Data annotation supply chain collaborative management system and method based on big data analysis

Abstract

The invention discloses a data annotation supply chain collaborative management system and method based on big data analysis, and relates to the technical field of supply chain collaborative management, and the system comprises a data annotation presetting module, a manufacturing progress analysis module, a supply progress analysis module, a distribution progress analysis module and a supply chain completion degree analysis module. The method comprises the steps of setting a preset data label for a supply chain, marking a supply chain name on the preset data label, collecting a product manufacturing process of the supply chain, analyzing the manufacturing process and then converting the manufacturing process into a manufacturing data label, collecting a total product sales progress of the supply chain, analyzing the total sales progress and then converting the total sales progress into a sales data label, and collecting a product sub-sales progress of the supply chain. After the sub-sales progress is converted into the sub-sales data annotation, the sub-sales data annotation is filled with the preset annotation, the unique data annotation of any supply chain is generated, and the predicted production capacity of the next production cycle is stipulated through the data annotation, so that a mode of predicting the production capacity required by the supply chain is provided, the market demand is met, and the product retention time is shortened.

Description

Technical Field

[0001] The present invention relates to the field of supply chain collaborative management, and specifically to a data annotation supply chain collaborative management system and method based on big data analysis. Background Art

[0002] Supply chain management refers to optimizing the operation of the supply chain, from procurement to satisfying the end customer at the lowest cost. MBA, EMBA and other management education all include enterprise supply chain management. With the advent of the big data era, supply chain management has gradually been used by major companies. Supply chain management is to coordinate internal and external resources of the enterprise to jointly meet consumer needs. When we regard the enterprises in each link of the supply chain as a virtual enterprise alliance, and regard any enterprise as a department in this virtual enterprise alliance, the internal management of the alliance is supply chain management. However, the composition of the alliance is dynamic and changes at any time according to market needs.

[0003] There is a lack of data-annotated supply chain collaborative management systems based on big data analysis on the market. On the one hand, existing supply chain collaborative management systems often focus on the yield of a single product and track the manufacturing process of defective parts. They are unable to observe the completion process of the supply chain within a cycle from the perspective of the entire supply chain. On the other hand, there is a lack of a standard that can compare different supply chains. It is difficult for existing supply chain management systems to intuitively compare the processes of multiple supply chains. On the other hand, existing supply chain management systems lack flexibility and currently rely on manual planning for urgent orders. The manual planning model lacks standardization and efficiency.

[0004] Therefore, a data annotation supply chain collaborative management system and method based on big data analysis is needed to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a data annotation supply chain collaborative management system and method based on big data analysis to solve the problems raised in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a data annotation supply chain collaborative management system based on big data analysis, the system comprising: a data annotation preset module, a manufacturing progress analysis module, a supply progress analysis module, a distribution progress analysis module and a supply chain completion analysis module;

[0007] Setting a preset data annotation for any supply chain through the data annotation preset module, and marking the preset data annotation with the name of the supply chain;

[0008] The manufacturing progress analysis module collects the product manufacturing process of any supply chain, analyzes the manufacturing process and converts it into manufacturing data annotation;

[0009] The total sales progress of products in any supply chain is collected through the supply progress analysis module, and the total sales progress is analyzed and converted into sales data annotation;

[0010] The distribution progress analysis module collects the sales progress of products in any supply chain and converts the sales progress into sales data annotation;

[0011] The supply chain completion analysis module summarizes and analyzes the data annotations collected by the manufacturing progress analysis module, the supply progress collection module and the distribution progress collection module, fills in the preset annotations, generates unique data annotations for any supply chain, and specifies the estimated production volume for the next production cycle through the data annotations.

[0012] Furthermore, the manufacturing progress analysis module monitors the product manufacturing process of any supply chain, and records the expected product manufacturing output, the actual product manufacturing output, the number of defective products, the average unit manufacturing time and the supply.

[0013] Furthermore, the supply progress analysis module records the inventory, total sales, return volume and emergency order volume of any supply chain, wherein the return volume refers to the volume of products returned by the distributor to the supplier, and the emergency order volume refers to the volume of ordered products after setting the expected production output of the product.

[0014] Furthermore, the distribution progress analysis module records the distribution volume, return volume and average unit consumption time of any distributor, and the average unit consumption time refers to the average time required for the distributor to sell a product.

