Intelligent supply chain collaborative platform intelligent scheduling system based on multi-source data fusion

Abstract

The invention discloses an intelligent supply chain collaborative platform intelligent scheduling system based on multi-source data fusion, and relates to the field of supply chain scheduling, and the system comprises an acquisition module which is used for collecting historical supply product association information of each supply chain in real time; the analysis module is used for traversing the supply chain supply product associated information acquired by the acquisition module, and analyzing each supply chain trust index based on the supply chain supply product associated information; the receiving module is used for receiving supply chain service target product supply demand information; according to the invention, through deep mining and analysis of massive historical supply data in the operation process, the credibility of each supply chain can be accurately evaluated, a powerful basis is provided for an enterprise to screen partners, the scientificity of supply chain selection is greatly improved, and the supply chain selection efficiency is greatly improved in the face of product supply demands. According to the method, options meeting requirements can be quickly matched from a plurality of supply chains and are sorted and recommended according to trust indexes, the supply response speed is greatly increased in the process, and the timeliness of production and operation of enterprises is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of supply chain scheduling, in particular to an intelligent scheduling system of a smart supply chain collaborative platform based on multi-source data fusion. BACKGROUND

[0002] A supply chain is a functional network structure around a core enterprise, through the control of information flow, logistics and capital flow, starting from the procurement of raw materials, making intermediate products and final products, and then delivering products to consumers through a sales network.

[0003] The application number 202010241091.3 discloses a smart supply chain system for managing the supply chain of multiple commodities, which has: a commodity classification device that classifies multiple commodities based on historical data; a sales prediction device that predicts sales for each commodity based on historical data and the classification results of the commodity classification device; and an intelligent replenishment device that generates replenishment decisions using an automatic replenishment model based on the prediction results of the sales prediction device. The intelligent replenishment device includes a mixed replenishment unit that generates periodic and quantitative replenishment decisions based on the prediction results of the point prediction and interval prediction of the sales prediction device, solving the problem of "current inventory management strategies, which mostly use a single method for management, have low flexibility and adapt to a single scenario, so new inventory control methods are urgently needed."

[0004] However, in the current multi-supply chain scenario, users with replenishment needs cannot quickly select and match the best supply chain for replenishment. Therefore, we propose an intelligent scheduling system of a smart supply chain collaborative platform based on multi-source data fusion. SUMMARY

[0005] To solve the above-mentioned shortcomings of the prior art, the present application provides an intelligent scheduling system of a smart supply chain collaborative platform based on multi-source data fusion, which can effectively solve the problems of the prior art.

[0006] To achieve the above purpose, the present application is realized by the following technical scheme; The present application discloses an intelligent scheduling system of a smart supply chain collaborative platform based on multi-source data fusion, which comprises: The collection module is used for collecting historical supply chain product association information in real time; the analysis module is used for traversing the supply chain product association information collected by the collection module, analyzing the trust index of each supply chain based on the supply chain product association information; the receiving module is used for receiving supply chain service target product supply demand information; the identification module is used for obtaining the supply chain service target product supply demand information received by the receiving module, identifying a matched supply chain based on the product supply demand information; the recommendation module is used for receiving the identification result of the identification module, and generating a supply chain recommendation; and the feedback module is used for feeding back the supply chain recommendation generated by the recommendation module to a system user. The collection module is connected with a storage unit and a management unit through wireless network interaction, the collection module is connected with an analysis module through wireless network interaction, the analysis module is connected with a selection unit through wireless network interaction, the selection unit is connected with the storage unit through wireless network interaction, the analysis module is connected with a receiving module and an identification module through wireless network interaction, the identification module is connected with a queue unit through wireless network interaction, and the identification module is connected with a recommendation module and a feedback module through wireless network interaction.

[0007] Further, the collection module is connected with a sub-module, including: The storage unit is used for receiving the supply chain product association information collected by the collection module, and storing the supply chain product association information. The management unit is used for distinguishing and storing the supply chain product association information stored in the storage unit. The supply chain product association information collected by the collection module includes product historical supply times, product historical supply expected arrival times, product historical supply actual arrival times, product historical supply amounts, and product historical supply qualified rates.

