required in the save stage are preloaded. This feedback loop enables the system to dynamically optimize the subsequent verification process and reduce user waiting time. Based on the prediction results, the rule modules and resources required in the next business link are preloaded. For example, the rule script of the next stage is compiled into WASM code and loaded into memory in advance. The preloading operation ensures that when the user actually enters the next link, the relevant rules are ready for immediate execution of verification, thereby improving the system response speed.

[0054] As a specific implementation, the predictive loading controller predicts the next rules or modules that need to be loaded based on the rule dependency analysis results and user historical behavior data, and prepares for loading in advance. The predictive loading controller includes an LSTM behavior prediction model that predicts the next possible operation and the required rules by analyzing the user's historical operation sequence. The prediction results are used to generate a priority queue, based on which the predictive loading controller preloads high-priority rules modules to shorten the waiting time for subsequent verification. There is a control feedback between the predictive loading controller and the rule dependency analyzer: when new rule dependencies are generated, the predictive loading controller can re-predict the loading requirements. The predictive loading controller outputs preloading instructions or directly loads related rules, and communicates with the hot update compiler through efficient protocols such as Protobuf to trigger the pre-compilation of rule code.

[0055] Specifically, the user historical behavior data includes the user's past business operation links, historical business scenarios, and matching records of corresponding verification rules. The user historical behavior data serves as the basis for time series analysis, helping the LSTM model capture user operation habit patterns to ensure that the prediction results fit the user's actual operation logic and avoid invalid predictions that deviate from actual use scenarios. Through standardized processing of user historical behavior data, such as converting user historical operation sequences like operation types and business stage into vector format, and mapping structured data like rule types and scenario labels in rule dependency analysis results into features recognizable by the model, it is ensured that the two types of input data can participate in analysis together. The long short-term memory behavior prediction model predicts the most likely subsequent operation based on user historical behavior data and rule dependency analysis results, and reversely deduces the preloading verification rules required for the operation.

[0056] Specifically, in combination with the predicted verification rules and the current rule dependency relationship, the priority weight is set according to the rule importance and historical call frequency; the predicted preloading verification rules are sorted according to the priority weight to generate a rule preloading priority queue, ensuring that high-priority rules are loaded first and avoiding wasting resources on rules with low probability of use.

[0057] Illustratively, in combination with the characteristics of financial business, the rule importance is defined from three core dimensions of business impact degree, compliance risk level, and verification result relevance, and the importance score of a single rule is calculated through a weighted sum formula. The business impact degree is determined by evaluating the blocking range of rule failure on the financial business process; the verification result relevance is determined by evaluating the impact of rule verification results on subsequent rule execution; and the compliance risk level can be evaluated by referring to the financial and tax system and the enterprise's internal financial system.

[0058] In a statistical period, the effective call times, the daily average call times, and the peak call period frequency of a single rule are quantified as historical call frequency, and the three indicators are normalized, and then the call frequency score is obtained by weighted summation. The statistical period can be configured according to the business scenario.

[0059] The priority weight of the verification rule is determined according to the rule importance score and the historical call frequency score.

[0060] It should be noted that the rule preloading priority queue is a priority sorting result of the verification rules required for the subsequent execution operation predicted, but also contains the priority of the necessary verification rules of the current business scenario. To ensure that the current business verification is not delayed, the necessary verification rules of the current business scenario, that is, the minimum rule set calculated by the rule dependency analyzer, are included in the head of the queue and are given the highest priority. This design makes the queue cover the current urgently needed rules and the subsequent predicted rules at the same time, but the core sorting logic still focuses on the verification rules of the subsequent execution operation, that is, the high priority of the current necessary rules is the basis for guaranteeing the current process, and the sorting of the subsequent predicted rules is the core of realizing the preloading zero waiting, and the two together constitute the complete sorting system of the queue.

[0061] After the predictive loading controller receives the minimum rule set, it will only preload and compile the rule modules in the set based on the set, rather than load the entire rule library, which greatly reduces the memory occupation and network requests.

[0062] Step 4: Based on the rule preloading priority queue, the necessary verification rule scripts and the preloaded verification rule scripts of the current business scenario are syntax analyzed and compiled to obtain target code, and the target code is executed according to the rule execution priority sequence to verify in combination with the business feature data.

[0063] In this embodiment, the necessary verification rule scripts and the preloaded verification rule scripts of the current business scenario are syntax analyzed, and the verification logic in the scripts is converted into a structured abstract syntax tree; based on the rule preloading priority queue, the abstract syntax tree is compiled into target code in the form of bytecode; when the business operation occurs, the target code is executed according to the real-time business context data according to the rule dependency order to complete the verification.

