A multimodal data processing system and method supporting customized processing

Abstract

The present invention relates to the field of data processing technology, and discloses a multimodal data processing system and method supporting custom processing, the system comprising: a collection module configured to collect query information; a data processing module configured to summarize the query data type of the query terms, count the query times and query interval duration of the query data type, and determine whether to add the data type based on the query result of the query data type and in combination with the query times and query interval duration; a data storage module configured to determine whether to adjust the storage mode of the data type according to the query times and query interval duration of each data type; a data response module configured to dynamically adjust the response time of the query result according to the query interval duration of the data type and the interval duration influence coefficient. The present invention can effectively improve query efficiency, optimize resource management, enhance user experience, and has strong scalability and flexibility.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a multimodal data processing system and method supporting customized processing. Background Art

[0002] With the continuous development of information technology, especially the increasing popularity of big data and artificial intelligence, enterprises and organizations are facing a large number of processing needs from multimodal data. Multimodal data usually includes different types of data such as images, text, audio, video, etc. The structure, storage method and query requirements of these data are different, which brings huge challenges to data management and processing. Existing processing systems are often unable to efficiently cope with the integration and processing of such heterogeneous data. How to design a flexible and efficient system architecture based on the diversity and variability of data has become an important topic in the field of modern data processing.

[0003] In traditional data processing systems, due to the lack of effective dynamic adaptability to queries and storage modes for different data types, system performance is often affected by factors such as query frequency fluctuations and storage resource management. For example, data with high frequency queries may take up a lot of computing and storage resources, while data with low frequency queries may lead to a waste of storage resources. In addition, the query interval will also affect the response time of the system. Therefore, how to adjust the query response time and optimize the storage mode according to different data types and query behaviors has become the key to improving system efficiency.

[0004] Therefore, it is necessary to provide a multimodal data processing method and system that supports customized processing to solve the problem that traditional data processing systems lack the ability to dynamically adapt to different data types and query behaviors, resulting in system performance being affected by factors such as query frequency fluctuations and storage resource management. Summary of the invention

[0005] In view of this, the present invention proposes a multimodal data processing system and method that supports customized processing, aiming to solve the problem that traditional data processing systems lack the ability to dynamically adapt to different data types and query behaviors, resulting in system performance being affected by factors such as query frequency fluctuations and storage resource management.

[0006] In one aspect, the present invention provides a multimodal data processing system supporting customized processing, comprising:

[0007] A collection module, configured to collect query information; wherein the query information includes query terms and query results;

[0008] A data processing module is configured to summarize the query data types of the query terms, count the query times and query intervals of the query data types, and determine whether to add a data type based on the query results of the query data types and in combination with the query times and query intervals;

[0009] The data storage module is configured to count the number of queries and query intervals of all data types within a preset time period, and determine whether to adjust the storage mode of each data type according to the number of queries and query intervals of each data type; wherein the storage mode includes active storage and backup storage;

[0010] The data response module is configured to determine whether it is necessary to adjust the response time of the query result corresponding to the data type based on the query interval duration of the data type. If it is determined to be adjusted, the interval duration impact coefficient is calculated based on the query interval duration and the query interval duration change value, and the response time of the query result is dynamically adjusted based on the interval duration impact coefficient.

[0011] Further, when the data processing module is configured to summarize the query data type of the query term, it includes:

[0012] Removing noise words from the query terms; wherein the noise words include spaces and punctuation marks;

[0013] Performing a word segmentation operation on the query terms after removing noise words, and marking the part of speech of each word in the query terms;

[0014] Analyze the meaning and intent of the query terms according to the part of speech of each word in the query terms to identify the query needs of the customer;

[0015] Match each word in the entry with the system predefined data types to determine the data type requested by the user.

