A multi-dimensional archive collection resource intelligent management system and method

Abstract

The present application relates to the field of archives digitization asset management and warehouse microenvironment intelligent monitoring, and particularly relates to a multi-dimensional archives collection resource intelligent management system and method, comprising: the system extracts the text segmentation sequence and the silence duration data of the target archives digital copy, and outputs the dimensionless retention value, performs reverse constraint operation based on the preset base to generate a dynamic humidity safety upper limit; the current relative humidity measurement value of the grid is subtracted from the safety upper limit to calculate the deviation degree as the initial state of the system; the state and the control variable are combined with the microenvironment parameters to construct a first-order differential equation, the performance index functional is constructed based on the differential equation and the energy consumption weight, and the partial derivative is optimized to generate the optimal control sequence; finally, the sequence power value is converted into a pulse width modulation instruction to issue the microcontroller to overwrite the register control dehumidification fan, and the humidity measurement value is reacquired to form a data physical closed loop.

Description

Technical Field

[0001] This invention relates to the field of digital archival asset management and intelligent monitoring of the microenvironment of storage facilities, and in particular to a multi-dimensional intelligent management system and method for archival resources. Background Technology

[0002] In large-scale integrated archives' mobile shelving storage scenarios, traditional microenvironment control systems typically employ a static management model with fragmented data. Specifically, existing environmental control hardware performs coarse start-stop dehumidification actions based solely on preset global fixed humidity thresholds, completely detached from the actual semantic information density and business access frequency of the digital copies corresponding to the physical archives. The aforementioned global fixed humidity threshold triggering mechanism and simple open-loop control logic not only lead to delayed response to moisture-proofing and rescue of high-value historical archives and continuous consumption of redundant dehumidification energy by low-utilization archives, but also, because the aerodynamic physical characteristics of the local grid are not incorporated into the optimization solution model of the underlying control commands, it is highly susceptible to high-frequency nonlinear oscillations in the relative humidity of the microenvironment, accelerating alternating stress fatigue and irreversible physical damage to the fibers inside the paper medium. Summary of the Invention

[0003] To overcome the above shortcomings, this invention provides a multi-dimensional intelligent management system and method for archival resources, aiming to improve the problem that traditional archival environmental control mostly adopts static threshold start-stop logic, which lacks a physical dynamic optimization mechanism, thus causing high-frequency oscillation of microenvironment humidity to accelerate the damage of media.

[0004] In a first aspect, the present invention provides the following technical solution: a multi-dimensional intelligent management system for archival resources, comprising the following modules:

[0005] The digital asset evaluation module is used to extract the text segmentation sequence and silent duration data of the target file digital copy, and to use the text segmentation sequence and silent duration data to perform calculations to output the dimensionless retention value.

[0006] The physical threshold mapping module is used to perform reverse constraint calculations on the dimensionless retention value output by the digital asset evaluation module based on a preset base to generate a dynamic humidity safety upper limit.

[0007] The environmental status acquisition module is used to acquire the current relative humidity measurement value of the grid where the target file is located, subtract the dynamic humidity safety upper limit output by the physical threshold mapping module from the current relative humidity measurement value to calculate the deviation, and define the deviation as the initial state of the system.

[0008] The dynamic optimization module is used to receive the initial state of the system output by the environmental state acquisition module, extract the state variables and control variables and combine them with micro-environment parameters to construct a first-order differential equation, construct a performance index functional based on the first-order differential equation and energy consumption weight, and perform partial derivative optimization calculation to generate the optimal control sequence.

[0009] The bottom-level driving closed-loop module is used to extract the power value from the optimal control sequence output by the dynamic optimization module, convert the power value into a pulse width modulation command and send it to the microcontroller at the bottom of the grid, drive the microcontroller to overwrite the register to control the dehumidifying fan, and trigger the environmental state acquisition module to reacquire the current relative humidity measurement value.

[0010] By adopting the above technical solution, the value mapping dynamic humidity upper limit is retained and the optimal control sequence is generated by combining the first-order differential equation to achieve precise pulse width modulation of the dehumidifying fan. This improves the problem that traditional archive environmental control mostly adopts static threshold start-stop logic, which lacks a physical dynamic optimization mechanism, resulting in high-frequency oscillation of microenvironment humidity that accelerates the damage of the medium.

[0011] Secondly, this invention provides the following technical solution: a multi-dimensional intelligent management method for archival resources, comprising the following steps:

[0012] S1. Extract the text segmentation sequence and silent duration data of the target file digital copy, and use the text segmentation sequence and silent duration data to perform calculations to output the dimensionless retention value.

[0013] S2. Based on a preset base, perform reverse constraint calculation on the dimensionless retention value output by the digital asset evaluation module to generate a dynamic humidity safety upper limit;

[0014] S3. Obtain the current relative humidity measurement value of the grid where the target file is located, subtract the dynamic humidity safety upper limit output by the physical threshold mapping module from the current relative humidity measurement value to calculate the deviation, and define the deviation as the initial state of the system.

[0015] S4. Receive the initial state of the system output by the environmental state acquisition module, extract the state variables and control variables and combine them with micro-environment parameters to construct a first-order differential equation, construct a performance index functional based on the first-order differential equation and energy consumption weight, and perform partial derivative optimization calculation to generate the optimal control sequence.

[0016] S5. Extract the power value from the optimal control sequence output by the dynamic optimization module, convert the power value into a pulse width modulation command and send it to the microcontroller at the bottom of the grid, drive the microcontroller to overwrite the register to control the dehumidifying fan, and trigger the environmental status acquisition module to reacquire the current relative humidity measurement value.

[0017] The present invention has the following beneficial effects:

[0018] 1. In this invention, the optimal control sequence is generated by mapping the dynamic humidity upper limit to the retention value and combining it with the first-order differential equation to achieve precise pulse width modulation of the dehumidifying fan. This improves the problem that traditional archive environmental control mostly adopts static threshold start-stop logic, which lacks a physical dynamic optimization mechanism, resulting in high-frequency oscillation of micro-environment humidity that accelerates the damage of the medium.

[0019] 2. In this invention, by mapping the retention value to a dynamic humidity upper limit and combining it with dynamic optimization control of the dehumidifying fan, differentiated environmental control is achieved. This improves the problem that traditional microenvironment management mostly uses static and uniform thresholds, which, due to the lack of cross-modal data linkage, results in lagging protection of high-value archives and unnecessary energy waste.

[0020] 3. In this invention, by using word segmentation sequences to calculate information entropy and combining the silent duration to output retention value to adjust the upper limit of humidity, the density of the true content of archives and business popularity are accurately quantified. This improves the problem of misallocation of environmental control and dehumidification resources caused by the traditional archive classification, which mostly uses a single filing year and fails to explore semantic and temporal features.

