Environment monitoring method, system and equipment for archival repository and medium

Abstract

The invention discloses an environment monitoring method, system and device for an archival repository and a medium, relates to the technical field of environment detection, realizes generation of a heat sensitivity index vector for each archive, can provide a personalized environment management strategy for characteristics of different archives, and avoids blind protection of all archives under a unified environment condition. Secondly, environment parameters in the warehouse are collected, and an environment feature set is generated, so that the whole system can monitor important environment factors such as temperature, humidity and illumination of the warehouse in real time, and a data basis is provided for subsequent thermal sensitivity evaluation and environment adjustment. And furthermore, through matching according to the heat sensitivity index vector and the environment characteristic set, a generated risk assessment value set provides quantitative risk early warning for each file, and the file which is easy to damage under a specific environment condition can be accurately identified, so that the potential harm of environmental factors to the file is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental detection, and specifically to an environmental monitoring method, system, device and medium for an archive storage. Background Art

[0002] In modern society, archive management has become one of the important tasks of various institutions. Especially for the management of archive materials with strong historical, cultural or legal nature, archive protection is particularly important. With the advancement of informatization and digitalization, traditional archives, such as paper archives, photos and document files, are facing new challenges. As the core place for storing these physical archives, the archive storage needs to provide a stable environment to ensure the long-term preservation of archives. However, the impact of environmental factors, such as temperature, humidity, light, etc., on archives, especially on temperature-sensitive archive materials, is still an urgent problem to be solved in the current archive protection field.

[0003] Currently, although many archive storages have been equipped with environmental monitoring devices that can collect data such as temperature, humidity, and air quality in real time, most monitoring systems still adopt a unified temperature and humidity adjustment strategy and fail to make refined adjustments according to the needs of different types of archives. Archives made of different materials have extremely different sensitivities to temperature and humidity. For example, paper archives and photos are more vulnerable to humidity changes compared to electronic media archives, and some precious manuscripts, ancient books, etc. are more easily damaged by temperature changes. At present, the temperature and humidity control in many storages does not take these differences into account, and the unified adjustment strategy may not meet the needs of different types of archives.

[0004] The shortcoming of this approach is that it ignores the individual differences of archives and the specific environmental requirements of the archives. Especially for those archives that are extremely sensitive to temperature and humidity changes, the unified environmental standards may not provide the most suitable preservation conditions. For example, some paper archives are prone to moisture and mildew in high humidity, and may become brittle and fragile in low humidity. In addition, some precious photos, files, etc. may show image fading, paper brittleness, etc. when the environment is too dry or humid. Therefore, the existing environmental control methods cannot fully consider the thermal sensitivity of different types of archives, resulting in insufficient or inaccurate archive preservation conditions, thus increasing the risk of archive damage. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides an environmental monitoring method, system, device and medium for an archive storage, which solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An environmental monitoring method, system, device and medium for an archive storage, including the following steps:

[0007] S1. Build a communication channel for the archive management system, collect initial archive information, and generate a thermal sensitivity index vector for each archive;

[0008] S2. Collect environmental parameters in the storage room and generate an environmental feature set;

[0009] S3. Match the thermal sensitivity and environmental status according to the thermal sensitivity index vector and the environmental feature set, and generate a set of risk assessment values;

[0010] S4. Calculate the temperature and humidity adjustment strategy based on the obtained set of risk assessment values, and obtain a set of adjustment strategies for the temperature and humidity adjustment amounts in the storage room;

[0011] S5. Dynamically adjust the environmental parameters by executing the set of adjustment strategies and generate a new set of executed environmental features;

[0012] S6. Evaluate the risk change of the archives in the storage room after executing the set of adjustment strategies by evaluating the set of executed environmental features, the thermal sensitivity index vector, and the set of risk assessment values, generate a risk vector, and trigger an iterative optimization mechanism according to the risk vector.

[0013] Preferably, the S1 includes S11 and S12;

[0014] S11. Obtain the initial archive information of each archived file stored from the archive management system, including material type information, preservation period information, and importance level information, and obtain the material type Tmat, preservation period Yage, and importance level Iimp by numerically processing the initial archive information, and form a parameter set P of the i-th archive;

[0015] Among them, the material type Tmat reflects the sensitivity of the archive to temperature and humidity changes;

[0016] The preservation period Yage reflects the influence of the preservation time of the archive on its stability;

[0017] The importance level Iimp represents the value weight of the archive in the overall management system;

[0018] The numerical processing includes numerically processing by scoring the archive attribute mapping table pre-matched with the material type information;

[0019] Numerically process the preservation period information by assigning the difference between the current time and the archive creation time;

[0020] Numerically process the importance level Iimp by matching the preset archive storage level mapping table;

[0021] S12. A weighted formula of a thermal sensitivity evaluation algorithm is constructed for the parameter set P of the ith file to obtain a thermal sensitivity index HS of the ith file, and a thermal sensitivity index vector HSI is obtained by integrating the thermal sensitivity indexes generated for each file.

[0022] Preferably, S2 includes S21 and S22;

[0023] S21, by integrating an environmental sensor group in the archive warehouse, real-time collection of environmental data in the archive warehouse, and real-time reflection of the air temperature, air humidity, light intensity, air pressure and carbon dioxide concentration in the archive warehouse;

[0024] The environmental sensor group includes temperature and humidity sensors, air pressure sensors, light sensors and carbon dioxide concentration sensors. The environmental data include temperature Tw, humidity Hs, light intensity Lg, air pressure Pq and carbon dioxide concentration CO. 2 ;

[0025] By performing data cleaning preprocessing on the collected environmental data, the noise values ​​in the environmental data are eliminated and the missing values ​​are filled, and the environmental feature set En is obtained by integrating the preprocessed environmental data and then using the minimum-maximum standardization for standardization.