[0016] Furthermore, when the data processing module is configured to count the number of queries and the query interval duration of the query data type, it includes:

[0017] Pre-set the statistical time period to collect the query times and query intervals of all queried data types within the statistical time period;

[0018] The data type is recorded and stored in the form of {data type, query times, query interval, statistical time period};

[0019] Construct a query count statistics graph for all queried data types, and construct a query interval duration change graph for each data type.

[0020] Further, the data processing module is configured to determine whether to add a data type based on the query result of the query data type and in combination with the query number and the query interval duration, including:

[0021] If the query result of the query data type is that no data is found, determining whether to add the data type according to the query times and the query interval time;

[0022] If the query result of the query data type is that it has been queried, it is directly determined that there is no need to add the data type.

[0023] Further, if the query result of the query data type is not found, judging whether to add a data type according to the query number and the query interval duration includes:

[0024] Set the minimum number of queries and the minimum query interval duration;

[0025] If the query number is greater than the minimum query number, and / or the query interval is less than the minimum query interval, it is determined that the query data type needs to be added;

[0026] If the query times are less than or equal to the minimum query times, and the query interval duration is greater than or equal to the minimum query interval duration, it is determined that there is no need to add the query data type.

[0027] Further, the data storage module is configured to count the number of queries and query intervals of all data types within a preset time period, and to determine whether to adjust the storage mode of each data type according to the number of queries and query interval of each data type, including:

[0028] Count the number of queries for each data type within a preset time period. If the number of queries for a data type is zero, back up and store the data type.

[0029] If the query times of the data type is not zero, the average query interval duration of each data type within the preset time period is calculated. If the average query interval duration is less than the minimum query interval duration, the data type is actively stored; if the average query interval duration is greater than or equal to the minimum query interval duration, the data type is backed up and stored;

[0030] The query priority of the active storage is higher than the query priority of the backup storage.

[0031] Further, the data response module is configured to determine whether it is necessary to adjust the response time of the query result corresponding to the data type according to the query interval length of the data type, including:

[0032] Setting a maximum query interval duration, if the average query interval duration of the data type within a preset time period is less than or equal to the maximum query interval duration, then determining that the response time of the query result corresponding to the data type needs to be adjusted;

[0033] If the average query interval duration of the data type within the preset time period is greater than the maximum query interval duration, it is determined that there is no need to adjust the response time of the query result corresponding to the data type.

[0034] Further, the data response module is configured to, if it is determined to be an adjustment, calculate the interval duration influence coefficient according to the query interval duration and the query interval duration change value, including:

[0035] Calculate the query interval duration change value of each two adjacent query interval durations within a preset time period;

[0036] The interval duration impact coefficient is calculated according to the average query interval duration and the query interval duration change value of the data type within the preset time period by the following formula:

[0037] ;

[0038] In the above formula, F represents the interval length influence coefficient, n represents the number of queries within the preset time period, and F i represents the duration of the i-th query interval, F i-1 Indicates the duration of the i-1th query interval.

[0039] Furthermore, when the data response module is configured to dynamically adjust the response time of the query result according to the interval duration influence coefficient, it includes:

[0040] Setting a first influence coefficient and a second influence coefficient, wherein the first influence coefficient is smaller than the second influence coefficient;

[0041] If the interval duration influence coefficient is less than or equal to the first influence coefficient, adjusting the response time of the query result by the first adjustment coefficient;

[0042] If the interval duration influence coefficient is greater than the first influence coefficient and less than or equal to the second influence coefficient, adjusting the response time of the query result by the second adjustment coefficient;

[0043] If the interval duration influence coefficient is greater than the second influence coefficient, adjusting the response time of the query result by a third adjustment coefficient;

[0044] The value range of the adjustment coefficient is: 0<first adjustment coefficient<second adjustment coefficient<third adjustment coefficient<1, and the response time after adjustment is the product of the response time before adjustment and the adjustment coefficient.

