Intelligent data analysis and storage device based on large model, medium and electronic equipment

Abstract

The invention provides an intelligent data analysis and storage device based on a large model, a medium and electronic equipment, and belongs to the technical field of data analysis and storage, and the device comprises a demand analysis module which is used for obtaining a data processing demand and carrying out demand extraction to obtain a plurality of demand entries, obtaining a demand analysis target and a target weight of each demand entry; the data analysis module is used for constructing a large model framework according to the demand analysis target and the target weight, and inputting the to-be-processed data into the large model framework to obtain analysis results of the sub-data under different data sources; and the storage distribution module is used for distributing storage lakes to the corresponding sub-data according to the processing paths corresponding to the analysis results and the data exception conditions in combination with the source security of the data sources, and storing the storage lakes. Security analysis of the data is achieved, and the security of data storage is improved.

Description

Technical Field

[0001] The present invention relates to the field of data analysis and storage technology, and in particular to an intelligent data analysis and storage device, medium and electronic equipment based on a large model. Background Art

[0002] In the current surging wave of digitalization, big models are like a bright new star, rising strongly and quickly becoming the focus of the science and technology field. From the initial theoretical exploration to the current widespread application in various industries, big models are reshaping the current life and work mode at an astonishing speed. Using big models for data analysis to determine data anomalies is a common way, and data analysis is closely related to data storage.

[0003] A common data storage method is to directly transfer certain data to a designated location (for example, different disks) for storage. However, during the storage process, the data that needs to be stored may be subject to security threats such as hacker attacks and virus infections, leading to data leakage and tampering. If the data itself and the storage device are not properly protected, hackers may easily exploit vulnerabilities to steal sensitive information, resulting in reduced security of data storage.

[0004] Therefore, the present invention proposes an intelligent data analysis and storage device, medium and electronic device based on a large model. Summary of the invention

[0005] The present invention provides an intelligent data analysis and storage device, medium and electronic device based on a large model, which is used to build a large model according to data processing requirements and analyze the data to determine the existence of abnormal situations, and then subsequently combine the source security and processing path to realize the security analysis of the data to improve the security of data storage.

[0006] The present invention provides an intelligent data analysis and storage device based on a large model, comprising: The demand analysis module is used to obtain data processing requirements and extract requirements to obtain a number of requirement items, and then obtain the demand analysis target and target weight of each requirement item; A data analysis module is used to build a large model according to the demand analysis target and target weight, and input the data to be processed into the large model to obtain the analysis results of the sub-data under different data sources; The storage allocation module is used to allocate a storage lake to the corresponding sub-data and store it according to the processing path and data anomalies corresponding to the analysis results and in combination with the source security of the data source.

[0007] Preferably, the demand analysis module includes: A description extraction unit, used to extract the data processing requirements respectively based on the set predefined indicators, and obtain the requirement description under each predefined indicator; A parameter extraction unit, used to extract the requirement parameters related to the corresponding predefined indicators from the requirement description, obtain parameter expressions, and regard them as requirement items; The matching unit is used to match the demand items with the item-target comparison table to obtain corresponding demand analysis targets and target weights.

[0008] Preferably, the data analysis module includes: An initialization unit, used for acquiring an initial model consistent with all predefined indicators from an indicator-model database, and initializing relevant parameters of each predefined indicator in the initial model; The allocation unit is used to allocate the demand analysis objectives and target weights under each set predefined indicator to the corresponding initial layers to construct a large model.

[0009] Preferably, the initial model comprises initial layers whose number is consistent with the set predefined indicators, and each initial layer corresponds to a set predefined indicator.

