Intelligent archival repository intelligent early warning method and system based on Internet of Things

Abstract

The invention discloses a smart archival repository intelligent early warning method and system based on the Internet of Things, and relates to the technical field of archival repository early warning, and the smart archival repository intelligent early warning method comprises the steps: transmitting the pre-collected spatial layout data and environment monitoring data of an archival repository to a central management platform through the Internet of Things communication technology; dividing the spatial layout of the archival repository based on the spatial layout data, and judging an archival storage type in each archival storage space; identifying environment monitoring characteristics corresponding to each file storage space based on the environment monitoring data, and judging file storage conditions in each file storage space in combination with the environment monitoring characteristics; and establishing a partition early warning mechanism by combining the file storage type and the file storage condition in each file storage space. According to the invention, environment regulation and control can be effectively carried out according to storage requirements of archives in different areas, and resource waste and environment inadaptation caused by unified regulation and control are avoided.

Description

Technical Field

[0001] The present invention relates to the field of archive warehouse early warning technology, and in particular to an intelligent early warning method and system for a smart archive warehouse based on the Internet of Things. Background Art

[0002] Archives warehouses are spaces dedicated to storing and protecting various archival materials. They are designed to meet the needs of long-term storage and ensure that archives are protected from environmental or external factors during storage. The design and management of archives warehouses must take into account a variety of factors, such as temperature and humidity, air quality, light, pollutant control, and fire safety, to ensure the safety, integrity, and long-term preservation reliability of archives.

[0003] In archive warehouse management, intelligent early warning plays a key role. By real-time monitoring of environmental parameters, potential risks that may affect archive preservation can be discovered and handled in a timely manner. Although intelligent early warning can trigger response measures based on preset thresholds, there is still an important shortcoming: it is difficult to closely integrate spatial layout with environmental monitoring.

[0004] Archives stored in different areas of an archive warehouse often differ in importance and sensitivity. However, due to the inability to combine the specific storage layout, the environmental control methods of all areas are usually unified, and it is impossible to perform differentiated environmental control based on the special needs of different archive types. This defect means that high-value or sensitive archive areas cannot receive more stringent environmental monitoring and control measures, thereby increasing the risk of archive damage.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In view of the problems in the related technology, the present invention proposes an intelligent early warning method and system for a smart archive warehouse based on the Internet of Things to overcome the above-mentioned technical problems existing in the existing related technology.

[0007] To this end, the specific technical solution adopted by the present invention is as follows:

[0008] According to one aspect of the present invention, there is provided an intelligent early warning method for a smart archive warehouse based on the Internet of Things, the intelligent early warning method for a smart archive warehouse comprising:

[0009] S1. Use the Internet of Things communication technology to transmit the pre-collected spatial layout data and environmental monitoring data of the archive warehouse to the central management platform;

[0010] S2. Divide the spatial layout of the archive warehouse based on the spatial layout data, and determine the archive storage type in each archive storage space;

[0011] S3, identifying the environmental monitoring features corresponding to each archive storage space based on the environmental monitoring data, and determining the archive storage conditions in each archive storage space in combination with the environmental monitoring features;

[0012] S4. Establish a zoning early warning mechanism based on the archive storage type and archive storage conditions in each archive storage space, and formulate response measures based on the zoning early warning mechanism.

[0013] Furthermore, the spatial layout data and environmental monitoring data of the archive warehouse collected in advance are transmitted to the central management platform by using the Internet of Things communication technology, including:

[0014] S11. Using a space collection device installed inside the archive warehouse to collect spatial layout data of the archive warehouse, the spatial layout data includes spatial structure and archive storage facilities;

[0015] S12, using the temperature and humidity sensor, air quality sensor and light sensor installed inside the archive warehouse to collect environmental monitoring data of the archive warehouse, and the environmental monitoring data includes temperature and humidity data, air quality data and light intensity data;

[0016] S13. Convert the spatial layout data and environmental monitoring data into standardized data formats in turn, and transmit the data to the central management platform through the Internet of Things communication protocol.

[0017] Furthermore, the spatial layout of the archive warehouse is divided based on the spatial layout data, and the archive storage types in each archive storage space are determined to include:

[0018] S21. Establish a spatial graph of the archive warehouse based on the spatial layout data, and represent nodes in the spatial graph as archive distribution spaces, and edges represent connection channels between archive distribution spaces;

[0019] S22. Using the minimum edge weight segmentation technique to preliminarily divide the spatial graph into several subgraphs, and combining the whale clustering algorithm to cluster the subgraphs with similar features into the same feature group, to obtain several archive storage spaces;

[0020] S23, extracting the identification code of the archive cover by using optical character recognition technology, identifying the archive storage type in each archive storage space, and identifying the importance of the archive information in combination with the usage frequency of each archive storage type;

[0021] S24. The archive storage type and the importance of archive information in each archive storage space are displayed as a heat map.

[0022] Furthermore, the whale clustering algorithm is combined to cluster subgraphs with similar features into the same feature group, and several archive storage spaces are obtained, including:

[0023] S221, treating each subgraph as a whale individual, and setting the population size and initial clustering position of the whale algorithm based on the number of subgraphs;

[0024] S222, calculating the local density of the subgraph based on the attribute characteristics of the subgraph, and analyzing the local density change using the slope measurement formula to obtain a slope change trend graph of each subgraph;

[0025] S223, selecting an initial cluster center from the slope change trend graph, and gradually assigning each subgraph to a cluster of similar features based on the initial cluster center, and calculating the clustering accuracy based on the initial clustering result;

[0026] S224, comparing the cutoff distance corresponding to the maximum clustering accuracy with a preset threshold, if it is less than the preset threshold, returning to step S222, otherwise, outputting the cutoff distance greater than the preset threshold to obtain the optimal cutoff distance;

[0027] S225. Complete clustering of each subgraph based on the optimal cutoff distance to form a number of archive storage spaces.

[0028] Further, identifying the environmental monitoring features corresponding to each archive storage space based on the environmental monitoring data, and determining the archive storage conditions in each archive storage space in combination with the environmental monitoring features includes:

[0029] S31, based on the light intensity data in each archive storage space, simulate the propagation path of light and identify the radiant illumination on the archive surface;

[0030] S32, establishing a fluid mechanics model based on the air quality data in each archive storage space, and using the fluid mechanics model to predict the hot spots of pollution sources;

[0031] S33. Comprehensively analyze the radiation illumination on the archive surface and the hot spots of pollution sources to determine the archive storage conditions in each archive storage space.

