Cycle index early warning method for java business process

Abstract

The invention provides a java business process cycle index early warning method, and relates to the technical field of low-code business process processing, and the method specifically comprises the steps: S1, setting a cycle threshold; s2, setting an early warning point; s3, performing an automatic processing flow; s4, recording execution times; s5, judging a threshold value; according to the invention, through dynamic calculation of the flow cycle threshold and a multi-stage early warning mechanism, the abnormal flow can be found and processed in time; the requirements of different scenes are met by dynamically calculating and optimizing the threshold value; dynamically calculating a process circulation threshold according to factors such as historical times, node complexity, node collaboration degree and a process time window of the business process; through a multi-level early warning mechanism, process anomalies of different degrees are classified and processed; the weight gradient and the self-adaptive learning rate are combined to dynamically optimize and update the weight value; and the flow cycle threshold value can be more accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of low-code business process processing, in particular to a java business process cycle number early warning method. BACKGROUND

[0002] In the Java business process, loop operation is a common operation, but if the execution number of some nodes is too large, it may cause the process execution time to be too long, the resource consumption to be too large, and even cause system lag or errors. Therefore, real-time monitoring and early warning of the process cycle number is an important means to improve the efficiency and stability of the business process.

[0003] An early warning point is set at the key node of the single-node loop for monitoring the execution number of the node. By dynamically obtaining the historical node execution number and the process cycle threshold, the process execution state can be judged in real time.

[0004] In the prior art, a method and device for detecting code logic dead loop are disclosed, and the method comprises the following steps: S1, data preprocessing; S2, extracting loop conditions; S3, judging loop termination conditions; S4, extracting dead loop logic features; and S5, constructing a deep learning model. Compared with the prior art, the present application outputs a data feature model related to the dead loop logic, which includes: a program flowchart, a loop structure information extraction method, a loop condition extraction method, a loop termination condition extraction method and a dead loop logic feature extraction method. The efficiency of artificial intelligence for detecting, positioning and warning dead loop logic in the program is improved.

[0005] The above information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The present application aims to provide a java business process cycle number early warning method to solve the problems raised in the above background.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0008] A java business process cycle number early warning method, the specific steps comprising:

[0009] S1, setting a cycle threshold: setting a process cycle threshold in the low-code platform business process, and storing the set process cycle threshold in the database;

[0010] S2, setting an early warning point: the backend code is set as an early warning point at each sub-node of the business process;

[0011] S3, automatic processing flow: when the business process is started, the process each sub-node acquires the process cycle threshold value;

[0012] S4, record the execution times: record the execution times of the current node and store it in the memory when the node is executed;

[0013] S5, threshold determination: compare the process cycle threshold value with the execution times, if the execution times exceeds the process cycle threshold value, throw an exception, form an exception log, display in the exception log module, and give a warning, if the execution times does not exceed the process cycle threshold value, execute the process normally until the end.

[0014] Further, the formula for calculating the process cycle threshold value is as follows:

[0015] Y l =w1×X s +w2×X Ncp +w3×X Ncd +w4×X Tw ;

[0016] Where Y l represents the process cycle threshold value, i.e. the execution times threshold value of the business process, X s represents the historical times of the business process, X Ncp represents the execution times threshold value caused by node complexity, X Ncd represents the execution times threshold value caused by node coordination degree, X Tw represents the execution times threshold value caused by process time window, w1, w2, w3, w4 represent the weight values of the historical times of the business process, the execution times threshold value caused by node complexity, the execution times threshold value caused by node coordination degree, and the execution times threshold value caused by process time window.

[0017] Further, the threshold determination is divided as follows:

[0018] According to the deviation degree of the process cycle threshold value and the execution times, a multi-level warning mechanism is realized, and the specific division standard is as follows:

[0019] Normal range: the execution times of the process sub-node does not exceed the dynamically calculated process cycle threshold value Y l ;

[0020] Normal warning range: the execution times of the process sub-node slightly exceeds the threshold value, but does not reach the emergency level, i.e. Y l < execution times < Y l ×30%.

[0021] Emergency warning range: the execution times seriously exceeds the threshold value, causing process lag or error; i.e. execution times > Y l ×30%.