[0008] Further, the supply chain product association information collected by the collection module is marked with a supply chain name, and the collection module synchronously uploads the supply chain product association information to the storage unit after collecting the supply chain product association information. The management unit further traverses the supply chain product association information loaded into the storage unit, identifies the marked content of the supply chain product association information, sets a distinguished storage interval in the storage unit based on the marked content, and performs distinguished storage management on the supply chain product association information based on the distinguished storage interval.

[0009] Further, the analysis module is connected with a sub-module, including: The selecting unit is configured to select the supply chain product association information in the storage unit, and forward the selected supply chain product association information to the analysis module for performing the analysis operation of the supply chain trust index. The selecting unit is configured to select the supply chain product association information in the storage unit, and forward the selected supply chain product association information to the analysis module for performing the analysis operation of the supply chain trust index.

[0010] Further, the analysis logic of the supply chain trust index in the analysis module is represented as: ; In the formula: is the supply chain trust index; is the supply chain historical product supply times; is the supply chain i-th product supply quantity; is the supply chain i-th product qualified rate; is the supply chain i-th product expected arrival time and actual arrival time; is the normalization factor; In the formula, represents the average of , and the normalization factor is between 0 and 1, and the supply chain trust index is larger, indicating that the supply chain is more reliable. .

[0011] Further, in the receiving module running stage, the received supply chain service target product supply demand information includes supply chain service target manual upload, and the supply chain service target product supply demand information includes: product demand supply quantity, product supply demand date interval.

[0012] Further, the logic of the identification module running to identify the matched supply chain is: Based on the calculation of the historical supply product related information of each supply chain, the historical single supply product quantity average of each supply chain and the expected arrival time and actual arrival time difference average of the supply chain product are calculated. The supply chain that meets the following conditions is recorded as a matched supply chain. No1: The historical single supply product quantity average of the supply chain is not less than the product demand supply quantity in the supply chain service target product supply demand information. No2: The average difference between the predicted arrival time and the actual arrival time of the product supplied by the supply chain is not greater than the date interval of the product supply demand in the supply chain service target product supply demand information.

[0013] Further, the identification module is arranged with a sub-module, comprising: A queue unit is arranged to receive each matched supply chain identified by the identification module, obtain the trust index of each matched supply chain, and sort each matched supply chain based on the trust index of each supply chain to generate a queue. In the sorting process, the matched supply chains are arranged in descending order.

[0014] Further, the supply chain recommendation generated in the recommendation module is the most front supply chain in the supply chain queue in the queue unit. The feedback module is connected with a computer device having a display function, the feedback module transmits the supply chain recommendation to the computer device, displays the supply chain corresponding name as the display content, and the system user reads the supply chain recommendation on the computer device.

[0015] Compared with the known prior art, the technical scheme provided by the present application has the following beneficial effects: The present application provides a smart scheduling system of a smart supply chain collaboration platform based on multi-source data fusion. In the running process, the system can accurately evaluate the credibility of each supply chain by deep mining and analyzing massive historical supply data, provide a strong basis for enterprises to select partners, greatly improve the scientific nature of supply chain selection, quickly match the options meeting the demand from numerous supply chains when facing product supply demand, and recommend the options according to the trust index. This process greatly improves the supply response speed, guarantees the timeliness of enterprise production and operation, and based on accurate data analysis and matching, the enterprise can effectively reduce the risks of supply interruption and substandard product quality caused by improper selection of supply chain, and reduce the operation cost. Overall, the system improves the intelligent level of supply chain collaboration management, enhances the competitiveness of enterprises in the market, and helps enterprises achieve sustainable development. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical scheme in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0017] Figure 1A structural schematic diagram of an intelligent scheduling system of a multi-source data fusion based intelligent supply chain collaboration platform. DETAILED DESCRIPTION

[0018] To make the objects, technical solutions, and advantages of the embodiments of the present application clearer, the following will be combined with the accompanying drawings to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0019] The present application will be further described below in combination with the embodiments.