[0021] 4. In this invention, by overwriting the microcontroller register with the pulse width modulation instruction and triggering the re-acquisition of the state, the underlying hardware adaptive control closed loop is realized, thereby improving the problem that most traditional environmental control nodes adopt open-loop unidirectional command, which lacks underlying state feedback verification after the dehumidification action is executed, resulting in the failure of micro-environment intervention. Attached Figure Description

[0022] Figure 1 This is an architecture diagram of a multi-dimensional intelligent management system for archival resources proposed in this invention;

[0023] Figure 2 This is a flowchart of the digital asset evaluation and physical threshold mapping processing network for a multi-dimensional intelligent management system and method for archival resources proposed in this invention.

[0024] Figure 3 This is a flowchart illustrating the environmental dynamics optimization control solution process of a multi-dimensional intelligent management system and method for archival resources proposed in this invention.

[0025] Figure 4 This is a flowchart illustrating the underlying drive control and environmental feedback wake-up closed-loop execution process of a multi-dimensional intelligent management system and method for archival resources proposed in this invention.

[0026] Figure 5 This is a flowchart of a multi-dimensional intelligent management method for archival resources proposed in this invention. Detailed Implementation

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Example 1:

[0029] In a first embodiment of the present invention, the present invention provides a multi-dimensional intelligent management system for archival resources, such as... Figures 1-4 As shown, it includes the following modules:

[0030] The digital asset evaluation module is used to extract the text segmentation sequence and silent duration data of the target file's digital copy, and to use the text segmentation sequence and silent duration data to perform calculations and output the dimensionless retention value.

[0031] Furthermore, the digital asset assessment module extracts the text segmentation sequence and silent duration data of the target file's digital copy, and uses the text segmentation sequence and silent duration data to calculate and output a dimensionless retention value, including:

[0032] The log extraction subunit is used to connect to the business database to retrieve the last access time anchor of the target file digital copy, subtract the last access time anchor from the current system time anchor to obtain the silent duration data, and call the natural language processing engine to segment the content of the target file digital copy to generate a text segmentation sequence.

[0033] The word segmentation statistics subunit is used to count the total number of all independent words in the text segmentation sequence and the occurrence frequency of each individual independent word. The occurrence frequency is divided by the total number to obtain the occurrence probability of each individual independent word. The occurrence probability is extracted and the logarithm of the occurrence probability is multiplied to obtain the word feature entropy. The word feature entropy is summed and the negative value is taken to convert it into text information entropy.

[0034] The coupled dimensionality reduction subunit is used to extract the borrowing attenuation constant preset by the system, multiply the borrowing attenuation constant by the silent duration data to obtain the attenuation exponent, calculate the negative exponent power function value of the attenuation exponent with the natural constant as the base, and perform dimensionality reduction processing by multiplying the negative exponent power function value by the text information entropy to output the dimensionless retention value.

[0035] Specifically, the log extraction subunit constructs a data retrieval link to the business database. The log extraction subunit sends a query signal to retrieve the last access time anchor of the target file's digital copy. Simultaneously, the log extraction subunit extracts the current system time anchor provided by the system motherboard's hardware clock. It executes a subtraction comparison instruction, subtracting the last access time anchor from the current system time anchor, directly outputting the silence duration data with absolute time span attributes. The log extraction subunit calls the natural language processing engine deployed in the computing power center through an internal software interface. It uses the part-of-speech segmentation and stop word filtering algorithms within the natural language processing engine to segment the plain text content of the target file's digital copy to be evaluated, generating a text segmentation sequence composed of independent words arranged in the original text's physical order. The log extraction subunit submits the text segmentation sequence to the segmentation statistics subunit for semantic feature parsing, and simultaneously allocates the silence duration data to the coupled dimensionality reduction subunit for caching.

[0036] The word segmentation statistics subunit receives the input text word segmentation sequence. It traverses and scans all data nodes within the text word segmentation sequence. It counts the total number of all independent words in the text word segmentation sequence. For each single independent word that has undergone deduplication, it counts the number of times it appears in the entire sequence. It performs basic division operations, dividing the specific occurrence count by the total number of occurrences to calculate the probability of occurrence of the single independent word.

[0037] The word segmentation and statistics subunit performs mathematical transformations and feature extraction based on the probability of occurrence. The specific probability calculation model configuration is as follows:

[0038] ;

[0039] in For the first The probability parameter of occurrence of a single independent word. The first one determined by scanning statistics The specific number of times a single, independent word appears. This represents the total number of all independent words contained in the text segmentation sequence. All three parameters are dimensionless, purely numerical variables.

[0040] After obtaining the occurrence probabilities of all independent words, the word segmentation statistics subunit extracts the occurrence probabilities and multiplies them by their logarithmic values ​​to generate word feature entropy. The calculated word feature entropies are then linearly summed, and a negative sign is added to the front of the final result to force a conversion to text information entropy, which reflects the density of text information content. The relevant entropy calculation model configuration is as follows:

[0041] ;

[0042] in The text information entropy obtained for the final transformation. This is the limit value for the sum of a single independent word category after deduplication of the traversed text segmentation sequence. Maintaining the above definition represents the probability parameter of occurrence. It is specified as the mathematical symbol for logarithmic operations with base 2. After completing the calculation, the word segmentation and statistics subunit delivers the text information entropy to the coupled dimensionality reduction subunit.

[0043] The coupled dimensionality reduction subunit receives two data sources: silent duration data and text information entropy. It reads the underlying environment configuration script to extract the system's preset borrowing attenuation constant. It performs a multiplication operation, multiplying the borrowing attenuation constant by the silent duration data to obtain the attenuation exponent. It then calls the floating-point unit of the computing power center to calculate the negative exponent function value of the attenuation exponent using the natural constant as the base. Finally, it performs a dimensionality reduction process based on the multiplication of semantic and temporal features, multiplying the negative exponent function value by the text information entropy and outputting the result. The specific dimensionality reduction model is as follows:

[0044] ;

[0045] in This refers to the dimensionless retention value generated through cross-dimensional feature fusion calculation. This represents the text information entropy injected into the word segmentation statistics subunit. It is the base of the natural constants within the scope of mathematics. This represents the borrowing decay constant extracted from the configuration script. Its dimension is strictly set to the reciprocal of the time unit to ensure the dimensionless nature of the values ​​in the exponential region. The silent duration data injected by the log extraction subunit is aligned with the time unit of the borrowing decay constant.

