Supplier information processing system and method based on multi-source collection data fusion

Abstract

The invention discloses a supplier information processing system and method based on multi-source collection data fusion, and belongs to the technical field of information processing, and the system comprises a multi-source data acquisition module which collects data information related to suppliers from a plurality of data sources so as to form an initial database; the processing module is used for performing related processing on the data information in the initial database to obtain an associated database; the comprehensive management center generates a deviation coefficient and a score value of each supplier so as to obtain a matching value of each supplier; the selection module is used for selecting the most suitable supplier for cooperation according to the matching value of each supplier; and the risk monitoring module is used for monitoring and evaluating the risk of the cooperative suppliers. Whether the supplier has a potential abnormal risk is judged according to the delivery punctuality condition and the profit condition of the cooperative supplier, so that an enterprise can conveniently and timely carry out corresponding countermeasure adjustment, and the normal production efficiency of the enterprise is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of information processing, and in particular relates to a supplier information processing system and method based on multi-source collected data fusion. Background Art

[0002] Suppliers are companies and individuals that supply various resources needed by an enterprise and its competitors. These resources may include raw materials, equipment, energy, labor, etc. They can be manufacturers of these resources or middlemen. Suppliers play a vital role in the production and operation of an enterprise. The resources they provide are the basis for the production activities of the enterprise.

[0003] There is usually a mutually dependent, mutually beneficial relationship between enterprises and suppliers. Enterprises rely on suppliers to provide high-quality, low-cost resources to maintain their operations, while suppliers rely on enterprises to obtain orders and revenue. This relationship needs to be based on mutual trust and cooperation to ensure that both parties can obtain the greatest benefits.

[0004] Due to the wide variety of suppliers and the complexity of their information, companies currently select suppliers based on their self-promotion and industry reputation. Although this method can select suppliers relatively quickly, companies cannot verify the accuracy of the information provided by suppliers, which can easily lead to deviations in subsequent cooperation; in addition, this method can only select suppliers from self-promotion, and cannot cover more potential suppliers, which may cause companies to miss the most suitable suppliers. Summary of the invention

[0005] The purpose of the present invention is to provide a supplier information processing system and method based on multi-source acquisition data fusion to solve the problems faced in the above-mentioned background technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A supplier information processing system based on multi-source collected data fusion, the processing system comprising: A multi-source data acquisition module, which is used to collect data information related to suppliers from multiple data sources to form an initial database; A processing module, wherein the processing module is used to perform relevant processing on the data information in the initial database to obtain a related database; An integrated management center, wherein the integrated management center is provided with a data calculation unit, a feedback processing unit and an integrated calculation unit, wherein the data calculation unit is used to analyze the data information in the associated database to generate a deviation coefficient of each supplier, the feedback processing unit is used to analyze the feedback data of each supplier to generate a score value of each supplier, and the integrated calculation unit is used to generate a matching value of each supplier according to the generated deviation coefficient and score value; A selection module, wherein the selection module selects the most suitable supplier for cooperation according to the matching value of each supplier; The risk monitoring module is used to obtain key data information of cooperative suppliers from big data, so as to monitor and evaluate the risks of cooperative suppliers.

[0007] Furthermore, the method in which the processing module performs relevant processing on the data information in the initial database includes: The data information in the initial database is cleaned, feature extracted, format converted and standardized to obtain pre-processed data information. Then, data fusion technology is used to integrate the data information collected from multiple data sources to obtain multiple sets of related items related to suppliers, which together constitute a related database.

[0008] Furthermore, the method for the data calculation unit to generate the deviation coefficient is: Develop a standard requirement project set based on the required commodity data ,in is the characteristic function of the first associated item, is the characteristic function of the i-th associated item, is the characteristic function of the nth associated item, is the total number of associated items, and ; Obtain the relevant project information of each supplier from the key database to form a proposal project set for each supplier , is the proposed item set of the jth supplier, is the characteristic function of the first associated item of the jth supplier, is the characteristic function of the i-th associated item of the j-th supplier, is the characteristic function of the nth associated item of the jth supplier; Then we get the project deviation set of each supplier , is the item deviation set of the jth supplier, is the deviation function of the first associated item of the jth supplier, and , is the deviation function of the ith associated item of the jth supplier, and , is the deviation function of the first associated item of the jth supplier, and ; Finally, the formula Determine the coefficient of variation for each supplier , is the weight coefficient of the i-th associated item.

