Data low-code processing method and system based on dynamic binding and template technology

Abstract

This invention relates to the field of low-code platform technical debt management technology, specifically to a data low-code processing method and system based on dynamic binding and template technology. The method includes: real-time capture of data source contracts; real-time capture of template gray-box metadata; real-time capture of user binding relationships; construction of a weighted directed dependency graph with adaptive risk weights; calculation of binding vulnerability index and template healing cost; performance of change impact analysis; calculation of failure propagation probability and failure chain; calculation of change source probability and change impact coupling factor; calculation of the total application technical debt risk score; calculation of the contribution value of the binding vulnerability index to the application technical debt risk; generation of proactive risk alerts; derivation of edge risk indicators based on the binding vulnerability index; and feedback of the edge risk indicators to dynamically update the adaptive risk weights of the weighted directed dependency graph. This invention achieves a shift from passive repair to proactive defense, avoiding technical debt at the source.

Description

Technical Field

[0001] This invention relates to the field of low-code platform technology debt management technology, specifically to a data low-code processing method and system based on dynamic binding and template technology. Background Technology

[0002] In the field of software development, low-code platforms accelerate application building through dynamic binding of data sources and templates. However, this binding can easily create rigid coupling between the abstraction layer and the implementation layer, forming implicit dependencies. As data contracts evolve, these dependencies accumulate into serious technical debt, leading to a surge in maintenance costs later on.

[0003] Existing technical methods mostly rely on static dependency analysis and passive repair models. These methods generally lack the ability to quantify and predict the costs of future changes in real time during the development phase, resulting in subjective and delayed risk assessment. At the same time, they cannot provide proactive risk intervention and immediate feedback when binding operations occur, making it difficult to avoid high-risk coupling at the source. This leads to the platform sacrificing long-term maintainability while improving development efficiency. Therefore, how to provide a data processing method that can quantify, assess, and proactively manage the risks of low-code technical debt in real time is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide a low-code data processing method and system based on dynamic binding and template technology, in order to solve the technical debt problem caused by the rigid coupling of abstraction and implementation in existing low-code platforms. This method can transform the vague and lagging technical debt risk in low-code applications into real-time visible and measurable risk indicators during the development phase, and realize the transformation from passive repair to active defense. Specifically, the technical solution of this invention is as follows:

[0005] A low-code data processing method based on dynamic binding and template technology includes the following steps:

[0006] Step S1: Capture the data source contract in real time; capture the template gray box metadata in real time; capture the user binding relationship in real time; based on the data source contract, the template gray box metadata and the user binding relationship, construct a weighted directed dependency graph with adaptive risk weights;

[0007] Step S2: Calculate the binding vulnerability index; and calculate the template healing cost;

[0008] Step S3: Based on the binding vulnerability index and the adaptive risk weight of the weighted directed dependency graph, perform change impact analysis; and calculate the failure propagation probability and failure chain.

[0009] Step S4: Calculate the probability of the change source; based on the topology of the failure chain, calculate the coupling factor of the change impact;

[0010] Step S5: Aggregate the change source probability, the change impact coupling factor, the failure propagation probability, and the template healing cost to calculate the total application technology debt risk score;

[0011] Step S6: Calculate the contribution value of the binding vulnerability index to the application technology debt risk; in response to the contribution value or the binding vulnerability index exceeding a preset threshold, generate an active risk alarm;

[0012] Step S7: Derive the edge risk index based on the binding vulnerability index; and feed back the edge risk index to dynamically update the adaptive risk weight of the weighted directed dependency graph.

[0013] Furthermore, the calculation of the binding vulnerability index includes:

[0014] Query pattern rigidity from the template graybox metadata;

[0015] Query the transformation complexity from the template graybox metadata;

[0016] Query the logical coupling degree from the template graybox metadata;

[0017] The binding vulnerability index is obtained by weighted summation of the pattern rigidity, the transformation complexity, and the logical coupling degree.

[0018] Furthermore, the calculation of the template healing cost includes:

[0019] Obtain the preset base working hours;

[0020] Query the black-box complexity factor from the template gray-box metadata;

[0021] Query the number of fan-out nodes of the template from the weighted directed dependency graph;

[0022] Based on the aforementioned basic working hours, and combined with the black-box complexity factor and the number of node fan-outs, a nonlinear correction calculation is performed to obtain the template healing cost.

[0023] Furthermore, the change impact analysis described in step S3 includes:

[0024] Simulate data source change events;

[0025] Perform graph traversal on the weighted directed dependency graph;

[0026] The binding vulnerability index and the adaptive risk weight are introduced as transmission coefficients in the propagation process;

[0027] Based on the graph traversal and the propagation coefficient, the failure propagation probability is calculated.

[0028] Furthermore, the calculation of the change-affect coupling factor in step S4 includes:

[0029] The number of nodes is counted from the failed chain;

[0030] Calculate the graph-theoretic centrality of nodes in the failure chain based on the weighted directed dependency graph;

[0031] The change impact coupling factor is calculated based on the number of nodes and the graph centrality.

[0032] Furthermore, the calculation of the change source probability in step S4 includes:

[0033] Obtain the static declaration probabilities marked in the data source contract;

[0034] Run the background monitor;

[0035] The background monitor polls the data source and compares it with historical snapshots to count the actual frequency of historical changes;

[0036] The static declaration probability and the historical actual change frequency are adaptively weighted and fused to generate the change source probability.

