Comprehensive operation and maintenance system and method of digital supply chain platform based on general artificial intelligence

Abstract

The invention discloses a comprehensive operation and maintenance system and method for a digital supply chain platform based on general artificial intelligence, and the system comprises a data collection module and an early warning measure module, and the data collection module is in control connection with a data processing module; the AI algorithm is used to analyze historical data, predict future demands, help to formulate purchase plans and inventory strategies, monitor market trends and consumer behavior changes in real time, dynamically adjust a prediction model, analyze factors such as the market trends, supplier performance and natural disasters, assess potential risks, and provide countermeasures; a key performance index threshold value is set, once the key performance index threshold value exceeds a set range, an alarm is triggered, related personnel are notified to take actions, and potential problems are actively recognized and early warning is given out through an AI-driven anomaly detection system, so that the safety of the whole system is improved; self-learning, continuous experience accumulation and model precision improvement can be realized, and changing market conditions can be better adapted.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated operation and maintenance systems, and particularly to an integrated operation and maintenance system and method for a digital supply chain platform based on general artificial intelligence. Background Technique

[0002] The integrated operation and maintenance system for a digital supply chain platform based on general artificial intelligence can bring significant advantages to enterprises in many aspects, covering multiple levels from efficiency improvement to risk management; by integrating data from all links of the supply chain and using AI technology for in-depth analysis, it provides more accurate demand forecasting and inventory management suggestions; with the real-time data analysis ability, the management can quickly obtain the latest information and make fast and accurate decisions; by continuously optimizing aspects such as logistics routes and inventory levels, it reduces operating costs; it provides personalized recommendations and service experiences according to users' preferences and behavior patterns; however, the currently popular integrated operation and maintenance systems for digital supply chain platforms based on general artificial intelligence have obvious defects: First, they cannot adjust strategies in a timely manner according to changes in market trends or the impact of emergencies to cope with new situations; second, the large model cannot continuously learn and adapt; finally, there is no alarm mechanism; therefore, it is very necessary to design an integrated operation and maintenance system and method for a digital supply chain platform based on general artificial intelligence. Summary of the Invention

[0003] The purpose of the present invention is to provide an integrated operation and maintenance system and method for a digital supply chain platform based on general artificial intelligence to solve the problems raised in the above background technique.

[0004] To achieve the above purpose, the present invention provides the following technical solutions: An integrated operation and maintenance system and method for a digital supply chain platform based on general artificial intelligence, including a data collection module, a data processing module, an intelligent analysis module, a repository module, an auxiliary decision-making module, a continuous improvement module, a data security module, and an early warning measure module. The data collection module is controlled and connected to the data processing module, the data processing module is respectively controlled and connected to the intelligent analysis module, the intelligent analysis module is respectively controlled and connected to the repository module, the continuous improvement module, the data security module, and the early warning measure module, the repository module is controlled and connected to the auxiliary decision-making module, and the auxiliary decision-making module is controlled and connected to the continuous improvement module.

[0005] As a further technical solution of the present invention, the data processing module is composed of a data cleaning module, a missing value processing module, an outlier processing module, a consistency checking module, a missing value detection module, an outlier definition module, a unified format module, a processing strategy module, a solution strategy module, a spelling difference resolution module, a data conversion module, a data standard module, a feature creation module, a feature selection module, a data splitting module, and a secondary checking module. The data cleaning module controls and connects to the missing value processing module, the outlier processing module, and the consistency checking module respectively.

[0006] As a further technical solution of the present invention, the missing value processing module controls and connects to the missing value detection module, the missing value detection module controls and connects to the processing strategy module, the outlier processing module controls and connects to the outlier definition module, the outlier definition module controls and connects to the solution strategy module, the consistency checking module controls and connects to the unified format module, and the unified format module controls and connects to the spelling difference resolution module.

[0007] As a further technical solution of the present invention, the missing value processing module, the outlier processing module, and the consistency checking module control and connect to the data conversion module, the data conversion module controls and connects to the data standard module, the data standard module controls and connects to the feature creation module, the feature creation module controls and connects to the feature selection module, and the feature selection module controls and connects to the data splitting module.

