Business assessment and bill management method and device, equipment and storage medium

Abstract

The invention relates to the field of computer data processing, in particular to a business assessment and bill management method and device, equipment and a storage medium. The business assessment and bill management method comprises the following steps: acquiring an assessment business, and extracting associated business data according to the assessment business; obtaining each assessment rule of the enterprise, extracting key features of the assessment rules, and summarizing and sorting all the assessment rules and the key features thereof to form an assessment rule library; calculating a performance amount corresponding to the assessment service according to the assessment service and the service data thereof by adopting a dynamic priority algorithm in combination with the assessment rule base; and forming a bill according to the performance amount, and detecting the bill before issuing according to the system record. According to the invention, the business data is automatically matched with the assessment rule, and the assessment amount is automatically calculated according to the assessment rule, so that the calculation efficiency is improved, and the problem of calculation error caused by mutual conflict of multiple assessment rules is effectively solved.

Description

Technical Field

[0001] This invention relates to the field of computer data processing, and in particular to a method, apparatus, device, and storage medium for business performance evaluation and billing management. Background Technology

[0002] In the operation and management of large enterprises, conducting business quality assessments of branch offices (such as outlets) is a routine but complex core task. Current mainstream technical solutions generally adopt a fixed assessment rule system, which often exposes numerous problems when faced with multiple internal assessment rules in actual operational scenarios. For example, departments at different levels may introduce or modify assessment rules from time to time, and these rules may conflict. Manual calculation of assessment amounts makes it difficult to detect these conflicts or temporary adjustments, leading to significant errors in the calculated amounts. Furthermore, manual calculation is prone to errors when dealing with more complex assessment rules. For enterprises with a large number of assessment tasks, calculating assessment amounts requires a significant investment of manpower and is inefficient. Summary of the Invention

[0003] In view of the shortcomings of the prior art, the purpose of this invention is to provide a business assessment and billing management method, apparatus, equipment and storage medium, which aims to solve the technical problem that the calculation of assessment amounts for large enterprises is prone to errors due to too many assessment rules in the prior art.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of this invention provides a business performance evaluation and billing management method, comprising the following steps: acquiring evaluation business, extracting associated business data based on the evaluation business; acquiring various evaluation rules of the enterprise, extracting key features of the evaluation rules, summarizing and organizing all evaluation rules and their key features to form an evaluation rule library; using a dynamic priority algorithm, combined with the evaluation rule library, calculating the performance amount corresponding to the evaluation business based on the evaluation business and its business data; generating a bill based on the performance amount, and checking the bill before issuance based on system records.

[0005] Optionally, in a first implementation of the first aspect of the present invention, the step of obtaining assessment business and extracting associated business data based on the assessment business specifically includes: obtaining assessment business, extracting assessment information from the assessment business, the assessment information including: business unit information, assessment personnel information, business type, and assessment scope; extracting associated business data from the enterprise system based on the assessment information, organizing the associated business data to form an assessment dataset; using an association algorithm to calculate the correlation between the business data in the assessment dataset and the assessment business, and outputting a prompt if the calculated correlation is lower than a threshold.

[0006] Optionally, in the second implementation of the first aspect of the present invention, the step of acquiring various assessment rules of the enterprise, extracting key features of the assessment rules, and summarizing and organizing all assessment rules and their key features to form an assessment rule library specifically includes: acquiring various assessment rules of the enterprise; using a word segmentation algorithm and combining it with an industry word segmentation dictionary to extract multiple key features of the assessment rules; correcting the key features to obtain corrected features, and associating the corrected features with the key features; using a pre-trained feature classification model to classify and label each corrected feature of the assessment rules, and adding the assessment rules and their labeled corrected features to the assessment rule library.

[0007] Optionally, in the third implementation of the first aspect of the present invention, the step of using a pre-trained feature classification model to classify and label each modified feature of the assessment rule, and adding the assessment rule and its labeled modified features to the assessment rule library, specifically includes: constructing feature classification rules, which include business type description, data type description, region description, business unit description, and job level description; obtaining all historical assessment rules, preprocessing and labeling the historical assessment rules according to the feature classification rules to form a training dataset, training the XGBoost-based model using the training dataset to obtain the feature classification model; and using the pre-trained feature classification model combined with a thesaurus to classify and label each modified feature of the assessment rule, and adding the assessment rule and its labeled modified features to the assessment rule library.

[0008] Optionally, in the fourth implementation of the first aspect of the present invention, the step of employing a dynamic priority algorithm, combined with an assessment rule base, to calculate the performance amount corresponding to the assessment business based on the assessment business and its business data, specifically includes: employing a dynamic priority algorithm to extract business features from the assessment business and its business data, and finding relevant rules in the assessment rule base based on the business features; sorting the relevant rules according to a preset priority, and selecting the assessment rule with the highest priority as the selected rule; and employing a semantic module to calculate the performance amount based on the selected rule, the assessment business, and its business data.