[0064] As a specific implementation, the hot update compiler is responsible for compiling the necessary verification rule scripts of the current business scenario and the preloaded verification rule scripts into executable code in real time and supporting runtime hot updates. The hot update compiler receives the preloaded verification rule module list from the predictive loading controller, parses the rule scripts using an abstract syntax tree parser, and then generates optimized target code, such as WebAssembly bytecode. The WASM bytecode has the characteristics of cross-platform and high performance and can be quickly executed in a browser or server environment. After compilation, the hot update compiler pushes the new rule code to the verification rule execution engine, realizing hot replacement of the rules without restarting the system. The entire compilation process is performed asynchronously in the background, and when the new rules are ready, the verification rule execution engine seamlessly switches to the new version of the rules, thereby realizing hot updates. The hot update technology allows updating modules without reloading the entire system, improving development and deployment efficiency.

[0065] The abstract syntax tree parser parses the verification rule scripts, specifically: the verification rule scripts are split into the smallest syntax units, the Token sequence is combined into a structured abstract syntax tree according to the predefined verification rule syntax specification, and a hierarchical clear syntax structure is formed. Taking the example of “if the amount > 5000, then perform invoice verification”, the root node of the AST is “conditional judgment”, and the child nodes are “left operand, i.e., amount”, “operator, i.e., >”, “right operand, i.e., 5000”, and “node to be executed when the condition is met, i.e., invoice verification logic”.

[0066] The task triggering and processing sequence of the hot update compiler is directly determined by the rule preloading priority queue generated by the predictive loading controller. The rule preloading priority queue clearly distinguishes the priorities of the current necessary verification rules and the preloaded verification rules. The hot update compiler will preferentially process the scripts with high priority in the queue, for example, the current necessary rules have the highest priority and are preferentially compiled; the rules that are most likely to be used in the next step in the preloaded rules are second, and then they are compiled.

[0067] As a specific implementation, after the hot update compiler receives the preloaded rule module list, it starts the background asynchronous compilation process, and the specific operations are as follows:

[0068] The abstract syntax tree parser is used to perform syntax analysis on the rule scripts, and the rules are converted into a structured abstract syntax tree, which facilitates subsequent code optimization and generation;

[0069] Based on the syntax tree, compilation optimization is performed, such as eliminating redundant logic and optimizing the condition judgment order, to generate WebAssembly bytecode. The WASM bytecode has the characteristics of cross-platform and high performance and can meet the verification speed requirements of financial business;

[0070] After the compilation is completed, the hot update compiler performs integrity checks on the WASM bytecode, such as checking for syntax errors, missing dependencies, etc., to ensure that the generated rule code can be executed normally.

[0071] As a specific implementation, the verification rule execution engine serves as the execution core, responsible for actually executing the verification rules and returning the results. The verification rule execution engine obtains the latest rule code such as the WASM module from the hot update compiler and loads it into the running environment. When a business operation occurs, the execution engine executes the corresponding verification logic according to the context data provided by the business feature engine, in the order of rule dependencies. The verification rule execution engine traverses the dynamic rule dependency graph, triggers the execution of related rules in sequence, and collects the verification results of all rules. The verification rule execution engine supports concurrent execution and priority scheduling to ensure that critical rules are executed first. Finally, the verification rule execution engine returns the verification results, such as pass or reject, to the front-end user interface or the calling party. The verification rule execution engine usually has a built-in runtime for rule execution, including rule matching and execution queue management mechanisms, to efficiently handle the real-time execution of a large number of rules.

[0072] In this embodiment, after the hot update compiler completes rule compilation, it pushes the verification-passing WASM bytecode to the verification rule execution engine, realizing rule hot replacement and loading. The specific process is as follows:

[0073] After receiving the WASM bytecode, the verification rule execution engine loads the new rule code into the running environment, such as the rule execution container in memory, without restarting the system, while retaining the currently executing old rules to avoid affecting current business verification;

[0074] When the new rule is loaded and confirmed to be running normally, the verification rule execution engine automatically routes subsequent verification requests to the newly loaded pre-compiled rules through a seamless switching mechanism, completing hot deployment;

[0075] The verification rule execution engine will initialize the preloaded rules, such as pre-allocating computing resources and establishing a context data connection channel with the business feature perception engine, to ensure that when the user triggers a pre-judgment operation, the preloaded rule can be immediately called to perform verification, achieving zero-wait response.

[0076] After receiving the rule execution priority sequence, the verification rule execution engine will schedule rule execution in sequence, while supporting concurrent execution of high-priority rules to avoid the lag caused by traditional synchronous execution of all rules. In addition, the priority sequence ensures that critical verification logic is prioritized for result feedback, improving user operation experience, such as allowing users to know whether the core verification has passed without waiting for low-priority rule execution.

