An Internet of Things-based electronic record management method and system

Abstract

This invention provides an Internet of Things (IoT)-based electronic record management method and system. The method includes: acquiring classification information of target records and deploying IoT devices based on the classification information; standardizing and converting the target records into metadata packages, including personal records, contract records, process records, and permission records; cleaning the metadata packages using an adaptive anomaly detection algorithm corresponding to the classification information to generate a structured record dataset; acquiring feature vectors corresponding to the record dataset; performing integrated classification of the record dataset based on the feature vectors; and distributing the metadata packages of different classifications; registering corresponding event channels based on the classification information; acquiring IoT device data through the event channels; and updating the distributed record data.

Description

Technical Field

[0001] This invention relates to the field of records management technology, and in particular to an electronic records management method and system based on the Internet of Things. Background Technology

[0002] Traditional electronic archiving involves a series of steps, including data capture, registration, processing, packaging, archiving, and dynamic management. Each step requires the coordination of a significant number of staff.

[0003] However, in the current processing technology, in addition to relying on human resources, there may be problems such as the difficulty in unified management and utilization due to the diverse sources of archives, forming "information silos", as well as low data utilization rate and easy "expiration" of data due to low data quality. Summary of the Invention

[0004] To address the problems existing in the prior art, embodiments of the present invention provide an electronic record management method and system based on the Internet of Things.

[0005] This invention provides an electronic records management method based on the Internet of Things, the method comprising:

[0006] Obtain the classification information of the target file, deploy IoT devices based on the classification information, and package the target file into a meta data package through standardized conversion. The target file includes personal files, contract files, process files, and permission files.

[0007] Based on the classification information, the corresponding adaptive anomaly detection algorithm is introduced into the metadata package to perform data cleaning and generate a structured archive dataset.

[0008] Obtain the feature vectors corresponding to the archive dataset, perform integrated classification on the archive dataset based on the feature vectors, and distribute the metadata data of different classifications.

[0009] Register the corresponding event channel based on the classification information, obtain IoT device data through the event channel, and then update the archive data in the distributed storage.

[0010] In one embodiment, the method further includes:

[0011] Based on the classification information, the metadata data packet is routed to the corresponding type of data cleaning pipeline, and intelligent completion of the metadata data packet is performed based on the data cleaning pipeline, including:

[0012] For short missing data in personal files, linear interpolation is performed, and for long missing data, KNN time-series completion is performed.

[0013] Fill in the missing information in the contract file with the average value of the equipment group;

[0014] Fill in the missing information in the process file with business rules.

[0015] In one embodiment, the method further includes:

[0016] Read the sensitive fields in the metadata of the personal files, contract files, and process files, and perform sensitive processing on the sensitive fields;

[0017] Read the metadata of the permission file and perform sensitive processing;

[0018] The distributed storage of metadata data of different categories includes:

[0019] The metadata of sensitive processing is stored in the simulation sandbox according to its feature vector.

[0020] In one embodiment, the method further includes:

[0021] Extract the data features of the archive dataset, and calculate the correlation between the data features and the business objectives corresponding to the archive type using a preset association algorithm;

[0022] Based on the correlation, data features with a correlation coefficient less than a preset threshold are removed.

[0023] In one embodiment, the method further includes:

[0024] It integrates the enterprise's permission model, permission adjustment rules, and permission conflict rules, and maps them to the corresponding type of distributed storage archive system.

[0025] This invention provides an Internet of Things (IoT) based electronic records management system, the system comprising:

[0026] The acquisition module is used to acquire the classification information of the target file, deploy IoT devices based on the classification information, and package the target file into a meta data package through standardized conversion. The target file includes personal files, contract files, process files and permission files.

[0027] The cleaning module is used to clean the metadata data by introducing an adaptive anomaly detection algorithm corresponding to the classification based on the classification information, and generate a structured archive dataset.

[0028] The storage module is used to obtain the feature vectors corresponding to the archive dataset, perform integrated classification on the archive dataset based on the feature vectors, and perform distributed storage of metadata data of different classifications.

[0029] The update module is used to register a corresponding event channel based on the classification information, obtain IoT device data through the event channel, and then update the archive data in the distributed storage.

[0030] In one embodiment, the system further includes:

[0031] The completion module is used to route the metadata data packet to a corresponding type of data cleaning pipeline based on the classification information, and to perform intelligent completion of the metadata data packet based on the data cleaning pipeline, including:

[0032] For short missing data in personal files, linear interpolation is performed, and for long missing data, KNN time-series completion is performed.