[0037] Furthermore, the proactive risk alert includes:

[0038] Display the application's total technical debt risk score to developers in real time;

[0039] It also provides decoupling suggestions.

[0040] A low-code data processing system based on dynamic binding and template technology includes:

[0041] An overall dependency graph construction engine is used to capture data source contracts, template gray box metadata, and user binding relationships in real time, and construct a weighted directed dependency graph with adaptive risk weights based on the data source contracts, template gray box metadata, and user binding relationships.

[0042] A technology debt quantification engine is used to calculate the binding vulnerability index and the template healing cost;

[0043] A change impact analyzer is used to perform change impact analysis based on the bound vulnerability index and the adaptive risk weights of the weighted directed dependency graph, and to calculate the failure propagation probability and failure chain.

[0044] The technology debt quantification engine is also used to calculate the probability of change sources; calculate the change impact coupling factor based on the topology of the failure chain; and aggregate the probability of change sources, the change impact coupling factor, the failure propagation probability, and the template healing cost to calculate the total application technology debt risk score.

[0045] The Evolutionary Force Insight and Proactive Alarm Module is used to calculate the contribution value of the Binding Vulnerability Index to the application technology debt risk, and generate a proactive risk alarm in response to the contribution value or the Binding Vulnerability Index exceeding a preset threshold.

[0046] The technology debt quantification engine is also used to derive edge risk indicators based on the binding vulnerability index;

[0047] The overall dependency graph construction engine is also used to receive the edge risk indicators fed back by the technology debt quantification engine, and dynamically update the adaptive risk weights of the weighted directed dependency graph.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. This invention transforms abstract and delayed technical debt risks into a real-time visible and measurable total risk score during the development phase. By quantifying expected repair time, it achieves a shift from subjective assessment to objective measurement, thus solving the problem of delayed risk assessment.

[0050] 2. This invention changes the traditional passive repair mode. When high-risk binding operations are performed during the development phase, it can immediately generate proactive risk warnings and provide specific decoupling suggestions, realizing the transformation from passive repair to proactive defense and avoiding technical debt at the source.

[0051] 3. This invention overcomes the limitations of static analysis by introducing a risk feedback loop and dynamically updating the adaptive risk weights of the dependency graph, enabling the assessment model to learn and evolve. The system becomes more sensitive to vulnerable paths over time, significantly improving the accuracy of risk assessment.

[0052] 4. This invention achieves refined and multi-dimensional quantification of risk, deconstructing fuzzy binding risks into specific indicators such as pattern rigidity, transformation complexity, and logical coupling, and introducing graph theory centrality to evaluate topological impact, so that risk calculation is no longer a subjective guess, but a refined and objective analysis. Attached Figure Description

[0053] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0054] Figure 1 This is a flowchart of the method of the present invention;

[0055] Figure 2This is a structural diagram of the system of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0057] Example 1:

[0058] Please see Figure 1 A low-code data processing method based on dynamic binding and template technology includes the following steps:

[0059] Step S1: Capture the data source contract in real time; capture the template gray box metadata in real time; capture the user binding relationship in real time; based on the data source contract, the template gray box metadata and the user binding relationship, construct a weighted directed dependency graph with adaptive risk weights;

[0060] Step S2: Calculate the binding vulnerability index; and calculate the template healing cost;

[0061] Step S3: Based on the binding vulnerability index and the adaptive risk weight of the weighted directed dependency graph, perform change impact analysis; and calculate the failure propagation probability and failure chain.

[0062] Step S4: Calculate the probability of the change source; based on the topology of the failure chain, calculate the coupling factor of the change impact;

[0063] Step S5: Aggregate the change source probability, the change impact coupling factor, the failure propagation probability, and the template healing cost to calculate the total application technology debt risk score;

[0064] Step S6: Calculate the contribution value of the binding vulnerability index to the application technology debt risk; in response to the contribution value or the binding vulnerability index exceeding a preset threshold, generate an active risk alarm;

[0065] Step S7: Derive the edge risk index based on the binding vulnerability index; and feed back the edge risk index to dynamically update the adaptive risk weight of the weighted directed dependency graph.

[0066] This invention provides a low-code data processing method based on dynamic binding and template technology, aiming to solve the technical debt problem caused by the rigid coupling between abstraction and implementation in existing low-code platforms. The core idea of ​​this method is to quantify and manage the change costs or technical debt risks that may be caused in the future Day2...N due to implicit dependencies in real time during the development phase Day1, thereby realizing the transformation from passive repair to active defense.

