Multi-party data real-time interaction and collaboration method and system of digital intelligent purchasing platform

Abstract

The invention discloses a multi-party data real-time interaction and collaboration method and system for a digital-intelligence purchasing platform, and relates to the technical field of data processing and interaction.The method comprises the steps that the digital-intelligence purchasing platform used for multi-party data interaction is established in combination with the block chain technology and the big data technology; based on a message queue technology, a data real-time interaction channel is established in a digital intelligent purchasing platform, and meanwhile, a data interaction process is automatically controlled by using an intelligent contract technology; in the data interaction process, a machine learning algorithm is introduced for data analysis and prediction, and an intelligent selection scheme is provided. According to the invention, the problems of delayed data interaction, poor collaboration, low transparency and insufficient security in the existing purchasing mode are solved, real-time interaction and collaboration of multi-party data in the purchasing process are realized, and the purchasing efficiency, transparency and security are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and interaction, and more specifically, to a method and system for real-time multi-party data interaction and collaboration in a digital and intelligent procurement platform. Background Art

[0002] During the long-term operation of the traditional procurement model, a series of drawbacks have gradually emerged, significantly hindering the efficient, fair, and secure advancement of procurement activities. The procurement process involves multiple parties, including purchasers, suppliers, agencies, and evaluation experts. However, the information transmission channels among these parties are complex and lack efficient integration, and the problem of poor information communication is extremely prominent. After a purchaser puts forward a procurement requirement, it is often difficult to quickly and accurately convey it to the appropriate supplier. In the process of the supplier's feedback information being transmitted back to the purchaser, delays or deviations often occur due to the complexity of the intermediate links, resulting in a significant increase in unnecessary waiting time in the procurement process and extremely low efficiency.

[0003] In the traditional mode, data interaction mainly relies on manual operations, such as sending emails and copying files for transmission. This method is not only time-consuming and laborious but also extremely error-prone. Once the data volume increases, the phenomenon of data interaction delay will become more and more serious, making the various links of the procurement project unable to be closely connected, further exacerbating the complexity of the procurement process.

[0004] In addition, there are huge differences in the systems or tools used by different participants. Purchasers may use a set of internally customized procurement management systems, suppliers may adopt different types of sales management software based on their own business characteristics, and agencies and evaluation experts also have their own independent working platforms. There is a lack of unified data interfaces and interaction standards among these systems, making it extremely difficult to integrate and share data among different systems. It is difficult for data to circulate and integrate, making it difficult for all parties to grasp the overall picture of the procurement project from an overall perspective, and greatly reducing the transparency of procurement.

[0005] During the evaluation process, evaluation experts need to comprehensively understand various information of suppliers, such as past performance, product quality, and credit status, in order to make objective and fair evaluations. However, in actual operation, the channels for experts to obtain this information are limited and cumbersome, often requiring separate inquiries through multiple channels, consuming a large amount of time and energy. Moreover, due to the untimely and incomplete acquisition of information, it is difficult for experts to quickly form a comprehensive judgment of suppliers, which greatly affects the accuracy and fairness of the evaluation results and cannot ensure that the procurement project can select the most excellent suppliers.

[0006] Therefore, how to provide a method and system for real-time interaction and collaboration of multi-party data in a digital and intelligent procurement platform, break the information barriers among all parties, achieve real-time interaction and efficient collaboration of data, improve procurement efficiency, enhance transparency, and ensure data security, and then comprehensively improve the overall efficiency of procurement activities is an urgent problem that needs to be solved by those skilled in the art. Summary of the Invention

[0007] In view of this, the present invention provides a method and system for real-time interaction and collaboration of multi-party data in a digital and intelligent procurement platform, which solves the problems of delayed data interaction, poor collaboration, low transparency, and insufficient security in the existing procurement mode, and realizes real-time interaction and collaboration of multi-party data in the procurement process, improving procurement efficiency, transparency, and security.

