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CN-122022740-A - Contract intelligent verification and risk detection method

CN122022740ACN 122022740 ACN122022740 ACN 122022740ACN-122022740-A

Abstract

The invention discloses an intelligent contract checking and risk detecting method. The method comprises multi-contract analysis and structured extraction, material demand, delivery time and process requirement acquisition, global constraint modeling and conflict detection, linkage enterprise real-time inventory, on-the-way and productivity data, material and productivity conflict identification, performance risk quantitative evaluation and grading, delivery risk coefficient calculation and grade marking based on conflict, intelligent treatment and dynamic scheduling, automatic generation of treatment suggestions and optimization of execution sequence aiming at high risk contracts, global collaborative visualization decision, centralized display of risks, suggestions and schedules in a billboard, and dynamic desensitization according to user roles. The method solves the problems of low information extraction efficiency, resource conflict discovery hysteresis and subjective risk judgment in multi-contract parallel performance, and realizes contract performance full-flow intellectualization and global collaborative decision.

Inventors

  • Jiang Yanhu
  • ZHAO KAIWEN

Assignees

  • 南昌师范学院

Dates

Publication Date
20260512
Application Date
20260402

Claims (9)

  1. 1. The intelligent contract checking and risk detecting method is characterized by comprising the following steps: S1, multi-contract analysis and structured extraction, namely, receiving a plurality of contract documents uploaded by users in batches, analyzing each contract, and extracting structured fulfillment constraint information including a bill of material requirements, delivery time and production process requirements; S2, global constraint modeling and conflict detection, namely carrying out linkage comparison on the extracted performance constraint information of all contracts and inventory data, in-transit material data and capacity planning data in enterprise real-time operation data, establishing a global resource constraint model, and detecting and identifying material resource conflicts and key capacity conflicts existing among multiple contracts; s3, performing quantitative assessment and grading, namely calculating a quantitative delivery risk coefficient for each contract based on detection results of resource conflict and capacity conflict, and performing grade marking on contract performance risks according to the coefficient; s4, intelligent disposal and dynamic scheduling, namely automatically generating disposal suggestions including adjustment of delivery date, replacement of materials and starting of emergency purchase aiming at the contracts marked as high risk, and meanwhile, carrying out dynamic simulation and priority rearrangement on the execution sequence of all the contracts based on a preset optimization target to output an optimized contract execution sequence; s5, global collaborative visual decision-making, namely, centralizing and displaying the execution sequences of the multi-contract execution risk grading result, the treatment suggestion and the optimization in a global collaborative visual billboard and supporting interactive decision-making.
  2. 2. The method for intelligently checking and detecting risk of contract according to claim 1, wherein step S2 specifically comprises: S21, acquiring real-time operation data, namely calling an application programming interface of an enterprise resource planning system to acquire real-time inventory data G and on-road material data G, wherein the real-time inventory data comprises material codes and available inventory quantity, and the on-road material data comprises the material codes, expected arrival dates and on-road quantity; calling an application programming interface of a manufacturing execution system to acquire a capacity calendar data set in a future preset period The productivity calendar data comprises production line or equipment identification, available man-hours, occupied man-hours and corresponding time windows; s22, material resource conflict detection, namely aggregating the material demand lists extracted from all contracts, and summarizing according to material codes to obtain the total demand of each material; calculating the total demand of materials of each contract according to the bill of material demand , The sum of all material demand amounts for the contract; For each material, adding the real-time inventory data G and the in-transit quantity of the in-transit material data G, wherein the expected date of the in-transit quantity is earlier than the latest demand time point of the material, and calculating the total available quantity of the material; comparing the total demand with the total available amount, if the total demand is larger than the total available amount, judging that the material has resource conflict, and calculating the number of gaps of the material to be the total demand minus the total available amount; adding the gap numbers of all materials with resource conflict related to the contract to obtain the total number of material gaps of the contract ; S23, detecting key productivity conflicts, namely, combining production process requirements and estimated working hours of each contract Converting into a task to be scheduled, and pushing back to generate a suggested start time window according to the delivery date agreed by the contract; sequentially or parallelly arranging tasks to be scheduled into a productivity calendar data set The available working hours of the corresponding production line or equipment in the corresponding time window are occupied; When working hour requirements generated by two or more contracted tasks in the same time window of the same production line or equipment are overlapped and exceed available labor of the time window, judging that key productivity conflict exists, and recording total length of the conflict time window And the number of critical production resources involved in the conflict Wherein, the method comprises the steps of, The sum of the days of all conflict periods that lead to delays in contract i in simulated production; For the number of unique production resources that cannot be used in the original scheduled time period due to capacity conflicts during the performance of contract i; The total number of key production resources that need to be occupied to fulfill contract i itself is an inherent attribute of the contract.