[0067] The method includes the following steps:

[0068] Step S1: Construct a weighted directed dependency graph with adaptive risk weights

[0069] The weighted directed dependency graph is also referred to in this embodiment as It refers to a mathematical model used to represent the dependencies between all entities in a low-code application;

[0070] in, Nodes represent data entities such as API fields and database columns, and application entities such as templates and UI components; Directed edges represent dependencies between entities, such as data binding and template references; The weight is the adaptive risk weight;

[0071] The purpose of this step is not only to establish a static dependency topology graph, but also to construct a dynamic risk-aware model; its non-obvious innovation lies in the edge weights. It is not a traditional 1 or a static value, but an adaptive risk weight that represents the dependency. The risk costs;

[0072] This step captures three types of input information in real time: real-time capture of data source contracts, for example, obtaining the names, types, and metadata of API fields, such as the static declaration probabilities defined in Example 6, by parsing the OpenAPI specification JSON / YAML file; real-time capture of template gray-box metadata, for example, obtaining the contracts of its internal implementation's data dependencies, such as the schema rigidity, transformation complexity, and logical coupling defined in Example 2, by parsing the manifest.json file attached to the template; and real-time capture of user binding relationships, for example, detecting the user's operation of dragging and binding the data source API.field_A to the template Template_B.input_X in the IDE;

[0073] Based on the above inputs, the system constructs a graph. Weight initial value It can be set to 1.0; this weight will be dynamically updated by the feedback closed-loop mechanism described in step S7, which is the core of achieving adaptivity;

[0074] Step S2: Calculate the binding vulnerability index and template healing cost

[0075] This step aims to calculate two unique intermediate core parameters for subsequent risk assessment; and to bind the vulnerability index, also referred to in this embodiment. This refers to a quantified 0-1 metric used to assess the inherent risk level of a single dynamic binding operation; template healing cost, also referred to in this embodiment... It refers to a quantified cost indicator with the physical dimension of man-hours, used to assess the estimated workload required to repair a template when it becomes invalid due to changes in upstream data;

[0076] Binding Vulnerability Index The calculation is performed according to the implementation method described in Example 2. By querying the template gray box metadata, the risk index of three dimensions, namely pattern rigidity, transformation complexity and logical coupling, is obtained and they are weighted and summed.

[0077] Template healing cost Calculation: This method calculates according to the implementation method described in Example 3. By obtaining the basic working hours and combining the black-box complexity factor queried from the template gray-box metadata and the number of node fan-outs queried from the weighted directed dependency graph, nonlinear correction calculation is performed.

[0078] Step S3: Perform change impact analysis and calculate failure propagation probability and failure chain.

[0079] The purpose of this step is to simulate a potential change event, such as a change in the type of an API field, and to quantitatively predict the probability that the change will cause failures in downstream nodes as it propagates along the dependency graph.

[0080] This step invokes the Change Impact Analyzer, which is based on the Binding Vulnerability Index. The adaptive risk weights derived from S2 and the weighted directed dependency graph From S1, perform analysis;

[0081] Simulate a change event And will change the source node Failure propagation probability, Set as ;

[0082] According to the implementation method described in Example 4, in Figure Perform graph traversal;

[0083] During the traversal Static fragility and Dynamic risk weights are used as key transmission coefficients to calculate the risk from upstream nodes. to downstream nodes The probability of failure;

[0084] Failure propagation probability It is a probability value between 0 and 1, representing a node. The possibility of failure due to upstream changes; failure chain. This refers to the source of the change. All of the incidents The subgraph formed by the downstream nodes;

[0085] Step S4: Calculate the coupling factor between change source probability and change impact.

[0086] This step calculates two additional advanced risk parameters to refine the final total risk score; the source change probability, also referred to in this embodiment... This refers to a probability value between 0 and 1, used to quantify the likelihood that a specific data source, such as an API, will change in the future; the change impact coupling factor, also referred to in this embodiment. It is The dimensionless multiplier is used to quantify the nonlinear amplification effect of the failure chain originating from the topology of S3 on the overall risk.

[0087] Change source probability Calculation: This method calculates according to the implementation method described in Example 6, and dynamically generates the value by adaptively weighting and fusing the static declaration probability of the developer and the historical actual change frequency counted by the background monitor.

[0088] Changes affect coupling factors The calculation: This method is based on the topology of the failure chain and is performed according to the implementation method described in Example 5. It solves the problem by analyzing the number of nodes in the failure chain and the graph theory centrality of these nodes in the whole graph, so as to identify high-risk propagation paths that can have a ripple effect.

[0089] Step S5: Aggregate and calculate the total risk score of applied technology debt.

[0090] This step is the top-level output of risk quantification, aiming to calculate a single, measurable application technology debt risk. Total score; This step aggregates all key indicators calculated in S2, S3, and S4;

[0091] The computational logic is to perform calculations on the system. One possible source of change arrive Sum the risk contributions for each change source; Its risk contribution is equal to its probability of occurrence. Multiply by its topological severity Then multiply by the failure chain triggered by the source of the change. The sum of the expected healing costs of all nodes is calculated using the following formula:

[0092] ;

[0093] in: The probability of the change source is calculated in step S4 and is a dimensionless value.

[0094] The change impact coupling factor is calculated in step S4 and is a dimensionless value.

[0095] The failure propagation probability is calculated by step S3 and is a dimensionless value.

[0096] The cost of template healing is calculated in step S2, and its dimension is man-hours.

[0097] because The dimension of is working hours, while all other factors are dimensionless probabilities or multipliers, therefore The final physical dimension is the risk-adjusted expected man-hours; this is a measurable objective indicator used for IT governance and decision-making; the model described in this embodiment mainly focuses on the technical debt related to maintenance workload caused by changes in data contracts and quantifies it as expected repair man-hours. Other dimensions of technical debt risks, such as performance, security or business reliability risks, can be evaluated in parallel by those skilled in the art using other similar models.