[0032] Further, based on the light intensity data in each archive storage space, simulating the propagation path of light and identifying the radiant illumination of the archive surface includes:

[0033] S311, randomly defining the light emitting position and light emitting direction in the archive storage space to simulate the divergence of light in different directions and obtain the emitting light equation;

[0034] S312, taking the archive storage facility as an obstacle, determining the collision point when the light ray contacts the archive storage facility and generating the coordinates of the collision point;

[0035] S313, calculating the reflection path of the light according to the reflection coefficient of the archive storage facility, and simulating and tracing the reflection path until the light energy is dissipated to calculate the coordinates of the reflection point;

[0036] S314, dividing the archive storage space into grids and counting the number of collision points and reflection points in each grid, and calculating the radiation illumination of the archive surface in combination with the light value carried by the light and the grid area.

[0037] Furthermore, a partition warning mechanism is established in combination with the archive storage type and archive storage conditions in each archive storage space, and response measures are formulated based on the partition warning mechanism, including:

[0038] S41, setting an optimal environmental measurement threshold for each archive storage space in combination with the archive storage type, the importance of archive information and the archive storage conditions in each archive storage space;

[0039] S42, collecting the environmental measurement value of each archive storage space in real time and comparing it with the optimal environmental measurement threshold, and dividing the warning level of each archive storage space based on the comparison result;

[0040] S43. Trigger the warning instruction according to the priority, and initiate response measures according to the warning level of each archive storage space after the warning instruction is triggered.

[0041] Furthermore, the optimal environment measurement threshold is set for each archive storage space in combination with the archive storage type, the importance of archive information and the archive storage condition in each archive storage space, including:

[0042] S411, the archive storage type, the importance of archive information and the archive storage conditions in each archive storage space are used as different hierarchical structures, and the hierarchical structures include a target layer, a criterion layer and a sub-criterion layer;

[0043] S412, constructing a judgment matrix for the importance of each criterion in the criterion layer based on the target layer, and performing pairwise comparisons on each sub-criteria to generate a judgment matrix of the sub-criteria;

[0044] S413, calculating the weights of each criterion and sub-criterion by decomposing the eigenvalues ​​of the judgment matrix, and detecting the logical consistency of the judgment matrix by using the consistency ratio;

[0045] S414. Assign importance to the archive storage type, archive information importance, and archive storage conditions in each archive storage space in combination with the weight calculation result, and set an optimal environment measurement threshold for each archive storage space based on the importance assignment result.

[0046] Furthermore, the slope measurement formula is:

[0047]

[0048] In the formula, h i represents the slope value of the i-th subgraph; i represents the subgraph number; δ i represents the local density of the i-th subgraph; δ i+1 represents the local density of the i+1th subgraph; δ max represents the maximum local density among all subgraphs; δ min represents the minimum local density among all subgraphs.

[0049] According to another aspect of the present invention, there is also provided an intelligent early warning system for a smart archive warehouse based on the Internet of Things, the intelligent early warning system for a smart archive warehouse comprising a data measurement module, a file type identification module, a storage condition identification module and a partition early warning module;

[0050] The data measurement module is used to transmit the pre-collected spatial layout data and environmental monitoring data of the archive warehouse to the central management platform using the Internet of Things communication technology;

[0051] The file type identification module is used to divide the spatial layout of the archive warehouse based on the spatial layout data and determine the file storage type in each file storage space;

[0052] A storage condition identification module, used to identify the environmental monitoring characteristics corresponding to each archive storage space based on the environmental monitoring data, and determine the archive storage conditions in each archive storage space in combination with the environmental monitoring characteristics;

[0053] The partition warning module is used to establish a partition warning mechanism based on the archive storage type and archive storage conditions in each archive storage space, and formulate response measures based on the partition warning mechanism.

[0054] The beneficial effects of the present invention are:

[0055] 1. The present invention constructs a spatial map of the archive warehouse through spatial layout data, and uses the whale clustering algorithm to cluster sub-graphs with similar characteristics, so as to more accurately divide the archive storage space and perform differentiated management for the environment of different regions. It can effectively regulate the environment according to the storage needs of archives in different regions, avoiding resource waste and environmental inadaptability caused by unified regulation. At the same time, the identification code of the archive cover is extracted and the importance of the archive is identified in combination with the frequency of use, so that key archive areas can be given priority according to the importance of the archive, thereby improving the efficiency of management and maintenance.

[0056] 2. The present invention can accurately calculate the radiation illumination on the archive surface through simulation and analysis of light intensity data, ensure a comprehensive understanding of the actual impact of light on archives, help to accurately judge archive storage conditions, and accurately identify hot spots where pollution sources are concentrated in the warehouse by predicting air flow and pollution source distribution. At the same time, the radiation illumination is combined with pollution source hot spots for comprehensive analysis, thereby not only providing a more comprehensive archive storage condition assessment, but also cross-judging the suitability of archive storage through multiple factors, thereby improving the scientificity and rationality of archive protection.

[0057] 3. The present invention can set an exclusive optimal environmental measurement threshold for each area by combining the archive storage type, the importance of archive information and the storage conditions in each archive storage space. This refined setting ensures that different areas are subject to differentiated environmental monitoring based on their specific needs and importance, avoiding a one-size-fits-all management approach. According to the warning level and the importance of the archive, the warning instructions for high-priority archive areas can be triggered first and response measures can be quickly initiated, thereby not only improving the protection of key archives, but also improving the efficiency of emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only 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.

[0059] Figure 1 This is a flow chart of an intelligent early warning method for a smart archive warehouse based on the Internet of Things according to an embodiment of the present invention;

[0060] Figure 2 The present invention is a block diagram of an intelligent early warning system for a smart archive warehouse based on the Internet of Things.

[0061] In the figure:

[0062] 1. Data measurement module; 2. File type identification module; 3. Storage condition identification module; 4. Partition warning module. DETAILED DESCRIPTION

[0063] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

[0064] According to an embodiment of the present invention, a method and system for intelligent early warning of a smart archive warehouse based on the Internet of Things are provided.

[0065] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to an embodiment of the present invention, an intelligent early warning method for a smart archive warehouse based on the Internet of Things, the intelligent early warning method for a smart archive warehouse includes:

[0066] S1. Use the Internet of Things communication technology to transmit the pre-collected spatial layout data and environmental monitoring data of the archive warehouse to the central management platform.

[0067] Among them, the use of IoT communication technology to transmit the pre-collected spatial layout data and environmental monitoring data of the archive warehouse to the central management platform includes:

[0068] S11. Use the space acquisition equipment installed inside the archive warehouse to collect the space layout data of the archive warehouse, and the space layout data includes the space structure and archive storage facilities.

[0069] It should be noted that the spatial layout data of the warehouse is obtained through the spatial acquisition equipment (such as 3D scanners, laser rangefinders, cameras, etc.) installed inside the archive warehouse. The spatial layout data includes two aspects:

[0070] Spatial structure: the physical structure of the warehouse, including the size, shape, partitions and passages of the room.