[0010] Preferably, the storage allocation module includes: A path drawing unit is used to process nodes in the corresponding large model in sequence according to the sub-data under each data source captured in real time by the capture tool, and draw a processing path; a variation calculation unit, for determining a processing variation coefficient of the processing path in combination with a processing log of each processing node; A situation determination unit, used to determine the data anomaly situation of the corresponding sub-data according to the analysis result, wherein the data anomaly situation includes: an anomaly coefficient of the data itself and a set of factors that affect the generation of data anomaly; ; in, Indicates the data anomaly coefficient of the corresponding sub-data; Indicates the normalized value of the j2th parameter under the corresponding sub-data; represents the standard normalized value of the j2th parameter under the corresponding sub-data; represents the total number of parameters under the corresponding sub-data; j2 represents the j2th parameter under the corresponding sub-data; A reliability determination unit, configured to determine the reliability of the corresponding sub-data according to the processing variation coefficient and in combination with data anomalies and source security of the corresponding data source; A lake determination unit, configured to determine a storage lake from a type-storage comparison table according to a data type of corresponding sub-data; A storage analysis unit, configured to directly store the corresponding sub-data in a corresponding storage lake if the reliability is greater than or equal to a preset reliability; Otherwise, the security level to be adjusted is determined based on reliability and presetness; A security encryption unit, used to retrieve a specified number of encryption algorithms from an encryption database according to the security level to be adjusted and in combination with reliability, and to combine the algorithms to securely encrypt the corresponding sub-data; A security upgrade unit, used to perform security upgrade on the corresponding storage space according to the space security of the designated storage space of the corresponding sub-data in the corresponding storage lake; The storage unit is used to store the securely encrypted sub-data in the security-upgraded storage space, wherein the storage space is a storage unit in the corresponding storage lake, and the storage lake includes several storage units.

[0011] Preferably, the variation calculation unit is used to: ; in, represents the processing variation coefficient of the processing path; n1 represents the number of processing nodes existing on the processing path; Indicates the historical actual working times of the i1th processing node; represents the number of historical set jobs of the i1th processing node; represents the number of times the i1th processing node repeats work on the processing path; Indicates the processing log of the i1th processing node and setting log Similarity function of ; Represents the data after the corresponding sub-data is processed by the i1th processing node The data before the corresponding subprocessing of the i1th processing node Similarity function of ; i1 represents the i1th processing node.

[0012] Preferably, the reliability determination unit is used to: ; ; in, Indicates the reliability of the corresponding sub-data; Represents the factor set under the corresponding sub-data Reliable influence coefficient on data; Indicates the source security of the data source of the corresponding sub-data; Represents the corresponding factor set The number of influencing factors involved; It represents the average value of the data impact length of all historical sub-data corresponding to the j1th historical factor; it represents the variance of the corresponding number of lengths obtained by random screening from the data impact lengths of all historical sub-data corresponding to the j1th historical factor; Indicates the length of the impact of the j1th influencing factor on the corresponding sub-data; Indicates the data length of the corresponding sub-data, and rand indicates the random function symbol.

[0013] Preferably, the security encryption unit comprises: A number calculation subunit, used to calculate the specified number Zn of the encryption algorithm; ; in, Indicates the preset nature of the corresponding sub-data; It represents the unit quantity of safety degradation based on reliability; represents the floor function; represents the ceiling function; An algorithm locking subunit, used to sequentially lock the first algorithm with a predetermined security level from the encryption database according to the specified number Zn; The random sorting unit is used to randomly sort and combine all the acquired first algorithms, and securely encrypt the corresponding sub-data in sequence according to the first algorithms in the combination result.

[0014] The present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes any intelligent data processing method of an intelligent data processing device based on a large model.

[0015] The present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes any intelligent data processing method of an intelligent data processing device based on a large model.

[0016] Compared with the prior art, the present invention has the following beneficial effects: Build a large model according to data processing requirements and analyze the data to determine any anomalies. Then, combine source security and processing paths to perform security analysis of the data to improve the security of data storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 is a structural diagram of an intelligent data analysis and storage device based on a large model provided by an embodiment of the present invention; Figure 2 A structural diagram of a large model provided by an embodiment of the present invention; Figure 3 A flowchart of an intelligent data processing method of an intelligent data processing device based on a large model provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The present invention provides an intelligent data analysis and storage device based on a large model, such as Figure 1 As shown, including: The demand analysis module is used to obtain data processing requirements and extract requirements to obtain a number of requirement items, and then obtain the demand analysis target and target weight of each requirement item; A data analysis module is used to build a large model according to the demand analysis target and target weight, and input the data to be processed into the large model to obtain the analysis results of the sub-data under different data sources; The storage allocation module is used to allocate a storage lake to the corresponding sub-data and store it according to the processing path and data anomalies corresponding to the analysis results and in combination with the source security of the data source.

