Demand processing system based on block chain

Abstract

The invention relates to the technical field of information service, in particular to a demand processing system based on a block chain, which comprises a total node unit, a sub-node unit, a data storage unit and user login equipment, and is characterized in that the total node unit is in communication connection with the sub-node unit through a wireless network; the user login device is in communication connection with the sub-node unit and the total node unit through a wireless network, and the total node unit and the sub-node unit are in communication connection with the data storage unit through the wireless network. According to the invention, keyword identification processing is carried out on data received by the user login module through the data identification module, whether a demand sent by a user accords with an enterprise operation scope is rapidly judged, and then a demand processing scheme is rapidly generated by comparing user demand information with previously recorded demand information; the data processed in the process is relatively less, the processing speed can be improved, relatively strong computing power is not needed, and the activity cost of an enterprise can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of information services, and in particular, to a demand processing system based on a blockchain. Background Art

[0002] With the development of computer technology, online platforms have provided convenience for people's lives. For example, people can purchase goods in aspects such as clothing, food, housing, and transportation through online platforms. For example, people can buy clothes, food, etc. through online platforms, and can also order meals, takeaways, etc. Although online platforms have brought convenience to people, currently, the goods that online platforms can provide are usually general goods and cannot provide personalized services according to individual needs.

[0003] For example, a method and system for commercializing services with an application number of CN201810504077.0 and a publication date of November 6, 2018. The method includes: Step 1: Obtain the service requirements of the service buyer, and send the processed service requirements to the service seller; Step 2: Receive the feedback information of the service seller, and transmit the information to the service buyer for the service buyer to select one of them as the final trading partner; Step 3: Obtain the trading partner information of the service buyer, and transmit the information to the service seller; Step 4: After the service seller receives the trading partner information, the transaction begins. The present invention can greatly facilitate service interactions among the public, arrange the service commodity buyer and the seller on a legal and compliant online sunshine platform for direct communication, reduce the intermediate links in the dissemination of service information, and is of great significance for increasing social employment, improving social interaction efficiency, guiding the establishment of a social integrity mechanism, and improving the offline commodity market environment.

[0004] In order to provide personalized services according to individual needs, the prior art solves this problem by establishing a demand processing system. However, the demand processing system needs to collect technologies such as personal preferences and personal behaviors, and subsequently, through a series of behaviors such as clustering analysis of the collected data, provide personalized services for individuals. However, the data processed in the above process is relatively large, the processing process is time-consuming, and requires strong computing power, increasing the operating costs of enterprises. Therefore, it is urgent to design a demand processing system based on a blockchain to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a demand processing system based on a blockchain to solve the above deficiencies in the prior art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A blockchain-based demand processing system, including a master node unit, a slave node unit, a data storage unit, and a user login device. The master node unit is communicatively connected to the slave node unit via a wireless network. The user login device is communicatively connected to the slave node unit and the master node unit respectively via a wireless network. The master node unit and the slave node unit are communicatively connected to the data storage unit via a wireless network. The data storage unit is constructed based on a cloud database and is used to collect all enterprise data required by the master node unit and the slave node unit. The user login device is selected from a mobile phone or a computer, and the user login device logs in to the demand processing system by installing corresponding software.

[0008] Both the master node unit and the slave node unit include a node connection module, a user login module, a data recognition module, a solution generation module, a fault monitoring module, a data security module, and a data feedback module.

[0009] The data recognition module is used to identify, analyze, and process the information received by the user login module. The data recognition module includes a keyword recognition sub-module, a comparison sub-module, and a model sub-module. The model sub-module stores the demand data received by the enterprise, the data of the enterprise's operating products, the keyword information of the enterprise's operating products, and the keyword information of the demand data previously received by the enterprise. The keyword recognition sub-module identifies the information received by the user login module according to the data recorded in the model sub-module, and the identification process is as follows:

[0010] Step S1-1. Scan the information received by the user login module and call the keyword data stored in the model sub-module;

[0011] Step S1-2. Extract keywords from the received information according to the keyword data stored in the model sub-module;

[0012] Step S1-3. Confirm whether the received information is related to the enterprise's operation according to the extraction result;

[0013] The comparison module calculates the similarity of the information received by the user login module according to the comparison formula, and the comparison formula is as follows:

[0014] Where is the frequency value of the i-th keyword in the information received by the user login module, and is the frequency value of the j-th keyword in the information stored in the model sub-module. If 0.3 ≤ S ≤ 1, it indicates that the information received by the user login module is highly similar to the information stored in the model sub-module, and the data recognition module will output the j-th information stored in the model sub-module as the final result. If 0 < S < 0.3, it indicates that the information received by the user login module is lowly similar to the information stored in the model sub-module, and further calculation is required.

