Energy-saving and environment-friendly file storage and management system

Abstract

The invention discloses an energy-saving and environment-friendly archive storage and management system, and belongs to the technical field of archive management. An energy-saving and environment-friendly file storage and management system comprises a green storage management platform, an energy consumption optimization unit, an intelligent storage supervision unit, an energy efficiency evaluation unit, a consulting authentication unit, a safety tracking unit, an energy recovery unit and a display management unit. Through an energy consumption hierarchical storage mechanism, archives are divided into three storage levels of high-frequency dormancy, intermediate-frequency dormancy and low-frequency dormancy according to archive access frequency, storage media of different energy efficiency levels are used, energy optimization configuration is achieved, and system energy consumption is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of archive management, and in particular to an energy-saving and environment-friendly archive storage and management system. Background Art

[0002] With the advent of the information age, archive digitization has become a major trend in archive management. Traditional archive storage and management systems primarily focus on data security, retrieval convenience, and storage capacity, while paying relatively little attention to system energy consumption. Existing digital archive storage systems commonly suffer from high energy consumption and operating costs. This is particularly true in large archives, where servers and storage devices operate 24 / 7, resulting in significant waste of electricity resources. Furthermore, the access frequency of various archive types varies significantly, but existing systems typically utilize the same storage strategy for all archives, without tiered storage based on importance and frequency of use. This results in low-frequency access archives occupying energy-intensive storage resources.

[0003] Currently, there are a variety of digital archive storage systems on the market. For example, CN112256789A discloses a digital archive storage intelligent management system based on data analysis. Although this system achieves digital storage and intelligent management of archives, it still has significant room for improvement in energy utilization. CN108765432B proposes an archive security management system, which focuses primarily on information security and lacks energy-saving and environmentally friendly design concepts. The archive management system disclosed in CN109876543A implements remote access capabilities but does not consider the energy consumption of the system.

[0004] Therefore, there is an urgent need for an energy-saving and environmentally friendly archive storage and management system that can perform hierarchical storage according to the frequency of archive access and has energy recycling and utilization functions to reduce energy consumption, lower system operating costs, and achieve green archive management. Summary of the Invention

[0005] The purpose of the present invention is to provide an energy-saving and environmentally friendly archive storage and management system, aiming to solve the problems of high energy consumption and high operating costs of existing archive storage systems. Through an energy consumption hierarchical storage mechanism, energy consumption index calculation and energy recovery technology, energy saving and environmental protection are achieved in the archive storage and management process.

[0006] To achieve the above-mentioned objectives, the present invention provides an energy-saving and environmentally friendly archive storage and management system, including a green storage management platform, an energy consumption optimization unit, an intelligent storage supervision unit, an energy efficiency evaluation unit, a review and certification unit, a security tracking unit, an energy recovery unit and a display management unit; when the green storage management platform generates an environmental operation and management instruction, the environmental operation and management instruction is sent to the energy consumption optimization unit, and when the energy consumption optimization unit receives the environmental operation and management instruction, it immediately performs archive information storage classification and energy consumption classification operations, and sends the obtained energy-saving storage signal to the intelligent storage supervision unit; after receiving the energy-saving storage signal, the intelligent storage supervision unit immediately collects the environmental and energy consumption risk data of the storage device, and the environmental and energy consumption risk data include an external information risk value, an internal information risk value and an energy consumption index, and sends the environmental and energy consumption risk data to the energy efficiency evaluation unit, and simultaneously performs archive security and energy efficiency evaluation and analysis, and sends the obtained energy consumption alarm signal or energy efficiency control signal or control adjustment signal to the display management unit. , and sends the obtained low-energy consumption authentication signal to the review authentication unit; after receiving the environmental and energy consumption risk data, the energy efficiency assessment unit performs query obstruction and energy consumption supervision assessment analysis on the environmental and energy consumption risk data, and sends the obtained information energy consumption assessment coefficient curve to the intelligent storage supervision unit; after receiving the low-energy consumption authentication signal, the review authentication unit immediately collects the authentication data of the reviewer, the authentication data includes the authentication string and facial feature image, and performs query authority and energy consumption assessment feedback analysis on the authentication data, sends the obtained unauthorized signal to the display management unit, and sends the obtained energy-saving authorization signal to the security tracking unit; after receiving the energy-saving authorization signal, the security tracking unit immediately performs information query energy consumption and safety analysis, and sends the obtained energy consumption risk signal to the display management unit; the energy recovery unit monitors the energy consumption status of each unit in the system in real time. When the energy consumption exceeds the preset threshold, the energy recovery mechanism is triggered to convert the redundant heat energy generated by the system operation into reusable electrical energy.

[0007] Furthermore, the archive information storage grading and energy consumption classification operation process of the energy consumption optimization unit is as follows: set a monitoring period and mark it as a time threshold, obtain the digital archives uploaded by the enterprise within the time threshold, and then obtain the archive data of the digital archives. The archive data includes keyword proportion value, word meaning frequency value and file access frequency value. The keyword proportion value represents the ratio of the number of keywords set in the digital archive to the total text in the archive. The word meaning frequency value represents the number of text groups consisting of more than two consecutive keywords. The file access frequency value represents the average number of visits to this type of archive in unit time, and the keyword proportion value is compared with the word meaning frequency value and file access frequency value. The weighted product value obtained after normalization of the access frequency value is marked as the energy consumption key field rate. The energy consumption key field rate is combined with the file access frequency value to construct an energy consumption prediction model. Through this model, the archives are divided into three storage levels: high-frequency access and low-energy consumption class, medium-frequency access and medium-energy consumption class, and low-frequency access and dormant class. The archives are stored in storage media with different energy efficiency levels according to their energy consumption classification. The high-frequency access class is stored in solid-state drives, the medium-frequency access class is stored in hybrid hard drives, and the low-frequency access and dormant class is stored in low-energy storage devices such as tapes or optical disks. The corresponding energy-saving storage signals are sent to the intelligent storage supervision unit, and the digitized archives are included in the corresponding classification items.