[0008] As a further technical solution of the present invention, the intelligent analysis module is composed of a demand prediction module, a risk assessment module, a risk management module, an optimization suggestion module, and an automated task scheduling module. The demand prediction module controls and connects to the risk assessment module, the risk assessment module controls and connects to the risk management module, the risk management module controls and connects to the optimization suggestion module, and the optimization suggestion module controls and connects to the automated task scheduling module.

[0009] As a further technical solution of the present invention, the auxiliary decision-making module is composed of a historical call module, a comparison and analysis module, and a targeted decision-making module. The historical call module controls and connects to the comparison and analysis module, and the comparison and analysis module controls and connects to the targeted decision-making module.

[0010] As a further technical solution of the present invention, the continuous improvement module is composed of an effect evaluation module and a learning and adaptation module. The effect evaluation module controls and connects to the learning and adaptation module.

[0011] As a further technical solution of the present invention, the data security module is composed of a data security protection module, a privacy protection module, and a compliance review module. The data security protection module controls and connects to the privacy protection module, and the privacy protection module controls and connects to the compliance review module.

[0012] As a further technical solution of the present invention, the early warning measure module is composed of a real-time monitoring module and an early warning mechanism module, and the real-time monitoring module is controllably connected to the early warning mechanism module.

[0013] An integrated operation and maintenance method for a digital supply chain platform based on general artificial intelligence, comprising the following steps: Step 1, data collection and processing; Step 2, intelligent analysis and early warning; Step 3, continuous upgrade and improvement; Step 4, data security guarantee;

[0014] Among them, in the above Step 1, data sources from different supply chain links are integrated in the data collection module, including suppliers, manufacturers, warehouses, logistics service providers, etc., covering order information, inventory levels, transportation status, market demand forecasts, etc. The data is processed in the data processing module. In the missing value detection module of the missing value processing module, it is checked which columns have missing values and the missing ratio. In the processing strategy module, records or variables with a large number of missing values are deleted, numerical data is filled with the mean / median, and categorical data is filled with the mode. Missing values are filled based on model prediction. In the outlier definition module of the outlier processing module, outliers are defined based on business logic or statistical methods. In the solution strategy module, obvious error data points are corrected, extreme outliers are deleted or marked as special status. In the unified format module of the consistency check module, the consistency of dates, currency units, etc. is ensured. In the spelling difference resolution module, the same entity names in different sources are unified. In the data conversion module, the data type of each column is ensured to be correct. In the data standard module, the data conforms to the standard normal distribution and the numerical values are scaled to the interval [0,1]. In the feature creation module, new meaningful features are generated based on existing features. In the feature selection module, redundant or irrelevant features are removed to reduce the impact of the curse of dimensionality. Common methods include correlation coefficient analysis, principal component analysis, etc. In the data splitting module, the data is divided into a training set and a test set according to a certain ratio, and sometimes a part is set aside as a validation set. In the secondary inspection module, statistical descriptions and visualization tools are run again to confirm that all problems have been properly solved;

[0015] Among them, in step 2 above, in the demand forecasting module of the intelligent analysis module, AI algorithms are used to analyze historical sales data to predict future demand, help formulate procurement plans and inventory strategies, and monitor market trends and changes in consumer behavior in real time, dynamically adjusting the prediction model. In the risk assessment module, factors such as market trends, supplier performance, and natural disasters are analyzed to evaluate potential risks and propose countermeasures. In the risk management module, natural language processing technology is used to analyze unstructured data such as news and social media to early warn of risk factors that may affect the supply chain. In the optimization recommendation module, based on real-time data analysis, optimization recommendations are provided for aspects such as inventory management, distribution route planning, and cost control, and advanced AI technologies such as reinforcement learning are used to optimize complex supply chain network designs. In the automated task scheduling module, according to the prediction results and optimization recommendations, production plans, shipping arrangements, etc. are automatically adjusted to reduce manual intervention, and strategies can be adjusted in a timely manner to cope with new situations according to changes in market trends or the impact of emergencies. In the real-time monitoring module of the early warning measures module, sensors and Internet of Things devices are deployed to achieve real-time monitoring of goods transportation, warehousing environment, etc., timely detect abnormal situations, and use computer vision technology to monitor warehouse operations to identify abnormal operations or safety hazards. In the early warning mechanism module, key performance indicator thresholds are set, and once the set range is exceeded, an alarm is triggered to notify relevant personnel to take actions, and through an AI-driven anomaly detection system, potential problems are actively identified and early warnings are issued, improving the security of the entire system;

