Environment intelligent supervision and energy efficiency optimization system suitable for archival repository management and control

Abstract

The invention relates to the technical field of archival repository management, in particular to an intelligent environment supervision and energy efficiency optimization system suitable for archival repository management and control, and discloses an archival repository environment monitoring module, an archival repository environment prediction module, an archival repository environment control module and an energy efficiency optimization environment regulation and control module. Through an archival repository environment monitoring module, an archival repository environment prediction module, an archival repository environment control module and an energy efficiency optimization environment regulation and control module, different regulation and control strategies of environment regulation and control equipment in an archival repository are simulated through a simulation technology; different regulation and control strategies feed back temperature, humidity, particulate matter concentration and energy consumption conditions, and through analysis modes of environment variability correlation, environment variability independence and the like between warehouse areas, an environment regulation and control strategy with the best overall environment, energy consumption condition and practical applicability comprehensive performance is selected to carry out environment regulation and control and energy consumption optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of archive repository management, and more specifically, to an environmental intelligent supervision and energy efficiency optimization system applicable to the control of archive repositories. Background Art

[0002] As an important carrier for recording history and inheriting culture, the preservation status of archives is directly affected by the repository environment. Different types of archive files are usually stored in archive repositories, including paper archives, film archives, optical disc archives, etc. The storage requirements of different types of archive files are different. For example, the storage temperature and humidity of film archives are more sensitive than those of paper archives. Therefore, different types of archive files are usually classified into multiple areas for partitioned storage. However, due to the layout requirements of archive repositories, in order to facilitate the access of archives, each repository area cannot be completely isolated. Therefore, the temperature, humidity, and air quality in the repository area have a certain mutual influence.

[0003] In the traditional environmental supervision process, the environment of each repository area is usually supervised by corresponding sensors. When the environment in a certain repository area is abnormal, the sensors in the corresponding repository area send out an alarm. This environmental supervision method can only respond passively to environmental changes, but cannot actively prevent and regulate. With the rapid development of Internet of Things, big data, and artificial intelligence technologies, an intelligent and automated archive repository management system has gradually become possible. The archive repository management system can realize environmental prediction and thus make corresponding adjustments actively. However, this adjustment method lacks scientificity and accuracy in formulating environmental prediction and control strategies, and cannot fully consider the mutual influence between each repository area in actual situations, resulting in poor control effects of the adjustment strategy.

[0004] In view of the above problems, the present invention proposes an environmental intelligent supervision and energy efficiency optimization system applicable to the control of archive repositories. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide an environmental intelligent supervision and energy efficiency optimization system applicable to the control of archive repositories.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An environmental intelligent supervision and energy efficiency optimization system applicable to the control of archive repositories, including an archive repository environment monitoring module, an archive repository environment prediction module, an archive repository environment control module, and an energy efficiency optimization environmental regulation module;

[0008] The archival repository environment monitoring module divides the archival repository into multiple repository areas according to the type of archives, and collects the temperature, humidity, and particulate matter concentration of each repository area in real time;

[0009] The archival repository environment prediction module regularly determines the predicted temperature characteristics, predicted humidity characteristics, and predicted particulate matter concentration characteristics of each repository area, and simultaneously determines the standard temperature range, standard humidity range, and standard particulate matter concentration range of each repository area;

[0010] The archival repository environment control module obtains the archival repository simulation model, controls the archival repository simulation model to perform m times of simulation regulation, each simulation regulation corresponds to an environmental regulation strategy, and obtains the environmental energy regulation index of each environmental regulation strategy;

[0011] The energy efficiency optimization environmental regulation module is used to select an environmental regulation strategy and regulate the environmental regulation equipment of each repository area in the next cycle based on this environmental regulation strategy.

[0012] Further, the methods for determining the predicted temperature characteristics, predicted humidity characteristics, and predicted particulate matter concentration characteristics of the repository area are as follows: Determine a repository area, obtain the time-series temperature characteristic set, time-series humidity characteristic set, and time-series particulate matter concentration characteristic set of this repository area, determine the temperature characteristic prediction model, humidity characteristic prediction model, and particulate matter concentration characteristic prediction model of this repository area, input the time-series temperature characteristic set into the temperature characteristic prediction model, input the time-series humidity characteristic set into the humidity characteristic prediction model, input the time-series particulate matter concentration characteristic set into the particulate matter concentration characteristic prediction model, and output the predicted temperature characteristics, predicted humidity characteristics, and predicted particulate matter concentration characteristics of this repository area.