[0009] Furthermore, the feedback processing unit generates a scoring value by: Get feedback data on the same product from various suppliers, including sales volume over a period of time , Return volume , number of positive reviews And the amount of negative reviews ; So through the formula Get the rating value ; in, The brand value points for the supplier.

[0010] Furthermore, the method by which the comprehensive calculation unit generates the matching value of each supplier is: By formula Find the matching value ,in The matching function preset by the system.

[0011] Furthermore, the selection module works as follows: Get the matching value of each supplier, sort the suppliers from large to small according to the matching value, and select the top-ranked suppliers for cooperation.

[0012] Furthermore, the risk monitoring module works as follows: Obtain the on-time rate of each product delivery of the cooperative supplier within a period of time , thus developing a curve function of on-time rate changing with delivery times , and obtain the supplier's profit E over time during the period ; By formula Derive the supplier's risk index ; when , then the supplier is judged to have a high potential risk; in, is the reputation value of the supplier during this period. is the start time of this period, is the end time of this period, The basic profit curve of the supplier is set as time changes. is the total number of product deliveries during this period. as well as is the preset coefficient, is the preset risk threshold.

[0013] A supplier information processing method based on multi-source collected data fusion, the processing method is controlled and implemented by the supplier information processing system based on multi-source collected data fusion described above.

[0014] Beneficial effects of the present invention: The present invention collects data information related to suppliers from multiple data sources to form an initial database, which can expand the scope of supplier selection, and then processes the data information, calculates the deviation coefficient of each supplier through a data calculation unit, and then obtains the feedback data of each supplier from the big data through a feedback processing unit for analysis to generate a scoring value, and finally generates a matching value for each supplier based on the generated deviation coefficient and the scoring value, and selects a suitable supplier for cooperation based on the matching value. In this way, not only the relevant data provided by the supplier is analyzed, but also the market feedback from the supplier is analyzed, so that the most suitable supplier for the enterprise can be matched more accurately, thereby bringing the highest profit to the enterprise.

[0015] The present invention determines whether a supplier has potential abnormal risks based on the delivery punctuality and profit situation of the cooperative supplier, so that the enterprise itself can make corresponding countermeasure adjustments in a timely manner to ensure the normal production efficiency of the enterprise.

[0016] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0019] 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.

[0020] In one embodiment, a supplier information processing system based on multi-source collected data fusion is disclosed, such as Figure 1 As shown, the processing system includes: A multi-source data acquisition module is used to collect data information related to suppliers from multiple data sources to form an initial database; A processing module, which is used to perform relevant processing on the data information in the initial database to obtain a related database; The integrated management center is provided with a data calculation unit, a feedback processing unit and an integrated calculation unit. The data calculation unit is used to analyze the data information in the associated database to generate the deviation coefficient of each supplier. The feedback processing unit analyzes the feedback data of each supplier to generate the score value of each supplier. The integrated calculation unit is used to generate the matching value of each supplier according to the generated deviation coefficient and score value. Selection module: The selection module selects the most suitable supplier for cooperation based on the matching value of each supplier; The risk monitoring module is used to obtain key data information of cooperative suppliers from big data, so as to monitor and evaluate the risks of cooperative suppliers.