[0016] In step 3 above, in the historical call module of the auxiliary decision-making module, past data is called. In the comparison and analysis module, the actual effects of various decisions are regularly evaluated, compared with the expected goals, gaps are found, and new suggestions are put forward in the targeted decision-making module. In the effect evaluation module of the continuous improvement module, the A / B test method is used to verify the effects of different strategies, select the optimal solution, and self-learn in the learning and adaptation module, continuously accumulating experience and improving the model accuracy to better adapt to changing market conditions;

[0017] In step 4 above, in the data security protection module of the data security module, encryption technologies and access control strategies are adopted to ensure the security of sensitive information and implement data desensitization technology to protect user privacy without affecting the analysis effect. In the privacy protection module, differential privacy technology is used to protect individual data privacy during the data sharing process. In the compliance review module, internal audits are regularly conducted to check whether the system meets the latest compliance requirements.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: The integrated operation and maintenance system and method of a digital supply chain platform based on general artificial intelligence analyze historical sales data using AI algorithms to predict future demand, assist in formulating procurement plans and inventory strategies, and monitor market trends and changes in consumer behavior in real time, dynamically adjusting the prediction model, analyzing factors such as market trends, supplier performance, and natural disasters, evaluating potential risks, and proposing countermeasures, and can adjust strategies in a timely manner according to changes in market trends or the impact of emergencies to cope with new situations; set key performance indicator thresholds, trigger an alarm once the set range is exceeded, notify relevant personnel to take actions, and through an AI-driven anomaly detection system, actively identify potential problems and issue early warnings, improving the security of the entire system; self-learn, continuously accumulate experience, and improve the model accuracy, and can better adapt to changing market conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0020] Figure 2 It is a schematic diagram of the architecture of the data processing module in the present invention;

[0021] Figure 3 It is a schematic diagram of the architecture of the intelligent analysis module in the present invention;

[0022] Figure 4 It is a schematic diagram of the architecture of the auxiliary decision-making module in the present invention;

[0023] Figure 5 It is a schematic diagram of the architecture of the continuous improvement module in the present invention;

[0024] Figure 6 It is a schematic diagram of the architecture of the data security module in the present invention;

[0025] Figure 7 It is a schematic diagram of the architecture of the early warning measure module in the present invention;

[0026] Figure 8 It is a flowchart of the method of the present invention.

[0027] In the figure: 1. Data collection module; 2. Data processing module; 3. Intelligent analysis module; 4. Repository module; 5. Auxiliary decision-making module; 6. Continuous improvement module; 7. Data security module; 8. Early warning measure module; 201. Data cleaning module; 202. Missing value processing module; 203. Outlier processing module; 204. Consistency check module; 205. Missing value detection module; 206. Outlier definition module; 207. Unified format module; 208. Processing strategy module; 209. Solution strategy module; 210. Spelling difference resolution module; 211. Data conversion module; 212. Data standard module; 213. Feature creation module; 214. Feature selection module; 215. Data splitting module; 216. Secondary check module; 301. Demand forecasting module; 302. Risk assessment module; 303. Risk management module; 304. Optimization suggestion module; 305. Automated task scheduling module; 501. Historical call module; 502. Comparison and analysis module; 503. Targeted decision-making module; 601. Effect evaluation module; 602. Learning and adaptation module; 701. Data security protection module; 702. Privacy protection module; 703. Compliance review module; 801. Real-time monitoring module; 802. Early warning mechanism module. Detailed implementation manners