[0013] Further, the methods for obtaining the time-series temperature characteristic set, time-series humidity characteristic set, and time-series particulate matter concentration characteristic set are as follows: Obtain the temperature characteristics, humidity characteristics, and particulate matter concentration characteristics of the repository area in multiple consecutive previous cycles, sort the temperature characteristics of multiple cycles in time series and combine them into a time-series temperature characteristic set in the form of a data set, sort the humidity characteristics of multiple cycles in time series and combine them into a time-series humidity characteristic set in the form of a data set, and sort the particulate matter concentration characteristics of multiple cycles in time series and combine them into a time-series particulate matter concentration characteristic set in the form of a data set.

[0014] Further, the selected environmental regulation strategy is the environmental regulation strategy with the largest environmental energy regulation index value.

[0015] Further, the environmental energy regulation index of the environmental regulation strategy is determined based on the following method: After the archive storage simulation model starts a simulation regulation and sets i time points with equal time differences within a cycle, at each time point passed, the temperature, humidity, and particulate matter concentration of each warehouse area model at that time point are collected. After a cycle ends, the energy consumption of each environmental regulation equipment entity in each warehouse area model within the cycle is collected, and then the comprehensive energy consumption of each warehouse area model, Energy(pg), is determined, where p = 1, 2, …, P, p is the serial number of the warehouse area model, and P is the total number of warehouse area models. Set the comprehensive energy consumption coefficient of the warehouse as wg, determine the temperature change index, humidity change index, and particulate matter concentration change index of each warehouse area model, count every two adjacent warehouse area models as a warehouse linkage impact area, determine the environmental linkage value of each warehouse linkage impact area, perform a sum and average calculation on the environmental linkage values of all warehouse linkage impact areas, calculate the average environmental linkage value value(ve), set the environmental linkage standard value. When the environmental linkage value of a warehouse linkage impact area is greater than the environmental linkage standard value, mark this warehouse linkage impact area as a warehouse synchronous linkage area, mark the total number of warehouse synchronous linkage areas as Number(dd), determine the comprehensive environmental change index Index(tp) of each warehouse area model, and through The environmental energy regulation index Index(mns) of this simulation regulation is calculated, where a1 is the first coefficient and a2 is the second coefficient.

[0016] Further, the comprehensive energy consumption of a warehouse area model is determined based on the following method: Determine a warehouse area model, obtain the energy consumption of each environmental regulation equipment entity in this warehouse area model within the cycle, perform a sum calculation on the energy consumption of each environmental regulation equipment entity within the cycle, and calculate the comprehensive energy consumption of this warehouse area model, Energy(pg).

[0017] Further, the comprehensive environmental change index of a warehouse area model is determined based on the following method: Determine a warehouse area model, perform a sum and average calculation on the temperature change index, humidity change index, and particulate matter concentration change index of this warehouse area model, and calculate the integrated change index, the comprehensive environmental change index Index(tp).

[0018] Further, the environmental linkage value of a warehouse linkage impact area is determined based on the following method: Mark the two warehouse area models within the warehouse linkage impact area as warehouse area model M and warehouse area model N respectively, and mark the temperature change index, humidity change index, and particulate matter concentration change index of warehouse area model M as [M 1 ,M 2 ,M 3 ,M 1is the temperature change index of the warehouse area model M, M 2 is the humidity change index of the warehouse area model M, M 3 is the particulate matter concentration change index of the warehouse area model M. Mark the temperature change index, humidity change index, and particulate matter concentration change index of the warehouse area model N as [N 1 ,N 2 ,N 3 ,N 1 is the temperature change index of the warehouse area model N, N 2 is the humidity change index of the warehouse area model N, N 3 is the particulate matter concentration change index of the warehouse area model N. Use the cosine similarity formula to calculate the environmental linkage value of the warehouse linkage impact area.

