CN115660774A - Material supply chain system credit evaluation method based on block chain - Google Patents
Material supply chain system credit evaluation method based on block chain Download PDFInfo
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Abstract
The invention relates to a block chain-based material supply chain system credit evaluation method, which comprises the steps of acquiring supplier data; respectively acquiring obvious risks and potential risks according to data of a supplier; quantifying the obvious risk and the potential risk respectively to obtain an obvious risk grade score and a potential risk grade score; processing the obvious risk grade scores by adopting a deep learning network to obtain a first evaluation result, and performing weighted calculation on the potential risk grade scores by adopting a subjective weighting method to obtain a second evaluation result; obtaining a credit evaluation result according to the first evaluation result and the second evaluation result; supplier data, obvious risks, potential risks and credit evaluation results are subjected to chain loading and certificate storage through the block chain, evaluation is performed through the obvious risks and the potential risks, accuracy of the evaluation results is guaranteed, data such as the credit evaluation results can be subjected to chain loading and certificate storage, and uplink and downlink of the data through the block chain can guarantee privacy, safety and publicity of the data, and tampering is avoided.
Description
Technical Field
The invention relates to the technical field of credit evaluation, in particular to a material supply chain system credit evaluation method based on a block chain.
Background
The problems of difficult operation, delayed delivery, quality defect, non-fit and the like of suppliers in the contract performance process are increasingly highlighted under the influence of uncertain factors such as construction environment, market change and the like, so that risks such as project delay, capital loss and the like are caused, and economic benefits and power grid safety are influenced.
In the face of a large number of suppliers, due to the lack of scientific division and objective quantification means, the material department often cannot analyze the problems of qualification characteristics, performance capability, potential risks and the like of each supplier one by one, so that the material performance personnel cannot find the performance risks of the suppliers in time, and cannot make countermeasures immediately after finding the risks, and finally, construction period delay or asset loss is caused.
Disclosure of Invention
In view of this, the invention provides a material supply chain system credit evaluation method based on a block chain, which can systematically perform credit evaluation on a supplier and store a credit evaluation result on the block chain for other enterprises to obtain.
The technical scheme of the invention is realized as follows:
a material supply chain system credit evaluation method based on a block chain comprises the following steps:
s1, obtaining supplier data, wherein the supplier data comprises basic information, historical performance information and change information;
s2, acquiring the obvious risk of the supplier according to the historical performance information, and acquiring the potential risk of the supplier according to the change information;
s3, quantifying the obvious risks and the potential risks respectively to obtain obvious risk grade scores and potential risk grade scores;
s4, processing the obvious risk grade scores by adopting a deep learning network to obtain a first evaluation result, and performing weighted calculation on the potential risk grade scores by adopting a subjective weighting method to obtain a second evaluation result;
s5, obtaining a credit evaluation result according to the first evaluation result and the second evaluation result;
and S6, carrying out uplink certificate storage on the supplier data, the obvious risk, the potential risk and the credit evaluation result through a block chain.
Preferably, the suppliers include material suppliers and service suppliers, the basic information includes enterprise type, enterprise nature, operation range and established time, the historical fulfillment information includes whether delivery time is on time, qualification rate of delivered products or services and accuracy of delivery quantity, wherein whether the delivery time is on time includes whether delivered goods are on time and whether logistics are on time, and the variation information includes staff number variation, floor area variation, operation condition variation, staff quality variation, technology and management level variation and engineering equipment variation.
Preferably, the step S2 of acquiring the obvious risk of the provider according to the historical fulfillment information includes:
s21, constructing an obvious risk database, wherein the obvious risk database contains risk words, and each risk word corresponds to an obvious risk;
s22, extracting keywords from the historical fulfillment information, and performing synonymy expansion and approximate expansion on the keywords to obtain a keyword combination;
and S23, traversing and comparing the keyword combination and the risk words in the obvious risk database, acquiring the risk word most closely matched with the keyword combination, and acquiring the obvious risk according to the risk word.