[0022] Further, the calculation of the execution times threshold caused by the node complexity is as follows:

[0023]

[0024] wherein C i represents the complexity parameter of the i-th node, x fzd represents the service logic complexity of the i-th node, x ldl represents the data flow amount of the i-th node, x yx represents the influence degree of the i-th node on the subsequent process;

[0025]

[0026] wherein X Ncp represents the execution times threshold caused by the node complexity, N represents the total number of nodes in the process, C i represents the complexity parameter of the i-th node;

[0027] The complexity parameter C i of each node contributes linearly to the process cycle times, i.e., the complexity parameters of each node are added to obtain the process cycle times.

[0028] Further, the calculation of the execution times threshold caused by the node synergy is as follows:

[0029]

[0030] wherein C d represents the synergy parameter of the i-th node, x ljyl represents the number of logical dependency relationships between nodes of the i-th node, x ldl represents the data flow amount of the i-th node;

[0031]

[0032] wherein X Ncd represents the execution times threshold caused by the node synergy, N represents the total number of nodes in the process, C d represents the synergy parameter of the i-th node;

[0033] The synergy parameter C d of each node contributes linearly to the process cycle times, i.e., the synergy parameters of each node are added to obtain the process cycle times.

[0034] Further, the calculation of the execution times threshold caused by the process time window is as follows:

[0035]

[0036] Among them, C w Represented as the process time window parameter of the i-th node, x dsjck Represented as the total number of executions in the current time window, x sjck Expressed as the total number of executions in the time window;

[0037]

[0038] Among them, X Ncd It represents the execution count threshold caused by the current process time window, N represents the total number of time windows, and C w It is represented as the process time window parameter of the i-th node;

[0039] Each process time window parameter C d The contribution to the number of process cycles is linear, that is, the process time window parameters of each time window are added together to obtain the number of process cycles.

[0040] Furthermore, the update optimization calculation of w1, w2, w3, and w4 in the process cycle threshold is as follows:

[0041] Combine the weight gradient and adaptive learning rate to update and optimize the weight value;

[0042] The parameter update rule is:

[0043] Among them, η represents the learning rate, Expressed as the gradient of the objective function with respect to the weight, w t Expressed as the weight value at time t, w t+1 Represents the updated optimized weight value.

[0044] Furthermore, the setting of the warning points is as follows:

[0045] Injection point description: Set early warning injection points at key nodes of a single-node loop to monitor the number of executions of the nodes;

[0046] Monitoring target: the number of executions of the current node; and the number of executions of historical nodes are obtained;

[0047] Abnormal conditions: When the number of executions exceeds the dynamically calculated process cycle threshold, the process cycle threshold is divided into different levels according to the degree of deviation from the execution number, thus implementing a multi-level early warning mechanism.

[0048] Trigger warning: When the number of executions exceeds the process cycle threshold, the warning mechanism is triggered, an error message is generated and processed.

[0049] Further, the automation process includes automatic retry mechanism, process optimization, and error information feedback.

[0050] Automatic retry mechanism:

[0051] Trigger condition: when the execution times of the process node exceed the process cycle threshold, trigger automatic retry;

[0052] Retry times and waiting time:

[0053] Retry times: set to 3 or more to prevent infinite loop;

[0054] Waiting time: set a waiting time between each retry to prevent system overload;

[0055] Implementation: set retry logic, use try-catch-finally structure; set retry strategy and waiting time in configuration management;

[0056] Process optimization:

[0057] Abnormal node identification: identify specific nodes and reasons that cause threshold exceptions by analyzing exception logs;

[0058] Optimization strategy:

[0059] Replacement optimization: replace abnormal nodes with more efficient logic or calls;

[0060] Process reconstruction: optimize the entire process to reduce the number of node executions or optimize data flow;

[0061] Rule adjustment: dynamically adjust process logic according to business rules;

[0062] Optimization implementation: use process optimization tools or custom scripts for optimization; combine machine learning models to automatically adjust process optimization strategies based on historical data and current state;

[0063] Error information feedback

[0064] Error information collection: collect exception logs, execution duration, and resource consumption details;

[0065] Feedback channel:

[0066] Email notification: send exception reports to relevant responsible persons;

[0067] ERP system integration: synchronize error information to enterprise resource planning system;

[0068] Third-party collaboration tools: feedback through Slack, Microsoft Teams tools;

[0069] Error handling process:

[0070] Quick response: When an exception is found, trigger an alarm and start the handling process immediately;

[0071] Detailed analysis: In-depth analysis of the cause of the exception to find the root problem;

[0072] Problem solving: According to the analysis results, take appropriate measures

[0073] Further,

[0074] Compared with the prior art, the beneficial effects of the present application are:

[0075] The present application can timely discover and handle abnormal processes, reduce unnecessary loop operations, and improve overall process execution efficiency by dynamically calculating the loop threshold and implementing a multi-level early warning mechanism. When the number of process executions is too high, the system may face high load, resource depletion and other problems. By early warning mechanism, avoid process lag or error, reduce the occupation of system resources; By dynamically adjusting the process optimization strategy and error handling process, it can quickly respond to and solve abnormal problems, and ensure the stable operation of the system.