[0071] Archive storage facilities: specific storage facility layout, such as the specific location, size and placement of bookshelves, filing cabinets, storage shelving, etc.

[0072] S12. Use the temperature and humidity sensors, air quality sensors and light sensors installed inside the archive warehouse to collect environmental monitoring data of the archive warehouse, and the environmental monitoring data includes temperature and humidity data, air quality data and light intensity data.

[0073] It should be noted that environmental monitoring data is collected in real time through various sensors in the archive warehouse (such as temperature and humidity sensors, air quality sensors, light sensors, etc.), among which the environmental monitoring data includes:

[0074] Temperature and humidity data: Real-time temperature and humidity information, monitoring the heat and humidity environment in the warehouse to prevent mold, corrosion or paper embrittlement of files caused by inappropriate temperature and humidity.

[0075] Air quality data: including the concentration of pollutants such as carbon dioxide, PM2.5, and formaldehyde, to ensure clean air in the warehouse and prevent pollutants from damaging archives.

[0076] Light intensity data: monitor the intensity and duration of light exposure to prevent damage to files caused by strong light or ultraviolet rays.

[0077] S13. Convert the spatial layout data and environmental monitoring data into standardized data formats in turn, and transmit the data to the central management platform through the Internet of Things communication protocol.

[0078] It should be noted that the collected spatial layout data and environmental monitoring data are converted into standardized data formats (such as JSON, XML, etc.). Standardized data formats can ensure that different types of data (such as spatial structure data and sensor data) can be uniformly understood and processed in the central management platform, and the standardized data can be transmitted to the central management platform through the Internet of Things communication protocol (such as MQTT, HTTP, CoAP, etc.). The Internet of Things communication protocol can effectively manage and transmit real-time data and ensure the accuracy and integrity of the data during the transmission process.

[0079] S2. Divide the spatial layout of the archive warehouse based on the spatial layout data, and determine the archive storage type in each archive storage space.

[0080] Among them, the spatial layout of the archive warehouse is divided based on the spatial layout data, and the archive storage types in each archive storage space are determined to include:

[0081] S21. A spatial graph of the archive warehouse is established based on the spatial layout data, and the nodes in the spatial graph are represented as archive distribution spaces, and the edges represent connection channels between the archive distribution spaces.

[0082] It should be noted that the spatial graph of the archive warehouse is established based on the spatial layout data, and the nodes in the spatial graph are represented as archive distribution spaces, and the edges represent the connection channels between the archive distribution spaces, including:

[0083] Establish a spatial graph of the archive warehouse. Based on the spatial layout data, the nodes in the spatial graph are represented as archive distribution spaces, and the edges represent the connection channels between archive distribution spaces. The specific implementation steps include:

[0084] A node represents a specific file distribution space, and each file distribution space corresponds to a specific area, such as a storage area or storage unit (such as a row of file cabinets or a part of a floor).

[0085] The information contained in each node includes: storage space number, location coordinates, space size, storage density, etc.; the edge represents the connection channel between the archive distribution spaces, which describes the accessibility between the various archive distribution spaces in the warehouse.

[0086] The collected raw spatial layout data is processed and standardized into a data format suitable for graph construction. For example, the boundary and distance data of each area are converted into a standardized coordinate system to facilitate the subsequent graph structure construction.

[0087] The specific distribution locations of the archive storage space are divided according to the spatial layout data. These areas can be divided into multiple independent areas according to physical partitions, different storage functions or floors.

[0088] Analyze the connection between each storage space, confirm which spaces have direct connection channels, and calculate the length, width and other data of these channels, establish attributes for the edges in the subsequent graph, and create a graph node for each divided archive distribution space. Each node can be assigned a unique ID to describe the specific attributes of the node (such as space name, number, size, storage function, etc.).

[0089] According to the spatial layout data, confirm whether there is a direct connection channel between each node (archive distribution space); if so, create an edge for the two nodes in the graph, and the attributes of the edge include information such as channel width.

[0090] Each node is visualized on a 2D or 3D graph according to its position in the actual space, showing the relative position of each archive distribution space. Each node can use different colors or sizes to represent different types of archive storage space (for example, dense filing cabinets, ordinary bookshelves, etc.).

[0091] S22. Use the minimum edge weight segmentation technology to preliminarily divide the spatial graph into several subgraphs, and combine the whale clustering algorithm to cluster the subgraphs with similar features into the same feature group to obtain several archive storage spaces.

[0092] It should be noted that the spatial graph is initially divided into several subgraphs using the minimum edge weight segmentation technique, including:

[0093] Based on the spatial graph established above, each node represents the archive distribution space, each edge represents the channel connecting these spaces, and a weight is assigned to each edge. The weight of the edge is usually set according to the actual situation, which can be the actual distance between nodes, the width or capacity of the channel. A wide channel can be assigned a lower weight, while a narrow channel can be assigned a higher weight.

[0094] The spatial graph is divided into several subgraphs (or sub-regions) so that the nodes inside the subgraphs are closely connected (low-weight edges), while the connections between subgraphs are sparse (high-weight edges). The minimum partitioning technique aims to partition the graph by minimizing the edge weights. In other words, it finds a set of edges and cuts them off so that the graph is divided into several parts, while the total weight of the cut edges is minimized. It mainly includes:

[0095] Determine the number of segments: Before segmentation, you need to determine how many sub-graphs (or sub-regions) to divide into. This number can be determined based on specific needs, for example:

[0096] There are different area functions in the archive warehouse (such as intensive storage area, ordinary storage area, etc.).

[0097] It needs to be divided according to the size or type of storage facilities.

[0098] According to the physical structure of the warehouse, for example, different floors or different areas can be divided.

[0099] If there is no predetermined number, the number of splits is determined automatically based on the graph structure and weights or dynamically based on certain constraints (such as the maximum number of nodes in a subgraph).

[0100] Construct edge weight matrix: According to the weight of each edge in the graph, construct an edge weight matrix of size n×n (where n is the number of nodes). Each element w in the matrix ij Represents the weight of the edge between node i and node j. If there is no edge between the two nodes, the weight of the corresponding position can be set to infinity (or a very large value).

[0101] The spatial graph is segmented using a minimum segmentation algorithm, for example, a node is randomly selected from the graph as the starting point; the minimum weight cut edge is found by continuously merging its neighboring nodes; the above operation is repeated until the graph is segmented into several subgraphs, while ensuring that the total weight of the cut edges is minimized.

[0102] In practical applications, edges with smaller weights usually indicate that the channel is easier to pass, and edges with larger weights usually indicate that the passage cost is higher. Therefore, cutting off edges with higher weights can ensure that the internal connection of the subgraph is tight and the connection between subgraphs is relatively sparse.