[0008] Furthermore, the query obstacle and energy consumption supervision evaluation and analysis process of the energy efficiency evaluation unit is as follows: T1: real-time acquisition of the out-of-information risk value of the storage device within the time threshold, the out-of-information risk value represents the ratio between the number of authorized connected devices of the storage device and the total number of connected devices, and then the product value obtained by normalizing the data with the interference risk value, the interference risk value represents the number of times the difference between the maximum and minimum values of the product values obtained after normalizing the real-time network parameters of each connected device exceeds the preset threshold, and the real-time network parameters include network delay value, network packet loss rate and network power consumption value; T2: real-time acquisition of the in-information risk value of the storage device within the time threshold, the in-information risk value represents the maximum value of the analysis target in the environment inside the storage device, and the product value obtained by normalizing the number of electrical components running over-temperature inside the storage device to the total number of electrical components, the analysis target represents the number of values corresponding to the monitoring items required for environmental monitoring inside the storage device that exceed the preset value requirements, the monitoring items include humidity and temperature, and electrical components running over-temperature represent operation. The maximum value of the row temperature exceeds the rated operating temperature by more than the preset threshold for two consecutive times, and the risk value inside the information and the risk value outside the information are labeled XN and XW respectively; T3: Real-time acquisition of the energy consumption index EI of the storage device. The energy consumption index EI is calculated by the formula EI=P×t×(1+α×ΔT), where P is the power of the storage device, t is the operating time, α is the temperature correction coefficient, and ΔT is the difference between the device temperature and the optimal ambient temperature; T4: According to the formula H=(a1×XN+a2×XW)× The information energy consumption assessment coefficient is obtained by calculating (1+a3×sin(XN×XW×π))×(1+a4×EI), where a1 and a2 are the preset proportional factor coefficients of the internal risk value and the external risk value of the information, respectively. Both a1 and a2 are greater than zero. A3 is the preset correction factor coefficient, which is 2.118. A4 is the energy consumption impact factor, which is 0.025. H is the information energy consumption assessment coefficient. A rectangular coordinate system is established with time as the X-axis and the information energy consumption assessment coefficient H as the Y-axis to obtain the information energy consumption assessment coefficient curve.

[0009] Furthermore, the archive security and energy efficiency evaluation and analysis process of the intelligent storage supervision unit is as follows: the information energy consumption evaluation coefficient curve of the storage device within the time threshold is obtained, and the information energy consumption evaluation coefficient curve is subjected to preliminary discrimination analysis with the preset information energy consumption evaluation coefficient curve: if there is no line segment of the information energy consumption evaluation coefficient curve located above the preset information energy consumption evaluation coefficient curve, a curve splitting analysis is performed; if there is a line segment of the information energy consumption evaluation coefficient curve located above the preset information energy consumption evaluation coefficient curve, the number of areas enclosed by the upper line segment and the preset information energy consumption evaluation coefficient curve is obtained, and marked as the total number of energy consumption risks, the area of each area is obtained, and it is marked as an energy consumption risk area, and then construct a set A of energy consumption risk areas, and further discriminate and analyze the elements in set A: if there is a subset in set A that is larger than the preset energy consumption risk area threshold, an energy consumption alarm signal is generated, and the intelligent energy-saving mechanism is started at the same time, and the files are temporarily migrated to the low-energy consumption storage area; if there is no subset in set A that is larger than the preset energy consumption risk area threshold, the interval length between each area is obtained, and then the average of the interval length is obtained, and it is marked as the energy consumption tendency rate, and the energy consumption tendency rate is discriminated: if the energy consumption tendency rate is less than or equal to the preset energy consumption tendency rate threshold, a curve splitting analysis is performed; if the energy consumption tendency rate is greater than the preset energy consumption tendency rate threshold, an energy efficiency control signal is generated.

[0010] Furthermore, the curve splitting and analysis process is as follows: the sum of the total length corresponding to all descending line segments in the information energy consumption evaluation coefficient curve and the total length corresponding to the horizontal line segments connected to the right end of the descending line segments is obtained, and marked as the energy efficiency safety value, and the ratio between the energy efficiency safety value and the total length corresponding to the information energy consumption evaluation coefficient curve is marked as the energy efficiency steady-state evaluation coefficient, and the energy efficiency steady-state evaluation coefficient is compared and analyzed with the preset energy efficiency steady-state evaluation coefficient threshold stored internally: if the energy efficiency steady-state evaluation coefficient is greater than or equal to the preset energy efficiency steady-state evaluation coefficient threshold, a low energy consumption authentication signal is generated; if the energy efficiency steady-state evaluation coefficient is less than the preset energy efficiency steady-state evaluation coefficient threshold, a pipe adjustment signal is generated, and the energy-saving optimization algorithm is triggered at the same time to rebalance the system's energy consumption.

