Intelligent guiding method and system for intelligent inspection work order processing

Abstract

The invention is suitable for the field of intelligent work order processing, and provides an intelligent guiding method and system for intelligent inspection work order processing, and the method comprises the steps: obtaining historical inspection work order data, carrying out the cleaning and preprocessing of the data, and removing repeated data and noise data; obtaining a new inspection work order, and collecting abnormal data features of the new inspection work order; inputting the abnormal data features of the new inspection work order into the intelligent processing guidance model, and generating a processing flow and a rectification suggestion based on the abnormal data features; setting a time interval threshold value, and obtaining new inspection work order data and related business data in real time; updating the historical inspection work order data by using the obtained new inspection work order data, and optimizing the intelligent processing guidance model by using a decision tree algorithm according to the updated data; the intelligent inspection work order processing is realized, the processing efficiency and accuracy are improved, the manual intervention is reduced, the service change is adapted through the model dynamic optimization, and the system practicability and reliability are enhanced.

Description

Technical Field

[0001] This invention belongs to the field of intelligent work order processing, and particularly relates to an intelligent guidance method and system for intelligent audit work order processing. Background Technology

[0002] In today's rapidly developing digital age, industries are increasingly demanding higher efficiency and accuracy in their business processes. In the field of audit work order processing, traditional methods face numerous challenges. With the continuous expansion of business scale, the number of audit work orders has exploded. Manually processing these work orders not only consumes significant human, material, and time resources but is also prone to human error, leading to low efficiency and inaccurate results. For example, in electricity marketing audits, staff need to manually classify a large number of work orders, analyze abnormal data characteristics, develop processing procedures, and provide rectification suggestions. This process is tedious and error-prone, significantly impacting work efficiency and service quality.

[0003] Currently, while some technologies attempt to utilize computer systems to assist in processing audit work orders, significant limitations remain. Existing systems lack in-depth analysis and effective utilization of historical audit work order data, failing to automatically generate accurate processing flows and rectification suggestions based on the abnormal data characteristics of the work orders. Furthermore, low integration with other related business systems hinders automated workflow processing, resulting in excessive manual intervention and severely restricting efficiency improvements. Therefore, a novel intelligent guidance method for audit work order processing is urgently needed to address these issues. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent guidance method and system for processing intelligent inspection work orders, aiming to solve the problems mentioned in the background art.

[0005] This invention is implemented as follows: On one hand, an intelligent guidance method for processing intelligent audit work orders, the method comprising: Obtain historical audit work order data, clean and preprocess the data to remove duplicate and noisy data; Logistic regression algorithm is used to analyze preprocessed historical audit work order data to construct an intelligent processing guidance model: ; In the formula, Represents a given feature vector In this case, the probability that a work order belongs to a specific processing category, For the intercept term, Features The corresponding coefficients, It is a natural constant; Obtain new audit work orders and collect abnormal data characteristics of the new audit work orders; The abnormal data characteristics include: abnormal work order type, abnormal occurrence time, description of abnormal phenomenon, business process involved, amount involved, and scope of impact; Input the abnormal data characteristics of new inspection work orders into the intelligent processing guidance model, and generate processing procedures and rectification suggestions based on the abnormal data characteristics; Set time interval threshold Real-time acquisition of new audit work order data and related business data; The historical inspection work order data is updated using the newly acquired inspection work order data. Simultaneously, based on the updated data, a decision tree algorithm is used to optimize the intelligent processing guidance model. The formula for the decision tree algorithm is as follows: ; In the formula, Represents the dataset Information entropy The number of categories in the dataset. For the data belonging to the first The proportion of class samples; When the intelligent processing guidance model recommends a processing flow that matches the business operation in the preset marketing system, the audit work order information will be transmitted to the preset marketing system.

[0006] As a further aspect of the present invention, the step of using a logistic regression algorithm to analyze the preprocessed historical audit work order data and constructing an intelligent processing guidance model specifically includes: Cross-validation is used to optimize the parameters of the logistic regression algorithm. The cross-validation formula is as follows: ; In the formula, Indicates the cross-validation error. The number of folds for cross-validation. For the first Errors during verification.

[0007] As a further aspect of the present invention, the step of inputting the abnormal data characteristics of the new inspection work order into the intelligent processing guidance model, and generating a processing flow and rectification suggestions based on the abnormal data characteristics, includes: A similarity calculation method is used to match historical processing records and new work orders. The similarity calculation formula is as follows: ; In the formula, For auditing work orders and inspection work orders similarity, and These are inspection work orders. and inspection work orders The One eigenvalue; Based on the similarity matching results, the recommended results are sorted, and the processing flow and rectification suggestions with high similarity to work orders with good historical processing results are displayed first.