[0103] Among them, the whale clustering algorithm is combined to cluster subgraphs with similar features into the same feature group, and several archive storage spaces are obtained, including:

[0104] S221. Treat each subgraph as an individual whale, and set the population size and initial clustering position of the whale algorithm based on the number of subgraphs.

[0105] It should be noted that the whale clustering algorithm is an intelligent optimization algorithm based on the foraging behavior of whale groups. It optimizes complex problems by simulating the behavior of whale groups searching and hunting around prey (targets). Specifically, each subgraph can be regarded as a "whale individual", and initial clustering and optimization of truncation distance are performed by minimizing edge weights and density change trend graphs, thereby realizing intelligent management of archive warehouses.

[0106] S222. Calculate the local density of the subgraph based on the attribute characteristics of the subgraph, and use the slope measurement formula to analyze the local density change to obtain a slope change trend graph of each subgraph.

[0107] The slope measurement formula is:

[0108]

[0109] In the formula, h i represents the slope value of the i-th subgraph; i represents the subgraph number; δ i represents the local density of the i-th subgraph; δ i+1 represents the local density of the i+1th subgraph; δ max represents the maximum local density among all subgraphs; δ min represents the minimum local density among all subgraphs.

[0110] It should be noted that the local density of the subgraph is calculated based on the attribute characteristics of the subgraph, and the slope change trend diagram of each subgraph obtained by analyzing the local density change using the slope measurement formula includes:

[0111] Determine the attribute characteristics of the subgraph, which determine the calculation method of local density, where the attribute characteristics include: the number of nodes (such as the number of filing cabinets or storage units); the number of archive storage (the actual number of archives in each subgraph); the area or storage capacity of the subgraph (such as physical storage area or available space).

[0112] The local density of the subgraph δ i Usually calculated based on some attribute characteristics of the subgraph, local density calculation methods include:

[0113] The density based on the number of nodes includes: the area of ​​the subgraph of the number of nodes δ i =Subgraph area × number of nodes / subgraph area, where the number of nodes is the number of storage facilities or filing cabinets within the subgraph; and the subgraph area is the physical area or available storage space of the subgraph.

[0114] Density based on storage utilization includes: Number of files Total storage capacity δ i =Total storage capacity number of files / storage capacity; where the number of files refers to the number of files actually stored in the sub-map; the storage capacity refers to the maximum storage capacity of all storage facilities in the sub-map.

[0115] According to the local density of each subgraph, the slope of its change trend is calculated, and the local density and slope value of each subgraph are calculated. The local density δ of each subgraph is i Use the above slope metric formula to calculate local density, for example:

[0116] Assume that there are five subgraphs, and the calculated local densities are: δ1 = 10, δ2 = 8, δ3 = 6, δ4 = 4, δ5 = 2, and the maximum and minimum local densities are determined as δ max =10,δ min =2.

[0117] Calculate the slope change h of each subgraph i , and use the slope metric formula to gradually calculate the slope value of each subgraph:

[0118]

[0119] The slope value h i The corresponding sub-graph number i is drawn into a trend graph, with the horizontal axis being the sub-graph number i and the vertical axis being the slope value h i , find the subgraphs with drastic slope changes from the trend graph. These subgraphs usually represent local density mutation areas, and these points can be selected as initial cluster centers. For example, h3 = 0.5 and h4 = 0.75 are locations with large slope changes, which are candidate points for cluster centers.

[0120] S223, selecting an initial cluster center from the slope change trend graph, and gradually assigning each subgraph to a cluster of similar features based on the initial cluster center, and calculating the clustering accuracy based on the initial clustering result;

[0121] S224, comparing the cutoff distance corresponding to the maximum clustering accuracy with a preset threshold, if it is less than the preset threshold, returning to step S222, otherwise, outputting the cutoff distance greater than the preset threshold to obtain the optimal cutoff distance;

[0122] S225. Complete clustering of each subgraph based on the optimal cutoff distance to form a number of archive storage spaces.

[0123] It should be noted that clustering of each subgraph is completed based on the optimal cutoff distance to form several archive storage spaces including:

[0124] After determining the optimal cutoff distance, the actual clustering operation begins. Based on the slope change trend graph, the subgraph with the largest density change is determined as the cluster center; each subgraph is assigned to the nearest cluster center according to the optimal cutoff distance to form a preliminary subgraph clustering result; the subgraphs are further merged or subdivided according to the optimal cutoff distance and clustering results to ensure that the subgraph characteristics (such as density, position) within each cluster are highly similar; after clustering is completed, each cluster represents a relatively independent archive storage space; the clustering results are displayed using graphics or heat maps, and each storage space is represented by a different color or block, so that warehouse managers can intuitively understand the size of each storage area, archive distribution and storage density.

[0125] S23. Use optical character recognition technology to extract the identification code of the archive cover, identify the archive storage type in each archive storage space, and identify the importance of the archive information based on the usage frequency of each archive storage type.

[0126] It should be noted that optical character recognition (OCR) technology can extract text information from paper or electronic file covers. It is mainly used to identify the file's identification code, storage type and other key information. The specific process includes:

[0127] Use a scanning device or camera to scan the file cover to generate a high-quality image file. For electronic files or other types of files, directly obtain the file cover image file without physical scanning.

[0128] The image of the file cover is processed by OCR software (such as Tesseract OCR technology or other OCR tools) to extract the identification code and other text information. The extracted identification code usually includes key information such as file number, storage type, date, classification number, etc.

[0129] Identify the storage type of each file based on the identification code and related information extracted by OCR, where the file storage type includes:

[0130] Paper physical archives: traditional paper documents, including various handwritten or printed written materials, such as contracts, agreements, documents, reports, books, etc.

[0131] Image or photo archives: Pictures or photo archives in physical form, usually washed or printed image materials, used to record certain historical events, people, geographical information, etc., such as historical photos, archaeological site records, architectural pictures, news photos.

[0132] Audio or video physical archives: Audio and video archives stored in physical media, such as tapes, CDs, films, etc. Such archives may record meetings, interviews, historical events or audio and video materials.

[0133] Storage requirements: It is necessary to avoid direct sunlight and high temperature environment to prevent aging and data loss of media such as tapes and CDs. It is recommended to store them in a dedicated cabinet and check the media condition regularly.

[0134] Project archives: physical documents and drawings related to construction or engineering projects, usually paper design drawings, construction plans, supervision reports, etc.

[0135] Storage requirements: Large-sized drawings usually need to be stored in rolls or placed flat in large storage cabinets to prevent folding and damage, and to be fireproof and moisture-proof.

[0136] Specimen or experimental archives: physical archives related to scientific research, including biological specimens, chemical experimental samples, etc.

[0137] Storage requirements: Specific storage environment is required, such as low-temperature storage, dry storage, and sometimes even anti-corrosion measures are required.

[0138] The extracted identification code is matched to the predefined storage type according to the specific field in the file number or classification number. For example, the file number "DOC-2023-001" corresponds to the electronic document type.