[0026] S22. Integrate the environmental feature sets Enu at different times t to form an environmental feature set ENi.

[0027] Preferably, said S3 includes S31 and S32;

[0028] S31, according to the thermal sensitivity index vector HSI and the environmental feature set ENi, the thermal sensitivity is matched with the environmental state, and the temperature Tw, humidity Hs, light intensity Lg, air pressure Pq and carbon dioxide concentration CO are obtained. 2 The temperature influence function fT, humidity influence function fH, light influence function fL, air pressure influence function fP and carbon dioxide concentration influence function fCO 2 By integrating the temperature influence function fT, humidity influence function fH, light influence function fL, air pressure influence function fP and carbon dioxide concentration influence function fCO 2 Analyze the risk assessment value R of each file, perform bubble sort on the risk assessment value R of each file, and then integrate it with the integrated thermal sensitivity index vector HSI to obtain the risk assessment value set Risk;

[0029] S32, traversing and comparing each risk assessment value R with the risk threshold Rthe according to the acquired risk assessment value set Risk, obtaining the comparison result of each risk assessment value R with the risk threshold Rthe, and synchronously associating it with the risk assessment value R;

[0030] The specific comparison result is obtained through the following comparison methods:

[0031] When the risk assessment value R ≥ the risk threshold Rthe, mark the current file as a risk file, generate a risk mark FX, and associate it with the risk assessment value R of the current file.

[0032] When the risk assessment value R < the risk threshold Rthe, do not mark the current file as a risk file and do not generate a risk mark FX.

[0033] Preferably, the S4 includes S41;

[0034] S41. Extract the files with the risk mark FX according to the obtained risk assessment value set Risk, calculate the temperature and humidity adjustment strategies of the warehouse, obtain the warehouse temperature adjustment amount △T and the warehouse humidity adjustment amount △H, and form an adjustment strategy set Adj of the temperature and humidity adjustment amount in the warehouse according to the obtained warehouse temperature adjustment amount △T and the warehouse humidity adjustment amount △H.

[0035] Preferably, the S5 includes S51;

[0036] S51. Dynamically adjust the environmental parameters according to the obtained adjustment strategy set Adj, obtain the adjusted application temperature Tw(u, t + 1) and the application humidity Hs(u, t + 1), integrate the obtained application temperature Tw(u, t + 1) and the application humidity Hs(u, t + 1), obtain the execution environment feature set ZENu, and then generate an adjustment instruction through the execution environment feature set ZENu to execute the application temperature Tw(u, t + 1) and the application humidity Hs(u, t + 1) for the environmental parameters in the current warehouse to dynamically adjust the environmental parameters in the warehouse.

[0037] Preferably, the S6 includes S61 and S62;

[0038] S61. Evaluate the risk change of the files in the warehouse after executing the adjustment strategy set Adj through the execution environment feature set ZENu, the thermal sensitivity index vector HSI, and the risk assessment value set Risk. Specifically, obtain the fluctuation values of the execution environment feature set ZENu, the thermal sensitivity index vector HSI, and the risk assessment value set Risk by accumulating fixed periods, obtain the execution environment feature fluctuation value △ZENu, the thermal sensitivity index vector fluctuation value △HSI, and the risk assessment value set fluctuation value △Risk, and generate a risk vector Ropt.

[0039] S62. Calculate the total fluctuation value within a fixed period according to the obtained risk vector Ropt, obtain the total risk vector fluctuation index △Ropt, and compare it with the preset adjustment strategy execution evaluation threshold AdjThe to obtain the trigger result of the iterative optimization mechanism.

[0040] The triggering result of the iterative optimization mechanism is obtained through the following comparison method:

[0041] When the total risk vector fluctuation index △Ropt ≥ the adjustment strategy execution evaluation threshold AdjThe, the triggering result of the iterative optimization mechanism is obtained as the triggering result. At this time, the iterative optimization mechanism is triggered, and steps S2, S3, S4, S5, and S6 are repeatedly executed until the iterative optimization mechanism exits;

[0042] When the total risk vector fluctuation index △Ropt < the adjustment strategy execution evaluation threshold AdjThe, the triggering result of the iterative optimization mechanism is obtained as the non-triggering result. At this time, the already executed iterative optimization mechanism exits.

[0043] An environmental monitoring system for an archive storage room includes a data acquisition module, a risk assessment module, an adjustment generation module, and an iterative optimization module;

[0044] The data acquisition module collects initial archive information by constructing a communication channel for the archive management system, generates a thermal sensitivity index vector for each archive, and collects environmental parameters in the storage room to generate an environmental feature set;

[0045] The risk assessment module matches the thermal sensitivity and environmental status based on the thermal sensitivity index vector and the environmental feature set, generates a set of risk assessment values, and calculates the temperature and humidity adjustment strategy according to the obtained set of risk assessment values to obtain a set of adjustment strategies for the temperature and humidity adjustment amount in the storage room;

[0046] The adjustment generation module dynamically adjusts environmental parameters by executing the set of adjustment strategies and generates a new set of executed environmental features;

[0047] The iterative optimization module evaluates the risk change of the archives in the storage room after executing the set of adjustment strategies through the set of executed environmental features, the thermal sensitivity index vector, and the set of risk assessment values, generates a risk vector, and triggers the iterative optimization mechanism based on the risk vector.