[0009] Optionally, in the fifth implementation of the first aspect of the present invention, the step of using a semantic module to calculate the performance amount based on selected rules, assessment business, and business data specifically includes: using the semantic module to call a first semantic understanding and calculation model to calculate the performance amount based on the selected rules, assessment business, and business data, and obtain a first calculated value; calling a second semantic understanding and calculation model to calculate the performance amount based on the selected rules, assessment business, and business data, and obtain a second calculated value; comparing the first calculated value with the second calculated value; if the first calculated value and the second calculated value are the same, the first calculated value is used as the performance amount; if the first calculated value and the second calculated value are different, a warning notification is issued.

[0010] Optionally, in the sixth implementation of the first aspect of the present invention, the step of generating a bill based on the performance amount and checking the bill before issuance based on system records specifically includes: constructing a filling template, filling the performance amount into the filling template, and extracting basic information from the assessment business and filling it into the filling template to generate a bill; associating the bill with the assessment rules on which the performance amount is calculated, so as to quickly verify it when a review request is received; checking the type of the bill in the system records, and if the bill type registered in the system records is "deferred assessment", intercepting the issuance of the bill.

[0011] A second aspect of the present invention provides a business performance evaluation and billing management device, comprising: an extraction module for acquiring performance evaluation business and extracting associated business data based on the performance evaluation business; a rule module for acquiring various performance evaluation rules of the enterprise, extracting key features of the performance evaluation rules, and summarizing and organizing all performance evaluation rules and their key features to form a performance evaluation rule library; a calculation module for using a dynamic priority algorithm, combined with the performance evaluation rule library, to calculate the performance amount corresponding to the performance evaluation business based on the performance evaluation business and its business data; and a generation module for generating a bill based on the performance amount and checking the bill before issuance based on system records.

[0012] Optionally, in a first implementation of the second aspect of the present invention, the extraction module includes: a first extraction unit, used to acquire assessment business, and extract assessment information from the assessment business, the assessment information including: business unit information, assessment personnel information, business type, and assessment scope; a second extraction unit, used to extract related business data from the enterprise system based on the assessment information, organize the related business data, and form an assessment dataset; and a correlation calculation unit, used to calculate the correlation degree between the business data in the assessment dataset and the assessment business using a correlation algorithm, and output a prompt if the calculated correlation degree is lower than a threshold.

[0013] Optionally, in a second implementation of the second aspect of the present invention, the rule module includes: a third extraction unit, used to acquire various assessment rules of the enterprise, and extract multiple key features of the assessment rules by using a word segmentation algorithm and combining an industry word segmentation dictionary; an error correction unit, used to correct the key features, acquire corrected features, and associate the corrected features with the key features; and a classification unit, used to classify and label each corrected feature of the assessment rule using a pre-trained feature classification model, and add the assessment rule and its labeled corrected features to the assessment rule library.

[0014] Optionally, in a third implementation of the second aspect of the present invention, the classification unit includes: a construction subunit for constructing feature classification rules, the feature classification rules including business type description, data type description, region description, business unit description, and job level description; a training subunit for acquiring all historical assessment rules, preprocessing and labeling the historical assessment rules according to the feature classification rules to form a training dataset, and training the XGBoost-based model using the training dataset to obtain a feature classification model; and a classification subunit for using the pre-trained feature classification model combined with a thesaurus to classify and label each modified feature of the assessment rule, and adding the assessment rule and its labeled modified feature to the assessment rule library.

[0015] Optionally, in a fourth implementation of the second aspect of the present invention, the calculation module includes: a matching submodule, used to extract business features from the assessment business and its business data using a dynamic priority algorithm, and to find relevant rules in the assessment rule base based on the business features; a sorting submodule, used to sort the relevant rules according to a preset priority, and to select the assessment rule with the highest priority; and a calculation submodule, used to calculate the performance amount based on the selected rule, the assessment business and its business data using a semantic module.

[0016] Optionally, in a fifth implementation of the second aspect of the present invention, the calculation submodule includes: a first calculation unit, configured to use a semantic module to call a first semantic understanding and calculation model, and calculate a performance amount based on selected rules for the assessment business and its business data, thereby obtaining a first calculated value; a second calculation unit, configured to call a second semantic understanding and calculation model, and calculate a performance amount based on selected rules for the assessment business and its business data, thereby obtaining a second calculated value; a comparison unit, configured to compare the first calculated value with the second calculated value, and if the first calculated value and the second calculated value are the same, the first calculated value shall be used as the performance amount; and an alert unit, configured to issue an alert notification if the first calculated value and the second calculated value are different.