[0015] Furthermore, the supply chain completion analysis module generates unique data annotations for any supply chain, and the annotation contents include: manufacturing data annotations, sales data annotations, and sales data annotations.

[0016] The data annotation supply chain collaborative management method based on big data analysis includes the following steps:

[0017] S1: Set a preset data label for any supply chain and add the name of the supply chain to the preset data label;

[0018] S2: Monitor any supply chain process and analyze the manufacturing data annotation, sales data annotation and sub-sales data annotation of any supply chain;

[0019] S3: Summarize and analyze manufacturing data annotation, sales data annotation, and sales data annotation, optimize preset annotations, generate unique data annotations for any supply chain, and set subordinate files for unique data annotations;

[0020] S4: Specify the expected production volume for the next supply chain cycle through data annotation.

[0021] Further, in step S1, a preset data annotation {A i ,B i ,C i , D i}, and label the preset data with the supply chain name i, where A i represents the manufacturing data annotation of the i-th supply chain, B i represents the sales data annotation of the ith supply chain, C i represents the sales data annotation of the ith supply chain, D i Indicates the analytical data annotation of the i-th supply chain. In the field of artificial intelligence, data annotation is a crucial link. It involves a series of processing and transformation of the original data to make it a format that can be understood and used by the machine learning model, providing labeled training data for the machine learning model.

[0022] Further, in step S2, the i-th supply chain process is monitored, the manufacturing data annotation, sales data annotation and sub-sales data annotation of the i-th supply chain are analyzed, the supply chain process includes the product manufacturing process, product sales process and product sub-sales process, the product manufacturing process of the i-th supply chain is monitored, and the manufacturing estimated output a of the i-th supply chain is recorded. i , Actual output of product manufacturing b i 、Number of defective products c i , Unit average manufacturing time d i and supply e i The supply quantity is the quantity of products sent from the manufacturer to the supplier, and the average manufacturing time per unit is d i represents the average manufacturing time of the product in the i-th supply chain, and the actual manufacturing output of the product b i A represents the real-time monitoring of the output of the ith supply chain, and the manufacturing data of the ith supply chain is annotated i :

[0023]

[0024] Analyze the product defect rate while considering the manufacturing quantity, so as to obtain a manufacturing data annotation that is more in line with the actual situation of the supply chain. i Reflects the urgency of product production, A i The larger the value, the more urgent the product production is. Monitor the product sales process of the i-th supply chain and record the inventory f of the i-th supply chain. i Total sales volume i 、Return amount h i and emergency order quantityj i , analyze the sales data of the i-th supply chain and label B i :

[0025]

[0026] Taking into account the sales volume, the return volume and emergency orders are comprehensively analyzed to more intuitively represent the progress of the sales link of the supply chain, thereby obtaining the real sales data annotation. Sales data annotation B i Reflects the sales progress of products in the supply chain, sales data marked B i The larger the value, the smoother the sales progress. The number of distributors in the i-th supply chain is N. The distribution progress of the n-th distributor is monitored and the distribution volume L of the n-th distributor is recorded. n 、Return amount M n and the average unit consumption time P n , where n = 1, 2, ..., N, analyze the sales data of the ith supply chain and label C i :

[0027]

[0028] The return amount represents M n The number of products returned from the nth distributor to the supplier, the average unit consumption time P n It represents the average selling time of each product from the nth distributor. Each distributor is monitored. Considering the adverse effect of the distributor's return volume on the sales progress, the supply chain analysis data annotation is analyzed. The sales data annotation C i Reflects the degree of sales success of products in the supply chain, and the sales data is marked with C i The larger the value, the smoother the sales process. The above steps standardize any stage of the supply chain, thereby obtaining a real-time and more realistic progress of the production chain.

[0029] Further, in step S3, the manufacturing data annotation, sales data annotation and sales data annotation are summarized and analyzed, and the preset annotation is optimized, considering the unit average manufacturing time d i and the average unit consumption time P n , analyze the analytical data annotation D of the i-th supply chain i :

[0030]

[0031] Thus, the unique data annotation of the i-th supply chain is generated {A i ,B i ,C i ,D i}, label the data A i Set subordinate files Q i-A , the document content includes: the estimated manufacturing output a of the i-th supply chain i , Actual output of product manufacturing bi 、Number of defective products c i , Unit average manufacturing time d i and supply e i ; Annotate the data B i Set subordinate files Q i-B , the file content includes: the inventory volume f of the i-th industrial chain i Total sales volume i 、Return amount h i and emergency order quantityj i ; Annotate the data B i Set subordinate files Q i-C , the file content includes: the distribution volume L of any distributor in the i-th supply chain n 、Return amount M n and the average unit consumption time P n , unifying the progress of multiple production chains into a unified format, making it convenient to intuitively observe the progress of the production chains, providing comparative progress and facilitating comparison of the completion efficiency of different production chains.