[0045] Compared with the prior art, the beneficial effect of the present invention is that the present invention significantly improves the query efficiency and system response capability through flexible multimodal data processing and dynamic adjustment mechanism. First, the acquisition module collects query information in real time, including query terms and query results, to provide a basis for subsequent data processing. The data processing module effectively determines whether it is necessary to add new data types by summarizing the query data types of the query terms, counting the number of queries and the query interval duration, and combining the query results. This allows the system to expand data support in real time according to user needs and improve its adaptability and flexibility. In addition, the data storage module determines whether it is necessary to adjust the data storage mode according to these factors by counting the number of queries and the interval duration. The data is divided into active storage and backup storage to ensure that high-frequency query data is processed first, and the infrequently used data is transferred to the backup storage, thereby optimizing the use of storage resources and reducing the burden on the system. At the same time, the data response module dynamically adjusts the query response time by monitoring the query interval duration. When the system detects that the query frequency has changed, the interval duration impact coefficient is calculated and the response time is adjusted according to this coefficient. This adaptive adjustment based on query behavior helps to ensure that the system always provides efficient response when the query fluctuates greatly, avoiding resource waste and response delay. Therefore, the system can effectively improve query efficiency, optimize resource management, enhance user experience, and has strong scalability and flexibility.

[0046] On the other hand, the present application also provides a multimodal data processing method supporting customized processing, including:

[0047] Collecting query information; wherein the query information includes query terms and query results;

[0048] Summarize the query data types of the query terms, count the query times and query intervals of the query data types, and determine whether to add data types based on the query results of the query data types and in combination with the query times and query intervals;

[0049] Count the number of queries and query intervals for all data types within a preset time period, and determine whether to adjust the storage mode of each data type based on the number of queries and query intervals for each data type; wherein the storage mode includes active storage and backup storage;

[0050] Based on the query interval duration of the data type, determine whether the response time of the query result corresponding to the data type needs to be adjusted. If it is determined to be adjusted, calculate the interval duration impact coefficient based on the query interval duration and the query interval duration change value, and dynamically adjust the response time of the query result based on the interval duration impact coefficient.

[0051] It is understandable that the multimodal data processing system and method supporting customized processing provided by the present application have the same beneficial effects, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0053] Figure 1 A functional block diagram of a multimodal data processing system supporting customized processing provided by an embodiment of the present invention;

[0054] Figure 2 A flowchart of a multimodal data processing method supporting customized processing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0056] In some embodiments of the present application, see Figure 1 As shown, this embodiment provides a multimodal data processing system supporting customized processing, including:

[0057] A collection module is configured to collect query information; wherein the query information includes query terms and query results;

[0058] The data processing module is configured to summarize the query data types of the query terms, count the query times and query intervals of the query data types, and determine whether to add the data types based on the query results of the query data types and in combination with the query times and query intervals;

[0059] The data storage module is configured to count the number of queries and query intervals of all data types within a preset time period, and determine whether to adjust the storage mode of each data type according to the number of queries and query intervals of each data type; wherein the storage mode includes active storage and backup storage;

[0060] The data response module is configured to determine whether the response time of the query result corresponding to the data type needs to be adjusted based on the query interval duration of the data type. If it is determined to be adjusted, the interval duration impact coefficient is calculated based on the query interval duration and the query interval duration change value, and the response time of the query result is dynamically adjusted based on the interval duration impact coefficient.

[0061] It can be understood that the present invention significantly improves query efficiency and system response capability through flexible multimodal data processing and dynamic adjustment mechanism. First, the acquisition module collects query information in real time, including query terms and query results, to provide a basis for subsequent data processing. The data processing module effectively determines whether new data types need to be added by summarizing the query data types of the query terms, counting the number of queries and the query interval duration, and combining the query results. This allows the system to expand data support in real time according to user needs and improve its adaptability and flexibility. In addition, the data storage module determines whether the data storage mode needs to be adjusted based on these factors by counting the number of queries and the interval duration. The data is divided into active storage and backup storage to ensure that high-frequency query data is processed first, and infrequently used data is transferred to backup storage, thereby optimizing the use of storage resources and reducing the burden on the system. At the same time, the data response module dynamically adjusts the query response time by monitoring the query interval duration. When the system detects that the query frequency has changed, the interval duration impact coefficient is calculated and the response time is adjusted according to this coefficient. This adaptive adjustment based on query behavior helps to ensure that the system always provides efficient response when the query fluctuates greatly, avoiding resource waste and response delay. Therefore, the system can effectively improve query efficiency, optimize resource management, enhance user experience, and has strong scalability and flexibility.