Preferably, the specific steps of obtaining the obvious risk grade score and the potential risk grade score in step S2 include:
s24, constructing an enterprise change database, wherein the enterprise change database comprises change threshold values of various information of enterprises;
step S25, calculating to obtain a variation amount according to the variation information;
step S26 compares the fluctuation amount with the fluctuation threshold value, and outputs fluctuation information corresponding to the fluctuation amount larger than the fluctuation threshold value as a risk potential.
Preferably, the specific step of obtaining the obvious risk level score and the potential risk level score in step S2 further includes:
and S27, acquiring credit evaluation results of upstream and downstream enterprises cooperating with the suppliers, and acquiring potential risks according to the credit evaluation results of the upstream and downstream enterprises.
Preferably, the specific steps of step S3 are: and constructing a quantitative score library, wherein the quantitative score library comprises all risks and grade scores corresponding to the risks, searching the corresponding risks from the quantitative score library according to the obvious risks and the potential risks, and outputting the obvious risk grade scores and the potential risk grade scores after obtaining the grade scores corresponding to the risks.
Preferably, in the step S4, the processing of the obvious risk level score by using the deep learning network, and the specific step of obtaining the first evaluation result includes:
s41, constructing an enterprise risk type library, wherein the enterprise risk type library comprises enterprise risks, a risk score range corresponding to each enterprise risk and enterprise evaluation results;
s42, embedding the enterprise risk type library into a deep learning network;
and S43, the deep learning network identifies the obvious risk grade scores, searches for enterprise risks corresponding to risk grade ranges containing the obvious risk grade scores, and outputs and adds enterprise evaluation results corresponding to the enterprise risks to obtain a first evaluation result.
Preferably, in the step S4, a subjective weighting method is used to perform weighted calculation on the potential risk level score, and the specific step of obtaining the second evaluation result includes:
s44, constructing an expert scoring library, wherein the expert scoring library comprises a weight factor corresponding to each potential risk;
and S45, assigning the weight factors in the expert score library to the potential risk grade scores, and adding the assigned potential risk grade scores to obtain a second evaluation result.
Preferably, the specific step of step S5 includes:
s51, when the first evaluation result is larger than the second evaluation result, outputting the first evaluation result as a credit evaluation result;
and S52, when the second evaluation result is larger than the first evaluation result, multiplying the first evaluation result and the second evaluation result by the corresponding reduction factors respectively, summing the results, and outputting a sum as a credit evaluation result, wherein the reduction factor of the first evaluation result is smaller than the reduction factor of the second evaluation result.
Preferably, the specific steps of step S6 are: and carrying out data uplink on the supplier data, the obvious risk, the potential risk and the credit evaluation result through a node server of the supplier, and symmetrically encrypting uplink data through block chain middleware service software on the node server.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a block chain-based material supply chain system credit evaluation method, which comprises the steps of after basic information, historical performance information and change information of a supplier are collected, judging obvious risks and potential risks of the supplier are judged, after the obvious risks and the potential risks are obtained, carrying out specific quantitative scoring, selecting different methods according to the types of the risks to process grade scores, obtaining each evaluation result of a chain, and finally obtaining credit evaluation results of the supplier comprehensively according to the two evaluation results, wherein data contained in the whole process, such as supplier data, the obvious risks, the potential risks and the credit evaluation results, can be subjected to uploading chain evidence storage through a block chain, other enterprises can obtain credit evaluation results of corresponding suppliers through the block chain, so that whether the enterprises cooperate with the enterprises or not is considered, objective and complete credit evaluation of the suppliers in a supply chain system is realized, and objective, private and transparent data can be guaranteed through a block chain uploading and downloading chain mode.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only preferred embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a general flowchart of a credit evaluation method of a material supply chain system based on a block chain according to the present invention;
fig. 2 is a flowchart of step S2 of the material supply chain system credit evaluation method based on the block chain according to the present invention;
fig. 3 is a flowchart of step S4 of the method for evaluating credit of a material supply chain system based on a block chain according to the present invention;
fig. 4 is a flowchart of step S5 of a material supply chain system credit evaluation method based on a block chain according to the present invention.