[0032] Further, in step S4, when a supply chain cycle set by the system is completed, the system analyzes the unique data annotation of the i-th supply chain, considering the average manufacturing time per unit and the average total consumption time per unit. When the system analyzes the consumption rate, the estimated production volume R1 is:

[0033]

[0034] when When the system analyzes the consumption rate, the estimated production volume R2 is:

[0035]

[0036] Through the specific analysis of this production chain, the relationship between the production speed and consumption speed of the products in the production chain was taken into consideration, and an estimated production volume that was more in line with the actual situation was set to meet market demand and reduce the length of time that products stay in the warehouse.

[0037] Compared with the prior art, the beneficial effects achieved by the present invention are: providing a data annotation supply chain collaborative management system and method based on big data analysis; on the one hand, the system observes the completion process of the supply chain within a cycle from the perspective of the entire supply chain, meeting the user's overall control needs for the production chain; on the other hand, it sets a data annotation standard and provides a method for digitizing the supply chain process, which is convenient for users to compare the efficiency of different supply chains; on the other hand, it meets the user's flexibility requirements for the supply chain system, and can correct data annotations when there are urgent orders. Users can more intuitively feel the impact of urgent orders on the stability of the supply chain, thereby taking timely regulatory measures; and at the same time, it provides a method for predicting the production volume of supply chain demand, thereby achieving the effect of both meeting market demand and reducing the length of time products stay in warehouses. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0039] Figure 1 It is a structural diagram of the data annotation supply chain collaborative management system based on big data analysis of the present invention;

[0040] Figure 2 It is a flow chart of the data annotation supply chain collaborative management method based on big data analysis of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] See also Figure 1 and Figure 2 , the present invention provides a technical solution: a data annotation supply chain collaborative management system based on big data analysis, the system includes: a data annotation preset module, a manufacturing progress analysis module, a supply progress analysis module, a distribution progress analysis module and a supply chain completion analysis module;

[0043] Setting a preset data annotation for any supply chain through the data annotation preset module, and marking the preset data annotation with the name of the supply chain;

[0044] The manufacturing progress analysis module collects the product manufacturing process of any supply chain, analyzes the manufacturing process and converts it into manufacturing data annotation;

[0045] The total sales progress of products in any supply chain is collected through the supply progress analysis module, and the total sales progress is analyzed and converted into sales data annotation;

[0046] The distribution progress analysis module collects the sales progress of products in any supply chain and converts the sales progress into sales data annotation;

[0047] The supply chain completion analysis module summarizes and analyzes the data annotations collected by the manufacturing progress analysis module, the supply progress collection module and the distribution progress collection module, fills in the preset annotations, generates unique data annotations for any supply chain, and specifies the estimated production volume for the next production cycle through the data annotations.

[0048] The manufacturing progress analysis module monitors the product manufacturing process of any supply chain, and records the expected output of product manufacturing, the actual output of product manufacturing, the number of defective products, the average unit manufacturing time and the supply.

[0049] The supply progress analysis module records the inventory, total sales, return volume and emergency order volume of any supply chain. The return volume refers to the volume of products returned by the distributor to the supplier, and the emergency order volume refers to the volume of ordered products after setting the expected production output of the product.

[0050] The distribution progress analysis module records the distribution volume, return volume and average unit consumption time of any distributor, and the average unit consumption time refers to the average time required for the distributor to sell a product.

[0051] The supply chain completion analysis module generates unique data annotations for any supply chain, and the annotation contents include: manufacturing data annotations, sales data annotations, and sales data annotations.