[0062] Specifically, the present invention combines the metadata description mechanism with multimodal data processing, and realizes the compatibility, scalability and dynamic adjustment of various data sources through metadata-driven strategies. First, the acquisition module collects query information, including query terms and query results, to provide key input for subsequent data analysis. On this basis, the data processing module summarizes the relevant data types of the query terms, and counts the number of queries and the query interval duration. Through the metadata description mechanism, the system structure is dynamically updated and optimized to adapt to the ever-changing business needs. This process follows the MOF specification to ensure that the system can flexibly add, delete and modify data types and automatically adjust the data storage mode at runtime. The system abstracts the data structure of business objects through metadata management to ensure the comprehensive management of data assets, objects and interfaces. At the same time, by dynamically configuring metadata, the system can determine whether to add new data types or adjust the data storage mode in real time based on information such as query data type, query number, query interval, etc. The data storage module dynamically determines the storage mode (such as active storage or backup storage) of each data type according to the number of queries and interval duration in a preset time period, thereby optimizing storage resources and ensuring efficient operation of the system. In addition, the data response module monitors the query interval in real time, calculates the interval impact coefficient, and dynamically adjusts the response time of the query results. This metadata-driven query optimization strategy helps improve the performance of full-text retrieval and provides a general decoupled interface capability through MetaAPI, ensuring that the system can flexibly respond to different query requirements without modifying the backend code, thereby enhancing the scalability and flexibility of the system.

[0063] In general, the system, by integrating a metadata-driven architecture, can adjust query and data storage strategies in real time, which not only improves query efficiency and system response time, but also enhances the convenience of business expansion and system maintenance, greatly improving delivery efficiency and scalability.

[0064] In some embodiments of the present application, when the data processing module is configured to summarize the query data type of the query term, it includes:

[0065] Remove query term noise words; wherein noise words include spaces and punctuation marks;

[0066] Perform word segmentation on the query terms after removing noise words, and mark the part of speech of each word in the query terms;

[0067] Analyze the meaning and intent of the query terms according to the parts of speech of each word in the query terms to identify the customer's query needs;

[0068] Match each word in the entry with the system predefined data types to determine the data type requested by the user.

[0069] It is understandable that the data processing module of the present invention helps to improve the accuracy and parsability of query terms by removing noise words (such as spaces and punctuation marks) in query terms and performing word segmentation and part-of-speech tagging on the remaining terms. By analyzing the part-of-speech of each word, the system can more accurately understand the meaning of the query terms and the user's intention, thereby identifying the customer's actual query needs. This processing method based on semantic analysis and part-of-speech recognition not only improves the accuracy of the query, but also ensures that the system can accurately match the data type requested by the user, thereby providing more accurate information support for subsequent responses and data processing.

[0070] In some embodiments of the present application, when the data processing module is configured to count the number of queries and the query interval duration of the query data type, it includes:

[0071] Pre-set the statistical time period to collect the query times and query intervals of all queried data types within the statistical time period;

[0072] The data type is recorded and stored in the form of {data type, query times, query interval, statistical time period};

[0073] Construct a query count statistics graph for all queried data types, and construct a query interval duration change graph for each data type.

