Blockchain-based verification system for information engineering consulting services

Abstract

This invention relates to the field of information consulting technology, and in particular to a blockchain-based verification system for information engineering consulting services. The system includes a data acquisition module, a data analysis module, a tag construction module, a data judgment module, and a data priority processing module. The data acquisition module determines the relevance and completeness of the data. The data analysis module performs a rationality analysis on the engineering consulting data to determine whether the data meets reasonable standards. The tag construction module sets tags and determines the data storage status. The data judgment module determines whether the consulting type of the engineering consulting data needs to be adjusted based on the consulting type matching value, and stores the engineering consulting data in the consulting database corresponding to the consulting type. The data priority processing module determines whether to increase the data processing priority of the engineering consulting data. This invention improves the space utilization rate of data storage and the data processing efficiency by analyzing and processing engineering consulting data.

Description

Technical Field

[0001] This invention relates to the field of information consulting technology, and in particular to a blockchain-based verification system for information engineering consulting services. Background Technology

[0002] In the field of information engineering consulting services, due to the complexity and diversity of information, the high requirements for authenticity and reliability, and the involvement of multiple parties in collaboration, traditional centralized verification institutions suffer from risks, insecure information transmission and storage, and low verification efficiency. Meanwhile, blockchain technology continues to develop due to its decentralized, immutable, distributed ledger, and smart contract characteristics. Therefore, combining blockchain with information engineering consulting services has become a trend, which can improve information security and credibility, optimize business processes, and promote cross-organizational collaboration. As a result, blockchain-based verification systems for information engineering consulting services have emerged. It is also crucial to prioritize engineering consulting data, process massive amounts of data, and determine priorities to quickly resolve important issues.

[0003] Chinese Patent Publication No. CN120013441A discloses an accident prevention and control system for enterprise safety consulting services, including a source extraction module, a standard specification module, a risk identification module, and an anticipatory control module. The source extraction module is used to collect engineering risk information and employee review information, and transmits the preprocessed engineering risk information and employee review information to the standard specification module. The standard specification module is used to standardize and evaluate the operating procedures in the safety consulting process and verify the differences between actual operation and standard operation. The risk identification module is used to assess the risk points in the safety consulting service, establish a risk assessment model to classify the risk level, and the anticipatory control module is used to manage anticipatory risk control according to the risk level. It can effectively model and assess accident risks in the safety consulting service process, diagnose risk levels, and provide control strategies.

[0004] Chinese Patent Publication No. CN114331383A discloses an integrated management system for engineering supervision services. Addressing the issues of limited functionality and low security in existing engineering supervision service management systems, which lack backend data monitoring, this invention proposes the following solution, comprising: a login module electrically connected to a security verification module, used for login by both users and backend administrators; and a security verification module electrically connected to a data modification module. This invention features a novel structure, provides various online consultation functions, and adds the ability to schedule appointments for supervisors, better facilitating the matching of suitable supervision companies and personnel with construction companies. Furthermore, it allows for the tracking of backend administrator login information, ensuring system information security and further enhancing the device's practicality.

[0005] However, the following problems still exist in the existing technology.

[0006] In reality, user terminals contain a large amount of engineering consulting data. This data may be filled out incorrectly or due to misunderstandings, omissions, or errors by consultants, resulting in ineffective or disordered consulting content. The system cannot distinguish the data and cannot resolve it successfully. At this time, a large amount of useless data occupies the data storage space, resulting in low space utilization. Staff cannot solve problems in a timely manner, and the data processing efficiency of the engineering consulting data is reduced. Summary of the Invention

[0007] To address this issue, the present invention provides a blockchain-based verification system for information engineering consulting services. This system aims to solve the problem that, in practice, users often have a large amount of engineering consulting data. This data may be scrambled due to misunderstandings, blank entries, or errors by consultants, resulting in invalid or disordered consulting content. The system cannot distinguish this information and is unable to resolve the issues successfully. In such cases, a large amount of useless data occupies data storage space, leading to low space utilization and hindering timely problem-solving by staff. Consequently, the data processing efficiency of the engineering consulting data is reduced.

[0008] To achieve the above objectives, the present invention provides a blockchain-based information engineering consulting service verification system, comprising:

[0009] The data acquisition module is used to acquire engineering consulting data uploaded by users and the type of consulting data to which it belongs, and to determine the data relevance and data completeness of the engineering consulting data;

[0010] A data analysis module, connected to the data acquisition module, is used to perform a rationality analysis on the engineering consulting data based on the data relevance and the data completeness, and to determine whether the engineering consulting data meets the rationality standards.

[0011] A tag building module, which is connected to the data analysis module, is used to set tags for the engineering consulting data based on whether the engineering consulting data meets reasonable standards, and to determine the data storage status.