[0052] The data annotation supply chain collaborative management method based on big data analysis includes the following steps:

[0053] S1: Set a preset data label for any supply chain and add the name of the supply chain to the preset data label;

[0054] S2: Monitor any supply chain process and analyze the manufacturing data annotation, sales data annotation and sub-sales data annotation of any supply chain;

[0055] S3: Summarize and analyze manufacturing data annotation, sales data annotation, and sales data annotation, optimize preset annotations, generate unique data annotations for any supply chain, and set subordinate files for unique data annotations;

[0056] S4: Specify the expected production volume for the next supply chain cycle through data annotation.

[0057] In step S1, a preset data annotation {A i,B i ,C i , D i}, and label the preset data with the supply chain name i, where A i represents the manufacturing data annotation of the i-th supply chain, B i represents the sales data annotation of the ith supply chain, C i represents the sales data annotation of the ith supply chain, D i Indicates the analytical data annotation of the i-th supply chain. In the field of artificial intelligence, data annotation is a crucial link. It involves a series of processing and transformation of raw data to make it a format that can be understood and used by machine learning models, providing labeled training data for machine learning models.

[0058] In step S2, the i-th supply chain process is monitored, and the manufacturing data annotation, sales data annotation and sub-sales data annotation of the i-th supply chain are analyzed. The supply chain process includes the product manufacturing process, product sales process and product sub-sales process. The product manufacturing process of the i-th supply chain is monitored, and the manufacturing estimated output a of the i-th supply chain is recorded. i , Actual output of product manufacturing b i 、Number of defective products c i , Unit average manufacturing time d i and supply e i The supply quantity is the quantity of products sent from the manufacturer to the supplier, and the average manufacturing time per unit is d i represents the average manufacturing time of the product in the i-th supply chain, and the actual manufacturing output of the product b i A represents the real-time monitoring of the output of the ith supply chain, and the manufacturing data of the ith supply chain is annotated i :

[0059]

[0060] Analyze the product defect rate while considering the manufacturing quantity, so as to obtain a manufacturing data annotation that is more in line with the actual situation of the supply chain, monitor the product sales process of the i-th supply chain, and record the inventory f of the i-th industrial chain i Total sales volume i 、Return amount h i and emergency order quantityj i , analyze the sales data of the i-th supply chain and label B i :

[0061]

[0062] Taking into account the sales volume, the return volume and emergency orders are comprehensively analyzed to more intuitively represent the progress of the sales link of the supply chain, so as to obtain the real sales data annotation. The number of distributors in the i-th supply chain is N, the distribution progress of the n-th distributor is monitored, and the distribution volume L of the n-th distributor is recorded. n 、Return amount M n and the average unit consumption time P n , where n = 1, 2, ..., N, analyze the sales data of the ith supply chain and label C i :

[0063]

[0064] The return amount represents M n The number of products returned from the nth distributor to the supplier, the average unit consumption time P n It represents the average selling time of each product from the nth distributor. Each distributor is monitored. Taking into account the adverse impact of the distributor's returns on the sales progress, the analytical data annotation of the supply chain is analyzed. The above steps standardize any stage of the supply chain, so as to obtain a real-time and more realistic progress of the production chain.

[0065] In step S3, the manufacturing data annotation, sales data annotation and sales data annotation are summarized and analyzed, and the preset annotation is optimized, considering the unit average manufacturing time d i and the average unit consumption time P n , analyze the analytical data annotation D of the i-th supply chain i :

[0066]

[0067] Thus, the unique data annotation of the i-th supply chain is generated {A i ,B i ,C i ,D i}, label the data A i Set subordinate files Q i-A , the document content includes: the estimated manufacturing output a of the i-th supply chain i , Actual output of product manufacturing b i 、Number of defective products c i , Unit average manufacturing time d i and supply e i ; Annotate the data B i Set subordinate files Q i-B , the file content includes: the inventory volume f of the i-th industrial chain i Total sales volume i 、Return amount h i and emergency order quantityji ; Annotate the data B i Set subordinate files Q i-C , the file content includes: the distribution volume L of any distributor in the i-th supply chain n 、Return amount M n and the average unit consumption time P n , unifying the progress of multiple production chains into a unified format, making it convenient to intuitively observe the progress of the production chains, providing comparative progress and facilitating comparison of the completion efficiency of different production chains.