[0046] After solving the above model, the coupled dimensionality reduction subunit encapsulates the final dimensionless retention value in the memory address space and sends a data ready hardware interrupt signal to the physical threshold mapping module, providing a complete basis for cross-modal value constraint judgment. This completes the underlying data flow closed loop from digital features to numerical variables.

[0047] The physical threshold mapping module is used to perform reverse constraint calculations on the dimensionless retention value output by the digital asset evaluation module based on a preset base to generate a dynamic humidity safety upper limit.

[0048] Furthermore, the physical threshold mapping module is used to perform reverse constraint calculations on the dimensionless retention value output by the digital asset evaluation module based on a preset base to generate a dynamic humidity safety upper limit, including:

[0049] The reference reading subunit is used to read the basic moisture absorption alarm tolerance parameters and preset base values ​​for paper media from the memory;

[0050] The penalty multiplier derivation subunit is used to scan the global archive database to extract the global maximum extreme value of the dimensionless retention value in all archives, subtract the dimensionless retention value from the global maximum extreme value to obtain the value difference value, divide the value difference value by the global maximum extreme value to obtain the normalized difference value, and multiply the normalized difference value by the preset base to obtain the penalty multiplier for the target archive.

[0051] The dynamic upper limit synthesis subunit is used to extract a constant and add it to the penalty multiplier to obtain the tolerance adjustment ratio. The basic moisture absorption alarm tolerance parameter is multiplied by the tolerance adjustment ratio to obtain the safe relative humidity threshold. The safe relative humidity threshold is defined as the dynamic humidity safe upper limit output.

[0052] Specifically, the physical threshold mapping module acts as a mandatory conversion hub between the digital asset domain and the physical environmental control domain. The execution unit includes a benchmark reading subunit, a penalty multiplier derivation subunit, and a dynamic upper limit synthesis subunit. Input data nodes receive the dimensionless retained value transmitted by the digital asset evaluation module. Output data nodes are directly connected to the threshold comparison channel of the underlying environmental status acquisition module.

[0053] The reference reading subunit accesses the non-volatile memory via the system's internal bus. It retrieves the preset basic moisture absorption alarm tolerance parameters, tailored to the hardware characteristics of paper media, from the designated memory sector. It also synchronously reads the preset base values ​​stored in the configuration file according to a preset clock cycle. The reference reading subunit extracts these two sets of parameters and writes them into the first-level cache space for subsequent computational logic calls.

[0054] The penalty multiplier derivation subunit retrieves the dimensionless retention value from the memory receiving address. It generates a traversal retrieval signal and sends it to the global archive database. It scans and compares the retention value data recorded in all archive nodes, extracting the global maximum value with the highest digit. Within the computing logic unit, it calls a subtraction instruction to perform an operation, subtracting the dimensionless retention value from the global maximum value to output the value difference. It then initiates a floating-point divider to extract the value difference value and divide it by the global maximum value to obtain the normalized difference. Finally, it reads the preset base resident in the cache and performs a multiplication operation between the preset base and the normalized difference to generate a penalty multiplier for the target archive.

[0055] The following mathematical constraint model is established for the underlying derivation of the penalty multiplier:

[0056] ;

[0057] in This represents the penalty multiplier generated through computation. This represents the maximum global extremum of the retained value extracted from the global archive database. The dimensionless retention value of the target file passed to the preceding processing node. This represents the preset base number read from non-volatile memory. All of the above-mentioned related parameters are dimensionless digital floating-point variables.

[0058] The dynamic upper limit synthesis subunit receives the penalty multiplier and the basic moisture absorption alarm tolerance parameter temporarily stored in the cache. It extracts a constant 1 from the accumulation register of the arithmetic logic unit as the adjustment bias reference. It performs an addition operation between the constant 1 and the input penalty multiplier to obtain the tolerance adjustment ratio. It triggers a low-level multiplication instruction to multiply the basic moisture absorption alarm tolerance parameter and the tolerance adjustment ratio. The physical output of the product operation is confirmed as the safe relative humidity threshold. The dynamic upper limit synthesis subunit redefines the safe relative humidity threshold in memory as the dynamic humidity safe upper limit and stores and outputs it.

[0059] The following physical dimension mapping model is constructed for the synthetic processing of dynamic safety upper limits:

[0060] ;

[0061] in Defined as the dynamic humidity safety upper limit of the final generated output, it has a percentage dimension attribute representing the relative humidity of the air. This represents the basic moisture absorption alarm tolerance parameter retrieved from memory, and also has a percentage-based dimension representing relative humidity. The constant 1 is a preset dimensionless bias integer. To penalize the dimensionless floating-point variables injected into the multiplier derivation subunit.

[0062] After the synthesis node completes its work, the dynamic upper limit synthesis subunit extracts and encapsulates the dynamic humidity safety upper limit. It then uses the hardware control bus to send a data frame containing the dynamic humidity safety upper limit to the physical environment status acquisition module. The non-physical value characteristics of the digital dimension are forcibly parsed and converted into absolute trigger threshold standards for intervention actions in the underlying physical microenvironment. The entire module completes an absolutely unbroken closed-loop data flow from digital bus reception to hardware bus delivery.

[0063] The environmental status acquisition module is used to obtain the current relative humidity measurement value of the grid where the target file is located, subtract the dynamic humidity safety upper limit output by the physical threshold mapping module from the current relative humidity measurement value to calculate the deviation, and define the deviation as the initial state of the system.

[0064] Furthermore, the environmental status acquisition module is used to obtain the current relative humidity measurement value of the grid where the target file is located, subtract the dynamic humidity safety upper limit output by the physical threshold mapping module from the current relative humidity measurement value to calculate the deviation, and define the deviation as the initial state of the system, including:

[0065] The hardware addressing subunit is used to extract the physical rack space coordinates of the target file, resolve the physical rack space coordinates into the sensor node network address of the grid where the target file is located, send data polling control commands to the sensor node network address, and receive the returned current relative humidity measurement value.

[0066] The difference extraction subunit is used to perform a subtraction comparison logic between the current relative humidity measurement value and the dynamic humidity safety upper limit output by the physical threshold mapping module, calculate the difference result of the relative humidity value, and extract the absolute value of the difference result as the deviation.

[0067] The state anchoring subunit is used to obtain the current execution timestamp of the subtraction comparison logic, bind and encapsulate the deviation with the current execution timestamp, and define the deviation of the bound time attribute as the initial state of the system within the continuous time domain.

[0068] Specifically, the execution framework of the environmental status acquisition module includes a hardware addressing subunit, a difference extraction subunit, and a state anchoring subunit. The upstream data interface receives the dynamic humidity safety upper limit submitted by the physical threshold mapping module. The downstream data outflow channel is directly connected to the state boundary input of the dynamic optimization module.

