WO2021052031A1 - Procédé et système de pré-alerte de risque de stock de marchandises basés sur un écart interquartile statistique, et support de stockage lisible par ordinateur - Google Patents

Procédé et système de pré-alerte de risque de stock de marchandises basés sur un écart interquartile statistique, et support de stockage lisible par ordinateur Download PDF

Info

Publication number
WO2021052031A1
WO2021052031A1 PCT/CN2020/105964 CN2020105964W WO2021052031A1 WO 2021052031 A1 WO2021052031 A1 WO 2021052031A1 CN 2020105964 W CN2020105964 W CN 2020105964W WO 2021052031 A1 WO2021052031 A1 WO 2021052031A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
inventory
early warning
abnormal
interquartile range
Prior art date
Application number
PCT/CN2020/105964
Other languages
English (en)
Chinese (zh)
Inventor
欧文祥
徐亮
蒋旭曦
Original Assignee
苏宁云计算有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 苏宁云计算有限公司 filed Critical 苏宁云计算有限公司
Priority to CA3155689A priority Critical patent/CA3155689A1/fr
Publication of WO2021052031A1 publication Critical patent/WO2021052031A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders

Definitions

  • the invention belongs to the application of big data in the field of retail risk control, and specifically relates to a method, system and computer-readable storage medium for early warning of commodity inventory risks based on statistical interquartile range.
  • the research on data outlier detection methods is currently mainly focused on unsupervised anomaly detection.
  • Commonly used detection methods include statistical and probability model methods, linear model-based methods, and similarity-based measurement models.
  • the methods based on statistics mainly include the 3 ⁇ principle and the method based on box plot analysis.
  • the methods based on the linear model mainly include PCA (principal component) analysis and One-class SVM (support vector machine), etc., based on the similarity measurement model
  • the main methods include k-nearest neighbor and Isolation Forest (isolated forest). Due to the wide variety of commodities, the amount of data is very large, and the commodity inventory data belongs to a one-dimensional time series, the calculation cost based on the linear model and the similarity measurement model is relatively large.
  • the present invention will adopt a statistical method .
  • the 3 ⁇ principle only applies to data that obey a normal distribution.
  • an outlier is defined as the deviation between the observed value and the average value by more than 3 times the standard deviation, P(
  • the probability of occurrence of a value greater than 3 ⁇ is less than 0.003, which is a small probability event, so it can be regarded as an outlier.
  • Inventory data belongs to time series. At present, many detection methods do not consider the time series characteristics of time series, but consider from the complete set of data. Local outliers are easy to be missed. In addition, inventory data has some characteristics of its own. For certain categories of goods , May remain unchanged for a long duration, that is, there is a lot of duplicate data.
  • the purpose of the present invention is to provide a method and system for early warning of commodity inventory risk based on statistical interquartile range, to overcome the high calculation cost and the large amount of data in the prior art. Problems such as low timeliness.
  • a method for early warning of commodity inventory risk based on statistical interquartile range comprising:
  • the number of bits, MAX is the threshold.
  • calculating the inventory increment data according to the original commodity inventory data includes the following steps:
  • the original product inventory data is first grouped by store and product, and sorted by time, and the missing data is filled with zero values to get the preliminarily organized historical data;
  • the calculation process of the interquartile range includes:
  • the method further includes adopting a sliding time window mode, and recalculating a new abnormality detection threshold at intervals of a period of time.
  • the latest inventory data is collected every day.
  • the data is used to determine the abnormality of the inventory data for a period of time in the future to improve the data judgment Timeliness.
  • the method further includes, after the front-end receives the abnormal data push, a business person manually reviews it to determine whether it is abnormal data. After being judged as abnormal data, manual detection can further improve the accuracy of judgment.
  • the spark data platform is used to process the grouping and sorting of the original product data and the difference operation. Using the spark platform can improve computing power and processing efficiency.
  • a risk identification system for suspected actual controllers based on knowledge graphs includes:
  • the data collection module is used to obtain the original product inventory data of all stores in a certain historical time period from the inventory database;
  • the data processing module performs processing operations on the original product inventory data to obtain the inventory incremental data
  • the threshold calculation module calculates the upper and lower quartiles of the inventory increment data, and calculates the interquartile range and abnormal detection threshold according to the upper and lower quartiles;
  • the early warning module detects whether the new inventory increment exceeds the abnormal detection threshold. If it exceeds, it will be judged as abnormal data and sent to the front-end early warning.
  • the data processing module includes:
  • the data grouping unit groups the original product inventory data
  • the data sorting unit sorts the original product inventory data according to time, and fills in missing data with zero values
  • the difference calculation unit performs a difference operation on the grouped and sorted data, takes the absolute value of the result, and removes all zero values to obtain the final inventory increment data.
  • the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is run by a processor, each step of the method in the present invention is executed.
  • the present invention uses the method of statistical interquartile range to calculate the threshold of abnormal inventory increment.
  • the calculation efficiency is high, and the risk is quickly and accurately positioned. Compared with the traditional manual audit and inventory, the workload is greatly reduced, and it can be avoided. Differences caused by human subjective factors.
  • the present invention actively warns the user or the front end when the monitoring exceeds the threshold, and can realize the T+1 early warning mode.
  • the abnormality detection and judgment of the inventory data every day greatly increases the risk of abnormal inventory. Timeliness of discovery.
  • the present invention uses the spark platform to process and calculate inventory incremental data, utilizes its computing power under a large amount of data and its advantages in iterative computing scenarios, and uses multiple threads for concurrent processing, which greatly improves data performance. Processing efficiency.
  • FIG. 1 is a schematic flowchart of a method for early warning of commodity inventory risk based on statistical interquartile range in an embodiment of the present invention.
  • Fig. 2 is a schematic diagram of setting interquartile range, interquartile range and threshold value in an embodiment of the present invention.