[0012] The data determination module, which is connected to the tag construction module, is used to respond to the storage status of the data storage state, determine the topic contradiction rate and correlation existence rate of sample keywords for different consultation types, calculate the consultation type matching value, determine whether the consultation type of the engineering consultation data needs to be adjusted, and store the engineering consultation data into the consultation database of the corresponding consultation type based on the determined consultation type.

[0013] The data priority processing module, which is connected to the data determination module, is used to organize the engineering consulting dataset, determine the probability of occurrence of a single data point and the probability of mention of a data point, calculate the data importance characterization value, and classify the data importance tendency in order to determine whether to increase the data processing priority of the engineering consulting data.

[0014] The engineering consulting dataset includes several engineering consulting datasets of the same consulting type.

[0015] The data acquisition module determines the data relevance and data completeness of the engineering consulting data, including:

[0016] Several keywords used to extract the engineering consulting data;

[0017] Used to calculate the keyword similarity between each of the stated keywords and the predetermined keywords;

[0018] Used to sort in descending order based on the keyword similarity;

[0019] This is used to determine the similarity of the top-ranked keyword as the data relevance.

[0020] The reciprocal of the information entropy of the engineering consulting data is used to determine the data completeness.

[0021] The data analysis module determines whether the engineering consulting data meets reasonable standards, wherein...

[0022] If the data relevance is greater than a preset data relevance threshold and the data completeness is greater than a preset data completeness threshold, then the engineering consulting data is determined to meet reasonable standards.

[0023] If the data relevance is less than or equal to a preset data relevance threshold and / or the data completeness is less than or equal to a preset data completeness threshold, then the engineering consulting data is determined to be non-compliant with reasonable standards.

[0024] The tag building module sets tags and determines the data storage status, including...

[0025] If the engineering consulting data meets reasonable standards, then a tag is set to indicate that the data storage status is pending storage.

[0026] If the engineering consulting data does not meet reasonable standards, an abandonment label is set, and the data storage status is determined to be non-storage status.

[0027] Furthermore, the data determination module is used to determine the topic contradiction rate and correlation rate of sample keywords for different consultation types, including,

[0028] Used to calculate the keyword similarity between keywords in engineering consulting data and sample keywords of consulting types;

[0029] Used to determine the number of keywords whose keyword similarity is less than the keyword similarity threshold;

[0030] The ratio of the number of keywords to the total number of keywords is used to determine the theme contradiction rate;

[0031] This is used to divide engineering consulting data into several engineering consulting data segments;

[0032] Used to determine the keywords corresponding to each of the aforementioned engineering consulting data segments;

[0033] The number of keywords that are the same as the sample keywords for the consultation type;

[0034] The ratio of the number of keywords to the total number of engineering consulting data segments is used to determine the association existence rate.

[0035] Furthermore, the data determination module calculates the consultation type matching value, including:

[0036] Used to retrieve the topic contradiction rate and correlation existence rate corresponding to the consultation type;

[0037] The ratio of the threshold for the topic conflict rate to the topic conflict rate is used as the conflict influence factor;

[0038] The ratio of the correlation existence rate to the correlation existence rate threshold is used to determine the correlation influence factor;

[0039] The weighted sum of the contradictory influencing factors and the related influencing factors is used to determine the matching value between the engineering consulting data and the consulting type.

[0040] Furthermore, the data determination module determines whether the consultation type of the engineering consultation data needs to be adjusted, and stores the engineering consultation data into the corresponding consultation type's consultation database based on the determined consultation type.

[0041] If the consultation type matching value is greater than the consultation type matching value threshold, the consultation type of the engineering consultation data will not be adjusted, and the engineering consultation data will be directly stored in the consultation database of the corresponding consultation type according to the consultation type of the engineering consultation data.

[0042] If the consultation type matching value is less than or equal to the consultation type matching value threshold, then the consultation type of the engineering consultation data is adjusted to the consultation type of the engineering consultation data corresponding to the maximum consultation type matching value, and the engineering consultation data is stored in the consultation database of the corresponding consultation type according to the adjusted consultation type of the engineering consultation data.

[0043] Furthermore, the data priority processing module determines the probability of a single data occurrence and the probability of data mention, including,

[0044] This is used to determine the average keyword similarity between a keyword in a single piece of engineering consulting data and each keyword in the remaining engineering consulting data in the engineering consulting dataset, and to determine the average keyword similarity as the probability of occurrence of a single piece of data in the engineering consulting dataset;

[0045] This is used to determine the average probability of each keyword in the engineering consulting data appearing in the remaining engineering consulting data, and the average probability of appearance is determined as the data mention probability for the engineering consulting data.