[0139] Record the number of times each file is retrieved or accessed, and obtain the usage frequency of each file through the usage log of the file management system.

[0140] Number of retrievals: records the number of times the file has been consulted or borrowed.

[0141] Access time: records the last access time or the longest time the file has not been accessed.

[0142] According to the frequency of use of files, they are divided into different levels of importance, including:

[0143] Frequently used files: These are files that are frequently retrieved and used recently. They are usually considered important files and need to be stored first.

[0144] Moderate frequency of use files: occasionally used or regularly retrieved, of moderate importance.

[0145] Low-frequency files: rarely or never used, may be inactive files, and are of low importance.

[0146] According to the archive storage type and usage frequency, the archive information is divided into three categories: important, medium and unimportant:

[0147] Important files: such as frequently accessed legal documents and contracts, should be stored in a secure area first.

[0148] Moderately important files: such as reports and statistical data that are reviewed regularly.

[0149] Unimportant files: such as old files that have not been retrieved for a long time, are suitable for storage in low-priority space.

[0150] S24. The archive storage type and the importance of archive information in each archive storage space are displayed as a heat map.

[0151] S3. Identify the environmental monitoring features corresponding to each archive storage space based on the environmental monitoring data, and determine the archive storage conditions in each archive storage space in combination with the environmental monitoring features.

[0152] Among them, identifying the environmental monitoring features corresponding to each archive storage space based on the environmental monitoring data, and judging the archive storage conditions in each archive storage space in combination with the environmental monitoring features include:

[0153] S31. Based on the light intensity data in each archive storage space, simulate the light propagation path and identify the radiant illumination on the archive surface.

[0154] Among them, based on the light intensity data in each archive storage space, simulating the propagation path of light and identifying the radiant illumination on the archive surface includes:

[0155] S311, randomly define the light emitting position and light emitting direction in the archive storage space to simulate the divergence of light in different directions and obtain the emitting light equation,

[0156] It should be noted that in the archive storage space, several light emission positions are randomly selected. The emission position is any point in the space, but it is usually emitted from the simulated light source position, such as the lamps on the ceiling, the lighting equipment on the side wall, and the sunlight. In order to accurately simulate, the light source point is randomly generated in the space. Assume that the light source height is h s , and randomly define the x and y coordinates in combination with the width and length of the space, where the exit position is expressed as: P origin =(x, y, h s ).

[0157] In three-dimensional space, the direction of light is usually represented by the direction vector d. In order to simulate the random divergence of light in different directions, each component (x, y and z) of the light's direction vector can be generated by uniform random distribution to meet the normalization of the direction vector. The angle range of x, y, and z can be randomly selected so that the light can be emitted to different areas of the storage space at any angle. origin and direction vector d, the equation of the light is expressed as: r(t) = P origin +t×d where t is a parameter describing the distance the light travels along the path in a particular direction.

[0158] S312, taking the archive storage facility as an obstacle, determining the collision point when the light hits the archive storage facility and generating the coordinates of the collision point.

[0159] It should be noted that the archive storage facilities are obstacles, and the archive storage facilities are modeled as geometric bodies (such as rectangles, cubes or polyhedrons) to represent the actual physical structure. Each facility has clear boundary coordinates and surface equations. The surface equation can be expressed as the equation of the plane, and the intersection of the light equation r(t) and the equation of the facility surface is used to calculate the collision point between the light and the storage facility.

[0160] S313. Calculate the reflection path of the light according to the reflection coefficient of the archive storage facility, and simulate and track the reflection path until the light energy is dissipated to calculate the coordinates of the reflection point.

[0161] It should be noted that the surface of each archival storage facility has a specific reflection coefficient, which determines the intensity and direction of the reflection of light. Reflection follows the law of reflection, that is, the angle of incidence is equal to the angle of reflection. The direction of the incident light d incident The relationship between d and the normal vector n is: reflected =d incident -2(d incident ×n)n, where n is the normal vector of the storage facility surface, calculated using the plane equation.

[0162] The new reflected light direction d is calculated by the above reflection equation reflected , and use the light equation to continue simulating the propagation path of the reflected light. Repeat the collision and reflection steps until the energy of the light dissipates to a certain threshold (such as the number of reflections exceeds a certain limit or the light intensity drops to a certain limit value). At each reflection, record the new reflection point until the light stops propagating.

[0163] S314, dividing the archive storage space into grids and counting the number of collision points and reflection points in each grid, and calculating the radiation illumination of the archive surface in combination with the light value carried by the light and the grid area.

[0164] It should be noted that the archive storage space is divided into uniform three-dimensional grids, each grid represents a specific area, and the size of the grid is determined according to the required accuracy. For example, assuming the length, width and height of the space are L, W, and H, and the number of grids is Nx×Ny×Nz, the size of each grid is Δx=L / Nx, Δy=W / Ny, Δz=H / Nz; in each grid, the number of collision points and reflection points of light passing through the grid is counted. The number of collision points Nc and the number of reflection points Nr in each grid are important factors in calculating the radiation illumination. Based on the total number of collision points and reflection points Ni=Nc+Nr in the grid, the radiation illumination distribution in each grid is obtained, which reflects the illumination intensity of light in the archive storage space.

[0165] S32. A fluid mechanics model is established based on the air quality data in each archive storage space, and the hot spots of pollution sources are predicted using the fluid mechanics model.

[0166] It should be noted that a fluid dynamics model is established based on the air quality data in each archive storage space. The hot spots of pollution sources predicted by the fluid dynamics model include:

[0167] Monitoring equipment should be arranged at multiple points within the storage space to ensure that the data covers different areas of the space, especially those close to potential pollution sources (such as vents, doors and windows, air conditioning equipment, etc.).

[0168] Conduct preliminary analysis of air quality data to determine the spatial distribution and temporal variation of pollutants. Use statistical analysis tools to generate heat maps to identify areas and time periods with high pollutant concentrations as input for subsequent models.

[0169] According to the geometric structure of the archive storage space, a three-dimensional space model is established. The model should include important structural features such as walls, archive racks, ventilation systems, doors and windows, and use computational fluid dynamics (CFD) software to construct a spatial grid, divide the storage space into several small units, and form a gridded simulation space.

[0170] Set boundary conditions for the fluid mechanics model to simulate the flow of air:

[0171] Inlet boundary conditions: Define the velocity, temperature, and contaminant concentration of air entering from a vent or air conditioning system.

[0172] Outlet boundary conditions: Set the flow of air discharged from exhaust vents or doors and windows to simulate ventilation and air exchange.

[0173] Wall boundary conditions: Surfaces of objects such as walls and filing racks are defined as no-slip boundaries to simulate the flow resistance of air over these surfaces.