[0021] Through the above technical scheme, the present application collects data information related to suppliers from multiple data sources to form an initial database, which can expand the scope of supplier selection, and then processes the data information, calculates the deviation coefficient of each supplier through the data calculation unit, and then obtains the feedback data of each supplier from the big data through the feedback processing unit for analysis to generate a scoring value, and finally generates a matching value for each supplier based on the generated deviation coefficient and the scoring value, and selects a suitable supplier for cooperation based on the matching value. In this way, not only the relevant data provided by the supplier is analyzed, but also the market feedback from the supplier is analyzed, which can more accurately match the most suitable supplier for the enterprise, thereby bringing the highest benefits to the enterprise; in addition, the present application also has a risk monitoring module, which determines whether the supplier has potential abnormal risks based on the on-time delivery and profit situation of the cooperative supplier, so that the enterprise itself can make corresponding countermeasures and adjustments in a timely manner to ensure the normal production efficiency of the enterprise.

[0022] The method for the processing module to perform relevant processing on the data information in the initial database includes: first, cleaning, feature extraction, format conversion and standardization of the data information in the initial database to obtain pre-processed data information, so that the data is uniformly converted into a unified format to facilitate subsequent calculation and processing, and some irrelevant influencing factors can be removed to facilitate subsequent analysis, and then data fusion technology is used to integrate the data information collected by multiple data sources to obtain multiple related project sets related to suppliers, thereby forming a related database together. This not only allows direct analysis based on the data in the related database to facilitate subsequent calculations, but also improves the accuracy and credibility of the data.

[0023] The method for the data calculation unit to generate the deviation coefficient is: according to the required commodity data, a standard demand item set is formulated. ,in is the characteristic function of the first associated item, is the characteristic function of the i-th associated item, is the characteristic function of the nth associated item, is the total number of associated items, and ; Obtain the relevant project information of each supplier from the key database to form a proposal project set for each supplier , is the proposed item set of the jth supplier, is the characteristic function of the first associated item of the jth supplier, is the characteristic function of the i-th associated item of the j-th supplier, is the characteristic function of the nth associated item of the jth supplier; Then we get the project deviation set of each supplier , is the item deviation set of the jth supplier, is the deviation function of the first associated item of the jth supplier, and , is the deviation function of the ith associated item of the jth supplier, and , is the deviation function of the first associated item of the jth supplier, and ; Finally, the formula Determine the coefficient of variation for each supplier , is the weight coefficient of the i-th associated item.

[0024] The above scheme mainly provides a method for generating the deviation coefficient. First, a standard requirement project set is formulated based on the commodity data required by the enterprise. , and then obtain the related project information of each supplier from the key database to form a proposal project set for each supplier , and finally obtain the project deviation set of each supplier , calculate each related item in the item deviation machine, through the formula Determine the coefficient of variation for each supplier For example, an enterprise forms a standard demand item set based on price, quality, service, reputation and other related items. The price-related items can be divided into multiple levels from low to high according to the price. The characteristic function of the price-related items is formulated according to the pre-assigned levels. The price required by the enterprise is input into the characteristic function of the proposed price-related items to obtain the required price level of the enterprise. By analogy, the standard demand item set of the enterprise is obtained. , and the proposal project set of each supplier , and then compare the proposed project sets of each supplier with the enterprise's standard demand project set to obtain the project deviation set of each supplier , in general, when the project deviation set Inside The smaller the value, the more the associated items in the supplier match the associated items required by the enterprise. Therefore, the formula is finally used to Determine the coefficient of variation for each supplier ,in is the weight coefficient of the ith related project, which can be determined based on historical experience data. It can be seen that the smaller the value of the deviation coefficient is, the higher the matching degree between the supplier and the enterprise's requirements is. In this way, the deviation coefficient can be obtained by analyzing the difference between the related project data disclosed by each supplier and the related project data required by the enterprise, so as to more accurately and conveniently select the most suitable institutional supplier for the enterprise.

[0025] The feedback processing unit generates the rating value by obtaining feedback data of the same product from each supplier, including sales volume within a period of time. , Return volume , number of positive reviews And the amount of negative reviews , So through the formula Get the rating value ; in, The brand value points for the supplier.