  • FIG. 3 is a statistical schematic diagram of the change in the inventory quantity of a certain commodity in the past year and the corresponding abnormality detection threshold in the embodiment of the invention.
  • FIG. 4 is a structural diagram of a commodity inventory risk early warning system based on statistical interquartile range in an embodiment of the present invention.
  • the embodiment of the present invention discloses a method for early warning of commodity inventory risk based on statistical interquartile range.
  • the method includes the following steps:
  • the product inventory data of all stores for a period of time before the current date is obtained from the product inventory database. For example, based on one year, the data in the first 12 months from this month is counted.
  • the product inventory The data in the database can be synchronously transmitted to the HDFS (distributed file storage) system of the HADOOP cluster at regular intervals, so that it can be directly obtained from the HDFS platform.
  • HDFS distributed file storage
  • this step includes:
  • the original product inventory data is first grouped by store and product, and sorted by time.
  • the missing data can be filled in with zero values on a day-to-day basis to get the preliminarily organized historical data;
  • the inventory increment data is the daily inventory increment during the historical period.
  • the sorting time can also be counted according to week and month, which is the weekly or monthly inventory increment data.
  • the system monitors the new inventory increment in real time.
  • the new inventory increment changes and exceeds the threshold, it actively reminds the front end and users to remind the financial staff to pay attention.
  • the detected abnormal result data will also be synchronized to the database of the application system, pre-stored in the Mysql (relational database management system) database, the process engine automatically initiates the abnormal process to the corresponding financial manager, and the financial manager can use the abnormal data Perform manual verification and feed back the final judgment result.
  • the quartile is also called the quartile point, which refers to the reduction of all values in statistics from small to Large array and divided into four equal parts, the numerical value at the position of the three dividing points.
  • the first quartile Q1 also known as the "lower quartile”
  • the second quartile Q2 also known as the "median ", equal to the 50% digit after all the values in the sample are arranged in descending order.
  • the third quartile Q3 also known as the "upper quartile" is equal to all the values in the sample arranged in descending order The number 75%.
  • step (7) Send the store, date and product information corresponding to the abnormal value detected in step (6) to the relevant business department.
  • the business department will check with the information of all parties and on-site investigations. If it is determined that there is a risk, it can be carried out by the company's legal department.
  • the next step is to deal with it to avoid greater losses; as shown in Figure 3, the figure shows the inventory risk early warning case of a store from June 2018 to June 2019. From the results in Figure 3, it can be seen that the risk level in January 19 was obvious Above the threshold, it can basically be determined that the store has data abnormalities and large financial risks.
  • the present invention provides a method for early warning of commodity inventory risk based on statistical interquartile range. According to the characteristics of commodity inventory data time series that are easily affected by the macroeconomic situation, seasons, promotional activities, etc., a sliding window is used to count the four points of the sample The number of digits is used to calculate the abnormality detection threshold, so that the abnormal value of the inventory data can be detected more accurately.
  • the method of the present invention has low calculation overhead, short running time of the computer program, and can realize quasi real-time detection.
  • the workload is huge and the efficiency is low.
  • an audit is performed only in a few months or a longer period, and each audit time also needs to last several days or longer; the use of the present invention
  • the T+1 form of once-a-day detection can be realized, and the task execution averages 15 minutes, and the detected possible abnormal data can be pushed to the corresponding financial manager through the process, and the relevant personnel will arrange the targeted review.
  • the results can be fed back on the same day, and the entire process closed-loop of risk discovery, risk early warning, abnormal push, risk review, result feedback, and post-event accountability can be realized, effectively detecting and avoiding abnormal risks in a timely manner.
  • the program can be stored in a judging machine storage medium, and the storage medium can include : Read-only memory ROM, random access memory RAM, magnetic disk or optical disk, etc.
  • the present invention also provides a risk identification system for suspected actual controllers based on a knowledge graph, the system including:
  • the data collection module is used to obtain the original product inventory data of all stores in a certain historical time period from the product inventory database of the enterprise platform;
  • the data processing module performs processing operations on the original product inventory data to obtain the inventory incremental data
  • the early warning module detects whether the new inventory increment exceeds the abnormal detection threshold. If it exceeds, it will be judged as abnormal data and sent to the front-end early warning. Front-end personnel, such as financial personnel, can manually check after receiving the warning information to further confirm the risk.
  • the invention realizes the rapid and accurate detection of the abnormal value of the commodity inventory, and can effectively avoid the abnormal risk in time.
  • the data processing module includes:
  • the data grouping unit groups the original product inventory data according to stores and products
  • the data sorting unit sorts the original product inventory data according to time, such as day as a unit, and fills in the missing data with zero values. For example, if there are no products in stock on a certain day, fill in 0;
  • the difference calculation unit performs a difference operation on the grouped and sorted data, takes the absolute value of the result, and removes all zero values to obtain the final inventory increment data.
  • the amount of data is large, for example, a platform has data of the order of 20 billion, it is basically not feasible to use traditional data analysis directly for differential calculation, and it is difficult to use traditional JAVA or database calculation schemes.
  • spark is used for data processing, using its computing power under a large amount of data and its advantages in an iterative computing scenario, and simultaneously using multiple threads for concurrent processing.
  • the actual initialization only takes a few hours. It can be completed, greatly improving the computing efficiency.
  • the functional modules in the embodiments of the present invention may be integrated into one processing module, or each module may exist alone physically.
  • the above-mentioned integrated modules, systems, and platforms can be implemented in hardware or software functions. The form of the unit is realized.