[0046] Furthermore, the data priority processing module calculates the data importance representation value, including,

[0047] The ratio of the probability of a single data point occurring to a threshold for the probability of a single data point occurring is used to determine the single influence factor.

[0048] The ratio of the probability of data mention to the data mention probability threshold is used to determine the data mention impact factor;

[0049] The weighted sum of the single impact factor and the data mention impact factor is used to determine the data importance characterization value for the engineering consulting data.

[0050] Furthermore, the data priority processing module categorizes data based on importance to determine whether to increase the data processing priority of the engineering consulting data.

[0051] If the data importance indicator value is greater than the data importance indicator value threshold, then the data importance tendency is classified as an importance tendency, thereby increasing the data processing priority of the engineering consulting data;

[0052] If the data importance value is less than or equal to the data importance value threshold, the data importance tendency is classified as normal tendency, and the data processing priority of the engineering consulting data is not increased.

[0053] Compared with existing technologies, this invention sets up a data acquisition module, a data analysis module, a tag construction module, a data judgment module, and a data priority processing module. The data acquisition module determines the relevance and completeness of the data; the data analysis module performs a rationality analysis on the engineering consulting data to determine whether the engineering consulting data meets reasonable standards; the tag construction module sets tags for the engineering consulting data and determines the data storage status; the data judgment module determines whether the consulting type of the engineering consulting data needs to be adjusted based on the consulting type matching value, and stores the engineering consulting data in the consulting database corresponding to the consulting type; the data priority processing module determines whether to increase the data processing priority of the engineering consulting data. This invention improves the space utilization of data storage space and the data processing efficiency by analyzing and processing engineering consulting data.

[0054] In particular, by determining the relevance and completeness of engineering consulting data, this invention provides a theoretical basis for labeling engineering consulting data. In practice, engineering consulting data is abundant, and while some data is filled out as required for easy recording and analysis, some data remains blank or randomly filled. If these useless data are not distinguished, they will accumulate over time, occupying a large amount of storage space, reducing space utilization, and consequently reducing data processing efficiency. Calculating data relevance ensures that engineering consulting data is relevant to the project, rather than irrelevant content; calculating data completeness ensures that the engineering consulting data is complete and meaningful, rather than lacking key information. Based on this, this invention determines data relevance and completeness, providing a theoretical basis for subsequently determining whether engineering consulting data meets reasonable standards and for labeling, ensuring the usability of the data to be stored and improving analysis efficiency.

[0055] In particular, by determining whether engineering consulting data meets reasonable standards, labeling the engineering consulting data, and determining the data storage status, this invention addresses the issue that in practice, if the storage status of the data is uncertain and the acquired engineering consulting data is stored directly, then engineering consulting data that does not meet reasonable standards—such as empty engineering consulting data, or data that is irrelevant to the project or is randomly written—will waste storage space, thereby reducing storage efficiency and even affecting subsequent data storage processes. Therefore, this invention considers conducting a reasonableness analysis of the engineering consulting data, screening the data before storage, starting from its relevance and completeness to the project, labeling the engineering consulting data, determining the data storage status, directly removing engineering consulting data in non-storage states, and further analyzing the engineering consulting data in the state to be stored. This greatly ensures that the engineering consulting data to be stored meets reasonable standards, improves storage efficiency, and enhances data storage quality.

[0056] In particular, determining the topic contradiction rate and correlation existence rate provides a data foundation for calculating the consultation type matching value of engineering consulting data. It is understandable that even engineering consulting data awaiting storage may contain content that does not match the problem due to the diverse acquisition channels. For example, due to user errors or misunderstandings, the content of the engineering consulting data may differ from the actual problem encountered. If such engineering consulting data is directly stored, invalid data will increase, affecting not only the speed of subsequent problem localization but also the efficiency and accuracy of resolving the corresponding problem. Therefore, this invention considers calculating the consultation type matching value of engineering consulting data by determining the topic contradiction rate and correlation existence rate. The topic contradiction rate determines that the content of the engineering consulting data contains content corresponding to the problem, while the correlation existence rate determines that most of the content of the engineering consulting data describes the corresponding problem. Further analysis of the engineering consulting data is necessary to store engineering consulting data in a one-to-one correspondence with the problem. Engineering consulting data of different consultation types can be adjusted to their corresponding consultation types before storage, improving the accuracy of the engineering consulting data and increasing the efficiency of subsequent data processing.