[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] Please refer to the attached Figure 1 - attached Figure 7, an embodiment provided by the present invention: an integrated operation and maintenance system for a digital supply chain platform based on general artificial intelligence, including a data collection module 1, a data processing module 2, an intelligent analysis module 3, a repository module 4, an auxiliary decision-making module 5, a continuous improvement module 6, a data security module 7, and an early warning measure module 8. The data collection module 1 is controlled and connected to the data processing module 2. The data processing module 2 is respectively controlled and connected to the intelligent analysis module 3. The intelligent analysis module 3 is respectively controlled and connected to the repository module 4, the continuous improvement module 6, the data security module 7, and the early warning measure module 8. The repository module 4 is controlled and connected to the auxiliary decision-making module 5. The auxiliary decision-making module 5 is controlled and connected to the continuous improvement module 6. The data processing module 2 is composed of a data cleaning module 201, a missing value processing module 202, an outlier processing module 203, a consistency check module 204, a missing value detection module 205, an outlier definition module 206, a unified format module 207, a processing strategy module 208, a solution strategy module 209, a spelling difference resolution module 210, a data conversion module 211, a data standard module 212, a feature creation module 213, a feature selection module 214, a data segmentation module 215, and a secondary check module 216. The data cleaning module 201 is respectively controlled and connected to the missing value processing module 202, the outlier processing module 203, and the consistency check module 204. The missing value processing module 202 is controlled and connected to the missing value detection module 205. The missing value detection module 205 is controlled and connected to the processing strategy module 208. The outlier processing module 203 is controlled and connected to the outlier definition module 206. The outlier definition module 206 is controlled and connected to the solution strategy module 209. The consistency check module 204 is controlled and connected to the unified format module 207. The unified format module 207 is controlled and connected to the spelling difference resolution module 210. The missing value processing module 202, the outlier processing module 203, and the consistency check module 204 are controlled and connected to the data conversion module 211. The data conversion module 211 is controlled and connected to the data standard module 212. The data standard module 212 is controlled and connected to the feature creation module 213. The feature creation module 213 is controlled and connected to the feature selection module 214. The feature selection module 214 is controlled and connected to the data segmentation module 215. The intelligent analysis module 3 is composed of a demand forecasting module 301, a risk assessment module 302, a risk management module 303, an optimization suggestion module 304, and an automated task scheduling module 305. The demand forecasting module 301 is controlled and connected to the risk assessment module 302. The risk assessment module 302 is controlled and connected to the risk management module 303. The risk management module 303 is controlled and connected to the optimization suggestion module 304. The optimization suggestion module 304 is controlled and connected to the automated task scheduling module 305. The auxiliary decision-making module 5 is composed of a historical call module 501, a comparison and analysis module 502, and a targeted decision-making module 503. The historical call module 501 is controlled and connected to the comparison and analysis module 502. The comparison and analysis module 502 is controlled and connected to the targeted decision-making module 503.The continuous improvement module 6 consists of an effect evaluation module 601 and a learning and adaptation module 602, and the effect evaluation module 601 is controllably connected to the learning and adaptation module 602; the data security module 7 consists of a data security protection module 701, a privacy protection module 702, and a compliance review module 703, the data security protection module 701 is controllably connected to the privacy protection module 702, and the privacy protection module 702 is controllably connected to the compliance review module 703; the early warning measure module 8 consists of a real-time monitoring module 801 and an early warning mechanism module 802, and the real-time monitoring module 801 is controllably connected to the early warning mechanism module 802.;

[0030] Please refer to the appendix Figure 8 An embodiment provided by the present invention: A comprehensive operation and maintenance method for a digital supply chain platform based on general artificial intelligence, including the following steps: Step 1, data collection and processing; Step 2, intelligent analysis and early warning; Step 3, continuous upgrade and improvement; Step 4, data security guarantee;

[0031] Among them, in the above Step 1, data sources from different supply chain links are integrated in the data collection module 1, including suppliers, manufacturers, warehouses, logistics service providers, etc., covering order information, inventory levels, transportation status, market demand forecasts, etc. The data is processed in the data processing module 2. In the missing value detection module 205 of the missing value processing module 202, it is checked which columns have missing values and the missing ratio. In the processing strategy module 208, records or variables with a large number of missing values are deleted, numerical data is filled with the mean / median, and categorical data is filled with the mode. Missing values are filled based on model prediction. In the outlier definition module 206 of the outlier processing module 203, outliers are defined based on business logic or statistical methods. In the solution strategy module 209, obvious error data points are corrected, extreme outliers are deleted or marked as special status. In the unified format module 207 of the consistency check module 204, the consistency of dates, currency units, etc. is ensured. In the spelling difference resolution module 210, the same entity names in different sources are unified. In the data conversion module 211, the data type of each column is ensured to be correct. In the data standard module 212, the data conforms to the standard normal distribution and the numerical values are scaled to the [0,1] interval. In the feature creation module 213, new meaningful features are generated based on existing features. In the feature selection module 214, redundant or irrelevant features are removed to reduce the impact of the curse of dimensionality. Common methods include correlation coefficient analysis, principal component analysis, etc. In the data splitting module 215, the data is divided into a training set and a test set according to a certain ratio, and sometimes a part is set aside as a validation set. In the secondary check module 216, statistical descriptions and visualization tools are run again to confirm that all problems have been properly solved;