[0069] The hardware addressing subunit calls the underlying database interface to read the physical rack space coordinates of the target file. Through the system's internally fixed topology address mapping routing table, the physical rack space coordinates are directly converted into the network address of the sensor node bound to the grid where the target file is located. Data polling control commands conforming to the underlying field communication bus protocol are generated. The hardware addressing subunit sends the data polling control commands along the physical communication isolation cable to the sensor node network address. The underlying bus receives the current relative humidity measurement value returned by the hardware probe. After completing the extraction of physical environmental variables, the hardware addressing subunit directly pushes the current relative humidity measurement value into the shared memory buffer area.

[0070] The difference extraction subunit extracts the dynamic humidity safety upper limit transmitted across modules by the physical threshold mapping module from the preceding communication queue. It synchronously reads the current relative humidity measurement value residing in the shared memory buffer area. The arithmetic logic unit of the central processing unit executes the subtraction comparison logic. Specifically, it subtracts the current relative humidity measurement value from the dynamic humidity safety upper limit to obtain the difference in relative humidity values. It intercepts and extracts the absolute value of the difference result and forcibly assigns it as the deviation degree.

[0071] The absolute difference evaluation model built inside the difference extraction subunit is configured as follows:

[0072] ;

[0073] in. It represents the deviation of the calculated output, and has the dimension of relative humidity difference expressed as a percentage. This is the current relative humidity measurement returned by the underlying sensor probe. This refers to the maximum safe limit for dynamic humidity injected by the pre-amplifier module. An absolute value calculation mechanism is introduced to ensure this. It always maintains the non-negative real scalar property to satisfy the non-negative boundary requirements of subsequent functional integrals.

[0074] The state anchoring subunit connects to the real-time clock generator on the system motherboard. It precisely captures the current execution timestamp at the instant the level of the subtraction comparison logic is completed in the arithmetic logic unit. It allocates consecutive blocks of non-volatile memory to perform data assembly operations. It performs low-level binary binding and encapsulation of the deviation representing the absolute humidity state of the space with the current execution timestamp representing the absolute position on the time axis. The state anchoring subunit strictly defines the deviation of the completed binding time attribute as the initial state of the system within a continuous time domain.

[0075] The system-level anchored encapsulation model for state space is described as follows:

[0076] ;

[0077] in It represents the system's initial state that has been formally established and output, serving as the zero-time boundary reference matrix for solving subsequent higher-order partial differential equations. Identify the starting time anchor point for calculus in the continuous time domain. This is the deviation scalar calculated in the previous step. The current execution timestamp is fetched from the register, and the value of the current execution timestamp is compared with... Absolutely aligned on the system's global timeline.

[0078] After completing the encapsulation and assembly process, the state anchoring subunit establishes the inter-process communication channel within the system. The fully encapsulated initial system state is then pushed to the underlying state input buffer queue of the dynamics optimization module. This completes the data transmission loop from the retrieval of physical environment probe data to the establishment of boundary conditions for the mathematical dynamic equations.

[0079] The dynamic optimization module receives the initial system state output by the environmental state acquisition module, extracts the state variables and control variables, combines them with micro-environmental parameters to construct a first-order differential equation, constructs a performance index functional based on the first-order differential equation and energy consumption weight, and performs partial derivative optimization calculations to generate the optimal control sequence.

[0080] Furthermore, the dynamic optimization module is used to extract state variables and control variables, combined with microenvironment parameters, to construct first-order differential equations, including:

[0081] The variable mapping definition subunit is used to receive the initial state of the system, map the humidity deviation value within the continuous time domain as a state variable, and map the input electrical power value of the dehumidifier fan as a control variable.

[0082] The physical parameter loading subunit is used to read the background moisture recovery parameters that characterize the spontaneous humidity change of the space, the physical dehumidification efficiency parameters that characterize the intervention attributes of artificial equipment, and the external moisture load disturbance parameters that characterize the disturbance attributes of personnel entering and exiting from the underlying environment configuration library to form microenvironment parameters.

[0083] The state equation construction sub-unit is used to multiply the background moisture recovery parameter with the state variable to obtain the spontaneous humidity rate of the environment, multiply the physical dehumidification efficiency parameter with the control variable to obtain the artificial intervention dehumidification rate, and subtract the artificial intervention dehumidification rate from the spontaneous humidity rate of the environment and add the external moisture load disturbance parameter to generate a first-order differential equation describing the mathematical correlation logic of the dynamic change of humidity deviation rate.

[0084] The dynamics optimization module is used to construct performance index functionals based on first-order differential equations and energy consumption weights, including:

[0085] The weight reading subunit is used to extract pre-calibrated humidity deviation penalty parameters and energy consumption weights used to constrain power consumption characteristics from the memory.

[0086] The risk power consumption quantification subunit is used to calculate the quadratic value of the state variable and multiply it by the humidity deviation penalty parameter to obtain the penalty cost rate characterizing the risk of physical damage, and to calculate the quadratic value of the control variable and multiply it by the energy consumption weight to obtain the operating cost rate characterizing the amount of power consumption.

[0087] The functional integration fusion subunit is used to sum the penalty cost rate and the operating cost rate. The final result of the summation operation is used to perform time integration operation logic within a specified control period to generate a performance index functional to measure the overall operating economy of the system.

[0088] The dynamics optimization module is used to perform partial derivative optimization calculations to generate the optimal control sequence, including:

[0089] The costate variable is introduced into a sub-unit to introduce the costate variable representing the shadow price of the dynamic economics of the risk of deterioration due to dampness. The costate variable is then multiplied and coupled with the first-order differential equation. The result of the multiplication and coupling operation is accumulated into the performance index functional to construct the Hamiltonian algebra model entity.

[0090] The partial derivative zeroing optimization sub-unit is used to perform the partial derivative operation of pure textual mathematical rules on the control variables in the Hamiltonian algebra model entity. It forces the final result of the partial derivative operation to be equal to zero to satisfy the necessary condition of the theoretical minimum. It derives the power extreme value algebraic correlation formula composed only of physical dehumidification efficiency parameters, energy consumption weights and costate variables.

[0091] The adjoint equation solver unit is used to construct the adjoint differential equation for the costate variables, perform numerical solution of boundary conditions by combining the power extremum algebraic correlation, calculate the absolute power value at each discrete time point on the preset time axis, and compile and merge all the absolute power values ​​in chronological order to output the optimal control sequence.

[0092] Specifically, the dynamics optimization module, as the core execution carrier of the system's computing power center, receives the initial system state from the environmental state acquisition module via its upstream data interface. The downstream data output bus directly connects to the control sequence receiving end of the underlying driver closed-loop module.