  3. 3. The intelligent checking and risk detecting method according to claim 1, wherein in step S3, a quantized delivery risk factor is calculated for each contract, specifically by the following formula: Wherein, the The calculation formula of the sub-index of the material risk of the contract i is as follows Wherein To fulfill the total number of materials missing from contract i, The total demand for the materials for contract i; is a time risk sub-indicator of contract i; the productivity risk sub-index of the contract i; 、 、 for preset weight coefficient, satisfy + + =1。
  4. 4. A method for intelligently verifying and risk detecting a contract as defined in claim 3, wherein said time risk sub-indicator The calculation formula of (2) is as follows: Wherein, the method comprises the steps of, The available response time of the contract i is the difference between the contracted delivery date of the contract i and the current system date; average procurement or production lead time of critical materials or processes required to fulfill contract i.
  5. 5. A method for intelligently verifying and risk detecting a contract as defined in claim 3, wherein said capacity risk sub-indicator The calculation formula of (2) is as follows: Wherein, the method comprises the steps of, The total length of a time window for simulating the scheduled conflict caused by the contract i in production; Total duration for planned production for contract i; in order to fulfill the contract i, the critical production resource quantity which cannot be used in the original planning period due to the capacity conflict is determined, if no conflict exists =0; The total number of key production resources required to be occupied to fulfill contract i is an inherent attribute of the contract; And (3) with For the preset sub-weight coefficient, satisfy + =1, And is determined by constructing a multiple linear regression model based on historical scheduling conflict data with conflict time window length and conflict resource number as independent variables and contract actual delay time as dependent variables, normalizing regression coefficients to obtain normalized coefficients as And (3) with Is a value of (a).
  6. 6. A method for intelligent checking and risk detection of a contract as set forth in claim 3, characterized in that said weight coefficients 、 、 Dynamically determined by: The first mode is to perform normalization calculation based on element importance scores input by users, namely , , Wherein 、 、 Importance scores of material supply stability, delivery timeliness and productivity utilization rate are respectively given; and selecting from preset typical weight templates, wherein the typical weight templates comprise a material priority mode, a delivery priority mode, a productivity priority mode and an equalization mode.
  7. 7. The method for intelligently checking and detecting risks of contracts according to claim 1, wherein in step S4, the execution sequence of all contracts is dynamically simulated and rearranged based on a preset optimization target, and the method specifically comprises the following steps: Setting a global optimization objective function, wherein the objective function is the maximum weighted overall contract on-time delivery rate Or minimizing the overall capacity idle rate Wherein, the method comprises the steps of, For indicating functions, if the contract j can be delivered on time in the simulated scheduling scheme, the value is 1, otherwise, the value is 0; for the preset weight of contract j, the weight is determined according to one or more of contract amount, customer level, or delivery urgency; to simulate the total length of resource idle in the scheduling scheme, A planned total duration for the simulated scheduling scheme; The method comprises the steps of carrying out repeated arrangement on the execution sequence of the executable contracts in the algorithm iteration process by adopting a genetic algorithm to generate candidate scheduling schemes, carrying out forward pushing simulation on each candidate scheduling scheme according to a global resource constraint model established in the step S2, verifying whether the material and productivity constraints of each contract in the scheme are met at the assumed execution time point, calculating objective function values corresponding to all the possible candidate scheduling schemes, and selecting the objective function values to enable the material and productivity constraints to be met Maximum or The smallest solution is used as the final recommended optimization contract execution sequence.
  8. 8. The method for intelligently checking and detecting risk of contract according to claim 1, wherein in step S5, the global collaborative visual bulletin board comprises: the risk overview instrument panel displays the part ratio of each risk level contract by using an annular chart and displays the sum accumulation of the high risk contract by using a histogram; a collaborative line Cheng Gante diagram, which shows the multi-contract production schedule optimized in the step S4, wherein the horizontal axis is a time axis, the vertical axis is a resource axis, each contract is represented by a color cross bar with definite starting and ending time scales, and the color of the cross bar corresponds to the risk level; The intelligent disposal workbench displays disposal suggestions for each high-risk contract in a list form, wherein each suggestion entry comprises risk positioning, influence analysis and a recommendation operation button, and the recommendation operation button comprises automatic generation of a deferred negotiation document, adoption of alternative materials and creation of engineering change requests and approval of emergency purchase applications.
  9. 9. The method for intelligently checking and detecting the risk of a contract according to claim 1, wherein in step S5, the global collaborative visual bulletin board is based on a role-based access control model, and sensitive fields are filtered in real time according to the roles of users in a data display layer, and are replaced by desensitization symbols, so that dynamic desensitization is realized, specifically: When the user role is "production planner", the automatic shielding contract content in the sign board is "final customer name" and "single-chip selling price" field, and when the user role is "external provider representative", the automatic shielding contract content in the sign board is "My production cost composition" and "core technological parameter" field.