[0015] The node connection module is used to connect the total node unit and the sub-node unit together. The node connection module is built based on blockchain technology. The user login module is used to receive the information transmitted by the user login device, and the user login module is built according to the client-server (C / S) connection mode.

[0016] The solution generation module generates a requirement processing document according to the results identified and analyzed by the data identification module. The solution generation module includes a document AI summary sub-module and a conversion sub-module. The AI summary sub-module retrieves the data stored in the data storage unit according to the results identified by the data identification module and performs a summary process on the data. The conversion sub-module is a PDF conversion tool, and the conversion sub-module is used to convert the data summarized by the AI summary sub-module.

[0017] The fault monitoring module is used to monitor the communication status among the total node unit, the sub-node unit, the user login device, and the data storage unit. The fault monitoring module is established based on the PhiAccrualFailure algorithm. The processing steps of the fault monitoring module are as follows:

[0018] Step S2-1. Collect heartbeat signals: The algorithm continuously collects heartbeat signals from each target and records the arrival time of each heartbeat signal.

[0019] Step S2-2. Calculate the average interval time: Using the sliding window technique, the algorithm calculates the average interval time of the heartbeat signals.

[0020] Step S2-3. Estimate the failure probability: According to the average interval time, use the exponential distribution formula to calculate the probability of node failure.

[0021] Step S2-4. Decision-making: According to the calculated failure probability, the algorithm decides whether to mark the target as failed. If the probability exceeds a certain threshold, it is considered that the target may have failed.

[0022] Step S2-5. Regulation: According to the decision result of the above steps, switch off the faulty target.

[0023] The data security module is used to ensure the security of data in the total node unit and the sub-node unit. The data security module includes a data tracking sub-module, a judgment sub-module, a firewall sub-module, and a self-check sub-module. The data tracking sub-module selects the website analysis tool Google Analytics. The data tracking sub-module is used to track user behavior and generate detailed reports and visualization data. The judgment sub-module judges the information collected by the data tracking sub-module through data analysis technology to confirm whether the user behavior is normal. The firewall sub-module is constructed based on network firewall technology. The firewall sub-module can build a network firewall for the total node unit and the sub-node unit. The self-check sub-module is established based on data classification and collection. The self-check sub-module is used to analyze and process the data in the total node unit and the sub-node unit to judge whether the data in the total node unit and the sub-node unit is secure.

[0024] The data feedback module is used to feedback the processing results of the total node unit and the sub-node unit to the user login device. The data feedback module is established based on the pop-up push technology. The data feedback module can transmit the requirement processing solution generated by the solution generation module, the problem data monitored by the fault monitoring module, and the dangerous data judged by the data security module to the user login device.

[0025] In the above technical solution, a requirement processing system based on blockchain provided by the present invention has the following beneficial effects:

[0026] (1) The present invention performs keyword recognition processing on the data received by the user login module through the data recognition module, quickly judges whether the requirements issued by the user conform to the business scope of the enterprise, and then quickly generates a requirement processing solution by comparing the user requirement information with the previously recorded requirement information. The data processed in this process is relatively small, which can improve the processing speed and does not require strong computing power, thereby reducing the activity cost of the enterprise.

[0027] (2) The requirement processing system designed by the present invention is constructed based on blockchain technology, which can reduce transaction costs, improve transaction efficiency, reduce the possibility of human intervention and fraud. At the same time, the present invention can monitor the communication status of the requirement processing system through the fault monitoring module, and timely switch off the faulty sub-node unit or the total node unit, greatly improving the disaster tolerance ability of the system, avoiding affecting the business continuity of the system, and ensuring the high availability of the system. Description of the Drawings

[0028] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0029] Figure 1 It is a schematic diagram of the system process provided by an embodiment of a blockchain-based demand processing system of the present invention.

[0030] Figure 2 It is a schematic diagram of the structure of the data recognition module provided by an embodiment of a blockchain-based demand processing system of the present invention.

[0031] Figure 3 It is a schematic diagram of the structure of the solution generation module provided by an embodiment of a blockchain-based demand processing system of the present invention.