[0048] An environmental monitoring device for an archive storage room includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements an environmental monitoring method for an archive storage room.

[0049] An environmental monitoring medium for an archive storage room stores a computer program. When the computer program is executed, it implements an environmental monitoring method for an archive storage room.

[0050] The present invention provides an environmental monitoring method, system, device, and medium for an archive storage room, having the following beneficial effects:

[0051] (1) It realizes the generation of a thermal sensitivity index vector for each file, can provide personalized environmental management strategies according to the characteristics of different files, and avoids the blind protection of all files under unified environmental conditions. Secondly, it collects the environmental parameters in the storage and generates an environmental feature set, enabling the entire system to monitor important environmental factors such as temperature, humidity, and light in the storage in real time, which provides a data basis for subsequent thermal sensitivity assessment and environmental regulation. Moreover, by matching according to the thermal sensitivity index vector and the environmental feature set, the generated set of risk assessment values provides a quantitative risk warning for each file, and can accurately identify the files that are easily damaged under specific environmental conditions. Then, based on these sets of risk assessment values, the system can calculate the temperature and humidity adjustment strategy in real time, and dynamically optimize the environmental parameters in the storage by executing the adjustment strategy, thereby significantly reducing the potential harm of environmental factors to the files.

[0052] (2) By integrating a variety of environmental sensors in the file storage, the obtained environmental feature set ENu is combined with the thermal sensitivity index vector HSI for thermal sensitivity assessment, which helps to accurately identify the risk assessment value R of each file, can quickly identify the files with high risks, and realizes file classification management by generating a risk mark FX, reducing file damage caused by environmental factors. By calculating the temperature and humidity adjustment amount in the storage, based on the risk assessment value of each file, an accurate set of environmental adjustment strategies Adj is formulated to ensure the dynamic optimization of environmental parameters, timely adjust the temperature and humidity to adapt to the changing environmental conditions, thereby effectively extending the preservation period of the files and improving the intelligent level of file protection.

[0053] (3) By obtaining the set of adjustment strategies Adj and generating environmental adjustment instructions, it ensures that the temperature and humidity in the storage are always within the range suitable for file preservation, compares and evaluates the total risk fluctuation index △Ropt, and judges whether to trigger the iterative optimization mechanism. If the total risk fluctuation index exceeds the preset threshold AdjThe, the system will trigger the iterative optimization mechanism for further adjustment until the optimal state is reached. This real-time dynamic feedback mechanism makes file management more intelligent, can significantly reduce the potential risks caused by environmental fluctuations to the files, provides a more refined and efficient file protection solution, and has stronger adaptability and foresight compared with traditional static adjustment methods. Description of the Drawings

[0054] Figure 1 Schematic diagram of the steps of the environmental monitoring method for the file storage of the present invention;

[0055] Figure 2 Schematic block diagram of the environmental monitoring system for the file storage of the present invention. Detailed Embodiments

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0057] Embodiment 1

[0058] The present invention provides an environmental monitoring method for an archive storage room. Please refer to Figure 1 , which includes the following steps:

[0059] S1. Build a communication channel for the archive management system, collect initial archive information, and generate a thermal sensitivity index vector for each archive;

[0060] S2. Collect environmental parameters in the storage room and generate an environmental feature set;

[0061] S3. Match the thermal sensitivity and environmental status according to the thermal sensitivity index vector and the environmental feature set, and generate a risk assessment value set;

[0062] S4. Calculate the temperature and humidity adjustment strategy according to the obtained risk assessment value set, and obtain an adjustment strategy set for the temperature and humidity adjustment amount in the storage room;

[0063] S5. Dynamically adjust the environmental parameters by executing the adjustment strategy set and generate a new execution environmental feature set;

[0064] S6. Evaluate the risk change of the archives in the storage room after executing the adjustment strategy set through the execution environmental feature set, the thermal sensitivity index vector, and the risk assessment value set, generate a risk vector, and trigger an iterative optimization mechanism according to the risk vector.

[0065] In this embodiment, through steps S1 to S6, the dynamic adjustment and precise management of the archive storage environment are realized, thus effectively solving the problem of archive damage caused by environmental changes in the traditional archive management method. First, by constructing the communication channel of the archive management system and collecting the initial archive information, a thermal sensitivity index vector is generated for each archive, which can provide personalized environmental management strategies according to the characteristics of different archives, avoiding the blind protection of all archives under the same environmental conditions. Secondly, the environmental parameters in the storage are collected and an environmental feature set is generated, enabling the entire system to monitor important environmental factors such as temperature, humidity, and light in the storage in real time, which provides a data basis for subsequent thermal sensitivity assessment and environmental adjustment. Furthermore, by matching the thermal sensitivity index vector and the environmental feature set, the generated risk assessment value set provides a quantitative risk warning for each archive, enabling accurate identification of archives that are vulnerable to damage under specific environmental conditions. Then, based on these risk assessment value sets, the system can calculate the temperature and humidity adjustment strategy in real time and dynamically optimize the environmental parameters in the storage by executing the adjustment strategy, thus significantly reducing the potential harm of environmental factors to archives. Most importantly, after executing the adjustment strategy, the system further evaluates the executed environmental feature set, thermal sensitivity index vector, and risk assessment value set, generates a risk vector and triggers an iterative optimization mechanism to continuously optimize the adjustment strategy, ensuring the long-term safe storage of each archive in the storage environment, realizing the personalization, real-time, and dynamic optimization of the archive management environment. Compared with the traditional fixed environmental conditions, it avoids the problem of archive damage caused by fluctuations in factors such as temperature and humidity, and also solves the deficiency in the previous archive management that it is impossible to accurately respond to different archive materials and preservation periods. This system based on the thermal sensitivity index vector of archives and the real-time environmental feature set can accurately control according to the risk characteristics of different archives, thus greatly improving the effect and efficiency of archive protection.

