Data query method for intellectual property big data analysis system

Abstract

The invention discloses a data query method for an intellectual property big data analysis system, and relates to the technical field of data query. The data query method for the intellectual property big data analysis system comprises the following steps of query-update judgment, query-update adjustment judgment and adoption of query-update resource allocation measures. According to the method, the data query parameters of the various intellectual property data in the preset query period are obtained, the query-update judgment result is obtained by performing the query-update judgment, and then the corresponding query-update adjustment judgment is performed according to the query-update judgment result; and finally, corresponding query-update resource allocation measures of the intellectual property data are taken for the optimized query period obtained by query-update adjustment judgment, so that the effect of improving the query accuracy of the intellectual property data is achieved. The problem that in the prior art, when intellectual property data query is conducted through an intellectual property big data analysis system, the association degree of query performance and system updating is low is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data query, and in particular to a data query method for an intellectual property big data analysis system. Background Art

[0002] With the increasing importance of intellectual property protection, intellectual property big data analysis systems have emerged as the times require. These systems integrate and analyze massive amounts of data such as patents, trademarks, and copyrights to help enterprises, research institutions, and government departments make better decisions and risk assessments. The data query method is one of its core functions. Through efficient retrieval technologies, it can accurately extract key information from complex databases, support the mining, monitoring, and protection of intellectual property rights, and provide strong support for innovation-driven and economic development.

[0003] The existing data query methods for intellectual property big data analysis systems mainly rely on technologies such as keyword retrieval, natural language processing (NLP), and machine learning. Keyword retrieval enables fast query by matching keywords in documents such as patents and trademarks; natural language processing technologies can understand the semantics of query statements and improve retrieval accuracy; machine learning methods automatically identify and recommend relevant intellectual property information through learning and training on big data. Although these technologies have been widely applied, there are still certain challenges in dealing with complex data and efficient query.

[0004] For example, a scientific and technological innovation management system based on big data analysis disclosed in a patent application with the publication number of CN116308116A includes: supporting query and retrieval according to permissions; full life cycle management of scientific and technological projects, addition and carry-forward of declared projects, and management of various different levels of scientific and technological projects in the scientific and technological project control system; the national scientific and technological project control system conveys declaration notices and declaration documents, establishes project files, and conducts process management of projects; the scientific and technological innovation evaluation system conducts project approval evaluation, task book evaluation, mid-term evaluation, acceptance evaluation, post-evaluation, major scientific and technological research project evaluation, patent evaluation, and science and technology award evaluation; the scientific and technological data management system generates relevant requirement formats; the scientific and technological declaration management system conducts online award declaration, viewing, review, award classification, and statistics of scientific and technological projects; the intellectual property management system conducts intellectual property workflow management, summarization, and early warning; the scientific and technological assessment management system sets evaluation methods and standards for each assessment element.

[0005] For example, the data query method and device in the invention patent announcement with the announcement number: CN108241934B include: continuously receiving operation data from multiple databases, where the operation data includes multiple data elements of multiple data objects; temporarily storing and processing the operation data to make each operation data value in the operation data have a unique encoding; when all data elements of a data object are obtained, adding a data record about the data object to one of multiple wide tables; storing multiple wide tables for display and query.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, it is found that the above technology has at least the following technical problems:

[0007] In the prior art, many existing intellectual property data systems still rely on batch processing rather than real-time update. In the batch processing mode, data updates are usually not immediate, but are carried out regularly or at fixed times, and there is a problem that the correlation between query performance and system update is low when querying intellectual property data through an intellectual property big data analysis system. Summary of the Invention

[0008] The embodiments of the present application provide a data query method for an intellectual property big data analysis system, which solves the problem that the correlation between query performance and system update is low when querying intellectual property data through an intellectual property big data analysis system in the prior art, and realizes the improvement of the accuracy of intellectual property data query.

[0009] The embodiments of the present application provide a data query method for an intellectual property big data analysis system, including the following steps: obtaining the data query parameters of various types of intellectual property data within a preset query period, and performing a query-update judgment to obtain a query-update judgment result; performing a corresponding query-update adjustment determination according to the query-update judgment result, where the query-update adjustment determination includes a real-time query determination and a predictive query determination. The real-time query determination is used to determine and adjust the data query situation of the intellectual property big data analysis system in real time, and the predictive query determination is used to perform a pre-adjustment according to the data query situation of the intellectual property big data analysis system; taking corresponding query-update resource allocation measures for the optimized query period obtained by the query-update adjustment determination, where the query-update resource allocation measures represent a processing method of improving data query timeliness by adjusting the update resources of various types of intellectual property data.

[0010] Further, obtain the data query parameters for various types of intellectual property data within a preset query period, and perform query-update judgment. The specific process is as follows: Evaluate the data query timeliness requirements based on the data query parameters for various types of intellectual property data within the preset query period to obtain a query-update rating index. The data query timeliness requirement evaluation is used to quantify the degree of data query timeliness requirements for various types of intellectual property data in the intellectual property big data analysis system; Map the obtained query-update rating index to obtain the query priorities for the corresponding types of intellectual property data; Obtain the cumulative value of the query priorities for all types of intellectual property data within the preset query period, and compare the cumulative value of the query priorities with the query-update limit value obtained from the preset database: If within the preset query period, the cumulative value of the query priorities exceeds the query-update limit value, perform real-time query determination; If within the preset query period, the cumulative value of the query priorities does not exceed the query-update limit value, perform predictive query determination.