[0076] Through dynamic calculation and optimization of the threshold value, the process execution strategy can be flexibly adjusted according to the changes of business rules to meet the needs of different scenarios; Through the automatic retry mechanism and process optimization strategy, it can reduce the errors caused by too many loop times, and improve the reliability and execution efficiency of the process; Through the early warning mechanism and error information feedback, users can timely understand the process execution status, avoid business delay or error caused by process exception;

[0077] The application dynamically calculates the process cycle threshold according to the historical number of times of the business process, the node complexity, the node coordination degree, and the process time window, etc. factors; by combining these factors, the rationality and applicability of the threshold are ensured, thereby effectively warning the process abnormalities; the complexity parameter of each node reflects the business logic complexity, data flow amount, and the influence degree on the subsequent process of the node; by accumulating the node complexity parameter, the linear contribution of the process cycle number of times can be calculated; the node coordination degree parameter reflects the logical dependency relationship and data flow amount between nodes; by accumulating the node coordination degree parameter, the linear contribution of the process cycle number of times can be calculated; the process time window parameter reflects the total execution number of times within the current time window; by dynamically updating the time window parameter, the recent process execution situation can be better reflected, helping to identify abnormalities; according to the deviation degree of the process cycle threshold and the execution number of times, it is divided into a normal range, an ordinary warning range, and an urgent warning range; through the multi-level warning mechanism, different degrees of process abnormalities can be classified and processed; the weight value is dynamically optimized and updated in combination with the weight gradient and the adaptive learning rate; by optimizing the weight value, the process cycle threshold can be more accurate, and the accuracy of the warning and the effect of the process optimization can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0078] Fig. 1 It is the overall method flowchart of the application;

[0079] Fig. 2 It is the setting flowchart of the warning point of the application. DETAILED DESCRIPTION

[0080] In order to make the purpose, technical scheme and advantages of the application clearer and more apparent, the application will be further described in detail below in combination with specific embodiments.

[0081] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the application should be understood as the usual meaning understood by those skilled in the art to which the application belongs. The "first", "second" and similar words used in the application do not represent any order, quantity or importance, but are only used to distinguish different components. "Include" or "contain" and similar words mean that the elements or objects before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connected" or "connected" and similar words are not limited to physical or mechanical connection, but can include electrical connection, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to represent the relative positional relationship, when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0082] Example one:

[0083] Please refer to Figs. 1-2 The application provides a technical solution: a java business process cycle number early warning method, the specific steps comprising:

[0084] S1, set the cycle threshold: set the process cycle threshold in the low-code platform business process, and store the set process cycle threshold in the database;

[0085] S2, set the early warning point: the backend code sets each sub-node of the business process as an early warning point;

[0086] S3, automatic process: when the business process starts, each sub-node of the process obtains the process cycle threshold;

[0087] S4, record the execution number: record the execution number of the current node when the node is executed and store it in the memory;

[0088] S5, threshold determination: compare the process cycle threshold with the execution number, if the execution number exceeds the process cycle threshold, throw an exception, form an exception log, display it in the exception log module, and perform early warning, if the execution number does not exceed the process cycle threshold, execute the process normally until the end.

[0089] In this embodiment, preferably, the calculation formula of the process cycle threshold is as follows:

[0090] Y l =w1×X s +w2×X Ncp +w3×X Ncd +w4×X Tw ;

[0091] Wherein, Y l represents the process cycle threshold, i.e. the execution number threshold of the business process, X s represents the historical number of the business process, X Ncp represents the execution number threshold caused by node complexity, X Ncd represents the execution number threshold caused by node coordination degree, X Tw represents the execution number threshold caused by process time window, and w1, w2, w3, w4 represent the weight values of the historical number of the business process, the execution number threshold caused by node complexity, the execution number threshold caused by node coordination degree, and the execution number threshold caused by process time window.