[0032] Figure 4 It is a schematic diagram of the structure of the data security module provided by an embodiment of a blockchain-based demand processing system of the present invention. Detailed implementation manners

[0033] To enable those skilled in the art to better understand the technical solutions of the present invention, the following will further introduce the present invention in detail in conjunction with the accompanying drawings.

[0034] As Figures 1-4 shown, a blockchain-based demand processing system provided by an embodiment of the present invention includes a total node unit, a sub-node unit, a data storage unit, and a user login device. The total node unit is communicatively connected to the sub-node unit through a wireless network. The user login device is communicatively connected to the sub-node unit and the total node unit respectively through a wireless network. The total node unit and the sub-node unit are communicatively connected to the data storage unit through a wireless network. The data storage unit is constructed based on a cloud database. The data storage unit is used to collect all enterprise data required by the total node unit and the sub-node unit. The user login device selects one of a mobile phone and a computer. The user login device logs in to the demand processing system by installing corresponding software.

[0035] Both the total node unit and the sub-node unit include a node connection module, a user login module, a data recognition module, a solution generation module, a fault monitoring module, a data security module, and a data feedback module.

[0036] The data recognition module is used to identify, analyze, and process the information received by the user login module. The data recognition module includes a keyword recognition sub-module, a comparison sub-module, and a model sub-module. The model sub-module stores the demand data received by the enterprise, the data of the enterprise's operating products, the keyword information of the enterprise's operating products, and the keyword information of the demand data received by the enterprise in the past. The keyword recognition sub-module identifies the information received by the user login module based on the data recorded in the model sub-module, and the recognition process is as follows:

[0037] Step S1-1. Scan the information received by the user login module and call the keyword data stored in the model sub-module;

[0038] Step S1-2. Extract keywords from the received information according to the keyword data stored in the model sub-module;

[0039] Step S1-3. Confirm whether the received information is related to the enterprise's operation according to the extraction result;

[0040] The comparison module calculates the similarity of the information received by the user login module according to the comparison formula, and the comparison formula is as follows:

[0041]

[0042] Among them, is the frequency value of the i-th keyword in the information received by the user login module, is the frequency value of the j-th keyword in the information stored in the model sub-module. If 0.3 ≤ S ≤ 1, it indicates that the information received by the user login module is highly similar to the information stored in the model sub-module, and the data recognition module will output the j-th piece of information stored in the model sub-module as the final result. If 0 < S < 0.3, it indicates that the information received by the user login module is lowly similar to the information stored in the model sub-module, and further calculation is required.

[0043] The node connection module is used to connect the total node unit and the sub-node unit together. The node connection module is built based on blockchain technology. The user login module is used to receive the information transmitted by the user login device, and the user login module is built according to the client-server (C / S) connection mode.

[0044] The solution generation module generates a demand processing document according to the result of the identification and analysis of the data recognition module. The solution generation module includes a document AI summary sub-module and a conversion sub-module. The AI summary sub-module retrieves the data stored in the data storage unit according to the result of the identification of the data recognition module and summarizes the data. The conversion sub-module is a PDF conversion tool, and the conversion sub-module is used to convert the data summarized by the AI summary sub-module.

[0045] It should be noted that the AI summary sub-module is established based on AI copywriting processing technology, which refers to the copywriting generated by using artificial intelligence technology.

[0046] The fault monitoring module is used to monitor the communication status among the total node unit, the sub-node unit, the user login device, and the data storage unit. The fault monitoring module is established based on the PhiAccrualFailure algorithm. The processing steps of the fault monitoring module are as follows:

[0047] Step S2-1. Collect heartbeat signals: The algorithm continuously collects heartbeat signals from each target and records the arrival time of each heartbeat signal.

[0048] Step S2-2. Calculate the average interval time: Using the sliding window technique, the algorithm calculates the average interval time of the heartbeat signals.

[0049] Step S2-3. Estimate the fault probability: According to the average interval time, use the exponential distribution formula to calculate the probability of node failure.

[0050] Step S2-4. Decision: According to the calculated fault probability, the algorithm decides whether to mark the target as failed. If the probability exceeds a certain threshold, it is considered that the target may have failed.

[0051] Step S2-5. Regulation: According to the decision result of the above steps, switch off the faulty target.

[0052] It should be noted that PhiAccrualFailureDetector is a fault detection algorithm used in distributed systems. It estimates the probability of node failure by statistically analyzing the arrival time of heartbeat signals. The core idea of this algorithm is to use the exponential distribution to estimate the probability of node failure and decide whether to mark the node as failed based on this probability.