[0066] Embodiment 2

[0067] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically: S1 includes S11 and S12;

[0068] S11. Obtain the initial archive information of each archived file stored in the archive management system, including material type information, preservation period information, and importance level information, and through numerical processing of the initial archive information, obtain the material type Tmat, preservation period Yage, and importance level Iimp, and form the parameter set P of the i-th archive;

[0069] Among them, the material type Tmat reflects the sensitivity of the archive to temperature and humidity changes. The thermal sensitivity of different materials varies significantly. For example, paper archives are prone to mildew under high humidity conditions, while film archives are prone to deformation under high temperature conditions;

[0070] The preservation period Yage reflects the impact of the preservation time of the file on its stability. The longer the preservation time of the file, the higher the degree of material deterioration and the stronger the thermal sensitivity may be;

[0071] The importance level Iimp represents the value weight of the file in the overall management system. More important files require more refined protection strategies;

[0072] The numerical processing includes performing numerical processing by scoring the file attribute mapping table for pre-matching of material type information. For example, paper files are assigned a value of 0.8, film files are assigned a value of 0.9, and digital media files are assigned a value of 0.4;

[0073] The preservation period information is numerically processed by assigning the difference between the current time and the file creation time;

[0074] The importance level Iimp is numerically processed by matching the preset file storage level mapping table;

[0075] S12. By constructing a weighted formula for the thermal sensitivity evaluation algorithm for the parameter set P of the i-th file, the thermal sensitivity index HS of the i-th file is obtained. By integrating the thermal sensitivity indices generated for each file, the thermal sensitivity index vector HSI is obtained;

[0076] The thermal sensitivity index vector HSI is obtained through the following calculation formula:

[0077] HS(i) = h1 * Tmat(i) + h2 * Yage(i) + h3 * Iimp(i);

[0078] In the formula, HS(i) represents the thermal sensitivity index of the i-th file, Tmat(i) represents the material type of the i-th file, Yage(i) represents the preservation period of the i-th file, Iimp(i) represents the importance level of the i-th file, and h1, h2, and h3 respectively represent the preset weight values of the material type Tmat, preservation period Yage, and importance level Iimp of the i-th file, and h1 + h2 + h3 = 1. The specific values are set by the user.

[0079] In this embodiment, by obtaining the initial information of each file from the file management system, including material type information, preservation period information, and importance level information, and numerically processing this information, a parameter set PPP including material type Tmat, preservation period Yage, and importance level Iimp is successfully generated for each file. This numerical processing method provides clear basic data for subsequent thermal sensitivity assessment, enabling the characteristics of different files to be accurately quantified and distinguished. By performing weighted calculation of the thermal sensitivity assessment algorithm on the parameter set P of each file, a thermal sensitivity index vector HSI is generated. This vector can reflect the unique thermal sensitivity characteristics of each file under temperature and humidity changes. It provides a systematic and quantitative assessment mechanism for file management, enabling each file to be dynamically adjusted according to the different characteristics of its material type Tmat, preservation period Yage, and importance level Iimp during the protection process. Compared with the traditional single and unified environmental control method, this solution can achieve more precise and personalized file management, avoiding file damage or inadaptability caused by unified environmental conditions. Especially for files with different materials and preservation periods, more targeted protection measures can be implemented. This not only improves the efficiency of file protection but also ensures the long-term stability of files in different environments, reducing risks and losses caused by changes in environmental factors.

[0080] Embodiment 3

[0081] This embodiment is an explanatory description carried out in Embodiment 2. Please refer to Figure 1 , specifically: S2 includes S21 and S22;

[0082] S21. By integrating an environmental sensor group in the file storage, the environmental data in the file storage is collected in real time to reflect the air temperature, air humidity, light intensity, air pressure, and carbon dioxide concentration in the file storage in real time;

[0083] Among them, the environmental sensor group includes a temperature and humidity sensor, a pressure sensor, a light sensor, and a carbon dioxide concentration sensor, and the environmental data includes temperature Tw, humidity Hs, light intensity Lg, air pressure Pq, and carbon dioxide concentration CO 2 ;

[0084] By performing data cleaning and preprocessing on the collected environmental data, the noise values in the environmental data are eliminated and the missing values are filled, and after integrating the preprocessed environmental data, min-max normalization is used for normalization processing to obtain the environmental feature set En;

[0085] S22. By integrating the environmental feature sets Enu at different times t, the environmental feature set ENu is formed.