[0019] Furthermore, the temperature change index, humidity change index, and particulate matter concentration change index of the warehouse area model are determined based on the following method: Determine a warehouse area model, construct a temperature coordinate system, a humidity coordinate system, and a particulate matter concentration coordinate system, and determine the temperature change index, humidity change index, and particulate matter concentration change index based on the temperature coordinate system, humidity coordinate system, and particulate matter concentration coordinate system.

[0020] Furthermore, the temperature change index, humidity change index, and particulate matter concentration change index are determined based on the following method: Mark the temperature corresponding to each time point in the temperature coordinate system in the form of coordinate points, and mark i coordinate points. Connect the adjacent coordinate points to obtain multiple temperature connection lines. Obtain the slope of each temperature connection line, take the absolute value of the slopes of all temperature connection lines, and perform a summation calculation to obtain the temperature change index;

[0021] Mark the humidity corresponding to each time point in the humidity coordinate system in the form of coordinate points, and mark i coordinate points. Connect the adjacent coordinate points to obtain multiple humidity connection lines. Obtain the slope of each humidity connection line, take the absolute value of the slopes of all humidity connection lines, and perform a summation calculation to obtain the humidity change index;

[0022] Mark the particulate matter concentration corresponding to each time point in the particulate matter concentration coordinate system in the form of coordinate points, and mark i coordinate points. Connect the adjacent coordinate points to obtain multiple particulate matter concentration connection lines. Obtain the slope of each particulate matter concentration connection line, take the absolute value of the slopes of all particulate matter concentration connection lines, and perform a summation calculation to obtain the particulate matter concentration change index.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] Through the archival repository environment monitoring module, archival repository environment prediction module, archival repository environment control module, and energy efficiency optimization environment regulation module, the archival repository is divided into areas according to the type of archives, and the temperature, humidity, and particulate matter concentration of each repository area are monitored in real time to refine the different environmental monitoring standards for different types of archives. Combining with the LSTM model, the temperature, humidity, and particulate matter concentration of each repository area are predicted. Through simulation technology, different regulation strategies of the environmental regulation equipment in the archival repository are simulated. On the premise of ensuring compliance with the standard environmental range, the temperature, humidity, particulate matter concentration, and energy consumption conditions fed back by different regulation strategies are analyzed, and through analysis methods such as environmental variability correlation and environmental variability independence between repository areas, the overall environment, energy consumption situation, and practical applicability of the archival repository are analyzed, and the environmental regulation strategy with the best comprehensive performance of the overall environment, energy consumption situation, and practical applicability is selected for environmental regulation and energy consumption optimization. Description of the Drawings

[0025] Figure 1 It is a block diagram of the modules of the environmental intelligent supervision and energy efficiency optimization system applicable to the management and control of archival repositories;

[0026] Figure 2 It is a flowchart for determining the predicted temperature characteristics, predicted humidity characteristics, and predicted particulate matter concentration characteristics of the repository area;

[0027] Figure 3 It is a flowchart for determining the environmental linkage value of the linked influence area of the repository;

[0028] Figure 4 It is a flowchart of the system operation. Detailed Implementation Manner

[0029] Refer to Figures 1 to 4 An environmental intelligent supervision and energy efficiency optimization system applicable to the management and control of archival repositories includes an archival repository environment monitoring module, an archival repository environment prediction module, an archival repository environment control module, and an energy efficiency optimization environment regulation module.

[0030] Archival repository environment monitoring module: The archival repository is divided into repository areas according to the type of archives (usually different types of archives are stored on different archive shelves in the archival repository. The types of archives include paper archive types, film archive types, CD archive types, etc. The storage requirements of different types of archives are different. For example, the storage temperature and humidity of film archive type archives are more sensitive than those of paper archive type archives), and multiple repository areas are divided (each repository area has an independent dehumidifier, humidifier, air conditioner, and air purifier), and the temperature, humidity, and particulate matter concentration of each repository area are collected in real time.

[0031] Archives storage environment prediction module: Regularly determine the predicted temperature characteristics, predicted humidity characteristics, and predicted particulate matter concentration characteristics of each storage area, and simultaneously determine the standard temperature range, standard humidity range, and standard particulate matter concentration range for each storage area (the standard temperature range, standard humidity range, and standard particulate matter concentration range are all preset ranges. Ensuring that the corresponding parameters are within this range can ensure that the archives in the storage area meet the storage requirements).