[0053] The data security module is used to ensure the security of the data in the total node unit and the sub-node unit. The data security module includes a data tracking sub-module, a judgment sub-module, a firewall sub-module, and a self-check sub-module. The data tracking sub-module selects the website analysis tool GoogleAnalytics. The data tracking sub-module is used to track user behavior and generate detailed reports and visualization data. The judgment sub-module judges the information collected by the data tracking sub-module through data analysis technology to confirm whether the user behavior is normal. The firewall sub-module is built based on network firewall technology. The firewall sub-module can build a network firewall for the total node unit and the sub-node unit. The self-check sub-module is established based on data classification and collection. The self-check sub-module is used to analyze and process the data in the total node unit and the sub-node unit to judge whether the data in the total node unit and the sub-node unit is secure.

[0054] It should be noted that:

[0055] The firewall sub-module will form a stateful inspection firewall. The stateful inspection firewall not only checks the header information of each data packet but also records the connection state, can track sessions, extend to the application layer, and filter traffic based on the session state.

[0056] The steps of data classification and aggregation mainly include the following aspects:

[0057] Review data: First, it is necessary to conduct a comprehensive review of the data to understand information such as the data source, format, and quality;

[0058] Define data types: According to the attributes and characteristics of the data, define different data types to lay a foundation for subsequent classification;

[0059] Sub-divide data according to data types: According to the defined data types, further sub-divide the data to ensure the accuracy and integrity of the data;

[0060] Sort out the details of the data: Conduct a detailed sorting of the data, including information such as the data source, format, and quality, to ensure the reliability of the data;

[0061] Classify data according to rules: According to predefined criteria and rules, classify the data to form a unified data structure and management system;

[0062] Summarize data: Summarize the classified data, extract valuable information, and form reports or analysis results;

[0063] Check the consistency of the result data: Check the consistency of the classified and summarized data to ensure the accuracy and consistency of the data;

[0064] Form a report: Organize the classification, summary, and consistency check results into a report.

[0065] The data feedback module is used to feedback the results processed by the master node unit and the slave node unit to the user login device. The data feedback module is established based on the pop-up push technology. The data feedback module can transmit the requirement processing solution generated by the solution generation module, the problem data monitored by the fault monitoring module, and the dangerous data judged by the data security module to the user login device.

[0066] Working principle: The user sends requirements to the master node unit or sub-node unit through the user login device. In this process, the user login module will receive the information transmitted by the user login device. Subsequently, the data recognition module will identify, analyze, and process the information received by the user login module. When processing, the keyword recognition sub-module will identify the information received by the user login module according to the data recorded by the model sub-module. And subsequently, the comparison module will calculate the similarity of the information received by the user login module through the comparison formula. According to the calculation result, the solution generation module will quickly generate a requirement processing copywriting and convert the copywriting into PDF format. After that, the data feedback module can send the requirement processing solution generated by the solution generation module to the user login device based on the pop-up push technology, so that the user can understand the personalized service information provided by the enterprise for the user. And when the system runs, the fault monitoring module can monitor the communication status among the master node unit, sub-node unit, user login device, and data storage unit. In this process, the data tracking sub-module will track the user behavior and generate a detailed report and visualization data. And subsequently, the judgment sub-module will judge the information collected by the data tracking sub-module to confirm whether the user behavior is normal. At the same time, the firewall sub-module can build a network firewall for the master node unit and sub-node unit. The self-check sub-module will analyze and process the data in the master node unit and sub-node unit to judge whether the data in the master node unit and sub-node unit is secure. And through the data feedback module, the results monitored by the data security module will be transmitted to the user login device, so that the user can understand whether the login process is secure.

[0067] Only some exemplary embodiments of the present invention have been described by way of illustration. Without doubt, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of the claims of the present invention.