[0057] In particular, by calculating the data importance value through the probability of occurrence of a single data point and the probability of mention of a data point, a data foundation is provided for subsequently determining the priority of data processing. The probability of occurrence of a single data point represents the probability of different users consulting the same question, while the probability of mention of a data point represents the probability of that question appearing in all engineering consulting data. In reality, a question may be consulted by different users, which indicates that the question is of high importance to a certain group of users. At the same time, if the question also frequently appears in the consultation questions of other users, it means that the question needs to be addressed in a timely manner. It is understandable that if the data importance value is not calculated and the data is stored directly, not only will the importance of the data not be reflected, but the processing efficiency of the corresponding questions in the engineering consulting data will also be reduced. Based on this, this invention considers analyzing the probability of occurrence of a single data point and the probability of mention of a data point, and then calculates the data importance value, classifies the data importance tendency, determines the processing priority of the engineering consulting data, and improves the data processing efficiency corresponding to the engineering consulting data. Attached Figure Description

[0058] Figure 1 A schematic diagram of the structure of a blockchain-based information engineering consulting service verification system according to an embodiment of the invention;

[0059] Figure 2 A logic block diagram illustrating whether the engineering consulting data conforms to reasonable standards, as described in an embodiment of the invention;

[0060] Figure 3 A logical block diagram for setting tags and determining data storage status in an embodiment of the invention;

[0061] Figure 4 To determine whether the consultation type of the engineering consultation data needs to be adjusted in the embodiments of the invention, and to store the engineering consultation data into the consultation database corresponding to the consultation type based on the determined consultation type;

[0062] Figure 5 This is a logical block diagram illustrating the data importance prioritization in an embodiment of the invention to determine whether to increase the data processing priority of the engineering consulting data. Detailed Implementation

[0063] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0064] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0065] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0066] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of a blockchain-based information engineering consulting service verification system according to an embodiment of the invention. The blockchain-based information engineering consulting service verification system of the present invention includes:

[0067] The data acquisition module is used to acquire engineering consulting data uploaded by users and the type of consulting data to which it belongs, and to determine the data relevance and data completeness of the engineering consulting data;

[0068] A data analysis module, connected to the data acquisition module, is used to perform a rationality analysis on the engineering consulting data based on the data relevance and the data completeness, and to determine whether the engineering consulting data meets the rationality standards.

[0069] A tag building module, which is connected to the data analysis module, is used to set tags for the engineering consulting data based on whether the engineering consulting data meets reasonable standards, and to determine the data storage status.

[0070] The data determination module, which is connected to the tag construction module, is used to respond to the storage status of the data storage state, determine the topic contradiction rate and correlation existence rate of sample keywords for different consultation types, calculate the consultation type matching value, determine whether the consultation type of the engineering consultation data needs to be adjusted, and store the engineering consultation data into the consultation database of the corresponding consultation type based on the determined consultation type.

[0071] The data priority processing module, which is connected to the data determination module, is used to organize the engineering consulting dataset, determine the probability of occurrence of a single data point and the probability of mention of a data point, calculate the data importance characterization value, and classify the data importance tendency in order to determine whether to increase the data processing priority of the engineering consulting data.

[0072] The engineering consulting dataset includes several engineering consulting datasets of the same consulting type.

[0073] Specifically, there are no restrictions on the source of engineering consulting data. For example, it can be data uploaded by authorized users themselves, or query texts used to consult information engineering-related content. This will not be elaborated further.

[0074] It is understandable that there is no limitation on the classification of engineering consulting data by type. For example, it can be classified as consulting process type, consulting installation environment type, consulting software configuration type, etc., and those skilled in the art can adjust it according to their needs.

[0075] It is understandable that the labels set are not labels in the physical sense, but rather digital virtual identifiers that exist in the form of data and serve only as a means of distinguishing consultation types; this will not be elaborated further.

[0076] Specifically, in practice, the engineering consulting dataset for a single consulting type is constantly updated, and processed engineering consulting data can be deleted from the engineering consulting dataset.

[0077] Specifically, the data acquisition module determines the data relevance and data completeness of the engineering consulting data, including:

[0078] Several keywords used to extract the engineering consulting data;

[0079] Used to calculate the keyword similarity between each of the stated keywords and the predetermined keywords;

[0080] Used to sort in descending order based on the keyword similarity;

[0081] This is used to determine the similarity of the top-ranked keyword as the data relevance.

[0082] The reciprocal of the information entropy of the engineering consulting data is used to determine the data completeness.

[0083] In practice, the predetermined keywords are engineering information. Of course, those skilled in the art can determine the predetermined keywords according to the actual situation, which will not be elaborated here.

[0084] Specifically, there are no restrictions on the specific method for extracting keywords. For example, the TF-IDF method or the POS Tagging method can be used. In practice, the TF-IDF method is used to extract keywords. By calculating the frequency of a word in the current document and its inverse document frequency, the TF-IDF value of each word is obtained. It can be understood that the larger the TF-IDF value, the more critical the corresponding word is. Of course, those skilled in the art can also use other methods, as long as the keywords can be extracted. This will not be elaborated further.