[0098] Step S6: Generate proactive risk alerts

[0099] The purpose of this step is to transform the abstract risks calculated in S5 into proactive interventions for developers during the development phase, thereby avoiding technical debt in real time before it is generated.

[0100] When a developer performs a new binding operation in the IDE, the system immediately triggers a recalculation or incremental calculation of S1 to S5, and specifically evaluates the new binding:

[0101] Calculate the binding vulnerability index The value comes from S2;

[0102] Calculate the contribution of the binding vulnerability index to the application technology debt risk. For example, calculate the impact of performing this binding operation. Increment of total score ;

[0103] The system sets a preset threshold. For example Working hours; the method for determining this threshold can be set by the enterprise's IT governance strategy, and its technical meaning is to represent the upper limit of acceptable risk for a single operation;

[0104] In response to the contribution value Or the aforementioned binding vulnerability index Exceeding the preset threshold The system immediately generates a proactive risk alarm; the specific form of this alarm is detailed in the implementation of Example 7.

[0105] Step S7: Feedback update of adaptive risk weights

[0106] This step is one of the core innovations of the architecture of this invention. It constructs a risk-weighted feedback loop. Its purpose is to enable the weighted directed dependency graph from S1 itself to have learning ability, so that it adaptively reflects the dependency paths in the system that are repeatedly evaluated as high risk.

[0107] Derivation: Based on the aforementioned binding vulnerability index Derive the edge risk indicator; edge risk indicator, It is a dimensionless index used to quantify the risk value fed back to the graphical model; in this embodiment, it can be simply defined as... ;

[0108] Feedback: The system will The metrics are fed back into the weighted directed dependency graph in S1;

[0109] Update: After receiving the metric, the graph model dynamically updates the adaptive risk weights of the weighted directed dependency graph. ;

[0110] ;

[0111] in, Indicates the current weight of the edge; It is the learning rate, which is a dimensionless system configuration parameter of 0-1, used to control how quickly the weights adapt to the risk assessment results; This refers to the marginal risk indicator identified in this assessment; for example, a smaller value could be set, such as... To ensure weight To ensure a smooth and stable evolution, avoid drastic fluctuations caused by a single high-risk assessment; or use a higher value in the early stages of the system to accelerate learning, and then lower it in the later stages to achieve convergence;

[0112] The synergistic effect of this feedback mechanism lies in achieving risk hardening; a binding high risk assessed by S2. →High This will cause it to appear in the diagram. Adaptive risk weights in Continue to rise When S3 performs the impact analysis for other changes next time, the graph traversal algorithm will calculate... At that time, it will be magnified because of this. The transmission coefficient results in a higher failure probability.

[0113] The present invention provides a low-code data processing method based on dynamic binding and template technology, which constructs a closed-loop evaluation and feedback system comprising seven steps.

[0114] This method, through the calculation of S2, S4, and S5, transforms the vague and delayed technical debt risk in low-code applications into real-time visible and measurable risk-adjusted projected man-hours during the development phase. This achieves a shift from black-box to white-box operation; through S6's proactive risk alerts, this method can provide immediate alerts and suggestions the instant a high-risk binding operation occurs, transforming the maintenance mode from passively repairing high TCO to proactively defending against low TCO; through S7's feedback loop... Unlike S4's dynamic probabilities, this system's evaluation model is not static but learnable and evolvable. Over time, the system becomes more sensitive to historically vulnerable dependency paths, greatly improving the accuracy of the evaluation. It resolves the fundamental contradiction between development efficiency and long-term maintainability. It ensures that while maintaining high development efficiency, the low-code platform's applications still possess high evolvability and can adapt to future business and data changes, truly realizing the core value of low-code.

[0115] Example 2:

[0116] The calculation of the binding vulnerability index includes:

[0117] Query pattern rigidity from the template graybox metadata;

[0118] Query the transformation complexity from the template graybox metadata;

[0119] Query the logical coupling degree from the template graybox metadata;

[0120] The binding vulnerability index is obtained by weighted summation of the pattern rigidity, the transformation complexity, and the logical coupling degree.

[0121] This embodiment is a binding vulnerability index described in step S2 of embodiment 1. A specific implementation of computation;

[0122] This embodiment creatively deconstructs the binding of this vague and singular engineering operation into three quantifiable and orthogonal risk dimensions; this is something that does not exist in the prior art, and it makes the risk assessment of the binding no longer a binary success / failure, but a multidimensional and quantitative one.