[0074] It is understandable that the data processing module helps to fully and systematically understand the query behavior and usage patterns of different data types by pre-setting the statistical time period and collecting the number of queries and query interval duration during the period. By recording and storing the data type and its corresponding number of queries, query interval duration, and statistical time period in detail, the system can clearly track the usage of each data type, thereby providing accurate data support for subsequent analysis. At the same time, constructing a query frequency statistical graph and a query interval duration change graph can intuitively display the query trend and activity changes of each data type, helping system administrators identify high-frequency query types, optimize storage and resource allocation, and improve system response efficiency. This method of data visualization analysis can better support the dynamic adjustment of storage modes and response strategies, thereby improving the flexibility and performance of the system.

[0075] In some embodiments of the present application, the data processing module is configured to determine whether to add a data type based on the query result of the query data type and in combination with the query number and the query interval duration, including:

[0076] If the query result of the query data type is not found, determine whether to add the data type based on the query times and query interval time;

[0077] If the query result of the query data type is that it has been queried, it is directly determined that there is no need to add the data type.

[0078] In some embodiments of the present application, if the query result of the query data type is not found, then judging whether to add the data type according to the query number and the query interval duration includes:

[0079] Set the minimum number of queries and the minimum query interval duration;

[0080] If the query times are greater than the minimum query times, and / or the query interval is less than the minimum query interval, it is determined that the query data type needs to be added;

[0081] If the query times are less than or equal to the minimum query times, and the query interval duration is greater than or equal to the minimum query interval duration, it is determined that there is no need to add query data types.

[0082] It is understandable that the data processing module intelligently determines whether new data types need to be added by combining query results, query times and query interval duration, which helps the system to more accurately and dynamically adjust the coverage of data types. When the query results fail to find the required data, the system sets the minimum thresholds for the number of queries and the query interval duration to ensure that new data types are added only when the query behavior is frequent and / or the intervals are short, thereby avoiding unnecessary type additions and improving the flexibility and resource utilization efficiency of the system. When the query results have found the required data, the system directly determines that there is no need to add data types, avoiding repeated and redundant data processing. This method not only ensures the response efficiency of the system, but also dynamically optimizes the management of data types according to actual query needs, which helps to improve the intelligence and adaptability of the system.

[0083] In some embodiments of the present application, the data storage module is configured to count the number of queries and query intervals of all data types within a preset time period, and to determine whether to adjust the storage mode of each data type according to the number of queries and query interval of each data type, including:

[0084] Count the number of queries for each data type within a preset time period. If the number of queries for a data type is zero, back up and store the data type.

[0085] If the query times of a data type is not zero, the average query interval of each data type within the preset time period is calculated. If the average query interval is less than the minimum query interval, the data type is actively stored; if the average query interval is greater than or equal to the minimum query interval, the data type is backed up and stored;

[0086] Among them, the query priority of the active storage is higher than the query priority of the backup storage.

[0087] It can be understood that the data storage module improves the efficiency of the use of system storage resources by intelligently adjusting the storage mode of the data type according to the number of queries and the query interval length. For data types with zero query times, they are directly converted to backup storage to avoid occupying active storage space, thereby saving resources. For data types with query behavior, the system decides whether to maintain active storage based on the length of its query interval to ensure that high-frequency queried data can be responded to quickly, while low-frequency queried data is converted to backup storage to reduce storage costs. This data storage mode optimization based on dynamic query behavior helps to automatically manage storage resources and ensure efficient data access and storage. It also ensures the system's reasonable allocation of data storage priorities, improving the overall performance and flexibility of the system.

[0088] In some embodiments of the present application, when the data response module is configured to determine whether it is necessary to adjust the response time of the query result corresponding to the data type according to the query interval length of the data type, it includes:

[0089] Set the maximum query interval duration. If the average query interval duration of a data type within a preset time period is less than or equal to the maximum query interval duration, it is determined that the response time of the query result corresponding to the data type needs to be adjusted.

[0090] If the average query interval duration of the data type within the preset time period is greater than the maximum query interval duration, it is determined that there is no need to adjust the response time of the query result corresponding to the data type.