Description

Contract intelligent verification and risk detection method Technical Field The invention relates to the technical field of intelligent manufacturing and supply chain management, in particular to a contract intelligent checking and risk detecting method. Background With the rapid development of electronic product markets such as automobile electronics, energy storage equipment, industrial Internet terminals and the like, the number of OEM/ODM contracts simultaneously accepted by manufacturing enterprises is increased rapidly, multiple contract parallel performance becomes normal, one automobile central control contract possibly relates to dozens of core materials such as automobile standard MCU, DDR and the like, the delivery cycle is tight, the production process requirements are high, and the limited material inventory and production line productivity resources are often shared by the automobile central control contract and the energy storage BMS contracts and industrial gateway contracts, at present, the enterprises usually adopt the following technical means when processing the multiple contract performance, firstly, the material demand and inventory are managed through an enterprise resource scheduling system, secondly, the occupied capacity is recorded through a manufacturing execution system, thirdly, a schedule is manually compared with a bill of materials and delivery time of the multiple contracts, whether resource conflicts exist or not is judged by combining experience, the prior art has obvious defects that firstly, the material demand information is dispersed in the multiple contract documents, the manual extraction and the summarization efficiency is low, the mistakes are easy to be easy, the material resource conflicts across contracts are difficult to discover in time, secondly, inventory data and productivity data are separated into different information systems, the whole production rate is not easy to be established, the whole production rate is difficult to realize by the fact that the whole production rate is difficult to realize by the optimal, the whole production rate can be completely accurate to be compared with the whole production rate, and the whole production rate is difficult to realize, and the whole production rate can be completely estimated, and the whole production rate is difficult to be completely by the whole production rate can be judged, and can be completely by the best by the whole production rate, and the whole production rate is difficult to be required to be completely and accurately by the full by the accurate, and can be compared, and can be judged by the basis due to the fact, A contract intelligent verification and risk detection method for performing quantitative evaluation and intelligent scheduling optimization on performance risks. Disclosure of Invention The technical problem to be solved by the invention is to overcome the defects of the prior art, and provide the contract intelligent verification and risk detection method capable of realizing closed-loop optimization from global resource modeling, multidimensional risk quantification, intelligent treatment scheduling to collaborative visual decision, and the intelligent level of multi-contract performance is improved through a dynamic regression weighting and optimization-verification mechanism. A contract intelligent verification and risk detection method comprises the following steps of; S1, multi-contract analysis and structured extraction, namely, receiving a plurality of contract documents uploaded by users in batches, analyzing each contract, and extracting structured fulfillment constraint information including a bill of material requirements, delivery time and production process requirements; S2, global constraint modeling and conflict detection, namely carrying out linkage comparison on the extracted performance constraint information of all contracts and inventory data, in-transit material data and capacity planning data in enterprise real-time operation data, establishing a global resource constraint model, and detecting and identifying material resource conflicts and key capacity conflicts existing among multiple contracts; s3, performing quantitative assessment and grading, namely calculating a quantitative delivery risk coefficient for each contract based on detection results of resource conflict and capacity conflict, and performing grade marking on contract performance risks according to the coefficient; s4, intelligent disposal and dynamic scheduling, namely automatically generating disposal suggestions including adjustment of delivery date, replacement of materials and starting of emergency purchase aiming at the contracts marked as high risk, and meanwhile, carrying out dynamic simulation and priority rearrangement on the execution sequence of all the contracts based on a preset optimization target to output an optimized contract execution sequence; s5, global collaborative visual