High-Definition and High-Cost-Performance Data Storage Model and Method in the Scenario of Multi-Source Grid-Connected Power Supply

Abstract

The present invention relates to the technical field of data storage security and energy conservation, specifically a high-definition and high-cost-performance data storage model and method in the scenario of multi-source grid-connected power supply, which can provide security protection and system energy conservation for the use of data storage servers, data centers or computing centers from the perspective of new high-quality power sources in a new power system, reduce the increase in the PUE value caused by new power quality factors, so as to achieve high-definition and high-cost-performance data storage. Through a dedicated data storage transformer or a data storage security and energy-saving machine, based on its internal circuit structure, it can bidirectionally block, reduce, filter and absorb the unstable, uncertain, uncontrollable and large-fluctuation new energy and traditional energy integrated power supply under the conditions of photovoltaic power, wind power and distributed grid connection, as well as internal and external electrical pollution, so as to obtain high-quality electric energy with stability, cleanliness, small impact, light pollution and low superposition, and provide security protection and system energy conservation for data storage servers and other external devices.

Description

Technical Field

[0001] The present invention relates to the technical field of data storage security and energy saving, specifically to a high-definition and high cost-effective data storage model and method in the scenario of multi-source grid-connected power supply. Background Art

[0002] Storage capacity is one of the key infrastructures of the four core elements of the digital economy (algorithm, computing power, data, storage capacity). Its security, reliability, stability, economy, etc. directly affect the green, high-quality and sustainable development of the digital economy. Storage capacity refers to the ability of data storage, including many aspects such as storage capacity, efficiency, reliability, economy and security.

[0003] For a long time, people have attached great importance to the software and hardware development of storage capacity. The endless emergence of the third-generation Internet web3.0 decentralized data storage technology is a typical example. However, the power quality has been ignored or underestimated, especially the photovoltaic power, wind power and a large number of distributed new energy grid-connected power generation which "rely on the sky for food", resulting in the power system changing from the instability at one end of the power consumption side to the instability, uncontrollability and large uncertainty fluctuations at both ends of the power consumption side and the power generation side, leading to a serious challenge to the security of data storage posed by the new power quality. In addition, components such as hard disks, optical disks, random access memories, controllers, interfaces, etc. for data storage are all non-linear pollution-type "high-precision and sophisticated" power-consuming components. The power pollution generated during operation forms the superposition of the phenomenon of wandering power pollution in groups and clusters inside the computing center / data center, posing a serious threat to the development of the data storage industry.

[0004] In addition, the greening and low-carbonization of data centers / computing centers have become topics that people highly concern and have to solve.

[0005] The full name of PUE is Power Usage Effectiveness, which represents the ratio of the total energy consumption of a data center to the energy consumption of IT equipment. The core of its optimization lies in reducing the energy consumption of non-IT equipment. Without considering traditional storage medium optimization and heat dissipation design, the new power quality of the data storage server itself is also an important optimization aspect. On the one hand, it is the security of data storage itself, such as end-to-end encryption to ensure the static and dynamic security of data. On the other hand, it is the data security during the storage process, which is mainly reflected in storage failures caused by new power problems, such as downtime and data corruption, resulting in the interruption of storage services, or a large number of "transient currents", "surges" and "harmonics" generated by the frequent startup of electrical equipment itself and the power supply network, harmonic interference, overload, aging of lines and equipment, etc., forming a phenomenon of current wandering in the local power grid, and data insecurity caused by power supply or from the power source perspective, which poses challenges to continuous and reliable data storage. For the latter, in the case of the popularization of 5G and Internet of Things devices, a large amount of data is brought. If the storage is unreliable and insecure, it will lead to service interruption, affecting user experience and business decisions. At the same time, in the multi-source power supply scenario of photovoltaic power, wind power and distributed power grid connection, many new issues are brought to the healthy development of the storage industry.

