Purchase supervision early warning system based on AI intelligent analysis and control method

Abstract

The invention relates to the technical field of purchasing systems, in particular to a purchasing supervision and early warning system based on AI intelligent analysis and a management and control method. The input unit is used for inputting product purchase information, product type information and product quantity information in a first time gradient; the data monitoring statistical unit is used for monitoring each evaluation data of the supplier in real time and acquiring a time sequence data sequence of the evaluation data in different time periods according to different evaluation data and evaluation generation time; the storage unit is used for recording and storing evaluation data corresponding to different time sequences; and the AI risk assessment unit is used for predicting risk total assessment data of a second time gradient according to the product information input by the input unit and the assessment data corresponding to different time sequences stored in the storage module of the data monitoring statistical unit, and judging whether risk early warning exists or not. The system can predict and evaluate the corresponding qualification of the supplier, and can effectively ensure that the purchase demand of an enterprise is met.

Description

Technical Field

[0001] The present invention relates to the technical field of procurement systems, and in particular to a procurement supervision and early warning system and a control method based on AI intelligent analysis. Background Art

[0002] Procurement system is an information management system used by enterprises in the procurement process, which aims to improve procurement efficiency and accuracy. The system usually includes multiple modules such as supplier management, procurement demand management, purchase order management, contract management, receipt management and financial reconciliation; the procurement system is an information platform that integrates functions such as supplier management, demand forecasting, procurement plan formulation, order processing, contract management, receipt warehousing and financial settlement. It digitizes each link of procurement activities through Internet technology and database management, realizes real-time information sharing and seamless connection of processes. In enterprise operations, procurement links, as the core part of supply chain management, are directly related to cost control, inventory management and production efficiency. Traditional manual procurement methods are inefficient, prone to errors, and difficult to track order status in real time. When establishing a procurement system in an enterprise, the financial status, delivery capabilities, technical capabilities and customer satisfaction of suppliers are constantly changing. If the different qualifications of suppliers are not processed, judged and predicted in a timely manner, it is likely to cause corresponding losses to the enterprise, or the products cannot meet the actual needs of the company.

[0003] Based on the above problems, there is an urgent need for a procurement supervision and early warning system that can predict and evaluate the corresponding qualifications of suppliers, effectively ensure that the procurement needs of enterprises are met, and reduce economic losses. Summary of the invention

[0004] In view of the current difficulty in timely predicting, processing and judging the different qualifications of suppliers, the present invention provides a procurement supervision and early warning system and a management and control method based on AI intelligent analysis to solve the above problem.

[0005] To achieve the above object, the present invention is implemented through the following technical solutions: The present application embodiment discloses a procurement supervision and early warning system based on AI intelligent analysis, including: Input unit: input product purchase information, product category information and product quantity information within the first time gradient through the input unit; Data monitoring and statistics unit: used to monitor various evaluation data of suppliers in real time, and obtain the time series data of evaluation data in different time periods according to different evaluation data and evaluation generation time; Storage unit: used to record and store various evaluation data corresponding to different time series; AI risk assessment unit: predicts the overall risk assessment data of the second time gradient based on the product information input by the input unit and the assessment data corresponding to different time series stored in the storage module of the data monitoring and statistical unit, and determines whether there is a risk warning.

[0006] Adopt the above technical solution: The above solution collects relevant data of suppliers through the input unit and the data monitoring and statistical unit, and transmits the data information to the AI ​​risk assessment unit, which conducts evaluation. It can predict the supplier's financial status, delivery capability, technical capability, customer satisfaction and other coefficients in real time, determine whether there is a risk warning, and if there is a risk warning, it will be dealt with in a timely manner.

[0007] Preferably, the risk assessment unit comprises: Risk data assessment module: retrieve the assessment data corresponding to different time series recorded in the storage module, and perform data conversion analysis on different types of assessment data according to the influencing factor coefficients to obtain the overall risk assessment data; Risk range prediction module: calculates the risk threshold range based on the input product procurement information, product type information and product quantity information; Judgment module: used to determine whether the overall risk assessment data falls within the risk threshold range. If not, a risk alarm message is generated.

[0008] Adopt the above technical solution: the above solution can evaluate the risk data of suppliers and calculate the risk threshold of supplier products. The judgment module can judge whether there is a risk warning based on the above two types of data.

[0009] Further preferably, the algorithm formula set in the risk data assessment unit is: , in, is the evaluation data of the ith item, is the impact factor coefficient corresponding to the i-th evaluation data, It is the overall risk assessment data.