[0017] Optionally, in the sixth implementation of the second aspect of the present invention, the generation module includes: a filling unit, used to construct a filling template, fill in the performance amount into the filling template, and extract basic information from the assessment business and fill it into the filling template to form an invoice; a binding unit, used to associate the invoice with the assessment rules on which the performance amount is calculated, so as to quickly verify it when a review request is received; and an interception unit, used to detect the type of invoice in the system records, and if the invoice type registered in the system records is "deferred assessment", to intercept the issuance of the invoice.

[0018] A third aspect of the present invention provides a business performance evaluation and billing management device, including a memory and at least one processor, wherein the memory stores computer-readable instructions; the at least one processor invokes the computer-readable instructions in the memory to execute the various steps of the business performance evaluation and billing management method described above.

[0019] A fourth aspect of the present invention provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the various steps of the business assessment and billing management method described above.

[0020] Beneficial Effects: This invention provides a business performance evaluation and billing management method. The method first acquires evaluation business data, and the system automatically extracts related business data based on the evaluation business, eliminating the need for manual data retrieval and improving efficiency. Then, it acquires various enterprise evaluation rules, extracts key features of these rules, and summarizes and organizes all evaluation rules and their key features to form an evaluation rule library. This provides complete data support for subsequent evaluation rule matching and forms a format easy for algorithms to recognize. Next, by employing a dynamic priority algorithm combined with the evaluation rule library, the system calculates the performance amount corresponding to the evaluation business based on its data. It automatically selects the corresponding rule from multiple complex evaluation rules and calculates the performance amount, greatly reducing manual intervention and improving processing efficiency and accuracy. Finally, by generating bills based on performance amounts and checking the pre-issuance bills against system records, the method achieves full automation from rule configuration to bill adjustment, avoiding erroneous evaluations and fines. Attached Figure Description

[0021] Figure 1 This is a first flowchart of a business performance evaluation and billing management method provided in an embodiment of the present invention; Figure 2 This is a second flowchart of the business assessment and billing management method provided in an embodiment of the present invention; Figure 3 This is a third flowchart of the business assessment and billing management method provided in the embodiments of the present invention; Figure 4 This is a fourth flowchart of the business assessment and billing management method provided in the embodiments of the present invention; Figure 5 This is a fifth flowchart of the business assessment and billing management method provided in an embodiment of the present invention; Figure 6 The sixth flowchart of the business assessment and billing management method provided in the embodiments of the present invention; Figure 7 The seventh flowchart of the business assessment and billing management method provided in the embodiments of the present invention; Figure 8 A schematic diagram of a business assessment and billing management device provided in an embodiment of the present invention; Figure 9 This is another structural schematic diagram of the business assessment and billing management device provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of the business assessment and billing management device provided in an embodiment of the present invention. Detailed Implementation

[0022] This invention provides a method, apparatus, device, and storage medium for business performance evaluation and billing management. First, the system acquires evaluation business data, automatically extracting related business data based on the evaluation business, eliminating the need for manual data retrieval and improving efficiency. Then, it acquires the various evaluation rules applicable to the enterprise, accurately extracting the key features of each rule, and then uniformly summarizing and standardizing all evaluation rules and their corresponding key features to construct an evaluation rule library. This rule library provides comprehensive and complete data support for subsequent intelligent matching of evaluation rules and is also transformed into a standardized format easy for algorithms to recognize and call. Next, by employing a dynamic priority algorithm combined with the evaluation rule library, the system calculates the performance amount corresponding to the evaluation business based on its business data. By introducing a dynamic priority algorithm based on organizational hierarchy, the problem of multiple rule conflicts is perfectly solved, ensuring the uniqueness and rationality of the evaluation results. Finally, by generating bills based on performance amounts and checking the bills before issuance according to system records, the system achieves fully automated control from evaluation rule configuration to bill generation and verification, effectively avoiding abnormal situations such as incorrect evaluations and fines.

[0023] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 The first embodiment of the business assessment and billing management method in this invention includes: S101. Obtain assessment business data and extract related business data based on the assessment business data; S102. Obtain all assessment rules of the enterprise, extract the key features of the assessment rules, and summarize and organize all assessment rules and their key features to form an assessment rule library; S103. Employ a dynamic priority algorithm, combined with an assessment rule base, to calculate the performance amount corresponding to the assessment business based on the assessment business and its business data. S104. Generate a bill based on the performance amount, and check the bill before it is issued based on the system records.

[0024] Traditional performance evaluation adjustments rely heavily on manual operations, requiring managers to review and manually modify each evaluation item. This method is inefficient, error-prone, and difficult to implement with large volumes of data. Furthermore, when situations arise requiring performance evaluation reductions or adjustments (such as system failures or special policies), problems commonly exist, including rigid processing methods, conflicting rules and unclear priorities, inconvenient configuration and management, and poor data consistency.