[0011] Further, the specific method for obtaining the query-update rating index is as follows: Obtain the data query parameters for various types of intellectual property data and perform normalization processing. The data query parameters include data query frequency, data query duration, and data change frequency; Obtain the data query analysis degree values from the preset database. The data query analysis degree values include data query frequency analysis degree value, data query duration analysis degree value, and data change frequency analysis degree value; Number various types of intellectual property data, and couple the data query parameters for various types of intellectual property data by weighting them with the data query analysis degree values to obtain the query-update rating index. The query-update rating index is used to quantify the degree of the data query for intellectual property data's demand for data update.

[0012] Further, the specific steps for real-time query determination are as follows: A1, directly update the intellectual property data within the preset query period, and obtain the optimized query period by getting the duration corresponding to when the cumulative value of the query priorities exceeds the query-update limit value; A2, obtain the query-update rating indexes for various types of intellectual property data within the optimized query period, and perform a similarity operation with the query-update rating index of the preset query period to obtain a similarity value. The similarity operation is used to quantify the similarity degree of the query-update rating indexes for various types of intellectual property data within the optimized query period and the preset query period; A3, judge the similarity value with the similarity limit value obtained from the preset database: If the similarity value is higher than the similarity limit value, continue to use the optimized query period; otherwise, obtain the corresponding query priorities based on the query-update rating index of the optimized query period, and continue to perform query-update judgment in the next preset query period according to the query priorities.

[0013] Furthermore, the specific steps for predicting query determination are as follows: B1. During the preset query period, the intellectual property data is not directly updated, and the difference between the cumulative query priority value and the query-update limit value is calculated to obtain the query-update difference; B2. The query-update difference is compared with the preset difference obtained from the preset database: if the query-update difference is not less than the preset difference, no additional processing is performed; if the query-update difference is less than the preset difference, the query-update difference and the preset difference are used for ratio analysis operation to obtain the query-update difference degree value, and the ratio analysis operation is used to quantify the proportion of the query-update difference within the preset difference range; B3. Based on the query-update difference degree value, the corresponding prediction adjustment factor is obtained, and the update period of the intellectual property data is predicted and adjusted through the prediction adjustment factor. The prediction adjustment factor represents the proportion of the prediction and adjustment of the update period of the intellectual property data.

[0014] Furthermore, the update period of the intellectual property data is predicted and adjusted through the prediction adjustment factor, and the specific process is as follows: the update period of the intellectual property data is predicted through the prediction adjustment factor to obtain the optimized update period, and the prediction operation represents the processing method of obtaining the optimized update period through the prediction adjustment factor; after obtaining the result of the query-update judgment after the optimized update period, if the result of the query-update judgment is that the cumulative query priority value exceeds the query-update limit value, then when the next prediction adjustment is performed, a re-query notice is automatically issued before the optimized update period, otherwise, the prediction adjustment factor is reduced by a ratio operation according to the preset ratio. The re-query notice is used to inform the current data query personnel to query the data again after the optimized update period.

[0015] Furthermore, corresponding query-update resource allocation measures for the intellectual property data are taken for the optimized query period obtained from the query-update adjustment determination, and the specific process is as follows: the initial query matching rate of data query within the preset query period and the optimized query matching rate of data query within the optimized query period are obtained; the initial query matching rate is compared with the optimized query matching rate: if the initial query matching rate is not less than the optimized query matching rate, query-update resource allocation evaluation is performed and query-update resource allocation measures are taken; if the initial query matching rate is less than the optimized query matching rate, the optimized query period is continued to be used.

[0016] Further, the specific steps for taking the query-update resource allocation measure are as follows: Step 1, conduct a query-update resource allocation assessment on various types of intellectual property data to obtain a query-update resource allocation index. The query-update resource allocation assessment is used to evaluate the compliance of the update resources corresponding to various types of intellectual property data; Step 2, construct a mapping set between the query-update resource allocation index of various types of intellectual property data and the preset resource allocation weights, and input the real-time query-update resource allocation index into the mapping set to obtain the corresponding resource allocation weights; Step 3, re-allocate the update resources of various types of intellectual property data according to the obtained resource allocation weights.

[0017] Further, the specific method for obtaining the query-update resource allocation index is as follows: Obtain the resource allocation rate of various types of intellectual property data in each query cycle and the corresponding query priorities; Number the preset query cycle and the optimized query cycle in chronological order to obtain query cycle numbers, and obtain the reference allocation influence degree values from the preset database. The reference allocation influence degree values include the allocation rate influence degree value and the priority influence degree value; Perform change rate equalization operations on the resource allocation rates and query priorities of various types of intellectual property data in each query cycle respectively to obtain the average resource change rate and the average priority change rate; Based on the average resource change rate and the average priority change rate, combine the corresponding reference allocation influence degree values to perform a weighting operation and then couple to obtain the query-update resource allocation index. The query-update resource allocation index is used to quantify the degree of resource allocation adjustment of various types of intellectual property data.

[0018] Further, re-allocate the update resources of various types of intellectual property data according to the obtained resource allocation weights. The specific method is as follows: Perform a proportional operation on the resource allocation weights of various types of intellectual property data to obtain a resource allocation sequence. The proportional operation is used to obtain the resource allocation proportion of each intellectual property data. The resource allocation sequence represents a set of the resource allocation proportions of various types of intellectual property data; Adjust the resource allocation rate corresponding to each type of intellectual property data to the corresponding resource allocation proportion in the resource allocation sequence; Obtain the optimized query matching rate in the next optimized query cycle after the update resources are re-allocated. If the initial query matching rate is still not less than the optimized query matching rate, adjust the optimized query cycle to the preset cycle limit, otherwise do not perform additional processing.