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Strategic Management (AREA)
  • Quality & Reliability (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Development Economics (AREA)
  • General Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Educational Administration (AREA)
  • Game Theory and Decision Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention concerne un procédé et un système de pré-alerte de risque de stock de marchandises basés sur un écart interquartile statistique, et un support de stockage lisible par ordinateur comprenant le procédé. Le procédé consiste : à acquérir des données de stock de marchandises d'origine de tous les magasins dans une certaine période historique ; à effectuer un calcul, en fonction des données de stock de marchandises d'origine, de manière à obtenir des données de stock à valeur ascendante ; à calculer des quartiles supérieur et inférieur des données de stock à valeur ascendante, et à calculer un écart interquartile et un seuil de détection d'anomalie en fonction des quartiles supérieur et inférieur ; et à détecter si un nouvel incrément de stock dépasse le seuil de détection d'anomalie, si tel est le cas, à déterminer ledit incrément comme données anormales et à pousser lesdites données vers une extrémité frontale en vue d'une pré-alerte. Le procédé résout les problèmes de l'état de la technique, tels que le manque de détermination d'une valeur anormale, et la faible rapidité de publication dans le cas d'importantes surcharges de calcul et d'une importante quantité de données.
PCT/CN2020/105964 2019-09-20 2020-07-30 Procédé et système de pré-alerte de risque de stock de marchandises basés sur un écart interquartile statistique, et support de stockage lisible par ordinateur WO2021052031A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CA3155689A CA3155689A1 (fr) 2019-09-20 2020-07-30 Procede et systeme de pre-alerte de risque de stock de marchandises bases sur un ecart interquartile statistique, et support de stockage lisible par ordinateur