[0025] In this embodiment, after receiving the assessment request, the system first automatically associates it with specific business data (such as call ID, branch information, etc.) and extracts the business related to the assessment request, providing a reliable data source for subsequent rule matching and performance calculation. For example, after receiving the performance assessment request for all delivery personnel at branch A in November 2025, the system automatically retrieves data such as delivery timeliness, order quality, and special order data for each courier from the branch management system, with the data period being November 1st to November 30th.

[0026] Before calculating performance-based compensation, the system collects assessment rules from all levels and business lines of the express delivery and logistics company, accurately extracts core features, and standardizes and summarizes them to build a unified and reusable assessment rule library. This provides a standardized basis for subsequent data matching and indicator calculation, ensuring the standardization of the assessment process and the comparability of the assessment results. For example, logistics companies have formulated different types of assessment rules at the provincial, municipal, and regional levels, and even at the branch level. These assessment rules include those related to couriers, as well as those for attendance, overall branch performance, and single-item delivery commissions. In terms of time span, these assessment rules include monthly, quarterly, and special period assessment rules.

[0027] Then, through a dynamic priority algorithm, the system intelligently matches the standards in the assessment rule base with the actual data of the assessment business, achieving accurate and efficient calculation of performance-based compensation. This breaks through the limitations of traditional fixed-weight calculations, dynamically adjusting indicator weights based on business importance, urgency, and business fluctuations within the assessment period, ensuring that performance-based compensation truly reflects the contribution value of the assessed individual. Specifically, the dynamic priority algorithm searches through all effective rules. When a piece of data matches multiple rules, the system selects the single rule with the highest priority according to a preset priority order as the standard for calculating the performance-based compensation.

[0028] Finally, the system automatically generates standardized performance bills based on the calculated performance amounts and uses a detection mechanism to check for anomalies in the bills, ensuring that the issued bills meet the requirements. For example, the system provides a bill issuance function that performs business logic verification before issuance. If a record has "Adjustment Type" set to "Postponed Assessment" selected, the system will intercept and issue a warning to prevent accidental issuance.

[0029] Please see Figure 2 The second embodiment of the business performance evaluation and billing management method in this invention includes: S201. Obtain assessment business, and extract assessment information from the assessment business, wherein the assessment information includes: business unit information, assessment personnel information, business type and assessment scope; S202. Extract relevant business data from the enterprise system based on the assessment information, organize the relevant business data, and form an assessment dataset; S203. Use an association algorithm to calculate the correlation between the business data in the assessment dataset and the assessment business. If the calculated correlation is lower than the threshold, output a prompt.

[0030] In this embodiment, for acquiring assessment business data, the associated business data is extracted based on the assessment business. Specifically, this includes extracting assessment information based on the content of the assessment business. The assessment business will detail the assessment content, time frame, and assessment targets. For example, the "Performance Assessment of Delivery Volume of All Delivery Personnel at Branch A in November 2025" records that the assessment targets are the delivery personnel at Branch A, the time is November 2025, and the content is the delivery volume. The system can then retrieve the corresponding business data from the system based on this information, without the need for manual searching and retrieval.

[0031] After acquiring the relevant business data, it is also necessary to organize the collected raw business data, such as standardizing the data content, converting the format, removing abnormal content, etc., and finally forming a structured assessment dataset.

[0032] After acquiring business data, to ensure the accuracy of the data content being retrieved, this embodiment further employs an association algorithm to calculate the correlation between the business data and the performance evaluation business. The association algorithm can use a matching algorithm to detect the matching of the content description of the performance evaluation business with the data type names in the performance evaluation dataset. For example, if the data names in the performance evaluation dataset include data names such as November, 2025, A-site, and delivery volume, it includes all keywords in the business "Performance Evaluation of Delivery Volume of All Delivery Personnel at A-site in November 2025". If the coverage rate (correlation) reaches the set threshold, the correlation is considered high. If the calculated correlation is lower than the threshold, the system automatically outputs a prompt message to remind the management personnel that the data retrieved may be incorrect and stops the subsequent calculation.

[0033] Please see Figure 3 The third embodiment of the business performance evaluation and billing management method in this invention includes: S301. Obtain the various assessment rules of the enterprise, use word segmentation algorithm, and combine with industry word segmentation dictionary to extract multiple key features of the assessment rules; S302. Correct the key features, obtain the corrected features, and associate the corrected features with the key features; S303. Using a pre-trained feature classification model, classify and label each modified feature of the assessment rule, and add the assessment rule and its labeled modified features to the assessment rule library.

[0034] This embodiment focuses on the standardized construction of the assessment rule base. Through key feature extraction, error correction optimization, and intelligent classification and labeling, it realizes the structured storage and efficient reuse of assessment rules, providing accurate and standardized rule support for the intelligent adjustment of subsequent business assessments and billing management.