[0092] It should be noted that by introducing the node complexity parameter, the node cooperation degree parameter and the process time window parameter, the single node cycle problem in the Java business process can be more comprehensively monitored and warned; the setting of the dynamic threshold not only considers the traditional business rules, historical execution data and system performance parameters, but also introduces more comprehensive monitoring points and parameters, so that the calculation of the threshold is more accurate and flexible; the threshold can be dynamically adjusted according to the specific business scene and running state, the possible process problems can be found in time, and the efficiency and stability of the business process are improved.

[0093] In this embodiment, preferably, the threshold determination is divided as follows:

[0094] According to the deviation degree of the process cycle threshold and the execution frequency, a multi-level warning mechanism is realized, and the specific division standard is as follows:

[0095] Normal range: the execution frequency of the process sub-node does not exceed the dynamically calculated process cycle threshold Y l ;

[0096] Normal warning range: the execution frequency of the process sub-node slightly exceeds the threshold, but does not reach the emergency level, i.e. Y l <execution frequency < Y l x 30%;

[0097] Emergency warning range: the execution frequency seriously exceeds the threshold, causing process jam or error; i.e. execution frequency > Y l x 30%;

[0098] Intelligent warning is realized, time series analysis is used to predict possible abnormal points; combined with business rules, the abnormality is intelligently judged to determine whether the warning needs to be triggered; for high-frequency abnormality, automatic processing process is automatically triggered;

[0099] It should be noted that the process cycle frequency warning method realizes multi-level dynamic monitoring and automatic processing, effectively prevents process abnormalities, improves the efficiency and stability of the system, and provides a flexible and efficient solution for complex business process management, which can meet the needs of different scenes.

[0100] In this embodiment, preferably, the calculation of the execution frequency threshold caused by the node complexity is as follows:

[0101]

[0102] Wherein, C i represents the complexity parameter of the i-th node, x fzd represents the business logic complexity of the i-th node, x ldl represents the data flow of the i-th node, x yxrepresents the influence degree of the i-th node on the subsequent flow process;

[0103]

[0104] wherein X Ncp represents the execution times threshold value caused by the node complexity, N represents the total number of nodes in the flow process, and C i represents the complexity parameter of the i-th node.

[0105] The complexity parameter C i The contribution to the flow cycle times is linear, that is, the complexity parameters of each node are added to obtain the flow cycle times.

[0106] It should be noted that the complexity parameter of each node reflects the business logic complexity, data flow amount, and influence degree on the subsequent flow process of the node; by accumulating the complexity parameters of the nodes, the linear contribution of the flow cycle times can be calculated.

[0107] In this embodiment, preferably, the calculation of the execution times threshold value caused by the node coordination degree is as follows:

[0108]

[0109] wherein C d represents the coordination parameter of the i-th node, x ljyl represents the number of logical dependency relationships between the i-th node and other nodes, x ldl represents the data flow amount of the i-th node.

[0110]

[0111] wherein X Ncd represents the execution times threshold value caused by the node coordination degree, N represents the total number of nodes in the flow process, and C d represents the coordination parameter of the i-th node.

[0112] The coordination parameter C d The contribution to the flow cycle times is linear, that is, the coordination parameters of each node are added to obtain the flow cycle times.

[0113] It should be noted that the node coordination parameter reflects the logical dependency relationship and data flow amount between the nodes; by accumulating the node coordination parameters, the linear contribution of the flow cycle times can be calculated.

[0114] In this embodiment, preferably, the calculation of the execution times threshold value caused by the flow time window is as follows:

[0115]

[0116] wherein C w represents the process time window parameter of the i-th node, x dsjck represents the total execution times in the current time window, x sjck represents the total execution times in the time window;

[0117]

[0118] wherein X Ncd represents the execution times threshold caused by the current process time window, N represents the total number of time windows, C w represents the process time window parameter of the i-th node;

[0119] Each process time window parameter C d The contribution of the process cycle times is linear, that is, the process time window parameters of each time window are added to obtain the process cycle times.

[0120] It should be noted that the process time window parameter reflects the total execution times in the current time window; by dynamically updating the time window parameter, the recent process execution situation can be better reflected, and the abnormality can be identified.