[0006] For example, new energy sources such as photovoltaic power and wind power have inherent defects such as instability, uncontrollability, uncertainty, and large fluctuations. Moreover, these power generation methods relying on natural resources have strong dependence. For example, in wind power generation, the fluctuation of wind speed will lead to unstable power output, and in photovoltaic power generation, affected by clouds and day-night alternation, the power generation power will have large fluctuations within dozens of minutes or hours. And these instabilities and fluctuations are difficult to predict through conventional prediction means. For example, although meteorological prediction can complete the prediction several days in advance, the overall error rate is relatively large, and these errors will exacerbate the instability of multi-source distributed power grid power supply. This poses new challenges to data storage servers. There are still some cases where only the performance improvement of the construction of computing power centers / data centers is emphasized, and some blindly pursue the high-end nature of data storage, resulting in frequent failures or accidents in computing power centers / data centers. At the same time, the fluctuation of this new power quality will lead to an increase in the PUE value and line loss, reducing the overall cost performance of data storage servers. Although in the existing technology, a combination of energy storage systems and VSG control technology is used to resist the fluctuations of new energy to meet the needs of civilian equipment, it is still not enough for professional computing power / data centers. Usually, emergency power supplies such as UPS power supplies, filtering devices and power factor compensation devices are also used to optimize the power quality indicators, but these are all passive. For example, after a power supply circuit is interrupted, the UPS power supply is used for power supply, or restricted by hardware, it is impossible to actively perform disaster recovery switching at any time under the new power quality indicators. Summary of the Invention

[0007] The object of the present invention is to provide a high-definition and cost-effective data storage model and method in a multi-source grid-connected power supply scenario for the above-mentioned existing problems.

[0008] The technical solution adopted by the present invention is as follows. A high-definition and cost-effective data storage model in a multi-source grid-connected power supply scenario is applicable to power supply scenarios under the grid connection conditions of photovoltaic power, wind power, and distributed power grids, and can optimize the power quality of data storage. It is characterized in that it includes a new high-quality power supply optimization model. The new high-quality power supply optimization model includes a new high-quality power supply device. The input end of the new high-quality power supply device is connected to a multi-source distributed power grid, and the output end of the new high-quality power supply device is connected to a data storage server. A generator, an automatic transfer switch, a power quality optimization mechanism, and a controller are arranged in the new high-quality power supply device;

[0009] The controller can receive the control of the data storage server, is signal-connected to the automatic transfer switch, and controls the start and stop of the generator;

[0010] The generator can supply power to the data storage server via the automatic transfer switch;

[0011] The multi-source distributed power grid can supply power to the data storage server via the automatic transfer switch;

[0012] The power quality optimization mechanism can optimize the power quality of the data storage server.

[0013] Further, the power quality optimization mechanism is a data storage safety and energy-saving machine, which is arranged in the new high-quality power supply device. The data storage safety and energy-saving machine is arranged between the sampling points of the automatic transfer switch and the controller, and can optimize the new power quality output to the data storage server.

[0014] Further, the power quality optimization mechanism is a special transformer for data storage, which is arranged in the new high-quality power supply device. The special transformer for data storage can optimize the power quality of the power input from the multi-source distributed power grid to the automatic transfer switch.

[0015] Further, it also includes a software power quality optimization model. The software power quality optimization model is deployed on the data storage server, and is internally provided with a power quality acquisition module, a power quality early warning module, a disaster recovery switching module, an energy consumption grid module, and a data storage module;

[0016] The power quality acquisition module is used to collect the power quality indexes at the output end of the multi-source distributed power grid and the output end of the new high-quality power supply device;

[0017] The power quality early warning module is used to analyze and give early warnings to the power quality indexes collected by the power quality acquisition module;

[0018] The disaster recovery switching module is used to feedback the results obtained by the new high-quality power supply device processing the power quality warning module;

[0019] The energy consumption grid module is used to display the energy digestion under the current power quality scenario.