[0008] To achieve the above object, the present invention adopts the following technical solutions: A method for real-time interaction and collaboration of multi-party data in a digital and intelligent procurement platform, including:

[0009] Combining blockchain technology and big data technology to establish a digital and intelligent procurement platform for multi-party data interaction;

[0010] Based on message queue technology, build a real-time data interaction channel in the digital and intelligent procurement platform, and at the same time, use smart contract technology to automatically control the data interaction process;

[0011] In the data interaction process, introduce machine learning algorithms for data analysis and prediction to provide intelligent selection solutions.

[0012] Preferably, the digital and intelligent procurement platform includes a front-end interaction layer, an intermediate business logic layer, and a back-end data storage layer;

[0013] The front-end interaction layer provides personalized operation interfaces for purchasers, suppliers, agencies, and evaluation experts respectively;

[0014] The intermediate business logic layer is used for data verification, process control, and permission management;

[0015] The back-end data storage layer uses a distributed database to centrally store and manage data.

[0016] Preferably, during the data access process of the digital and intelligent procurement platform, data preprocessing operations are performed in real time, and through data standardization processing, various formats of data are uniformly converted into standard formats.

[0017] Preferably, based on message queue technology, building a real-time data interaction channel in the digital and intelligent procurement platform includes:

[0018] When a user on one side performs a data update operation, the updated data is encapsulated into a message and sent to the message queue. The message queue, following the principle of first in, first out, quickly and accurately pushes the message to the relevant parties;

[0019] Use smart contract technology to automate the control of the data interaction process, including:

[0020] When the purchaser releases a procurement project, the smart contract automatically screens potential suppliers according to preset rules and sends them procurement invitations.

[0021] Preferably, combine blockchain technology and big data technology to establish a digital and intelligent procurement platform for multi-party data interaction, including:

[0022] Build a private blockchain network and connect it to the public chain Bumeng blockchain network;

[0023] Define the data specifications of the enterprise database based on the data situation of the enterprise database;

[0024] Analyze the requirements of the business system and build blockchain nodes according to the requirements;

[0025] Write relevant smart contracts and deploy them to the blockchain network;

[0026] Build a blockchain data center to encrypt and chain-record data;

[0027] Based on the blockchain data center, establish a digital and intelligent procurement platform for multi-party data interaction.

[0028] Preferably, introduce machine learning algorithms for data analysis and prediction to provide intelligent selection solutions, including:

[0029] Extract procurement historical data from the backend data storage layer;

[0030] Determine the influencing factors and prediction factors, and establish a prediction model based on machine learning algorithms;

[0031] Use the improved quantum grey wolf algorithm to optimize the parameters of the prediction model respectively to obtain the optimal parameters;

[0032] Apply the optimal parameter combination obtained by the improved quantum grey wolf algorithm to the machine learning algorithm;

[0033] Use the optimized prediction model to analyze and predict procurement data;

[0034] Integrate the analysis and prediction results into the front-end interaction layer of the digital and intelligent procurement platform to provide decision-making solutions for users on all sides.

[0035] Preferably, use the improved quantum grey wolf algorithm to optimize the parameters of the prediction model respectively to obtain the optimal parameters, including:

[0036] (1) Initialize the algorithm parameters and the population positions. Among them, the population size is N, the maximum number of iterations is N maxgen , the number of qubits is M, the number of decision variables is P, the range of decision variables is [V min , V max , and the maximum number of times the global optimum has not been updated is N maxup ;

[0037] (2) Let the iteration number N gen = 0, and the number of times the global optimum has not been updated N up = 0. Perform qubit probability amplitude encoding on each individual in the population. All probability amplitudes (α ij , β ij ) have initial values of to form the initial population position Q. Among them, (α ij , β ij ) represents the probability amplitude of the j - th qubit of the i - th individual;

[0038] (3) Perform binary encoding on all individuals in Q and convert the binary to decimal to determine the population X = [X1, X2,..., X i ,..., X N . Then calculate the fitness value of each individual in the population. The binary encoding method is shown in the following formula:

[0039]

[0040] where b ij is the binary encoding, r is a random variable, r ∈ (0, 1);

[0041] (4) Sort in ascending order according to the fitness value, and select and save the three individuals with the smallest fitness values: X α , X β , X δ . The corresponding fitness values are f α , f β , f δ . Among them, X α is the global - optimal gray wolf with the smallest fitness value;

[0042] (5) Introduce the preference - ratio weight strategy and update all gray - wolf individuals in the population according to X α , X β , X δ ;

[0043] (6) Judge whether the number of times the global optimum has not been updated N up is greater than N maxup, if so, perform a quantum catastrophe operation once, and then proceed to step (2); otherwise, skip the quantum catastrophe operation and proceed to the next step;

[0044] Determine whether the number of iterations has reached N maxgen , if so, terminate the operation and output the result; if not, return to step (4).