[0035] Specifically, this embodiment first obtains the full text of assessment rules by calling or importing from the enterprise's internal system interface. Then, it uses a word segmentation algorithm and combines it with an industry word segmentation dictionary to extract multiple key features of the assessment rules. Subsequently, the assessment rules can be classified based on the key features in the assessment rules (such as keywords such as time, assessment object, and assessment type).

[0036] The assessment rules are manually formulated, and their terminology may be non-standard, or some commonly used words may change due to market variations, potentially leading to errors in subsequent rule classification. By correcting the terminology used for each key feature, the system can correct new or incorrect terms to commonly used words, ensuring accurate identification by the subsequent feature classification model. Specifically, semantic algorithms can be used to correct questionable key features (keywords). After correction, the original word segmentation is retained for easy follow-up.

[0037] After obtaining multiple keywords, the system uses a trained classification model to classify and label each modified feature. For example, keywords such as "refrigerated" and "low temperature" are classified into "cold chain" and labeled before being added to the assessment rule base. When it is necessary to find the assessment criteria for express shipments involving "cold chain", the assessment rules can be quickly filtered based on the "cold chain" label.

[0038] Please see Figure 4 The fourth embodiment of the business assessment and billing management method in this invention includes: S401. Construct feature classification rules, wherein the feature classification rules include descriptions of business type, data type, region, business unit, and job level; S402. Obtain all historical assessment rules, preprocess and label the historical assessment rules according to the feature classification rules to form a training dataset, and use the training dataset to train the XGBoost-based model to obtain the feature classification model; S403. Using a pre-trained feature classification model combined with a thesaurus, each modified feature of the assessment rule is classified and labeled, and the assessment rule and its labeled modified features are added to the assessment rule library.

[0039] In this embodiment, a training dataset needs to be constructed for model training before obtaining the feature classification model.

[0040] This involves constructing feature classification rules, which include descriptions of the assessment rule type, data type, region, business unit, and job level. Then, historical assessment rules are organized based on these feature classification rules, such as filling in descriptions of the historical assessment rule type and data type, and labeling the historical assessment tasks with the corresponding rules, thus forming a dataset suitable for model training.

[0041] After obtaining the training dataset, this embodiment trains the model based on XGBoost, aiming to teach the model to classify the features of the assessment rules according to preset feature classification rules. The classification of assessment rules often relies on the "interaction of multi-dimensional features" (such as "business type + region + job level" jointly determining the classification), and this association is non-linear. XGBoost excels at capturing "non-linear associations of multi-feature interactions." Specifically, during training, the dataset is split into training, validation, and test sets after labeling to avoid model overfitting.

[0042] Since the terms involved in the assessment rules may involve multiple synonyms, in order to improve the accuracy of the assessment rules, when the feature classification model makes predictions, the modified features (keywords) of the assessment rules will be converted into synonyms through a preset synonym list. After being converted into synonyms, the predictions will be made to improve the accuracy of model classification and labeling.

[0043] Please see Figure 5 The fifth embodiment of the business assessment and billing management method in this invention includes: S501. Employ a dynamic priority algorithm to extract business characteristics from the assessment business and its data, and find relevant rules in the assessment rule base based on the business characteristics. S502. Sort the relevant rules according to preset priority, and select the assessment rule with the highest priority; S503. Using a semantic module, performance amounts are calculated based on selected rules, assessing business operations and their data.

[0044] In this embodiment, a dynamic priority algorithm is used for matching assessment rules. Specifically, this includes extracting business characteristics from the assessment business and its data. These business characteristics include data names (e.g., data time characteristics, such as 2025 and November); assessment task content characteristics (e.g., delivery volume); assessment target characteristics (e.g., branch A); and the assessment target's jurisdiction (e.g., southern business province). Based on these characteristics, the dynamic priority algorithm first matches the rules with features in the assessment rule base. The assessment rule base includes assessment rule text and multiple key features. The business features are matched against multiple key features of the assessment rules, and then sorted and output according to the degree of matching. If multiple assessment rules have the same degree of matching, they are further sorted according to other priorities, such as regional rules > provincial business rules > branch-level rules > specified ID-level rules.

[0045] Specifically, the assessment rule base also records the applicable time range of each assessment rule. When business data includes data from different time periods, such as early November and late November corresponding to different assessment rules, operators can match the data within different ranges to their respective assessment standards based on the matched rules. Through these steps, the corresponding assessment rules can be accurately selected, while avoiding situations such as expired or conflicting rules. Once the operator confirms the selected rule, the system calculates the performance bonus through a semantic module. The semantic module uses natural language understanding technology to break down the core logic of the selected rule and automatically calculates the performance bonus based on the business data values.

[0046] Please see Figure 6 The sixth embodiment of the business assessment and billing management method in this invention includes: S601. Using the semantic module, the first semantic understanding and calculation model is invoked, and the performance amount is calculated based on the selected rules, the business and its business data, to obtain the first calculated value; S602. Call the second semantic understanding and calculation model, and calculate the performance amount based on the selected rules, assessing the business and its business data to obtain the second calculated value; S603. Compare the first calculated value with the second calculated value. If the first calculated value is the same as the second calculated value, the first calculated value shall be used as the performance amount. S604. If the first calculated value is different from the second calculated value, issue a warning notification.