[0033] Fill in the missing information in the contract file with the average value of the equipment group;

[0034] Fill in the missing information in the process file with business rules.

[0035] In one embodiment, the system further includes:

[0036] The first processing module is used to read sensitive fields from the metadata of the personal files, contract files, and process files, and to perform sensitive processing on the sensitive fields;

[0037] The second processing module is used to read the metadata of the permission file and perform sensitive processing.

[0038] A sandbox storage module, used for distributed storage of metadata data of different categories, includes:

[0039] The metadata of sensitive processing is stored in the simulation sandbox according to its feature vector.

[0040] This invention provides an electronic device, including a processor and a memory;

[0041] The processor is connected to the memory;

[0042] The memory is used to store executable program code;

[0043] The processor runs a program corresponding to the executable program code stored in the memory to perform the methods described in one or more embodiments.

[0044] This invention provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described Internet of Things-based electronic record management method.

[0045] In view of the above, in one or more embodiments of this specification, classification information of the target archives is obtained, and IoT devices are deployed based on the classification information. The target archives are standardized and packaged into metadata data packages, including personal archives, contract archives, process archives, and permission archives. Based on the classification information, an adaptive anomaly detection algorithm corresponding to the classification is introduced into the metadata data packages for data cleaning, generating a structured archive dataset. Feature vectors corresponding to the archive datasets are obtained, and the archive datasets are integrated and classified based on the feature vectors. Metadata packages of different categories are distributed and stored. Based on the classification information, corresponding event channels are registered, and IoT device data is obtained through the event channels to update the archive data in the distributed storage. Thus, the storage strategy and data value can be matched through the Internet of Things, the management process is automated and intelligent, the utilization efficiency is improved, and the environment is perceived through IoT devices, responding to external changes to ensure the real-time performance and accuracy of the archive data. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating an embodiment of an Internet of Things-based electronic record management method provided in this specification.

[0048] Figure 2 This is a schematic diagram of the structure of an Internet of Things-based electronic records management system provided in one embodiment of this specification.

[0049] Figure 3 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this specification. Detailed Implementation

[0050] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed merely to enable those skilled in the art to better understand and implement the subject matter described herein, and are not intended to limit the scope, applicability, or examples set forth in the claims. The function and arrangement of the elements discussed may be changed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the various examples. For example, the described methods may be performed in a different order than described, and steps may be added, omitted, or combined. Furthermore, features described in some examples may be combined in other examples.

[0051] As used herein, the term "comprising" and its variations are open terms meaning "including but not limited to". The term "based on" means "at least partially based on". The terms "one embodiment" and "an embodiment" mean "at least one embodiment". The term "another embodiment" means "at least one other embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other definitions, whether explicit or implicit, may be included below. Unless explicitly indicated by the context, the definition of a term shall remain consistent throughout the specification.

[0052] like Figure 1 As shown, this embodiment of the invention provides an electronic records management method based on the Internet of Things, including:

[0053] Step S102: Obtain the classification information of the target file, deploy IoT devices based on the classification information, and package the target file into a meta data package through standardized conversion. The target file includes personal files, contract files, process files, and permission files.

[0054] Specifically, for different types of files, corresponding differentiated IoT devices are deployed. For personal files, wearable devices and identity verification devices can be used to determine the ownership of personal data. Contract files can be transmitted via NB-IoT protocol using industrial sensors. Process files can be captured by deploying card readers and other devices to collect operation logs. Permission files can be captured by deploying biometric sensors and other devices to collect relevant permission scheme data via TCP / IP protocol. All these differentiated devices are then registered to the IoT platform for centralized management, protocol adaptation, and data channel establishment.

[0055] Furthermore, the data streams uploaded from different IoT devices are inspected, and key fields are checked to ensure data integrity. The data is then standardized and encapsulated, meaning the data stream is associated with business implications. For example, 28.5 in the data stream can be associated with the meaning of "temperature," allowing the data to be understood during file processing. Then, the data is encapsulated according to the type of IoT device. For example, personal data can be encapsulated with a "P" prefix. The temperature 28.5 in the example above can be encapsulated based on its IoT device source. For example, if it comes from a personal wearable device, it is encapsulated with "P," and if it comes from contract data (such as the temperature of an item in a contract), it is encapsulated with "C." The encapsulated metadata can be in JSON format, thus unifying the chaotic data formats from different device sources into a defined format.