[0121] In this embodiment, preferably, the update optimization calculation of w1, w2, w3 and w4 in the process cycle threshold is as follows:

[0122] The weight value is updated and optimized in combination with the gradient of the weight and the adaptive learning rate;

[0123] The parameter update rule is:

[0124] wherein η represents the learning rate, represents the gradient of the objective function with respect to the weight, w t represents the weight value at time t, w t+1 represents the updated and optimized weight value;

[0125] It should be noted that through the multi-level early warning mechanism, different degrees of process abnormalities can be classified and processed; in combination with the weight gradient and the adaptive learning rate, the weight value is dynamically optimized and updated; by optimizing the weight value, the process cycle threshold can be more accurate, and the accuracy of the early warning and the effect of the process optimization can be improved.

[0126] In this embodiment, preferably, the early warning point is set as follows:

[0127] Injection point description: set an early warning injection point at a key node of a single node cycle to monitor the execution times of the node;

[0128] Monitoring target: the number of executions for the current node; and obtaining the historical node execution times;

[0129] Abnormal condition: when the number of executions exceeds the dynamically calculated process cycle threshold, the deviation between the process cycle threshold and the number of executions is classified to implement a multi-level warning mechanism;

[0130] Triggering a warning: when the number of executions exceeds the process cycle threshold, triggering a warning mechanism to form an error message and processing it;

[0131] It should be noted that by setting the warning point, the execution of the key node can be monitored in real time, and abnormalities can be found in time. According to the severity of the abnormal situation, a multi-level warning processing is realized to improve the flexibility and accuracy of the warning;

[0132] By introducing the setting of the warning point, the system can more comprehensively and accurately monitor and handle the abnormal situation in the process, improve the efficiency and stability of the process management, reduce the loss caused by the process abnormality, and provide more efficient business process support.

[0133] In this embodiment, preferably, the automatic processing flow includes an automatic retry mechanism, process optimization, and error information feedback;

[0134] Automatic retry mechanism:

[0135] Triggering condition: when the number of executions of the process node exceeds the process cycle threshold, triggering an automatic retry;

[0136] Retry times and waiting time:

[0137] Retry times: set to 3 or more to prevent infinite loops;

[0138] Waiting time: set a waiting time between each retry to prevent system overload;

[0139] Implementation: set retry logic using try-catch-finally structure; set retry strategy and waiting time in configuration management;

[0140] Process optimization:

[0141] Abnormal node identification: identify the specific node and reason causing the threshold abnormality by analyzing abnormal logs;

[0142] Optimization strategy:

[0143] Replacement optimization: replace the abnormal node with more efficient logic or call;

[0144] Process reconstruction: optimize the entire process to reduce the number of node executions or optimize data flow;

[0145] Rule Adjustment: Dynamically adjust the process logic according to business rules;

[0146] Optimization Implementation: Use process optimization tools or custom scripts for optimization; combine machine learning models to automatically adjust process optimization strategies based on historical data and current state;

[0147] Error Information Feedback

[0148] Error Information Collection: Collect exception logs, execution duration, and detailed resource consumption information;

[0149] Feedback Channels:

[0150] Email Notification: Send exception reports to relevant responsible persons;

[0151] ERP System Integration: Synchronize error information to the enterprise resource planning system;

[0152] Third-party Collaboration Tools: Feedback through Slack, Microsoft Teams tools;

[0153] Error Handling Process:

[0154] Quick Response: When an exception is found, trigger an alarm and start the handling process immediately;

[0155] Detailed Analysis: Conduct in-depth analysis of the causes of the exception to find the root problem;

[0156] Problem Solving: According to the analysis results, take appropriate measures to solve the problem.

[0157] It should be noted that the automated handling process can monitor and handle exceptions in the process through automatic retry, process optimization and error information feedback, improve the execution efficiency and stability of the process, and reduce business losses caused by exceptions; The automated handling process not only supports the normal operation of the process, but also can quickly respond and solve problems when problems occur, ensuring the efficiency and reliability of the process, and is an efficient and intelligent process management solution.