[0032] In step 2 above, in the demand forecasting module 301 of the intelligent analysis module 3, an AI algorithm is used to analyze historical sales data to predict future demand, assist in formulating procurement plans and inventory strategies, and monitor market trends and changes in consumer behavior in real time, dynamically adjusting the prediction model. In the risk assessment module 302, factors such as market trends, supplier performance, and natural disasters are analyzed to evaluate potential risks and propose countermeasures. In the risk management module 303, natural language processing technology is used to analyze unstructured data such as news and social media to early warn of risk factors that may affect the supply chain. In the optimization recommendation module 304, based on real-time data analysis, optimization recommendations are provided for aspects such as inventory management, distribution route planning, and cost control, and advanced AI technologies such as reinforcement learning are used to optimize complex supply chain network designs. In the automated task scheduling module 305, according to the prediction results and optimization recommendations, production plans, shipping arrangements, etc. are automatically adjusted to reduce manual intervention and can adjust strategies in a timely manner according to changes in market trends or the impact of emergencies to cope with new situations. In the real-time monitoring module 801 of the early warning measure module 8, sensors and Internet of Things devices are deployed to achieve real-time monitoring of goods transportation, warehousing environment, etc., timely detect abnormal situations, and use computer vision technology to monitor warehouse operations to identify abnormal operations or safety hazards. In the early warning mechanism module 802, key performance indicator thresholds are set, and once the set range is exceeded, an alarm is triggered to notify relevant personnel to take actions, and through an AI-driven anomaly detection system, potential problems are actively identified and early warnings are issued, improving the security of the entire system;

[0033] In step 3 above, in the historical call module 501 of the auxiliary decision-making module 5, past data is called. In the comparison and analysis module 502, the actual effects of various decisions are regularly evaluated, compared with the expected goals, and the gaps are found. In the targeted decision-making module 503, new suggestions are put forward. In the effect evaluation module 601 of the continuous improvement module 6, the A / B test method is used to verify the effects of different strategies and select the optimal solution. In the learning and adaptation module 602, self-learning is carried out, experience is continuously accumulated, and the model accuracy is improved to better adapt to changing market conditions;

[0034] In step 4 above, in the data security protection module 701 of the data security module 7, encryption technology and access control strategies are adopted to ensure the security of sensitive information and implement data desensitization technology to protect user privacy without affecting the analysis effect. In the privacy protection module 702, differential privacy technology is used to protect individual data privacy during the data sharing process. In the compliance review module 703, internal audits are regularly carried out to check whether the system meets the latest compliance requirements.

[0035] Based on the above, the advantages of this system are as follows: Analyze historical sales data using AI algorithms to predict future demand, assist in formulating procurement plans and inventory strategies, and monitor market trends and changes in consumer behavior in real time, dynamically adjust the prediction model, analyze factors such as market trends, supplier performance, natural disasters, etc., evaluate potential risks, and propose countermeasures, and can adjust strategies in a timely manner according to changes in market trends or the impact of emergencies to respond to new situations; Set key performance indicator thresholds, trigger an alarm once the set range is exceeded, notify relevant personnel to take actions, and through an AI-driven anomaly detection system, actively identify potential problems and issue early warnings, improving the security of the entire system; Self-learn, continuously accumulate experience, improve the model accuracy, and be able to better adapt to changing market conditions.