[0123] The calculation relies on the manifest.json file captured in S1 as template gray-box metadata; this metadata file is a dependency contract that template developers must provide, and the calculation formula is as follows:

[0124] ;

[0125] Query pattern rigidity from the template graybox metadata. rigidity of the model It is a dimensionless exponent of 0-1, derived from the schemaRigidity field declared in the metadata; it quantifies the strength of the template's internal implementation's dependency on the input data schema it is bound to; for example, StrongTyped, a strongly typed, rigid schema, maps to... WeakTyped, a weakly typed, flexible mode mapping, is... ;

[0126] Query the transformation complexity from the template graybox metadata. : Conversion complexity It is a dimensionless exponent of 0-1, derived from the `transformComplexity` field declared in the metadata; it quantifies the complexity of the template's internal transformation logic of the input data; for example, High represents nested loops, recursive transformations, etc. Low, such as direct pass-through mapping, is ;

[0127] Query the logical coupling degree from the template graybox metadata. Logical coupling It is a dimensionless exponent of 0-1, derived from the declaration of the logicCoupling field in the metadata; it quantifies whether the template's internal business logic is coupled to specific values ​​of the input data, rather than just its type; for example, critical logic such as if(data.status==APPROVED)... maps to... The None logic is independent of data values ​​and is mapped as follows: ;

[0128] The rigidity of the pattern, the transformation complexity, and the logical coupling are weighted and summed as follows:

[0129] These are three-dimensional weighting factors, which can be obtained from expert systems or regression calibration based on historical data, and in this embodiment... ;

[0130] This embodiment transforms heavyweight static code analysis, which may take minutes, into lightweight metadata queries that only take milliseconds through a gray-box metadata contract, thereby enabling... The calculation is completed in real time the moment the user drags and binds the data; this is the key engineering aspect of implementing S6 proactive risk alerts and has extremely high non-obviousness. In another optional embodiment, to prevent discrepancies between the metadata declared by the developer and the actual implementation, the system can be configured with a background static analyzer to periodically sample and scan the template code. If the cyclomatic complexity or data coupling of the code is found to be inconsistent with the metadata declaration... or If the metric deviates beyond the preset threshold, the metadata will be automatically corrected or an alert will be sent to the template developer to ensure the objectivity and accuracy of the gray-box data.

[0131] Example 3:

[0132] The calculation of template healing costs includes:

[0133] Obtain the preset base working hours;

[0134] Query the black-box complexity factor from the template gray-box metadata;

[0135] Query the number of fan-out nodes of the template from the weighted directed dependency graph;

[0136] Based on the aforementioned basic working hours, and combined with the black-box complexity factor and the number of node fan-outs, a nonlinear correction calculation is performed to obtain the template healing cost.

[0137] This embodiment addresses the template healing cost described in step S2 of Embodiment 1. A specific implementation of computation;

[0138] The standard software cost model in this embodiment cannot accurately assess the actual maintenance cost in a low-code environment if it only estimates man-hours. This invention solves the problem of the standard model underestimating the maintenance cost of black boxes and highly reusable components by introducing two non-linear correction factors: black box characteristics and fan-out effect.

[0139] ;

[0140] Get the preset base working hours This parameter It is a repair template The baseline workload is measured in man-hours; its source can be the system's preset configuration parameters, such as an estimate based on the average repair time of similar historical templates.

[0141] Query the black-box complexity factor from the template gray-box metadata. This parameter It is a dimensionless multiplier, derived from the `type` or `opaqueness` field in the metadata; it quantifies the unobservability and modifiability of the template; for example, a black-box might be mapped to a compiled binary package as... ;white-box is mapped as a readable script ;

[0142] Query the number of fan-out nodes of the template from the weighted directed dependency graph. This parameter It is a dimensionless integer, derived from the weighted directed dependency graph in S1. Real-time query node The out-degree is obtained; it quantifies how many other downstream components depend on the template. ;

[0143] Based on the aforementioned base working hours, and combined with the black-box complexity factor and the node fan-out number, a nonlinear correction calculation is performed; as shown in the formula. As an amplifier, it significantly increases the repair cost of the black box template; The nonlinear marginal effect used to simulate the fan-out effect is diminishing, meaning that the cost increase from 1 dependency to 10 dependencies is much greater than the cost increase from 100 to 110 dependencies.

[0144] The gain effect of this embodiment is that it can more accurately identify high-risk templates; a widely relied-upon high-risk template... And it is difficult to modify the high The template, its This will be significantly amplified; this makes the S5 calculation... The overall risk is more realistic, preventing an underestimation of the risk on the critical path.

[0145] Example 4:

[0146] The change impact analysis described in step S3 includes:

[0147] Simulate data source change events;

[0148] Perform graph traversal on the weighted directed dependency graph;

[0149] The binding vulnerability index and the adaptive risk weight are introduced as transmission coefficients in the propagation process;

[0150] Based on the graph traversal and the propagation coefficient, the failure propagation probability is calculated.

[0151] This embodiment is a specific implementation of the change impact analysis described in step S3 of embodiment 1;

[0152] Traditional dependency analysis methods such as DFS / BFS can only provide a binary relationship indicating whether something is affected, but cannot quantify the degree of impact. This invention introduces a binding vulnerability index. and adaptive risk weights As a transmission coefficient in the propagation process, it enables a refined quantification of the failure probability;

[0153] Simulate data source change events For example, simulate a change in the type of field X in data source A; set a type impact factor for this change event. This factor is a dimensionless parameter ranging from 0 to 1, and its source can be a system-preset configuration table, for example: delete = 1.0, type change = 0.8; the source node of Set to 1.0;

[0154] Perform a graph traversal on the weighted directed dependency graph; starting from the source node. Begin, along the dependency graph Traverse the edges from S1;

[0155] The binding vulnerability index and the adaptive risk weight are introduced as transmission coefficients in the propagation process;

[0156] Based on the graph traversal and the propagation coefficient, the failure propagation probability is calculated. ;

[0157] For downstream nodes ,That :

[0158] ;

[0159] in: Indicates upstream node The failure probability is calculated from the previous step in the graph traversal.