[0011] Furthermore, the query authority and energy consumption assessment feedback analysis process of the access authentication unit is as follows: the authentication data of the person who accesses the file within the time threshold is obtained, the authentication data includes an authentication string and a facial feature image, the authentication string represents a string composed of text and digital feature extraction of the account and password, and the authentication data is compared and analyzed with the preset authentication data entered and stored internally: if the authentication string is not equal to the preset authentication string, or the facial feature image is not equal to the preset facial feature image, an unauthorized signal is generated; if the authentication string is equal to the preset authentication string, and the facial feature image is equal to the preset facial feature image, an energy-saving authorization signal is generated, and the system automatically calculates the energy consumption value required for this query file and adds it to the user's energy consumption quota record.

[0012] Furthermore, the information query energy consumption and security analysis process of the security tracking unit is as follows: a real-time facial feature image of the querying person is collected once every t time period, where t is a natural number greater than zero, the number of facial feature images of the person is extracted from the real-time facial feature image, and the number of facial feature images of the person is discriminated and analyzed one by one: if the number of facial feature images of the person is not equal to 1, a verification instruction is generated. When the verification instruction is generated, the facial feature image of the person is extracted from the real-time facial feature image, and the facial feature image of the person is matched and analyzed one by one with the authorized facial feature image: if the facial feature image of the person falls within the authorized facial feature image, no signal is generated, and the system continues to monitor the energy consumption data during the query process; if the facial feature image of the person does not fall within the authorized facial feature image, an energy consumption risk signal is generated; if the number of facial feature images of the person is equal to 1, the real-time facial feature image is compared and analyzed with the facial feature image: if the real-time facial feature image and the facial feature image are the same, no signal is generated; if the real-time facial feature image and the facial feature image are different, an interrupt signal is generated and the current query session is terminated, and an abnormal energy consumption mark is recorded at the same time.

[0013] Furthermore, the working process of the energy recovery unit is as follows: real-time monitoring of the energy consumption status of each hardware device in the system, converting the waste heat generated by the system operation into electrical energy through thermoelectric power generation elements; dynamic power allocation according to the equipment load conditions, automatically reducing the power supply of non-critical components when the system is running at low load; through the intelligent sleep algorithm, controlling the storage devices corresponding to low-frequency access archives to enter a deep sleep state to reduce standby energy consumption; setting up a multi-level energy consumption early warning mechanism, when the system energy consumption exceeds the preset threshold, automatically adjusting the archive storage strategy, queuing non-urgent access needs, and reducing the overall energy consumption of the system.

[0014] The beneficial effects of the present invention are:

[0015] Through the energy consumption classification storage mechanism, the files are divided into three storage levels according to the access frequency: high frequency, medium frequency and low frequency dormancy. And the storage media with different energy efficiency levels are used to achieve energy optimization configuration and significantly reduce system energy consumption.

[0016] The energy consumption index calculation method is introduced to accurately evaluate the system energy consumption status by considering the influence of equipment power, operating time and temperature, providing a scientific basis for energy consumption optimization;

[0017] An energy recovery unit is designed to convert waste heat generated by system operation into reusable electricity, improving energy utilization efficiency;

[0018] Through the intelligent energy-saving mechanism, when energy consumption risks are detected, files are automatically temporarily migrated to low-energy storage areas to reduce the overall energy consumption of the system;

[0019] A multi-level energy consumption warning mechanism and intelligent sleep algorithm are adopted to dynamically adjust the power supply and storage strategy according to the system load status to achieve energy saving and environmental protection during system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a structural diagram of an energy-saving and environmentally friendly archive storage and management system. DETAILED DESCRIPTION

[0021] Please refer to Figure 1, the present invention provides a technical solution:

[0022] like Figure 1As shown, the present invention provides an energy-saving and environmentally friendly archive storage and management system, including a green storage management platform, an energy consumption optimization unit, an intelligent storage supervision unit, an energy efficiency evaluation unit, a review and certification unit, a security tracking unit, an energy recovery unit and a display management unit; when the green storage management platform generates an environmental operation and management instruction, the environmental operation and management instruction is sent to the energy consumption optimization unit, and when the energy consumption optimization unit receives the environmental operation and management instruction, it immediately performs archive information storage classification and energy consumption classification operations, and sends the obtained energy-saving storage signal to the intelligent storage supervision unit; after receiving the energy-saving storage signal, the intelligent storage supervision unit immediately collects the environment and energy consumption risk data of the storage device, and the environment and energy consumption risk data include an external information risk value, an internal information risk value and an energy consumption index, and sends the environment and energy consumption risk data to the energy efficiency evaluation unit, and simultaneously performs archive security and energy efficiency evaluation and analysis, and sends the obtained energy consumption alarm signal or energy efficiency control signal or control signal to the display management unit, The obtained low-energy consumption authentication signal is sent to the review authentication unit; after receiving the environmental and energy consumption risk data, the energy efficiency assessment unit performs query obstacle and energy consumption supervision assessment analysis on the environmental and energy consumption risk data, and sends the obtained information energy consumption assessment coefficient curve to the intelligent storage supervision unit; after receiving the low-energy consumption authentication signal, the review authentication unit immediately collects the authentication data of the reviewer, the authentication data includes the authentication string and facial feature image, and performs query authority and energy consumption assessment feedback analysis on the authentication data, sends the obtained unauthorized signal to the display management unit, and sends the obtained energy-saving authorization signal to the security tracking unit; after receiving the energy-saving authorization signal, the security tracking unit immediately performs information query energy consumption and safety analysis, and sends the obtained energy consumption risk signal to the display management unit; the energy recovery unit monitors the energy consumption status of each unit in the system in real time. When the energy consumption exceeds the preset threshold, the energy recovery mechanism is triggered to convert the redundant heat energy generated by the system operation into reusable electrical energy.