Detailed Description
For a better understanding of the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described with reference to the accompanying drawings.
Referring to fig. 1 to 4, the method for evaluating credit of a material supply chain system based on a block chain according to the present invention includes the following steps:
s1, collecting and acquiring supplier data from a plurality of channels such as a social credit platform, an enterprise information inquiry network and the like, wherein the supplier data comprises basic information, historical performance information and change information.
The suppliers include material suppliers and service suppliers, and for the material suppliers, the quality, amount and logistics degree of the material may be judged to judge whether the suppliers meet the past agreement with other enterprises, and for the service suppliers, the service may be evaluated in credit, including service time limit, service attitude, etc.
In the supplier data, the basic information comprises enterprise type, enterprise property, operation range and establishment time, the historical fulfillment information comprises whether delivery time is on time or not, the qualification rate of delivered products or services and whether the delivery quantity is accurate or not, wherein the delivery time is on time or not, whether goods are delivered or not and whether logistics are on time or not, and the variation information comprises staff number variation, floor area variation, operation condition variation, staff quality variation, technology and management level variation and engineering equipment variation.
The basic information can be used for other enterprises to know the basic content of the supplier, the cooperation condition of the supplier and the past enterprises can be intuitively known through the historical fulfillment information, so that the credit of the supplier is judged, the change information can show the change of the strength of the supplier to judge whether the follow-up supplier has corresponding capacity to bear the order requirement of other enterprises, but the historical fulfillment information and the change information contain more contents, and if the enterprise needs to verify the historical fulfillment information and the change information of each supplier, more time is wasted.
S2, acquiring the obvious risk of the supplier according to the historical performance information and acquiring the potential risk of the supplier according to the change information, wherein the method specifically comprises the following steps:
s21, constructing an obvious risk database, wherein the obvious risk database contains risk words, and each risk word corresponds to an obvious risk;
s22, extracting keywords from the historical fulfillment information, and performing synonymy expansion and approximate expansion on the keywords to obtain a keyword combination;
and S23, traversing and comparing the keyword combination and the risk words in the obvious risk database, obtaining the risk word most closely matched with the keyword combination, and obtaining the obvious risk according to the risk word.
When the obvious risk is obtained, the judgment and the obtaining can be directly carried out according to the contents contained in the history fulfillment information, for the history fulfillment information, the contents of whether the delivery time is on time, the qualification rate of the delivered products or services, whether the delivery quantity is accurate and the like can be contained, wherein the on-time rate, the advance rate and the late rate can exist if the delivery time is on time, the corresponding embodiment can be realized in the complete order information of the suppliers, and the descriptions of the orders by different suppliers are different, so the historical fulfillment information has the difference in the aspect, therefore, after the keywords are extracted from the history fulfillment information, the keywords need to be uniformly expanded and approximately expanded, and finally the corresponding keyword combination is obtained, and in the constructed obvious risk database, the risk words such as 'delay', 'unqualified', 'defective goods', and the like can be found out through the comparison between the keyword combination and the risk words, and the closest risk words in the obvious risk database can be obtained, so that the obvious risk corresponding to the risk can be obtained.
In the evaluation of the credit of the supplier, in addition to considering the obvious risk of the supplier, the potential risk of the supplier needs to be considered, the potential risk is related to the change information of the supplier in the recent period, and the change information includes the change of staff number, the change of floor area, the change of business condition, the change of staff quality, the change of technology and management level and the change of engineering equipment, and the data is considered to obtain the potential risk, so the step S2 further includes:
s24, constructing an enterprise change database, wherein the enterprise change database comprises change threshold values of various information of enterprises;
step S25, calculating to obtain a variation amount according to the variation information;
step S26 compares the fluctuation amount with the fluctuation threshold value, and outputs fluctuation information corresponding to the fluctuation amount larger than the fluctuation threshold value as a risk potential.