[0086] The above S3 includes S31 and S32;

[0087] S31 matches the thermal sensitivity and environmental status based on the thermal sensitivity index vector HSI and the environmental feature set ENu to obtain the temperature Tw, humidity Hs, light intensity Lg, air pressure Pq, and carbon dioxide concentration CO 2 's temperature influence function fT, humidity influence function fH, light influence function fL, air pressure influence function fP, and carbon dioxide concentration influence function fCO 2 , and analyzes the risk assessment value R of each file by integrating the temperature influence function fT, humidity influence function fH, light influence function fL, air pressure influence function fP, and carbon dioxide concentration influence function fCO 2 . After bubble sorting the risk assessment value R of each file, it is then integrated with the integrated thermal sensitivity index vector HSI to obtain the risk assessment value set Risk;

[0088] S32 traverses and compares each risk assessment value R in the obtained risk assessment value set Risk with the risk threshold Rthe, obtains the comparison result of each risk assessment value R and the risk threshold Rthe, and synchronously associates it with the risk assessment value R;

[0089] The above comparison result is specifically obtained through the following comparison method:

[0090] When the risk assessment value R ≥ the risk threshold Rthe, mark the current file as a risk file, generate a risk mark FX, and associate it with the risk assessment value R of the current file;

[0091] When the risk assessment value R < the risk threshold Rthe, do not mark the current file as a risk file and do not generate a risk mark FX;

[0092] The above risk assessment value set Risk is obtained through the following calculation formula:

[0093] R(u) = HSI(u) * fenu(u, fT, fH, fL, fP, fCO 2 );

[0094] In the formula, R(u) represents the risk assessment value of the u-th file, HSI(u) represents the thermal sensitivity index vector of the u-th file, fenu represents the influence function, and fenu(u, fT, fH, fL, fP, fCO 2 ) represents the temperature influence function fT, humidity influence function fH, light influence function fL, air pressure influence function fP, and carbon dioxide concentration influence function fCO of the u-th file 2 ;

[0095] The above temperature influence function fT is obtained through the following calculation formula:

[0096]

[0097] In the formula, exp represents the exponential function, T0 represents the target temperature, which is usually the optimal temperature value recommended for file storage, similar to a reference temperature, and σT 2 represents the temperature sensitivity coefficient, which is used to reflect the influence intensity of temperature change on the thermal sensitivity of the file. A larger temperature sensitivity parameter σT 2 value indicates that the influence of temperature change on the file is more moderate, while a smaller value indicates a greater influence on the file. The temperature influence function fT simulates the influence of temperature change on the file. If the warehouse temperature Tw is close to the optimal temperature T0, the influence is close to 1, indicating that the influence of temperature on the file load is small. When the temperature deviates from T0, the influence increases sharply, and the risk of temperature to the file also increases;

[0098] The humidity influence function fH is obtained through the following calculation formula:

[0099] fH = log(1 + αH * |Hs - H0);

[0100] In the formula, log represents the logarithmic function, αH represents the humidity sensitivity coefficient, which represents the influence intensity of humidity deviation from the target value on the file, and H0 represents the target humidity, which is usually the optimal humidity value for file storage. The humidity influence function fH reflects the influence of humidity change on the file by performing a logarithmic calculation on the humidity deviation ∣Hs - H0∣. When deviating from H0, the influence of humidity increases non-linearly, and the influence is small when the humidity change is small. As the humidity gap increases, the load effect increases rapidly;

[0101] The light influence function fL is obtained through the following calculation formula:

[0102]

[0103] In the formula, βL represents the light sensitivity coefficient, which represents the influence degree of light intensity on the file. A larger light sensitivity coefficient βL indicates that the influence of light intensity on the file is more moderate. The light influence function fL simulates the load influence of light intensity on the file. As the light intensity Lg increases, the influence of light also increases, but the increase amplitude tends to be gentle, which is applicable to the non-linear sensitivity of the file to light. Extremely high light intensity may cause serious influence on the file, while under low light conditions, the influence is small;

[0104] The air pressure influence function fP is obtained through the following calculation formula:

[0105]

[0106] Wherein, P0 represents the target air pressure value, Pmax represents the upper limit of air pressure change, specifically representing the maximum value of air pressure. The air pressure influence function fP reflects the influence degree of air pressure on the archives by the ratio of the difference between the air pressure P and its target value P0 to the upper limit of change. Generally, the change of air pressure has little influence on most archives, but in special environments (such as when the air pressure changes violently), the influence may be aggravated;

[0107] The carbon dioxide concentration influence function fCO 2 is obtained through the following calculation formula:

[0108]

[0109] Wherein, γCO 2 represents the carbon dioxide sensitivity coefficient, which is used to reflect the sensitivity degree of the influence of carbon dioxide concentration on the archives. A smaller value of the carbon dioxide sensitivity coefficient γCO 2 indicates that a higher concentration of carbon dioxide has a greater influence on the archives. The carbon dioxide concentration influence function fCO 2 represents the influence of carbon dioxide concentration on the thermal sensitivity of the archives through a proportional relationship. As the carbon dioxide concentration CO 2 increases, the influence degree increases, and the influence degree tends to be gentle with the further increase of the concentration.

[0110] The S4 includes S41;

[0111] S41. Extract the archives with risk markers FX according to the obtained risk assessment value set Risk, calculate the temperature adjustment strategy of the warehouse, obtain the warehouse temperature adjustment amount △T and the warehouse humidity adjustment amount △H, and form an adjustment strategy set Adj of the temperature and humidity adjustment amount in the warehouse according to the obtained warehouse temperature adjustment amount △T and warehouse humidity adjustment amount △H;

[0112] The warehouse temperature adjustment amount △T is obtained through the following calculation formula:

[0113] ΔT = κT(Risk(u,t)) * (Tw - T0);

[0114] Wherein, κT represents the temperature adjustment coefficient, which represents the sensitivity of the influence of temperature on the risk of archives. A higher value indicates that the change of temperature has a greater influence on the risk of archives. Risk(u, t) represents the risk assessment value set of the u-th archive at time t;

[0115] The warehouse humidity adjustment amount △H is obtained through the following calculation formula:

[0116] ΔH = κH(Risk(u,t)) * (Hs - H0);

[0117] In the formula, κH represents the humidity adjustment coefficient, which indicates the sensitivity of humidity to the risk of archives. A higher value indicates that the change in humidity has a greater impact on the risk of archives.