[0032] The methods for determining the predicted temperature characteristics, predicted humidity characteristics, and predicted particulate matter concentration characteristics of the storage area are as follows: Determine a storage area, obtain the time-series temperature characteristic set, time-series humidity characteristic set, and time-series particulate matter concentration characteristic set of this storage area, determine the temperature characteristic prediction model, humidity characteristic prediction model, and particulate matter concentration characteristic prediction model of this storage area, input the time-series temperature characteristic set into the temperature characteristic prediction model, input the time-series humidity characteristic set into the humidity characteristic prediction model, input the time-series particulate matter concentration characteristic set into the particulate matter concentration characteristic prediction model, and output the predicted temperature characteristics, predicted humidity characteristics, and predicted particulate matter concentration characteristics of this storage area.

[0033] The methods for obtaining the time-series temperature characteristic set, time-series humidity characteristic set, and time-series particulate matter concentration characteristic set are as follows: Obtain the temperature characteristics, humidity characteristics, and particulate matter concentration characteristics of the storage area in multiple consecutive previous cycles (temperature characteristics include temperature change rate, average temperature, temperature peak and valley values, temperature change trend direction; humidity characteristics include humidity change rate, average humidity, humidity peak and valley values, humidity change trend direction; particulate matter concentration characteristics include particulate matter concentration change rate, average particulate matter concentration, particulate matter concentration peak and valley values, particulate matter concentration change trend direction. The above characteristics are obtained after data processing and feature extraction for the corresponding cycles), sort the temperature characteristics of multiple cycles in time series and combine them into a time-series temperature characteristic set in the form of a data set, sort the humidity characteristics of multiple cycles in time series and combine them into a time-series humidity characteristic set in the form of a data set, and sort the particulate matter concentration characteristics of multiple cycles in time series and combine them into a time-series particulate matter concentration characteristic set in the form of a data set.

[0034] The temperature feature prediction model, humidity feature prediction model, and particulate matter concentration feature prediction model for different warehouse areas are different. All temperature feature prediction models, humidity feature prediction models, and particulate matter concentration feature prediction models are constructed based on the LSTM model. The construction processes of the three models are similar. In this embodiment, the specific construction processes of the three models are not listed. Taking the temperature feature prediction model of warehouse area A as an example, the construction process of the temperature feature prediction model will be disclosed: construct an LSTM model, collect multiple time-series temperature feature sets of warehouse area A (if constructing the humidity feature prediction model of warehouse area B, collect multiple time-series humidity feature sets of warehouse area B), use the multiple time-series temperature feature sets as the training data of the LSTM model, assign a predicted temperature feature to each training data, and the predicted temperature feature is the predicted temperature feature of warehouse area A in the next cycle (if constructing the humidity feature prediction model of warehouse area B, use the multiple time-series humidity feature sets as the training data of the LSTM model, assign a predicted humidity feature to each training data, and the predicted humidity feature is the predicted humidity feature of warehouse area A in the next cycle), divide the training data into a training set, a validation set, and a test set according to the set ratio of 5:1:1, train the training set, the validation set, and the test set, and after training is completed, obtain the temperature feature prediction model of warehouse area A.

[0035] Archives warehouse environment control module: Obtain the archives warehouse simulation model, input the standard temperature range, standard humidity range, standard particulate matter concentration range, predicted temperature feature, predicted humidity feature, and predicted particulate matter concentration feature of each warehouse area into the archives warehouse simulation model (the archives warehouse simulation model can simulate the temperature change situation, change situation, and particulate matter concentration change situation of each warehouse area in the next cycle, and perform environmental regulation on each warehouse area based on the standard temperature range, standard humidity range, and standard particulate matter concentration range of each warehouse area), control the archives warehouse simulation model to perform m times of simulation regulation, and each simulation regulation corresponds to an environmental regulation strategy (each environmental regulation strategy includes the regulation means, regulation frequency, etc. of all environmental regulation equipment in all warehouse areas in one cycle), and obtain the environmental energy regulation index of each environmental regulation strategy.