[0045] Preferably, a multi-party data real-time interaction and collaboration system for a digital and intelligent procurement platform includes:

[0046] A platform establishment module for establishing a digital and intelligent procurement platform for multi-party data interaction by combining blockchain technology and big data technology;

[0047] A real-time interaction module for building a data real-time interaction channel in the digital and intelligent procurement platform based on message queue technology, and at the same time, using smart contract technology to automate the control of the data interaction process;

[0048] An analysis and prediction module for introducing machine learning algorithms for data analysis and prediction during the data interaction process to provide intelligent selection solutions.

[0049] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a multi-party data real-time interaction and collaboration method and system for a digital and intelligent procurement platform. The present invention has the following beneficial effects: (1) Improve data security and credibility: Through the distributed ledger and encrypted storage of blockchain technology, the decentralization, immutability and traceability of data are guaranteed, enhancing the trust of all participating parties in the data.

[0050] (2) Message queue technology realizes asynchronous transmission of data, and smart contract automates the control of the data interaction process, reducing data congestion and human intervention, and improving the interaction efficiency.

[0051] (3) Machine learning algorithms combined with improved quantum grey wolf algorithms deeply analyze and accurately predict procurement data, providing intelligent decision-making solutions for all parties of users, which helps to reduce procurement costs and ensure the stability of the supply chain.

[0052] (4) The hierarchical architecture design makes the functions of the digital and intelligent procurement platform clear. The front-end interaction layer is convenient for users to operate, the middle business logic layer ensures data security and business process norms, and the back-end data storage layer ensures reliable storage and efficient retrieval of data. Description of the Drawings

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on the provided drawings.

[0054] Figure 1 Schematic diagram of the process for real-time multi-party data interaction and collaboration in a digital and intelligent procurement platform provided by the present invention.

[0055] Figure 2 Schematic diagram of the structure of a system for real-time multi-party data interaction and collaboration in a digital and intelligent procurement platform provided by the present invention. Detailed implementation manners

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0057] The embodiments of the present invention disclose a method for real-time multi-party data interaction and collaboration in a digital and intelligent procurement platform. As Figure 1 shown, it includes:

[0058] Combining blockchain technology and big data technology, establish a digital and intelligent procurement platform for multi-party data interaction; utilize the distributed ledger feature of the blockchain to ensure the decentralization and immutability of data storage, and ensure the security and credibility of the data of each participating party. Through big data technology, collect, organize and store hundreds of millions of procurement-related data, covering supplier information, product price fluctuations, market supply and demand trends, etc., and build a comprehensive procurement data resource pool for subsequent data interaction and analysis;

[0059] Based on message queue technology, build a real-time data interaction channel in the digital and intelligent procurement platform. At the same time, use smart contract technology to automatically control the data interaction process; the message queue can orderly arrange data requests and responses in different formats and from different sources to achieve asynchronous data transmission, greatly improving the data interaction efficiency and avoiding delays caused by data congestion. At the same time, use smart contract technology to automatically control the data interaction process. The smart contract pre-sets data interaction rules in the form of code, such as data transmission time nodes, data format requirements, authorization access mechanisms, etc. Once the contract conditions are triggered, the corresponding data interaction operations will be automatically executed without manual intervention, effectively reducing human errors and communication costs.

[0060] During the data interaction process, machine learning algorithms are introduced for data analysis and prediction to provide intelligent selection solutions. Machine learning algorithms are used to deeply mine historical procurement data and analyze information such as procurement behavior patterns, supplier performance, and cost change rules. Through the analysis of these data, the operating conditions of the procurement business can be comprehensively understood, providing accurate data support for subsequent decision-making.

