Enterprise output value settlement system based on intelligent algorithm and workflow

Abstract

The invention relates to an enterprise output value settlement system based on an intelligent algorithm and workflow. The enterprise output value settlement system comprises a data collection module, a data cleaning module, a submission settlement module and a performance allocation module. Through integrated data collection and an automatic settlement process, the system greatly reduces the demand of manual intervention, thereby improving the overall working efficiency. Due to real-time synchronization and automatic processing of the data, the settlement speed is increased, the risk of human errors is greatly reduced, and the accuracy and consistency of the data are ensured; a large amount of human input is reduced through automatic and intelligent means, frequent and tedious offline communication and checking work is avoided, and precious time and human resources are saved; in addition, accurate cost accounting is beneficial for enterprises to better control budget and realize optimal configuration of resources.

Description

Technical Field

[0001] The present invention belongs to the technical field of enterprise output value settlement, and particularly relates to an enterprise output value settlement system based on intelligent algorithms and workflows. Background Art

[0002] In the context of the ever-changing and constantly innovating information technology era, in the work related to project output value settlement of enterprises, the traditional output value settlement method has been difficult to meet the needs of efficient operation and refined management of enterprises. Specifically, in the approval link of contract confirmed amount and received amount, it mainly relies on manual operation. Different personnel have inconsistent operation habits and data entry specifications for Excel tables, which are extremely prone to negligence and errors; there are also prone to repeated settlement problems, and frequent amount errors. Once there are deviations in either the contract confirmed amount or the cost accounting amount, it not only causes waste of enterprise funds, but may also lead to financial risks and internal management chaos, interfering with the decision-making of enterprises. There is an urgent need for a more intelligent and efficient output value settlement system to address the above problems. Therefore, designing an enterprise output value settlement system based on intelligent algorithms and workflows that can reduce project data errors and optimize the settlement method has become a technical problem that needs to be solved urgently. Summary of the Invention

[0003] The present invention provides an enterprise output value settlement system based on intelligent algorithms and workflows to solve the above problems.

[0004] The technical solution of the present invention, an enterprise output value settlement system based on intelligent algorithms and workflows, includes a data collection module, a data cleaning module, a settlement request module, and a performance distribution module; The data collection module is based on a big data framework of RPC technology, connects to other external systems to collect output value settlement data, uses a visual ETL platform to extract, transform, and load the data into a unified specification, then builds a data storage architecture, and uses a relational database to store structured data and a NoSQL database to store unstructured data; the output value settlement data includes project confirmed amount, received amount records, and project basic information including vehicle usage costs, subcontracting costs, other costs, personnel organizational structure, and contract amount; The data cleaning module is based on a data processing library in Java and big data algorithms to process the data stored by the data collection module. The processing steps include duplicate removal and consistency check, missing value processing, anomaly detection and correction, and format standardization; The settlement request module monitors the data information stored in the system in real time through an intelligent trigger algorithm. After the data meets the preset settlement conditions, it automatically changes the project status to settlement request, enters the current settlement request process, and sends a notice to the project leader recorded in the data. Then, it uses a mathematical calculation library and model analysis to calculate the confirmed amount, received amount, production cost, and subcontracting cost of the output value settlement data in this settlement request record, and analyzes the algorithm model to obtain historical settlement request data for prediction and calibration processing of the settlement amount in the data. The calibration processing conducts a comparison check according to the project system, collects the output value data of the projects that fail the comparison check, and generates a list of non-compliant data. The performance distribution module screens out the production department members participating in the distribution from the output value settlement data by obtaining the settlement request record, calculates the performance data that each member should receive using the performance distribution algorithm, and sets up an objection feedback window for checking the performance data. This objection feedback window sends a confirmation performance notice to the members participating in the distribution.