[0008] As a further aspect of the present invention, the step of inputting the abnormal data characteristics of the new inspection work order into the intelligent processing guidance model, and generating a processing flow and rectification suggestions based on the abnormal data characteristics, specifically includes: If the abnormal data characteristics indicate that the abnormal type is data entry error and the amount involved is small, the processing flow is as follows: the front-line business personnel should check the original vouchers, correct the erroneous data, and submit them for review; the rectification suggestions are: strengthen the training of business personnel, establish a data entry review mechanism, and set data verification rules to automatically intercept obvious errors; If the abnormal data characteristics indicate that the abnormal type is data entry error, but the amount involved is large or the scope of impact is wide, the processing procedure is as follows: initiate the emergency processing procedure, freeze relevant business operations, organize a special team to trace and verify the source, simultaneously correct the data in the related systems, and submit a written rectification report; the rectification suggestion is: introduce OCR recognition technology to assist in data entry and implement a dual-person, dual-position data entry mechanism.

[0009] As a further aspect of the present invention, the step of inputting the abnormal data characteristics of the new inspection work order into the intelligent processing guidance model, and generating a processing flow and rectification suggestions based on the abnormal data characteristics, specifically includes: If the abnormal data characteristics indicate that the abnormal type is a system failure and the duration is less than 2 hours, the following processing procedure is generated: notify the system operation and maintenance personnel to immediately investigate the failure, activate the backup system or temporary processing procedure, and record the failure time and scope of impact; the rectification suggestion is: conduct system stress tests regularly, improve the system monitoring and early warning mechanism, and reserve spare parts for critical systems. If the abnormal data characteristics indicate that the abnormal type is a system failure, and the duration is greater than or equal to 2 hours or affects core business, the processing procedure is as follows: activate the disaster recovery plan, switch to the disaster recovery system, report to the superior competent authority, organize technical experts to jointly tackle the problem, and conduct a comprehensive data verification after the failure is resolved; the rectification suggestions are: establish a disaster recovery center in the same city but in a different location, formulate a system failure graded response mechanism, and conduct disaster recovery drills regularly.

[0010] As a further aspect of the present invention, the step of inputting the abnormal data characteristics of the new inspection work order into the intelligent processing guidance model, and generating a processing flow and rectification suggestions based on the abnormal data characteristics, specifically includes: If the abnormal data characteristics indicate that the abnormality type is business violation and the severity is moderate, the processing flow is as follows: send a notice of rectification within a specified period, requiring the responsible department to submit a rectification plan, track the rectification progress and conduct acceptance; the rectification suggestions are: strengthen business compliance training, optimize business process approval links, and set up a function to block violations. If the abnormal data characteristics indicate that the anomaly type is a business violation and the severity is major, the following processing procedure will be generated: suspend relevant business permissions, conduct a special audit investigation, transfer the case to the discipline inspection or judicial department based on the investigation results, and circulate the violation case throughout the system; the rectification suggestion is: establish a business operation risk scoring model, implement real-time risk warning, and strengthen the internal supervision and accountability mechanism.

[0011] As a further aspect of the present invention, the step of transmitting the audit work order information to the preset marketing system when the intelligent processing guidance model recommends a processing flow that matches a business operation in the preset marketing system specifically includes: Data interaction is achieved through API interfaces with pre-set marketing systems; Access control and security authentication are implemented for API calls to ensure the security and accuracy of data transmission. Send a work order transmission request to the preset marketing system through the API interface, and carry the converted and verified audit work order information; Receive response information from the preset marketing system, determine whether the work order transmission was successful. If the transmission is successful, record the transmission time and operation log information; if it fails, resend the request based on the error message and notify the system administrator to troubleshoot the interface.

[0012] As a further aspect of the present invention, the intelligent guidance method for intelligent audit work order processing also includes: When the processing flow and rectification suggestions recommended by the intelligent processing guidance model do not match the actual business situation, staff will make manual adjustments. The manually adjusted information will be added to the historical audit work order data as new sample data. The intelligent processing guides the model to optimize and learn again based on the new data.

[0013] As a further aspect of the present invention, another feature is an intelligent guidance system for processing intelligent inspection work orders, the system comprising: The acquisition and processing module is used to acquire historical audit work order data, clean and preprocess the data, and remove duplicate and noisy data. The intelligent processing guidance model module is used to analyze the pre-processed historical audit work order data using logistic regression algorithms to build an intelligent processing guidance model. The data acquisition module is used to acquire new audit work orders and collect abnormal data characteristics of new audit work orders; The generation module is used to input the abnormal data characteristics of new inspection work orders into the intelligent processing guidance model, and generate processing flow and rectification suggestions based on the abnormal data characteristics; The real-time acquisition module is used to set the time interval threshold. Real-time acquisition of new audit work order data and related business data; The update and optimization module is used to update the historical audit work order data using the acquired new audit work order data, and at the same time, it uses the decision tree algorithm to optimize the intelligent processing guidance model based on the updated data. The matching and transmission module transmits the audit work order information to the preset marketing system when the intelligent processing guidance model recommends a processing flow that matches the business operation in the preset marketing system.