[0158] The exception log uses more fine-grained exception detection and log recording:

[0159] More diverse information is recorded in the exception log, such as: the specific time of the exception (accurate to milliseconds); the node name and path of the call exception; the relevant business process context (such as process number, executor or request source); exception type and error code; corresponding threshold and number of executions; intelligent analysis of exception logs, such as automatically identifying exception types and potential causes through natural language processing technology (NLP) or machine learning models;

[0160] Set multi-level alarm mechanism, for example: normal range: the execution times of process sub-nodes do not exceed the dynamically calculated process cycle threshold Y l ;

[0161] Normal warning range: the execution times of process sub-nodes slightly exceed the threshold, but do not reach the emergency level, i.e. Y l <Execution times < Y l × 30%;

[0162] Emergency warning range: the execution times seriously exceed the threshold, causing process jam or error; i.e. Execution times > Y l × 30%;

[0163] Technical means: use log analysis tools (such as ELK, Splunk) to store and retrieve abnormal logs; introduce intelligent analysis module, use NLP or statistical learning method to automatically analyze logs; implement multi-level alarm mechanism to ensure that abnormalities can be discovered and handled in time.

[0164] Comparative analysis table

[0165]

[0166]

[0167] The technical points proposed in this application have significant advantages in dynamic threshold calculation, multi-level warning mechanism, node complexity and coordination degree parameters, process optimization, and error information feedback compared to existing technologies, which can more comprehensively and accurately monitor and handle abnormal situations in business processes, and improve the efficiency and stability of process management.

[0168] It should be noted that all the calculation formulas in this application file use regression analysis in machine learning algorithms, including but not limited to, to deeply analyze the collected relevant parameters and identify their natural trends and mutual relationships. Professional software such as Python's Scikit-learn library or R language is used to automatically generate mathematical models that match the data. Then, through cross-validation and other methods, the performance of the model is objectively evaluated, and combined with continuous feedback and optimization, to ensure that the created formula truly reflects the inherent law of the data, thereby ensuring its effectiveness and accuracy. In all the calculation formulas in this application, the parameters in each formula are processed by consistent range of dimensionless to ensure that different physical quantities are compared on the same scale; the dimensionless technical means include but are not limited to Min-Max Normalization, Z-Score standardization;

[0169] The technical solutions of the present application can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a FLASH memory, a hard disk, or an optical disc, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present application.

[0170] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a list of instructions for implementing logic functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, a processor- based system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or in conjunction with which the instructions may be executed. For the purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0171] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of the claims of the present application.

[0172] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A Java business process cycle count warning method, characterized in that: The specific steps include: S1. Set the cycle threshold: Set the process cycle threshold in the low-code platform business process and store the set process cycle threshold in the database; S2. Set warning points: The backend code sets warning points at each sub-node of the business process; S3. Automated processing flow: When a business process starts, each sub-node of the process obtains the process cycle threshold; S4. Record the number of executions: When a node is executed, the number of executions of the current node is recorded and stored in memory; S5. Threshold determination: Compare the process cycle threshold with the execution times. If the execution times exceed the process cycle threshold, an exception is thrown, an exception log is generated, and it is displayed in the exception log module, and an early warning is issued. If the execution times do not exceed the process cycle threshold, the process is executed normally until the end.

2. A Java business process cycle count warning method according to claim 1, characterized in that: The calculation formula of the process cycle threshold is as follows: AND l =w1×X s +w2×X Ncp +w3×X Ncd +w4×X Tw ; Among them, Y l It is expressed as the process cycle threshold, that is, the execution number threshold of the business process, X s Expressed as the historical number of business processes, X Ncp It is expressed as the execution number threshold caused by the complexity of the node, X Ncd It is expressed as the execution number threshold caused by node coordination, X Tw It is represented by the execution count threshold caused by the process time window, and w1, w2, w3, and w4 are the weight values ​​of the historical number of business processes, the execution count threshold caused by node complexity, the execution count threshold caused by node coordination, and the execution count threshold caused by the process time window.

3. The method for early warning of the number of cycles of a Java business process according to claim 2, characterized in that: The threshold determination is divided as follows: Based on the degree of deviation between the process cycle threshold and the number of executions, a multi-level early warning mechanism is implemented. The specific classification criteria are as follows: Normal range: The number of executions of the process sub-node does not exceed the dynamically calculated process cycle threshold Y l ; Normal warning range: The number of executions of the process subnode slightly exceeds the threshold, but does not reach the emergency level, that is, Y l <Number of executions <Y l ×30%; Emergency warning range: The number of executions seriously exceeds the threshold, causing process jams or errors; that is, the number of executions > Y l ×30%.