[0160] It is a bound vulnerability index, calculated in step S2, which serves as a static, inherent vulnerability coefficient.

[0161] These are adaptive risk weights, maintained and provided by steps S1 and S7, serving as dynamic, learned risk amplification coefficients. ;

[0162] This refers to the type of impact factor of the change event defined in step one above;

[0163] This embodiment features a dual risk transmission mechanism; Static risks and The combined effect of dynamic risks allows for a more precise identification of risk paths; a binding mechanism itself is very fragile and high-risk. The path in question has a high probability of failure; simultaneously, a path historically marked as high-risk by the S7 feedback mechanism... The path, even if its The probability of failure is not high, and its failure rate will also be... The magnification is significantly increased, thus enabling accurate identification of potential hazard paths.

[0164] Example 5:

[0165] The calculation of the change impact coupling factor in step S4 includes:

[0166] The number of nodes is counted from the failed chain;

[0167] Calculate the graph-theoretic centrality of nodes in the failure chain based on the weighted directed dependency graph;

[0168] The change impact coupling factor is calculated based on the number of nodes and the graph centrality.

[0169] This embodiment describes the change of the coupling factor described in step S4 of embodiment 1. A specific implementation of computation;

[0170] The technical motivation behind this embodiment lies in the standard risk model. It is a linear superposition, in which Represents the probability of failure. Representing failure cost, it cannot distinguish the topological structure of failure chains; however, a small change that hits the system hub is far more harmful than a large change that affects the peripheral edges; this invention creatively introduces the idea of ​​graph centrality to quantify this topological amplification effect.

[0171] The calculation is based on the failure chain output by S3. Weighted directed dependency graph constructed with S1 ;

[0172] ;

[0173] The number of nodes in the failure chain is counted. :

[0174] This parameter It is a dimensionless integer, its origin is The total number of nodes included;

[0175] Calculate the graph centrality of nodes in the failure chain based on the weighted directed dependency graph. :

[0176] This parameter It is a dimensionless index; its source is in the graph. Above, to Each node in Calculate its graph theory centrality, such as PageRank, betweenness centrality, etc., and sum these centrality indices;

[0177] Based on the number of nodes and the graph centrality, calculate the coupling factor of the change's impact:

[0178] As shown in the formula, It is a multiplier greater than or equal to 1;

[0179] This embodiment implements a non-linear risk assessment; if a change The resulting failure chain Small in scale Small, but all hits are high centrality High core nodes, The value will become very large; this will non-linearly amplify the source of the change. In S5 The contribution of the total score enables the system to accurately identify and warn of such catastrophic risks that have far-reaching consequences.

[0180] Example 6:

[0181] The calculation of the change source probability in step S4 includes:

[0182] Obtain the static declaration probabilities marked in the data source contract;

[0183] Run the background monitor;

[0184] The background monitor polls the data source and compares it with historical snapshots to count the actual frequency of historical changes;

[0185] The static declaration probability and the historical actual change frequency are adaptively weighted and fused to generate the change source probability.

[0186] This embodiment describes the change of source probability in step S4 of embodiment 1. A specific implementation of computation;

[0187] In this embodiment, the probability of an external API changing in the future is a key input for risk assessment. However, traditional methods, such as relying on developers to manually set the @Volatile tag, are extremely subjective and easily become outdated. This invention solves this problem through an adaptive mechanism that combines static declaration and dynamic learning.

[0188] ;

[0189] Get the static declaration probability marked in the data source contract This parameter It is a dimensionless probability of 0-1, which comes from the subjective estimate provided by the developer in the data source contract such as OpenAPI through meta tags such as @Stable→0.1, @Volatile→0.7; it is suitable for the cold start of the system.

[0190] Run the background monitor; this monitor, also known as the Schema Evolution Monitor, is a background service.

[0191] The background monitor polls the data source and compares it with historical snapshots to calculate the actual frequency of historical changes. This parameter It is a dimensionless probability of 0-1, derived from calculations by a monitor, for example, the monitor polls the API once a day. The schema is then compared with yesterday's snapshot; if the fields have changed in the past 100 days... There were 8 changes, then ;

[0192] An adaptive weighted fusion is performed between the static declaration probability and the historical actual change frequency;

[0193] In the formula It is a 0-1 static / dynamic weighting factor, derived from system-level configuration parameters; the value of this parameter can be set according to the system maturity: for newly connected systems lacking historical data, It can be set to trust developers close to version 1.0; for mature systems that have been running for a long time, It can be set to have near-zero trust in objective historical data;

[0194] This embodiment makes The parameters have been transformed from a subjective, static guess into an objective, dynamically evolving metric that adaptively learns from the production environment; this greatly enhances the performance of S5. The accuracy and objectivity of the overall risk assessment score.

[0195] Example 7:

[0196] Proactive risk alerts include:

[0197] Display the application's total technical debt risk score to developers in real time;

[0198] It also provides decoupling suggestions.