[0010] Adopt the above technical solution: the above solution can calculate the corresponding risk summary data based on the evaluation data and influencing factors through the system algorithm, and can determine whether there is a corresponding risk warning based on the risk summary data.

[0011] Further preferably, the algorithm formula set in the risk range prediction unit is: , in, is the risk threshold, is the product quantity information, and λ is the influence coefficient corresponding to the product.

[0012] Adopting the above technical solution: The above solution can predict the risk range data and determine the risk threshold coefficient of the supplier's corresponding product; Further preferably, the judgment unit is used to judge the data calculated by the risk data assessment unit and the risk range prediction unit to analyze whether a risk warning occurs.

[0013] By adopting the above technical solution, the judgment unit can calculate and judge whether there is a risk warning based on the risk threshold and the overall risk evaluation data.

[0014] Further preferably, it also includes: a risk data report generation unit: after a risk warning occurs, the risk data report generation unit is used to generate a data report corresponding to the data monitored by the data monitoring and statistical unit and the data analyzed by the AI ​​risk assessment unit.

[0015] Adopt the above technical solution: The above solution can generate corresponding risk data reports after the risk warning occurs, so that the enterprise procurement department can intuitively understand the data in the report, analyze and make corresponding decisions.

[0016] Further preferably, it also includes an uploading unit: the uploading unit is used to upload risk warning information and risk data reports.

[0017] By adopting the above technical solution, the uploading unit can upload the risk data report.

[0018] Further preferably, it also includes a processing unit: the enterprise procurement department receives the risk warning information and risk data report uploaded by the uploading unit, analyzes the risk data report, and makes corresponding adjustments to the procurement business.

[0019] Adopting the above technical solution: the processing unit provided by the above solution can enable the enterprise to analyze various data on the risk warning information volume risk data report and make corresponding business adjustments.

[0020] A management and control method is applied to the procurement supervision and early warning system based on AI intelligent analysis as described in any one of the above, characterized by comprising: S1: Input product purchase information, product category information and product quantity information within the first time gradient through an input unit; S2: Acquire the time data series of supplier evaluation data in different time periods in real time and transmit them to the storage unit for data storage; S3: Predict the overall risk assessment data of the second time gradient according to the product information input by the input unit and the assessment data corresponding to different time series stored in the storage module of the data monitoring and statistical unit, and determine whether there is a risk warning.

[0021] Further preferably, the step S3 comprises: S31: Retrieving the assessment data corresponding to different time series recorded in the storage module, and performing data conversion analysis on different types of assessment data according to the influencing factor coefficients to obtain the overall risk assessment data; S32: Calculate the risk threshold range based on the input product purchase information, product type information, and product quantity information; S33: Determine whether the overall risk assessment data falls within the risk threshold range. If not, generate risk alarm information. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 A connection diagram of the regulatory early warning system was procured for this application; Figure 2 This is a flow chart of the control method for this application; Figure 3 For this application Figure 1 Flowchart of step S3 in FIG.

[0024] In the figure: 1. Input unit; 2. Data monitoring and statistics unit; 3. Storage unit; 4. AI risk assessment unit; 5. Risk data assessment module; 6. Risk range prediction module; 7. Judgment module; 8. Risk data report generation unit; 9. Upload unit; 10. Processing unit. DETAILED DESCRIPTION

[0025] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0026] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0027] See also Figure 1-Figure 3 , such as the traditional manual procurement method is inefficient, prone to errors, and difficult to track order status in real time. ‌ When establishing a procurement system in an enterprise, the financial status, delivery capabilities, technical capabilities, and customer satisfaction of suppliers are constantly changing. If the different qualifications of suppliers are not processed, judged, and predicted in a timely manner, it is likely to cause corresponding losses to the enterprise, or the product cannot meet the actual needs of the company. Based on the above problems, this application mentions a procurement supervision and early warning system based on AI intelligent analysis and a control method including: Input unit 1: input product purchase information, product category information and product quantity information within the first time gradient through input unit 1; Data monitoring and statistics unit 2: used to monitor various evaluation data of suppliers in real time, and obtain the time series data of evaluation data in different time periods according to different evaluation data and evaluation generation time; Storage unit 3: used to record and store various evaluation data corresponding to different time series; AI risk assessment unit 4: predict the overall risk assessment data of the second time gradient based on the product information input by input unit 1 and the assessment data corresponding to different time series stored in the storage module of data monitoring and statistics unit 2, and determine whether there is a risk warning. It is worth mentioning that the above scheme collects the relevant data of the supplier through input unit 1 and data monitoring and statistics unit 2, and transmits the data information to AI risk assessment unit 4, which is evaluated by AI risk assessment unit 4. It can predict the supplier's financial status, delivery capabilities, technical capabilities, customer satisfaction and other coefficients in real time, determine whether there is a risk warning, and if there is a risk warning, it will be handled in a timely manner.