[0026] The above scheme mainly provides a specific method for generating the score value. First, the feedback data of the same product from each supplier, including the sales volume within a period of time, , Return volume , number of positive reviews And the amount of negative reviews So, through the formula Get the rating value ,in is the brand value score of the supplier, which can be determined based on the industry reputation of the supplier. The larger the value, the higher the brand value score of the supplier. As can be seen from the formula, when The larger the ratio is, the better the sales volume of the supplier's products is and the fewer the returns are. The larger the ratio is, the more favorable comments there are than unfavorable ones, indicating that the supplier's products are more recognized. The larger the value is, the better the feedback from the supplier in the market is, and the better the benefits can be obtained by cooperating with the supplier.

[0027] The method for the comprehensive calculation unit to generate the matching value of each supplier is: Find the matching value ,in It is the matching function preset by the system; The working method of the selection module is: obtain the matching value of each supplier, sort the suppliers from large to small according to the matching value, and select the top-ranked suppliers for cooperation.

[0028] The above scheme provides a specific method for selecting suppliers. After obtaining the score value and deviation coefficient, continuous simulation training is carried out based on historical data to obtain a preset matching function. , and then bring the score value and the deviation coefficient into the matching function to get the matching value of each supplier. Generally speaking, the higher the score of a supplier, or the smaller the deviation coefficient, the more it matches the requirements of the enterprise. Therefore, the higher its matching value, the more it matches the enterprise. Therefore, after obtaining the matching value of each supplier, sort the suppliers from large to small according to the matching value, and select the top-ranked suppliers for cooperation. For example, the top three suppliers can be selected for cooperation. This can not only find the most suitable supplier for the enterprise, but also avoid the impact of problems with a single supplier on the enterprise.

[0029] The working method of the risk monitoring module is to obtain the on-time rate of each delivery of goods by the cooperative supplier within a period of time. , thus developing a curve function of on-time rate changing with delivery times , and obtain the supplier's profit E over time during the period ; By formula Derive the supplier's risk index ; when , then the supplier is judged to have a high potential risk; in, is the reputation value of the supplier during this period. is the start time of this period, is the end time of this period, The basic profit curve of the supplier is set as time changes. is the total number of product deliveries during this period. as well as is the preset coefficient, is the preset risk threshold.

[0030] The above scheme provides a method for the risk monitoring module to monitor and evaluate the potential risks of cooperative suppliers. First, the on-time rate of each delivery of goods by the cooperative supplier within a period of time is obtained. , thus developing a curve function of on-time rate changing with delivery times , and obtain the supplier's profit E over time during the period , through the formula Derive the supplier's risk index ;in is the reputation value of the supplier in that period. It is independently formulated according to the reputation of the corresponding supplier in that period. The greater the negative impact, the greater the value. The reputation value has a great impact on the product. For example, a supplier has caused widespread discussion due to food safety issues in that period, which has a greater impact on the sales of subsequent products. Therefore, the greater the reputation value of a supplier, the greater the potential risk it has. The formula It indicates the difference between the supplier's profit and the basic profit during the period. The larger the value, the less profit the supplier itself generates, which means that the possibility of abnormal operation is greater. The formula It represents the difference between the on-time rate of the first half of the cycle and the on-time rate of the second half of the cycle. If the difference is larger, it means that the supplier's delivery on-time rate is lower and its delivery is more unstable, so its impact on the company will be greater. Therefore, through the formula Derive the supplier's risk index Then, compare it with the preset risk threshold For comparison, when When the supplier is judged to have high potential risk, in order to ensure the effective and normal operation of the enterprise, corresponding countermeasures can be adjusted, such as reducing orders from the supplier or terminating subsequent cooperation, so as to ensure the normal production efficiency of the enterprise.

[0031] It should be noted that the cycle length can be determined manually, and the basic profit amount of the supplier set changes over time. , preset coefficient as well as , preset risk threshold All of these can be determined based on historical data and empirical data, so I will not go into details here.

[0032] A supplier information processing method based on multi-source collected data fusion, the processing method is controlled and implemented by the above-mentioned supplier information processing system based on multi-source collected data fusion.