[0091] In some embodiments of the present application, the data response module is configured to, if it is determined to be an adjustment, calculate the interval duration impact coefficient according to the query interval duration and the query interval duration change value, including:

[0092] Calculate the query interval duration change value of each two adjacent query interval durations within a preset time period;

[0093] The interval duration impact coefficient is calculated using the following formula based on the average query interval duration and query interval duration change value of the data type within the preset time period:

[0094] ;

[0095] In the above formula, F represents the interval length influence coefficient, n represents the number of queries within the preset time period, and F i represents the duration of the i-th query interval, F i-1 Indicates the duration of the i-1th query interval.

[0096] It is understandable that the data response module intelligently determines whether it is necessary to adjust the response time of the query results by setting the maximum query interval duration and combining the average query interval duration of the query data type. By dynamically calculating the interval duration impact coefficient according to the changes in the query interval duration, the system can flexibly optimize the query response strategy. When the query interval duration of the data type is short, the system will adjust the query response time to improve query efficiency; for data types with longer query interval durations, frequent adjustments to the response time are avoided to reduce unnecessary resource consumption. This method can automatically optimize the response time based on actual query behavior, ensure that the system can respond quickly to high-frequency queries, and reduce unnecessary resource burdens in low-frequency queries, thereby improving the response efficiency and performance of the system.

[0097] In some embodiments of the present application, when the data response module is configured to dynamically adjust the response time of the query result according to the interval duration influence coefficient, it includes:

[0098] Setting a first influence coefficient and a second influence coefficient, wherein the first influence coefficient is smaller than the second influence coefficient;

[0099] If the interval duration influence coefficient is less than or equal to the first influence coefficient, the response time of the query result is adjusted by the first adjustment coefficient;

[0100] If the interval duration influence coefficient is greater than the first influence coefficient and less than or equal to the second influence coefficient, the response time of the query result is adjusted by the second adjustment coefficient;

[0101] If the interval duration influence coefficient is greater than the second influence coefficient, the response time of the query result is adjusted by the third adjustment coefficient;

[0102] The value range of the adjustment coefficient is: 0<first adjustment coefficient<second adjustment coefficient<third adjustment coefficient<1, and the response time after adjustment is the product of the response time before adjustment and the adjustment coefficient.

[0103] It is understandable that the data response module flexibly adjusts the response time of the query results according to different ranges of the interval duration impact coefficient by setting different impact coefficients and corresponding adjustment coefficients. This hierarchical adjustment mechanism can accurately control the adjustment range of the response time to avoid excessive impact on query performance. When the query interval is short, optimization is performed through a smaller first adjustment coefficient; when the query interval is moderate, adjustment is performed through the second adjustment coefficient; when the query interval is long, adjustment is performed through a larger third adjustment coefficient. This dynamic, hierarchical adjustment method ensures that the system can respond flexibly according to changes in actual query frequency and intervals, which not only improves the response efficiency of high-frequency queries, but also avoids unnecessary optimization of low-frequency queries, thereby maximizing system performance and user experience in different usage scenarios.

[0104] On the other hand, see Figure 2 As shown, the present application also provides a multimodal data processing method supporting custom processing, which is applied to the above-mentioned multimodal data processing system supporting custom processing, and includes the following steps:

[0105] S100, collecting query information; wherein the query information includes query terms and query results;

[0106] S200, summarizing the query data types of the query terms, counting the query times and query intervals of the query data types, and determining whether to add the data types based on the query results of the query data types and in combination with the query times and query intervals;

[0107] S300, counting the number of queries and query intervals of all data types within a preset time period, and determining whether to adjust the storage mode of each data type according to the number of queries and query intervals of each data type; wherein the storage mode includes active storage and backup storage;

[0108] S400. Determine whether the response time of the query result corresponding to the data type needs to be adjusted based on the query interval duration of the data type. If it is determined to be adjusted, calculate the interval duration impact coefficient based on the query interval duration and the query interval duration change value, and dynamically adjust the response time of the query result based on the interval duration impact coefficient.