[0093] Within the dynamic optimization module, during the first-order differential equation construction process, the variable mapping definition sub-unit allocates temporary storage space within the system to receive the system's initial state. It extracts the humidity deviation value within the continuous time domain from the system's initial state and maps this value to a state variable in the computation address. A new address block is allocated to map the input electrical power value of the dehumidifier fan to a control variable.

[0094] The physical parameter loading subunit executes bus addressing to access the underlying environment configuration library. It retrieves and reads the background moisture recovery parameters characterizing the spontaneous humidity change of the space. It reads the physical dehumidification efficiency parameters characterizing the intervention attributes of artificial equipment. It extracts the external moisture load disturbance parameters characterizing the disturbance attributes of personnel entering and exiting. These three parameters are then aggregated into a complete set of system microenvironment parameters and injected into the arithmetic logic unit cache.

[0095] The state equation construction sub-unit triggers a low-level floating-point multiplication instruction to multiply the background moisture recovery parameter with the state variable to obtain the spontaneous humidity change rate of the environment. Simultaneously, a multiplier is invoked to multiply the physical dehumidification efficiency parameter with the control variable to obtain the artificial intervention dehumidification rate. In the adder-subtractor accumulator, the spontaneous humidity change rate of the environment is subtracted from the artificial intervention dehumidification rate, and the applied external moisture load disturbance parameter is added. The calculated algebraic polynomial establishes, in a physical sense, a first-order differential equation describing the mathematical correlation logic of the dynamic change in humidity deviation rate.

[0096] The underlying dynamic differential model is established based on the construction logic of the state equations as follows:

[0097] ;

[0098] in The first-order differential equation represents the instantaneous rate of change of relative humidity deviation. This represents the state variable obtained through mapping. Refers to the control variables obtained through mapping. This represents the background moisture recovery parameters extracted from the configuration library. This represents the physical dehumidification efficiency parameter. This represents the parameters of external wet load disturbance.

[0099] In the subsequent execution flow of constructing the performance index functional, the weight reading subunit establishes a data channel to the non-volatile memory. It extracts pre-calibrated humidity deviation penalty parameters from the storage sector. It also reads the energy consumption weights used to constrain the power consumption characteristics of the underlying system devices.

[0100] The risk-based power consumption quantification subunit performs system-level quadratic multiplication on the state variables and multiplies them with the humidity deviation penalty parameter to obtain the penalty cost rate characterizing the risk of physical damage. The calculation threads calculate the quadratic value of the control variables and multiply it with the input energy consumption weight to obtain the operating cost rate characterizing the amount of power consumption.

[0101] The functional integration fusion subunit feeds the penalty cost rate and operating cost rate into the adder for summation. The numerical integration module then executes time integration logic within a specified control period on the final combined result of the summation operation. Finally, it outputs a functional data package with performance metrics that provide economic evaluation dimensions.

[0102] Based on the assembly rules of the performance index functional, the following functional integral model is established:

[0103] ;

[0104] in This represents a performance index functional generated through integration. and The start and end times of the integration for the specified control period are identified respectively. Refers to the humidity deviation penalty parameter obtained from the reading. Energy consumption weight representing the constrained electrical power. This is the squared term of the state variable. This is the squared term of the control variable.

[0105] Entering the final data processing stage of partial derivative optimization calculation, the costate variable introduction sub-unit declares and introduces a costate variable in the memory address space, representing the shadow price of the dynamic economics of the risk of moisture deterioration. The costate variable and the previously generated first-order differential equation are fed into the arithmetic unit for product coupling operation. The result of the product coupling operation is accumulated into the performance index functional through registers. The computation processing node completes the full construction of the Hamiltonian algebra model entity.

[0106] The following algebraic model is established for the assembly operation of Hamiltonian entities:

[0107] ;

[0108] in Defined as a completed Hamiltonian algebraic model entity. This represents a newly declared costate variable introduced by the system. The referential relationships of the remaining algebraic symbols remain absolutely aligned with those of the aforementioned differential and integral models.

[0109] The partial derivative zeroing optimization sub-unit locks the Hamiltonian algebraic model entity. The differentiation paths for other variables are cut off, and the partial derivative calculation instructions, based on pure textual mathematical rules, are executed for the internal control variables. A digital comparator forces the final feedback result of the partial derivative calculation to be equal to an absolute zero value, satisfying the necessary condition for the theoretical minimum value according to the Pontryagin maximum principle. Through internal algebraic rearrangement instructions, the power extremum algebraic correlation formula, composed solely of the physical dehumidification efficiency parameter, energy consumption weight, and costate variables, is derived.

[0110] The following algebraic correlation model is extracted for the extreme value calculation of partial derivative operations:

[0111] ;

[0112] in This represents the theoretically optimal electric power extremum obtained through the current derivation and calculation. This formula rigorously demonstrates the mathematically necessary process of obtaining the extremum variable by setting the partial derivatives to zero.

[0113] The adjoint equation solving subunit constructs the adjoint differential equation for the costate variables according to control theory rules. Combining the derived algebraic correlation of power extrema, it applies preset time boundary conditions and executes a difference numerical solution algorithm. The central processing unit calculates the absolute power values ​​corresponding to each consecutive discrete time point on the preset time axis. A queue assembly tool is used to compile and merge all absolute power values ​​ordered according to physical time sequence. The dynamics optimization module outputs the optimal control sequence carrying all control data through the underlying communication protocol. This sequence is then submitted to the underlying drive closed-loop module to implement the final electromechanical equipment speed control action.

[0114] The bottom-level drive closed-loop module is used to extract the power value from the optimal control sequence output by the dynamic optimization module, convert the power value into a pulse width modulation command and send it to the microcontroller at the bottom of the grid, drive the microcontroller to overwrite the register to control the dehumidifying fan, and trigger the environmental status acquisition module to reacquire the current relative humidity measurement value.

[0115] Furthermore, the underlying drive closed-loop module is used to extract the power values ​​from the optimal control sequence output by the dynamics optimization module, and convert the power values ​​into pulse width modulation commands, which are then sent to the microcontroller at the bottom of the grid. This includes:

[0116] The sequence parsing discrete subunit is used to receive the optimal control sequence, extract the individual power values ​​corresponding to the current control segment one by one according to the system's underlying clock beat, and extract the rated maximum power parameter of the dehumidifier and the maximum reload unsigned count value of the microcontroller's internal timer.

[0117] The duty cycle ratio conversion subunit is used to divide the single-unit power value by the rated maximum power parameter to obtain the power allocation percentage coefficient, perform product conversion between the power allocation percentage coefficient and the maximum heavy load unsigned count value, and extract the integer part of the product conversion to generate a hardware-recognized pulse width modulation command.