[0085] Specifically, there is no limitation on the method for determining keyword similarity. For example, it can be the cosine similarity method or the Jaccard similarity method. In practice, the cosine similarity method is used to determine keyword similarity. The keywords and the keywords in the consultation title are vectorized, and the similarity between the two is calculated using the cosine similarity formula. It can be understood that the closer the similarity is to 1, the more similar the two are. Of course, those skilled in the art can also use other methods, as long as the keyword similarity can be determined. This will not be elaborated further.

[0086] Specifically, in practice, information entropy is used to characterize the completeness of data. Information entropy is negatively correlated with data completeness. There is no limitation on the calculation method of information entropy. It can be understood that information entropy corresponds to several formulas, such as the Shannon entropy formula, the joint entropy formula, and the conditional entropy formula. These are existing formulas. Those skilled in the art can choose the formula according to the actual situation, which will not be elaborated here.

[0087] Specifically, by determining the relevance and completeness of engineering consulting data, this invention provides a theoretical basis for labeling engineering consulting data. In practice, engineering consulting data is abundant. While some data is filled out as required for recording and analysis, some data remains blank or randomly filled. If this useless data is not distinguished, it will accumulate over time, occupying a large amount of storage space, reducing space utilization, and consequently reducing data processing efficiency. Calculating data relevance ensures that engineering consulting data is relevant to the project, rather than irrelevant content. Calculating data completeness ensures that the engineering consulting data is complete and meaningful, rather than lacking key content. Based on this, this invention determines data relevance and completeness, providing a theoretical basis for subsequently determining whether engineering consulting data meets reasonable standards and for labeling, ensuring the usability of the data to be stored and improving analysis efficiency.

[0088] Please see Figure 2 , Figure 2This is a logic block diagram illustrating whether the engineering consulting data meets reasonable standards, as described in an embodiment of the invention. Specifically, the data analysis module determines whether the engineering consulting data meets reasonable standards, wherein...

[0089] If the data relevance is greater than a preset data relevance threshold and the data completeness is greater than a preset data completeness threshold, then the engineering consulting data is determined to meet reasonable standards.

[0090] If the data relevance is less than or equal to a preset data relevance threshold and / or the data completeness is less than or equal to a preset data completeness threshold, then the engineering consulting data is determined to be non-compliant with reasonable standards.

[0091] Specifically, the preset data relevance threshold is calculated from historical data. Several historical engineering consulting data are obtained in advance to obtain several historical data relevances. The preset data relevance threshold is selected within [1.1, 1.23] times the average value of each historical data relevance.

[0092] Specifically, the preset data integrity threshold is calculated from historical data. Several historical engineering consulting data are obtained in advance to obtain several data integrity values. The preset data integrity threshold is selected within [1.07, 1.21] times the average value of each data integrity value.

[0093] Please see Figure 3 , Figure 3 A logical block diagram for setting tags and determining data storage status in an embodiment of the invention. Specifically, the tag construction module sets tags and determines data storage status, including:

[0094] If the engineering consulting data meets reasonable standards, then a tag is set to indicate that the data storage status is pending storage.

[0095] If the engineering consulting data does not meet reasonable standards, an abandonment label is set, and the data storage status is determined to be non-storage status.

[0096] It is understandable that engineering consulting data in the pending storage state can be stored in the blockchain to ensure data security and availability.

[0097] Specifically, by determining whether engineering consulting data meets reasonable standards, tags are set for the engineering consulting data, and the data storage status is determined. In practice, if the storage status of the data is uncertain and the acquired engineering consulting data is stored directly, then engineering consulting data that does not meet reasonable standards, such as empty engineering consulting data or engineering consulting data that is irrelevant to the project or is randomly written, will waste storage space, thereby reducing storage efficiency and even affecting the subsequent data storage process. Based on this, the present invention considers to conduct a reasonableness analysis of engineering consulting data, and screens the engineering consulting data before data storage, starting from the relevance and completeness of the project, sets tags for the engineering consulting data, determines the data storage status, and directly removes engineering consulting data corresponding to non-storage status, while engineering consulting data corresponding to the storage status is further analyzed. This greatly ensures that the engineering consulting data to be stored meets reasonable standards, improves storage efficiency, and improves data storage quality.