[0036] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. An integrated operation and maintenance system for a digital supply chain platform based on general artificial intelligence, comprising a data collection module (1), a data processing module (2), an intelligent analysis module (3), a repository module (4), an auxiliary decision module (5), a continuous improvement module (6), a data security module (7) and an early warning measure module (8), characterized in that: The data collection module (1) controls the connection to the data processing module (2), the data processing module (2) controls the connection to the intelligent analysis module (3), the intelligent analysis module (3) controls the connection to the storage repository module (4), the continuous improvement module (6), the data security module (7) and the early warning measure module (8), the storage repository module (4) controls the connection to the auxiliary decision-making module (5), and the auxiliary decision-making module (5) controls the connection to the continuous improvement module (6).

2. According to claim 1, a comprehensive operation and maintenance system for a digital supply chain platform based on general artificial intelligence is characterized by: The data processing module (2) is composed of a data cleaning module (201), a missing value processing module (202), an abnormal value processing module (203), a consistency check module (204), a missing value detection module (205), an abnormal value definition module (206), a unified format module (207), a processing strategy module (208), a solution strategy module (209), a spelling difference solution module (210), a data conversion module (211), a data standard module (212), a feature creation module (213), a feature selection module (214), a data segmentation module (215) and a secondary check module (216). The data cleaning module (201) controls the connection of the missing value processing module (202), the abnormal value processing module (203) and the consistency check module (204) respectively.

3. The integrated operation and maintenance system of a digital supply chain platform based on general artificial intelligence according to claim 2, characterized in that: The missing value processing module (202) controls the connection to the missing value detection module (205), the missing value detection module (205) controls the connection to the processing strategy module (208), the abnormal value processing module (203) controls the connection to the abnormal value definition module (206), the abnormal value definition module (206) controls the connection to the solution strategy module (209), the consistency check module (204) controls the connection to the unified format module (207), and the unified format module (207) controls the connection to the spelling difference solution module (210).

4. The integrated operation and maintenance system of a digital supply chain platform based on general artificial intelligence according to claim 3 is characterized by: The missing value processing module (202), the abnormal value processing module (203) and the consistency check module (204) control the connection data conversion module (211), the data conversion module (211) controls the connection data standard module (212), the data standard module (212) controls the connection feature creation module (213), the feature creation module (213) controls the connection feature selection module (214), and the feature selection module (214) controls the connection data segmentation module (215).

5. The integrated operation and maintenance system of a digital supply chain platform based on general artificial intelligence according to claim 1, characterized in that: The intelligent analysis module (3) is composed of a demand forecasting module (301), a risk assessment module (302), a risk management module (303), an optimization suggestion module (304) and an automated task scheduling module (305). The demand forecasting module (301) controls the connection to the risk assessment module (302), the risk assessment module (302) controls the connection to the risk management module (303), the risk management module (303) controls the connection to the optimization suggestion module (304), and the optimization suggestion module (304) controls the connection to the automated task scheduling module (305).

6. The integrated operation and maintenance system of a digital supply chain platform based on general artificial intelligence according to claim 1, characterized in that: The auxiliary decision-making module (5) is composed of a history calling module (501), a comparison and analysis module (502) and a targeted decision-making module (503). The history calling module (501) controls the connection to the comparison and analysis module (502), and the comparison and analysis module (502) controls the connection to the targeted decision-making module (503).

7. The integrated operation and maintenance system of a digital supply chain platform based on general artificial intelligence according to claim 1, characterized in that: The continuous improvement module (6) is composed of an effect evaluation module (601) and a learning adaptation module (602), and the effect evaluation module (601) controls the connection with the learning adaptation module (602).

8. The integrated operation and maintenance system of a digital supply chain platform based on general artificial intelligence according to claim 1, characterized in that: The data security module (7) is composed of a data security protection module (701), a privacy protection module (702) and a compliance review module (703). The data security protection module (701) controls the connection to the privacy protection module (702), and the privacy protection module (702) controls the connection to the compliance review module (703).

9. The integrated operation and maintenance system of a digital supply chain platform based on general artificial intelligence according to claim 1, characterized in that: The early warning measure module (8) is composed of a real-time monitoring module (801) and an early warning mechanism module (802), and the real-time monitoring module (801) controls the connection with the early warning mechanism module (802).