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910892438.8A CN110751371B (zh) 2019-09-20 2019-09-20 基于统计四分位距的商品库存风险预警方法、系统及计算机可读存储介质
CN201910892438.8 2019-09-20

Publications (1)

Publication Number Publication Date
WO2021052031A1 true WO2021052031A1 (fr) 2021-03-25

Family

ID=69276761

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/105964 WO2021052031A1 (fr) 2019-09-20 2020-07-30 Procédé et système de pré-alerte de risque de stock de marchandises basés sur un écart interquartile statistique, et support de stockage lisible par ordinateur

Country Status (3)

Country Link
CN (1) CN110751371B (fr)
CA (1) CA3155689A1 (fr)
WO (1) WO2021052031A1 (fr)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220327172A1 (en) * 2021-04-09 2022-10-13 Servicenow, Inc. Evaluation and Recommendation Engine for a Remote Network Management Platform
CN115188186A (zh) * 2022-06-28 2022-10-14 公安部交通管理科学研究所 一种区域内交通流量监测方法
CN115858894A (zh) * 2023-02-14 2023-03-28 温州众成科技有限公司 一种可视化的大数据分析方法
CN117454121A (zh) * 2023-12-22 2024-01-26 华能济南黄台发电有限公司 一种基于电厂安全预警的数据分析处理方法及系统
CN117556364A (zh) * 2024-01-12 2024-02-13 济南福深兴安科技有限公司 一种矿用矿压安全智能监测系统

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110751371B (zh) * 2019-09-20 2022-06-07 苏宁云计算有限公司 基于统计四分位距的商品库存风险预警方法、系统及计算机可读存储介质
CN111428886B (zh) * 2020-04-10 2023-08-04 青岛聚好联科技有限公司 一种故障诊断的深度学习模型自适应更新的方法及装置
CN111506625A (zh) * 2020-04-20 2020-08-07 中国建设银行股份有限公司 一种告警阈值确定方法及装置
CN111861410A (zh) * 2020-07-27 2020-10-30 北京百川盈孚科技有限公司 数据变动异常预警方法、系统及装置
CN111967345B (zh) * 2020-07-28 2023-10-31 国网上海市电力公司 一种实时判定摄像头遮挡状态的方法
CN112001621A (zh) * 2020-08-21 2020-11-27 广州云徙科技有限公司 一种关键指标的智能预警方法
CN112070408A (zh) * 2020-09-14 2020-12-11 唐山学院 基于大数据和深度学习的烧结矿成分预报模型
CN112116014A (zh) * 2020-09-24 2020-12-22 贵州电网有限责任公司 一种配电自动化设备测试数据离群值检测方法
CN112700085A (zh) * 2020-12-11 2021-04-23 华南理工大学 基于关联规则的复杂系统稳态运行参数优化方法、系统和介质
CN112768062A (zh) * 2021-01-26 2021-05-07 武汉大学 提高医疗数值型数据矫正效率的方法、系统及存储介质
CN113096393A (zh) * 2021-03-29 2021-07-09 中移智行网络科技有限公司 一种路况预警方法、装置及边缘云设备
CN113159597B (zh) * 2021-04-29 2022-08-16 浙江陀曼云计算有限公司 基于时序数据波动的机床生产间隔计算方法及系统