[0077] In the embodiment, the checksum of the current business operation and the preloading preparation are completed, and the process ends. When the user performs a subsequent operation, the process will repeat the above scheme to support dynamic checksum for the whole business process.

[0078] As an optional implementation, the transmission of the checksum rule is based on a differential update protocol, which is a key technical protocol supporting hot update of the checksum rule and efficient resource transmission. The core role is to transmit only the difference between the new and old rule codes instead of the full code when the checksum rule is updated, thereby greatly saving network traffic, shortening update time, and ensuring the real-time and efficiency of rule hot update.

[0079] As an optional implementation, the quantum compression algorithm is used to store and process the checksum rules in the rule library, reducing the memory occupation by 60%. Finally, edge computing support is developed to allow part of the checksum logic to be executed on the terminal device. These enhancements enable the system to maintain a checksum response speed of 200 ms within 10 million data volume scenarios.

[0080] Embodiment Two

[0081] The purpose of the embodiment is to provide a business checksum system, which comprises:

[0082] The acquisition module is configured to acquire and encapsulate current business key parameters to obtain business feature data;

[0083] The graph construction module is configured to abstract the checksum rules as nodes in the graph and the dependency relationship between the rules as edges in the graph according to the business feature data and the rule definitions related to the current business scenario in the rule library, construct a dynamic rule dependency graph, perform graph analysis on the dynamic rule dependency graph, and obtain the minimum rule set and the rule execution priority sequence under the current business scenario;

[0084] The prediction module is configured to analyze and predict the subsequent operation and the required preloaded checksum rule based on the rule dependency analysis result and the user historical behavior data through a behavior prediction model, and generate a rule preloading priority queue based on the prediction result;

[0085] The checksum module is configured to perform syntax analysis and compilation on the necessary checksum rule script and the preloaded checksum rule script of the current business scenario based on the rule preloading priority queue to obtain target code, and execute the target code according to the rule execution priority sequence to perform checksum in combination with the business feature data.

[0086] In more embodiments, the following is also provided:

[0087] An electronic device includes a memory and a processor, and computer instructions stored on the memory and run on the processor, when the computer instructions are run by the processor, the method described in embodiment one is completed. For brevity, it will not be described here.

[0088] It should be understood that in the embodiments, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSPs, application-specific integrated circuits ASICs, ready-to-program gate arrays FPGA or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0089] The memory can include read-only memory and random access memory, and provide instructions and data to the processor, and a part of the memory can also include non-volatile random access memory. For example, the memory can also store device type information.

[0090] A computer readable storage medium for storing computer instructions, when the computer instructions are executed by the processor, the method described in embodiment one is completed.

[0091] The method in embodiment one can be directly embodied as a hardware processor to complete, or be completed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, register, etc. The storage medium is located in the memory, and the processor reads the information in the memory to complete the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0092] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0093] The above describes the specific embodiments of the application in combination with the drawings, but is not a limitation on the protection scope of the application. Those skilled in the art should understand that various modifications or variations made by those skilled in the art on the basis of the technical solutions of the application without inventive labor are still within the protection scope of the application.

Claims

1. A business verification method, characterized in that, include: Collect key parameters of the current business and encapsulate them to obtain business feature data; Based on business characteristic data and combined with rule definitions related to the current business scenario in the rule base, the verification rules are abstracted as nodes in the graph, and the dependencies between rules are abstracted as edges in the graph. A dynamic rule dependency graph is constructed, and graph analysis is performed on the dynamic rule dependency graph to obtain the minimum rule set and rule execution priority sequence under the current business scenario. Based on the rule dependency analysis results and combined with user historical behavior data, a behavior prediction model is used to predict the subsequent operations and the required preloaded verification rules. A rule preload priority queue is generated based on the prediction results. The rule preload priority queue contains the priority of the necessary verification rules for the current business scenario. Based on the rule preloading priority queue, the necessary verification rule scripts and preloaded verification rule scripts for the current business scenario are parsed and compiled to obtain target code. The target code is then executed according to the rule execution priority sequence based on business feature data for verification. Specifically, by combining historical user behavior data and analyzing it through a behavior prediction model, the subsequent operations and required pre-loaded verification rules are predicted. Based on the prediction results, a rule pre-loading priority queue is generated, as follows: By using a behavior prediction model to analyze the user's past business operation links, historical business scenarios and corresponding verification rules matching records, the user's operation habit patterns are captured. Combined with the rule dependency analysis results of the current scenario, the subsequent operations and the required preloaded verification rules are predicted. By combining the importance of rules and the frequency of historical calls, priority weights are set, and the predicted preloaded verification rules are sorted according to their priority weights to generate a rule preload priority queue. It also includes: when a user enters the next business process, acquiring new scenario business feature data in real time, reconstructing a dynamic rule dependency graph based on the new scenario business feature data and performing graph analysis, obtaining a rule preloading priority queue based on the rule dependency analysis results, and realizing dynamic verification of the entire business process.