[0005] As a further improvement of the present invention, the deduplication and consistency check uses a set framework and custom comparator, deeply compares data from different sources through a recursive algorithm, then uses big data correlation analysis technology to perform correlation checks on data from different systems, and finally correlates the project numbers to establish the corresponding relationship between project cost data and revenue data. The missing value processing adopts a machine learning library combined with big data statistical analysis methods to establish filling strategies for different types of data. The filling strategies include using a linear regression model to predict missing values for numerical data and using a decision tree algorithm to infer missing categories for categorical data. The anomaly detection and correction uses a statistical analysis library combined with a big data anomaly detection algorithm to monitor data in real time, and corrects the detected anomaly values to corresponding normal values according to the preset correction rules. The format standardization uses a data conversion tool to unify the date format and amount data in the data based on regular expressions.

[0006] As a further improvement of the present invention, the settlement conditions for submitting settlement include the following judgment processes: 1) Calculate whether the total confirmed amount of the project obtained from the confirmed amount of the project is greater than 0. If not, mark it as an unconfirmed project not allowed to be submitted, and end the submission; 2) Whether the total confirmed amount of the project and the received amount do not exceed the total contract amount. If not, mark it as a project with the distribution ended not allowed to be submitted, and end the submission; 3) Whether the receipt time and confirmation time of the most recent received amount are not in the current month. If not, mark it as a project of this month not allowed to be submitted, and end the submission; 4) Whether the date of this month has reached the 10th. If not, mark it as a project in a non-submission stage not allowed to be submitted, and end the submission; 5) Whether the basic information of the project is complete. If not, mark it as an abnormal project not allowed to be submitted, and end the submission.

[0007] As a further improvement of the present invention, the performance data is obtained by calculating the total salary of the production department. The total salary = (20% of the settlement output value of the projects completed by the department personnel in the year and obtaining the stage confirmation amount + 10% of the settlement output value of the projects invoiced and received by the department personnel in the year + 3% of the confirmed amount of the projects self-signed by the department in the year + 5% of the project contract fees received by the department self in the year) × 94%, and is split into the project performance, department performance, signing reward, and receipt reward for the current submission of settlement. The proportion of splitting the total salary is determined according to the results of the following calculation formulas. The project performance = [(the current confirmed amount - the current subcontracting cost - the current vehicle cost - the current other cost) × 20% + (the current received amount - the current subcontracting cost - the current vehicle cost - the current other cost) × 8%] × the assessment coefficient; The department performance = (the current confirmed amount - the current subcontracting cost - the current vehicle cost - the current other cost) × 2%; The signing reward = (the current confirmed amount - the current subcontracting amount) × 0.03. If the current subcontracting amount is greater than the current confirmed amount, the signing reward is 0. The subcontracting cost data in the formula is reserved for the next submission of settlement process; The receipt reward = (the current received amount - the current subcontracting amount - the current vehicle cost - the current other cost) × 0.05. If the current total cost is greater than the current received amount, the receipt reward is 0. The cost data in the formula is reserved for the next submission of settlement process; The assessment coefficient is obtained based on the total score of the project progress, project completion quality, project satisfaction, and project difficulty score.

[0008] As a further improvement of the present invention, the assessment coefficient includes the following scores: Project progress coefficient: If the project is completed within the time specified in the management manual, the coefficient is rated as 1.05; If it is completed within 3 days, the coefficient is rated as 1.0; If it is 3 days or more, the coefficient is reduced by 0.01 for each day of delay until the coefficient reaches 0.7. The project completion quality coefficient is based on the technical quality assessment results. If the assessment result is 95 points or above, the coefficient is 1.05; if the result is between 90 and 95 points (excluding 95 points), the coefficient is 1.02; if the result is between 75 and 90 points (excluding 90 points), the coefficient is 1.0; if the result is between 60 and 75 points (excluding 75 points), the coefficient is 0.9; if the result is below 60 points, the coefficient is 0.7. The project satisfaction coefficient is evaluated by having the project stakeholders fill out a project satisfaction questionnaire. If the result is'very satisfied' or'satisfied', the coefficient is 1.0; if the result is 'basically satisfied', the coefficient is 0.9; if the result is 'dissatisfied', the coefficient is 0.7. The project difficulty coefficient is 1.05 for projects reviewed or approved by relevant departments at the provincial level or above, and 1.0 for the remaining projects.