[0118] In this embodiment, through S2 to S4, the environmental parameters in the archive storage can be accurately controlled. Based on the real-time data collection of the environmental sensor group, the storage conditions of the archives can be optimized, and the risks caused by the external environment to the archives can be reduced. First, by integrating a variety of environmental sensors in the archive storage, the temperature Tw, humidity Hs, light intensity Lg, air pressure Pq, and carbon dioxide concentration CO are collected in real time. 2 Multidimensional environmental data to ensure comprehensive monitoring of the environmental conditions. After data cleaning and standardization processing, the obtained environmental feature set En is further integrated to form the environmental feature set ENu, and combined with the heat sensitivity index vector HSI for heat sensitivity assessment, which helps to accurately identify the risk assessment value R of each archive. After risk assessment, by comparing with the risk threshold Rthe, the archives with high risks can be quickly identified, and the archive classification management can be realized by generating the risk mark FX, reducing the damage of archives caused by environmental factors. Furthermore, by calculating the adjustment amount of the temperature and humidity in the storage, based on the risk assessment value of each archive, an accurate environmental adjustment strategy set Adj is formulated to ensure the dynamic optimization of the environmental parameters, timely adjust the temperature and humidity to adapt to the changing environmental conditions, and through high-frequency data collection and real-time evaluation, personalized risk management and optimization adjustment can be carried out for each archive under the changing environmental conditions. Different from the traditional static environmental monitoring method, this solution enables the storage environment to respond to the heat sensitivity requirements of the archives at any time through dynamic calculation and feedback mechanism, reduces potential risks, optimizes the archive storage conditions, thereby effectively extending the preservation period of the archives and improving the intelligent level of archive protection.

[0119] Embodiment 4

[0120] This embodiment is an explanatory description based on Embodiment 3. Please refer to Figure 1 Specifically: The S5 includes S51;

[0121] S51. Dynamically adjust the environmental parameters according to the obtained adjustment strategy set Adj, obtain the adjusted applied temperature Tw(u, t + 1) and applied humidity Hs(u, t + 1), and integrate the obtained applied temperature Tw(u, t + 1) and applied humidity Hs(u, t + 1) to obtain the execution environmental feature set ZENu. Then, generate an adjustment instruction for the execution environmental feature set ZENu to execute the applied temperature Tw(u, t + 1) and applied humidity Hs(u, t + 1) for the environmental parameters in the current storage to dynamically adjust the environmental parameters in the storage;

[0122] The applied temperature Tw(u, t + 1) is obtained through the calculation formula Tw(u, t + 1)=Tw + △T;

[0123] The applied humidity Hs(u, t + 1) is obtained through the calculation formula Hs(u, t + 1)=Hs + △H.

[0124] The said S6 includes S61 and S62;

[0125] S61, evaluate the risk change of the archives in the warehouse after implementing the adjustment strategy set Adj on the execution environment feature set ZENu, the heat sensitivity index vector HSI, and the risk assessment value set Risk. Specifically, by accumulating the fluctuation values of the execution environment feature set ZENu, the heat sensitivity index vector HSI, and the risk assessment value set Risk over a fixed period, obtain the execution environment feature fluctuation value △ZENu, the heat sensitivity index vector fluctuation value △HSI, and the risk assessment value set fluctuation value △Risk, and generate the risk vector Ropt;

[0126] S62, according to the obtained risk vector Ropt, calculate the total fluctuation value within a statistical fixed period, obtain the total risk vector fluctuation index △Ropt, and compare it with the preset adjustment strategy execution evaluation threshold AdjThe to obtain the trigger result of the iterative optimization mechanism;

[0127] The trigger result of the iterative optimization mechanism is obtained through the following comparison method:

[0128] When the total risk vector fluctuation index △Ropt ≥ the adjustment strategy execution evaluation threshold AdjThe, the trigger result of the iterative optimization mechanism is obtained as the trigger result. At this time, trigger the iterative optimization mechanism, and repeat steps S2, S3, S4, S5, and S6 until exiting the iterative optimization mechanism;

[0129] When the total risk vector fluctuation index △Ropt < the adjustment strategy execution evaluation threshold AdjThe, the trigger result of the iterative optimization mechanism is obtained as the non-trigger result. At this time, exit the already executed iterative optimization mechanism.