[0014] As a further aspect of the present invention, the generation module specifically includes: The matching unit is used to perform similarity matching between historical processing records and new work orders using similarity calculation methods; The sorting and display unit is used to sort the recommended results based on similarity matching results, and prioritizes displaying the processing flow and rectification suggestions that are highly similar to work orders with good historical processing results.

[0015] The intelligent guidance method and system for intelligent audit work order processing provided by this invention realizes intelligent audit work order processing, improves processing efficiency and accuracy, reduces manual intervention, adapts to business changes through dynamic model optimization, enhances system practicality and reliability, and promotes business process automation and standardization. Attached Figure Description

[0016] Figure 1 This is the main flowchart of the intelligent guidance method for intelligent audit work order processing.

[0017] Figure 2 The flowchart of the first embodiment of the intelligent guidance method for intelligent audit work order processing is as follows: the abnormal data characteristics of new audit work orders are input into the intelligent processing guidance model, and the processing flow and rectification suggestions are generated based on the abnormal data characteristics.

[0018] Figure 3 The flowchart of the first embodiment of the intelligent guidance method for intelligent audit work order processing is as follows: the abnormal data characteristics of new audit work orders are input into the intelligent processing guidance model, and the processing flow and rectification suggestions are generated based on the abnormal data characteristics.

[0019] Figure 4 The flowchart of the second embodiment of the intelligent guidance method for intelligent inspection work order processing is as follows: the abnormal data characteristics of new inspection work orders are input into the intelligent processing guidance model, and based on the abnormal data characteristics, the processing flow and rectification suggestions are generated.

[0020] Figure 5 The flowchart of the third embodiment of the intelligent guidance method for intelligent audit work order processing is as follows: the abnormal data characteristics of new audit work orders are input into the intelligent processing guidance model, and the processing flow and rectification suggestions are generated based on the abnormal data characteristics.

[0021] Figure 6 The flowchart of the fourth embodiment of the intelligent guidance method for intelligent audit work order processing is as follows: The abnormal data characteristics of new audit work orders are input into the intelligent processing guidance model, and based on the abnormal data characteristics, the processing flow and rectification suggestions are generated.

[0022] Figure 7 This is the main structure diagram of the intelligent guidance system for intelligent audit work order processing.

[0023] Figure 8 This is a structural block diagram of the generation module in the intelligent guidance system for intelligent audit work order processing. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0025] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0026] The intelligent guidance method and system for intelligent inspection work order processing provided by this invention solves the technical problems in the background art.

[0027] like Figure 1 The diagram shown is the main flowchart of an intelligent guidance method for intelligent audit work order processing provided in an embodiment of the present invention. The intelligent guidance method for intelligent audit work order processing includes: Step S100: Obtain historical audit work order data, clean and preprocess the data to remove duplicate and noisy data; Step S200: Analyze the preprocessed historical audit work order data using a logistic regression algorithm to construct an intelligent processing guidance model. ; In the formula, Represents a given feature vector In this case, the probability that a work order belongs to a specific processing category, For the intercept term, Features The corresponding coefficients, It is a natural constant; Step S300: Obtain a new audit work order and collect abnormal data characteristics of the new audit work order; The abnormal data characteristics include: abnormal work order type, abnormal occurrence time, description of abnormal phenomenon, business process involved, amount involved, and scope of impact; Step S400: Input the abnormal data characteristics of the new inspection work order into the intelligent processing guidance model, and generate a processing flow and rectification suggestions based on the abnormal data characteristics; Step S500: Set the time interval threshold Real-time acquisition of new audit work order data and related business data; Step S600: Update the historical audit work order data using the acquired new audit work order data, and optimize the intelligent processing guidance model using the decision tree algorithm based on the updated data; The decision tree algorithm formula is as follows: ; In the formula, Represents the dataset Information entropy The number of categories in the dataset. For the data belonging to the first The proportion of class samples; Step S700: When the intelligent processing guidance model recommends a processing flow that matches the business operation in the preset marketing system, the audit work order information is transmitted to the preset marketing system. In this embodiment, historical audit work order data is first acquired and pre-processed to remove duplicate and noisy data, as raw data often contains impurities that affect subsequent analysis. Pre-processing lays the foundation for building a reliable model. Then, a logistic regression algorithm is used to analyze the pre-processed data and construct the model. The logistic regression algorithm can calculate the probability that a work order belongs to a specific processing category through feature vectors, achieving preliminary classification prediction. After acquiring new work orders, abnormal data features are collected. These abnormal data features include: abnormal work order type, abnormal occurrence time, description of abnormal phenomenon, involved business links, involved amount, and scope of impact. These abnormal data features are the core representation of work order problems and provide a basis for subsequent processing. The new work order features are input into the model to generate processing flow and rectification suggestions, enabling new work orders to receive processing solutions based on historical experience. Simultaneously, a time interval threshold is set. Real-time acquisition of new and business data enables timely capture of business changes. New data is used to update historical data, and the model is optimized using decision tree algorithms. Furthermore, the decision tree can extract rules from the updated data, improving model adaptability. When the model's recommendation process matches a pre-defined marketing system business operation, work order information is transmitted to that system, achieving seamless business process integration. Through data processing, model building and optimization, feature collection and analysis, and system interaction, a closed-loop processing system is formed, ultimately achieving integration with business systems.