[0199] This embodiment is a specific implementation of the proactive risk alarm described in step S6 of embodiment 1;

[0200] The technical objective of this embodiment is not only to provide alerts, but also to empower developers. Existing tools often lead to alert fatigue because they do not offer solutions. This invention provides actionable insights to guide developers to adopt better design patterns, such as decoupling, thereby eliminating technical debt at the source.

[0201] When S6 triggers or Exceeding the threshold When the condition is met, the alarm module is activated, and its implementation includes:

[0202] Display the application's total technical debt risk score to developers in real time:

[0203] This is a passive display, such as providing an evolution dashboard in the IDE to display the current application's performance in real time. The overall score comes from S5; this provides developers with global, real-time feedback on system health and offers decoupling suggestions:

[0204] This is a proactive alert; the system will pop up an alert window that includes not only a risk description but also specific decoupling recommendations.

[0205] Proactive risk warning: You are currently sending your monthly financial report template. The estimated working hours are 40 hours, bound to the data source API.v1_beta.sales. The evaluation score is 0.8, and this binding is... The assessment is 0.95; this action will reduce the total application risk. Increased by 25%; Recommendation: Add a data adapter between the two to decouple the templates and isolate changes;

[0206] This embodiment constructs a complete closed loop of discovery-alarm-recommendation; by providing specific decoupling suggestions such as adding data adapters, it transforms developers from problem discoverers to solution implementers, significantly improving the execution efficiency of risk avoidance and enabling low TCO to be truly realized.

[0207] Example 8:

[0208] Please see Figure 2 A low-code data processing system based on dynamic binding and template technology, comprising:

[0209] An overall dependency graph construction engine is used to capture data source contracts, template gray box metadata, and user binding relationships in real time, and construct a weighted directed dependency graph with adaptive risk weights based on the data source contracts, template gray box metadata, and user binding relationships.

[0210] A technology debt quantification engine is used to calculate the binding vulnerability index and the template healing cost;

[0211] A change impact analyzer is used to perform change impact analysis based on the bound vulnerability index and the adaptive risk weights of the weighted directed dependency graph, and to calculate the failure propagation probability and failure chain.

[0212] The technology debt quantification engine is also used to calculate the probability of change sources; calculate the change impact coupling factor based on the topology of the failure chain; and aggregate the probability of change sources, the change impact coupling factor, the failure propagation probability, and the template healing cost to calculate the total application technology debt risk score.

[0213] The Evolutionary Force Insight and Proactive Alarm Module is used to calculate the contribution value of the Binding Vulnerability Index to the application technology debt risk, and generate a proactive risk alarm in response to the contribution value or the Binding Vulnerability Index exceeding a preset threshold.

[0214] The technology debt quantification engine is also used to derive edge risk indicators based on the binding vulnerability index;

[0215] The overall dependency graph construction engine is also used to receive the edge risk indicators fed back by the technology debt quantification engine, and dynamically update the adaptive risk weights of the weighted directed dependency graph.

[0216] This invention provides a low-code data processing system based on dynamic binding and template technology. This system is configured to execute a low-code data processing method based on dynamic binding and template technology as described in any one of embodiments 1-7 above. The system architecture includes the following four core modules:

[0217] Overall Dependency Graph Building Engine

[0218] This engine serves as the system's data foundation and dynamic model; its function is to execute step S1: capturing data source contracts, template gray-box metadata, and user binding relationships in real time, and based on these inputs, constructing a weighted directed dependency graph with adaptive risk weights. Simultaneously, the engine also executes the receiving end of step S7: configured to receive the edge risk indicator fed back by the technology debt quantification engine. And dynamically update the adaptive risk weights of the weighted directed dependency graph. This achieves a closed-loop feedback mechanism in the architecture.

[0219] Technical Debt Quantitative Engine

[0220] This engine is the core computing unit of the system; its function is to execute step S2 and calculate the binding vulnerability index. As described in Example 2 and the template healing cost is calculated. As described in Example 3; perform step S4 to calculate the probability of the change source. As described in Example 6, and the coupling factor of the change's impact is calculated. As described in Example 5; Step S5 is executed to aggregate all the above indicators and those from the change impact analyzer. and Calculate the total risk score of applied technology debt. Furthermore, the sending end executing step S7 is configured to derive an edge risk index based on the binding vulnerability index. And feed it back to the overall dependency graph building engine;

[0221] Change Impact Analyzer

[0222] This engine is the system's simulation and deduction unit, typically invoked by the technical debt quantification engine. Its function is to execute step S3: configured to perform change impact analysis as described in Example 4, based on the binding vulnerability index from the quantification engine and the adaptive risk weights from the graph engine in the weighted directed dependency graph, and to calculate the failure propagation probability. With failure chain The calculation results and It is returned to the technical debt quantification engine for S5 calculations;

[0223] Evolutionary Force Insight and Proactive Alert Module

[0224] This engine is the system's human-computer interface (HMI); its function is to execute step S6: configured to receive the calculation results from the technical debt quantification engine. , Calculate the contribution of the binding vulnerability index to the risk of application technology debt. ;

[0225] In response to the contribution value or the binding vulnerability index exceeding a preset threshold, the module generates an active risk alarm and pushes the alarm content to the developer's IDE interface as described in Example 7.