[0118] The bus protocol packet sub-unit is used to load pulse width modulation instructions into the data frame payload area of ​​the standard serial communication protocol, add start parity bits, security parity bits and stop cutoff bits in sequence to pack and assemble them into serial control data packets, and send the serial control data packets to the microcontroller at the bottom of the grid through the underlying isolated physical communication cable.

[0119] The underlying driver closed-loop module is used to drive the microcontroller to overwrite registers to control the dehumidifier fan and trigger the environmental status acquisition module to reacquire the current relative humidity measurement value, including:

[0120] The unpacking and verification stripping subunit is used to obtain serial control data packets at the microcontroller receiving terminal, perform cyclic redundancy check security comparison, and strip the data frame shell to extract the original pulse width modulation command after confirming that the security comparison has passed.

[0121] The memory overwrite driver subunit is used to obtain the specific count value carried in the pulse width modulation instruction, and use the internal direct memory addressing bus to forcibly overwrite and update the specific count value to the comparison matching register of the pulse width modulation output channel. The switching waveform width output to the inverter bridge drive end of the dehumidifier fan is changed through the low-level hardware-level flip-level logic to steplessly adjust the speed of the drive motor.

[0122] The status feedback wake-up subunit is used to generate a hardware interrupt feedback wake-up signal after the speed adjustment action of the drive motor is completed and after a preset air mixing and circulation time is delayed. The hardware interrupt feedback wake-up signal is then sent back to the main control system to trigger the environmental status acquisition module to start a new round of physical status polling scan to obtain the current relative humidity measurement value.

[0123] Specifically, the underlying driver closed-loop module undertakes the final conversion task from digital space to physical space. The main execution unit includes a sequence parsing discrete subunit, a duty cycle ratio conversion subunit, and a bus protocol packet encapsulation subunit. The device terminal execution unit includes a depacketization and verification stripping subunit, a memory overwrite driver subunit, and a status feedback wake-up subunit. The data flow starts from the control matrix promoted by the dynamic optimization module and ends at the polling trigger port of the environmental status acquisition module.

[0124] The sequence parsing discrete subunit listens to the output bus of the dynamic optimization module to receive the optimal control sequence. Based on the system's underlying clock beat, it extracts the individual power values ​​corresponding to the current control segment one by one. The sequence parsing discrete subunit sends a query request to the device configuration library to extract the rated maximum electrical power parameter of the dehumidifier fan to be controlled. It synchronously reads the maximum reload unsigned count value of the microcontroller's internal timer deployed at the bottom of the target grid. All of these parameters are then uniformly pushed into a high-speed data cache for queuing and conversion.

[0125] The duty cycle proportional conversion subunit retrieves the previously prepared physical parameters from the high-speed data cache. It calls the underlying hardware divider to divide the individual unit's power value by the rated maximum power parameter to obtain the power allocation percentage coefficient. This power allocation percentage coefficient and the maximum reload unsigned count value are then allocated to the multiplication register for product conversion. The system logic discards the decimal residue of the product conversion result, extracting the absolute integer part to directly generate a pulse width modulation instruction that meets the microcontroller's hardware recognition requirements.

[0126] The following discrete control algebraic model is established for the hardware-level mapping transformation of instruction issuance:

[0127] ;

[0128] in This represents a pulse width modulation instruction generated after a round-down truncation operation, the value of which directly corresponds to the timer's compare-match target value. This represents the individual power values ​​extracted from the optimal control sequence. This represents the rated maximum electrical power parameter of the dehumidifier fan that has been established through the query. This refers to the maximum unsigned reload count value of the timer. The symbol is used to mark the integer down function executed by the underlying code.

[0129] The bus protocol packet subunit captures the generated pulse width modulation (PWM) command and loads it into the payload area of ​​a data frame based on the standard serial communication protocol. According to the serial communication specification, start parity bits, security parity bits, and stop bits are added sequentially outside the payload area. After the data bits are concatenated, they are packaged and assembled into a serial control data packet. The underlying communication interface transmits the serial control data packet to the microcontroller receiver at the bottom of the designated grid via an isolated physical communication cable.

[0130] In the microcontroller receiving terminal area, the unpacking and verification stripping subunit captures the serial control data packets arriving at the communication interface. It extracts the peripheral check bits of the data frame and performs a cyclic redundancy check (CRC) security comparison. After the hardware comparison logic confirms that the data link transmission is error-free and passes the security comparison, the data frame shell is stripped according to the communication protocol bit length. The unpacking and verification stripping subunit then extracts the original pulse width modulation (PWM) instructions carrying the core control values.

[0131] The memory overwrite driver subunit retrieves the specific count value carried in the pulse width modulation instruction. Utilizing the microcontroller's internal direct memory addressing bus to bypass conventional interrupt handling, the specific count value is forcibly overwritten and updated in the compare-match register of the pulse width modulation output channel. The update of the value within the compare-match register directly triggers the underlying hardware-level level-flipping logic. The change in the level-flipping period forcibly alters the switching waveform width output to the dehumidifier fan inverter bridge driver, thereby continuously adjusting the drive motor speed to perform dehumidification intervention.

[0132] The status feedback wake-up subunit monitors the completion signal of the memory overwrite driver subunit's overwrite action. It starts an internal timer to delay for a preset air mixing and circulation duration. Once the local mesh microenvironment temperature and humidity reach physical mixing equilibrium due to dehumidification intervention, it triggers a register to generate a hardware interrupt feedback wake-up signal. The status feedback wake-up subunit transmits the hardware interrupt feedback wake-up signal back to the main control system via the serial bus uplink channel. The main control system receives the signal and directly triggers the environmental status acquisition module. The environmental status acquisition module responds to the wake-up action by initiating a new round of physical status polling scans, re-acquiring the current relative humidity measurement value after the control action is executed. This completes the absolute closed-loop data flow from digital prediction to physical acquisition.