[0130] In this embodiment, by obtaining the adjustment strategy set Adj and dynamically adjusting the temperature Tw(u, t + 1) and humidity Hs(u, t + 1), the environmental conditions in the storage warehouse are adjusted. The adjustment amounts △T and △H of temperature and humidity are directly applied to the optimization of environmental parameters, generating the adjusted execution environment feature set ZENu, and generating an environmental adjustment instruction to ensure that the temperature and humidity in the storage warehouse are always within the range suitable for file preservation. By comprehensively evaluating and analyzing the fluctuations of the execution environment feature set ZENu, the heat sensitivity index vector HSI, and the risk assessment value set Risk, the risk changes of the storage warehouse environment and files can be tracked in real time. The fluctuations of environmental features, heat sensitivity indexes, and risk assessment values within a fixed period are accumulated to generate a risk vector Ropt, and the total risk fluctuation index △Ropt is compared and evaluated to determine whether to trigger the iterative optimization mechanism. If the total risk fluctuation index exceeds the preset threshold AdjThe, the system will trigger the iterative optimization mechanism for further adjustment until the optimal state is reached. Through continuous risk assessment and environmental parameter adjustment, the system can dynamically respond to the changes in the internal and external environments of the storage warehouse and flexibly cope with the fluctuations of file risks. By triggering the iterative optimization mechanism, the system can perform multiple evaluations and adjustments at different time points to ensure that each optimization can be accurately adjusted according to the latest environmental features, file heat sensitivity, and risk assessment. This real-time dynamic feedback mechanism makes file management more intelligent, can significantly reduce the potential risks caused by environmental fluctuations to files, provides a more refined and efficient file protection solution, and has stronger adaptability and foresight compared with traditional static adjustment methods.

[0131] Embodiment 5

[0132] For the environmental monitoring system of the file storage warehouse, please refer to Figure 2 , specifically: including a data acquisition module, a risk assessment module, an adjustment generation module, and an iterative optimization module;

[0133] The data acquisition module collects the initial file information by constructing a communication channel for the file management system, generates a heat sensitivity index vector for each file, and collects the environmental parameters in the storage warehouse to generate an environmental feature set;

[0134] The risk assessment module matches the heat sensitivity and environmental status according to the heat sensitivity index vector and the environmental feature set to generate a risk assessment value set, and calculates the temperature and humidity adjustment strategy according to the obtained risk assessment value set to obtain an adjustment strategy set for the temperature and humidity adjustment amount in the storage warehouse;

[0135] The adjustment generation module dynamically adjusts the environmental parameters by executing the adjustment strategy set and generates a new execution environment feature set;

[0136] The iterative optimization module evaluates the risk changes of the archives in the warehouse after implementing the adjustment strategy set through the execution environment feature set, the thermal sensitivity index vector, and the risk assessment value set, generates a risk vector, and triggers the iterative optimization mechanism based on the risk vector.

[0137] Embodiment 6

[0138] An environmental monitoring device for an archives warehouse, specifically: including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements an environmental monitoring method for an archives warehouse.

[0139] Embodiment 7

[0140] An environmental monitoring medium for an archives warehouse, specifically: the medium stores a computer program, and when the computer program is executed, it implements an environmental monitoring method for an archives warehouse.

[0141] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for environmental monitoring of archive warehouses, characterized in that: The following steps are involved: S1, build the communication channel of the archive management system, collect the initial information of the archive, and generate a thermal sensitivity index vector for each archive; S2, collect environmental parameters in the warehouse and generate an environmental feature set; S3, matching thermal sensitivity with environmental status according to the thermal sensitivity index vector and the environmental feature set, and generating a risk assessment value set; S4. Calculate the temperature and humidity adjustment strategy based on the acquired risk assessment value set, and obtain the adjustment strategy set of the temperature and humidity adjustment amount in the warehouse; S5. Dynamically adjust the environment parameters by executing the adjustment strategy set, and generate a new execution environment feature set; S6. Generate a risk vector by evaluating the risk changes of the files in the warehouse after the execution adjustment strategy set is executed by evaluating the execution environment feature set, thermal sensitivity index vector and risk assessment value set, and trigger an iterative optimization mechanism based on the risk vector.

2. The environmental monitoring method for archives warehouse according to claim 1, characterized in that: Said S1 includes S11 and S12; S11, by obtaining the initial archive information of each archive stored in the archive management system, including material type information, preservation age information and importance level information, and by numerically processing the initial archive information, obtaining the material type Tmat, preservation age Yage and importance level Iimp, and forming a parameter set P of the i-th archive; Among them, the material type Tmat reflects the sensitivity of the archive to changes in temperature and humidity; The preservation period Yage reflects the impact of the preservation time of the archive on its stability; The importance level Iimp indicates the value weight of the archive in the overall management system; The numerical processing includes scoring the material type information matching the pre-examined archive attribute mapping table to perform numerical processing; The preservation period information is digitized by assigning the difference between the current time and the archive creation time; The importance level Iimp is numerically processed by matching the preset archive storage level mapping table; S12. A weighted formula of a thermal sensitivity evaluation algorithm is constructed for the parameter set P of the ith file to obtain a thermal sensitivity index HS of the ith file, and a thermal sensitivity index vector HSI is obtained by integrating the thermal sensitivity indexes generated for each file.

3. The environmental monitoring method for archives warehouse according to claim 2 is characterized in that: The S2 includes S21 and S22; S21, by integrating an environmental sensor group in the archive warehouse, real-time collection of environmental data in the archive warehouse, and real-time reflection of the air temperature, air humidity, light intensity, air pressure and carbon dioxide concentration in the archive warehouse; Among them, the environmental sensor group includes temperature and humidity sensors, air pressure sensors, light sensors and carbon dioxide concentration sensors, and the environmental data include temperature Tw, humidity Hs, light intensity Lg, air pressure Pq and carbon dioxide concentration CO2; By performing data cleaning preprocessing on the collected environmental data, the noise values ​​in the environmental data are eliminated and the missing values ​​are filled, and the environmental feature set En is obtained by integrating the preprocessed environmental data and then using the minimum-maximum standardization for standardization. S22. Integrate the environmental feature sets Enu at different times t to form an environmental feature set ENi.