[0020] Embodiment The multi-source data fusion based intelligent supply chain collaboration platform intelligent scheduling system of the embodiment, as shown in the figure, comprises: Figure 1 A collection module for collecting historical supply chain product related information in real time; The collection module is provided with a sub-module at a lower level, comprising: A storage unit for receiving the collection module to collect supply chain product related information and storing the supply chain product related information; A management unit for traversing the supply chain product related information stored in the storage unit and storing the supply chain product related information; The supply chain product related information collected by the collection module comprises: product historical supply times, product historical supply expected arrival time, product historical supply actual arrival time, product historical supply amount, and product historical supply qualification rate; The supply chain product related information collected by the collection module is marked with a supply chain name. After the collection module collects the supply chain product related information, it synchronously uploads the supply chain product related information to the storage unit. The management unit further traverses the supply chain product related information loaded into the storage unit, identifies the marked content of the supply chain product related information, sets a differentiated storage interval in the storage unit based on the marked content, and manages the supply chain product related information based on the differentiated storage interval; An analysis module for traversing the supply chain product related information collected by the collection module and analyzing the trust index of each supply chain based on the supply chain product related information; The analysis module is provided with a sub-module at a lower level, comprising: ​The selecting unit is configured to select the supply chain product association information in the storage unit, and forward the selected supply chain product association information to the analysis module for performing an analysis operation of the supply chain trust index. In the selection of the supply chain product association information in the storage unit, the selecting unit selects the corresponding mark content of each supply chain product association information based on the supply chain product association information in the differentiated storage interval, so that the mark content of each supply chain product association information selected by the selecting unit is consistent each time the selecting unit is operated. The analysis module is operated in conjunction with the selecting unit, the number of continuous operations of the analysis module is equal to the number of the differentiated storage intervals in the storage unit, and the analysis module is reset when the supply chain product association information in the storage unit is updated. The analysis logic of the supply chain trust index in the analysis module is represented as: ; In the formula: is the supply chain trust index; is the number of historical product supply times of the supply chain; is the number of the i-th product supply of the supply chain; is the qualified rate of the i-th product supply of the supply chain; is the expected arrival time and the actual arrival time of the i-th product supply of the supply chain; is a normalization factor; wherein, represents the average of , and the normalization factor is between 0 and 1, and the supply chain trust index is larger, indicating that the supply chain is more reliable, and based on the above formula, each supply chain has . Through the above logical formula calculation, the trust index of each supply chain is calculated to provide data support for the operation of the subsequent modules in the embodiment.

[0021] The receiving module is configured to receive supply chain service target product supply demand information. In the receiving module operation stage, the received supply chain service target product supply demand information is manually uploaded by the supply chain service target, and the supply chain service target product supply demand information includes: product demand supply amount and product supply demand date interval. The identification module is configured to obtain the supply chain service target product supply demand information received by the receiving module, and identify a matched supply chain based on the product supply demand information. The logic of the identification module for identifying the matched supply chain is: The average historical single supply product quantity of each supply chain and the average difference between the estimated arrival time and the actual arrival time of the supply product of the supply chain are calculated based on the historical supply product related information of each supply chain; The supply chain satisfying the following conditions is recorded as a matched supply chain; No1: The average historical single supply product quantity of the supply chain is not less than the product demand supply quantity in the product supply demand information of the target product supply service; No2: The average difference between the estimated arrival time and the actual arrival time of the supply product of the supply chain is not greater than the date interval of the product supply demand in the target product supply service; The sub-module is arranged below the identification module and includes: The queue unit is configured to receive each matched supply chain identified by the identification module, obtain the trust index of each matched supply chain, sort each matched supply chain based on the trust index of each supply chain, and generate a queue; When the matched supply chains are sorted, they are arranged in descending order; The recommendation module is configured to receive the identification result of the identification module and generate a supply chain recommendation; The supply chain recommendation generated in the recommendation module is the most front supply chain in the supply chain queue in the queue unit; The feedback module is connected with a computer device having a display function, and the feedback module transmits the supply chain recommendation to the computer device, displays the supply chain corresponding name as the display content, and allows a system end user to read the supply chain recommendation on the computer device; The feedback module is configured to feed back the supply chain recommendation generated in the recommendation module to a system end user; The storage unit and the management unit are connected with the collection module through wireless network interaction, the analysis module is connected with the collection module through wireless network interaction, the selection unit is connected with the analysis module through wireless network interaction, the analysis module is connected with the receiving module and the identification module through wireless network interaction, the queue unit is connected with the identification module through wireless network interaction, and the recommendation module and the feedback module are connected with the identification module through wireless network interaction.