[0036] Archive storage simulation model: Select simulation software, create an archive storage model in the simulation software, and divide the archive storage model into multiple storage area models according to the actual layout of the archive storage and the installation positions of environmental control equipment. The multiple storage area models are set to be completely isolated (because it is difficult for simulation software to simulate the influence between storage area models in an incompletely isolated state), and corresponding environmental control equipment entities (environmental control equipment includes dehumidifiers, humidifiers, air conditioners, air purifiers) are added to each storage area model, and corresponding parameters are added to the environmental control equipment entities. The addition of the corresponding parameters is based on the model of the corresponding environmental control equipment (for example, parameters such as the cooling capacity, power, and air supply angle of different models of air conditioners are different. After setting according to the actual parameters, it can accurately simulate its influence on the temperature, humidity, and air flow in the area where it is located), and the construction of the archive storage simulation model is completed. The archive storage simulation model can simulate the environmental control of each environmental control equipment on each archive storage.

[0037] The environmental energy control index of the environmental control strategy is determined based on the following method: After the archive storage simulation model starts a simulation control, and i equidistant time points are set within a cycle. Every time a time point passes, the temperature, humidity, and particulate matter concentration of each storage area model at this time point are collected. After a cycle ends, the energy consumption of each environmental control equipment entity in each storage area model within the cycle is collected, and then the comprehensive energy consumption of each storage area model, Energy(pg), is determined, where p = 1, 2,..., P, p is the serial number of the storage area model, and P is the total number of storage area models. Set the comprehensive energy consumption coefficient of the storage as wg, where g = 1, 2,..., G, w1 < w2 < w3 <... < wG. Each comprehensive energy consumption coefficient of the storage corresponds to a range of comprehensive energy consumption of the storage. The value range of the comprehensive energy consumption of the storage includes (0, Energy(p1)], (Energy(p1), Energy(p2)],..., (Energy(pG - 1), Energy(pG)]. When Energy(pg) ∈ (0, Energy(p1)], the comprehensive energy consumption coefficient of the storage is w1. Determine the temperature change index, humidity change index, and particulate matter concentration change index of each storage area model. Every two adjacent storage area models are counted as a storage linkage influence area, and the environmental linkage value of each storage linkage influence area is determined. The environmental linkage values of all storage linkage influence areas are summed and averaged to calculate the average environmental linkage value, value(ve). Set the environmental linkage standard value (the environmental linkage standard value is a preset value used to compare with the environmental linkage value). When the environmental linkage value of the storage linkage influence area is greater than the environmental linkage standard value, mark this storage linkage influence area as a storage synchronous linkage area, mark the total number of storage synchronous linkage areas as Number(dd), and determine the comprehensive environmental change index Index(tp) of each storage area model. Through The energy and environment regulation index Index(mns) for this simulation regulation is calculated. a1 is the first coefficient and a2 is the second coefficient. The value of a1 can be 1.28 and the value of a2 can be 0.97.

[0038] The comprehensive energy consumption of the warehouse area model is determined based on the following method: Determine a warehouse area model, obtain the energy consumption of each environmental regulation equipment entity within the cycle in the warehouse area model, sum up the energy consumption of each environmental regulation equipment entity within the cycle, and calculate the comprehensive energy consumption Energy(pg) of the warehouse area model.

[0039] The comprehensive environment change index of the warehouse area model is determined based on the following method: Determine a warehouse area model, perform a summation and averaging calculation on the temperature change index, humidity change index, and particulate matter concentration change index of the warehouse area model, and calculate the integrated change index comprehensive environment change index Index(tp).