[0028] In a preferred embodiment of the present invention, the step of using a logistic regression algorithm to analyze the preprocessed historical audit work order data and constructing an intelligent processing guidance model specifically includes: Cross-validation is used to optimize the parameters of the logistic regression algorithm. The cross-validation formula is as follows: ; In the formula, Indicates the cross-validation error. The number of folds for cross-validation. For the first Errors during verification; In this embodiment, cross-validation is used to optimize the parameters of the logistic regression algorithm during model construction. The purpose is to divide the dataset into training and test sets multiple times to avoid random errors from a single partition. Error is calculated using the cross-validation formula, and parameters are adjusted with the goal of minimizing this error. This effectively prevents overfitting, ensuring the model performs well on different data subsets, improving the accuracy of predicting new work order processing categories, and making the constructed intelligent processing guidance model more stable and reliable.

[0029] like Figure 2 As shown, in a preferred embodiment of the present invention, the step of inputting the abnormal data characteristics of the new inspection work order into the intelligent processing guidance model, and generating a processing flow and rectification suggestions based on the abnormal data characteristics, includes: Step S401: Use a similarity calculation method to match the similarity between historical processing records and new work orders; The formula for calculating similarity is: ; In the formula, For auditing work orders and inspection work orders similarity, and These are inspection work orders. and inspection work orders The One eigenvalue; Step S402: Based on the similarity matching results, sort the recommended results and prioritize displaying the processing flow and rectification suggestions that have a high similarity to work orders with good historical processing results; In this embodiment, after inputting the abnormal data features of new work orders into the model, a similarity calculation method is used to match historical processing records with the new work orders. The similarity calculation borrows from matching techniques in the field of information retrieval, measuring the similarity between work orders through the distance or similarity of feature vectors. The similarity formula is used to calculate the similarity between new and historical work orders, and the recommendation results are ranked accordingly, prioritizing the display of solutions with high similarity to work orders with good historical processing results. This quickly provides feasible processing procedures and rectification suggestions for new work orders, improving processing efficiency and reducing repetitive work.

[0030] like Figure 3 As shown, in a preferred embodiment of the present invention, the step of inputting the abnormal data characteristics of the new inspection work order into the intelligent processing guidance model, and generating a processing flow and rectification suggestions based on the abnormal data characteristics, specifically includes: Step S411: If the abnormal data characteristics show that the abnormal type is data entry error and the amount involved is small, the processing flow is as follows: the front-line business personnel check the original vouchers, correct the erroneous data and submit them for review; the rectification suggestion is: strengthen the training of business personnel, establish a data entry review mechanism, and set data verification rules to automatically intercept obvious errors. Step S412: If the abnormal data characteristics indicate that the abnormal type is data entry error, but the amount involved is large or the scope of impact is wide, the processing flow is as follows: initiate the emergency processing flow, freeze the relevant business operations, organize a special team to trace and verify the source, simultaneously correct the data in the related systems, and submit a written rectification report; the rectification suggestion is: introduce OCR recognition technology to assist in data entry and implement a dual-person, dual-position data entry mechanism. It should be understood that when abnormal data characteristics indicate data entry errors and the amounts involved are small, a processing flow is generated where frontline staff verify vouchers, correct data, and submit for review. Simultaneously, suggestions for improvement include strengthening training, establishing a review mechanism, and setting verification rules. Small errors have a limited impact and are suitable for rapid handling by frontline staff. When large amounts are involved or the impact is wide-ranging, an emergency process is initiated, freezing business operations and organizing source tracing and verification. Because large errors have serious consequences, stricter process control is required, and the improvement suggestion is to introduce OCR technology and a dual-person, dual-role mechanism.