[0061] By using a prediction model to estimate the market price trend in advance, it helps the purchaser to grasp the best procurement timing and reduce procurement costs; by analyzing the historical delivery data of suppliers, it predicts the future delivery capabilities and risks of suppliers, enabling the purchaser to formulate countermeasures in advance, such as finding alternative suppliers or adjusting the procurement plan, to ensure the smooth progress of the procurement business.

[0062] Specifically, the digital procurement platform includes a front-end interaction layer, a middle business logic layer, and a back-end data storage layer;

[0063] The front-end interaction layer provides personalized operation interfaces for purchasers, suppliers, agencies, and evaluation experts respectively; supports access from multiple terminal devices to ensure that users can conveniently input data, query, and obtain feedback; purchasers can publish detailed procurement project requirements on the interface, including the specifications, quantities, quality requirements, delivery times, etc. of the procurement items; suppliers can upload tender documents, covering product introductions, quotations, after-sales service commitments, etc.; agencies can carry out project management and coordination work; evaluation experts can enter evaluation opinions and scores.

[0064] The middle business logic layer is used for data verification, process control, and permission management; it verifies the format, logic, and compliance of the data input by users to ensure the accuracy and integrity of the data. It controls according to the preset procurement business process to guide all parties to complete the operations of each link. According to the user roles and responsibilities, different operation permissions are accurately allocated to ensure data security. Among them, purchasers can create, modify, and publish procurement projects; suppliers can only view and operate on projects related to their own tenders; evaluation experts can only conduct evaluation operations on designated projects.

[0065] The back-end data storage layer uses a distributed database to centrally store and manage data. By utilizing the high availability, fault tolerance, and scalability of the distributed database, it ensures the security and reliability of the data and supports the rapid retrieval and update of data. Combining blockchain technology, key data is encrypted and stored in a chained manner to ensure that the data cannot be tampered with and is traceable. Among them, important data such as procurement contracts, transaction records, and evaluation results can be securely stored on the blockchain.

[0066] Specifically, during the data access process of the digital and intelligent procurement platform, data preprocessing operations are carried out in real time. The data denoising algorithm is used to eliminate duplicate, incorrect or incomplete data, and through data standardization processing, various formats of data are uniformly converted into the standard format that the platform can recognize and process.

[0067] Specifically, based on the message queue technology, a data real-time interaction channel is built in the digital and intelligent procurement platform, including:

[0068] When a user on one side performs a data update operation, the updated data is encapsulated into a message and sent to the message queue. The message queue, following the principle of first in first out, quickly and accurately pushes the message to the relevant parties.

[0069] Specifically, when a supplier submits a bidding document and an evaluation expert enters evaluation opinions, the operation data is immediately encapsulated into a message and sent to the message queue. The message queue, following the principle of first in first out, quickly and accurately pushes the message to relevant parties such as the purchaser and the agency, realizing the real-time interaction of data.

[0070] The intelligent contract technology is used to automate the control of the data interaction process, including:

[0071] When the purchaser releases a procurement project, the intelligent contract automatically screens potential suppliers according to preset rules and sends them a procurement invitation. It realizes the automation and intelligence of data interaction and greatly improves the interaction efficiency.

[0072] The preset rules include conditions such as the qualification level, credit score, business scope, and past performance of the supplier. The intelligent contract screens out the suppliers that meet the requirements from the database and automatically sends them an invitation containing the detailed information of the procurement project. In other links of the procurement process, such as the receipt of bidding documents and the publicity of evaluation results, the intelligent contract can also automatically execute corresponding operations according to the preset rules, realizing the automation and intelligence of data interaction and improving the interaction efficiency.

[0073] Specifically, combining blockchain technology and big data technology, a digital and intelligent procurement platform for multi-party data interaction is established, including:

[0074] Build a private blockchain network and connect it to the public chain Bumeng blockchain network; with the extensive connectivity of the public chain and the security of the private chain, provide a reliable network foundation for data interaction.