[0021] In this embodiment, data processing requirements refer to a description of what kind of processing needs to be performed on the data, for example, it is necessary to identify typos in the data, identify whether the typos are single errors or continuous errors, and the typos recognition accuracy is 90%. Or, it is necessary to identify missing information in the data, and the missing information recognition accuracy is 95%.

[0022] In this embodiment, the large model framework is based on the neural network model, and the processing of resource data under different data processing requirements and the extraction requirements under different predefined indicators are used as samples to train the neural network model. Therefore, there is an initial framework, but due to different requirements, the parameters of different layers in the model need to be adjusted to ensure that the model can strictly meet the requirements and ensure the reliability of data analysis.

[0023] In this embodiment, the data to be processed refers to network resource data, for example, resource data A1 is obtained from platform R1, resource data A2 is obtained from platform R2, etc., and the data to be processed refers to data that requires exception analysis and security analysis, for example, resource data required by users for building AI models. Since the data is distributed on different platforms and there may be data tampering due to unsafe factors during the data acquisition process from the platform, it is necessary to perform a preliminary analysis of the data to be processed based on the large model to determine the exceptions and the possible security impact of the processing path under the analysis process on the data, so as to further expand the security protection of the data and provide proactive protection.

[0024] In this embodiment, source security is obtained by matching the source type-security comparison table of the data source, and the source security under different source types is different, with a value range of 0 to 1. It is set before the relevant platform of the corresponding data source is built and is known content.

[0025] In this embodiment, the storage lake is allocated for secure storage of sub-data, and the storage lake refers to a medium such as a storage disk that can store data.

[0026] The beneficial effect of the above technical solution is: to build a large model according to data processing requirements and analyze the data to determine the existence of abnormal situations, and then subsequently combine the source security and processing path to realize the security analysis of the data to improve the security of data storage.

[0027] The present invention provides an intelligent data analysis and storage device based on a large model, wherein the demand analysis module comprises: A description extraction unit, used to extract the data processing requirements respectively based on the set predefined indicators, and obtain the requirement description under each predefined indicator; A parameter extraction unit, used to extract the requirement parameters related to the corresponding predefined indicators from the requirement description, obtain parameter expressions, and regard them as requirement items; The matching unit is used to match the demand items with the item-target comparison table to obtain corresponding demand analysis targets and target weights.

[0028] In this embodiment, predefined indicators are set as typos recognition description indicators and missing information description indicators, and then the corresponding descriptions can be extracted from the requirements according to the relevant indicators: that is, it is necessary to identify typos in the data, identify single typos, two consecutive typos, multiple consecutive typos, etc., and the typos recognition accuracy is 90%; it is necessary to identify missing information in the data, identify the missing of a single character, the missing of multiple characters, and the missing recognition accuracy is 95%.

[0029] The parameter expression of the requirement parameters under the typo recognition description indicator is: @1@2@3+90%, where the requirement parameters are a single typo @1, two consecutive typos @2, and multiple consecutive typos @3, and the typo recognition accuracy is 90%.

[0030] The parameter expression of the required parameters under the missing information description indicator is: #1#3+95%, and the required parameters are single missing character #1, multiple missing characters #3, and missing recognition accuracy 95%.

[0031] In this embodiment, the item-target comparison table includes several pre-stored requirement items and requirement analysis targets and indicator weights that match the items, and the requirement analysis target is: different numbers of typos need to be identified, etc., wherein the sum of the target weights of all predefined indicators is 1, and the target weight = the indicator weight of the corresponding predefined indicator / the sum of the indicator weights of all predefined indicators involved.

[0032] The beneficial effect of the above technical solution is: the requirements are extracted according to indicators to obtain the requirement description, and then the requirement analysis goals and target weights are obtained through parameter extraction and comparison table matching, providing a basis for the subsequent construction of a large model.