[0031] like Figure 4 As shown, in a preferred embodiment of the present invention, the step of inputting the abnormal data characteristics of the new inspection work order into the intelligent processing guidance model, and generating a processing flow and rectification suggestions based on the abnormal data characteristics, specifically includes: Step S421: If the abnormal data characteristics show that the abnormal type is a system failure and the duration is less than 2 hours, the following processing procedure is generated: notify the system operation and maintenance personnel to immediately investigate the fault, activate the backup system or temporary processing procedure, and record the fault time and scope of impact; the rectification suggestion is: conduct system stress tests regularly, improve the system monitoring and early warning mechanism, and reserve spare parts for critical systems. Step S422: If the abnormal data characteristics indicate that the abnormal type is a system failure, and the duration is greater than or equal to 2 hours or affects core business, the processing flow is as follows: activate the disaster recovery plan, switch to the disaster recovery system, report to the superior competent authority, organize technical experts to jointly tackle the problem, and conduct a comprehensive data verification after the fault is eliminated; the rectification suggestion is: establish a disaster recovery center in the same city but in a different location, formulate a system failure graded response mechanism, and conduct disaster recovery drills regularly. In this embodiment, when a system failure lasts less than 2 hours, the handling procedure is to notify system maintenance personnel to immediately investigate the fault, activate the backup system or temporary handling procedure, and record the fault time and scope of impact. The rectification recommendations include: conducting regular system stress tests, improving the system monitoring and early warning mechanism, and stockpiling critical system spare parts. Considering that the impact of short-term failures is relatively controllable, rapid response measures can be adopted. If the duration is ≥2 hours or affects core business operations, the disaster recovery plan should be activated, switching to the disaster recovery system, reporting to the superior authority, organizing technical experts to jointly tackle the problem, and conducting comprehensive data verification after troubleshooting. The rectification recommendations include establishing a local and off-site disaster recovery center, developing a system failure graded response mechanism, and conducting regular disaster recovery drills. Due to the long duration or significant impact of core business failures, a more comprehensive disaster recovery system is needed, and the fault can be graded according to its severity.

[0032] like Figure 5 As shown, in another preferred embodiment of the present invention, the step of inputting the abnormal data characteristics of the new inspection work order into the intelligent processing guidance model, and generating a processing flow and rectification suggestions based on the abnormal data characteristics, specifically includes: Step S431: If the abnormal data characteristics show that the abnormal type is business violation and the severity is moderate, the processing flow is generated as follows: send a notice of rectification within a time limit, requiring the responsible department to submit a rectification plan, track the rectification progress and accept it; the rectification suggestions are: strengthen business compliance training, optimize the business process approval process, and set up a function to block violations. Step S432: If the abnormal data characteristics show that the abnormal type is business violation and the severity is major, the processing flow is as follows: suspend relevant business permissions, conduct a special audit investigation, transfer the case to the discipline inspection or judicial department based on the investigation results, and circulate the violation case throughout the system; the rectification suggestion is: establish a business operation risk scoring model, implement real-time risk warning, and strengthen the internal supervision and accountability mechanism.

[0033] In this embodiment, if the abnormal data characteristics indicate an anomaly type of business violation, and the severity of the violation is moderate, the generated processing flow is as follows: send a rectification notice with a deadline, requiring the responsible department to submit a rectification plan, track the rectification progress, and conduct acceptance; the rectification suggestions are: strengthen business compliance training, optimize business process approval procedures, and set up a function to block violations. If the severity is significant, the generated processing flow is as follows: suspend relevant business permissions, conduct a special audit investigation, transfer the case to the disciplinary inspection or judicial department based on the investigation results, and circulate the violation case throughout the system; the rectification suggestions are: establish a business operation risk scoring model, implement real-time risk warnings, and strengthen the internal supervision and accountability mechanism. This tiered processing is based on the risk level classification principle of business compliance management, and different processing strategies are formulated according to the severity of the violation.

[0034] like Figure 6 As shown, in another preferred embodiment of the present invention, the step of transmitting the audit work order information to the preset marketing system when the intelligent processing guidance model recommends a processing flow that matches a business operation in the preset marketing system specifically includes: Step S701: Use the API interface to achieve data interaction with the preset marketing system; Step S702: Perform access control and security authentication on API interface calls to ensure the security and accuracy of data transmission; Step S703: Send a work order transmission request to the preset marketing system through the API interface, carrying the converted and verified audit work order information; Step S704: Receive the response information returned by the preset marketing system, determine whether the work order transmission was successful. If the transmission is successful, record the transmission time and operation log information; if it fails, resend the request based on the error message and notify the system administrator to troubleshoot the interface. In this embodiment, an API interface is used to achieve data interaction with the marketing system. API interfaces are a standard technology for system integration, possessing universality and standardization. Access control and security authentication are implemented for interface calls to ensure secure and accurate data transmission, preventing unauthorized access and data tampering. Work order transmission requests are sent via the interface, carrying converted and verified information to ensure correct data format. Simultaneously, the success of transmission is determined by receiving response information; if it fails, it is resent and the administrator is notified for investigation, ensuring reliable data transmission. When the model recommendation process matches the marketing system's business operations, seamless transmission of work order information is achieved through the API interface, realizing cross-system connection of business processes and improving the overall continuity and efficiency of business processing.