[0226] The system described in this embodiment of the invention, through the collaborative work of these four modules, especially the non-obvious feedback loop S7 between the technical debt quantification engine and the overall dependency graph construction engine, constructs a complete and adaptive risk assessment and management system. This architecture clearly separates the responsibilities of the data modeling graph engine, the core computational quantification engine, the simulation inference analyzer, and the user interaction alarm module, while tightly coupling them through precise data flow, thereby efficiently and in real time realizing all the technical effects described in Embodiment 1, providing quantitative evolvability assurance for low-code platforms.

[0227] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A low-code data processing method based on dynamic binding and template technology, characterized in that, Includes the following steps: Step S1: Capture the data source contract in real time; Real-time capture of template graybox metadata; Real-time capture of user binding relationships; Based on the data source contract, the template gray-box metadata, and the user binding relationship, a weighted directed dependency graph with adaptive risk weights is constructed. The data source contract includes the name, type, and metadata of the API fields; the template gray-box metadata includes schema rigidity, transformation complexity, and logical coupling; the user binding relationship represents the binding relationship between the data source and the template; the schema rigidity quantifies the strength of the dependence of the template's internal implementation on the input data schema it is bound to. Step S2: Calculate the binding vulnerability index; And calculate the template healing cost; Step S3: Based on the binding vulnerability index and the adaptive risk weight of the weighted directed dependency graph, perform change impact analysis; and calculate the failure propagation probability and failure chain. Step S4: Calculate the probability of the change source; Based on the topology of the failure chain, calculate the coupling factor of the impact of the change; Step S5: Aggregate the change source probability, the change impact coupling factor, the failure propagation probability, and the template healing cost to calculate the total application technology debt risk score; Step S6: Calculate the contribution of the binding vulnerability index to the risk of applied technology debt; In response to the contribution value or the binding vulnerability index exceeding a preset threshold, an active risk alarm is generated; Step S7: Derive the edge risk index based on the binding vulnerability index; And feed back the edge risk index to dynamically update the adaptive risk weights of the weighted directed dependency graph; The calculation of the binding vulnerability index includes: Query pattern rigidity from the template graybox metadata; Query the transformation complexity from the template graybox metadata; Query the logical coupling degree from the template graybox metadata; The binding vulnerability index is obtained by weighting and summing the pattern rigidity, the transformation complexity, and the logical coupling degree. The calculation of the template healing cost includes: Obtain the preset base working hours; Query the black-box complexity factor from the template gray-box metadata; Query the number of fan-out nodes of the template from the weighted directed dependency graph; Based on the aforementioned basic working hours, and combined with the black-box complexity factor and the number of node fan-outs, a nonlinear correction calculation is performed to obtain the template healing cost; The calculation of the change impact coupling factor in step S4 includes: The number of nodes is counted from the failed chain; Calculate the graph-theoretic centrality of nodes in the failure chain based on the weighted directed dependency graph; The change impact coupling factor is calculated based on the number of nodes and the graph centrality. The calculation of the change source probability in step S4 includes: Obtain the static declaration probabilities marked in the data source contract; Run the background monitor; The background monitor polls the data source and compares it with historical snapshots to count the actual frequency of historical changes; The static declaration probability and the historical actual change frequency are adaptively weighted and fused to generate the change source probability.

2. The low-code data processing method based on dynamic binding and template technology according to claim 1, characterized in that, The change impact analysis described in step S3 includes: Simulate data source change events; Perform graph traversal on the weighted directed dependency graph; The binding vulnerability index and the adaptive risk weight are introduced as transmission coefficients in the propagation process; Based on the graph traversal and the propagation coefficient, the failure propagation probability is calculated.

3. The low-code data processing method based on dynamic binding and template technology according to claim 1, characterized in that, The proactive risk alerts include: Display the application's total technical debt risk score to developers in real time; It also provides decoupling suggestions.

4. A low-code data processing system based on dynamic binding and template technology, characterized in that, A method for performing low-code data processing based on dynamic binding and template technology as described in any one of claims 1-3, comprising: An overall dependency graph construction engine is used to capture data source contracts, template gray box metadata, and user binding relationships in real time, and construct a weighted directed dependency graph with adaptive risk weights based on the data source contracts, template gray box metadata, and user binding relationships. A technology debt quantification engine is used to calculate the binding vulnerability index and the template healing cost; A change impact analyzer is used to perform change impact analysis based on the bound vulnerability index and the adaptive risk weights of the weighted directed dependency graph, and to calculate the failure propagation probability and failure chain. The technology debt quantification engine is also used to calculate the probability of change sources; calculate the change impact coupling factor based on the topology of the failure chain; and aggregate the probability of change sources, the change impact coupling factor, the failure propagation probability, and the template healing cost to calculate the total application technology debt risk score. The Evolutionary Force Insight and Proactive Alarm Module is used to calculate the contribution value of the Binding Vulnerability Index to the application technology debt risk, and generate a proactive risk alarm in response to the contribution value or the Binding Vulnerability Index exceeding a preset threshold. The technology debt quantification engine is also used to derive edge risk indicators based on the binding vulnerability index; The overall dependency graph construction engine is also used to receive the edge risk indicators fed back by the technology debt quantification engine, and dynamically update the adaptive risk weights of the weighted directed dependency graph.

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