[0109] It can be understood that the multimodal data digital processing method provided by the present invention significantly improves the processing efficiency and response performance of query data through precise steps and dynamic adjustment mechanisms. First, the collection of query information (including query terms and query results) provides a basis for subsequent data processing, ensuring accurate capture of user needs. Then, by counting the number of queries and interval duration of the query data type, and judging whether to add data types in combination with the query results, the data storage and access strategies can be optimized according to actual needs, effectively avoiding data redundancy or waste of storage resources. The following storage mode adjustment, by analyzing the number of queries and interval duration, determines whether to store the data as active or backup state, and realizes the reasonable allocation and efficient utilization of data storage resources. Finally, by adjusting the response time of the query results according to the query interval duration, and dynamically optimizing the response time according to the interval duration influence coefficient, it can ensure that high-frequency queries are responded to in a timely manner, while low-frequency queries maintain a low system load. This method can optimize system resource allocation, improve data processing efficiency and response time through a flexible dynamic adjustment mechanism, thereby enhancing user experience and improving system performance, especially in the face of large-scale and diversified queries.

[0110] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may 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 codes.

[0111] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multimodal data processing system supporting customized processing, characterized in that: include: A collection module, configured to collect query information; wherein the query information includes query terms and query results; A data processing module is configured to summarize the query data types of the query terms, count the query times and query intervals of the query data types, and determine whether to add a data type based on the query results of the query data types and in combination with the query times and query intervals; Among them, if the query result of the query data type is not found, determine whether to add the data type according to the query number and the query interval duration; set the minimum query number and the minimum query interval duration; if the query number is greater than the minimum query number, and / or the query interval duration is less than the minimum query interval duration, determine that the query data type needs to be added; if the query number is less than or equal to the minimum query number, and the query interval duration is greater than or equal to the minimum query interval duration, determine that the query data type does not need to be added; if the query result of the query data type is found, directly determine that the data type does not need to be added; The data storage module is configured to count the number of queries and query intervals of all data types within a preset time period, and determine whether to adjust the storage mode of each data type according to the number of queries and query intervals of each data type; wherein the storage mode includes active storage and backup storage; Among them, the number of queries for each data type within a preset time period is counted. If the number of queries for a data type is zero, the data type is backed up and stored; if the number of queries for a data type is not zero, the average query interval duration of each data type within the preset time period is calculated. If the average query interval duration is less than the minimum query interval duration, the data type is actively stored; if the average query interval duration is greater than or equal to the minimum query interval duration, the data type is backed up and stored; wherein the query priority of the active storage is higher than the query priority of the backup storage; A data response module is configured to determine whether it is necessary to adjust the response time of the query result corresponding to the data type according to the query interval duration of the data type, and if it is determined to be adjusted, calculate the interval duration impact coefficient according to the query interval duration and the query interval duration change value, and dynamically adjust the response time of the query result according to the interval duration impact coefficient; Among them, a maximum query interval duration is set. If the average query interval duration of the data type within the preset time period is less than or equal to the maximum query interval duration, it is judged that the response time of the query result corresponding to the data type needs to be adjusted; if the average query interval duration of the data type within the preset time period is greater than the maximum query interval duration, it is judged that the response time of the query result corresponding to the data type does not need to be adjusted.

2. The multimodal data processing system supporting customized processing according to claim 1, characterized in that: When the data processing module is configured to summarize the query data type of the query term, it includes: Removing noise words from the query terms; wherein the noise words include spaces and punctuation marks; Performing a word segmentation operation on the query terms after removing noise words, and marking the part of speech of each word in the query terms; Analyze the meaning and intent of the query terms according to the part of speech of each word in the query terms to identify the query needs of the customer; Match each word in the entry with the system predefined data types to determine the data type requested by the user.