[0068] In step S4, when a supply chain cycle set by the system is completed, the system analyzes the unique data annotation of the i-th supply chain, considering the average manufacturing time per unit and the average total consumption time per unit. When the system analyzes the consumption rate, the estimated production volume R1 is:

[0069]

[0070] when When the system analyzes the consumption rate, the estimated production volume R2 is:

[0071]

[0072] Through the specific analysis of this production chain, the relationship between the production speed and consumption speed of the products in the production chain was taken into consideration, and an estimated production volume that was more in line with the actual situation was set to meet market demand and reduce the length of time that products stay in the warehouse.

[0073] Example 1: Summarize and analyze manufacturing data annotation, sales data annotation and sales data annotation, optimize preset annotation, and consider the unit average manufacturing time d i and the average unit consumption time P n , the time consumed by each distributor in the supply chain Average manufacturing time per unit d i =5, manufacturing expected output a i =20000, actual production output of product b i =14000, inventory f i =1000Analysis of the analysis data of the i-th supply chain D i :

[0074]

[0075] The analysis data here is marked D i The larger the value, the more urgent the production order. i ≥ Estimated manufacturing output a i , the order is considered to be basically completed, thus generating the unique data annotation {Ai ,B i ,C i ,D i}, label the data A i Set subordinate files Q i-A , the document content includes: the estimated manufacturing output a of the i-th supply chain i , Actual output of product manufacturing b i 、Number of defective products c i , Unit average manufacturing time d i and supply e i ; Annotate the data B i Set subordinate files Q i-B , the file content includes: the inventory volume f of the i-th industrial chain i Total sales volume i 、Return amount h i and emergency order quantityj i ; Annotate the data B i Set subordinate files Q i-C , the file content includes: the distribution volume L of any distributor in the i-th supply chain n 、Return amount M n and the average unit consumption time P n , unifying the progress of multiple production chains into a unified format, making it convenient to intuitively observe the progress of the production chains, providing comparative progress and facilitating comparison of the completion efficiency of different production chains.

[0076] When a supply chain cycle set by the system is completed, the system analyzes the unique data annotation of the i-th supply chain, considering the average manufacturing time per unit and the average total consumption time per unit. When the system analyzes the consumption rate, the estimated production volume R1 is:

[0077]

[0078] when When the system analyzes the consumption rate, the estimated production volume R2 is:

[0079]

[0080] In this supply chain, Substitute the formula to calculate the expected production volume R2 of the product:

[0081]

[0082] When the supply chain cycle ends, the final product actual production total is substituted to get the final product estimated production volume. Through the specific analysis of this production chain, the relationship between the production speed and consumption speed of the products in the production chain is considered, and an estimated production volume that is more in line with the actual situation is set to meet market demand and reduce the length of time that products stay in the warehouse.

[0083] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any figure reference in a claim should not be considered as limiting the claim to which it relates.

Claims

1. Data annotation supply chain collaborative management system based on big data analysis, characterized by: The system includes: a data annotation preset module, a manufacturing progress analysis module, a supply progress analysis module, a distribution progress analysis module and a supply chain completion analysis module; Setting a preset data annotation for any supply chain through the data annotation preset module, and marking the preset data annotation with the name of the supply chain; The manufacturing progress analysis module collects the product manufacturing process of any supply chain, analyzes the manufacturing process and converts it into manufacturing data annotation; The total sales progress of products in any supply chain is collected through the supply progress analysis module, and the total sales progress is analyzed and converted into sales data annotation; The distribution progress analysis module collects the sales progress of products in any supply chain and converts the sales progress into sales data annotation; The supply chain completion analysis module summarizes and analyzes the data annotations collected by the manufacturing progress analysis module, the supply progress collection module and the distribution progress collection module, fills in the preset annotations, generates unique data annotations for any supply chain, and specifies the estimated production volume for the next production cycle through the data annotations.

2. The data annotation supply chain collaborative management system based on big data analysis according to claim 1 is characterized by: The manufacturing progress analysis module monitors the product manufacturing process of any supply chain, and records the expected output of product manufacturing, the actual output of product manufacturing, the number of defective products, the average unit manufacturing time and the supply.

3. The data annotation supply chain collaborative management system based on big data analysis according to claim 1 is characterized by: The supply progress analysis module records the inventory, total sales, return volume and emergency order volume of any supply chain. The return volume refers to the volume of products returned by the distributor to the supplier, and the emergency order volume refers to the volume of ordered products after setting the expected production output of the product.