[0098] Specifically, the data judgment module is used to determine the topic contradiction rate and relevance rate of sample keywords for different types of consultations, including:

[0099] Used to calculate the keyword similarity between keywords in engineering consulting data and sample keywords of consulting types;

[0100] Used to determine the number of keywords whose keyword similarity is less than the keyword similarity threshold;

[0101] The ratio of the number of keywords to the total number of keywords is used to determine the theme contradiction rate;

[0102] This is used to divide engineering consulting data into several engineering consulting data segments;

[0103] Used to determine the keywords corresponding to each of the aforementioned engineering consulting data segments;

[0104] The number of keywords that are the same as the sample keywords for the consultation type;

[0105] The ratio of the number of keywords to the total number of engineering consulting data segments is used to determine the association existence rate.

[0106] Specifically, the keyword similarity threshold represents the situation where the keyword similarity is low, so the keyword similarity threshold is selected within the range [0.3, 0.4].

[0107] Specifically, there is no limitation on the division method. In practice, the division is based on the punctuation marks in the engineering consulting data. Each time a period is encountered, it is divided into a segment of engineering consulting data. Of course, those skilled in the art can also use other methods to divide the data, as long as they are reasonable. This will not be elaborated further.

[0108] Specifically, the data determination module calculates the consultation type matching value, including:

[0109] Used to retrieve the topic contradiction rate and correlation existence rate corresponding to the consultation type;

[0110] The ratio of the threshold for the topic conflict rate to the topic conflict rate is used as the conflict influence factor;

[0111] The ratio of the correlation existence rate to the correlation existence rate threshold is used to determine the correlation influence factor;

[0112] The weighted sum of the contradictory influencing factors and the related influencing factors is used to determine the matching value between the engineering consulting data and the consulting type.

[0113] Specifically, the subject contradiction rate threshold is determined based on historical data. Several engineering consulting data and matching consulting types are pre-selected by those skilled in the art, the subject contradiction rate of the engineering consulting data is calculated, and the average of the subject contradiction rates is determined as the subject contradiction rate threshold.

[0114] Specifically, the correlation existence rate threshold is determined based on historical data. A number of engineering consulting data and matching consulting types are selected by a person skilled in the art, the correlation existence rate of the engineering consulting data is calculated, and the average of the correlation existence rates is determined as the correlation existence rate threshold.

[0115] Specifically, the sum of the weight coefficients of the contradictory influence factor and the related influence factor is 1, the weight coefficient of the contradictory influence factor is 0.47, and the weight coefficient of the related influence factor is 0.53.

[0116] Specifically, determining the topic contradiction rate and correlation existence rate provides a data foundation for calculating the consultation type matching value of engineering consulting data. It is understandable that even engineering consulting data awaiting storage may contain content that does not match the problem due to the diverse acquisition channels. For example, due to user errors or misunderstandings, the content of the engineering consulting data may differ from the actual problem encountered. If such engineering consulting data is directly stored, invalid data will increase, affecting not only the speed of subsequent problem localization but also the efficiency and accuracy of solving the corresponding problems. Therefore, this invention considers calculating the consultation type matching value of engineering consulting data by determining the topic contradiction rate and correlation existence rate. The topic contradiction rate determines that the content of the engineering consulting data contains content corresponding to the problem, while the correlation existence rate determines that most of the content of the engineering consulting data describes the corresponding problem. Further analysis of the engineering consulting data is necessary to ensure a one-to-one correspondence between engineering consulting data and problems for storage. Engineering consulting data of different consultation types can be adjusted to their corresponding consultation types before storage, improving the accuracy of the engineering consulting data and increasing the efficiency of subsequent data processing.

[0117] Please see Figure 4 , Figure 4 To determine whether the consultation type of the engineering consultation data needs to be adjusted in this embodiment of the invention, and to store the engineering consultation data in a consultation database corresponding to the determined consultation type, specifically, the data determination module determines whether the consultation type of the engineering consultation data needs to be adjusted, and to store the engineering consultation data in a consultation database corresponding to the determined consultation type.

[0118] If the consultation type matching value is greater than the consultation type matching value threshold, the consultation type of the engineering consultation data will not be adjusted, and the engineering consultation data will be directly stored in the consultation database of the corresponding consultation type according to the consultation type of the engineering consultation data.

[0119] If the consultation type matching value is less than or equal to the consultation type matching value threshold, then the consultation type of the engineering consultation data is adjusted to the consultation type of the engineering consultation data corresponding to the maximum consultation type matching value, and the engineering consultation data is stored in the consultation database of the corresponding consultation type according to the adjusted consultation type of the engineering consultation data.

[0120] Specifically, the consultation type matching value threshold represents the minimum value at which the consultation type of engineering consultation data is successfully matched. Therefore, the consultation type matching value threshold is selected within the range [0.69, 0.86].