4. The method for early warning of the number of cycles of a Java business process according to claim 2, characterized in that: The calculation of the execution number threshold caused by the complexity of the node is as follows: Among them, C i Expressed as the complexity parameter of the i-th node, x fzd Expressed as the business logic complexity of the i-th node, x ldl Expressed as the data flow of the i-th node, x yx It is expressed as the degree of influence of the i-th node on the subsequent process; Among them, X Ncp It is represented by the execution number threshold caused by the complexity of the node, N is the total number of nodes in the process, C i Expressed as the complexity parameter of the i-th node; The complexity parameter C of each node i The contribution to the number of process cycles is linear, that is, the complexity parameters of each node are added together to obtain the number of process cycles.

5. The method for early warning of the number of cycles of a Java business process according to claim 2, characterized in that: The calculation of the execution number threshold caused by the node coordination degree is as follows: Among them, C d Expressed as the coordination parameter of the i-th node, x ljyl It is represented as the number of logical dependencies between nodes of the i-th node, x ldl It is expressed as the data flow of the i-th node; Among them, X Ncd It is represented by the execution number threshold caused by node coordination, N is the total number of nodes in the process, C d It is expressed as the coordination parameter of the i-th node; The coordination parameter C of each node d The contribution to the number of process cycles is linear, that is, the number of process cycles is obtained by adding the coordination parameters of each node.

6. The method for early warning of the number of cycles of a Java business process according to claim 2, characterized in that: The calculation of the execution count threshold caused by the process time window is as follows: Among them, C w Represented as the process time window parameter of the i-th node, x dsjck Represented as the total number of executions in the current time window, x sjck Expressed as the total number of executions in the time window; Among them, X Ncd It represents the execution count threshold caused by the current process time window, N represents the total number of time windows, and C w It is represented as the process time window parameter of the i-th node; Each process time window parameter C d The contribution to the number of process cycles is linear, that is, the process time window parameters of each time window are added together to obtain the number of process cycles.

7. The method for early warning of the number of cycles of a Java business process according to claim 2, characterized in that: The update optimization calculation of w1, w2, w3, and w4 in the process cycle threshold is as follows: Combine the weight gradient and adaptive learning rate to update and optimize the weight value; The parameter update rule is: Among them, η represents the learning rate, Expressed as the gradient of the objective function with respect to the weight, w t Expressed as the weight value at time t, w t+1 Represents the updated optimized weight value.

8. The method for early warning of the number of cycles of a Java business process according to claim 2, characterized in that: The settings of the warning points are as follows: Injection point description: Set early warning injection points at key nodes of a single-node loop to monitor the number of executions of the nodes; Monitoring target: the number of executions for the current node; And obtain the number of historical node executions; Abnormal conditions: When the number of executions exceeds the dynamically calculated process cycle threshold, the process cycle threshold is divided into different levels according to the degree of deviation from the execution number, thus implementing a multi-level early warning mechanism. Trigger warning: When the number of executions exceeds the process cycle threshold, the warning mechanism is triggered, an error message is generated and processed.

9. The method for early warning of the number of cycles of a Java business process according to claim 7, characterized in that: The automated processing flow includes an automatic retry mechanism, process optimization and error information feedback; Automatic retry mechanism: Trigger condition: When the number of executions of a process node exceeds the process cycle threshold, an automatic retry is triggered; Retry times and waiting time: Retry times: set to 3 or more to prevent infinite loops; Waiting time: Set a waiting time between each retry to prevent system overload; Implementation: Set retry logic and use the try-catch-finally structure; set the retry strategy and wait time in configuration management; Process optimization: Abnormal node identification: By analyzing abnormal logs, the specific nodes and causes that cause threshold abnormalities are identified; Optimization strategy: Replacement optimization: Replace abnormal nodes with more efficient logic or calls; Process reconstruction: Optimize the entire process, reduce the number of node executions or optimize data flow; Rule adjustment: Dynamically adjust process logic according to business rules; Optimization implementation: Use process optimization tools or custom scripts for optimization; combine machine learning models to automatically adjust process optimization strategies based on historical data and current status; Error information feedback Error information collection: collect exception logs, execution time, and detailed resource consumption information; Feedback channels: Email notification: Send abnormality reports to relevant persons in charge; ERP system integration: Synchronize error information to the enterprise resource planning system; Third-party collaboration tools: Feedback through Slack and Microsoft Teams; Error handling process: Quick response: When an anomaly is detected, an alarm is immediately triggered and the processing process is started; Detailed analysis: Conduct in-depth analysis of the cause of the abnormality to identify the root cause; Problem solving: Take appropriate solutions based on the analysis results.

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