[0040] The environmental linkage value of the warehouse linkage influence area is determined based on the following method: Mark the two warehouse area models within the warehouse linkage influence area as warehouse area model M and warehouse area model N respectively. Mark the temperature change index, humidity change index, and particulate matter concentration change index of warehouse area model M as M 1 ,M 2 ,M 3 ,M 1 is the temperature change index of warehouse area model M, M 2 is the humidity change index of warehouse area model M, M 3 is the particulate matter concentration change index of warehouse area model M. Mark the temperature change index, humidity change index, and particulate matter concentration change index of warehouse area model N as [N 1 ,N 2 ,N 3 ,N 1 is the temperature change index of warehouse area model N, N 2 is the humidity change index of warehouse area model N, N 3is the particulate matter concentration change index of the warehouse area model N. The environmental linkage value of the warehouse linkage impact area is calculated using the cosine similarity formula. The larger the environmental linkage value, the more similar the environmental changes of the warehouse area model M and the warehouse area model N. In this way, it is easier to maintain the environmental temperature of the warehouse area model M and the warehouse area model N (temperature perspective: when the temperature change rates of adjacent warehouse areas are similar, it means that their rates of temperature increase or decrease are relatively close, which can reduce the strong heat transfer caused by different temperature differences; humidity perspective: for humidity, similar change rates can make the diffusion and transfer of water vapor between adjacent warehouse areas relatively stable and orderly; particulate matter concentration perspective: ensuring similar particulate matter concentration change rates can keep the air purification conditions in adjacent areas relatively synchronized).

[0041]

[0042] The temperature change index, humidity change index, and particulate matter concentration change index of the warehouse area model are determined based on the following method: Determine a warehouse area model, construct a temperature coordinate system, a humidity coordinate system, and a particulate matter concentration coordinate system, and determine the temperature change index, humidity change index, and particulate matter concentration change index based on the temperature coordinate system, humidity coordinate system, and particulate matter concentration coordinate system.

[0043] The temperature coordinate system, humidity coordinate system, and particulate matter concentration coordinate system are constructed based on the following method: Construct a rectangular coordinate system with time as the X-axis and temperature as the Y-axis, and mark this rectangular coordinate system as the temperature coordinate system; construct a rectangular coordinate system with time as the X-axis and humidity as the Y-axis, and mark this rectangular coordinate system as the humidity coordinate system; construct a rectangular coordinate system with time as the X-axis and particulate matter concentration as the Y-axis, and mark this rectangular coordinate system as the particulate matter concentration coordinate system.

[0044] The temperature change index, humidity change index, and particulate matter concentration change index are determined based on the following method: Mark the temperature corresponding to each time point in the temperature coordinate system in the form of coordinate points, and mark i coordinate points. Connect the adjacent coordinate points, and multiple temperature connection lines are obtained (the connection line between each coordinate point is recorded as a temperature connection line). Obtain the slope of each temperature connection line, take the absolute value of the slopes of all temperature connection lines and then perform a summation calculation to calculate the temperature change index.

[0045] Mark the humidity corresponding to each time point in the humidity coordinate system in the form of coordinate points, and mark i coordinate points. Connect the adjacent coordinate points, and multiple humidity connection lines are obtained (the connection line between each coordinate point is recorded as a humidity connection line). Obtain the slope of each humidity connection line, take the absolute value of the slopes of all humidity connection lines and then perform a summation calculation to calculate the humidity change index.

[0046] Mark the particulate matter concentration corresponding to each time point in the particulate matter concentration coordinate system in the form of coordinate points, and obtain i coordinate points. Connect adjacent coordinate points to get multiple particulate matter concentration connection lines (the connection line between each coordinate point is recorded as a particulate matter concentration connection line). Obtain the slope of each particulate matter concentration connection line, take the absolute value of the slopes of all particulate matter concentration connection lines, and perform a summation calculation to obtain the particulate matter concentration change index.

[0047] Energy efficiency optimization environmental control module: Select the environmental control strategy with the largest value of the environmental energy control index, and based on the environmental control strategy, control each environmental control device in each warehouse area in the next cycle.

[0048] Through the archive warehouse environmental monitoring module, archive warehouse environmental prediction module, archive warehouse environmental control module, and energy efficiency optimization environmental control module, divide the archive warehouse into areas according to the type of archives, and conduct real-time temperature, humidity, and particulate matter concentration on each warehouse area to refine the different environmental monitoring standards for different types of archives. Combine the LSTM model to predict the temperature, humidity, and particulate matter concentration of each warehouse area. Through simulation technology, simulate different control strategies of environmental control equipment in the archive warehouse. On the premise of ensuring compliance with the standard environmental range, different control strategies will feedback the temperature, humidity, particulate matter concentration, and energy consumption situation. And through analysis methods such as environmental variability correlation and environmental variability independence between warehouse areas, analyze the overall environment, energy consumption situation, and practical applicability of the archive warehouse, and select the environmental control strategy with the best comprehensive performance of the overall environment, energy consumption situation, and practical applicability for environmental control and energy consumption optimization.