[0023] Furthermore, the archive information storage grading and energy consumption classification operation process of the energy consumption optimization unit is as follows: set a monitoring period and mark it as a time threshold, obtain the digital archives uploaded by the enterprise within the time threshold, and then obtain the archive data of the digital archives. The archive data includes keyword proportion value, word meaning frequency value and file access frequency value. The keyword proportion value represents the ratio of the number of keywords set in the digital archive to the total text in the archive. The word meaning frequency value represents the number of text groups consisting of more than two consecutive keywords. The file access frequency value represents the average number of visits to this type of archive in unit time, and the keyword proportion value is compared with the word meaning frequency value and file access frequency value. The weighted product value obtained after normalization of the access frequency value is marked as the energy consumption key field rate. The energy consumption key field rate is combined with the file access frequency value to construct an energy consumption prediction model. Through this model, the archives are divided into three storage levels: high-frequency access and low-energy consumption class, medium-frequency access and medium-energy consumption class, and low-frequency access and dormant class. The archives are stored in storage media with different energy efficiency levels according to their energy consumption classification. The high-frequency access class is stored in solid-state drives, the medium-frequency access class is stored in hybrid hard drives, and the low-frequency access and dormant class is stored in low-energy storage devices such as tapes or optical disks. The corresponding energy-saving storage signals are sent to the intelligent storage supervision unit, and the digitized archives are included in the corresponding classification items.

[0024] Furthermore, the query obstacle and energy consumption supervision evaluation and analysis process of the energy efficiency evaluation unit is as follows: T1: real-time acquisition of the out-of-information risk value of the storage device within the time threshold, the out-of-information risk value represents the ratio between the number of authorized connected devices of the storage device and the total number of connected devices, and then the product value obtained by normalizing the data with the interference risk value, the interference risk value represents the number of times the difference between the maximum and minimum values of the product values obtained after normalizing the real-time network parameters of each connected device exceeds the preset threshold, and the real-time network parameters include network delay value, network packet loss rate and network power consumption value; T2: real-time acquisition of the in-information risk value of the storage device within the time threshold, the in-information risk value represents the maximum value of the analysis target in the environment inside the storage device, and the product value obtained by normalizing the number of electrical components running over-temperature inside the storage device to the total number of electrical components, the analysis target represents the number of values corresponding to the monitoring items required for environmental monitoring inside the storage device that exceed the preset value requirements, the monitoring items include humidity and temperature, and electrical components running over-temperature represent operation. The maximum value of the row temperature exceeds the rated operating temperature by more than the preset threshold for two consecutive times, and the risk value inside the information and the risk value outside the information are labeled XN and XW respectively; T3: Real-time acquisition of the energy consumption index EI of the storage device. The energy consumption index EI is calculated by the formula EI=P×t×(1+α×ΔT), where P is the power of the storage device, t is the operating time, α is the temperature correction coefficient, and ΔT is the difference between the device temperature and the optimal ambient temperature; T4: According to the formula H=(a1×XN+a2×XW)× The information energy consumption assessment coefficient is obtained by calculating (1+a3×sin(XN×XW×π))×(1+a4×EI), where a1 and a2 are the preset proportional factor coefficients of the internal risk value and the external risk value of the information, respectively. Both a1 and a2 are greater than zero. A3 is the preset correction factor coefficient, which is 2.118. A4 is the energy consumption impact factor, which is 0.025. H is the information energy consumption assessment coefficient. A rectangular coordinate system is established with time as the X-axis and the information energy consumption assessment coefficient H as the Y-axis to obtain the information energy consumption assessment coefficient curve.

[0025] Furthermore, the archive security and energy efficiency evaluation and analysis process of the intelligent storage supervision unit is as follows: the information energy consumption evaluation coefficient curve of the storage device within the time threshold is obtained, and the information energy consumption evaluation coefficient curve is subjected to preliminary discrimination analysis with the preset information energy consumption evaluation coefficient curve: if there is no line segment of the information energy consumption evaluation coefficient curve located above the preset information energy consumption evaluation coefficient curve, a curve splitting analysis is performed; if there is a line segment of the information energy consumption evaluation coefficient curve located above the preset information energy consumption evaluation coefficient curve, the number of areas enclosed by the upper line segment and the preset information energy consumption evaluation coefficient curve is obtained, and marked as the total number of energy consumption risks, the area of each area is obtained, and it is marked as an energy consumption risk area, and then construct a set A of energy consumption risk areas, and further discriminate and analyze the elements in set A: if there is a subset in set A that is larger than the preset energy consumption risk area threshold, an energy consumption alarm signal is generated, and the intelligent energy-saving mechanism is started at the same time, and the files are temporarily migrated to the low-energy consumption storage area; if there is no subset in set A that is larger than the preset energy consumption risk area threshold, the interval length between each area is obtained, and then the average of the interval length is obtained, and it is marked as the energy consumption tendency rate, and the energy consumption tendency rate is discriminated: if the energy consumption tendency rate is less than or equal to the preset energy consumption tendency rate threshold, a curve splitting analysis is performed; if the energy consumption tendency rate is greater than the preset energy consumption tendency rate threshold, an energy efficiency control signal is generated.