[0174] Fluid dynamics models are used to describe the flow of air within the archival storage space, and potential sources of contamination are identified in the model, usually the entrances to the ventilation system, doors and windows, and objects that may contain hazardous substances (such as chemicals or unventilated archival materials).

[0175] According to the air flow model, the diffusion path of pollutants in the archive storage space is simulated. The simulation should take into account the turbulent diffusion and molecular diffusion processes, predict how pollutants enter different areas with air flow, extract pollutant concentration field data to generate pollutant distribution maps, and observe the changes in pollutant concentration at different time points and spatial locations.

[0176] S33. Comprehensively analyze the radiation illumination on the archive surface and the hot spots of pollution sources to determine the archive storage conditions in each archive storage space.

[0177] It should be noted that the archive storage conditions include:

[0178] The impact of radiation on archives:

[0179] The lighting environment of the archive storage space, especially the light intensity (radiant illumination), will directly affect the preservation life of the archive materials. Excessive light will cause photochemical reactions in materials such as paper, ink, and images, accelerating aging and fading. Therefore, the impact of light on archive storage can be judged from the following aspects:

[0180] Excessive irradiance:

[0181] If the radiation illumination on the surface of the archive is too high, it may cause the paper to turn yellow and the ink to fade, which will affect the readability and preservation quality of the archive.

[0182] In particular, archival storage facilities near windows, direct light sources or areas with strong scattered light have higher radiation illumination values ​​and require special attention.

[0183] Uneven distribution of irradiance:

[0184] Uneven light intensity in storage spaces can lead to overexposure of records in some areas and underexposure in others. Identifying which surfaces receive too much or too little light using a radiance grid distribution map can help adjust the layout of storage facilities.

[0185] Impact of pollution hotspots:

[0186] Pollutants in the air, especially particulate matter (such as PM2.5 and PM10) and harmful gases (such as volatile organic compounds (VOCs), may cause physical or chemical damage to archival materials. By analyzing the hot spots of pollution sources in the storage space, the impact of pollution on archival preservation can be determined.

[0187] Chemical reactions of pollutants:

[0188] Certain chemical pollutants can react with archival materials, causing deterioration of archival paper or other materials. For example, acidic gases can accelerate the acidification of paper, causing it to become brittle. If hot spots are concentrated near archival storage facilities, especially at storage heights close to corners with poor air flow, high concentrations of pollutants may have a greater destructive effect on stored archives.

[0189] Judgment of overlapping areas:

[0190] If high-irradiance areas overlap with pollution hotspots, it means that archives in these areas are under a double threat: on the one hand, strong light accelerates aging, and on the other hand, pollutant deposition or chemical reactions may further aggravate damage. For example, areas near vents or air inlets may be exposed to strong light and high concentrations of pollutants at the same time, and archives in such areas are particularly vulnerable.

[0191] S4. Establish a zoning early warning mechanism based on the archive storage type and archive storage conditions in each archive storage space, and formulate response measures based on the zoning early warning mechanism.

[0192] Among them, a partition warning mechanism is established in combination with the archive storage type and archive storage conditions in each archive storage space, and response measures are formulated based on the partition warning mechanism, including:

[0193] S41. Set an optimal environmental measurement threshold for each archive storage space based on the archive storage type, the importance of the archive information, and the archive storage conditions in each archive storage space.

[0194] Among them, the optimal environmental measurement threshold is set for each archive storage space in combination with the archive storage type, the importance of archive information and the archive storage conditions in each archive storage space, including:

[0195] S411. The archive storage type, the importance of archive information and the archive storage conditions in each archive storage space are made into different hierarchical structures, and the hierarchical structures include a target layer, a criterion layer and a sub-criterion layer.

[0196] It should be noted that the goal is to set the optimal environmental measurement threshold for each archive storage space.

[0197] The criteria layer includes three main influencing factors:

[0198] Archive storage type: The physical characteristics of different archives have different environmental requirements (such as paper, electronic, audio and video, etc.).

[0199] Importance of archival information: The importance of the information determines the priority of the archive for protection and maintenance (such as confidential archives, historical documents, etc.).

[0200] Archive storage conditions: the ability of existing storage conditions to control the environment, such as temperature and humidity control, air purification and other systems.

[0201] Sub-criteria layer: Each criterion is further divided into multiple sub-criteria.

[0202] S412, constructing a judgment matrix for the importance of each criterion in the criterion layer based on the target layer, and performing pairwise comparisons on each sub-criteria to generate a judgment matrix of the sub-criteria;

[0203] S413, calculating the weights of each criterion and sub-criterion by decomposing the eigenvalues ​​of the judgment matrix, and detecting the logical consistency of the judgment matrix by using the consistency ratio;

[0204] S414. Assign importance to the archive storage type, archive information importance, and archive storage conditions in each archive storage space in combination with the weight calculation result, and set an optimal environment measurement threshold for each archive storage space based on the importance assignment result.

[0205] It should be noted that the comprehensive importance score of each archive storage space is determined based on the weight distribution result, combined with the archive storage type, information importance and storage conditions in the archive storage space. For example:

[0206] If a storage space mainly stores confidential paper files and the storage conditions are poor, the importance score of the space is higher and a stricter environmental measurement threshold needs to be set.

[0207] S42, collecting the environmental measurement value of each archive storage space in real time and comparing it with the optimal environmental measurement threshold, and dividing the warning level of each archive storage space based on the comparison result;

[0208] S43. Trigger the warning instruction according to the priority, and initiate response measures according to the warning level of each archive storage space after the warning instruction is triggered.

[0209] It should be noted that the warning level is divided according to the comparison between the real-time collected data and the optimal environmental measurement threshold. The specific standards can be defined according to the degree of deviation of different parameters:

[0210] Low level warning:

[0211] Deviation: Environmental parameters slightly exceed the set optimal threshold range.

[0212] Impact: Long-term impact on archive preservation is small, but may accumulate without intervention.

[0213] Trigger conditions: If the temperature slightly exceeds the standard (for example, the set value of 22℃ changes to 23℃), the humidity is slightly higher or lower than the optimal range.

[0214] Medium level warning:

[0215] Deviation: Environmental parameters deviate significantly from the set optimal threshold. If not handled in time, it will have a significant impact on the archive.

[0216] Impact: Medium-term impact on archives, such as accelerated material aging, accumulation of pollutants, etc.

[0217] Trigger conditions: If humidity exceeds the safe range, or PM2.5 concentration rises to a level that may affect paper archives.

[0218] High-level warning:

[0219] Deviation: Environmental parameters deviate significantly from the threshold range, which may cause irreversible damage to the archives immediately or in the short term.

[0220] Impact: Significant threats to the preservation of archives, such as extremely high or low temperatures, excessive concentrations of pollutants, etc.