[0009] After adopting the above method, through the data collection module's big data framework based on RPC technology, it connects to other external systems to collect production value settlement data, ensuring the comprehensiveness and real-time nature of the data, breaking information silos, improving data acquisition efficiency, and guaranteeing the real-time nature of the project production and financial status; by using the visual ETL platform to unify the data specifications, it facilitates subsequent cost query and analysis, ensuring the integrity of cost data; through the data cleaning module's data processing library based on Java and big data algorithms, it processes the data stored in the data collection module, solving data accuracy and integrity problems; reducing manual operations and data entry in the data transfer process, and being able to correct data errors caused by inconsistent entry specifications among different personnel, which helps to more accurately conduct project settlement requests.

[0010] By the settlement request module, it monitors the data information stored in the system in real time. After the data meets the preset settlement request conditions, it automatically enters the current settlement request process, improving the timeliness of settlement; using a mathematical calculation library and model analysis to predict and calibrate the settlement amount in the data, ensuring the accuracy and reasonableness of the settlement amount; generating a list of non-compliant data through the calibration process, and promptly having manual intervention to rectify the non-compliant data; by the performance distribution module, it obtains the settlement request records, calculates the performance data that each member should receive using the performance distribution algorithm, and accepts the verification and feedback from the participating members, automatically calculating and flexibly adjusting the performance, achieving a more fair and reasonable performance distribution, reducing management difficulties, and enhancing employee satisfaction; preventing the problem of duplicate project settlements, avoiding waste of enterprise funds, reducing financial risks, optimizing internal management methods, assisting the enterprise in making decisions, and continuously improving the performance distribution mechanism.

[0011] In the data processing of the data cleaning module, through deduplication and consistency checking, the recursive algorithm deeply compares data from different sources to accurately identify duplicate data that needs to be deleted, and the data association check ensures the consistency of data logic; for missing value processing, corresponding filling strategies are adopted for different types of data to ensure data integrity; through anomaly detection and correction, data is corrected to corresponding normal values according to preset correction rules. For example, if the received payment amount is much higher or lower than the average received payment amount of the same type of project and exceeds the set threshold range, it is identified as an outlier. If the outlier is caused by data entry errors such as incorrect decimal point positions or reversed number inputs, it is automatically corrected according to the business logic of the project. For data that cannot be corrected by business logic, the system issues a notice for manual correction by relevant personnel; through format standardization based on regular expressions and data conversion tools, the date format and amount data are unified, and the amount data is uniformly retained to six decimal places to improve the efficiency of format standardization.

[0012] This system improves the production efficiency and accuracy of the project. Through integrated data collection and automated settlement processes, the system significantly reduces the need for manual intervention, thereby improving the overall work efficiency; the real-time synchronization and automated processing of data not only speed up the settlement speed but also greatly reduce the risk of human errors, ensuring data accuracy and consistency; the system reduces a large amount of human input through automated and intelligent means, avoiding frequent and cumbersome offline communication and verification work, saving valuable time and human resources; in addition, accurate cost accounting helps enterprises better control budgets and achieve optimal resource allocation; with the help of big data analysis and artificial intelligence algorithms, the system can provide more comprehensive and in-depth data insights to help enterprises make scientific and reasonable decisions; real-time updating of financial status and project progress information provides solid data support for management, enhancing the reliability and forward-looking of decision-making; the compliance check function of the system ensures that all financial operations comply with enterprise internal policies and regulatory requirements, enhancing the effectiveness of internal control; at the same time, the member verification mechanism in the performance distribution process increases the transparency of the process and promotes trust and collaboration among teams. Brief Description of the Drawings

[0013] Figure 1 The following shows the schematic diagram of the settlement judgment process proposed by the present invention. Detailed Embodiments