[0026] Furthermore, the curve splitting and analysis process is as follows: the sum of the total length corresponding to all descending line segments in the information energy consumption evaluation coefficient curve and the total length corresponding to the horizontal line segments connected to the right end of the descending line segments is obtained, and marked as the energy efficiency safety value, and the ratio between the energy efficiency safety value and the total length corresponding to the information energy consumption evaluation coefficient curve is marked as the energy efficiency steady-state evaluation coefficient, and the energy efficiency steady-state evaluation coefficient is compared and analyzed with the preset energy efficiency steady-state evaluation coefficient threshold stored internally: if the energy efficiency steady-state evaluation coefficient is greater than or equal to the preset energy efficiency steady-state evaluation coefficient threshold, a low energy consumption authentication signal is generated; if the energy efficiency steady-state evaluation coefficient is less than the preset energy efficiency steady-state evaluation coefficient threshold, a pipe adjustment signal is generated, and the energy-saving optimization algorithm is triggered at the same time to rebalance the system's energy consumption.

[0027] Furthermore, the query authority and energy consumption assessment feedback analysis process of the access authentication unit is as follows: the authentication data of the person who accesses the file within the time threshold is obtained, the authentication data includes an authentication string and a facial feature image, the authentication string represents a string composed of text and digital feature extraction of the account and password, and the authentication data is compared and analyzed with the preset authentication data entered and stored internally: if the authentication string is not equal to the preset authentication string, or the facial feature image is not equal to the preset facial feature image, an unauthorized signal is generated; if the authentication string is equal to the preset authentication string, and the facial feature image is equal to the preset facial feature image, an energy-saving authorization signal is generated, and the system automatically calculates the energy consumption value required for this query file and adds it to the user's energy consumption quota record.

[0028] Furthermore, the information query energy consumption and security analysis process of the security tracking unit is as follows: a real-time facial feature image of the querying person is collected once every t time period, where t is a natural number greater than zero, the number of facial feature images of the person is extracted from the real-time facial feature image, and the number of facial feature images of the person is discriminated and analyzed one by one: if the number of facial feature images of the person is not equal to 1, a verification instruction is generated. When the verification instruction is generated, the facial feature image of the person is extracted from the real-time facial feature image, and the facial feature image of the person is matched and analyzed one by one with the authorized facial feature image: if the facial feature image of the person falls within the authorized facial feature image, no signal is generated, and the system continues to monitor the energy consumption data during the query process; if the facial feature image of the person does not fall within the authorized facial feature image, an energy consumption risk signal is generated; if the number of facial feature images of the person is equal to 1, the real-time facial feature image is compared and analyzed with the facial feature image: if the real-time facial feature image and the facial feature image are the same, no signal is generated; if the real-time facial feature image and the facial feature image are different, an interrupt signal is generated and the current query session is terminated, and an abnormal energy consumption mark is recorded at the same time.

[0029] Furthermore, the working process of the energy recovery unit is as follows: real-time monitoring of the energy consumption status of each hardware device in the system, converting the waste heat generated by the system operation into electrical energy through thermoelectric power generation elements; dynamic power allocation according to the equipment load conditions, automatically reducing the power supply of non-critical components when the system is running at low load; through the intelligent sleep algorithm, controlling the storage devices corresponding to low-frequency access archives to enter a deep sleep state to reduce standby energy consumption; setting up a multi-level energy consumption early warning mechanism, when the system energy consumption exceeds the preset threshold, automatically adjusting the archive storage strategy, queuing non-urgent access needs, and reducing the overall energy consumption of the system.

[0030] The working process of the present invention is as follows:

[0031] First, the green storage management platform generates environmentally friendly operation and management instructions based on user needs and sends them to the energy consumption optimization unit.

[0032] After receiving the environmental protection operation management instruction, the energy consumption optimization unit performs archival information storage classification and energy consumption classification operations:

[0033] Step S201: Set a monitoring period and mark it as a time threshold;

[0034] Step S202: Acquire digital files uploaded by enterprises within a time threshold;

[0035] Step S203: Obtaining archive data of the digitized archive, including keyword proportion values, word meaning frequency values, and file access frequency values;

[0036] Step S204: normalizing the keyword proportion value, the word meaning frequency value, and the file access frequency value to obtain a weighted product value, which is marked as the energy consumption key field rate;

[0037] Step S205: constructing an energy consumption prediction model to classify the archives into three storage levels: high-frequency access and low-energy consumption class, medium-frequency access and medium-energy consumption class, and low-frequency access and dormant class;

[0038] Step S206: Classify the archives according to the storage level and store them in storage media with different energy efficiency levels. High-frequency access files are stored in solid-state drives, medium-frequency access files are stored in hybrid hard drives, and low-frequency access and dormant files are stored in low-energy storage devices such as tapes or optical disks.

[0039] Step S207: Generate an energy-saving storage signal and send it to the intelligent storage supervision unit, and at the same time include the digital archive in the corresponding classification item.

[0040] After receiving the energy-saving storage signal, the intelligent storage monitoring unit immediately collects the environmental and energy consumption risk data of the storage device and sends the data to the energy efficiency evaluation unit.

[0041] After receiving the environmental and energy consumption risk data, the energy efficiency assessment unit conducts query obstacle and energy consumption supervision assessment analysis:

[0042] Step S301: obtaining in real time the external information risk value XW of the storage device within a time threshold;

[0043] Step S302: obtaining in real time the risk value XN of the information of the storage device within a time threshold;

[0044] Step S303: Obtain the energy consumption index EI of the storage device in real time, calculated using the formula EI=P×t×(1+α×ΔT);

[0045] Step S304: Calculate the information energy consumption evaluation coefficient according to the formula H=(a1×XN+a2×XW)×(1+a3×sin(XN×XW×π))×(1+a4×EI);

[0046] Step S305: Establish a rectangular coordinate system with time as the X-axis and the information energy consumption evaluation coefficient H as the Y-axis to obtain an information energy consumption evaluation coefficient curve, and send it to the intelligent storage monitoring unit.