[0056] Step S104: Based on the classification information, introduce the corresponding adaptive anomaly detection algorithm for the metadata package to perform data cleaning and generate a structured archive dataset.

[0057] Specifically, while metadata packages unify the chaotic format, they may contain significant noise or missing data, making them unsuitable for direct use in profile creation. Data cleaning is necessary, requiring different cleaning schemes for different data types (e.g., personal profiles are high-frequency time series, while process profiles are discrete event streams). For missing data, different alarm thresholds and completion strategies are set for the sampling intervals of different data types. For example, for personal profiles, short missing data can be filled using linear interpolation, while long missing data can be filled using KNN time series imputation; for contract profiles, the mean of device groups (other sensor data) can be used; and for process profiles, the mean can be calculated and imputed based on business rules, etc. This ensures data integrity and continuity.

[0058] Furthermore, the anomaly detection and noise filtering steps for different types of data may include setting thresholds for different data types. Then, multiple anomaly detection methods are set, including but not limited to: detecting the physical rationality of the data, detecting data abrupt changes, detecting whether time-series data windows are abnormal, and analyzing contextual relationships to determine if data is abnormal. Based on the results of multiple detections, abnormal data is identified and removed.

[0059] Furthermore, after data cleaning, cross-device time synchronization is performed on similar data uploaded from different sensors. For example, for contract files, data on the same subject matter uploaded by multiple sensors can be aligned on the timeline. The starting point of the timeline can be set according to the type of data; for example, the starting point of the timeline for contract files can be the contract signing date.

[0060] Furthermore, after data preprocessing, feature extraction is performed. This includes extracting features from different types of data, such as personal information in personal files, contract duration and subject matter in contract files. After feature extraction, the corresponding Pearson correlation coefficient is calculated using appropriate algorithms to verify the relevance of the features to business objectives. Taking contract files as an example, by comparing contract performance time with the actual contract default rate, the correlation coefficient of contract performance time in the contract file can be determined (e.g., 0.85), and redundant features with correlation coefficients <0.3 are removed. Additionally, feature normalization is performed on each data feature to eliminate the influence of units and ensure comparability between features. Then, the normalized data is structured into archival data, ensuring that the structured data fields meet archival field requirements and can be directly written into the archive.

[0061] Step S106: Obtain the feature vector corresponding to the archive dataset, perform integrated classification on the archive dataset based on the feature vector, and perform distributed storage of the metadata data of different classifications.

[0062] Specifically, features from structured datasets are constructed into fixed-dimensional vectors. For example, the feature vector for personal files has a dimension of 9 (including 9 features), and the feature vector for contract files has a dimension of 15 (including 15 features), and so on. Then, a feature fingerprint library for file types is pre-built, with different file types containing corresponding key feature fingerprints. For example, personal files include personal health data and User_id format. This transforms heterogeneous structured data into vectors, making it processable by the classification model. Furthermore, the feature fingerprint library is used to intercept mislabeled data. Classification using feature vectors offers advantages over structured data: a stable number of fields (structured data may introduce new fields), clearer business meaning (structured data may contain multiple features with the same field), and unified dimensions.

[0063] Furthermore, an ensemble learning model is deployed, such as a model primarily based on XGBoost and supplemented by NLP. After training for classification, the model classifies the feature vectors of the data. The classification quality is then determined through validation of the classification results and human feedback. Once the classification quality meets the requirements, the classified files are stored in a distributed system.

[0064] Furthermore, to prevent unauthorized access risks for data in distributed storage, the enterprise's existing permission model can be mapped to the file system, such as which employees have which read / write permissions for which files. Then, rules for adjusting permissions and handling permission conflicts should be determined. Access permissions can also be controlled through key-based and dynamic access control rules. This allows for different access permissions to be assigned to different types of data.

[0065] Step S108: Register the corresponding event channel based on the classification information, obtain IoT device data through the event channel, and then update the archive data in the distributed storage.

[0066] Specifically, for archived repositories that require real-time data updates, dedicated event channels are registered based on the archive type. Events are data from the corresponding IoT devices via their respective event source types. For example, contract archives consist of event streams from various sensors. When acquiring data, the data prefix can include the device ID and the event topic. Then, using the event topic, corresponding feature vectors are extracted from the event data for data archiving.