[0014] As Figure 1 shown, the enterprise output value settlement system based on intelligent algorithms and workflows includes a data collection module, a data cleaning module, a settlement proposal module, and a performance distribution module; The data collection module is a big data framework based on RPC technology, which connects to other external systems to collect production value settlement data. It uses a visual ETL platform to extract, transform, and load the data into a unified specification, and then builds a data storage architecture, using a relational database to store structured data and a NoSQL database to store unstructured data. The production value settlement data includes the confirmed amount of the project, the record of the received payment amount, and the project basic information including vehicle use cost, subcontracting cost, other costs, personnel organizational structure, and contract amount. The data cleaning module is based on Java's data processing libraries and big data algorithms to process the data stored by the data collection module. The processing steps include deduplication and consistency checking, missing value processing, anomaly detection and correction, and format standardization. The settlement request module uses an intelligent trigger algorithm to monitor the data information stored in the system in real time. After the data meets the preset settlement conditions, it automatically changes the project status to settlement request, enters the current settlement request process, and sends a notice to the project responsible person recorded in the data. Then, it uses a mathematical calculation library and model analysis to calculate the confirmed amount, received payment amount, production cost, and subcontracting cost of the production value settlement data in this settlement request record, analyzes the algorithm model to obtain historical settlement request data, and performs prediction and calibration processing on the settlement amount in the data. The calibration processing conducts a comparison check according to the project system, collects the production value data of the projects that fail the comparison check, and generates a non-compliant data list. The performance distribution module obtains the settlement request records, screens out the members of the production departments participating in the distribution from the production value settlement data, calculates the performance data that each member should receive using a performance distribution algorithm, and sets up an objection feedback window for checking the performance data. This objection feedback window sends a confirmation performance notice to the members participating in the distribution.

[0015] For deduplication and consistency checking, a collection framework and a custom comparator are used. Through a recursive algorithm, in-depth comparison is performed on data from different sources, and then big data correlation analysis technology is used to perform correlation checks on data from different systems. Finally, the project numbers are associated to establish the corresponding relationship between project cost data and revenue data. For missing value processing, a machine learning library combined with big data statistical analysis methods is used to establish filling strategies for different types of data. The filling strategies include using a linear regression model to predict missing values for numerical data and using a decision tree algorithm to infer missing categories for categorical data. For anomaly detection and correction, a statistical analysis library combined with big data anomaly detection algorithms is used to monitor the data in real time, and the detected outliers are corrected to corresponding normal values according to the preset correction rules. For format standardization, a data conversion tool is used to unify the date format and amount data in the data based on regular expressions.

[0016] The settlement conditions for submitting for settlement include the following judgment processes: 1) Determine whether the total confirmed amount of the project obtained by calculating the confirmed amount of the project is greater than 0. If not, mark it as an unconfirmed project not allowed to be submitted, and end the submission; 2) Determine whether the total confirmed amount of the project and the received amount do not exceed the total contract amount. If not, mark it as a project with the distribution ended not allowed to be submitted, and end the submission; 3) Determine whether the receipt time and confirmation time of the most recent received amount are not the current month. If not, mark it as a project of this month not allowed to be submitted, and end the submission; 4) Determine whether the date of this month has reached the 10th. If not, mark it as a project in a non-submission stage not allowed to be submitted, and end the submission; 5) Determine whether the basic information of the project is complete. If not, mark it as an abnormal project not allowed to be submitted, and end the submission.