2. The business verification method as described in claim 1, characterized in that, Based on business characteristic data and rule definitions relevant to the current business scenario in the rule base, validation rules are abstracted as nodes in a graph, and dependencies between rules are abstracted as edges in the graph. A dynamic rule dependency graph is constructed, and graph analysis is performed on the dynamic rule dependency graph to obtain the minimum rule set and rule execution priority sequence under the current business scenario, specifically: Obtain the rule definitions in the rule base that match the current business scenario, abstract each independent validation rule as a node in the graph, abstract the dependencies between rules as edges in the graph, and construct a dynamic rule dependency graph; A graph traversal algorithm is used to analyze the dynamic rule dependency graph and select the minimum rule set. Based on the minimum set of rules, and combining rule dependencies and rule importance, a rule execution priority sequence is generated.

3. The business verification method as described in claim 1, characterized in that, Based on the aforementioned rule preloading priority queue, the necessary verification rule scripts and preloaded verification rule scripts for the current business scenario are parsed and compiled to obtain target code. The target code is then executed in conjunction with business feature data to perform verification according to the rule execution priority sequence, achieving dynamic verification of the entire business process. Specifically: The necessary validation rule scripts and preloaded validation rule scripts for the current business scenario are parsed to transform the validation logic in the scripts into a structured abstract syntax tree. Based on the rules, a priority queue is preloaded, and the abstract syntax tree is compiled into target code in bytecode form; When a business operation occurs, the target code is executed to complete the verification based on real-time business context data and in the order of rule dependencies.

4. The business verification method as described in claim 1, characterized in that, By combining rule importance and historical call frequency to set priority weights, the predicted preloaded verification rules are sorted according to their priority weights to generate a rule preload priority queue, specifically: The importance of rules is determined by considering the business characteristics, the degree of business impact, the level of compliance risk, and the relevance of verification results. The historical call frequency is determined based on the number of valid calls to a single rule, the average number of calls per day, and the frequency of peak call periods within the statistical period; Priority weights are set based on rule importance and historical call frequency. The predicted preloaded verification rules are sorted to generate a rule preload priority queue.

5. The business verification method as described in claim 1, characterized in that, Also includes: When the validation rules in the rule base are updated, the dynamic rule dependency graph is updated, and the updated information is synchronized to the preloaded validation rule prediction and validation rule execution. The validation rules in the rule base are stored using a quantum compression algorithm.

6. A business verification system, characterized in that, include: The data acquisition module is configured to: collect key parameters of the current business and encapsulate them to obtain business feature data; The graph construction module is configured to: based on business feature data and combined with rule definitions in the rule base that are relevant to the current business scenario, abstract the verification rules into nodes in the graph and the dependencies between rules into edges in the graph, construct a dynamic rule dependency graph, perform graph analysis on the dynamic rule dependency graph, and obtain the minimum rule set and rule execution priority sequence under the current business scenario; The prediction module is configured to: based on rule dependency analysis results and combined with historical user behavior data, analyze the data using a behavior prediction model to predict subsequent operations and required pre-loaded verification rules, and generate a rule pre-loading priority queue based on the prediction results; specifically: By using a behavior prediction model to analyze the user's past business operation links, historical business scenarios and corresponding verification rules matching records, the user's operation habit patterns are captured. Combined with the rule dependency analysis results of the current scenario, the subsequent operations and the required preloaded verification rules are predicted. By combining the importance of rules and the frequency of historical calls, priority weights are set, and the predicted preloaded verification rules are sorted according to their priority weights to generate a rule preload priority queue. The rule preloading priority queue contains the priorities of the necessary verification rules for the current business scenario; The verification module is configured to: based on the rule preload priority queue, perform syntax parsing and compilation of the necessary verification rule scripts and preloaded verification rule scripts for the current business scenario to obtain target code, and execute the target code according to the rule execution priority sequence in combination with business feature data to perform verification; The dynamic verification module is configured to: acquire new scenario business feature data in real time when the user enters the next business process, reconstruct the dynamic rule dependency graph based on the new scenario business feature data and perform graph analysis, obtain the rule preloading priority queue based on the rule dependency analysis results, and realize dynamic verification of the entire business process.

7. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-5.

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