3. The multimodal data processing system supporting customized processing according to claim 2, characterized in that: When the data processing module is configured to count the number of queries and the query interval duration of the query data type, it includes: Pre-set the statistical time period to collect the query times and query intervals of all queried data types within the statistical time period; The data type is recorded and stored in the form of {data type, query times, query interval, statistical time period}; Construct a query count statistics graph for all queried data types, and construct a query interval duration change graph for each data type.

4. The multimodal data processing system supporting customized processing according to claim 3, characterized in that: The data response module is configured to, if it is determined to be an adjustment, calculate the interval duration influence coefficient according to the query interval duration and the query interval duration change value, including: Calculate the query interval duration change value of each two adjacent query interval durations within a preset time period; The interval duration impact coefficient is calculated according to the average query interval duration and the query interval duration change value of the data type within the preset time period by the following formula: ； In the above formula, F represents the interval length influence coefficient, n represents the number of queries within the preset time period, and F i represents the duration of the i-th query interval, F i-1 Indicates the duration of the i-1th query interval.

5. The multimodal data processing system supporting customized processing according to claim 4, characterized in that: When the data response module is configured to dynamically adjust the response time of the query result according to the interval duration influence coefficient, it includes: Setting a first influence coefficient and a second influence coefficient, wherein the first influence coefficient is smaller than the second influence coefficient; If the interval duration influence coefficient is less than or equal to the first influence coefficient, adjusting the response time of the query result by the first adjustment coefficient; If the interval duration influence coefficient is greater than the first influence coefficient and less than or equal to the second influence coefficient, adjusting the response time of the query result by the second adjustment coefficient; If the interval duration influence coefficient is greater than the second influence coefficient, adjusting the response time of the query result by a third adjustment coefficient; The value range of the adjustment coefficient is: 0<first adjustment coefficient<second adjustment coefficient<third adjustment coefficient<1, and the response time after adjustment is the product of the response time before adjustment and the adjustment coefficient.

6. A method for processing multimodal data supporting custom processing, applied to the multimodal data processing system supporting custom processing as claimed in any one of claims 1 to 5, characterized in that: include: Collecting query information; wherein the query information includes query terms and query results; Summarize the query data types of the query terms, count the query times and query intervals of the query data types, and determine whether to add data types based on the query results of the query data types and in combination with the query times and query intervals; Among them, if the query result of the query data type is not found, determine whether to add the data type according to the query number and the query interval duration; set the minimum query number and the minimum query interval duration; if the query number is greater than the minimum query number, and / or the query interval duration is less than the minimum query interval duration, determine that the query data type needs to be added; if the query number is less than or equal to the minimum query number, and the query interval duration is greater than or equal to the minimum query interval duration, determine that the query data type does not need to be added; if the query result of the query data type is found, directly determine that the data type does not need to be added; Count the number of queries and query intervals for all data types within a preset time period, and determine whether to adjust the storage mode of each data type based on the number of queries and query intervals for each data type; wherein the storage mode includes active storage and backup storage; Among them, the number of queries for each data type within a preset time period is counted. If the number of queries for a data type is zero, the data type is backed up and stored; if the number of queries for a data type is not zero, the average query interval duration of each data type within the preset time period is calculated. If the average query interval duration is less than the minimum query interval duration, the data type is actively stored; if the average query interval duration is greater than or equal to the minimum query interval duration, the data type is backed up and stored; wherein the query priority of the active storage is higher than the query priority of the backup storage; According to the query interval duration of the data type, determine whether it is necessary to adjust the response time of the query result corresponding to the data type; if it is determined to be adjusted, calculate the interval duration impact coefficient according to the query interval duration and the query interval duration change value, and dynamically adjust the response time of the query result according to the interval duration impact coefficient; Among them, a maximum query interval duration is set. If the average query interval duration of the data type within the preset time period is less than or equal to the maximum query interval duration, it is judged that the response time of the query result corresponding to the data type needs to be adjusted; if the average query interval duration of the data type within the preset time period is greater than the maximum query interval duration, it is judged that the response time of the query result corresponding to the data type does not need to be adjusted.

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