[0017] The performance data is obtained by calculating the total salary of the production department. The total salary = (20% of the settlement output value of the projects completed by the department personnel in the year and obtaining the stage confirmation amount + 10% of the settlement output value of the invoiced and received amount of the projects completed by the department personnel in the year + 3% of the confirmed amount of the projects self-signed by the department in the year + 5% of the project contract cost of the projects self-received and received by the department in the year) × 94%. And it is split into the project performance, department performance, signing reward, and receipt reward for the current submission for settlement. The proportion of splitting the total salary is determined according to the results of the following calculation formulas. The project performance = [(the current confirmed amount - the current subcontracting cost - the current vehicle cost - the current other cost) × 20% + (the current received amount - the current subcontracting cost - the current vehicle cost - the current other cost) × 8%] × the assessment coefficient; The department performance = (the current confirmed amount - the current subcontracting cost - the current vehicle cost - the current other cost) × 2%; The signing reward = (the current confirmed amount - the current subcontracting amount) × 0.03. If the current subcontracting amount is greater than the current confirmed amount, the signing reward is 0. The subcontracting cost data in the formula is reserved for the next submission for settlement process; The receipt reward = (the current received amount - the current subcontracting amount - the current vehicle cost - the current other cost) × 0.05. If the current total cost is greater than the current received amount, the receipt reward is 0. The cost data in the formula is reserved for the next submission for settlement process; The assessment coefficient is obtained based on the total score of the project progress, project completion quality, project satisfaction, and project difficulty scores.

[0018] The assessment coefficient includes the following scoring: The project progress coefficient: If the project is completed within the time specified in the management manual, the coefficient is rated as 1.05; If it is completed within 3 days, the coefficient is rated as 1.0; If it is 3 days or more, the coefficient is reduced by 0.01 for each day of delay until the coefficient reaches 0.7. The project completion quality coefficient is based on the technical quality assessment results. If the assessment result is 95 points or above, the coefficient is 1.05; if the result is between 90 and 95 points (excluding 95 points), the coefficient is 1.02; if the result is between 75 and 90 points (excluding 90 points), the coefficient is 1.0; if the result is between 60 and 75 points (excluding 75 points), the coefficient is 0.9; if the result is below 60 points, the coefficient is 0.7. The project satisfaction coefficient is evaluated by having the project stakeholders fill out a project satisfaction questionnaire. If the result is "very satisfied" or "satisfied", the coefficient is 1.0; if the result is "basically satisfied", the coefficient is 0.9; if the result is "dissatisfied", the coefficient is 0.7. The project difficulty coefficient is 1.05 for projects reviewed or approved by relevant departments at the provincial level or above, and 1.0 for the remaining projects.

[0019] Through the data collection module, a big data framework based on RPC technology is used to connect to other external systems to collect production value settlement data, ensuring the comprehensiveness and real-time nature of the data, breaking information silos, improving data acquisition efficiency, and guaranteeing the real-time nature of project production and financial status; by using a visual ETL platform to unify data specifications, it facilitates subsequent cost query and analysis, ensuring the integrity of cost data; through the data cleaning module, a data processing library based on Java and big data algorithms are used to process the data stored by the data collection module, solving data accuracy and integrity problems; reducing manual operations and data entry in the data transfer process, and being able to correct data errors caused by inconsistent entry specifications among different personnel, which helps to more accurately initiate project settlements.

[0020] Through the settlement initiation module, it monitors the data information stored in the system in real time. After the data meets the preset settlement initiation conditions, it automatically enters the current settlement initiation process, improving the timeliness of settlement; using a mathematical calculation library and model analysis to predict and calibrate the settlement amount in the data, ensuring the accuracy and reasonableness of the settlement amount; generating a list of non-compliant data through the calibration process, and promptly having manual intervention to rectify the non-compliant data; through the performance distribution module, it obtains the settlement initiation records, calculates the performance data that each member should receive using the performance distribution algorithm, and accepts the verification and feedback from the participating members, automatically calculating and flexibly adjusting performance, achieving a more fair and reasonable performance distribution, reducing management difficulty, and enhancing employee satisfaction; preventing the problem of duplicate project settlements, avoiding waste of enterprise funds, reducing financial risks, optimizing internal management methods, assisting the enterprise in making decisions, and continuously improving the performance distribution mechanism.