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100114745A1 (en) * 2008-10-30 2010-05-06 Sap Ag System and method for calculating and applying market data change rate sets
CN103631681A (zh) * 2013-12-10 2014-03-12 国家电网公司 一种在线修复风电场异常数据的方法
CN104915846A (zh) * 2015-06-18 2015-09-16 北京京东尚科信息技术有限公司 一种电子商务时间序列数据的异常检测方法及系统
CN110751371A (zh) * 2019-09-20 2020-02-04 苏宁云计算有限公司 基于统计四分位距的商品库存风险预警方法、系统及计算机可读存储介质

Family Cites Families (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10453142B2 (en) * 2009-02-11 2019-10-22 Johnathan Mun System and method for modeling and quantifying regulatory capital, key risk indicators, probability of default, exposure at default, loss given default, liquidity ratios, and value at risk, within the areas of asset liability management, credit risk, market risk, operational risk, and liquidity risk for banks
CN102339288B (zh) * 2010-07-21 2013-09-25 中国移动通信集团辽宁有限公司 数据仓库异常数据的检测方法及装置
US10762173B2 (en) * 2011-12-05 2020-09-01 Omnicell, Inc. System and method for managing inventory at dispensing units
CN104636933B (zh) * 2015-02-11 2017-12-12 广州唯品会信息科技有限公司 电子商务网站超卖原因定位的方法及装置
CN104657503A (zh) * 2015-03-13 2015-05-27 浪潮集团有限公司 一种基于统计判别法对电商销售额异常值的预处理方法
CN105808368B (zh) * 2016-03-15 2019-04-30 南京联成科技发展股份有限公司 一种基于随机概率分布的信息安全异常检测的方法及系统
CN106407278B (zh) * 2016-08-26 2020-03-17 宝信软件(武汉)有限公司 一种大数据平台的架构设计系统
CN111615711A (zh) * 2018-02-26 2020-09-01 伯克顿迪金森公司 用于安全库存建模的视觉交互式应用
CN108830510B (zh) * 2018-07-16 2021-09-21 国网上海市电力公司 一种基于数理统计的电力数据预处理方法
CN109191283A (zh) * 2018-08-30 2019-01-11 成都数联铭品科技有限公司 风险预警方法及系统
CN109978386A (zh) * 2019-03-28 2019-07-05 六安致跃供应链管理有限公司 一种基于供应链的可视化库存系统及其可视化管理方法
CN110032670B (zh) * 2019-04-17 2022-11-29 腾讯科技（深圳）有限公司 时序数据的异常检测方法、装置、设备及存储介质

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100114745A1 (en) * 2008-10-30 2010-05-06 Sap Ag System and method for calculating and applying market data change rate sets
CN103631681A (zh) * 2013-12-10 2014-03-12 国家电网公司 一种在线修复风电场异常数据的方法
CN104915846A (zh) * 2015-06-18 2015-09-16 北京京东尚科信息技术有限公司 一种电子商务时间序列数据的异常检测方法及系统
CN110751371A (zh) * 2019-09-20 2020-02-04 苏宁云计算有限公司 基于统计四分位距的商品库存风险预警方法、系统及计算机可读存储介质