[0022] In the embodiment, the collection module runs to collect historical supply chain product association information in real time, the storage unit synchronously receives the supply chain product association information collected by the collection module, stores the supply chain product association information, the management unit traverses the supply chain product association information stored in the storage unit in real time, and stores the supply chain product association information. The analysis module runs after the collection module runs to collect the supply chain product association information, analyzes the trust index of each supply chain based on the supply chain product association information, the selection unit synchronously selects the supply chain product association information in the storage unit, and forwards the selected supply chain product association information to the analysis module. The analysis module performs the analysis operation of the supply chain trust index, the receiving module further receives the supply chain service target product supply demand information, the recognition module obtains the supply chain service target product supply demand information received by the receiving module, recognizes the matched supply chain based on the product supply demand information, the queue unit synchronously receives each matched supply chain recognized by the recognition module, obtains the trust index of each matched supply chain, sorts the matched supply chain based on the trust index of each supply chain to generate a queue, and the recommendation module receives the recognition result of the recognition module, generates a supply chain recommendation, and finally the feedback module feeds back the supply chain recommendation generated in the recommendation module to the system end user.

[0023] In summary, the system in the above embodiments can accurately evaluate the credibility of each supply chain through deep mining and analysis of massive historical supply data, provide a strong basis for enterprises to select partners, and greatly improve the scientific nature of supply chain selection. When facing product supply demand, it can quickly match the options that meet the demand from numerous supply chains and recommend them according to the trust index. This process greatly improves the supply response speed and ensures the timeliness of enterprise production and operation. At the same time, based on accurate data analysis and matching, enterprises can effectively reduce the risks of supply interruption and substandard product quality caused by improper selection of supply chains, and reduce operating costs. Overall, the system improves the intelligent level of supply chain collaborative management, enhances the competitiveness of enterprises in the market, and helps enterprises achieve sustainable development The above embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. Intelligent scheduling system of smart supply chain collaborative platform based on multi-source data fusion, characterized by: include: The collection module is used to collect historical supply product related information of each supply chain in real time; An analysis module is used to traverse the supply chain supply product association information collected by the collection module and analyze the trust index of each supply chain based on the supply chain supply product association information; A receiving module, used to receive supply demand information of target products for supply chain services; The identification module is used to obtain the supply chain service target product supply demand information received by the receiving module, and identify the matching supply chain based on the product supply demand information; The recommendation module is used to receive the recognition results of the recognition module and generate supply chain recommendations; The feedback module is used to feed back the supply chain recommendations generated in the recommendation module to the system end users.

2. The intelligent scheduling system for a smart supply chain collaboration platform based on multi-source data fusion according to claim 1 is characterized in that: The acquisition module is provided with submodules at the lower level, including: A storage unit is used to receive the supply chain product association information collected by the collection module and store the supply chain product association information; The management unit is used to traverse the supply chain supply product association information stored in the storage unit and distinguish and store the supply chain supply product association information; Among them, the supply chain product related information collected by the collection module includes: the number of historical product supplies, the estimated arrival time of historical product supplies, the actual arrival time of historical product supplies, the supply quantity of each historical product supply, and the qualified rate of each historical product supply.

3. The intelligent scheduling system for a smart supply chain collaboration platform based on multi-source data fusion according to claim 2 is characterized in that: The supply chain supply product association information collected by the collection module is marked with the supply chain name. After collecting the supply chain supply product association information, the collection module synchronously transfers the supply chain supply product association information to the storage unit. The management unit further traverses the supply chain supply product association information loaded into the storage unit, identifies the marking content of the supply chain supply product association information, sets differentiated storage intervals in the storage unit based on the marking content, and performs differentiated storage management on the supply chain supply product association information based on the differentiated storage intervals.