[0035] As another preferred embodiment of the present invention, the intelligent guidance method for intelligent audit work order processing further includes: When the processing flow and rectification suggestions recommended by the intelligent processing guidance model do not match the actual business situation, staff will make manual adjustments. The manually adjusted information will be added to the historical audit work order data as new sample data. The intelligent processing guides the model to optimize and learn again based on the new data.

[0036] In this embodiment, when the model's recommended processing flow and rectification suggestions do not match the actual business, staff manually adjust them. Considering the complexity of business scenarios, the model may have limitations, requiring manual intervention for correction. The manually adjusted information is then added to the historical data as new samples. The model learns and optimizes again based on the new data, enabling it to continuously learn from actual business feedback, correct biases, and improve recommendation accuracy. This allows the intelligent processing guidance model to continuously evolve in practical applications, better adapt to business changes, and ensure a continuous improvement in the level of intelligence in work order processing.

[0037] like Figure 7 As shown, in another preferred embodiment of the present invention, the intelligent audit work order processing intelligent guidance system includes: The acquisition and processing module 100 is used to acquire historical audit work order data, clean and preprocess the data, and remove duplicate and noisy data. The intelligent processing guidance model module 200 is used to analyze the pre-processed historical audit work order data using a logistic regression algorithm to construct an intelligent processing guidance model. The data acquisition module 300 is used to acquire new audit work orders and collect abnormal data characteristics of the new audit work orders. The generation module 400 is used to input the abnormal data characteristics of new inspection work orders into the intelligent processing guidance model, and generate processing flow and rectification suggestions based on the abnormal data characteristics. Real-time acquisition module 500 is used to set the time interval threshold. Real-time acquisition of new audit work order data and related business data; The update and optimization module 600 is used to update the historical audit work order data using the acquired new audit work order data, and at the same time, it uses the decision tree algorithm to optimize the intelligent processing guidance model based on the updated data. The matching transmission module 700 transmits the audit work order information to the preset marketing system when the intelligent processing guidance model recommends a processing flow that matches the business operation in the preset marketing system.

[0038] In this embodiment, during application, the processing module 100 acquires historical audit work order data, cleans and preprocesses the data to remove duplicate and noisy data, and the intelligent processing guidance model 200 module uses a logistic regression algorithm to analyze the preprocessed historical audit work order data to construct an intelligent processing guidance model. The acquisition module 300 acquires new audit work orders and collects abnormal data features of the new audit work orders. The generation module 400 inputs the abnormal data features of the new audit work orders into the intelligent processing guidance model and generates processing procedures and rectification suggestions based on the abnormal data features. The real-time acquisition module 500 sets time interval thresholds. The system acquires new audit work order data and related business data in real time. The update and optimization module 600 uses the acquired new audit work order data to update the historical audit work order data. At the same time, based on the updated data, the intelligent processing guidance model is optimized using a decision tree algorithm. When the intelligent processing guidance model recommends a processing flow that matches the business operation in the preset marketing system, the matching and transmission module 700 transmits the audit work order information to the preset marketing system.

[0039] like Figure 8 As shown, in another preferred embodiment of the present invention, the generation module 400 specifically includes: Matching unit 401 is used to perform similarity matching between historical processing records and new work orders using a similarity calculation method; The sorting and display unit 402 is used to sort the recommended results based on the similarity matching results, and prioritizes displaying the processing flow and rectification suggestions that have a high similarity to the work orders with good historical processing results.

[0040] In this embodiment, the matching unit 401 uses a similarity calculation method to match the historical processing records and new work orders. Based on the similarity matching results, the sorting and display unit 402 sorts the recommended results and prioritizes displaying the processing flow and rectification suggestions that have a high similarity to the historical processing results.