4. The environmental monitoring method for archives warehouse according to claim 3 is characterized in that: The S3 includes S31 and S32; S31, matching thermal sensitivity with environmental conditions according to the thermal sensitivity index vector HSI and the environmental feature set EMu, obtaining the temperature influence function fT, humidity influence function fH, illumination influence function fL, air pressure influence function fP and carbon dioxide concentration influence function fCO2 of temperature Tw, humidity Hs, illumination intensity Lg, air pressure Pq and carbon dioxide concentration CO2, analyzing the risk assessment value R of each file by integrating the temperature influence function fT, humidity influence function fH, illumination influence function fL, air pressure influence function fP and carbon dioxide concentration influence function fCO2, and then bubble sorting the risk assessment value R of each file, and then integrating it with the integrated thermal sensitivity index vector HSI to obtain the risk assessment value set Risk; S32, traversing and comparing each risk assessment value R with the risk threshold Rthe according to the acquired risk assessment value set Risk, obtaining the comparison result of each risk assessment value R with the risk threshold Rthe, and synchronously associating it with the risk assessment value R; The comparison result is obtained by the following comparison method: When the risk assessment value R ≥ the risk threshold Rthe, the current file is marked as a risk file, and a risk mark FX is generated and associated with the risk assessment value R of the current file; When the risk assessment value R is less than the risk threshold Rthe, the current file is not marked as a risk file, and the risk mark FX is not generated.

5. The environmental monitoring method for archives warehouse according to claim 4 is characterized in that: The S4 includes S41; S41. According to the obtained risk assessment value set Risk, the files with risk mark FX are extracted to calculate the temperature and humidity adjustment strategy, and the warehouse temperature adjustment amount △T and the warehouse humidity adjustment amount △H are obtained. According to the obtained warehouse temperature adjustment amount △T and warehouse humidity adjustment amount △H, the adjustment strategy set Adj of the temperature and humidity adjustment amount in the warehouse is formed.

6. The environmental monitoring method for archives warehouse according to claim 5, characterized in that: The S5 includes S51; S51. Dynamically adjust the environmental parameters according to the obtained adjustment strategy set Adj, obtain the adjusted application temperature Tw (u, t+1) and application humidity Hs (u, t+1), integrate the obtained application temperature Tw (u, t+1) and application humidity Hs (u, t+1), obtain the execution environment feature set ZENu, and then generate adjustment instructions for the execution environment feature set ZENu to execute the application temperature Tw (u, t+1) and application humidity Hs (u, t+1) on the environmental parameters in the current warehouse to dynamically adjust the environmental parameters in the warehouse.

7. The environmental monitoring method for archives warehouse according to claim 6 is characterized in that: The S6 includes S61 and S62; S61, by evaluating the risk change of the files in the warehouse after the execution adjustment strategy set Adj is executed on the execution environment feature set ZENu, the heat sensitivity index vector HSI and the risk assessment value set Risk, specifically by accumulating the fluctuation values ​​of the execution environment feature set ZENu, the heat sensitivity index vector HSI and the risk assessment value set Risk in a fixed period, obtaining the execution environment feature fluctuation value △ZENu, the heat sensitivity index vector fluctuation value △HSI and the risk assessment value set fluctuation value △Risk, and generating the risk vector Ropt; S62, according to the obtained risk vector Ropt, the total fluctuation value within a fixed period is counted to obtain the total risk vector fluctuation index △Ropt, and compared with the preset adjustment strategy execution evaluation threshold AdjThe to obtain the iterative optimization mechanism triggering result; The triggering result of the iterative optimization mechanism is obtained by the following comparison method: When the total risk vector volatility index △Ropt≥the adjustment strategy execution evaluation threshold AdjThe, the iterative optimization mechanism trigger result is obtained as the trigger result, and the iterative optimization mechanism is triggered at this time, and steps S2, S3, S4, S5 and S6 are repeatedly executed until the iterative optimization mechanism is exited; When the total risk vector fluctuation index △Ropt<the adjustment strategy execution evaluation threshold AdjThe, the iterative optimization mechanism trigger result is obtained as a non-triggering result, and the iterative optimization mechanism that has been executed is exited at this time.

8. An environmental monitoring system for archive warehouses, characterized in that: It includes data collection module, risk assessment module, adjustment generation module and iterative optimization module; The data acquisition module collects initial archive information by constructing an archive management system communication channel, generates a thermal sensitivity index vector for each archive, and collects environmental parameters in the warehouse to generate an environmental feature set; The risk assessment module matches the thermal sensitivity with the environmental state according to the thermal sensitivity index vector and the environmental feature set, generates a risk assessment value set, and calculates the temperature and humidity adjustment strategy according to the obtained risk assessment value set, and obtains the adjustment strategy set of the temperature and humidity adjustment amount in the warehouse; The adjustment generation module dynamically adjusts the environment parameters by executing the adjustment strategy set and generates a new execution environment feature set; The iterative optimization module generates a risk vector by evaluating the risk changes of the files in the warehouse after the execution adjustment strategy set is executed by evaluating the execution environment feature set, the thermal sensitivity index vector and the risk assessment value set, and triggers the iterative optimization mechanism according to the risk vector.

9. An environmental monitoring device for archive warehouse, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the environmental monitoring method for archive warehouses as described in any one of claims 1 to 7 is implemented.

10. An environmental monitoring medium for archive warehouses, characterized in that: The medium stores a computer program, and when the computer program is executed, the environmental monitoring method for an archive warehouse described in any one of claims 1 to 7 is implemented.

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