[0067] Furthermore, sensitive fields in the metadata of personal files, contract files, and process files, as well as permission files, undergo sensitive processing, including masking, generalization, or replacement. For feature vectors, synthetic data techniques can be used to generate a batch of simulated data with similar statistical properties but entirely new content. This data is then stored in a simulation sandbox based on its feature vectors. This eliminates the need for business users, data analysts, or AI algorithms to directly access the main sensitive archive. The data in the sandbox is beneficial for testing new algorithms within the archives, business process simulations, and other testing scenarios. If the algorithms tested prove valuable, they can be used to improve the archive management process.

[0068] This invention provides an IoT-based electronic records management method. The method acquires the classification information of target records and deploys IoT devices based on this information. The target records are standardized and packaged into metadata packages, including personal records, contract records, process records, and permission records. Based on the classification information, an adaptive anomaly detection algorithm corresponding to the classification is introduced into the metadata packages for data cleaning, generating a structured record dataset. Feature vectors corresponding to the record dataset are obtained, and the datasets are integrated and classified based on these feature vectors. Metadata packages of different categories are then distributed and stored. Event channels are registered based on the classification information, and data from IoT devices is obtained through these channels to update the distributed record data. This method enables the matching of storage strategies with data value through IoT, automating and intelligentizing the management process, improving utilization efficiency, and ensuring the real-time nature and accuracy of record data by sensing the environment and responding to external changes through IoT devices.

[0069] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of an Internet of Things-based electronic records management system provided in an embodiment of this application. Figure 2 As shown, the system includes:

[0070] The acquisition module S202 is used to acquire the classification information of the target file, deploy IoT devices based on the classification information, and package the target file into a meta data package through standardized conversion. The target file includes personal files, contract files, process files and permission files.

[0071] The cleaning module S204 is used to clean the metadata data by introducing an adaptive anomaly detection algorithm corresponding to the classification based on the classification information, and generate a structured archive dataset.

[0072] Storage module S206 is used to obtain the feature vector corresponding to the archive dataset, perform integrated classification on the archive dataset based on the feature vector, and perform distributed storage of metadata data of different classifications;

[0073] The update module S208 is used to register a corresponding event channel based on the classification information, obtain IoT device data through the event channel, and then update the archive data in the distributed storage.

[0074] In another embodiment, an Internet of Things-based electronic records management system further includes:

[0075] The completion module is used to route the metadata data packet to a corresponding type of data cleaning pipeline based on the classification information, and to perform intelligent completion of the metadata data packet based on the data cleaning pipeline, including:

[0076] For short missing data in personal files, linear interpolation is performed, and for long missing data, KNN time-series completion is performed.

[0077] Fill in the missing information in the contract file with the average value of the equipment group;

[0078] Fill in the missing information in the process file with business rules.

[0079] In another embodiment, an Internet of Things-based electronic records management system further includes:

[0080] The first processing module is used to read sensitive fields from the metadata of the personal files, contract files, and process files, and to perform sensitive processing on the sensitive fields;

[0081] The second processing module is used to read the metadata of the permission file and perform sensitive processing.

[0082] A sandbox storage module, used for distributed storage of metadata data of different categories, includes:

[0083] The metadata of sensitive processing is stored in the simulation sandbox according to its feature vector.

[0084] Those skilled in the art will clearly understand that the technical solutions of the embodiments of this application can be implemented by means of software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware that can independently complete or cooperate with other components to complete a specific function, wherein the hardware may be, for example, a field-programmable gate array (FPGA), an integrated circuit (IC), etc.

[0085] Each processing unit and / or module in the embodiments of this application can be implemented by an analog circuit that implements the functions described in the embodiments of this application, or by software that executes the functions described in the embodiments of this application.

[0086] See Figure 3It shows a schematic diagram of the structure of an electronic device according to an embodiment of this application, which can be used to implement... Figure 1 The method in the illustrated embodiment. (As shown) Figure 3 As shown, the electronic device 300 may include: at least one processor 301, at least one network interface 304, user interface 303, memory 305, and at least one communication bus 302.

[0087] The communication bus 302 is used to enable communication between these components.

[0088] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0089] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0090] The processor 301 may include one or more processing cores. The processor 301 connects to various parts within the electronic device 300 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0091] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. Figure 3 As shown, the memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.