[0019] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0020] 1. By obtaining the data query parameters of various types of intellectual property data within a preset query period, and performing query-update judgment to obtain a query-update judgment result, then making a corresponding query-update adjustment determination based on the query-update judgment result, and finally taking corresponding query-update resource allocation measures for the optimized query period obtained from the query-update adjustment determination, the response time of various types of intellectual property data to data queries is reduced, thereby improving the accuracy of intellectual property data queries, and effectively solving the problem of low correlation between query performance and system update in the prior art when querying intellectual property data through an intellectual property big data analysis system.

[0021] 2. By obtaining the data query parameters of various types of intellectual property data and performing normalization processing, then obtaining the data query analysis degree value from a preset database, then numbering various types of intellectual property data, and coupling after weighting the data query parameters of various types of intellectual property data with the data query analysis degree value to obtain a query-update rating index, thereby more accurately quantifying the degree of demand for data update in the data query of intellectual property data, and further improving the accuracy of intellectual property data queries.

[0022] 3. By obtaining the resource allocation rate of various types of intellectual property data in each query period and the corresponding query priority, then numbering the preset query period and the optimized query period in chronological order to obtain a query period number, and obtaining a reference allocation influence degree value from a preset database, then performing change rate equalization operations on the resource allocation rate and query priority of various types of intellectual property data in each query period respectively to obtain a resource change rate average value and a priority change rate average value, and finally performing a weighted operation and coupling based on the resource change rate average value and the priority change rate average value in combination with the corresponding reference allocation influence degree value to obtain a query-update resource allocation index, thereby more accurately quantifying the degree of resource allocation adjustment of various types of intellectual property data, and further more accurately allocating update resources for various types of intellectual property data. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of a data query method for an intellectual property big data analysis system provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] Embodiments of the present application provide a data query method for an intellectual property big data analysis system, which solves the problem in the prior art that the query performance has a low correlation with system updates when querying intellectual property data through an intellectual property big data analysis system. By evaluating the data query timeliness requirements through the data query parameters of various types of intellectual property data within a preset query period, a query-update rating index is obtained. Then, the query priorities of the corresponding types of intellectual property data are mapped through the obtained query-update rating index. Next, the cumulative value of the query priorities of all types of intellectual property data within the preset query period is obtained, and the cumulative value of the query priorities is compared with the query-update limit value obtained from a preset database: If within the preset query period, the cumulative value of the query priorities exceeds the query-update limit value, a real-time query determination is performed; if within the preset query period, the cumulative value of the query priorities does not exceed the query-update limit value, a predictive query determination is performed. Then, a corresponding query-update adjustment determination is made according to the query-update judgment result. Finally, corresponding query-update resource allocation measures for the intellectual property data are taken for the optimized query period obtained from the query-update adjustment determination. Then, a corresponding query-update adjustment determination is made according to the query-update judgment result. Finally, corresponding query-update resource allocation measures for the intellectual property data are taken for the optimized query period obtained from the query-update adjustment determination, achieving an improvement in the query accuracy of intellectual property data.

[0025] The technical solution in the embodiments of the present application aims to solve the problem that the query performance has a low correlation with system updates when querying intellectual property data through an intellectual property big data analysis system. The general idea is as follows:

[0026] By obtaining the data query parameters of various types of intellectual property data within a preset query period, and performing a query-update judgment to obtain a query-update judgment result, then making a corresponding query-update adjustment determination according to the query-update judgment result, and finally taking corresponding query-update resource allocation measures for the intellectual property data for the optimized query period obtained from the query-update adjustment determination, an improvement in the query accuracy of intellectual property data is achieved.

[0027] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0028] Such as Figure 1As shown in the figure, it is a flowchart of a data query method for an intellectual property big data analysis system provided by an embodiment of the present application. The method includes the following steps: obtaining data query parameters of various types of intellectual property data within a preset query period, and performing a query-update judgment to obtain a query-update judgment result; performing a corresponding query-update adjustment determination according to the query-update judgment result. The query-update adjustment determination represents corresponding adjustment analysis based on different results of the query-update judgment. The query-update adjustment determination includes a real-time query determination and a predictive query determination. The real-time query determination is used to determine and adjust the data query situation of the intellectual property big data analysis system in real time, and the predictive query determination is used to perform pre-adjustment according to the data query situation of the intellectual property big data analysis system; taking corresponding query-update resource allocation measures for the optimized query period obtained by the query-update adjustment determination. The query-update resource allocation measures represent a processing method of improving data query timeliness by adjusting the update resources of various types of intellectual property data.

[0029] In this embodiment, many existing intellectual property data systems for intellectual property big data analysis systems still rely on batch processing rather than real-time updates. Especially in the batch processing mode, data updates are usually not immediate but are carried out regularly or at fixed times. Therefore, there may be a situation where users cannot query in a timely manner the intellectual property-related data that has been uploaded to the database but has not been updated in time when performing data queries. The solution of the present application analyzes the needs of the intellectual property data queried by users and combines with adjusting the update period of the corresponding category of intellectual property data, which helps to improve the timeliness of users' queries for relevant category of intellectual property data and at the same time realizes the improvement of the accuracy of intellectual property data queries.