[0047] After receiving the information energy consumption assessment coefficient curve, the intelligent storage supervision unit conducts archive security and energy efficiency assessment analysis:

[0048] Step S401: obtaining an information energy consumption evaluation coefficient curve of a storage device within a time threshold, and comparing it with a preset information energy consumption evaluation coefficient curve;

[0049] Step S402: determining whether there is a line segment of the information energy consumption evaluation coefficient curve located above the preset information energy consumption evaluation coefficient curve;

[0050] Step S403: If there is no upper line segment, perform curve splitting analysis;

[0051] Step S404: If an upper line segment exists, the number of areas enclosed by the upper line segment and the preset information energy consumption assessment coefficient curve is obtained and marked as the total number of energy consumption risks;

[0052] Step S405: obtaining the area of each region, marking it as an energy consumption risk area, and constructing an energy consumption risk area set A;

[0053] Step S406: Determine whether there is a subset in set A with an area greater than a preset energy consumption risk threshold;

[0054] Step S407: If it exists, an energy consumption alarm signal is generated, an intelligent energy-saving mechanism is activated, and the file is temporarily moved to a low-energy storage area;

[0055] Step S408: If it does not exist, then obtain the interval duration between each area, calculate the average of the interval duration, and mark it as the energy consumption tendency rate;

[0056] Step S409: determining whether the energy consumption tendency rate is greater than a preset energy consumption tendency rate threshold;

[0057] Step S410: If it is less than or equal to the threshold, then perform curve splitting analysis;

[0058] Step S411: If it is greater than the threshold, an energy efficiency control signal is generated.

[0059] The curve splitting analysis process is as follows:

[0060] Step S412: Obtain the sum of the total length of all descending line segments in the information energy consumption assessment coefficient curve and the total length of the horizontal line segments connected to the right ends of the descending line segments, and mark it as the energy efficiency safety value;

[0061] Step S413: Calculate the ratio of the energy efficiency safety value to the total length corresponding to the information energy consumption evaluation coefficient curve, and mark it as the energy efficiency steady-state evaluation coefficient;

[0062] Step S414: determining whether the energy efficiency steady-state evaluation coefficient is greater than or equal to a preset energy efficiency steady-state evaluation coefficient threshold;

[0063] Step S415: If the value is greater than or equal to the threshold, a low energy consumption authentication signal is generated;

[0064] Step S416: If it is less than the threshold, a control signal is generated to trigger the energy-saving optimization algorithm to rebalance the energy consumption of the system.

[0065] After receiving the low energy consumption authentication signal, the authentication unit conducts query authority and energy consumption assessment feedback analysis:

[0066] Step S501: Acquire authentication data of a person who has reviewed the information within a time threshold, including an authentication string and a facial feature image;

[0067] Step S502: Compare and analyze the authentication data with the preset authentication data;

[0068] Step S503: determining whether the authentication string is equal to a preset authentication string, and whether the facial feature image is equal to a preset facial feature image;

[0069] Step S504: if the condition is not met, generating an unweighted signal;

[0070] Step S505: If the conditions are met, an energy-saving authorization signal is generated, and the energy consumption value required for this archive query is calculated and added to the user's energy consumption quota record.

[0071] After receiving the energy-saving authorization signal, the safety tracking unit conducts information query and energy consumption and safety analysis:

[0072] Step S601: collecting a real-time facial feature image of the person who is looking for information every t time period;

[0073] Step S602: extracting the number of facial feature images of the person from the real-time facial feature image;

[0074] Step S603: determining whether the number of facial feature images of the person is equal to 1;

[0075] Step S604: If it is not equal to 1, generate a verification instruction;

[0076] Step S605: extracting the person's facial feature image from the real-time facial feature image according to the verification instruction, and performing matching analysis with the authorized facial feature image;

[0077] Step S606: determining whether the facial feature image of the person is within the authorized facial feature image;

[0078] Step S607: If yes, no signal is generated and the system continues to monitor the energy consumption data during the query process;

[0079] Step S608: If not, generate an energy consumption risk signal;

[0080] Step S609: If the number of facial feature images of the person is equal to 1, the real-time facial feature image is compared and analyzed with the facial feature image;

[0081] Step S610: determining whether the real-time facial feature image is the same as the facial feature image;

[0082] Step S611: If they are the same, no signal is generated;

[0083] Step S612: If they are not the same, an interrupt signal is generated and the current query session is terminated, while recording an abnormal energy consumption mark.

[0084] The working process of the energy recovery unit is as follows:

[0085] Step S701: Real-time monitoring of the energy consumption status of each hardware device in the system;

[0086] Step S702: Determine whether the system energy consumption exceeds a preset threshold;

[0087] Step S703: If the threshold is exceeded, the waste heat generated by the system operation is converted into electrical energy through the thermoelectric power generation element;

[0088] Step S704: Dynamically allocate power based on device load conditions, automatically reducing the power supply to non-critical components when the system is running at low load;

[0089] Step S705: Using an intelligent sleep algorithm, the storage device corresponding to the low-frequency accessed files is controlled to enter a deep sleep state to reduce standby energy consumption;

[0090] Step S706: A multi-level energy consumption warning mechanism is set up. When the system energy consumption exceeds a preset threshold, the archive storage strategy is automatically adjusted to queue non-urgent access requests and reduce the overall energy consumption of the system.