[0133] Example 2:

[0134] This invention relates to a large-scale, comprehensive archive storage scenario using compact shelving to store massive amounts of physical paper archives and simultaneously provide digital copy retrieval services. In this scenario, the underlying physical environmental control hardware and the upper-level digital asset business system are chronically isolated, leading to serious technical deficiencies in the control of micro-environment humidity. Firstly, the environmental control system uses a fixed, uniform static humidity alarm threshold for the entire archive grid, failing to convert the semantic content density of the digital copies corresponding to the physical archives and the historical silent period span into differentiated physical fault-tolerant benchmarks. This results in delayed intervention for high-value historical archives becoming damp and a large amount of ineffective dehumidification energy consumption from low-utilization redundant archives. Secondly, the underlying variable frequency dehumidifiers mostly use simple start-stop triggering logic based on the absolute value of the deviation, failing to incorporate the background parameters of local grid air thermodynamics and electromechanical response characteristics into the optimization equation for minimizing energy consumption. This easily triggers high-frequency nonlinear oscillations in the relative humidity of the micro-environment, accelerating physical alternating stress fatigue damage to the fibers within the paper medium. To address these problems, this invention provides a multi-dimensional intelligent management method for archive resources, such as… Figure 5 As shown. The specific implementation process of this method is as follows:

[0135] The computing center extracts the text segmentation sequence of the target file's digital copy and performs coupled computation with the silent duration data to output a dimensionless retention value representing asset density. Based on a preset base, the system performs inverse constraint computation on the dimensionless retention value, forcibly mapping the digital features to a dynamic humidity safety upper limit in the physical space. The communication bus collects the current relative humidity measurement value of the grid where the target file is located, subtracts the dynamic humidity safety upper limit to obtain the deviation, and anchors it as the system's initial state. The computing center extracts state variables and control variables, combines them with micro-environmental parameters to construct a first-order differential equation, incorporates energy consumption weights to establish a performance index functional, and directly performs partial derivative minimization to generate the optimal control sequence. The underlying interface extracts the power values ​​from the control sequence, reduces their dimension, converts them into pulse width modulation instructions, and sends them to the microcontroller to forcibly overwrite the duty cycle register to continuously drive the dehumidifier fan. Upon completion of the action, the environmental probe is immediately triggered to re-acquire humidity measurements, constructing a physically adaptive closed loop.

[0136] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-dimensional intelligent management system for archival resources, characterized in that, Includes the following modules: The digital asset evaluation module is used to extract the text segmentation sequence and silent duration data of the target file digital copy, and to use the text segmentation sequence and silent duration data to perform calculations to output the dimensionless retention value. The physical threshold mapping module is used to perform reverse constraint calculations on the dimensionless retention value output by the digital asset evaluation module based on a preset base to generate a dynamic humidity safety upper limit. The environmental status acquisition module is used to acquire the current relative humidity measurement value of the grid where the target file is located, subtract the dynamic humidity safety upper limit output by the physical threshold mapping module from the current relative humidity measurement value to calculate the deviation, and define the deviation as the initial state of the system. The dynamic optimization module is used to receive the initial state of the system output by the environmental state acquisition module, extract the state variables and control variables and combine them with micro-environment parameters to construct a first-order differential equation, construct a performance index functional based on the first-order differential equation and energy consumption weight, and perform partial derivative optimization calculation to generate the optimal control sequence. The bottom-level driving closed-loop module is used to extract the power value from the optimal control sequence output by the dynamic optimization module, convert the power value into a pulse width modulation command and send it to the microcontroller at the bottom of the grid, drive the microcontroller to overwrite the register to control the dehumidifying fan, and trigger the environmental state acquisition module to reacquire the current relative humidity measurement value.

2. The multi-dimensional intelligent management system for archival resources according to claim 1, characterized in that, The digital asset evaluation module is used to extract the text segmentation sequence and silent duration data of the target file's digital copy, and to perform calculations using the text segmentation sequence and silent duration data to output a dimensionless retention value, including: The log extraction subunit is used to connect to the business database to retrieve the last access time anchor point of the target file digital copy, subtract the last access time anchor point from the current system time anchor point to obtain the silent duration data, and call the natural language processing engine to segment the content of the target file digital copy to generate the text segmentation sequence. The word segmentation statistics subunit is used to count the total number of all independent words in the text segmentation sequence and the occurrence frequency of each single independent word. The occurrence frequency is divided by the total number to obtain the occurrence probability of a single independent word. The occurrence probability is extracted and the logarithm of the occurrence probability is multiplied to obtain the word feature entropy. The word feature entropy is summed and the negative value is taken to convert it into text information entropy. The coupled dimensionality reduction subunit is used to extract the borrowing attenuation constant preset by the system, multiply the borrowing attenuation constant by the silent duration data to obtain the attenuation exponent, calculate the negative exponent power function value of the attenuation exponent with the natural constant as the base, and perform dimensionality reduction processing by multiplying the negative exponent power function value by the text information entropy to output the dimensionless retention value.

3. The multi-dimensional intelligent management system for archival resources according to claim 1, characterized in that, The physical threshold mapping module is used to perform reverse constraint calculations on the dimensionless retention value output by the digital asset evaluation module based on a preset base to generate a dynamic humidity safety upper limit, including: The reference reading subunit is used to read the basic moisture absorption alarm tolerance parameters for paper media and the preset base number from the memory; The penalty multiplier derivation subunit is used to scan the global archive database to extract the global maximum extreme value of the dimensionless retention value in all archives, subtract the dimensionless retention value from the global maximum extreme value to obtain the value difference value, divide the value difference value by the global maximum extreme value to obtain the normalized difference value, and multiply the normalized difference value by the preset base to obtain the penalty multiplier for the target archive. The dynamic upper limit synthesis subunit is used to extract a constant and add it to the penalty multiplier to obtain the tolerance adjustment ratio, multiply the basic moisture absorption alarm tolerance parameter with the tolerance adjustment ratio to obtain the safe relative humidity threshold, and define the safe relative humidity threshold as the dynamic humidity safe upper limit output.

4. The multi-dimensional intelligent management system for archival resources according to claim 1, characterized in that, The environmental status acquisition module is used to obtain the current relative humidity measurement value of the grid where the target file is located, subtract the dynamic humidity safety upper limit output by the physical threshold mapping module from the current relative humidity measurement value to calculate the deviation, and define the deviation as the system initial state, including: The hardware addressing subunit is used to extract the physical shelf space coordinates of the target file, resolve the physical shelf space coordinates into the sensor node network address of the grid where the target file is located, send a data polling control command to the sensor node network address, and receive the returned current relative humidity measurement value. The difference extraction subunit is used to perform a subtraction comparison logic on the current relative humidity measurement value and the dynamic humidity safety upper limit output by the physical threshold mapping module, calculate the difference result of the relative humidity value, and extract the absolute value of the difference result as the deviation. The state anchoring subunit is used to obtain the current execution timestamp of the subtraction comparison logic, bind and encapsulate the deviation with the current execution timestamp, and define the deviation with the bound time attribute as the initial state of the system within the continuous time domain.