[0020] The present invention also provides a high-definition and high cost-effective data storage method under a multi-source distributed power grid power supply scenario based on a high-definition and high cost-effective data storage model under a multi-source grid-connected power supply scenario. For the new high-quality power supply device, the method includes the following steps:

[0021] S1. The new high-quality power supply device obtains the power provided by the multi-source distributed power grid, and the power quality optimization mechanism performs power optimization processing;

[0022] S2. The new high-quality power supply device supplies power to the data storage server.

[0023] Further, it also includes the following steps:

[0024] S3. When the controller receives the data transmitted by the disaster recovery switching module, it disconnects the multi-source distributed power grid from the automatic switching switch;

[0025] S4. The generator supplies power to the data storage module;

[0026] S5. When the controller receives the data transmitted by the disaster recovery switching module again, it disconnects the generator from the automatic switching switch and turns on the connection between the multi-source distributed power grid and the automatic switching switch;

[0027] S6. The multi-source distributed power grid supplies power to the data storage server. The present invention also provides a high-definition and high cost-effective data storage method under a multi-source distributed power grid power supply scenario based on a high-definition and high cost-effective data storage model under a multi-source grid-connected power supply scenario. For the data storage server, the method includes the following steps:

[0028] A1. The power quality acquisition module collects the power quality indexes input by the multi-source distributed power grid and the power quality indexes output by the new high-quality power supply device to the data storage server within a set time series, and generates an evaluation data set for each power quality index;

[0029] A2. The power quality warning module analyzes the power quality indexes input by the multi-source distributed power grid and the power quality indexes output by the new high-quality power supply device to the data storage server from the evaluation data sets of each power quality index generated by the power quality acquisition module, and transmits the results to the disaster recovery switching module;

[0030] A3. The disaster recovery switching module processes according to the results issued by the power quality warning module.

[0031] Optionally, A1 further includes the following steps:

[0032] B1. Select the power quality indexes input from the multi-source distributed power grid. The voltage deviation, frequency deviation, harmonic content, three-phase unbalance parameter, and voltage fluctuation and flicker parameter are respectively marked as VD1, FD1, HD1, TI1, and VFF1. Select the power quality indexes output from the new high-quality power supply device to the data storage server. The voltage deviation, frequency deviation, harmonic content, three-phase unbalance parameter, and voltage fluctuation and flicker parameter are respectively marked as VD2, FD2, HD2, TI2, and VFF2;

[0033] B2. Establish an evaluation data set of the power quality indexes with respect to the date and time interval, which are respectively expressed as VD1[d][t], FD1[d][t], HD1[d][t], TI1[d][t], VFF1[d][t], VD2[d][t], FD2[d][t], HD2[d][t], TI2[d][t], and VFF2[d][t], where d represents the date and t represents the time interval point.

[0034] Optionally, A2 further includes the following steps:

[0035] C1. Select three consecutive dates in the power quality index evaluation data set, and select the same power quality index at the same time interval point to obtain the mean value μ and standard deviation σ of the power quality index;

[0036] C2. Select the fourth date in the above power quality index evaluation data set, and judge by the mean value μ and standard deviation σ obtained from the three dates, configure the confidence level, and select the filtering coefficient α;

[0037] C3. Through a first-order low-pass IIR filter, select the filtering coefficient to update the mean value μ to obtain the updated mean value μ';

[0038] C4. Based on the updated mean value μ', establish the time mean value sequence U1 of the power quality indexes at the output end of the multi-source distributed power grid and the time mean value sequence U2 of the power quality indexes output from the new high-quality power supply device to the data storage server;

[0039] C5. Select the time-averaged sequence U1 and the current time sequence D1 to obtain the deviation ERR1, where the current time sequence D1 includes the current values of power quality indicators such as voltage deviation, frequency deviation, harmonic content, three-phase unbalance parameters, and voltage fluctuation and flicker parameters input from the multi-source distributed power grid. Select the time-averaged sequence U2 and the current time sequence D2 to obtain the deviation ERR2, where the current time sequence D2 includes the current values of power quality indicators such as voltage deviation, frequency deviation, harmonic content, three-phase unbalance parameters, and voltage fluctuation and flicker parameters output from the new high-quality power supply device to the data storage server;

[0040] C6. Judge the deviation ERR1 or the deviation ERR2, and transfer the result to the disaster recovery switching module.