[0041] The above embodiments of the present invention provide an intelligent guidance method and system for intelligent audit work order processing. First, historical audit work order data is acquired and pre-processed to remove duplicate and noisy data. Since raw data often contains impurities that affect subsequent analysis, pre-processing lays the foundation for building a reliable model. Then, a logistic regression algorithm is used to analyze the pre-processed data and build the model. The logistic regression algorithm can calculate the probability that a work order belongs to a specific processing category through feature vectors, achieving preliminary classification prediction. After acquiring new work orders, abnormal data features are collected. These abnormal data features include: abnormal work order type, abnormal occurrence time, description of the abnormal phenomenon, involved business processes, involved amount, and scope of impact. These abnormal data features are the core representation of work order problems and provide a basis for subsequent processing. The new work order features are input into the model to generate a processing flow and rectification suggestions, enabling new work orders to receive processing solutions based on historical experience. Simultaneously, a time interval threshold is set. Real-time acquisition of new and business data enables timely capture of business changes. New data is used to update historical data, and the model is optimized using decision tree algorithms. Furthermore, the decision tree can extract rules from the updated data, improving model adaptability. When the model's recommended process matches a pre-defined marketing system business operation, work order information is transmitted to that system, achieving seamless business process integration. Through data processing, model building and optimization, feature collection and analysis, and system interaction, a closed-loop processing system is formed, ultimately achieving integration with the business system. This enables intelligent processing of audit work orders, improving processing efficiency and accuracy, reducing manual intervention, and dynamically optimizing the model to adapt to business changes, enhancing system usability and reliability, and promoting the automation and standardization of business processes.

[0042] In order for the above methods and systems to operate smoothly, the system may include more or fewer components than those described above, or combine certain components, or different components, in addition to the various modules mentioned above. For example, it may include input / output devices, network access devices, buses, processors, and memory.

[0043] The processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (OPGs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the system, connecting various parts via various interfaces and lines.

[0044] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0045] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0046] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent guidance method for processing intelligent inspection work orders, characterized in that, The method includes: Obtain historical audit work order data, clean and preprocess the data to remove duplicate and noisy data; Logistic regression algorithm is used to analyze preprocessed historical audit work order data to construct an intelligent processing guidance model: ； In the formula, Represents a given feature vector In this case, the probability that a work order belongs to a specific processing category, For the intercept term, Features The corresponding coefficients, It is a natural constant; Obtain new audit work orders and collect abnormal data characteristics of the new audit work orders; The abnormal data characteristics include: abnormal work order type, abnormal occurrence time, description of abnormal phenomenon, business process involved, amount involved, and scope of impact; Input the abnormal data characteristics of new inspection work orders into the intelligent processing guidance model, and generate processing procedures and rectification suggestions based on the abnormal data characteristics; Set time interval threshold Real-time acquisition of new audit work order data and related business data; The historical inspection work order data is updated using the newly acquired inspection work order data. Simultaneously, based on the updated data, a decision tree algorithm is used to optimize the intelligent processing guidance model. The formula for the decision tree algorithm is as follows: ； In the formula, Represents the dataset Information entropy The number of categories in the dataset. For the data belonging to the first The proportion of class samples; When the intelligent processing guidance model recommends a processing flow that matches the business operation in the preset marketing system, the audit work order information will be transmitted to the preset marketing system.

2. The intelligent guidance method for intelligent audit work order processing according to claim 1, characterized in that, The process of using logistic regression to analyze preprocessed historical audit work order data and constructing an intelligent processing guidance model specifically includes: Cross-validation is used to optimize the parameters of the logistic regression algorithm. The cross-validation formula is as follows: ； In the formula, Indicates the cross-validation error. The number of folds for cross-validation. For the first Errors during verification.

3. The intelligent guidance method for intelligent audit work order processing according to claim 1, characterized in that, The process of inputting the abnormal data characteristics of new inspection work orders into the intelligent processing guidance model, and generating processing procedures and rectification suggestions based on the abnormal data characteristics, includes: A similarity calculation method is used to match historical processing records and new work orders. The similarity calculation formula is as follows: ； In the formula, For audit work orders and inspection work orders similarity, and These are inspection work orders. and inspection work orders The One eigenvalue; Based on the similarity matching results, the recommended results are sorted, and the processing flow and rectification suggestions with high similarity to work orders with good historical processing results are displayed first.

4. The intelligent guidance method for intelligent audit work order processing according to claim 3, characterized in that, The process of inputting the abnormal data characteristics of new inspection work orders into the intelligent processing guidance model, and generating processing procedures and rectification suggestions based on the abnormal data characteristics, specifically includes: If the abnormal data characteristics indicate that the abnormal type is data entry error and the amount involved is small, the processing flow is as follows: the front-line business personnel should check the original vouchers, correct the erroneous data, and submit them for review; the rectification suggestions are: strengthen the training of business personnel, establish a data entry review mechanism, and set data verification rules to automatically intercept obvious errors; If the abnormal data characteristics indicate that the abnormal type is data entry error, but the amount involved is large or the scope of impact is wide, the processing procedure is as follows: initiate the emergency processing procedure, freeze relevant business operations, organize a special team to trace and verify the source, simultaneously correct the data in the related systems, and submit a written rectification report; the rectification suggestion is: introduce OCR recognition technology to assist in data entry and implement a dual-person, dual-position data entry mechanism.