Claims

1. A demand processing system based on blockchain, comprising a master node unit, a sub-node unit, a data storage unit and a user login device, characterized in that: The master node unit is in communication connection with the sub-node unit via a wireless network, the user login device is in communication connection with the sub-node unit and the master node unit respectively via a wireless network, and the master node unit and the sub-node unit are in communication connection with the data storage unit via a wireless network; The master node unit and the sub-node unit both include a node connection module, a user login module, a data identification module, a solution generation module, a fault monitoring module, a data security module, and a data feedback module. The node connection module is used to connect the master node unit and the sub-node unit together. The user login module is used to receive information transmitted by the user login device. The data identification module is used to identify, analyze, and process the information received by the user login module. The solution generation module generates a demand processing document based on the results of the identification and analysis of the data identification module. The fault monitoring module is used to monitor the communication status among the master node unit, the sub-node unit, the user login device, and the data storage unit. The data security module is used to ensure the security of the data in the master node unit and the sub-node unit. The data feedback module is used to feed back the results of the processing of the master node unit and the sub-node unit to the user login device. The data identification module includes a keyword identification submodule, a comparison submodule, and a model submodule. The model submodule includes demand data received by the enterprise, data of the enterprise's operating products, keyword information of the enterprise's operating products, and keyword information of demand data previously received by the enterprise. The keyword identification submodule identifies the information received by the user login module based on the data recorded by the model submodule, and the identification process is as follows: Step S1-1. Scan the user login module to receive information and call the keyword data collected by the model submodule; Step S1-2. Perform keyword extraction processing on the received information according to the keyword data collected by the model submodule; Step S1-3. Confirm whether the received information is related to business operations based on the extraction results; The comparison module calculates the similarity of the information received by the user login module according to the comparison formula, and the comparison formula is as follows: Among them, T i 用 is the frequency value of the i-th keyword in the information received by the user login module, T j 模 is the frequency value of the j-th keyword in the information included in the model sub-module. If 0.3 ≤ S ≤ 1, it indicates that the information received by the user login module is highly similar to the information included in the model sub-module, and the data recognition module will output the j-th piece of information included in the model sub-module as the final result. If 0 < S < 0.3, it indicates that the information received by the user login module is lowly similar to the information included in the model sub-module, and further calculation is required.

2. A blockchain-based demand processing system according to claim 1, characterized in that: The node connection module is constructed based on blockchain technology, and the user login module is constructed according to the client-server (C / S) connection mode.

3. A blockchain-based demand processing system according to claim 1, characterized in that: The solution generation module includes a copy AI summary submodule and a conversion submodule. The AI ​​summary submodule retrieves the data collected in the data storage unit according to the results of the data identification module and summarizes the data. The conversion submodule is a PDF conversion tool, and the conversion submodule is used to convert the data summarized by the AI ​​summary submodule.

4. A blockchain-based demand processing system according to claim 1, characterized in that: The fault monitoring module is established based on the PhiAccrualFailure algorithm, and the processing steps of the fault monitoring module are as follows: Step S2-1. Collect heartbeat signals: The algorithm continuously collects heartbeat signals from each target and records the arrival time of each heartbeat signal; Step S2-2. Calculate the average interval time: Use the sliding window technique to calculate the average interval time of the heartbeat signal; Step S2-3. Estimate the failure probability: Calculate the probability of node failure using the exponential distribution formula based on the average interval time; Step S2-4. Decision: Based on the calculated failure probability, the algorithm decides whether to mark the target as failed. If the probability exceeds a certain threshold, the target is considered to have failed. Step S2-5. Control: According to the decision results of the above steps, switch out the faulty target.

5. A blockchain-based demand processing system according to claim 1, characterized in that: The data security module includes a data tracking submodule, a judgment submodule, a firewall submodule, and a self-check submodule. The data tracking submodule uses the website analysis tool Google Analytics. The data tracking submodule is used to track user behavior and generate detailed reports and visual data. The judgment submodule uses data analysis technology to judge the information collected by the data tracking submodule to confirm whether the user behavior is normal.

6. A blockchain-based demand processing system according to claim 5, characterized in that: The firewall submodule is constructed based on the network firewall technology, and the firewall submodule can construct a network firewall for the main node unit and the sub-node unit.

7. A blockchain-based demand processing system according to claim 5, characterized in that: The self-check submodule is established based on data classification and collection. The self-check submodule is used to analyze and process the data in the main node unit and the sub-node unit to determine whether the data in the main node unit and the sub-node unit is safe.

8. A blockchain-based demand processing system according to claim 1, characterized in that: The data feedback module is established based on the pop-up window push technology. The data feedback module can transmit the demand processing solution generated by the solution generation module, the problem data monitored by the fault monitoring module, and the dangerous data judged by the data security module to the user login device.

9. A blockchain-based demand processing system according to claim 1, characterized in that: The data storage unit is constructed based on a cloud database and is used to collect all enterprise data required by the main node unit and the sub-node unit.

10. A blockchain-based demand processing system according to claim 1, characterized in that: The user login device is selected from a mobile phone and a computer, and the user login device logs in to the demand processing system by loading corresponding software.

Images