4. The intelligent scheduling system for a smart supply chain collaboration platform based on multi-source data fusion according to claim 1 is characterized in that: The analysis module is provided with submodules at the lower level, including: A selection unit is used to select supply chain supply product association information from the storage unit, and forward the selected supply chain supply product association information to the analysis module for the analysis module to perform an analysis operation of the supply chain trust index; Among them, when the selection unit selects the supply chain supply product association information in the storage unit, it selects based on the corresponding mark content of each supply chain supply product association information in the differentiated storage interval where the supply chain supply product association information is located, so that the mark content of each supply chain supply product association information selected by the selection unit each time is consistent, and the analysis module follows the selection unit to run in conjunction, and the number of consecutive runs of the analysis module is equal to the number of differentiated storage intervals in the storage unit. When the supply chain supply product association information in the storage unit is updated, the analysis module resets its operation.

5. The intelligent scheduling system for a smart supply chain collaboration platform based on multi-source data fusion according to claim 1 is characterized in that: The analysis logic of the supply chain trust index in the analysis module is expressed as follows: ； Where: is the supply chain trust index; The historical product supply times for the supply chain; The quantity of products supplied by the supply chain for the i-th time; The qualified rate of products supplied by the supply chain for the i-th time; The estimated arrival time and actual arrival time of the product supplied by the supply chain for the i-th time; is the normalization factor; in, Express The average, normalization factor The value is between 0 and 1, supply chain trust index The larger the value, the higher the credibility of the supply chain. Based on the above formula, the corresponding supply chains have .

6. The intelligent scheduling system for a smart supply chain collaboration platform based on multi-source data fusion according to claim 1 is characterized in that: During the operation phase of the receiving module, the received supply chain service target product supply demand information is manually uploaded by the supply chain service target, and the supply chain service target product supply demand information includes: product demand supply quantity and product supply demand date range.

7. The intelligent scheduling system for a smart supply chain collaboration platform based on multi-source data fusion according to claim 1 is characterized in that: The logic of the identification module to identify the matching supply chain is as follows: Based on the historical supply information of each supply chain, the average quantity of products supplied by each supply chain in a single batch and the average difference between the expected arrival time and the actual arrival time of the products supplied by the supply chain are calculated; The supply chain that meets the following conditions is recorded as a matching supply chain; No. 1: The average of the supply chain's historical single-time product supply volume is not less than the product demand supply volume in the supply chain service target product supply demand information; No2: The average difference between the estimated arrival time and the actual arrival time of the supply chain supply products is not greater than the date range of the product supply demand in the supply chain service target product supply demand information.

8. The intelligent scheduling system for a smart supply chain collaboration platform based on multi-source data fusion according to claim 7 is characterized in that: The identification module is provided with submodules at the lower level, including: A queue unit is configured to receive each matching supply chain identified by the identification module, obtain a trust index of each matching supply chain, and sort each matching supply chain based on the trust index to generate a queue; Among them, the matching supply chains are sorted in descending order.

9. The intelligent scheduling system for a smart supply chain collaboration platform based on multi-source data fusion according to claim 1 is characterized in that: The supply chain recommendation generated by the recommendation module is the frontmost supply chain in the supply chain queue of the queue unit; The feedback module is connected to a computer device with a display function. The feedback module transmits the supply chain recommendation to the computer device and displays the supply chain corresponding name as the display content, so that the system end user can read the supply chain recommendation on the computer device.

10. The intelligent scheduling system for a smart supply chain collaboration platform based on multi-source data fusion according to claim 1 is characterized in that: The acquisition module is interactively connected to a storage unit and a management unit at its lower level through a wireless network, the acquisition module is interactively connected to an analysis module through a wireless network, the analysis module is interactively connected to a selection unit through a wireless network, the selection unit is interactively connected to the storage unit through a wireless network, the analysis module is interactively connected to a receiving module and an identification module through a wireless network, the identification module is interactively connected to a queue unit at its lower level through a wireless network, and the identification module is interactively connected to a recommendation module and a feedback module through a wireless network.

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