[0091] In the information energy consumption assessment coefficient curve diagram, the horizontal axis represents time, and the vertical axis represents the information energy consumption assessment coefficient. Curve A represents the actual information energy consumption assessment coefficient curve, and curve B represents the preset information energy consumption assessment coefficient curve. When curve A exceeds curve B in certain areas, these areas are considered energy consumption risk areas, and the system will take appropriate measures based on the size and distribution characteristics of these areas.

[0092] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. An energy-saving and environmentally friendly archive storage and management system, characterized in that: It includes a green storage management platform, energy consumption optimization unit, intelligent storage supervision unit, energy efficiency assessment unit, review and certification unit, safety tracking unit, energy recovery unit and display management unit; When the green storage management platform generates an environmental protection operation and management instruction, it sends the environmental protection operation and management instruction to the energy consumption optimization unit. Upon receiving the environmental protection operation and management instruction, the energy consumption optimization unit immediately performs archival information storage classification and energy consumption classification operations, and sends the obtained energy-saving storage signal to the intelligent storage supervision unit; After receiving the energy-saving storage signal, the intelligent storage supervision unit immediately collects the environmental and energy consumption risk data of the storage device, which includes the external information risk value, the internal information risk value and the energy consumption index, and sends the environmental and energy consumption risk data to the energy efficiency evaluation unit. At the same time, it conducts file security and energy efficiency evaluation and analysis, sends the obtained energy consumption alarm signal or energy efficiency control signal or management adjustment signal to the display management unit, and sends the obtained low energy consumption certification signal to the review and certification unit; After receiving the environmental and energy consumption risk data, the energy efficiency assessment unit conducts query and energy consumption supervision assessment analysis on the environmental and energy consumption risk data, and sends the obtained information energy consumption assessment coefficient curve to the intelligent storage supervision unit; After receiving the low-energy authentication signal, the access authentication unit immediately collects the authentication data of the access person, including the authentication string and facial feature image, and performs query authority and energy consumption assessment feedback analysis on the authentication data, and sends the obtained unauthorized signal to the display management unit and the obtained energy-saving authorization signal to the security tracking unit; After receiving the energy-saving authorization signal, the safety tracking unit immediately conducts information query and energy consumption and safety analysis, and sends the obtained energy consumption risk signal to the display management unit; The energy recovery unit monitors the energy consumption status of each unit in the system in real time. When the energy consumption exceeds the preset threshold, the energy recovery mechanism is triggered to convert the redundant heat energy generated by the system operation into reusable electrical energy.

2. The energy-saving and environmentally friendly archive storage and management system according to claim 1 is characterized in that: The energy consumption optimization unit's archive information storage classification and energy consumption classification operation process is as follows: Set a monitoring cycle and mark it as a time threshold to obtain the digital archives uploaded by the enterprise within the time threshold, and then obtain the archival data of the digital archives. The archival data includes keyword proportion value, word meaning frequency value and file access frequency value. The keyword proportion value represents the ratio of the number of keywords set in the digital archive to the total text in the archive. The word meaning frequency value represents the number of text groups consisting of more than two consecutive keywords. The file access frequency value represents the average number of visits to this type of archive within a unit time, and the weighted product obtained by normalizing the keyword proportion value, the word meaning frequency value and the file access frequency value is calculated. The value is marked as the energy consumption key field rate, and the energy consumption key field rate is combined with the file access frequency value to construct an energy consumption prediction model. Through this model, the archives are divided into three storage levels: high-frequency access and low-energy consumption class, medium-frequency access and medium-energy consumption class, and low-frequency access and dormant class. The archives are stored in storage media with different energy efficiency levels according to their energy consumption classification. The high-frequency access class is stored in solid-state drives, the medium-frequency access class is stored in hybrid hard drives, and the low-frequency access and dormant class is stored in low-energy storage devices such as tapes or optical disks. The corresponding energy-saving storage signals are sent to the intelligent storage supervision unit, and the digitized archives are included in the corresponding classification items.

3. The energy-saving and environmentally friendly archive storage and management system according to claim 1 is characterized in that: The query barrier and energy consumption supervision evaluation and analysis process of the energy efficiency evaluation unit is as follows: T1: Real-time acquisition of the out-of-information risk value of storage devices within the time threshold. The out-of-information risk value represents the ratio of the number of authorized connected devices to the total number of connected devices, and the product of this ratio and the interference risk value after data normalization. The interference risk value represents the number of times the difference between the maximum and minimum values of the product of the real-time network parameters of each connected device after data normalization exceeds the preset threshold. Real-time network parameters include network latency, network packet loss rate, and network power consumption. T2: Real-time acquisition of the in-information risk value of the storage device within the time threshold. The in-information risk value represents the maximum value of the analysis target in the storage device environment, and the product of the ratio of the number of over-temperature electrical components in the storage device to the total number of electrical components after data normalization. The analysis target represents the number of times that the values corresponding to the monitoring items required for environmental monitoring within the storage device exceed the preset value requirements. The monitoring items include humidity and temperature. The over-temperature electrical component indicates that the maximum operating temperature exceeds the rated operating temperature by more than the preset threshold for two consecutive times. The in-information risk value and the out-of-information risk value are labeled XN and XW, respectively. T3: Obtain the energy consumption index (EI) of the storage device in real time.