And S27, acquiring credit evaluation results of upstream and downstream enterprises cooperating with the suppliers, and acquiring potential risks according to the credit evaluation results of the upstream and downstream enterprises.
Firstly, an enterprise change database is established according to corresponding change conditions, a change threshold value is set in the enterprise change database aiming at each change information in the enterprise operation process, for example, the personnel flow in the last 5 years should not exceed 20%, and the like, then after the corresponding change amount is calculated according to the collected change information, the change amount is compared with the change threshold value, when the change amount is greater than the change threshold value, the change information represents that the change information belongs to potential risks for a supplier, in addition, when the potential risks of the supplier are considered, credit evaluation results of upstream and downstream enterprises of the supplier are considered besides the supplier, if the credit evaluation results of the upstream and downstream enterprises are poor, normal order execution of the supplier can be influenced, and therefore the progress of the enterprises cooperating with the supplier is influenced finally, and therefore, the accuracy of the credit evaluation results of the supplier can be ensured by acquiring the potential risks through multiple ways.
And S3, constructing a quantitative score library, wherein the quantitative score library comprises all risks and grade scores corresponding to the risks, searching corresponding risks from the quantitative score library according to the obvious risks and the potential risks, and outputting the obvious risk grade scores and the potential risk grade scores after obtaining the grade scores corresponding to the risks.
The final credit evaluation result is not grade or form, but can be quantified through numbers, for this reason, after obtaining the obvious risk and the potential risk, the obvious risk and the potential risk are converted into corresponding grade scores through a quantitative score library, and the risk grade scores can be used for the subsequent calculation of the credit evaluation result.
And S4, processing the obvious risk grade scores by adopting a deep learning network to obtain a first evaluation result, and performing weighted calculation on the potential risk grade scores by adopting a subjective weighting method to obtain a second evaluation result.
When the final credit evaluation result is calculated, evaluation results corresponding to the obvious risk and the potential risk are not simply accumulated, but are correspondingly selected according to comparison results, so that the evaluation results need to be calculated respectively according to the obvious risk grade score and the potential risk grade score.
When a first evaluation result corresponding to the obvious risk grade score is calculated, a deep learning network is adopted for processing, and the method specifically comprises the following steps:
s41, constructing an enterprise risk type library, wherein the enterprise risk type library comprises enterprise risks, a risk score range corresponding to each enterprise risk and enterprise evaluation results;
s42, embedding the enterprise risk type library into a deep learning network;
and S43, the deep learning network identifies the obvious risk grade scores, searches for enterprise risks corresponding to risk grade ranges containing the obvious risk grade scores, and outputs and adds enterprise evaluation results corresponding to the enterprise risks to obtain a first evaluation result.
According to the method, the deep learning network is adopted to calculate the first evaluation result, before calculation, an enterprise risk type library is constructed, the enterprise risk type library contains various enterprise risks, one risk has a plurality of enterprise risks, the risk score range of each enterprise risk is different from the enterprise evaluation result, when the deep learning network processes the obvious risk grade score, the corresponding enterprise risk is searched in the enterprise risk type library according to the obvious risk corresponding to the obvious risk grade score, and then the obvious risk grade score is judged to fall into which risk score range, so that the first evaluation result can be correspondingly obtained.
The method comprises the following specific steps of adopting a subjective weighting method to carry out weighted calculation on the potential risk grade scores to obtain a second evaluation result:
s44, constructing an expert scoring library, wherein the expert scoring library comprises a weight factor corresponding to each potential risk;
and S45, assigning the weight factors in the expert score library to the potential risk grade scores, and adding the assigned potential risk grade scores to obtain a second evaluation result.