[0075] Define the data specifications of the enterprise database according to the data situation of the enterprise database; ensure the consistency of data in terms of format, content, etc., facilitating subsequent data processing and analysis.

[0076] Analyze the requirements of the business system and build blockchain nodes according to the requirements; these nodes are used for data interaction and storage.

[0077] Write relevant smart contracts and deploy them to the blockchain network; the smart contracts will play an automated control role in subsequent data interaction processes, such as screening potential suppliers according to preset rules, automatically processing the receipt of tender documents, etc.

[0078] Build a blockchain data center to encrypt and chain-record data; ensure the security, immutability, and traceability of data.

[0079] Build a digital and intelligent procurement platform for multi-party data interaction based on the blockchain data center. This platform integrates the front-end interaction layer, the middle business logic layer, and the back-end data storage layer to achieve real-time interaction and collaboration of multi-party data.

[0080] The digital and intelligent procurement platform established through the above steps provides a comprehensive and efficient solution for multi-party data interaction and collaboration.

[0081] Specifically, introduce machine learning algorithms for data analysis and prediction, and provide intelligent selection solutions, including:

[0082] Extract procurement historical data from the back-end data storage layer; such as the price fluctuations of suppliers, which reflect the dynamic changes in market prices and the pricing strategies of suppliers; the on-time delivery rate, which reflects the performance ability of suppliers and the stability of the supply chain; the product quality rating, which directly affects whether the purchased products can meet business requirements; and the dynamic change data of market supply and demand, etc. These data can help grasp the overall trend of the market; these data serve as the basis for subsequent algorithm processing and provide multi-dimensional information for model training;

[0083] Determine the influencing factors and prediction factors, and establish a prediction model based on machine learning algorithms; conduct in-depth analysis on the extracted procurement historical data, and combine the actual needs and goals of procurement business to determine the key factors affecting procurement decisions as the influencing factors.

[0084] Specifically, for procurement costs, the price fluctuations of suppliers, market supply and demand conditions, etc. may be important influencing factors; for the selection of suppliers, the on-time delivery rate, product quality rating, etc. are key factors. At the same time, clarify the indicators to be predicted as the prediction factors, such as future procurement prices, the delivery ability of suppliers, etc.

[0085] Specifically, the machine learning algorithms include neural network models, support vector machine models, long short-term memory network models, etc.

[0086] Use the improved quantum grey wolf algorithm to optimize the parameters of the prediction model respectively to obtain the optimal parameters;

[0087] Apply the optimal parameter combination obtained by the improved quantum grey wolf algorithm to the machine learning algorithm;

[0088] Analyze and predict the procurement data using the optimized prediction model;

[0089] Integrate the analysis and prediction results into the front-end interaction layer of the digital and intelligent procurement platform to provide decision-making solutions for all parties of users.

[0090] Specifically, the improved quantum grey wolf algorithm is an efficient optimization algorithm that combines the characteristics of quantum computing and the search ability of the grey wolf algorithm, and can quickly find the optimal solution in a complex parameter space. In this step, the parameters of the prediction model are used as the variables to be optimized, and the improved quantum grey wolf algorithm is used to optimize them; the improved quantum grey wolf algorithm is used to optimize the parameters of the prediction model respectively to obtain the optimal parameters, including:

[0091] (1) Initialize the algorithm parameters and the population position. Among them, the population size is N, the maximum number of iterations is N maxgen , the number of quantum bits is M, the number of decision variables is P, the range of decision variables is [V min , V max , and the maximum number of times that the global optimum has not been updated is N maxup ;

[0092] (2) Let the number of iterations N gen = 0, and the number of times that the global optimum has not been updated N up = 0. Perform quantum bit probability amplitude encoding on each individual in the population. All probability amplitudes (α ij , β ij ) have initial values of to form the initial population position Q. Among them, (α ij , β ij ) represents the jth quantum bit probability amplitude of the ith individual;

[0093] (3) Perform binary encoding on all individuals in Q, convert the binary to decimal, and determine the population X = [X1, X2,..., X i ,..., X N . Then calculate the fitness value of each individual in the population. The binary encoding method is shown in the following formula:

[0094]

[0095] Among them, b ij is the binary encoding, r is a random variable, and r ∈ (0, 1);

[0096] (4) Sort in ascending order according to the fitness value, and select and save the three individuals with the smallest fitness values: X α , X β , X δ, the corresponding fitness values are f α , f β , f δ , where X α is the globally optimal gray wolf, and its fitness value is the smallest;

[0097] (5) Introduce the preference ratio weight strategy and update all gray wolf individuals in the population according to X α , X β , X δ ;

[0098] (6) Judge whether the number of times the global optimum has not been updated, N up , is greater than N maxup . If so, perform a quantum cataclysm operation once, and then go to step (2); otherwise, skip the quantum cataclysm operation and go to the next step;

[0099] Judge whether the number of iterations has reached N maxgen . If so, terminate the operation and output the result; if not, return to step (4).

[0100] Specifically, the update equation of the gray wolf individual based on the preference ratio weight strategy is:

[0101] D α = |C·X α (k)-X i (k)|;

[0102] D β = |C·X β (k)-X i (k)|;

[0103] D δ = |C·X δ (k)-X i (k)|;

[0104] X1 = X α (k)-A·D α ;

[0105] X2 = X β (k)-A·D β ;

[0106] X3 = X δ (k)-A·D δ ;

[0107] ω1 = 1 - f α / (f α + f β + f δ );

[0108] ω2 = 1 - fβ / (f α +f β +f δ ));

[0109] ω3 = 1 - f δ / (f α +f β +f δ ));

[0110]

[0111] where k is the number of iterations, A = 2a × r1 - a, C = 2 × r2, a = 2 - 2k / N maxgen , and the parameters A, C, and a are all convergence factors; r1, r2 are random variables, r1, r2 ∈ (0, 1), D α , D β , D δ respectively represent the distances between individuals X α , X β , X δ and individual X i .

[0112] Specifically, the quantum catastrophe operation is as follows: retain the global optimal solution, and re - encode the probability amplitudes of the qubits for the remaining individuals. The update equations for all probability amplitudes (α, β) are:

[0113] Update of α:

[0114] Update of β:

[0115] where poi, r3, r4 are random variables, where poi ∈ (-1, 1), r3, r4 ∈ (0, 1).

[0116] In a specific embodiment of the present invention, a multi - party data real - time interaction and collaboration system for a digital and intelligent procurement platform, as Figure 2 shown, includes:

[0117] A platform establishment module, used to establish a digital and intelligent procurement platform for multi - party data interaction by combining blockchain technology and big data technology;

[0118] A real - time interaction module, used to build a data real - time interaction channel in the digital and intelligent procurement platform based on message queue technology, and at the same time, use smart contract technology to automatically control the data interaction process;

[0119] An analysis and prediction module, used to introduce machine learning algorithms for data analysis and prediction during the data interaction process, and provide intelligent selection solutions.

[0120] In a specific embodiment of the present invention, a method for real-time data interaction and collaboration of a digital and intelligent procurement platform includes:

[0121] Construction of a digital and intelligent procurement platform

[0122] Build a private blockchain network and connect it to the public chain Bumeng blockchain network. Relying on the extensive connectivity of the public chain and the security of the private chain, provide a reliable network foundation for data interaction;

[0123] Define the data specifications of the enterprise database based on the data situation of the enterprise database to ensure the consistency of data in terms of format, content, etc., facilitating subsequent data processing and analysis;

[0124] Analyze the requirements of the business system and build blockchain nodes according to the requirements for data interaction and storage;

[0125] Write relevant smart contracts and deploy them to the blockchain network. The smart contracts will play an automated control role in subsequent data interaction processes, such as screening potential suppliers according to preset rules, automatically processing the receipt of tender documents, etc.;

[0126] Build a blockchain data center to encrypt and store data in a chained manner to ensure the security, immutability, and traceability of data;

[0127] Build a digital and intelligent procurement platform based on the blockchain data center, integrating the front-end interaction layer, the middle business logic layer, and the back-end data storage layer.