5. The multi-dimensional intelligent management system for archival resources according to claim 1, characterized in that, The dynamic optimization module is used to extract state variables and control variables, combined with microenvironment parameters, to construct first-order differential equations, including: The variable mapping definition subunit is used to receive the initial state of the system, map the humidity deviation value within the continuous time domain to the state variable, and map the input power value of the dehumidifier fan to the control variable. The physical parameter loading subunit is used to read the background moisture recovery parameters characterizing the spontaneous humidity change attribute of the space, the physical dehumidification efficiency parameters characterizing the intervention attribute of artificial equipment, and the external moisture load disturbance parameters characterizing the disturbance attribute of personnel entering and exiting from the underlying environment configuration library to form the microenvironment parameters. The state equation construction sub-unit is used to multiply the background moisture recovery parameter with the state variable to obtain the spontaneous humidity rate of the environment, multiply the physical dehumidification efficiency parameter with the control variable to obtain the artificial intervention dehumidification rate, and subtract the artificial intervention dehumidification rate from the spontaneous humidity rate of the environment and add the external moisture load disturbance parameter to generate the first-order differential equation describing the mathematical correlation logic of the dynamic change of humidity deviation rate.

6. The multi-dimensional intelligent management system for archival resources according to claim 1, characterized in that, The dynamic optimization module is used to construct a performance index functional based on the first-order differential equation and energy consumption weights, including: The weight reading subunit is used to extract pre-calibrated humidity deviation penalty parameters and energy consumption weights used to constrain power consumption characteristics from the memory; The risk power consumption quantification subunit is used to calculate the quadratic value of the state variable and multiply it by the humidity deviation penalty parameter to obtain the penalty cost rate characterizing the risk of physical damage, and to calculate the quadratic value of the control variable and multiply it by the energy consumption weight to obtain the operating cost rate characterizing the amount of power consumption. The functional integration fusion subunit is used to add the penalty cost rate and the operating cost rate together, and to perform time integration operation logic within a specified control period on the final result of the addition operation to generate the performance index functional used to measure the overall operating economy of the system.

7. The multi-dimensional intelligent management system for archival resources according to claim 1, characterized in that, The dynamics optimization module is used to perform partial derivative optimization calculations to generate the optimal control sequence, including: A costate variable is introduced into a subunit to introduce a costate variable representing the shadow price of the dynamic economics of the risk of deterioration due to dampness. The costate variable is then multiplied and coupled with the first-order differential equation. The result of the multiplication and coupling operation is accumulated into the performance index functional to construct the Hamiltonian algebra model entity. The partial derivative zero-reduction optimization subunit is used to perform the partial derivative operation of pure textual mathematical rules on the control variables in the Hamiltonian algebra model entity, and to force the final result of the partial derivative operation to be equal to zero to satisfy the necessary condition of the theoretical minimum value, and to derive the power extreme value algebraic correlation formula composed only of the physical dehumidification efficiency parameter, the energy consumption weight and the costate variable. The adjoint equation solving subunit is used to construct the adjoint differential equation for the costate variable, perform boundary condition numerical solution in combination with the power extremum algebraic correlation, calculate the absolute power value at each discrete time point on the preset time axis, and compile and merge all the absolute power values ​​in chronological order to output the optimal control sequence.

8. The multi-dimensional intelligent management system for archival resources according to claim 1, characterized in that, The underlying drive closed-loop module is used to extract the power value from the optimal control sequence output by the dynamics optimization module, and convert the power value into a pulse width modulation command to be sent to the microcontroller at the bottom of the grid, including: The sequence parsing discrete subunit is used to receive the optimal control sequence, extract the individual power values ​​corresponding to the current control segment one by one according to the system's underlying clock beat, and extract the rated maximum power parameters of the dehumidifier and the maximum reload unsigned count value of the microcontroller's internal timer. The duty cycle ratio conversion subunit is used to divide the single-unit power value by the rated maximum power parameter to obtain the power allocation percentage coefficient, perform product conversion on the power allocation percentage coefficient and the maximum heavy load unsigned count value, and extract the integer part of the product conversion to generate the pulse width modulation instruction at the hardware recognition level. The bus protocol packet unit is used to load the pulse width modulation instruction into the data frame payload area of ​​the standard serial communication protocol, add start parity bit, security parity bit and stop cutoff bit in sequence to pack and assemble it into a serial control data packet, and send the serial control data packet to the microcontroller at the bottom of the grid through the underlying isolated physical communication cable.

9. The multi-dimensional intelligent management system for archival resources according to claim 8, characterized in that, The underlying driver closed-loop module is used to drive the microcontroller to overwrite registers to control the dehumidifier fan and trigger the environmental status acquisition module to reacquire the current relative humidity measurement value, including: The unpacking and verification stripping subunit is used to obtain serial control data packets at the microcontroller receiving terminal, perform cyclic redundancy check security comparison, and strip the data frame shell to extract the original pulse width modulation command after confirming that the security comparison has passed. The memory overwrite driver subunit is used to obtain the specific count value carried in the pulse width modulation instruction, and use the internal direct memory addressing bus to force overwrite and update the specific count value to the comparison matching register of the pulse width modulation output channel. The switching waveform width output to the inverter bridge drive end of the dehumidifier fan is changed through the low-level hardware-level flip-level logic to steplessly adjust the speed of the drive motor. The status feedback wake-up subunit is used to generate a hardware interrupt feedback wake-up signal after the speed adjustment action of the drive motor is completed and after a preset air mixing and circulation time is delayed. The hardware interrupt feedback wake-up signal is then sent back to the main control system to trigger the environmental status acquisition module to start a new round of physical status polling scan to obtain the current relative humidity measurement value.

10. A multi-dimensional intelligent management method for archival resources, characterized in that, The multi-dimensional intelligent management system for archival resources as described in any one of claims 1-9 includes the following steps: S1. Extract the text segmentation sequence and silent duration data of the target file digital copy, and use the text segmentation sequence and silent duration data to perform calculations to output the dimensionless retention value. S2. Based on a preset base, perform reverse constraint calculation on the dimensionless retention value output by the digital asset evaluation module to generate a dynamic humidity safety upper limit; S3. Obtain the current relative humidity measurement value of the grid where the target file is located, subtract the dynamic humidity safety upper limit output by the physical threshold mapping module from the current relative humidity measurement value to calculate the deviation, and define the deviation as the initial state of the system. S4. Receive the initial state of the system output by the environmental state acquisition module, extract the state variables and control variables and combine them with micro-environment parameters to construct a first-order differential equation, construct a performance index functional based on the first-order differential equation and energy consumption weight, and perform partial derivative optimization calculation to generate the optimal control sequence. S5. Extract the power value from the optimal control sequence output by the dynamic optimization module, convert the power value into a pulse width modulation command and send it to the microcontroller at the bottom of the grid, drive the microcontroller to overwrite the register to control the dehumidifying fan, and trigger the environmental status acquisition module to reacquire the current relative humidity measurement value.

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