[0033] The present invention provides an intelligent data analysis and storage device based on a large model, wherein the data analysis module comprises: An initialization unit, used for acquiring an initial model consistent with all predefined indicators from an indicator-model database, and initializing relevant parameters of each predefined indicator in the initial model; The allocation unit is used to allocate the demand analysis objectives and target weights under each set predefined indicator to the corresponding initial layers to construct a large model.

[0034] Preferably, the initial model comprises initial layers whose number is consistent with the set predefined indicators, and each initial layer corresponds to a set predefined indicator.

[0035] In this embodiment, the indicator-model database includes different predefined indicator combinations and an initial model matching the combination. The purpose of initialization is to facilitate the redistribution of targets and weights according to the indicators to ensure that the model meets the needs as closely as possible. It should be noted that the initial model is pre-planned, and only the relevant parameters need to be initialized and reset. For example, the weight of the layer corresponding to the indicator is set to 0, and replaced by the newly obtained target weight.

[0036] For example, Figure 2 As shown, it is a structural diagram of a large model, including an H1 layer, an H2 layer, etc., and the H1 layer corresponds to setting a predefined indicator a1, the H2 layer corresponds to setting a predefined indicator a2, and so on.

[0037] In this embodiment, the number of predefined indicators is at least 3.

[0038] For example, first identify typos, then identify missing information, and finally analyze data anomalies.

[0039] The beneficial effect of the above technical solution is: by obtaining the initial model under the predefined indicator combination from the database, and then resetting the target and weight of the initial layer, a large model is obtained to ensure the rationality of data analysis.

[0040] The present invention provides an intelligent data analysis and storage device based on a large model, wherein the storage allocation module comprises: A path drawing unit is used to process nodes in the corresponding large model in sequence according to the sub-data under each data source captured in real time by the capture tool, and draw a processing path; a variation calculation unit, for determining a processing variation coefficient of the processing path in combination with a processing log of each processing node; A situation determination unit, used to determine the data anomaly situation of the corresponding sub-data according to the analysis result, wherein the data anomaly situation includes: an anomaly coefficient of the data itself and a set of factors that affect the generation of data anomaly; ; in, Indicates the data anomaly coefficient of the corresponding sub-data; Indicates the normalized value of the j2th parameter under the corresponding sub-data; represents the standard normalized value of the j2th parameter under the corresponding sub-data; represents the total number of parameters under the corresponding sub-data; j2 represents the j2th parameter under the corresponding sub-data; A reliability determination unit, configured to determine the reliability of the corresponding sub-data according to the processing variation coefficient and in combination with data anomalies and source security of the corresponding data source; A lake determination unit, configured to determine a storage lake from a type-storage comparison table according to a data type of corresponding sub-data; A storage analysis unit, configured to directly store the corresponding sub-data in a corresponding storage lake if the reliability is greater than or equal to a preset reliability; Otherwise, the security level to be adjusted is determined based on reliability and presetness; A security encryption unit, used to retrieve a specified number of encryption algorithms from an encryption database according to the security level to be adjusted and in combination with reliability, and to combine the algorithms to securely encrypt the corresponding sub-data; A security upgrade unit, used to perform security upgrade on the corresponding storage space according to the space security of the designated storage space of the corresponding sub-data in the corresponding storage lake; The storage unit is used to store the securely encrypted sub-data in the security-upgraded storage space, wherein the storage space is a storage unit in the corresponding storage lake, and the storage lake includes several storage units.

[0041] Preferably, the variation calculation unit is used to: ; in, represents the processing variation coefficient of the processing path; n1 represents the number of processing nodes existing on the processing path; Indicates the historical actual working times of the i1th processing node; represents the number of historical set jobs of the i1th processing node; represents the number of times the i1th processing node repeats work on the processing path; Indicates the processing log of the i1th processing node and setting log Similarity function of ; Represents the data after the corresponding sub-data is processed by the i1th processing node The data before the corresponding subprocessing of the i1th processing node Similarity function of ; i1 represents the i1th processing node.