For the potential risk, the method selects the weighting factors for each potential risk grade score in an expert scoring mode, assigns the weighting factors to the potential risk grade scores according to different specific settings of the types of the supply chains, and then accumulates the weighting factors to obtain a second evaluation result.
The expert scoring is determined according to reference weights obtained by summarizing and summarizing a large amount of collected historical data of supply chain enterprises, and the weighting factors are selected by taking the historical data as a reference, so that the selected weighting factors can be guaranteed to be matched with suppliers.
And S5, comprehensively obtaining a credit evaluation result according to the first evaluation result and the second evaluation result.
S51, when the first evaluation result is larger than the second evaluation result, outputting the first evaluation result as a credit evaluation result;
and S52, when the second evaluation result is larger than the first evaluation result, multiplying the first evaluation result and the second evaluation result by the corresponding reduction factors respectively, summing the results, and outputting a sum as a credit evaluation result, wherein the reduction factor of the first evaluation result is smaller than the reduction factor of the second evaluation result.
The first evaluation result and the second evaluation result are both data which can be used for quantification, and for the credit evaluation of the supplier, the potential risk is not a defect which is really existed in the supplier, but the obvious risk is a real defect which can be intuitively obtained from a past order, therefore, the priority of the first evaluation result is higher than that of the second evaluation result, if the first evaluation result is higher than the second evaluation result, the first evaluation result is taken as the credit evaluation result, and when the first evaluation result is lower than the second evaluation result, the first evaluation result and the second evaluation result are respectively weakened and then summed, and the sum is taken as the credit evaluation result, and when the weakening is carried out, a reduction factor is used, the reduction factor is a constant which is higher than 0 and lower than 1, the actual setting can be carried out according to the type of the supply chain, and the reduction factor of the first evaluation result is lower than that of the second evaluation result, so that when the two results are weakened, the degree of weakening of the second evaluation result is higher than that of the first evaluation result.
And S6, carrying out data uplink on the supplier data, the obvious risk, the potential risk and the credit evaluation result through a node server of the supplier, and symmetrically encrypting uplink data through block chain middleware service software on the node server.
After the credit evaluation result is obtained, all related data related in the whole process are subjected to uplink chain deposit, related enterprises can obtain the credit evaluation results of all suppliers in the supply chain through the block chain, and basic information, historical performance information, change information, obvious risks, potential risks and the like of the related enterprises can be visually checked, so that the suppliers can be known in detail.
When the block chain is used as a storage and transmission medium of data, the privacy, the disclosure and the transparency of the data can be ensured, and the situation that a supplier randomly tampers the content on the chain can not occur.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A credit evaluation method of a material supply chain system based on a block chain is characterized by comprising the following steps:
step S1, obtaining supplier data, wherein the supplier data comprises basic information, historical fulfillment information and change information;
s2, acquiring the obvious risk of the supplier according to the historical performance information, and acquiring the potential risk of the supplier according to the change information;
s3, quantifying the obvious risks and the potential risks respectively to obtain obvious risk grade scores and potential risk grade scores;
s4, processing the obvious risk grade scores by adopting a deep learning network to obtain a first evaluation result, and performing weighted calculation on the potential risk grade scores by adopting a subjective weighting method to obtain a second evaluation result;
s5, obtaining a credit evaluation result according to the first evaluation result and the second evaluation result;
and S6, carrying out uplink certificate storage on the supplier data, the obvious risk, the potential risk and the credit evaluation result through a block chain.
2. The credit evaluation method of a block chain-based material supply chain system as claimed in claim 1, wherein the suppliers include material suppliers and service suppliers, the basic information includes enterprise type, enterprise property, operation range and establishment time, the historical performance information includes whether delivery time is on time, qualification rate of delivered products or services and accuracy of delivered quantity, wherein the delivery time is on time, whether delivery is on time, whether logistics are on time, and the variation information includes staff number variation, floor area variation, operation condition variation, staff quality variation, technology and management level variation and engineering equipment variation.