[0030] It should be explained that before designing the data query method for the intellectual property big data analysis system, a preset database for storing various types of set data is established by preset personnel. The preset database includes but is not limited to a preset query period, query-update limit value, query priority, data query analysis degree value, similarity limit value, preset difference value, reference allocation influence degree value, period limit value, and mapping set, etc. Among them, various values are directly set by preset professionals. For example, by pre-constructing a mapping set between the query-update rating index and the query priority in the preset database, and inputting the real-time query-update rating index into the corresponding mapping set to obtain the query priority. There is a one-to-one mapping relationship between the query-update rating index and the query priority in this mapping set.

[0031] Further, obtain the data query parameters for various types of intellectual property data within a preset query period, and perform query-update judgment. The specific process is as follows: Evaluate the data query timeliness requirements based on the data query parameters for various types of intellectual property data within the preset query period to obtain a query-update rating index. The data query timeliness requirement evaluation is used to quantify the degree of data query timeliness requirements for various types of intellectual property data in the intellectual property big data analysis system; Map the obtained query-update rating index to obtain the query priorities for the corresponding types of intellectual property data; Obtain the cumulative value of the query priorities for all types of intellectual property data within the preset query period. The cumulative value of the query priorities is the sum of the query priorities for the corresponding types of all the queried intellectual property data within the preset query period. Compare the cumulative value of the query priorities with the query-update limit value obtained from the preset database: If within the preset query period, the cumulative value of the query priorities exceeds the query-update limit value, then perform a real-time query determination; otherwise, perform a predictive query determination. Among them, the query-update limit value represents the maximum value at which the cumulative value of the query priorities triggers the update mechanism, and is preset by the preset staff in the preset database.

[0032] In this embodiment, the higher the query priority, the higher the query and update requirements for the corresponding type of intellectual property data. Conversely, the lower the query priority, the lower the query and update requirements for the corresponding type of intellectual property data. Through the above analysis, it helps to promptly respond to the user's demand for the timeliness of data query for a certain type of data, and improves the accuracy of intellectual property data query.

[0033] Further, the specific method for obtaining the query-update rating index is as follows: First, obtain the data query parameters for various types of intellectual property data and perform normalization processing. The data query parameters include data query frequency, data query duration, and data change frequency.

[0034] Specifically, the data query frequency is obtained through the database log of the intellectual property big data analysis system. The data query frequency represents the frequency at which various types of intellectual property data are queried. In the intellectual property big data analysis system, this usually refers to the number of times certain intellectual property-related data (such as patents, trademarks, etc.) are queried within a certain period of time; The data query duration is obtained through the database query log of the intellectual property big data analysis system. The data query duration refers to the average time consumed for each query of various types of intellectual property data; The data change frequency is obtained through the data change log of the intellectual property big data analysis system. The data change frequency represents the number of changes that occur to intellectual property data (such as patents, trademarks, etc.) within a certain time range. The changes that occur can be data updates, adding new data, or deleting data, etc.

[0035] Next, obtain the data query analysis degree value from the preset database. The data query analysis degree value includes the data query frequency analysis degree value, the data query duration analysis degree value, and the data change frequency analysis degree value.

[0036] Specifically, the data query analysis degree value is obtained from the preset database, and the data query analysis degree value represents the influence degree of data query parameters on the query-update rating index. Each data query parameter has a unique mapping relationship with the data query analysis degree value, and the value range is between 0 and 1. For example, construct a mapping set of data query parameters and the preset data query analysis degree value, and input the real-time data query frequency, data query duration, and data change frequency into the mapping set to obtain the corresponding data query frequency analysis degree value, data query duration analysis degree value, and data change frequency analysis degree value, which respectively represent the influence degrees of data query frequency, data query duration, and data change frequency on the query-update rating index, and the sum of the three is 1.

[0037] Finally, number various types of intellectual property data, and after weighting the data query parameters of various types of intellectual property data through the data query analysis degree value and coupling them, obtain the query-update rating index, which is used to quantify the degree of demand for data update by the data query of intellectual property data.

[0038] The specific constraint expression of the query-update rating index is as follows:

[0039] S_U_R g =sf g ×θ g +st g ×γ g +sc g ×δ g ;

[0040] In the formula, g represents the category number of intellectual property data, g = 1, 2,..., G, G represents the total number of categories of intellectual property data, sf g represents the data query frequency, st g represents the data query duration, sc g represents the data change frequency, θ g represents the data query frequency analysis degree value, γ g represents the data query duration analysis degree value, δ g represents the data change frequency analysis degree value, S_U_R g represents the query-update rating index.

[0041] In this embodiment, the algorithm combines data query parameters and data query analysis degree values for analysis to obtain a query-update rating index. In the formula, the query-update rating index increases as the data query frequency of the corresponding category of intellectual property data increases, indicating that the higher the data query frequency of the corresponding category of intellectual property data, the higher the corresponding query demand. Therefore, the timeliness requirement for the corresponding category of intellectual property data is higher, so the corresponding update requirement is higher, and the corresponding query-update rating index is larger. Similarly, it can be known that when the data query duration and data change frequency of the corresponding category of intellectual property data are larger, it also indicates that the query and update demand degrees for the corresponding category of intellectual property data are higher, and then the corresponding query-update rating index is larger. Through the analysis of the query-update rating index, it is helpful to more accurately quantify the query timeliness demand degree of the corresponding category of intellectual property data, so as to update the intellectual property data in a timely manner, and then improve the timeliness of data query for intellectual property data.