[0021] In the data cleaning module's data processing, through deduplication and consistency checking, the recursive algorithm deeply compares data from different sources to accurately identify duplicate data that needs to be deleted, and the data association check ensures the consistency of data logic; through missing value processing, corresponding filling strategies are adopted for different types of data to ensure data integrity; through anomaly detection and correction, according to the preset correction rules, the data is corrected to the corresponding normal values. For example, if the collection amount is much higher or lower than the average collection amount of the same type of project and exceeds the set threshold range, it is identified as an outlier. If the outlier is caused by data entry errors such as incorrect decimal point positions or reversed digit inputs, it is automatically corrected according to the business logic of the project. For data that cannot be corrected by the business logic, the system issues a notice for manual correction by relevant personnel; through format standardization based on regular expressions and data conversion tools, the date format and amount data are unified, and the amount data is uniformly retained to six decimal places to improve the efficiency of format standardization.

[0022] This system improves the production efficiency and accuracy of the project. Through integrated data collection and automated settlement processes, the system significantly reduces the need for manual intervention, thereby improving the overall work efficiency; the real-time synchronization and automated processing of data not only speed up the settlement speed but also greatly reduce the risk of human errors, ensuring data accuracy and consistency; the system reduces a large amount of human input through automated and intelligent means, avoiding frequent and cumbersome offline communication and verification work, saving valuable time and human resources; in addition, accurate cost accounting helps enterprises better control budgets and achieve optimal allocation of resources; with the help of big data analysis and artificial intelligence algorithms, the system can provide more comprehensive and in-depth data insights to help enterprises make scientific and reasonable decisions; real-time updating of financial status and project progress information provides solid data support for management, enhancing the reliability and forward-looking of decision-making; the compliance check function of the system ensures that all financial operations comply with enterprise internal policies and regulatory requirements, enhancing the effectiveness of internal control; at the same time, the member verification mechanism in the performance distribution process increases the transparency of the process and promotes trust and collaboration among teams.

Claims

1. The enterprise output value settlement system based on intelligent algorithms and workflows is characterized by: It includes data collection module, data cleaning module, settlement request module and performance distribution module; The data collection module is based on the big data framework of RPC technology, connects to other external systems to collect output value settlement data, uses a visual ETL platform to extract, convert and load data into a unified specification, and then builds a data storage architecture, using a relational database to store structured data and a NoSQL database to store unstructured data; the output value settlement data includes the project confirmation amount, the collection amount record, and the basic project information including vehicle use cost, subcontracting cost, other costs, personnel organization structure, and contract amount; The data cleaning module processes the data stored in the data collection module based on the Java data processing library and big data algorithms. The processing steps include deduplication and consistency check, missing value processing, anomaly detection and correction, and format standardization. The settlement request module monitors the data information stored in the system in real time through an intelligent trigger algorithm, and automatically changes the project status to settlement request after the data meets the preset settlement conditions, enters the current settlement request process, and sends a notification to the project leader recorded in the data; Then use the mathematical calculation library and model analysis to calculate the confirmed amount, collection amount, production cost and subcontracting cost of the output value settlement data in the settlement record of this time, analyze the algorithm model to obtain historical submission data, and predict and calibrate the settlement amount in the data; the calibration process is carried out according to the project system for comparative inspection, and the output value data of the projects that fail the comparative inspection are collected to generate a list of non-compliant data; The performance distribution module obtains the settlement request records, screens out the production department members participating in the distribution from the output value settlement data, uses the performance distribution algorithm to calculate the performance data that each member deserves, and sets up an objection feedback window for verifying the performance data. The objection feedback window sends a performance confirmation notification to the members participating in the distribution.

2. The enterprise output value settlement system based on intelligent algorithm and workflow according to claim 1 is characterized by: The deduplication and consistency check uses a collection framework and a custom comparator to perform in-depth comparisons of data from different sources through a recursive algorithm, and then uses big data association analysis technology to perform association checks on data from different systems, and finally associates the project numbers to establish a corresponding relationship between project cost data and revenue data; The missing value processing adopts a machine learning library combined with a big data statistical analysis method to establish a filling strategy for different types of data. The filling strategy includes predicting missing values ​​using a linear regression model for numerical data and inferring missing categories using a decision tree algorithm for categorical data; The anomaly detection and correction described herein monitors data in real time through a statistical analysis library combined with a big data anomaly detection algorithm, and corrects the abnormal values ​​found in the monitoring to corresponding normal values ​​according to preset correction rules; The format standardization is performed by using a data conversion tool to unify the date format and amount data in the data based on regular expressions.