[0221] Trigger conditions: If the temperature exceeds 24°C and the PM2.5 concentration far exceeds the safety standard.

[0222] Response measures vary according to the warning level. The following are the response strategies corresponding to each level.

[0223] Low-level warning response:

[0224] Response Strategy: Monitor and record, prompt staff to take minor adjustments.

[0225] Temperature adjustment: Adjust the air conditioner or temperature control system to restore the temperature to the normal range. For example, adjust the temperature from 23℃ back to the set range of 20℃-22℃.

[0226] Humidity adjustment: Start the humidification or dehumidification equipment to control the humidity within the set threshold.

[0227] Pollutant control: Monitor pollutant concentrations and record and alert operators if they exceed the standard by a small amount, but do not take immediate large-scale measures.

[0228] Notification mechanism:

[0229] The system sends automatic reminders to responsible personnel, indicating that equipment needs to be checked or simple adjustments need to be made.

[0230] Record the time and parameter value of each slight exceedance to facilitate subsequent analysis.

[0231] Medium-level warning response:

[0232] Response strategy: Automatically start some control devices and send higher priority warnings.

[0233] Air purification: If the pollutant concentration is high (such as PM2.5 exceeding the standard), the system automatically starts the air purification device to reduce particulate matter and harmful gases in the air.

[0234] Temperature and humidity adjustment: If the temperature and humidity exceed the standard, activate additional air conditioning, dehumidification or humidification equipment to restore it to the safe threshold range.

[0235] Light control: If the light intensity exceeds the standard, the brightness of the light source can be adjusted or the automatic shading device can be activated to reduce the light exposure of the files.

[0236] Notification mechanism:

[0237] A medium-level alert is sent to operators, requiring them to check the problem area as soon as possible to ensure that the system adjustments are effective.

[0238] Automatically generate detailed alarm reports containing exceeded areas, parameters and actions taken for subsequent evaluation.

[0239] High-level warning response:

[0240] Response strategy: The system automatically executes emergency measures and notifies all relevant personnel for emergency intervention.

[0241] Emergency light source shutdown: If the light exceeds the standard seriously, the system will immediately shut down the relevant light source to prevent further damage.

[0242] Fully activate the air purification and ventilation system: If the concentration of pollutants reaches a dangerous level, the system will immediately fully activate the air purification equipment to increase air circulation and quickly reduce the concentration of pollutants.

[0243] File transfer: If the temperature or humidity exceeds the standard and cannot return to normal in a short time, the system can issue a file transfer instruction to transfer important files to the backup storage space.

[0244] Notification mechanism:

[0245] Send high-priority alerts to management and notify field operators to take immediate action.

[0246] Trigger emergency contact mechanisms such as phone calls and text messages to ensure that all relevant personnel respond in a timely manner.

[0247] like Figure 2 As shown, according to another embodiment of the present invention, there is also provided an intelligent early warning system for a smart archive warehouse based on the Internet of Things, the intelligent early warning system for a smart archive warehouse comprising a data measurement module 1, a file type identification module 2, a storage condition identification module 3 and a partition early warning module 4;

[0248] Data measurement module 1, used to transmit the pre-collected spatial layout data and environmental monitoring data of the archive warehouse to the central management platform using the Internet of Things communication technology;

[0249] The file type identification module 2 is used to divide the spatial layout of the file warehouse based on the spatial layout data and determine the file storage type in each file storage space;

[0250] The storage condition identification module 3 is used to identify the environmental monitoring characteristics corresponding to each archive storage space based on the environmental monitoring data, and determine the archive storage conditions in each archive storage space in combination with the environmental monitoring characteristics;

[0251] The partition warning module 4 is used to establish a partition warning mechanism in combination with the archive storage type and archive storage conditions in each archive storage space, and formulate response measures based on the partition warning mechanism.

[0252] In summary, with the help of the above technical solution of the present invention, the present invention constructs a spatial map of the archive warehouse through spatial layout data, and uses the whale clustering algorithm to cluster subgraphs with similar characteristics, so as to more accurately divide the archive storage space, and perform differentiated management for the environment of different regions, and can effectively perform environmental regulation according to the storage needs of archives in different regions, avoiding the waste of resources and environmental inadaptability caused by unified regulation, and at the same time extracting the identification code of the archive cover and combining the frequency of use to identify the importance of the archive, so as to give priority to the key archive area according to the importance of the archive, and improve the efficiency of management and maintenance. The present invention can accurately calculate the radiation illumination of the archive surface through simulation and analysis of light intensity data, ensure that the actual impact of light on the archive can be fully understood, help to accurately judge the archive storage conditions, and accurately identify the hot spots where pollution sources are concentrated in the warehouse by predicting air flow and pollution source distribution, and combine the radiation illumination with the pollution source hot spots for comprehensive analysis, so as to not only provide a more comprehensive archive storage condition evaluation, but also cross-judge the suitability of archive storage through multiple factors, thereby improving the scientificity and rationality of archive protection. The present invention can set an exclusive optimal environmental measurement threshold for each area by combining the archive storage type, the importance of archive information and the storage conditions in each archive storage space. This refined setting ensures that different areas are subject to differentiated environmental monitoring based on their specific needs and importance, avoiding a one-size-fits-all management approach. According to the warning level and the importance of the archive, the warning instructions for high-priority archive areas can be triggered first and response measures can be quickly initiated, thereby not only improving the protection of key archives, but also improving the efficiency of emergency response.

[0253] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent early warning method for a smart archive warehouse based on the Internet of Things, characterized in that: The intelligent early warning method of the smart archive warehouse includes: S1. Use the Internet of Things communication technology to transmit the pre-collected spatial layout data and environmental monitoring data of the archive warehouse to the central management platform; S2. Divide the spatial layout of the archive warehouse based on the spatial layout data, and determine the archive storage type in each archive storage space; S3, identifying the environmental monitoring features corresponding to each archive storage space based on the environmental monitoring data, and determining the archive storage conditions in each archive storage space in combination with the environmental monitoring features; S4. Establish a zoning early warning mechanism based on the archive storage type and archive storage conditions in each archive storage space, and formulate response measures based on the zoning early warning mechanism.

2. According to the method of claim 1, the intelligent early warning method of the smart archive warehouse based on the Internet of Things is characterized in that: The method of using the Internet of Things communication technology to transmit the pre-collected spatial layout data and environmental monitoring data of the archive warehouse to the central management platform includes: S11. Using a space collection device installed inside the archive warehouse to collect spatial layout data of the archive warehouse, the spatial layout data includes spatial structure and archive storage facilities; S12, using the temperature and humidity sensor, air quality sensor and light sensor installed inside the archive warehouse to collect environmental monitoring data of the archive warehouse, and the environmental monitoring data includes temperature and humidity data, air quality data and light intensity data; S13. Convert the spatial layout data and environmental monitoring data into standardized data formats in turn, and transmit the data to the central management platform through the Internet of Things communication protocol.