[0028] The AI ​​risk assessment system includes: Risk data assessment module 5: The assessment data corresponding to different time series recorded in the storage module are retrieved, and different types of assessment data are converted and analyzed according to the influencing factor coefficients to obtain the overall risk assessment data; the overall risk assessment data includes statistics on the supplier's financial status, delivery capabilities, technical capabilities, and customer satisfaction data, and then data conversion is performed to finally obtain the corresponding overall risk assessment data.

[0029] Risk Range Prediction Module 6: Calculate the risk threshold range based on the input product purchase information, product type information and product quantity information; this unit can further determine the maximum risk threshold of the product based on the information of the purchased goods. The risk threshold is the maximum risk range that the enterprise procurement system can accept. The specific information of the product, the price of the product and the quantity of the product all need to be evaluated and judged accordingly to determine whether there is a risk that may cause product procurement.

[0030] Judgment module 7: used to judge whether the overall risk assessment data falls within the risk threshold range. If not, a risk alarm message is generated. This unit can directly realize effective judgment on the procurement system.

[0031] It is worth mentioning that the above scheme can evaluate the risk data of suppliers and calculate the risk threshold of suppliers' products. The judgment module 7 can judge whether there is a risk warning based on the above two types of data.

[0032] The algorithm formula set in the risk data assessment unit is: , in, is the evaluation data of the ith item, is the impact factor coefficient corresponding to the i-th evaluation data, The above solution can calculate the corresponding risk summary data based on the evaluation data and influencing factors through the system algorithm. The evaluation data includes information such as the supplier's financial status, delivery capabilities, technical capabilities, and customer satisfaction. Based on the risk summary data, it can be determined whether there is a corresponding risk warning.

[0033] The algorithm formula set in the risk range prediction unit is: , in, is the risk threshold, is the product quantity information, and λ is the influence coefficient corresponding to the product.

[0034] The above solution can predict the risk range data and determine the risk threshold coefficient of the supplier's corresponding products; The judgment unit is used to judge the data calculated by the risk data assessment unit and the risk range prediction unit, and analyze whether a risk warning occurs.

[0035] The judgment unit can calculate and determine whether there is a risk warning based on the risk threshold and the overall risk assessment data.

[0036] It also includes: a risk data report generating unit 8: after a risk warning occurs, the risk data report generating unit 8 is used to generate a data report corresponding to the data monitored by the data monitoring and statistical unit 2 and the data analyzed by the AI ​​risk assessment unit 4. The above scheme can realize the generation of a corresponding risk data report after a risk warning occurs, so that the enterprise procurement department can intuitively understand the various data in the report, analyze and make corresponding decisions.

[0037] The system also includes an uploading unit 9: the uploading unit 9 is used to upload risk warning information and risk data reports. The uploading unit can upload risk data reports.

[0038] It also includes a processing unit 10: the enterprise procurement department receives the risk warning information and risk data report uploaded by the uploading unit 9, analyzes the risk data report, and makes corresponding adjustments to the procurement business. The processing unit 10 provided by the above solution enables the enterprise to analyze the various data on the risk warning information and risk data report, and make corresponding business adjustments.

[0039] A management and control method, applied to a procurement supervision and early warning system based on AI intelligent analysis as described in any one of the above, comprising: S1: Input product purchasing information, product category information and product quantity information within the first time gradient through input unit 1; S2: acquiring the time data series of the supplier's evaluation data in different time periods in real time, and transmitting it to the storage unit 3 for data storage; S3: Predict the overall risk assessment data of the second time gradient according to the product information input by the input unit 1 and the assessment data corresponding to different time series stored in the storage module of the data monitoring and statistical unit 2, and determine whether there is a risk warning.