[0041] Optionally, C6 further includes generating a warning detection table, and the warning detection table records the deviation ERR1 and / or the deviation ERR2 and transfers it to the disaster recovery switching module.

[0042] Optionally, in A3, the following steps are included:

[0043] L1. The disaster recovery switching module issues a data disaster recovery backup requirement to the data storage module according to the warning issued by the power quality warning module;

[0044] L2. The disaster recovery switching module issues an alarm externally according to the warning issued by the power quality warning module;

[0045] L3. The disaster recovery switching module obtains the result transferred by the power quality warning module and controls the controller in the new high-quality power supply device to turn on / off the disaster recovery generator and disconnect / turn on the multi-source distributed power grid.

[0046] The beneficial effects of the present invention at least include one of the following;

[0047] 1. A high-definition and high-cost-performance data storage model is provided, which can protect the use of the data storage server or the data center from the perspective of power supply, reduce the increase in the PUE value caused by power supply factors, and thus achieve high-definition and high-cost-performance data storage.

[0048] 2. In the new high-quality power supply optimization model, the special transformer for data storage can, based on its internal circuit structure, use the multi-source distributed power grid power supply network as the power input end, and bidirectionally block, reduce, filter, and absorb the electrical pollution generated by situations such as phase imbalance, insufficient power factor, frequent device startup, harmonic interference, and overload in the system during use, thereby improving the power quality and increasing the power saving rate, providing a guarantee for the safe use of the data storage server.

[0049] 3. In the new high-quality power optimization model, the data storage security and energy-saving machine can provide safety protection for power-using terminals such as data storage servers and other external devices based on its internal circuit structure, avoiding large fluctuations in power such as overvoltage or undervoltage.

[0050] 4. On the other hand, by setting up a generator and a controller, the controller can control the generator to turn on or off, and control the power supply network of the multi-source distributed power grid to turn off or on, realizing active power supply switching.

[0051] 5. A data storage server optimization model composed of a power quality acquisition module, an energy consumption grid module, a power quality warning module, a disaster recovery switching module, and a data storage module is deployed on the data storage server. It can evaluate and predict the power quality provided by the multi-source distributed power grid, so as to perform active disaster recovery switching when the power quality index is not ideal or is about to be not ideal.

[0052] 6. Adopting a more advantageous warning and evaluation strategy for the power quality warning module, which is applicable to the data storage server initially deployed in the power supply scenario of the multi-source distributed power grid. It can reduce the demand of the power quality warning module for the original power quality index samples, reduce the data adjustment time, improve the stability, controllability and continuity of the electric energy during use, ensure continuous power supply, and guarantee data security. Description of the Drawings

[0053] Figure 1 It is a schematic connection diagram of a high-definition and high-cost-performance data storage model in a multi-source grid-connected power supply scenario;

[0054] Figure 2 It is a schematic connection diagram of another high-definition and high-cost-performance data storage model in a multi-source grid-connected power supply scenario;

[0055] Figure 3 It is a circuit diagram of a special transformer for data storage;

[0056] Figure 4 It is a circuit diagram of a data storage security and energy-saving machine;

[0057] Figure 5 It is a schematic diagram of the negative feedback buck state;

[0058] Figure 6 It is a schematic diagram of the positive feedback boost state;

[0059] Figure 7 It is a schematic diagram of a soft switching circuit;

[0060] Figure 8 It is a processing flow chart of a new high-quality power supply device;

[0061] Figure 9Schematic diagram of power quality fluctuation;

[0062] Figure 10 Normal distribution diagram;

[0063] Figure 11 High-definition and high cost-effective data storage model framework diagram. Specific implementation manners

[0064] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some but not all of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0065] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0066] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0067] It should be noted that like reference numerals and letters denote like items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0068] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship when the product of the present invention is normally placed, or the orientation or positional relationship commonly understood by those skilled in the art. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, the terms "first", "second", etc. are only used for descriptive distinction and should not be construed as indicating or implying relative importance.