[0042] Further, the specific steps of real-time query determination are as follows: A1, directly update the intellectual property data within a preset query period, and obtain an optimized query period when the cumulative value of the query priority exceeds the corresponding duration of the query-update limit, where the duration from the start time of the preset query period to the time when the cumulative value of the query priority exceeds the query-update limit is obtained through a timer.

[0043] A2, obtain the query-update rating index of various types of intellectual property data within the optimized query period, and perform a similarity operation with the query-update rating index of the preset query period to obtain a similarity value. The similarity operation is used to quantify the similarity degree of the query-update rating index of various types of intellectual property data within the optimized query period and the preset query period. Among them, the similarity operation is implemented through the Euclidean distance algorithm. Obtaining the similarity value through the Euclidean distance algorithm helps to more intuitively understand the similarity degree between the query-update rating index of various types of intellectual property data within the optimized query period and the query-update rating index of the preset query period.

[0044] A3, judge the similarity value with the similarity limit obtained from the preset database: if the similarity value is higher than the similarity limit, continue to use the optimized query period; otherwise, obtain the corresponding query priority based on the query-update rating index of the optimized query period, and continue to perform query-update judgment within the next preset query period according to the query priority. It should be noted that the similarity limit is preset by preset staff in the preset database, indicating the minimum similarity degree limit between the query-update rating index of various types of intellectual property data within the optimized query period and the query-update rating index of the preset query period.

[0045] In this embodiment, through the above analysis, the update and query of intellectual property data can be optimized in real time, which is beneficial to improving the efficiency of querying and updating intellectual property data, and ensures the timeliness and accuracy of querying intellectual property data.

[0046] Further, the specific steps of predicting query determination are as follows: B1, do not directly update the intellectual property data within a preset query cycle, and perform a difference operation on the cumulative value of query priorities and the query-update limit value to obtain a query-update difference value; it should be noted that the difference operation means subtracting the query-update limit value from the cumulative value of query priorities.

[0047] B2, compare the query-update difference value with a preset difference value obtained from a preset database: if the query-update difference value is not less than the preset difference value, no additional processing is performed; if the query-update difference value is less than the preset difference value, a proportion analysis operation is performed on the query-update difference value and the preset difference value to obtain a query-update difference degree value, and the proportion analysis operation is used to quantify the proportion of the query-update difference value within the preset difference value range; it should be noted that the preset difference value is preset by preset staff and stored in the preset database; the proportion analysis operation means performing a ratio operation on the query-update difference value and the preset difference value.

[0048] B3, obtain a corresponding prediction adjustment factor based on the query-update difference degree value, and predict and adjust the update cycle of the intellectual property data through the prediction adjustment factor. The prediction adjustment factor represents the proportion of predicting and adjusting the update cycle of the intellectual property data. It should be added that a mapping set of the query-update difference degree value and the corresponding prediction adjustment factor is constructed in the preset database, and the real-time query-update difference degree value is input into the corresponding mapping set to output the corresponding prediction adjustment factor.

[0049] In this embodiment, the method of this embodiment not only helps to reduce the delay response time when querying intellectual property data, but also realizes the automatic prediction of the update of various types of intellectual property data in the intellectual property big data analysis system, thereby realizing the timely update of intellectual property data, and further improving the accuracy of querying intellectual property data.

[0050] Further, predicting and adjusting the update cycle of the intellectual property data through the prediction adjustment factor, the specific process is as follows: First, perform a prediction operation on the update cycle of the intellectual property data through the prediction adjustment factor to obtain an optimized update cycle. The prediction operation means the processing method of obtaining the optimized update cycle through the prediction adjustment factor; among them, the prediction operation means performing a product operation on the prediction adjustment factor and the duration of the update cycle of the intellectual property data.

[0051] Next, obtain the result of the query-update judgment after the optimized update period. If the result of the query-update judgment is that the cumulative value of the query priority exceeds the query-update limit, then when performing predictive adjustment next time, automatically issue a re-query notice before the optimized update period. Otherwise, perform a proportional reduction operation on the predictive adjustment factor according to a preset ratio. The re-query notice is used to inform the current data query personnel to perform data query again after the optimized update period. Among them, the preset ratio is preset by preset staff and stored in the preset database; the proportional reduction operation means performing a multiplication operation on the predictive adjustment factor by the preset ratio.

[0052] In this embodiment, through the above analysis, it can be obtained that the solution of the present application realizes the automatic evaluation and adjustment of the allocation of intellectual property data update resources, which not only reduces the waste of intellectual property data update resources, but also improves the accuracy and timeliness of intellectual property data query and update.

[0053] Further, corresponding intellectual property data query-update resource allocation measures are taken for the optimized query period obtained by the query-update adjustment determination. The specific process is as follows: First step, obtain the initial query matching rate of data query within the preset query period and the optimized query matching rate of data query within the optimized query period. It should be added that the initial query matching rate represents the matching rate of intellectual property data query within the preset query period, and the optimized query matching rate represents the matching rate of intellectual property data query within the optimized query period.

[0054] Second step, compare the initial query matching rate with the optimized query matching rate: If the initial query matching rate is not less than the optimized query matching rate, then perform query-update resource allocation evaluation and take query-update resource allocation measures; if the initial query matching rate is less than the optimized query matching rate, then continue to use the optimized query period.