[0092] exist Figure 3 In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and obtain user input data; while the processor 301 can be used to call the image-based interactive application stored in the memory 305 and specifically perform the following operations: obtain the classification information of the target file, deploy IoT devices based on the classification information, package the target file into a meta-data package through standardization conversion, the target file includes personal files, contract files, process files, and permission files; based on the classification information, introduce the corresponding adaptive anomaly detection algorithm to the meta-data package for data cleaning, and generate a structured file dataset; obtain the feature vector corresponding to the file dataset, perform integrated classification of the file dataset based on the feature vector, and distribute the meta-data packages of different categories; register the corresponding event channel based on the classification information, obtain IoT device data through the event channel, and then update the distributed file data.

[0093] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0094] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0095] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0096] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0097] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0098] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0099] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0100] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0101] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

Claims

1. An electronic record management method based on the Internet of Things, the method comprising: Obtain the classification information of the target file, deploy IoT devices based on the classification information, and package the target file into a meta data package through standardized conversion. The target file includes personal files, contract files, process files, and permission files. Based on the classification information, the corresponding adaptive anomaly detection algorithm is introduced into the metadata package to perform data cleaning and generate a structured archive dataset. Obtain the feature vectors corresponding to the archive dataset, perform integrated classification on the archive dataset based on the feature vectors, and distribute the metadata data of different classifications. Register the corresponding event channel based on the classification information, obtain IoT device data through the event channel, and then update the archive data in the distributed storage.

2. The method according to claim 1, characterized in that, The step of introducing an adaptive anomaly detection algorithm corresponding to the classification into the metadata for data cleaning includes: Based on the classification information, the metadata data packet is routed to the corresponding type of data cleaning pipeline, and intelligent completion of the metadata data packet is performed based on the data cleaning pipeline, including: For short missing data in personal files, linear interpolation is performed, and for long missing data, KNN time-series completion is performed. Fill in the missing information in the contract file with the average value of the equipment group; Fill in the missing information in the process file with business rules.

3. The method according to claim 2, characterized in that, The method further includes: Read the sensitive fields in the metadata of the personal files, contract files, and process files, and perform sensitive processing on the sensitive fields; Read the metadata of the permission file and perform sensitive processing; The distributed storage of metadata data of different categories includes: The metadata of sensitive processing is stored in the simulation sandbox according to its feature vector.

4. The method according to claim 1, characterized in that, After generating the structured archive dataset, the following is also included: Extract the data features of the archive dataset, and calculate the correlation between the data features and the business objectives corresponding to the archive type using a preset association algorithm; Based on the correlation, data features with a correlation coefficient less than a preset threshold are removed.

5. The method according to claim 1, characterized in that, The distributed storage of metadata data of different categories includes: It integrates the enterprise's permission model, permission adjustment rules, and permission conflict rules, and maps them to the corresponding type of distributed storage archive system.

6. An electronic records management system based on the Internet of Things, characterized in that, The system includes; The acquisition module is used to acquire the classification information of the target file, deploy IoT devices based on the classification information, and package the target file into a meta data package through standardized conversion. The target file includes personal files, contract files, process files and permission files. The cleaning module is used to clean the metadata data by introducing an adaptive anomaly detection algorithm corresponding to the classification based on the classification information, and generate a structured archive dataset. The storage module is used to obtain the feature vectors corresponding to the archive dataset, perform integrated classification on the archive dataset based on the feature vectors, and perform distributed storage of metadata data of different classifications. The update module is used to register a corresponding event channel based on the classification information, obtain IoT device data through the event channel, and then update the archive data in the distributed storage.

7. The system according to claim 6, characterized in that, The system also includes: The completion module is used to route the metadata data packet to a corresponding type of data cleaning pipeline based on the classification information, and to perform intelligent completion of the metadata data packet based on the data cleaning pipeline, including: For short missing data in personal files, linear interpolation is performed, and for long missing data, KNN time-series completion is performed. Fill in the missing information in the contract file with the average value of the equipment group; Fill in the missing information in the process file with business rules.

8. The system according to claim 6, characterized in that, The system also includes: The first processing module is used to read sensitive fields from the metadata of the personal files, contract files, and process files, and to perform sensitive processing on the sensitive fields; The second processing module is used to read the metadata of the permission file and perform sensitive processing. A sandbox storage module, used for distributed storage of metadata data of different categories, includes: The metadata of sensitive processing is stored in the simulation sandbox according to its feature vector.

9. An electronic device, comprising a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, in order to perform the method as described in any one of claims 1-5.

10. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of claims 1-5.

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