[0042] Preferably, the reliability determination unit is used to: ; ; in, Indicates the reliability of the corresponding sub-data; Represents the factor set under the corresponding sub-data Reliable influence coefficient on data; Indicates the source security of the data source of the corresponding sub-data; Represents the corresponding factor set The number of influencing factors involved; It represents the average value of the data impact length of all historical sub-data corresponding to the j1th historical factor; it represents the variance of the corresponding number of lengths obtained by random screening from the data impact lengths of all historical sub-data corresponding to the j1th historical factor; Indicates the length of the impact of the j1th influencing factor on the corresponding sub-data; Indicates the data length of the corresponding sub-data, and rand indicates the random function symbol.

[0043] In this embodiment, the processing nodes correspond one-to-one to the initial layer. For example, the typo recognition layer corresponds to one node, the missing information recognition layer corresponds to one node, and the data anomaly analysis layer corresponds to one node. For example, the typo recognition layer corresponds to 3 typo recognitions. At this time, the missing information recognition layer corresponds to 2 missing recognitions, and the data anomaly analysis layer corresponds to 1 anomaly analysis. At this time, the processing path is: typo recognition layer-typo recognition layer-typo recognition layer-missing information recognition layer-missing information recognition layer-data anomaly analysis layer.

[0044] In this embodiment, the processing log is the background data automatically generated by the corresponding node during the processing process, which can be directly captured, for example, the situation of wrong character recognition, for example, which character is the wrong character, and where it is located in the sub-data.

[0045] In this embodiment, for example, the character at position e1 should be "I", but the recognized character is "Oh", and so on.

[0046] In this embodiment, the set of factors that affect the generation of data anomalies is determined based on the analysis results, that is, there are U1 set influencing factors, and each factor is used to perform security tests on the sub-data to determine whether there are changes under the corresponding influencing factors. The factors that have changed are summarized to obtain a factor set, that is, the number of factors in the factor set is less than U1. For example, a Trojan virus of model w1 will change certain characters in the sub-data.

[0047] In this embodiment, the normalization process refers to normalizing the values ​​of the relevant parameters to between 0 and 1 to facilitate subsequent calculations, and the standard normalized value of the value of each parameter is pre-set.

[0048] The data type refers to the type of resource obtained. For example, the sub-data obtained from the search results of "semiconductor" can be regarded as the device type, etc.

[0049] In this embodiment, the type-storage comparison table includes different data types and storage lakes for the types, which facilitates direct allocation of storage.

[0050] In this embodiment, the preset property is pre-set and has a value of 0.8.

[0051] In this embodiment, the designated storage space refers to a storage address in the storage lake (disk), for example, address 0011-ff01.

[0052] In this embodiment, the space security is obtained by matching the space-security comparison table, and the table contains different address segments and the security of the space for the address segments, which are all set in advance, that is, the storage lake is factory tested before storage is used.

[0053] In this embodiment, security upgrade means that if the space security is greater than or equal to the preset security, no upgrade is required. Otherwise, the storage space needs to be securely upgraded according to the preset security. The upgrade can be performed using an upgrade package, which contains content related to the security encryption algorithm and belongs to the prior art.

[0054] In this embodiment, the setting log is pre-planned and is known content.

[0055] In this embodiment, the value of M1 is greater than 5, and a corresponding number of lengths are obtained by random screening. It should be noted that the number of random screening is a positive number.

[0056] For example, if the length of the sub-data is 100, and the length of the influence of factor a1 on the sub-data is 10, then according to The calculation result of factor a1 is: 10 / 100.

[0057] The beneficial effects of the above technical solution are: starting from the processing path, processing log, and analysis results to determine the processing variation coefficient and data anomalies, and then determine the security level to be adjusted for the sub-data and combine the reliability to obtain a specified number of encryption algorithms to ensure the security of data encryption, and further combine the security of the space to ensure the security of data storage from two aspects.

[0058] The present invention provides an intelligent data analysis and storage device based on a large model, wherein the security encryption unit comprises: A number calculation subunit, used to calculate the specified number Zn of the encryption algorithm; ; in, Indicates the preset nature of the corresponding sub-data; It represents the unit quantity of safety degradation based on reliability; represents the floor function; represents the ceiling function; An algorithm locking subunit, used to sequentially lock the first algorithm with a predetermined security level from the encryption database according to the specified number Zn; The random sorting unit is used to randomly sort and combine all the acquired first algorithms, and securely encrypt the corresponding sub-data in sequence according to the first algorithms in the combination result.