[0055] It should be added that the query matching rate refers to the matching degree between the query conditions input by the user and the intellectual property data stored in the intellectual property big data analysis system; the query matching rate represents the ratio of the number of query matching results to the total number of query results. The number of query matching results represents the number of data records that match the query conditions in the results returned by the query, and the total number of query results represents the total results returned after data retrieval according to the query conditions.

[0056] In this embodiment, through the evaluation of the query-update resource allocation index, the intellectual property big data analysis system can judge which categories of intellectual property data need to be updated in a timely manner and which intellectual property data can reduce the data query and update resources without adding additional computational burdens. Thus, it can be seen that the intellectual property big data analysis system realizes while maintaining the accuracy and timeliness of intellectual property data update, and also reduces the consumption of computing resources for data with a low degree of data query requirements.

[0057] Further, the specific steps for taking the query-update resource allocation measures are as follows: Step 1: Conduct a query-update resource allocation assessment on various types of intellectual property data to obtain a query-update resource allocation index. The query-update resource allocation assessment is used to evaluate the compliance of the update resources corresponding to various types of intellectual property data.

[0058] Step 2: Construct a mapping set between the query-update resource allocation index of various types of intellectual property data and the preset resource allocation weights, and input the real-time query-update resource allocation index into the mapping set to obtain the corresponding resource allocation weights. Among them, the mapping set between the query-update resource allocation index of various types of intellectual property data and the preset resource allocation weights is stored in a preset database and is preset and stored by preset staff.

[0059] Step 3: Reallocate the update resources of various types of intellectual property data according to the obtained resource allocation weights.

[0060] In this embodiment, through the above update resource allocation of various types of intellectual property data, the intellectual property big data analysis system can respond more quickly to user query requests. Especially when querying intellectual property data with a higher query priority, it helps to ensure the timeliness of data query; the optimized resource allocation enables the intellectual property big data analysis system to more centrally use update resources for updating intellectual property data with a higher query priority, avoiding excessive resource occupation by query operations with a lower query priority; and through the precise quantitative assessment of the priority and resource requirements of various types of intellectual property data, the intellectual property big data analysis system can ensure that intellectual property data with a higher query demand degree is updated more timely and accurately; through the above resource allocation method, the update delay of intellectual property data is reduced, ensuring the real-time and accuracy of intellectual property data update and query results.

[0061] Further, the specific method for obtaining the query-update resource allocation index is as follows: First, obtain the resource allocation rate of various types of intellectual property data in each query cycle and the corresponding query priorities.

[0062] It should be added that the start time, end time, and consumed computing resources (such as CPU time, memory, storage) of each update operation are recorded in the application log of the intellectual property big data analysis system; by analyzing the log file, the resource consumption of different types of intellectual property data is statistically analyzed to obtain the resource consumption amount when each type of intellectual property data (such as patents, trademarks, copyrights, etc.) is updated; calculate the resource allocation rate: According to the log statistics, calculate the resource occupation ratio of each type of intellectual property data update. For example, patent data update occupies 40% of the total CPU time, trademark data occupies 30%, and other data occupies 30%.

[0063] Next, number the preset query period and the optimized query period in chronological order to obtain query period numbers, and obtain reference allocation influence degree values from the preset database. The reference allocation influence degree values include allocation rate influence degree values and priority influence degree values.

[0064] Specifically, the reference allocation influence degree values are obtained from the preset database. The reference allocation influence degree values represent the influence degrees of the resource allocation rate and the query priority on the query-update resource allocation index. There is a unique mapping relationship between each resource allocation rate and query priority and their corresponding reference allocation influence degree values, and the value range is between 0 and 1. For example, construct a mapping set of the resource allocation rate and query priority with the preset reference allocation influence degree values, and input the real-time resource allocation rate and query priority into the mapping set to obtain the corresponding allocation rate influence degree value and priority influence degree value, which respectively represent the influence degrees of the resource allocation rate and query priority on the query-update resource allocation index, and the sum of the two is 1.

[0065] Finally, perform rate equalization operations on the resource allocation rates and query priorities of various types of intellectual property data in each query period to obtain the average resource change rate and the average priority change rate. Based on the average resource change rate and the average priority change rate, combined with the corresponding reference allocation influence degree values, perform weighted operations and then couple them to obtain the query-update resource allocation index. The query-update resource allocation index is used to quantify the degree of resource allocation adjustment of various types of intellectual property data. Among them, the rate equalization operation means performing a ratio operation on the resource allocation rates and query priorities of various types of intellectual property data in each query period and the corresponding data in the previous query period, and then performing a summation and averaging operation.

[0066] The specific limit expression of the query-update resource allocation index is as follows:

[0067]

[0068] In the formula, g represents the category number of the intellectual property data, g = 1, 2,..., G, G represents the total number of categories of the intellectual property data, t represents the query period number, t = 1, 2,..., T, and T ≥ 2, T represents the total number of query periods. represents the resource allocation rate of the g-th type of intellectual property data in the t-th query period. represents the query priority of the g-th type of intellectual property data in the t-th query period. represents the allocation rate influence degree value of the g-th type of intellectual property data, ω g represents the priority influence degree value of the g-th type of intellectual property data, S_U_A g represents the query-update resource allocation index of the g-th type of intellectual property data.