[0069] In the description of the present invention, it should also be noted that unless otherwise clearly specified and defined, the terms "set", "installed", "connected", and "coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0070] Embodiment 1

[0071] A high-definition and high-cost-performance data storage model in a multi-source grid-connected power supply scenario, which is applicable to power supply scenarios under the grid connection conditions of photovoltaic power, wind power, and distributed power grids, can optimize the power quality of data storage, including a new high-quality power supply optimization model. The new high-quality power supply optimization model includes a new high-quality power supply device. The input end of the new high-quality power supply device is connected to a multi-source distributed power grid, and the output end of the new high-quality power supply device is connected to a data storage server. A generator, an automatic transfer switch, a power quality optimization mechanism, and a controller are arranged in the new high-quality power supply device;

[0072] The controller can receive the control of the data storage server, is signal-connected to the automatic transfer switch, and controls the start and stop of the generator;

[0073] The generator can supply power to the data storage server via the automatic transfer switch;

[0074] The multi-source distributed power grid can supply power to the data storage server via the automatic transfer switch;

[0075] The power quality optimization mechanism can optimize the power quality of the data storage server.

[0076] The purpose of such a design is to provide a new high-quality power supply optimization model, which can provide safety protection for the use of the data storage server or data center from the perspective of power supply, reduce the increase in the PUE value caused by power supply factors, and thus achieve high-definition and high-cost-performance data storage.

[0077] It should be noted that the high-definition and high-cost-performance data storage model in this embodiment adopts the "1+N" mode. Here, "1" refers to the new high-quality power supply device, and "N" refers to various storage technologies for data storage. For example, the third-generation Internet Web3.0 decentralized storage technology is shown as follows Figure 11 as shown.

[0078] Embodiment 2

[0079] Such as Figure 1As shown in the figure, in this embodiment, a specific structure of a power quality optimization mechanism is provided. The power quality optimization mechanism is a data storage and security energy-saving machine, which is arranged inside a new high-quality power supply device. The data storage and security energy-saving machine is arranged between the sampling points of the automatic switching switch and the controller, and can optimize the power quality output to the data storage server.

[0080] The purpose of such a design is that in the hardware power quality optimization model, the data storage and security energy-saving machine can provide safety protection for power usage ends such as data storage servers and other external devices based on its internal circuit structure, and avoid the occurrence of large power fluctuations such as too high or too low voltage.

[0081] As Figures 4 to 7 shown, a specific composition of a data storage and security energy-saving machine is provided. The energy-saving and security part includes a sampling circuit, a PLC, a second AC contactor KM2, a third AC contactor KM3, a fourth AC contactor KM4, a fifth AC contactor KM5, a sixth AC contactor KM6, a seventh AC contactor KM7, three voltage output circuits, and three voltage control circuits;

[0082] The output ends of each of the voltage output circuits are respectively connected to the input end of the sampling circuit, and the output end of the sampling circuit is connected to the analog input end of the PLC;

[0083] The sampling circuit includes: a sampling processor and three sensors. Each of the sensors is connected to the voltage output circuit, and the sensors are used to collect voltage data of the voltage output circuit; one end of the sampling processor is respectively connected to the three sensors, and the other end of the sampling processor is connected to the analog input end of the PLC;

[0084] The signal output ends of the PLC are respectively connected to one end of the coil of the second AC contactor KM2, one end of the coil of the third AC contactor KM3, one end of the coil of the fourth AC contactor KM4, one end of the coil of the fifth AC contactor KM5, one end of the coil of the sixth AC contactor KM6, and one end of the coil of the seventh AC contactor KM7. The other ends of the coils of the second AC contactor KM2, the third AC contactor KM3, the fourth AC contactor KM4, the fifth AC contactor KM5, the sixth AC contactor KM6, and the seventh AC contactor KM7 are respectively connected to the neutral line of the power supply line;