[0047] This embodiment employs a dual verification mechanism in the "performance amount calculation" stage. The semantic module invokes a semantic understanding and computation model to understand the selected rules and calculates based on business data. The semantic understanding and computation model can utilize currently known large language models, such as GPT or the open-source DeepSeek large language model.

[0048] Because different semantic understanding and computational models have varying comprehension capabilities, there may be discrepancies in understanding relatively complex assessment rules, potentially leading to errors in the calculated amounts. Therefore, this embodiment employs different large language models for both the first and second semantic understanding and computational models, such as GPT and Deepseek, for simultaneous calculations. Only when the amounts calculated by both models match is the calculated performance amount used. If the two calculated values ​​are inconsistent, an alert is issued, manual intervention is initiated to verify the results, the accurate model is selected for execution, and the incorrect model is trained and optimized.

[0049] Please see Figure 7 The seventh embodiment of the business assessment and billing management method in this invention includes: S701. Construct a form, fill in the performance amount into the form, and extract the basic information from the assessment business and fill it into the form to form a bill; S702. Link the invoices to the assessment rules on which performance amounts are calculated, so as to quickly verify them when a review request is received; S703. Check the bill type in the system records. If the bill type registered in the system records is "deferred assessment", intercept the issuance of the bill.

[0050] This implementation focuses on the practical application and risk management of performance accounting results. It ensures the standardization, traceability, and business compliance of invoices by generating standardized templates, establishing a traceable link between invoices and assessment rules, and setting up special invoice interception mechanisms. Specifically, this implementation first standardizes the invoice format, integrating performance amounts and basic business information to form standardized invoices that can be directly used for settlement. Then, it establishes a traceability relationship between invoices and assessment rules to ensure quick identification of the accounting basis during review, improving audit efficiency. Finally, it controls the issuance of special invoice types to prevent irregular settlements and adapt to the business needs of enterprises that have temporarily suspended assessments. Through standardized generation, traceable linking, and type screening, the performance accounting results are transformed from "data" to "usable invoices," and business compliance is ensured through a risk management mechanism. This not only improves the efficiency of invoice generation and review but also adapts to the special scenario needs of enterprise performance management.

[0051] The above describes the business assessment and billing management method in the embodiments of the present invention. The following describes the business assessment and billing management device in the embodiments of the present invention. Please refer to [link / reference]. Figure 8 One embodiment of the business assessment and billing management device in this invention includes: Extraction module 10 is used to acquire assessment business data and extract related business data based on the assessment business data. Rule module 20 is used to acquire various assessment rules of the enterprise, extract the key features of the assessment rules, and summarize and organize all assessment rules and their key features to form an assessment rule library; Calculation module 30 is used to calculate the performance amount corresponding to the assessment business based on the assessment business and its business data by using a dynamic priority algorithm combined with the assessment rule library. The generation module 40 is used to generate invoices based on performance amounts and to check the invoices before issuance based on system records.