4. The energy-saving and environmentally friendly archive storage and management system according to claim 3 is characterized in that: The file security and energy efficiency evaluation and analysis process of the intelligent storage supervision unit is as follows: Obtain the information energy consumption evaluation coefficient curve of the storage device within the time threshold, and perform preliminary discrimination analysis on the information energy consumption evaluation coefficient curve and the preset information energy consumption evaluation coefficient curve: If there is no line segment of the information energy consumption evaluation coefficient curve located above the preset information energy consumption evaluation coefficient curve, then curve splitting analysis is performed; If there is a line segment of the information energy consumption assessment coefficient curve that is above the preset information energy consumption assessment coefficient curve, then the number of areas enclosed by the upper line segment and the preset information energy consumption assessment coefficient curve is obtained and marked as the total number of energy consumption risks. The area of each area is obtained and marked as the energy consumption risk area, and then a set A of energy consumption risk areas is constructed. The elements in set A are further discriminant analyzed: If there is a subset in set A that is larger than the preset energy consumption risk area threshold, an energy consumption alarm signal is generated, and the intelligent energy-saving mechanism is activated to temporarily migrate the files to a low-energy storage area; If there is no subset in set A with an area greater than the preset energy consumption risk area threshold, the interval lengths between each area are obtained, and then the average of the interval lengths is obtained and marked as the energy consumption tendency rate. The energy consumption tendency rate is then judged: If the energy consumption tendency rate is less than or equal to the preset energy consumption tendency rate threshold, curve splitting analysis is performed; If the energy consumption tendency rate is greater than the preset energy consumption tendency rate threshold, an energy efficiency control signal is generated.

5. The energy-saving and environment-friendly archive storage and management system according to claim 4 is characterized in that: The curve splitting analysis process is as follows: Obtain the sum of the total lengths of all descending line segments in the information energy consumption evaluation coefficient curve and the total lengths of the horizontal line segments connected to the right ends of the descending line segments, and mark it as the energy efficiency safety value. Mark the ratio between the energy efficiency safety value and the total length corresponding to the information energy consumption evaluation coefficient curve as the energy efficiency steady-state evaluation coefficient. Compare and analyze the energy efficiency steady-state evaluation coefficient with the preset energy efficiency steady-state evaluation coefficient threshold stored internally: If the energy efficiency steady-state evaluation coefficient is greater than or equal to the preset energy efficiency steady-state evaluation coefficient threshold, a low energy consumption authentication signal is generated; If the energy efficiency steady-state evaluation coefficient is less than the preset energy efficiency steady-state evaluation coefficient threshold, a control signal is generated, and the energy-saving optimization algorithm is triggered at the same time to rebalance the energy consumption of the system.

6. The energy-saving and environment-friendly archive storage and management system according to claim 1, characterized in that: The query authority and energy consumption assessment feedback analysis process of the authentication unit are as follows: The authentication data of the person who accessed the account within the time threshold is obtained. The authentication data includes an authentication string and a facial feature image. The authentication string represents a string composed of text and numeric features extracted from the account and password. The authentication data is then compared and analyzed with the preset authentication data stored internally: If the authentication string is not equal to the preset authentication string, or the facial feature image is not equal to the preset facial feature image, an unweighted signal is generated; If the authentication string is equal to the preset authentication string and the facial feature image is equal to the preset facial feature image, an energy-saving authorization signal is generated. At the same time, the system automatically calculates the energy consumption value required for this query file and adds it to the user's energy consumption quota record.

7. The energy-saving and environment-friendly archive storage and management system according to claim 6, characterized in that: The information query energy consumption and safety analysis process of the safety tracking unit is as follows: The real-time facial feature image of the person being consulted is collected every t time period, where t is a natural number greater than zero. The number of facial feature images of the person is extracted from the real-time facial feature image, and the number of facial feature images of the person is discriminated and analyzed one by one: If the number of facial feature images of the person is not equal to 1, a verification instruction is generated. When the verification instruction is generated, the facial feature image of the person is extracted from the real-time facial feature image, and a one-to-one matching analysis is performed between the facial feature image of the person and the authorized facial feature image: If the facial feature image of the person falls within the authorized facial feature image, no signal is generated and the system continues to monitor the energy consumption data during the query process; If the facial feature image of the person does not belong to the authorized facial feature images, an energy consumption risk signal is generated; If the number of facial feature images of the person is equal to 1, the real-time facial feature image is compared and analyzed with the facial feature image: If the real-time facial feature image is identical to the facial feature image, no signal is generated; If the real-time facial feature image is not identical to the facial feature image, an interrupt signal is generated and the current query session is terminated, and an abnormal energy consumption mark is recorded.

8. The energy-saving and environment-friendly archive storage and management system according to claim 1, characterized in that: The working process of the energy recovery unit is as follows: Real-time monitoring of the energy consumption status of each hardware device in the system, and conversion of waste heat generated by system operation into electrical energy through thermoelectric power generation elements; Dynamic power allocation based on equipment load, automatically reducing the power supply to non-critical components when the system is running at low load; Through intelligent sleep algorithm, the storage devices corresponding to low-frequency access files are controlled to enter deep sleep state, reducing standby energy consumption; A multi-level energy consumption warning mechanism is set up. When the system energy consumption exceeds the preset threshold, the archive storage strategy is automatically adjusted, non-urgent access requests are queued, and the overall energy consumption of the system is reduced.

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