[0059] In this embodiment, It is the security level to be adjusted, and its value is an integer.

[0060] In this embodiment, each encryption algorithm has its corresponding security, which is pre-tested. Therefore, the security of all encryption algorithms can be sorted in order to obtain an algorithm close to the preset security. For example, the security of the encryption algorithms sorted in order are: o1, o2, o3, o4, o5, o6. At this time, the preset security is between o4 and o5. At this time, Zn is 2, and the encryption algorithm corresponding to o4 and o5 is used as the first algorithm. If the preset security is after o6, Zn is 2, and the encryption algorithm corresponding to o5 and o6 is used as the first algorithm, and so on. The principle is to sort the algorithms closest to the preset security.

[0061] In this embodiment, the random ordering combination is to encrypt after disrupting the order to ensure encryption security.

[0062] In this embodiment, the reliability-based safety attenuation unit is generally 0.2.

[0063] The beneficial effect of the above technical solution is: based on the preset and reliability, the specified number is calculated, and then the first algorithm is obtained and randomly combined to achieve secure encryption of data and ensure the security of data.

[0064] The present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes any one of the intelligent data processing methods of the intelligent data processing device based on a large model, such as Figure 3 As shown: Step 1: Obtain data processing requirements and extract requirements to obtain several requirement items, and then obtain the requirement analysis target and target weight of each requirement item; Step 2: According to the demand analysis target and target weight, a large model is constructed, and the data to be processed is input into the large model to obtain the analysis results of the sub-data under different data sources; Step 3: According to the processing path and data anomalies corresponding to the analysis results, and in combination with the source security of the data source, a storage lake is allocated to the corresponding sub-data and stored.

[0065] The present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes any one of the intelligent data processing methods of the intelligent data processing device based on a large model: Step 1: Obtain data processing requirements and extract requirements to obtain several requirement items, and then obtain the requirement analysis target and target weight of each requirement item; Step 2: According to the demand analysis target and target weight, a large model is constructed, and the data to be processed is input into the large model to obtain the analysis results of the sub-data under different data sources; Step 3: According to the processing path and data anomalies corresponding to the analysis results, and in combination with the source security of the data source, a storage lake is allocated to the corresponding sub-data and stored.

[0066] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent data analysis and storage device based on a large model, characterized in that: include: The demand analysis module is used to obtain data processing requirements and extract requirements to obtain a number of requirement items, and then obtain the demand analysis target and target weight of each requirement item; A data analysis module is used to build a large model according to the demand analysis target and target weight, and input the data to be processed into the large model to obtain the analysis results of the sub-data under different data sources; The storage allocation module is used to allocate a storage lake to the corresponding sub-data and store it according to the processing path and data anomalies corresponding to the analysis results and in combination with the source security of the data source.

2. The large model-based intelligent data analysis and storage device according to claim 1, characterized in that: The demand analysis module includes: A description extraction unit, used to extract the data processing requirements respectively based on the set predefined indicators, and obtain the requirement description under each predefined indicator; A parameter extraction unit, used to extract the requirement parameters related to the corresponding predefined indicators from the requirement description, obtain parameter expressions, and regard them as requirement items; The matching unit is used to match the demand items with the item-target comparison table to obtain corresponding demand analysis targets and target weights.

3. The large model-based intelligent data analysis and storage device according to claim 1, characterized in that: The data analysis module comprises: An initialization unit, used for acquiring an initial model consistent with all predefined indicators from an indicator-model database, and initializing relevant parameters of each predefined indicator in the initial model; The allocation unit is used to allocate the demand analysis objectives and target weights under each set predefined indicator to the corresponding initial layers to construct a large model.

4. The large model-based intelligent data analysis and storage device according to claim 3, characterized in that: The initial model includes initial layers whose number is consistent with the set predefined indicators, and each initial layer corresponds to a set predefined indicator.