[0128] Real-time data interaction and collaboration

[0129] When a user on one side performs a data update operation, such as a supplier submitting a tender document or an evaluation expert entering evaluation opinions, the updated data is encapsulated into a message and sent to the message queue. The message queue, according to the first-in, first-out principle, quickly and accurately pushes the message to relevant parties such as the purchaser and the agency, realizing real-time data interaction;

[0130] After the purchaser releases a procurement project, the smart contract automatically screens potential suppliers according to preset rules (such as conditions such as the supplier's qualification level, credit score, business scope, past performance, etc.) and sends them a procurement invitation. In other links of the procurement process, such as the receipt of tender documents and the publicity of evaluation results, the smart contract also automatically executes corresponding operations according to preset rules, realizing the automation and intelligence of data interaction and improving the interaction efficiency.

[0131] Data analysis, prediction, and decision support

[0132] Extract procurement historical data from the back-end data storage layer and perform data preprocessing, including denoising and standardization processing;

[0133] Determine the influencing factors and predictive factors. For example, for procurement costs, the quotation fluctuations of suppliers and the market supply and demand situation are influencing factors, and the future procurement price is a predictive factor; for supplier selection, the on-time delivery rate and product quality rating are influencing factors, and the supplier's delivery ability is a predictive factor.

[0134] Select a suitable machine learning algorithm (such as neural network model, support vector machine model, long short-term memory network model, etc.) to establish a prediction model.

[0135] Use the improved quantum grey wolf algorithm to optimize the parameters of the prediction model, and set the algorithm parameters, such as the population size N = 50, the maximum number of iterations N maxgen = 200, the number of quantum bits M = 30, the number of decision variables P is determined according to the model parameters, and the decision variable range [V min , V max is set according to the actual situation, and the maximum number of times the global optimum has not been updated N maxup = 20. Perform iterative optimization to obtain the optimal parameters and apply them to the machine learning algorithm.

[0136] Use the optimized prediction model to analyze and predict procurement data, and integrate the analysis and prediction results into the front-end interaction layer of the digital and intelligent procurement platform to provide decision-making solutions for various users in the form of charts, reports, etc., such as recommending the best procurement timing and suppliers for purchasers, and providing market demand forecasts and competitive analyses for suppliers.

[0137] The embodiments of the present invention can effectively realize the real-time interaction and collaboration of multi-party data on the digital and intelligent procurement platform, and improve the overall efficiency and benefits of procurement operations.

[0138] Each embodiment in this specification is described in a progressive manner. The key points of each embodiment are the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0139] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for real-time data interaction and collaboration among multiple parties of a digital and intelligent procurement platform, characterized in that, Including: Combining blockchain technology and big data technology to establish a digital and intelligent procurement platform for multi-party data interaction; Based on message queue technology, building a real-time data interaction channel in the digital and intelligent procurement platform. Meanwhile, using smart contract technology to automate the control of the data interaction process; During the data interaction process, introducing machine learning algorithms for data analysis and prediction to provide intelligent selection solutions.

2. The multi-party data real-time interaction and collaboration method of a digital and intelligent procurement platform according to claim 1, characterized in that, The digital and intelligent procurement platform includes a front-end interaction layer, a middle business logic layer, and a back-end data storage layer; The front-end interaction layer provides personalized operation interfaces for purchasers, suppliers, agencies, and evaluation experts respectively; The middle business logic layer is used for data verification, process control, and permission management; The back-end data storage layer uses a distributed database to centrally store and manage data.

3. A multi-party data real-time interaction and collaboration method for a digital and intelligent procurement platform according to claim 1, characterized in that During the data access process of the digital and intelligent procurement platform, real-time data preprocessing operations are carried out, and through data standardization processing, various formats of data are uniformly converted into standard formats.

4. A method for real-time data interaction and collaboration among multiple parties of a digital and intelligent procurement platform according to claim 1, characterized in that Based on message queue technology, building a real-time data interaction channel in the digital and intelligent procurement platform, including: When a user on one side performs a data update operation, the updated data is encapsulated into a message and sent to the message queue. The message queue, following the principle of first in first out, quickly and accurately pushes the message to the relevant parties; Using smart contract technology to automate the control of the data interaction process, including: When the purchaser releases a procurement project, the smart contract automatically screens potential suppliers according to preset rules and sends them procurement invitations.