[0069] In this embodiment, the algorithm analyzes the resource allocation rate, query priority, and reference allocation influence degree value of various types of intellectual property data in each query cycle to obtain a query-update resource allocation index. In the formula, as the average value of the resource change rate increases, it indicates that the demand degree for the update resources of this type of intellectual property data is higher, and the corresponding query-update resource allocation index is higher. Similarly, as the average value of the priority change rate increases, it indicates that the data query demand degree for this type of intellectual property data is higher, then the timeliness requirement for this type of intellectual property data is higher, and the resource allocation demand for this type of intellectual property data is higher, and the corresponding query-update resource allocation index is higher. Through the analysis of the query-update resource allocation index, it helps to more accurately quantify the demand degree of various types of intellectual property data for data update resources, thereby improving the data update resources for intellectual property data with a higher timeliness demand degree, and further improving the data query timeliness of the intellectual property big data analysis system.

[0070] Furthermore, the update resources of various types of intellectual property data are reallocated according to the obtained resource allocation weights, and the specific method is as follows: D1, perform a ratio operation on the resource allocation weights of various types of intellectual property data to obtain a resource allocation sequence. The ratio operation is used to obtain the resource allocation proportion of each intellectual property data. The resource allocation sequence represents a set of resource allocation proportions of various types of intellectual property data, where the ratio operation means performing a ratio operation on the resource allocation weights of various types of intellectual property data.

[0071] D2, adjust the resource allocation rate corresponding to each type of intellectual property data to the corresponding resource allocation proportion in the resource allocation sequence; obtain the optimized query matching rate in the next optimized query cycle after the update resources are reallocated. If the initial query matching rate is still not less than the optimized query matching rate, then adjust the optimized query cycle to the preset cycle limit value, otherwise no additional processing is performed. Specifically, the cycle limit value represents the shortest duration of the preset query cycle, which is preset and stored in the preset database by the preset staff.

[0072] In this embodiment, through the above method, a more precise allocation of the update resources of various types of intellectual property data is achieved, and the query cycle is dynamically adjusted according to the determination result of the query matching rate, ensuring the efficiency and accuracy of the intellectual property data query and update process.

[0073] In summary, in the embodiments of the present application, by obtaining the data query parameters of various types of intellectual property data within a preset query period, and performing query-update judgment to obtain a query-update judgment result, then making a corresponding query-update adjustment determination according to the query-update judgment result, and finally taking corresponding query-update resource allocation measures for the optimized query period obtained from the query-update adjustment determination for various types of intellectual property data, the response time of various types of intellectual property data to data queries is reduced, thereby improving the timeliness of intellectual property data queries, and effectively solving the problem that the query performance has a low correlation with system updates when querying intellectual property data through an intellectual property big data analysis system in the prior art.

[0074] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0075] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the processes Figure 1 or multiple processes and / or blocks

[0076] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the processes Figure 1 or multiple processes and / or blocks

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide means for implementing the functions specified in Figure 1Steps of the functions specified in one process or multiple processes and / or one block or multiple blocks. Figure 1 Steps of the functions specified in one block or multiple blocks.

[0078] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0079] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A data query method for an intellectual property big data analysis system, characterized in that, It includes the following steps: Obtain the data query parameters of various types of intellectual property data within a preset query cycle, and perform a query-update judgment to obtain a query-update judgment result; Conduct corresponding query-update adjustment judgments according to the query-update judgment result. The query-update adjustment judgments include real-time query judgments and predictive query judgments. The real-time query judgment is used to judge and adjust the data query situation of the intellectual property big data analysis system in real time, and the predictive query judgment is used to perform pre-adjustment according to the data query situation of the intellectual property big data analysis system; Take corresponding query-update resource allocation measures for the optimized query cycle obtained from the query-update adjustment judgment. The query-update resource allocation measures represent a processing method of improving data query timeliness by adjusting the update resources of various types of intellectual property data.

2. The data query method for the intellectual property big data analysis system according to claim 1, wherein: The specific process of obtaining the data query parameters of various types of intellectual property data within a preset query cycle and performing a query-update judgment is as follows: Evaluate the data query timeliness requirements based on the data query parameters of various types of intellectual property data within a preset query cycle to obtain a query-update rating index. The data query timeliness requirement evaluation is used to quantify the data query timeliness requirement degree of various types of intellectual property data in the intellectual property big data analysis system; Map the obtained query-update rating index to obtain the query priorities of corresponding types of intellectual property data; Obtain the cumulative value of the query priorities of all types of intellectual property data within a preset query cycle, and compare the cumulative value of the query priorities with the query-update limit value obtained from the preset database: If within the preset query cycle, the cumulative value of the query priorities exceeds the query-update limit value, then perform a real-time query judgment; If within the preset query cycle, the cumulative value of the query priorities does not exceed the query-update limit value, then perform a predictive query judgment.

3. The data query method for the intellectual property big data analysis system according to claim 2, wherein: The specific method for obtaining the query-update rating index is as follows: Obtain the data query parameters of various types of intellectual property data and perform normalization processing. The data query parameters include data query frequency, data query duration, and data change frequency; Obtain the data query analysis degree values from the preset database. The data query analysis degree values include data query frequency analysis degree value, data query duration analysis degree value, and data change frequency analysis degree value; Number various types of intellectual property data, and couple the data query parameters of various types of intellectual property data after weighting with the data query analysis degree values to obtain a query-update rating index. The query-update rating index is used to quantify the requirement degree of data query of intellectual property data for data update.