[0121] Specifically, the data prioritization module determines the probability of a single data point occurring and the probability of the data being mentioned, including:

[0122] This is used to determine the average keyword similarity between a keyword in a single piece of engineering consulting data and each keyword in the remaining engineering consulting data in the engineering consulting dataset, and to determine the average keyword similarity as the probability of occurrence of a single piece of data in the engineering consulting dataset;

[0123] This is used to determine the average probability of each keyword in the engineering consulting data appearing in the remaining engineering consulting data, and the average probability of appearance is determined as the data mention probability for the engineering consulting data.

[0124] Specifically, the data priority processing module calculates the important characteristic values ​​of the data, including,

[0125] The ratio of the probability of a single data point occurring to a threshold for the probability of a single data point occurring is used to determine the single influence factor.

[0126] The ratio of the probability of data mention to the data mention probability threshold is used to determine the data mention impact factor;

[0127] The weighted sum of the single impact factor and the data mention impact factor is used to determine the data importance characterization value for the engineering consulting data.

[0128] Specifically, the probability threshold for a single data occurrence is calculated based on historical data. Several engineering consulting data and matching consulting types are pre-selected by those skilled in the art, several engineering consulting datasets are compiled, the historical probability of a single data occurrence in each engineering consulting dataset is calculated, and the average of the historical single data occurrence probabilities is determined as the probability threshold for a single data occurrence.

[0129] Specifically, the data mention probability threshold is calculated based on historical data. Several engineering consulting data and matching consulting types are pre-selected by those skilled in the art, several engineering consulting datasets are compiled, the historical data mention probability of engineering consulting data in each engineering consulting dataset is calculated, and the average of the historical data mention probabilities is determined as the data mention probability threshold.

[0130] Specifically, the sum of the weighting coefficients of the single impact factor and the data-mentioned impact factor is 1, the weighting coefficient of the single impact factor is 0.56, and the weighting coefficient of the data-mentioned impact factor is 0.44.

[0131] Specifically, this invention calculates the data importance value by using the probability of occurrence of a single data point and the probability of mention of a data point. This provides a data foundation for subsequently determining the priority of data processing. The probability of occurrence of a single data point represents the probability of different users consulting about the same issue, while the probability of mention represents the probability of that issue appearing in all engineering consulting data. In reality, a question may be consulted by different users, indicating that the question is of high importance to a specific group of users. At the same time, if the question also frequently appears in the consultation questions of other users, it means that the question needs to be addressed promptly. It is understandable that if the data importance value is not calculated and the data is stored directly, not only will the importance of the data not be reflected, but the processing efficiency of the corresponding questions in the engineering consulting data will also be reduced. Based on this, this invention considers analyzing the probability of occurrence of a single data point and the probability of mention of a data point to calculate the data importance value, classify the data importance tendency, determine the processing priority of the engineering consulting data, and improve the data processing efficiency corresponding to the engineering consulting data.

[0132] Please see Figure 5 , Figure 5 This is a logical block diagram illustrating the data importance prioritization method used in an embodiment of the invention to determine whether to increase the data processing priority of the engineering consulting data. Specifically, the data priority processing module prioritizes data based on its importance to determine whether to increase the data processing priority of the engineering consulting data.

[0133] If the data importance indicator value is greater than the data importance indicator value threshold, then the data importance tendency is classified as an importance tendency, thereby increasing the data processing priority of the engineering consulting data;

[0134] If the data importance value is less than or equal to the data importance value threshold, the data importance tendency is classified as normal tendency, and the data processing priority of the engineering consulting data is not increased.

[0135] Specifically, the data importance threshold represents the importance of engineering consulting data, so the data importance threshold is set within the range of [0.76, 0.93].