4. The data annotation supply chain collaborative management system based on big data analysis according to claim 1 is characterized by: The distribution progress analysis module records the distribution volume, return volume and average unit consumption time of any distributor, and the average unit consumption time refers to the average time required for the distributor to sell a product.

5. The data annotation supply chain collaborative management system based on big data analysis according to claim 1 is characterized by: The supply chain completion analysis module generates unique data annotations for any supply chain, and the annotation contents include: manufacturing data annotations, sales data annotations, and sales data annotations.

6. A data annotation supply chain collaborative management method based on big data analysis, characterized by: The following steps are involved: S1: Set a preset data label for any supply chain and add the name of the supply chain to the preset data label; S2: Monitor any supply chain process and analyze the manufacturing data annotation, sales data annotation and sub-sales data annotation of any supply chain; S3: Summarize and analyze manufacturing data annotation, sales data annotation, and sales data annotation, optimize preset annotations, generate unique data annotations for any supply chain, and set subordinate files for unique data annotations; S4: Specify the expected production volume for the next supply chain cycle through data annotation.

7. The data annotation supply chain collaborative management method based on big data analysis according to claim 6 is characterized by: In step S1, a preset data annotation {A i ,B i ,C i , D i }, and label the preset data with the supply chain name i, where A i represents the manufacturing data annotation of the i-th supply chain, B i represents the sales data annotation of the i-th supply chain, C i represents the sales data annotation of the ith supply chain, D i Represents the analytical data annotation of the i-th supply chain.

8. The data annotation supply chain collaborative management method based on big data analysis according to claim 7 is characterized by: In step S2, the i-th supply chain process is monitored, and the manufacturing data annotation, sales data annotation and sub-sales data annotation of the i-th supply chain are analyzed. The supply chain process includes the product manufacturing process, product sales process and product sub-sales process. The product manufacturing process of the i-th supply chain is monitored, and the manufacturing estimated output a of the i-th supply chain is recorded. i , Actual output of product manufacturing b i 、Number of defective products c i , Unit average manufacturing time d i and supply e i The supply quantity is the quantity of products sent from the manufacturer to the supplier, and the average manufacturing time per unit is d i represents the average manufacturing time of the product in the i-th supply chain, and the actual manufacturing output of the product b i A represents the real-time monitoring of the output of the ith supply chain, and the manufacturing data of the ith supply chain is annotated i : Monitor the product sales process of the i-th supply chain and record the inventory f of the i-th supply chain i Total sales volume i 、Return amount h i and emergency order quantityj i , analyze the sales data of the i-th supply chain and label B i : The number of distributors in the i-th supply chain is N. The distribution progress of the n-th distributor is monitored and the distribution volume L of the n-th distributor is recorded. n 、Return amount M n and the average unit consumption time P n , where n = 1, 2, ..., N, analyze the sales data of the i-th supply chain and label C i : The return amount represents M n The number of products returned from the nth distributor to the supplier, the average unit consumption time P n It represents the average selling time of each product from the nth distributor.

9. The data annotation supply chain collaborative management method based on big data analysis according to claim 8 is characterized by: In step S3, the manufacturing data annotation, sales data annotation and sales data annotation are summarized and analyzed, and the preset annotation is optimized, considering the unit average manufacturing time d i and the average unit consumption time P n , analyze the analytical data annotation D of the i-th supply chain i : Thus, the unique data annotation of the i-th supply chain is generated {A i ,B i ,C i ,D i }, label the data A i Set subordinate files Q i-A , the document content includes: the estimated manufacturing output a of the i-th supply chain i , Actual output of product manufacturing b i 、Number of defective products c i , Unit average manufacturing time d i and supply e i ; Annotate the data B i Set subordinate files Q i-B , the file content includes: the inventory volume f of the i-th industrial chain i Total sales volume i 、Return amount h i and emergency order quantityj i ; Annotate the data B i Set subordinate files Q i-C , the file content includes: the distribution volume L of any distributor in the i-th supply chain n 、Return amount M n and the average unit consumption time P n .

10. The data annotation supply chain collaborative management method based on big data analysis according to claim 9 is characterized by: In step S4, when a supply chain cycle set by the system is completed, the system analyzes the unique data annotation of the i-th supply chain, considering the average manufacturing time per unit and the average total consumption time per unit. When the system analyzes the consumption rate, the estimated production volume R1 is: when When the system analyzes the consumption rate, the estimated production volume R2 is:

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