4. The data query method for the intellectual property big data analysis system according to claim 2, characterized in that: The specific steps of the real-time query judgment are as follows: A1. Directly update the intellectual property data within a preset query cycle, and obtain the optimized query cycle by obtaining the duration corresponding to when the cumulative value of the query priorities exceeds the query-update limit value; A2. Obtain the query-update rating index for various types of intellectual property data within the optimized query period, and perform a similarity operation with the query-update rating index of the preset query period to obtain a similarity value. The similarity operation is used to quantify the similarity degree of the query-update rating indices of various types of intellectual property data within the optimized query period and the preset query period; A3. Judge the similarity value with the similarity limit value obtained from the preset database: If the similarity value is higher than the similarity limit value, continue to use the optimized query period; otherwise, obtain the corresponding query priority based on the query-update rating index of the optimized query period, and continue to perform query-update judgment in the next preset query period according to the query priority.

5. The data query method for the intellectual property big data analysis system according to claim 2, characterized in that: The specific steps of the predicted query determination are as follows: B1. Do not directly update the intellectual property data within the preset query period, and perform a difference operation on the cumulative value of the query priority and the query-update limit value to obtain a query-update difference value; B2. Compare the query-update difference value with the preset difference value obtained from the preset database: If the query-update difference value is not less than the preset difference value, no additional processing is performed; If the query-update difference value is less than the preset difference value, perform a proportion analysis operation on the query-update difference value and the preset difference value to obtain a query-update difference degree value. The proportion analysis operation is used to quantify the proportion of the query-update difference value within the preset difference value range; B3. Obtain the corresponding prediction adjustment factor based on the query-update difference degree value, and perform a prediction adjustment on the update period of the intellectual property data through the prediction adjustment factor. The prediction adjustment factor represents the proportion of the prediction adjustment of the update period of the intellectual property data.

6. The data query method for the intellectual property big data analysis system according to claim 5, wherein: The specific process of performing a prediction adjustment on the update period of the intellectual property data through the prediction adjustment factor is as follows: Perform a prediction operation on the update period of the intellectual property data through the prediction adjustment factor to obtain an optimized update period. The prediction operation represents the processing method of obtaining the optimized update period through the prediction adjustment factor; Obtain the result of the query-update judgment after obtaining the optimized update period. If the result of the query-update judgment is that the cumulative value of the query priority exceeds the query-update limit value, then automatically issue a re-query notice before the optimized update period during the next prediction adjustment; otherwise, perform a proportional reduction operation on the prediction adjustment factor according to a preset ratio. The re-query notice is used to inform the current data query personnel to perform data query again after the optimized update period.

7. The data query method for an intellectual property big data analysis system according to claim 1, characterized in that: The specific process of taking corresponding query-update resource allocation measures for the optimized query period obtained from the query-update adjustment determination is as follows: Obtain the initial query matching rate of data query within the preset query period and the optimized query matching rate of data query within the optimized query period; Compare the initial query matching rate with the optimized query matching rate: If the initial query matching rate is not less than the optimized query matching rate, perform a query-update resource allocation evaluation and take query-update resource allocation measures; If the initial query matching rate is less than the optimized query matching rate, continue to use the optimized query period.

8. The data query method for the intellectual property big data analysis system according to claim 7, wherein: The specific steps of taking query-update resource allocation measures are as follows: Step 1: Query and update the resource allocation evaluation for various types of intellectual property data to obtain the query-update resource allocation index, where the query-update resource allocation evaluation is used to evaluate the compliance of the update resources corresponding to various types of intellectual property data; Step 2: Construct a mapping set between the query-update resource allocation index of various types of intellectual property data and the preset resource allocation weights, and input the real-time query-update resource allocation index into the mapping set to obtain the corresponding resource allocation weights; Step 3: Reallocate the update resources of various types of intellectual property data according to the obtained resource allocation weights.

9. The data query method for the intellectual property big data analysis system according to claim 8, wherein: The specific method for obtaining the query-update resource allocation index is as follows: Obtain the resource allocation rate and the corresponding query priority of various types of intellectual property data in each query cycle; Number the preset query cycle and the optimized query cycle in chronological order to obtain the query cycle number, and obtain the reference allocation influence degree value from the preset database. The reference allocation influence degree value includes the allocation rate influence degree value and the priority influence degree value; Perform change rate equalization operations on the resource allocation rates and query priorities of various types of intellectual property data in each query cycle respectively to obtain the average resource change rate and the average priority change rate; Based on the average resource change rate and the average priority change rate, combine with the corresponding reference allocation influence degree value to perform a weighting operation and then couple to obtain the query-update resource allocation index, which is used to quantify the resource allocation adjustment degree of various types of intellectual property data.

10. The data query method for the intellectual property big data analysis system according to claim 8, characterized in that: The method for reallocating the update resources of various types of intellectual property data according to the obtained resource allocation weights is as follows: Perform a proportional operation on the resource allocation weights of various types of intellectual property data to obtain a resource allocation sequence. The proportional operation is used to obtain the resource allocation proportion of each intellectual property data, and the resource allocation sequence represents the set of resource allocation proportions of various types of intellectual property data; Adjust the resource allocation rate corresponding to various types of intellectual property data to the corresponding resource allocation proportion in the resource allocation sequence; Obtain the optimized query matching rate in the next optimized query cycle after the update resources are reallocated. If the initial query matching rate is still not less than the optimized query matching rate, adjust the optimized query cycle to the preset cycle limit, otherwise no additional processing is performed.

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