3. The method as claimed in claim 1, wherein the step S2 of obtaining the apparent risk of the supplier according to the historical fulfillment information comprises:
s21, constructing an obvious risk database, wherein the obvious risk database contains risk words, and each risk word corresponds to an obvious risk;
s22, extracting keywords from the historical fulfillment information, and performing synonymy expansion and approximate expansion on the keywords to obtain a keyword combination;
and S23, traversing and comparing the keyword combination and the risk words in the obvious risk database, obtaining the risk word most closely matched with the keyword combination, and obtaining the obvious risk according to the risk word.
4. The block chain-based material supply chain system credit evaluation method according to claim 1, wherein the step S2 of obtaining the obvious risk level score and the potential risk level score comprises the specific steps of:
s24, constructing an enterprise change database, wherein the enterprise change database comprises change threshold values of various information of enterprises;
step S25, calculating to obtain a variation amount according to the variation information;
step S26, comparing the variation amount with the variation threshold value, and outputting variation information corresponding to the variation amount larger than the variation threshold value as a potential risk.
5. The block chain-based material supply chain system credit evaluation method according to claim 1, wherein the specific step of obtaining the obvious risk level score and the potential risk level score in the step S2 further comprises:
and S27, acquiring credit evaluation results of upstream and downstream enterprises cooperating with the suppliers, and acquiring potential risks according to the credit evaluation results of the upstream and downstream enterprises.
6. The method for credit evaluation of a material supply chain system based on a block chain as claimed in claim 1, wherein the step S3 comprises the following steps: and constructing a quantitative score library, wherein the quantitative score library comprises all risks and grade scores corresponding to the risks, searching the corresponding risks from the quantitative score library according to the obvious risks and the potential risks, and outputting the obvious risk grade scores and the potential risk grade scores after obtaining the grade scores corresponding to the risks.
7. The credit evaluation method of the material supply chain system based on the block chain as claimed in claim 1, wherein the step S4 of processing the obvious risk level score by using a deep learning network, and the specific step of obtaining the first evaluation result comprises:
s41, constructing an enterprise risk type library, wherein the enterprise risk type library comprises enterprise risks, a risk score range corresponding to each enterprise risk and enterprise evaluation results;
s42, embedding the enterprise risk type library into a deep learning network;
and S43, the deep learning network identifies the obvious risk grade scores, searches for enterprise risks corresponding to risk grade ranges containing the obvious risk grade scores, and outputs and adds enterprise evaluation results corresponding to the enterprise risks to obtain a first evaluation result.
8. The credit evaluation method of the material supply chain system based on the block chain as claimed in claim 1, wherein the step S4 of weighting the potential risk level score by a subjective weighting method to obtain the second evaluation result comprises the following specific steps:
s44, constructing an expert scoring library, wherein the expert scoring library comprises a weight factor corresponding to each potential risk;
and S45, assigning the weight factors in the expert score library to the potential risk grade scores, and adding the assigned potential risk grade scores to obtain a second evaluation result.
9. The method for credit evaluation of a block chain-based material supply chain system according to claim 1, wherein the specific steps of the step S5 include:
s51, when the first evaluation result is larger than the second evaluation result, outputting the first evaluation result as a credit evaluation result;
and S52, when the second evaluation result is larger than the first evaluation result, multiplying the first evaluation result and the second evaluation result by the corresponding reduction factors respectively, summing the results, and outputting a sum as a credit evaluation result, wherein the reduction factor of the first evaluation result is smaller than the reduction factor of the second evaluation result.
10. The method for credit evaluation of a material supply chain system based on a block chain as claimed in claim 1, wherein the step S6 comprises the following steps: and carrying out data uplink on the supplier data, the obvious risk, the potential risk and the credit evaluation result through a node server of the supplier, and symmetrically encrypting uplink data through block chain middleware service software on the node server.
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