5. The intelligent guidance method for intelligent audit work order processing according to claim 3, characterized in that, The process of inputting the abnormal data characteristics of new inspection work orders into the intelligent processing guidance model, and generating processing procedures and rectification suggestions based on the abnormal data characteristics, specifically includes: If the abnormal data characteristics indicate that the abnormal type is a system failure and the duration is less than 2 hours, the following processing procedure is generated: notify the system operation and maintenance personnel to immediately investigate the failure, activate the backup system or temporary processing procedure, and record the failure time and scope of impact; the rectification suggestion is: conduct system stress tests regularly, improve the system monitoring and early warning mechanism, and reserve spare parts for critical systems. If the abnormal data characteristics indicate that the abnormal type is a system failure, and the duration is greater than or equal to 2 hours or affects core business, the processing procedure is as follows: activate the disaster recovery plan, switch to the disaster recovery system, report to the superior competent authority, organize technical experts to jointly tackle the problem, and conduct a comprehensive data verification after the failure is resolved; the rectification suggestions are: establish a disaster recovery center in the same city but in a different location, formulate a system failure graded response mechanism, and conduct disaster recovery drills regularly.

6. The intelligent guidance method for intelligent audit work order processing according to claim 3, characterized in that, The process of inputting the abnormal data characteristics of new inspection work orders into the intelligent processing guidance model, and generating processing procedures and rectification suggestions based on the abnormal data characteristics, specifically includes: If the abnormal data characteristics indicate that the abnormality type is business violation and the severity is moderate, the processing flow is as follows: send a notice of rectification within a specified period, requiring the responsible department to submit a rectification plan, track the rectification progress and conduct acceptance; the rectification suggestions are: strengthen business compliance training, optimize business process approval links, and set up a function to block violations. If the abnormal data characteristics indicate that the anomaly type is a business violation and the severity is major, the following processing procedure will be generated: suspend relevant business permissions, conduct a special audit investigation, transfer the case to the discipline inspection or judicial department based on the investigation results, and circulate the violation case throughout the system; the rectification suggestion is: establish a business operation risk scoring model, implement real-time risk warning, and strengthen the internal supervision and accountability mechanism.

7. The intelligent guidance method for intelligent audit work order processing according to claim 1, characterized in that, When the intelligent processing guidance model recommends a processing flow that matches a business operation in the preset marketing system, transmitting the audit work order information to the preset marketing system specifically includes: Data interaction is achieved through API interfaces with pre-set marketing systems; Access control and security authentication are implemented for API calls to ensure the security and accuracy of data transmission. Send a work order transmission request to the preset marketing system through the API interface, and carry the converted and verified audit work order information; Receive response information from the preset marketing system, determine whether the work order transmission was successful. If the transmission is successful, record the transmission time and operation log information; if it fails, resend the request based on the error message and notify the system administrator to troubleshoot the interface.

8. The intelligent guidance method for intelligent audit work order processing according to claim 1, characterized in that, The intelligent guidance method for processing intelligent audit work orders also includes: When the processing flow and rectification suggestions recommended by the intelligent processing guidance model do not match the actual business situation, staff will make manual adjustments. The manually adjusted information will be added to the historical audit work order data as new sample data. The intelligent processing guides the model to optimize and learn again based on the new data.

9. An intelligent guidance system for processing intelligent inspection work orders, characterized in that: The system, employing the intelligent guidance method for intelligent audit work order processing as described in any one of claims 1-8, comprises: The acquisition and processing module is used to acquire historical audit work order data, clean and preprocess the data, and remove duplicate and noisy data. The intelligent processing guidance model module is used to analyze the pre-processed historical audit work order data using logistic regression algorithms to build an intelligent processing guidance model. The data acquisition module is used to acquire new audit work orders and collect abnormal data characteristics of new audit work orders; The generation module is used to input the abnormal data characteristics of new inspection work orders into the intelligent processing guidance model, and generate processing flow and rectification suggestions based on the abnormal data characteristics; The real-time acquisition module is used to set the time interval threshold. Real-time acquisition of new audit work order data and related business data; The update and optimization module is used to update the historical audit work order data using the acquired new audit work order data, and at the same time, it uses the decision tree algorithm to optimize the intelligent processing guidance model based on the updated data. The matching and transmission module transmits the audit work order information to the preset marketing system when the intelligent processing guidance model recommends a processing flow that matches the business operation in the preset marketing system.

10. The intelligent guidance system for intelligent audit work order processing according to claim 9, characterized in that, The generation module specifically includes: The matching unit is used to perform similarity matching between historical processing records and new work orders using similarity calculation methods; The sorting and display unit is used to sort the recommended results based on similarity matching results, and prioritizes displaying the processing flow and rectification suggestions that are highly similar to work orders with good historical processing results.

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