[0040] The step S3 comprises: S31: Retrieving the assessment data corresponding to different time series recorded in the storage module, and performing data conversion analysis on different types of assessment data according to the influencing factor coefficients to obtain the overall risk assessment data; S32: Calculate the risk threshold range based on the input product purchase information, product type information, and product quantity information; S33: Determine whether the overall risk assessment data falls within the risk threshold range. If not, generate risk alarm information.

[0041] Unless otherwise specified, the device components involved in the above embodiments are all conventional device components, and the connection methods and control methods involved are all conventional connection methods and control methods unless otherwise specified.

[0042] The present invention has been described in detail above in conjunction with the embodiments. However, those skilled in the art will appreciate that, without departing from the spirit of the present invention, the specific parameters in the above embodiments may be changed to form a plurality of specific embodiments, which are all within the common variation range of the present invention and will not be described in detail herein.

Claims

1. A procurement supervision and early warning system based on AI intelligent analysis, characterized in that: include: Input unit: input product purchase information, product category information and product quantity information within the first time gradient through the input unit; Data monitoring and statistics unit: used to monitor various evaluation data of suppliers in real time, and obtain the time series data of evaluation data in different time periods according to different evaluation data and evaluation generation time; Storage unit: used to record and store various evaluation data corresponding to different time series; AI risk assessment unit: predicts the overall risk assessment data of the second time gradient based on the product information input by the input unit and the assessment data corresponding to different time series stored in the storage module of the data monitoring and statistical unit, and determines whether there is a risk warning.

2. According to claim 1, a procurement supervision and early warning system based on AI intelligent analysis is characterized in that: The risk assessment unit comprises: Risk data assessment module: retrieve the assessment data corresponding to different time series recorded in the storage module, and perform data conversion analysis on different types of assessment data according to the influencing factor coefficients to obtain the overall risk assessment data; Risk range prediction module: calculates the risk threshold range based on the input product procurement information, product type information and product quantity information; Judgment module: used to determine whether the overall risk assessment data falls within the risk threshold range. If not, a risk alarm message is generated.

3. According to claim 2, a procurement supervision and early warning system based on AI intelligent analysis is characterized in that: The algorithm formula set in the risk data assessment unit is: ， in, is the evaluation data of item i, is the impact factor coefficient corresponding to the i-th evaluation data, It is the overall risk assessment data.

4. According to claim 3, a procurement supervision and early warning system based on AI intelligent analysis is characterized in that: The algorithm formula set in the risk range prediction unit is: ， in, is the risk threshold, is the product quantity information, and λ is the influence coefficient corresponding to the product.

5. According to claim 4, a procurement supervision and early warning system based on AI intelligent analysis is characterized in that: The judgment unit is used to judge the data calculated by the risk data assessment unit and the risk range prediction unit, and analyze whether a risk warning occurs.

6. According to claim 1, a procurement supervision and early warning system based on AI intelligent analysis is characterized in that: Also includes: Risk data report generation unit: After a risk warning occurs, the risk data report generation unit is used to generate a data report corresponding to the data monitored by the data monitoring and statistics unit and the data analyzed by the AI ​​risk assessment unit.

7. According to claim 1, a procurement supervision and early warning system based on AI intelligent analysis is characterized in that: It also includes an uploading unit: the uploading unit is used to upload risk warning information and risk data reports.

8. According to claim 1, a procurement supervision and early warning system based on AI intelligent analysis is characterized in that: It also includes a processing unit: the enterprise procurement department receives risk warning information and risk data reports uploaded by the uploading unit, analyzes the risk data reports, and makes corresponding adjustments to the procurement business.

9. A management and control method, applied to the procurement supervision and early warning system based on AI intelligent analysis as described in any one of claims 1 to 8, characterized in that: include: S1: Input product purchase information, product category information and product quantity information within the first time gradient through an input unit; S2: Acquire the time data series of supplier evaluation data in different time periods in real time and transmit them to the storage unit for data storage; S3: Predict the overall risk assessment data of the second time gradient according to the product information input by the input unit and the assessment data corresponding to different time series stored in the storage module of the data monitoring and statistical unit, and determine whether there is a risk warning.

10. A control method according to claim 9, characterized in that: The step S3 comprises: S31: Retrieving the assessment data corresponding to different time series recorded in the storage module, and performing data conversion analysis on different types of assessment data according to the influencing factor coefficients to obtain the overall risk assessment data; S32: Calculate the risk threshold range based on the input product purchase information, product type information, and product quantity information; S33: Determine whether the overall risk assessment data falls within the risk threshold range. If not, generate risk alarm information.

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