[0049] The above formulas are all dimensionless and take their numerical values for calculation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0050] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0051] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0052] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0053] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0054] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical, or other forms.

[0055] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the essence of the technical solution of the present application, or the part that contributes to the prior art, or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, and other various media that can store program codes.

[0056] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An environmental intelligent supervision and energy efficiency optimization system applicable to archive warehouse management and control, characterized by: It includes archive warehouse environment monitoring module, archive warehouse environment prediction module, archive warehouse environment control module, and energy efficiency optimization environment control module; The archive warehouse environment monitoring module divides the archive warehouse into multiple warehouse areas according to the archive type, and collects the temperature, humidity and particle concentration of each warehouse area in real time; The archive warehouse environment prediction module regularly determines the predicted temperature characteristics, predicted humidity characteristics and predicted particle concentration characteristics of each warehouse area, and simultaneously determines the standard temperature range, standard humidity range and standard particle concentration range of each warehouse area; The archive warehouse environment control module obtains the archive warehouse simulation model, controls the archive warehouse simulation model to perform m simulation controls, each simulation control corresponds to an environment control strategy, and obtains the environmental energy control index of each environment control strategy; The energy efficiency optimization environment control module is used to select an environment control strategy, and based on the environment control strategy, control each environment control device in each warehouse area in the next cycle.

2. The environmental intelligent supervision and energy efficiency optimization system applicable to archive warehouse management and control according to claim 1 is characterized in that: The method for determining the predicted temperature characteristics, predicted humidity characteristics and predicted particle concentration characteristics of the warehouse area is as follows: determine a warehouse area, obtain the time-series temperature feature set, time-series humidity feature set and time-series particle concentration feature set of the warehouse area, determine the temperature feature prediction model, humidity feature prediction model and particle concentration feature prediction model of the warehouse area, input the time-series temperature feature set into the temperature feature prediction model, input the time-series humidity feature set into the humidity feature prediction model, input the time-series particle concentration feature set into the particle concentration feature prediction model, and output the predicted temperature characteristics, predicted humidity characteristics and predicted particle concentration characteristics of the warehouse area.

3. The environmental intelligent supervision and energy efficiency optimization system applicable to archive warehouse management and control according to claim 1 is characterized in that: The method for obtaining the time series temperature feature set, the time series humidity feature set and the time series particle concentration feature set is as follows: obtain the temperature characteristics, humidity characteristics and particle concentration characteristics of the warehouse area in the previous multiple consecutive cycles, sort the temperature characteristics of multiple cycles in time series and combine them into a time series temperature feature set in the form of a data set, sort the humidity characteristics of multiple cycles in time series and combine them into a time series humidity feature set in the form of a data set, sort the particle concentration characteristics of multiple cycles in time series and combine them into a time series particle concentration feature set in the form of a data set.

4. The environmental intelligent supervision and energy efficiency optimization system applicable to archive warehouse management and control according to claim 1 is characterized in that: The selected environmental control strategy is the one with the largest environmental energy control index value.

5. The environmental intelligent supervision and energy efficiency optimization system applicable to archive warehouse management and control according to claim 1 is characterized in that: The environmental energy control index of the environmental control strategy is determined based on the following method: after the archive warehouse simulation model starts a simulation control, i time points with equal time difference are set within a cycle. At each time point, the temperature, humidity and particulate matter concentration of each warehouse area model at that time point are collected. After a cycle, the energy consumption of each environmental control equipment entity in each warehouse area model is collected during the cycle, and then the comprehensive energy consumption Energy (pg) of each warehouse area model is determined, where p = 1, 2, ..., P, p is the serial number of the warehouse area model, and P is the total amount of the warehouse area model. The comprehensive energy consumption coefficient of the warehouse is set to wg, and the temperature change index and humidity change index of each warehouse area model are determined. Index, particle concentration change index, count every two adjacent warehouse area models as a warehouse linkage influence area, determine the environmental linkage value of each warehouse linkage influence area, sum and average the environmental linkage values ​​of all warehouse linkage influence areas, calculate the average environmental linkage value value(ve), set the environmental linkage standard value, when the environmental linkage value of the warehouse linkage influence area is greater than the environmental linkage standard value, mark the warehouse linkage influence area as a warehouse synchronous linkage area, mark the total number of warehouse synchronous linkage areas as Number(dd), determine the comprehensive environmental change index Index(tp) of each warehouse area model, through Index(mns)= The environmental energy control index Index (mns) of this simulation control is calculated, where a1 is the first coefficient and a2 is the second coefficient.