3. According to the intelligent early warning method of the smart archive warehouse based on the Internet of Things according to claim 2, it is characterized in that: The spatial layout of the archive warehouse is divided based on the spatial layout data, and the archive storage type in each archive storage space is determined, including: S21. Establish a spatial graph of the archive warehouse based on the spatial layout data, and represent nodes in the spatial graph as archive distribution spaces, and edges represent connection channels between archive distribution spaces; S22. Using the minimum edge weight segmentation technique to preliminarily divide the spatial graph into several subgraphs, and combining the whale clustering algorithm to cluster the subgraphs with similar features into the same feature group, to obtain several archive storage spaces; S23, extracting the identification code of the archive cover by using optical character recognition technology, identifying the archive storage type in each archive storage space, and identifying the importance of the archive information in combination with the usage frequency of each archive storage type; S24. The archive storage type and the importance of archive information in each archive storage space are displayed as a heat map.

4. According to the intelligent early warning method of the smart archive warehouse based on the Internet of Things according to claim 3, it is characterized in that: The combined whale clustering algorithm clusters subgraphs with similar features into the same feature group, and obtains several archive storage spaces including: S221, treating each subgraph as a whale individual, and setting the population size and initial clustering position of the whale algorithm based on the number of subgraphs; S222, calculating the local density of the subgraph based on the attribute characteristics of the subgraph, and analyzing the local density change using the slope measurement formula to obtain a slope change trend graph of each subgraph; S223, selecting an initial cluster center from the slope change trend graph, and gradually assigning each subgraph to a cluster of similar features based on the initial cluster center, and calculating the clustering accuracy based on the initial clustering result; S224, comparing the cutoff distance corresponding to the maximum clustering accuracy with a preset threshold, if it is less than the preset threshold, returning to step S222, otherwise, outputting the cutoff distance greater than the preset threshold to obtain the optimal cutoff distance; S225. Complete clustering of each subgraph based on the optimal cutoff distance to form a number of archive storage spaces.

5. According to the method of intelligent early warning of a smart archive warehouse based on the Internet of Things according to claim 4, it is characterized in that: The identifying of the environmental monitoring features corresponding to each archive storage space based on the environmental monitoring data, and determining the archive storage conditions in each archive storage space in combination with the environmental monitoring features includes: S31, based on the light intensity data in each archive storage space, simulate the propagation path of light and identify the radiant illumination on the archive surface; S32, establishing a fluid mechanics model based on the air quality data in each archive storage space, and using the fluid mechanics model to predict the hot spots of pollution sources; S33. Comprehensively analyze the radiation illumination on the archive surface and the hot spots of pollution sources to determine the archive storage conditions in each archive storage space.

6. According to the method of intelligent early warning of a smart archive warehouse based on the Internet of Things according to claim 5, it is characterized in that: The method of simulating the propagation path of light and identifying the radiant illumination of the archive surface based on the light intensity data in each archive storage space includes: S311, randomly defining the light emitting position and light emitting direction in the archive storage space to simulate the divergence of light in different directions and obtain the emitting light equation; S312, taking the archive storage facility as an obstacle, determining the collision point when the light ray contacts the archive storage facility and generating the coordinates of the collision point; S313, calculating the reflection path of the light according to the reflection coefficient of the archive storage facility, and simulating and tracing the reflection path until the light energy is dissipated to calculate the coordinates of the reflection point; S314, dividing the archive storage space into grids and counting the number of collision points and reflection points in each grid, and calculating the radiation illumination of the archive surface in combination with the light value carried by the light and the grid area.

7. According to claim 6, a smart early warning method for a smart archive warehouse based on the Internet of Things is characterized in that: The establishment of a partition warning mechanism in combination with the archive storage type and archive storage conditions in each archive storage space, and the formulation of response measures based on the partition warning mechanism include: S41, setting an optimal environmental measurement threshold for each archive storage space in combination with the archive storage type, the importance of archive information and the archive storage conditions in each archive storage space; S42, collecting the environmental measurement value of each archive storage space in real time and comparing it with the optimal environmental measurement threshold, and dividing the warning level of each archive storage space based on the comparison result; S43. Trigger the warning instruction according to the priority, and initiate response measures according to the warning level of each archive storage space after the warning instruction is triggered.

8. According to the method of intelligent early warning of a smart archive warehouse based on the Internet of Things according to claim 7, it is characterized in that: The step of setting the optimal environment measurement threshold for each archive storage space in combination with the archive storage type, the importance of archive information and the archive storage conditions in each archive storage space includes: S411, the archive storage type, the importance of archive information and the archive storage conditions in each archive storage space are used as different hierarchical structures, and the hierarchical structures include a target layer, a criterion layer and a sub-criterion layer; S412, constructing a judgment matrix for the importance of each criterion in the criterion layer based on the target layer, and performing pairwise comparisons on each sub-criteria to generate a judgment matrix of the sub-criteria; S413, calculating the weights of each criterion and sub-criterion by decomposing the eigenvalues ​​of the judgment matrix, and detecting the logical consistency of the judgment matrix by using the consistency ratio; S414. Assign importance to the archive storage type, archive information importance, and archive storage conditions in each archive storage space in combination with the weight calculation result, and set an optimal environment measurement threshold for each archive storage space based on the importance assignment result.

9. The method for intelligent early warning of a smart archive warehouse based on the Internet of Things according to claim 8 is characterized in that: The slope measurement formula is: In the formula, h i represents the slope value of the i-th subgraph; i represents the subgraph number; δ i represents the local density of the i-th subgraph; δ i+1 represents the local density of the i+1th subgraph; δ max represents the maximum local density among all subgraphs; δ min represents the minimum local density among all subgraphs.

10. An intelligent early warning system for a smart archive warehouse based on the Internet of Things, used to implement the intelligent early warning method for a smart archive warehouse based on the Internet of Things as described in any one of claims 1 to 9, characterized in that: The intelligent early warning system for the smart archive warehouse includes a data measurement module, an archive type identification module, a storage condition identification module and a partition early warning module; The data measurement module is used to transmit the pre-collected spatial layout data and environmental monitoring data of the archive warehouse to the central management platform using the Internet of Things communication technology; The file type identification module is used to divide the spatial layout of the file warehouse based on the spatial layout data and determine the file storage type in each file storage space; The storage condition identification module is used to identify the environmental monitoring characteristics corresponding to each archive storage space based on the environmental monitoring data, and determine the archive storage conditions in each archive storage space in combination with the environmental monitoring characteristics; The partition warning module is used to establish a partition warning mechanism in combination with the archive storage type and archive storage conditions in each archive storage space, and formulate response measures based on the partition warning mechanism.

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