[0136] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0137] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1.A blockchain-based information engineering consulting service verification system, characterized in that, The method comprises the following steps: a data acquisition module is configured to acquire engineering consultation data uploaded by a user terminal and a corresponding consultation type, determine a data relevance and a data completeness of the engineering consultation data; a data analysis module is connected to the data acquisition module and configured to perform rationality analysis on the engineering consultation data based on the data relevance and the data completeness, and determine whether the engineering consultation data meets a rationality standard; a label construction module is connected to the data analysis module and configured to set a label for the engineering consultation data according to whether the engineering consultation data meets the rationality standard, and determine a data storage state; a data determination module is connected to the label construction module and configured to, in response to a to-be-stored state of the data storage state, determine a theme contradiction rate and a correlation existence rate of sample keywords for different consultation types, calculate a consultation type matching value, determine whether the consultation type of the engineering consultation data needs to be adjusted, and store the engineering consultation data in a consultation database corresponding to the determined consultation type; a data priority processing module is connected to the data determination module and configured to sort engineering consultation data sets, determine a single data occurrence probability and a data mention probability, calculate a data importance representation value, and divide a data importance tendency, to determine whether to increase a data processing priority of the engineering consultation data. The engineering consultation data sets comprise a plurality of engineering consultation data of the same consultation type. The data acquisition module determines the data relevance and the data completeness of the engineering consultation data by: extracting a plurality of keywords of the engineering consultation data; calculating a keyword similarity between each of the keywords and a predetermined keyword; arranging the keyword similarities in descending order; determining the keyword similarity ranked first as the data relevance; determining a reciprocal of an information entropy of the engineering consultation data as the data completeness. The data determination module determines the theme contradiction rate and the correlation existence rate of the sample keywords for different consultation types by: calculating a keyword similarity between a keyword in the engineering consultation data and a sample keyword of a consultation type; determining a number of keywords whose keyword similarity is less than a keyword similarity threshold value; determining a theme contradiction rate as a ratio of the number of keywords to a total number of keywords; dividing the engineering consultation data into a plurality of engineering consultation data segments; determining a keyword corresponding to each of the engineering consultation data segments; recording the number of keywords that are the same as the sample keywords of the consultation type; determining a correlation existence rate as a ratio of the number of keywords to a total number of engineering consultation data segments. 2.The blockchain-based information engineering consulting service verification system according to claim 1, characterized in that, The data analysis module determines whether the engineering consultation data meets the rationality standard by: if the data relevance is greater than a preset data relevance threshold value and the data completeness is greater than a preset data completeness threshold value, determining that the engineering consultation data meets the rationality standard; if the data relevance is less than or equal to the preset data relevance threshold value and / or the data completeness is less than or equal to the preset data completeness threshold value, determining that the engineering consultation data does not meet the rationality standard. 3.The blockchain-based information engineering consulting service verification system of claim 2, wherein, The label construction module sets a label, determines a data storage state, including, if the engineering consultation data meets the reasonable standard, a present label is set, and the data storage state is determined as a to-be-stored state; if the engineering consultation data does not meet the reasonable standard, an abandoned label is set, and the data storage state is determined as a non-stored state. 4.The blockchain-based information engineering consulting service verification system of claim 1, wherein, The data determination module calculates a consultation type matching value, including, to call a subject contradiction rate and a correlation existence rate corresponding to the consultation type; to determine a ratio of the subject contradiction rate threshold value to the subject contradiction rate as a contradiction influence factor; to determine a ratio of the correlation existence rate to the correlation existence rate threshold value as a correlation influence factor; to determine a weighted sum value of the contradiction influence factor and the correlation influence factor as the consultation type matching value of the engineering consultation data and the consultation type. 5.The blockchain-based information engineering consulting service verification system of claim 1, wherein, The data determination module determines whether the consultation type of the engineering consultation data needs to be adjusted, and stores the engineering consultation data into a consultation database of a corresponding consultation type based on the determined consultation type, wherein, if the consultation type matching value is greater than a consultation type matching value threshold value, the consultation type of the engineering consultation data is not adjusted, and the engineering consultation data is directly stored into the consultation database of the corresponding consultation type according to the consultation type of the engineering consultation data; if the consultation type matching value is less than or equal to the consultation type matching value threshold value, the consultation type of the engineering consultation data is adjusted to the consultation type of the engineering consultation data corresponding to the maximum consultation type matching value, and the engineering consultation data is stored into the consultation database of the corresponding consultation type according to the adjusted consultation type of the engineering consultation data. 6.The blockchain-based information engineering consulting service verification system of claim 1, wherein, The data priority processing module determines a single data occurrence probability and a data mention probability, including, to determine a keyword similarity average value of a keyword in a single engineering consultation data in the engineering consultation data set and each keyword in the remaining engineering consultation data, and determine the keyword similarity average value as the single data occurrence probability for the engineering consultation data; to determine an occurrence probability average value of each keyword in the engineering consultation data in the remaining engineering consultation data, and determine the occurrence probability average value as the data mention probability for the engineering consultation data. 7.The blockchain-based information engineering consulting service verification system according to claim 6, characterized in that, The data priority processing module calculates a data important representation value, including, to determine a ratio of the single data occurrence probability to a single data occurrence probability threshold value as a single influence factor; to determine a ratio of the data mention probability to a data mention probability threshold value as a data mention influence factor; to determine a weighted sum value of the single influence factor and the data mention influence factor as the data important representation value for the engineering consultation data. 8.The blockchain-based information engineering consulting service verification system according to claim 7, characterized in that, The data priority processing module divides a data important tendency to determine whether to improve the data processing priority of the engineering consultation data, wherein, if the data important representation value is greater than a data important representation value threshold value, the data important tendency is divided as an important tendency, and the data processing priority of the engineering consultation data is improved; if the data important representation value is less than or equal to the data important representation value threshold value, the data important tendency is divided as a normal tendency, and the data processing priority of the engineering consultation data is not improved.

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