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220327172A1 (en) * 2021-04-09 2022-10-13 Servicenow, Inc. Evaluation and Recommendation Engine for a Remote Network Management Platform
CN115188186A (zh) * 2022-06-28 2022-10-14 公安部交通管理科学研究所 一种区域内交通流量监测方法
CN115188186B (zh) * 2022-06-28 2024-02-20 公安部交通管理科学研究所 一种区域内交通流量监测方法
CN115858894A (zh) * 2023-02-14 2023-03-28 温州众成科技有限公司 一种可视化的大数据分析方法
CN115858894B (zh) * 2023-02-14 2023-05-16 温州众成科技有限公司 一种可视化的大数据分析方法
CN117454121A (zh) * 2023-12-22 2024-01-26 华能济南黄台发电有限公司 一种基于电厂安全预警的数据分析处理方法及系统
CN117454121B (zh) * 2023-12-22 2024-04-05 华能济南黄台发电有限公司 一种基于电厂安全预警的数据分析处理方法及系统
CN117556364A (zh) * 2024-01-12 2024-02-13 济南福深兴安科技有限公司 一种矿用矿压安全智能监测系统
CN117556364B (zh) * 2024-01-12 2024-03-29 济南福深兴安科技有限公司 一种矿用矿压安全智能监测系统

Also Published As

Publication number Publication date
CA3155689A1 (fr) 2021-03-25
CN110751371B (zh) 2022-06-07
CN110751371A (zh) 2020-02-04

Similar Documents

Publication Publication Date Title
WO2021052031A1 (fr) Procédé et système de pré-alerte de risque de stock de marchandises basés sur un écart interquartile statistique, et support de stockage lisible par ordinateur
US11093519B2 (en) Artificial intelligence (AI) based automatic data remediation
CN107810500B (zh) 数据质量分析
WO2019056681A1 (fr) Procédé, dispositif, appareil terminal et support de stockage pour surveillance de données en temps réel
US10614073B2 (en) System and method for using data incident based modeling and prediction
US11182394B2 (en) Performing database file management using statistics maintenance and column similarity
WO2020062702A9 (fr) Procédé et dispositif pour envoyer des messages textuels, dispositif informatique et support de stockage
CN109934268B (zh) 异常交易检测方法及系统
US20150220868A1 (en) Evaluating Data Quality of Clinical Trials
CN107679734A (zh) 一种用于无标签数据分类预测的方法和系统
US20150339600A1 (en) Method and system for analysing data
US20210364999A1 (en) System and method for analyzing cause of product defect, computer readable medium
US11954945B2 (en) Systems and methods for analyzing machine performance
CN112465397A (zh) 一种审计数据的分析方法和装置
WO2019196502A1 (fr) Procédé d'évaluation de qualité d'activé de commercialisation, serveur et support d'informations lisible par ordinateur
CN115860465A (zh) 一种企业关联数据处理预警方法、系统及装置
CN115689713A (zh) 异常风险数据处理方法、装置、计算机设备和存储介质
CN112258095B (zh) 基于标准正态分布的评分方法、装置、设备及存储介质
CN114312930A (zh) 基于日志数据的列车运行异常诊断方法和装置
CN112966897A (zh) 一种基于维修平台的多维度数据分析方法
CN110737700A (zh) 一种基于贝叶斯算法的进销存用户分类方法及系统
US20210350305A1 (en) Notifying key performance indicators of interest in a business intelligence system
US20240242129A1 (en) Segmented machine learning-based modeling with period-over-period analysis
CN111626586B (zh) 数据质量检测方法、装置、计算机设备和存储介质
US20230334496A1 (en) Automated transaction clustering based on rich, non-human filterable risk elements

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20864908

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 3155689

Country of ref document: CA

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20864908

Country of ref document: EP

Kind code of ref document: A1