10. A comprehensive operation and maintenance method for a digital supply chain platform based on general artificial intelligence, comprising the following steps: Step 1: data collection and processing; Step 2: intelligent analysis and early warning; Step 3: continuous upgrading and improvement; Step 4: data security assurance; It is characterized by: In the above step 1, data sources from different supply chain links, including suppliers, manufacturers, warehouses, logistics service providers, etc., are integrated in the data collection module (1), covering order information, inventory levels, transportation status, market demand forecasts, etc., the data is processed in the data processing module (2), and the missing value detection module (205) of the missing value processing module (202) checks which columns have missing values ​​and the missing proportions, deletes records or variables with a large number of missing values ​​in the processing strategy module (208), uses the mean / median to fill in numerical data and the mode to fill in categorical data, fills in missing values ​​based on model prediction, defines outliers based on business logic or statistical methods in the definition outlier module (206) in the outlier processing module (203), corrects obviously erroneous data points and deletes extreme outliers or marks them as special states in the solution strategy module (209), and checks the consistency of the data in the consistency check module (208). The unified format module (207) in block (204) ensures the consistency of dates, currency units, etc. The spelling difference resolution module (210) unifies the same entity names in different sources. The data conversion module (211) ensures that the data type of each column is correct. The data standard module (212) makes the data conform to the standard normal distribution and scales the values ​​to the [0,1] interval. In the feature creation module (213), new meaningful features are generated based on existing features. In the feature selection module (214), redundant or irrelevant features are removed to reduce the impact of the curse of dimensionality. Commonly used methods include correlation coefficient analysis and principal component analysis. In the data segmentation module (215), the data is divided into training set and test set according to a certain ratio. Sometimes a part is reserved as a validation set. In the secondary check module (216), the statistical description and visualization tools are run again to confirm that all problems have been properly resolved. In the above step 2, in the demand forecasting module (301) of the intelligent analysis module (3), an AI algorithm is used to analyze historical sales data, predict future demand, help formulate procurement plans and inventory strategies, and monitor market trends and changes in consumer behavior in real time, and dynamically adjust the forecasting model. In the risk assessment module (302), market trends, supplier performance, natural disasters and other factors are analyzed to assess potential risks and propose countermeasures. In the risk management module (303), natural language processing technology is used to analyze unstructured data such as news and social media to provide early warning of risk factors that may affect the supply chain. In the optimization suggestion module (304), based on real-time data analysis, optimization suggestions are provided for inventory management, distribution route planning, cost control and other aspects, and advanced AI technologies such as reinforcement learning are used to optimize complex The supply chain network design is designed in such a way that the production plan, delivery schedule, etc. are automatically adjusted according to the prediction results and optimization suggestions in the automated task scheduling module (305), thereby reducing manual intervention. The strategy can be adjusted in time to cope with new situations according to changes in market trends or the impact of emergencies. Sensors and IoT devices are deployed in the real-time monitoring module (801) of the early warning measure module (8) to achieve real-time monitoring of cargo transportation, storage environment, etc., to detect abnormal situations in time and use computer vision technology to monitor warehouse operations and identify abnormal operations or safety hazards. In the early warning mechanism module (802), the threshold of key performance indicators is set. Once the set range is exceeded, an alarm is triggered to notify relevant personnel to take action and the AI-driven anomaly detection system is used to proactively identify potential problems and issue warnings, thereby improving the security of the entire system. In the above step 3, the historical call module (501) of the auxiliary decision-making module (5) calls up past data, the actual effects of various decisions are regularly evaluated in the comparison and analysis module (502), compared with the expected goals, and the gaps are found. New suggestions are put forward in the targeted decision-making module (503), and the effects of different strategies are verified by using the A / B test method in the effect evaluation module (601) of the continuous improvement module (6), and the optimal solution is selected. Self-learning is carried out in the learning and adaptation module (602), and experience is continuously accumulated to improve the model accuracy, so as to better adapt to changing market conditions; In the above step 4, encryption technology and access control strategy are used in the data security protection module (701) of the data security module (7) to ensure the security of sensitive information and implement data desensitization technology to protect user privacy without affecting the analysis effect. Differential privacy technology is used in the privacy protection module (702) to protect individual data privacy during data sharing. Internal audits are regularly conducted in the compliance review module (703) to check whether the system meets the latest compliance requirements.

Images