[0052] Please see Figure 9 One embodiment of the business assessment and billing management device in this invention includes: Extraction module 10 is used to acquire assessment business data and extract related business data based on the assessment business data. Rule module 20 is used to acquire various assessment rules of the enterprise, extract the key features of the assessment rules, and summarize and organize all assessment rules and their key features to form an assessment rule library; Calculation module 30 is used to calculate the performance amount corresponding to the assessment business based on the assessment business and its business data by using a dynamic priority algorithm combined with the assessment rule library. The generation module 40 is used to generate invoices based on performance amounts and to check the invoices before issuance based on system records. In this embodiment, the extraction module 10 includes: The first extraction unit 11 is used to acquire assessment business and extract assessment information from the assessment business. The assessment information includes: business unit information, assessment personnel information, business type and assessment scope. The second extraction unit 12 is used to extract related business data from the enterprise system based on the assessment information, organize the related business data, and form an assessment dataset. The correlation calculation unit 13 is used to calculate the correlation between the business data in the assessment dataset and the assessment business using a correlation algorithm. If the calculated correlation is lower than the threshold, a prompt will be output. The rule module 20 includes: The third extraction unit 21 is used to obtain various assessment rules of the enterprise. It uses a word segmentation algorithm and combines an industry word segmentation dictionary to extract multiple key features of the assessment rules. The error correction unit 22 is used to correct key features, obtain corrected features, and associate the corrected features with key features; Classification unit 23 is used to classify and label each modified feature of the assessment rule using a pre-trained feature classification model, and add the assessment rule and its labeled modified feature to the assessment rule library. In this embodiment, the classification unit 23 includes: Subunit 231 is constructed to construct feature classification rules, which include business type description, data type description, region description, business unit description, and job level description. Training subunit 232 is used to acquire all historical assessment rules, preprocess and label the historical assessment rules according to the feature classification rules to form a training dataset, and use the training dataset to train the XGBoost-based model to obtain the feature classification model. Classification subunit 233 is used to classify and label each modified feature of the assessment rule using a pre-trained feature classification model combined with a thesaurus, and to add the assessment rule and its labeled modified features to the assessment rule library. In this embodiment, the computing module 30 includes: The matching submodule 31 is used to extract business features from the assessment business and its business data using a dynamic priority algorithm, and to find relevant rules in the assessment rule base based on the business features. The sorting submodule 32 is used to sort the relevant rules according to a preset priority, and select the assessment rule with the highest priority as the selected rule; The calculation submodule 33 is used to calculate the performance amount based on the selected rules, the assessment business and its business data using the semantic module. In this embodiment, the computing submodule 33 includes: The first calculation unit 331 is used to employ a semantic module, call a first semantic understanding and calculation model, and calculate the performance amount based on the selected rules, assessing the business and its business data, to obtain a first calculated value. The second calculation unit 332 is used to call the second semantic understanding and calculation model, and calculate the performance amount based on the selected rules, the assessment business and its business data, and obtain the second calculated value. Comparison unit 333 is used to compare the first calculated value with the second calculated value. If the first calculated value and the second calculated value are the same, the first calculated value is used as the performance amount. The warning unit 334 is used to issue a warning notification if the first calculated value is different from the second calculated value; In this embodiment, the generation module 40 includes: The filling unit 41 is used to construct a filling template, fill in the performance amount into the filling template, and extract basic information from the assessment business and fill it into the filling template to form a bill; Binding unit 42 is used to associate the bill with the assessment rules on which the performance amount is calculated, so as to quickly verify it when a review request is received; Interception unit 43 is used to detect the type of bill in the system records. If the bill type registered in the system records is "deferred assessment", the bill issuance is intercepted.

[0053] The business performance evaluation and billing management device of this invention first acquires the performance evaluation business, and the system automatically extracts the associated business data based on the performance evaluation business, eliminating the need for manual searching of related data one by one, effectively improving processing efficiency. Then, it acquires various performance evaluation rules within the enterprise, accurately extracts the key features of each rule, and then uniformly summarizes and standardizes all performance evaluation rules and their corresponding key features to construct a performance evaluation rule library. This provides comprehensive and complete data support for the intelligent matching of subsequent performance evaluation rules, and the content of the performance evaluation rule library is in a standardized format that facilitates algorithm recognition and invocation. Next, a dynamic priority algorithm based on organizational hierarchy is used, combined with the aforementioned performance evaluation rule library, to calculate the performance amount corresponding to the performance evaluation business based on the performance evaluation business and its business data. This resolves the problem of multiple rule conflicts and ensures the uniqueness and rationality of the evaluation results. Finally, a bill is generated based on the performance amount, and the bill before issuance is checked in conjunction with system records. Ultimately, this achieves fully automated control of the entire process from performance evaluation rule configuration to bill generation and verification, effectively avoiding abnormal situations such as erroneous evaluations and fines.

[0054] The above describes the business assessment and billing management device in the embodiments of the present invention from the perspective of modular functional entities. The following describes the business assessment and billing management equipment in the embodiments of the present invention from the perspective of hardware processing.

[0055] Figure 10 This is a schematic diagram of a business assessment and billing management device 900 provided in an embodiment of the present invention. The business assessment and billing management device 900 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 910 (e.g., one or more processors) and a memory 920, and one or more storage media 930 (e.g., one or more mass storage devices) for storing application programs 933 or data 932. The memory 920 and storage media 930 can be temporary or persistent storage. The program stored in the storage media 930 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the business assessment and billing management device 900. Furthermore, the processor 910 may be configured to communicate with the storage media 930 and execute the series of instruction operations in the storage media 930 on the business assessment and billing management device 900 to implement the steps of the business assessment and billing management methods provided in the above-described method embodiments.

[0056] The performance evaluation and billing management device 900 may also include one or more power supplies 940, one or more wired or wireless network interfaces 950, one or more input / output interfaces 960, and / or one or more operating systems 931, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 10 The structure of the business assessment and billing management equipment shown does not constitute a limitation on the business assessment and billing management equipment. It may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0057] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the business assessment and billing management method.

[0058] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the device or apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0059] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0060] It is understood that those skilled in the art can make equivalent substitutions or modifications to the technical solution and inventive concept of the present invention, and all such substitutions or modifications should fall within the protection scope of the appended claims.