3. The enterprise output value settlement system based on intelligent algorithm and workflow according to claim 1 is characterized by: The settlement conditions for requesting settlement include the following judgment process: 1) whether the total confirmed amount of the project obtained by calculating the confirmed amount of the project is greater than 0, if not, it is marked as an unconfirmed project that is not allowed to be submitted, and the submission is terminated; 2) Whether the total confirmed amount and the received amount of the project do not exceed the total amount of the contract amount. If not, mark it as an allocation end project that is not allowed to be submitted and end the submission; 3) Whether the collection time and confirmation time of the most recent collection amount are not in the current month. If not, mark it as a project of this month that is not allowed to be submitted and end the submission; 4) Whether the date of this month has reached the 10th. If not, mark it as a non-submission stage project that is not allowed to be submitted and end the submission; 5) Whether the basic information of the project is complete. If not, mark it as an abnormal project that is not allowed to be submitted and end the submission.

4. The enterprise output value settlement system based on intelligent algorithm and workflow according to claim 1 is characterized by: The performance data is calculated by calculating the total salary of the production department, which is (the annual department staff completed the project and obtained the stage confirmation amount settlement output x 20% + the annual department staff completed the project invoice settlement output x 10% + the annual department signed the project confirmation amount x 3% + the annual department received the project contract fee x 5%) x 94%, and split the project performance, department performance, signing reward and collection reward submitted for settlement in this period. The proportion of splitting the total salary is determined according to the result of the following calculation formula: Project performance = [(this period confirmation amount - this period subcontracting cost - this period car cost - this period other costs) * 20% + (this period collection amount - this period subcontracting cost - this period car cost - this period other costs) This)*8%]*assessment coefficient; the department performance = (confirmed amount of this period - subcontract cost of this period - vehicle cost of this period - other costs of this period)*2%; the signing reward = (confirmed amount of this period - subcontract amount of this period)*0.03, if the subcontract amount of this period is greater than the confirmed amount of this period, the signing reward is 0, and the subcontract cost data in the formula will be retained until the next settlement process; the collection reward = (collection amount of this period - subcontract amount of this period - vehicle cost of this period·other costs of this period)*0.05, if the total cost of this period is greater than the collection amount of this period, the collection reward is 0, and the cost data in the formula will be retained until the next settlement process; the assessment coefficient is obtained based on the total score of project progress, project completion quality, project satisfaction and project difficulty.

5. The enterprise output value settlement system based on intelligent algorithm and workflow according to claim 4 is characterized by: The assessment coefficient includes the following scoring: The project progress coefficient is 1.05 if the project is completed within the time specified in the management book; 1.0 if the project is completed within 3 days; and 0.01 for each day of delay, up to 0.7 if the project is completed within 3 days. The project completion quality coefficient is based on the technical quality assessment results. If the assessment result is 95 points or above, the coefficient is 1.05; if the result is 90 points to below 95 points, the coefficient is 1.02; if the result is 75 points to below 90 points, the coefficient is 1.0; if the result is 60 points to below 75 points, the coefficient is 0.9; if the result is below 60 points, the coefficient is 0.7; The project satisfaction coefficient is evaluated by filling out a project satisfaction questionnaire by the project stakeholders. If the result is very satisfied or satisfied, the coefficient is 1.0; if the result is basically satisfied, the coefficient is 0.9; if the result is dissatisfied, the coefficient is 0.7; The project difficulty coefficient is 1.05 for projects reviewed or approved by relevant departments at the provincial level and above, and 1.0 for other projects.

Images