6. The environmental intelligent supervision and energy efficiency optimization system applicable to archive warehouse management and control according to claim 5 is characterized in that: The comprehensive energy consumption of the warehouse in the warehouse area model is determined based on the following method: determine a warehouse area model, obtain the energy consumption of each environmental control equipment entity in the warehouse area model during a period, sum up the energy consumption of each environmental control equipment entity during the period, and calculate the comprehensive energy consumption Energy (pg) of the warehouse area model.

7. The environmental intelligent supervision and energy efficiency optimization system applicable to archive warehouse management and control according to claim 5 is characterized in that: The comprehensive environmental change index of the warehouse area model is determined based on the following method: determine a warehouse area model, calculate the sum and average of the temperature change index, humidity change index, and particle concentration change index of the warehouse area model, and calculate the fusion change index comprehensive environmental change index Index(tp).

8. The environmental intelligent supervision and energy efficiency optimization system applicable to archive warehouse management and control according to claim 5 is characterized in that: The environmental linkage value of the warehouse linkage influence area is determined based on the following method: the two warehouse area models in the warehouse linkage influence area are marked as warehouse area model M and warehouse area model N respectively, and the temperature change index, humidity change index, and particle concentration change index of the warehouse area model M are marked as [M1, M2, M3], where M1 is the temperature change index of the warehouse area model M, M2 is the humidity change index of the warehouse area model M, and M3 is the particle concentration change index of the warehouse area model M; the temperature change index, humidity change index, and particle concentration change index of the warehouse area model N are marked as [N1, N2, N3], where N1 is the temperature change index of the warehouse area model N, N2 is the humidity change index of the warehouse area model N, and N3 is the particle concentration change index of the warehouse area model N; the environmental linkage value of the warehouse linkage influence area is calculated using the cosine similarity formula.

9. The environmental intelligent supervision and energy efficiency optimization system applicable to archive warehouse management and control according to claim 5 is characterized in that: The temperature change index, humidity change index, and particle concentration change index of the warehouse area model are determined based on the following method: determine a warehouse area model, construct a temperature coordinate system, a humidity coordinate system, and a particle concentration coordinate system, and determine the temperature change index, humidity change index, and particle concentration change index based on the temperature coordinate system, the humidity coordinate system, and the particle concentration coordinate system.

10. The environmental intelligent supervision and energy efficiency optimization system applicable to archive warehouse management and control according to claim 9 is characterized in that: The temperature change index, humidity change index, and particulate matter concentration change index are determined in the following manner: the temperature corresponding to each time point is marked in the temperature coordinate system in the form of coordinate points, i coordinate points are marked, adjacent coordinate points are connected to obtain multiple temperature lines, the slope of each temperature line is obtained, the absolute values ​​of the slopes of all temperature lines are taken and summed up to obtain the temperature change index; The humidity corresponding to each time point is marked in the form of coordinate points in the humidity coordinate system, i coordinate points are marked, adjacent coordinate points are connected to obtain multiple humidity lines, the slope of each humidity line is obtained, the absolute values ​​of the slopes of all humidity lines are taken and summed up to obtain the humidity change index; The particle concentration corresponding to each time point is marked in the particle concentration coordinate system in the form of coordinate points, i coordinate points are marked, adjacent coordinate points are connected to obtain multiple particle concentration lines, the slope of each particle concentration line is obtained, the absolute values ​​of the slopes of all particle concentration lines are taken and summed up to obtain the particle concentration change index.

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