Claims

1. A business performance evaluation and billing management method, characterized in that, Includes the following steps: Obtain assessment data and extract related business data based on the assessment data. Obtain all the assessment rules of the enterprise, extract the key features of the assessment rules, and summarize and organize all the assessment rules and their key features to form an assessment rule library; A dynamic priority algorithm is used, combined with an assessment rule base, to calculate the performance amount corresponding to the assessment business based on the assessment business and its business data. Bills are generated based on performance amounts, and the bills before issuance are checked against system records.

2. The business performance evaluation and billing management method according to claim 1, characterized in that, The acquisition of assessment business data, specifically including extracting related business data based on the assessment business data, includes: Acquire assessment business, and extract assessment information from the assessment business. The assessment information includes: business unit information, assessment personnel information, business type and assessment scope. Based on the assessment information, relevant business data is extracted from the enterprise system, and the relevant business data is organized to form an assessment dataset; An association algorithm is used to calculate the correlation between business data and assessment business in the assessment dataset. If the calculated correlation is lower than the threshold, a prompt is output.

3. The business performance evaluation and billing management method according to claim 1, characterized in that, The process of acquiring various enterprise assessment rules, extracting key features of these rules, and summarizing and organizing all assessment rules and their key features to form an assessment rule library specifically includes: We acquire various assessment rules from enterprises, use word segmentation algorithms, and combine them with industry word segmentation dictionaries to extract multiple key features of the assessment rules. Correct key features, obtain corrected features, and associate the corrected features with the key features; A pre-trained feature classification model is used to classify and label each modified feature of the assessment rule, and the assessment rule and its labeled modified feature are added to the assessment rule library.

4. The business performance evaluation and billing management method according to claim 3, characterized in that, The process involves using a pre-trained feature classification model to classify and label each modified feature of the assessment rule, and then adding the assessment rule and its labeled modified features to the assessment rule library. Specifically, this includes: Construct feature classification rules, which include descriptions of business type, data type, region, business unit, and job level; Obtain all historical assessment rules, preprocess and label the historical assessment rules according to the feature classification rules to form a training dataset, and use the training dataset to train the XGBoost-based model to obtain the feature classification model. A pre-trained feature classification model combined with a thesaurus is used to classify and label each modified feature of the assessment rule, and the assessment rule and its labeled modified features are added to the assessment rule library.

5. The business performance evaluation and billing management method according to claim 1, characterized in that, The process employs a dynamic priority algorithm, combined with an assessment rule base, to calculate the performance amount corresponding to the assessment business based on the assessment business and its data. Specifically, this includes: A dynamic priority algorithm is used to extract business characteristics from the assessment business and its data, and relevant rules in the assessment rule base are found based on the business characteristics. The relevant rules are sorted according to a preset priority, and the assessment rule with the highest priority is selected. A semantic module is used to calculate performance fees based on selected rules, assessment business, and business data.

6. The business performance evaluation and billing management method according to claim 5, characterized in that, The use of a semantic module to calculate performance fees based on selected rules, assessment business, and business data specifically includes: Using the semantic module, the first semantic understanding and calculation model is invoked, and the performance amount is calculated based on the selected rules, the business and its business data, to obtain the first calculated value. The second semantic understanding and calculation model is invoked, and the performance amount is calculated based on the selected rules, the business and its business data, to obtain the second calculated value. Compare the first calculated value with the second calculated value. If the first calculated value is the same as the second calculated value, the first calculated value shall be used as the performance amount. If the first calculated value is different from the second calculated value, a warning notification will be issued.

7. The business performance evaluation and billing management method according to claim 1, characterized in that, The process of generating invoices based on performance-based amounts and then reviewing these invoices against system records before issuance specifically includes: Create a form, fill in the performance amount into the form, and extract basic information from the assessment business and fill it into the form to generate a bill; Link bills to the assessment rules used to calculate performance amounts to quickly verify them when a review request is received; Check the bill type in the system records. If the bill type registered in the system records is "deferred assessment", intercept the issuance of the bill.

8. A business performance evaluation and billing management device, characterized in that, include: The extraction module is used to obtain assessment business data and extract related business data based on the assessment business data. The rules module is used to acquire various assessment rules of the enterprise, extract the key features of the assessment rules, and summarize and organize all assessment rules and their key features to form an assessment rule library. The calculation module is used to calculate the performance amount corresponding to the assessment business based on the assessment business and its business data, using a dynamic priority algorithm combined with the assessment rule library. The generation module is used to generate invoices based on performance amounts and to check the invoices before they are issued based on system records.

9. A business performance evaluation and billing management device, characterized in that, It includes a memory and at least one processor, wherein the memory stores computer-readable instructions; The at least one processor invokes the computer-readable instructions in the memory to perform the steps of the business assessment and billing management method as described in any one of claims 1-7.

10. A computer-readable storage medium storing computer-readable instructions thereon, characterized in that, When the computer-readable instructions are executed by the processor, they implement the steps of the business assessment and billing management method as described in any one of claims 1-7.

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