5. A method for real-time data interaction and collaboration among multiple parties of a digital and intelligent procurement platform according to claim 1, characterized in that, Combining blockchain technology and big data technology to establish a digital and intelligent procurement platform for multi-party data interaction, including: Building a private blockchain network and connecting it to the public chain Bumeng blockchain network; Defining the data specifications of the enterprise database according to the data situation of the enterprise database; Analyzing the requirements of the business system and building blockchain nodes according to the requirements; Writing relevant smart contracts and deploying them to the blockchain network; Building a blockchain data center to encrypt and store data in a chained manner; Based on the blockchain data center, establishing a digital and intelligent procurement platform for multi-party data interaction.

6. A method for real-time data interaction and collaboration among multiple parties of a digital and intelligent procurement platform according to claim 2, characterized in that Introducing machine learning algorithms for data analysis and prediction to provide intelligent selection solutions, including: Extracting procurement historical data from the back-end data storage layer; determining influencing factors and prediction factors, and establishing a prediction model based on machine learning algorithms; Using the improved quantum grey wolf algorithm to optimize the parameters of the prediction model respectively to obtain the optimal parameters; Applying the optimal parameter combination obtained by the improved quantum grey wolf algorithm to the machine learning algorithm; Using the optimized prediction model to analyze and predict procurement data; Integrating the analysis and prediction results into the front-end interaction layer of the digital and intelligent procurement platform to provide decision-making solutions for all parties of users.

7. A method for real-time data interaction and collaboration among multiple parties of a digital and intelligent procurement platform according to claim 6, characterized in that, Using the improved quantum grey wolf algorithm to optimize the parameters of the prediction model respectively to obtain the optimal parameters, including: (1) Initialize the algorithm parameters and the population positions, where the population size is N, the maximum number of iterations is N maxgen , the number of qubits is M, the number of decision variables is P, and the range of decision variables is [V min , V max , and the maximum number of times the global optimum has not been updated is N maxup ; (2) Let the number of iterations be N gen = 0, the number of times the global optimum has not been updated be N up = 0, perform quantum bit probability amplitude encoding on each individual in the population. All probability amplitudes (α ij , β ij ) have initial values of to form the initial population position Q. Among them, (α ij , β ij ) represents the probability amplitude of the j-th quantum bit of the i-th individual; (3) Binary code all individuals in Q, convert the binary to decimal, and determine the population X = [X1, X2,..., X i ,..., X N , and then calculate the fitness value of each individual in the population. The binary coding method is shown in the following formula: where b ij is a binary code, r is a random variable, and r ∈ (0, 1); (4) Sort in ascending order according to the fitness value, and select and save the three individuals with the smallest fitness values: X α , X β , X δ , and the corresponding fitness values are f α , f β , f δ . Among them, X α is the globally optimal grey wolf with the smallest fitness value; (5) Introduce the preference ratio weight strategy and update all gray wolf individuals in the population according to X α 、X β 、X δ ; (6) Judge the number of times N that the global optimum has not been updated up Is it greater than N maxup If so, perform a quantum cataclysm operation once, and then go to step (2); otherwise, skip the quantum cataclysm operation and proceed to the next step; Determine whether the number of iterations has reached N maxgen , if so, terminate the operation and output the result; if not, return to step (4).

8. A multi-party data real-time interaction and collaboration system for a digital and intelligent procurement platform, which applies the multi-party data real-time interaction and collaboration method for a digital and intelligent procurement platform according to any one of claims 1-7, characterized in that, Including: A platform establishment module, used for combining blockchain technology and big data technology to establish a digital and intelligent procurement platform for multi-party data interaction; A real-time interaction module, used for building a real-time data interaction channel in the digital and intelligent procurement platform based on message queue technology. Meanwhile, using smart contract technology to automate the control of the data interaction process; An analysis and prediction module, which is used to introduce machine learning algorithms for data analysis and prediction during the data interaction process, and provide intelligent selection solutions.

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