[0033] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A supplier information processing system based on multi-source data fusion, characterized in that: The processing system comprises: A multi-source data acquisition module, which is used to collect data information related to suppliers from multiple data sources to form an initial database; A processing module, wherein the processing module is used to perform relevant processing on the data information in the initial database to obtain a related database; An integrated management center, wherein the integrated management center is provided with a data calculation unit, a feedback processing unit and an integrated calculation unit, wherein the data calculation unit is used to analyze the data information in the associated database to generate a deviation coefficient of each supplier, the feedback processing unit is used to analyze the feedback data of each supplier to generate a score value of each supplier, and the integrated calculation unit is used to generate a matching value of each supplier according to the generated deviation coefficient and score value; A selection module, wherein the selection module selects the most suitable supplier for cooperation according to the matching value of each supplier; The risk monitoring module is used to obtain key data information of cooperative suppliers from big data, so as to monitor and evaluate the risks of cooperative suppliers.

2. A supplier information processing system based on multi-source data fusion according to claim 1, characterized in that: The method for the processing module to perform relevant processing on the data information in the initial database includes: The data information in the initial database is cleaned, feature extracted, format converted and standardized to obtain pre-processed data information. Then, data fusion technology is used to integrate the data information collected from multiple data sources to obtain multiple sets of related items related to suppliers, which together constitute a related database.

3. A supplier information processing system based on multi-source data fusion according to claim 1, characterized in that: The method for the data calculation unit to generate the deviation coefficient is: Develop a standard requirement project set based on the required commodity data ,in is the characteristic function of the first associated item, is the characteristic function of the i-th associated item, is the characteristic function of the nth associated item, is the total number of associated items, and ; Obtain the relevant project information of each supplier from the key database to form a proposal project set for each supplier , is the proposed item set of the jth supplier, is the characteristic function of the first associated item of the jth supplier, is the characteristic function of the i-th associated item of the j-th supplier, is the characteristic function of the nth associated item of the jth supplier; Then we get the project deviation set of each supplier , is the item deviation set of the jth supplier, is the deviation function of the first associated item of the jth supplier, and , is the deviation function of the ith associated item of the jth supplier, and , is the deviation function of the first associated item of the jth supplier, and ; Finally, the formula Determine the coefficient of variation for each supplier , is the weight coefficient of the i-th associated item.

4. A supplier information processing system based on multi-source data fusion according to claim 3, characterized in that: The method for the feedback processing unit to generate a rating value is: Get feedback data on the same product from various suppliers, including sales volume over a period of time , Return volume , number of positive reviews And the amount of negative reviews ; So through the formula Get the rating value ; in, The brand value points for the supplier.

5. A supplier information processing system based on multi-source data fusion according to claim 4, characterized in that: The method by which the comprehensive calculation unit generates the matching value of each supplier is: By formula Find the matching value ,in The matching function preset by the system.

6. A supplier information processing system based on multi-source data fusion according to claim 5, characterized in that: The selection module works as follows: Get the matching value of each supplier, sort the suppliers from large to small according to the matching value, and select the top-ranked suppliers for cooperation.

7. The supplier information processing system based on multi-source data fusion according to claim 1 is characterized in that: The working method of the risk monitoring module is: Obtain the on-time rate of each product delivery of the cooperative supplier within a period of time , thus developing a curve function of on-time rate changing with delivery times , and obtain the supplier's profit E over time during the period ; By formula Derive the supplier's risk index ; when , then the supplier is judged to have a high potential risk; in, is the reputation value of the supplier during this period. is the start time of this period, is the end time of this period, The basic profit curve of the supplier is set as time changes. is the total number of product deliveries during this period. as well as is the preset coefficient, is the preset risk threshold.

8. A supplier information processing method based on multi-source data fusion, characterized in that: The processing method is controlled and implemented by the supplier information processing system based on multi-source acquisition data fusion as described in any one of claims 1-7.

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