5. The large model-based intelligent data analysis and storage device according to claim 1, characterized in that: The storage allocation module comprises: A path drawing unit is used to process nodes in the corresponding large model in sequence according to the sub-data under each data source captured in real time by the capture tool, and draw a processing path; a variation calculation unit, for determining a processing variation coefficient of the processing path in combination with a processing log of each processing node; A situation determination unit, used to determine the data anomaly situation of the corresponding sub-data according to the analysis result, wherein the data anomaly situation includes: an anomaly coefficient of the data itself and a set of factors that affect the generation of data anomaly; ； in, Indicates the data anomaly coefficient of the corresponding sub-data; Indicates the normalized value of the j2th parameter under the corresponding sub-data; represents the standard normalized value of the j2th parameter under the corresponding sub-data; represents the total number of parameters under the corresponding sub-data; j2 represents the j2th parameter under the corresponding sub-data; A reliability determination unit, configured to determine the reliability of the corresponding sub-data according to the processing variation coefficient and in combination with data anomalies and source security of the corresponding data source; A lake determination unit, configured to determine a storage lake from a type-storage comparison table according to a data type of corresponding sub-data; A storage analysis unit, configured to directly store the corresponding sub-data in a corresponding storage lake if the reliability is greater than or equal to a preset reliability; Otherwise, the security level to be adjusted is determined based on reliability and presetness; A security encryption unit, used to retrieve a specified number of encryption algorithms from an encryption database according to the security level to be adjusted and in combination with reliability, and to combine the algorithms to securely encrypt the corresponding sub-data; A security upgrade unit, used to perform security upgrade on the corresponding storage space according to the space security of the designated storage space of the corresponding sub-data in the corresponding storage lake; The storage unit is used to store the securely encrypted sub-data in the security-upgraded storage space, wherein the storage space is a storage unit in the corresponding storage lake, and the storage lake includes several storage units.

6. The large model-based intelligent data analysis and storage device according to claim 5, characterized in that: The variation calculation unit is used for: ； in, represents the processing variation coefficient of the processing path; n1 represents the number of processing nodes existing on the processing path; Indicates the historical actual working times of the i1th processing node; represents the number of historical set jobs of the i1th processing node; represents the number of times the i1th processing node repeats work on the processing path; Indicates the processing log of the i1th processing node and setting log Similarity function of ; Represents the data after the corresponding sub-data is processed by the i1th processing node The data before the corresponding subprocessing of the i1th processing node Similarity function of ; i1 represents the i1th processing node.

7. The large model-based intelligent data analysis and storage device according to claim 6, characterized in that: The reliability determination unit is used to: ； ； in, Indicates the reliability of the corresponding sub-data; Represents the factor set under the corresponding sub-data Reliable influence coefficient on data; Indicates the source security of the data source of the corresponding sub-data; Represents the corresponding factor set The number of influencing factors involved; It represents the average value of the data impact length of the j1th influencing factor on all historical sub-data; Indicates that the data impact length of all historical sub-data corresponding to the j1th historical factor is calculated according to Random screening obtains the variance of the corresponding number of lengths; Indicates the length of the impact of the j1th influencing factor on the corresponding sub-data; Indicates the data length of the corresponding sub-data, and rand indicates the random function symbol.

8. The large model-based intelligent data analysis and storage device according to claim 7, characterized in that: The security encryption unit comprises: A number calculation subunit, used to calculate the specified number Zn of the encryption algorithm; ； in, Indicates the preset nature of the corresponding sub-data; It represents the unit quantity of safety degradation based on reliability; represents the floor function; represents the ceiling function; An algorithm locking subunit, used to sequentially lock the first algorithm with a predetermined security level from the encryption database according to the specified number Zn; The random sorting unit is used to randomly sort and combine all the acquired first algorithms, and securely encrypt the corresponding sub-data in sequence according to the first algorithms in the combination result.

9. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the processor executes the intelligent data processing method of the large model-based intelligent data processing device according to any one of claims 1-8.

10. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the intelligent data processing method of the large model-based intelligent data processing device according to any one of claims 1-8.

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