[0085] One end of each of the first primary coils LA is connected to the output end of each phase power supply in the power supply line, and the other end of the first primary coil LA is the voltage output end;

[0086] Each of the voltage control circuits includes: a first secondary coil L1, a second secondary coil L2, and a third secondary coil L3. The winding methods of the first secondary coil L1, the second secondary coil L2, and the third secondary coil L3 are the same and they are connected to the first primary coil LA through an iron core at the same time. The voltage input line of the voltage control circuit is connected to one end of the normally open contact of the third AC contactor KM3, one end of the normally open contact of the fourth AC contactor KM4, one end of the normally open contact of the fifth AC contactor KM5, and one end of the normally open contact of the sixth AC contactor KM6. One end of the first secondary coil L1 is respectively connected to the other end of the normally open contact of the second AC contactor KM2 and the other end of the normally open contact of the third AC contactor KM3. The other end of the first secondary coil L1 is respectively connected to one end of the second secondary coil L2 and the other end of the normally open contact of the fourth AC contactor KM4. The other end of the second secondary coil L2 is respectively connected to one end of the third secondary coil L3 and the other end of the normally open contact of the fifth AC contactor KM5. The other end of the third secondary coil L3 is respectively connected to the other end of the normally open contact of the sixth AC contactor KM6 and the other end of the normally open contact of the seventh AC contactor KM7. One end of the normally open contact of the second AC contactor KM2 and one end of the normally open contact of the seventh AC contactor KM7 are connected to the neutral line of the power supply line.

[0087] At the same time, the energy-saving safety device further includes a first AC contactor KM1;

[0088] One end of the coil of the first AC contactor KM1 is connected to the signal output end of the PLC, and the other end of the coil of the first AC contactor KM1 is connected to the neutral line of the power supply line;

[0089] The three normally open contacts of the first AC contactor KM1 are respectively connected in parallel across both ends of each of the first primary coils LA.

[0090] Furthermore, the energy-saving safety device further includes three soft switching circuits for switching the power supply circuit;

[0091] Each of the soft switching circuits is respectively connected in parallel across both ends of each of the first primary coils LA.

[0092] The data storage safety energy-saving machine further includes an eighth AC contactor KM8;

[0093] One end of the coil of the eighth AC contactor KM8 is connected to the signal output end of the PLC, and the other end of the coil of the eighth AC contactor KM8 is connected to the neutral line of the power supply line;

[0094] The three normally open contacts of the eighth AC contactor KM8 are respectively connected in series with each of the soft switching circuits and then are respectively connected in parallel across both ends of each of the first primary coils LA.

[0095] Meanwhile, the data storage security energy-saving machine further includes a ninth AC contactor KM9, a tenth AC contactor KM10, and an eleventh AC contactor KM11; one end of the coil of the ninth AC contactor KM9, one end of the coil of the tenth AC contactor KM10, and one end of the coil of the eleventh AC contactor KM11 are connected to the signal output end of the PLC, and the other end of the coil of the ninth AC contactor KM9, the other end of the coil of the tenth AC contactor KM10, and the other end of the coil of the eleventh AC contactor KM11 are connected to the power supply line.

[0096] Each of the soft switching circuits includes: a second primary coil LB, a fourth secondary coil L4, and a fifth secondary coil L5, and the second primary coil LB, the fourth secondary coil L4, and the fifth secondary coil L5 are connected through an iron core;

[0097] One end of the second primary coil LB is connected to one end of the normally open contact of the eighth AC contactor KM8, and the other end of the second primary coil LB is connected to the other end of the first primary coil LA; the normally open contact of the ninth AC contactor KM9 is connected in parallel with the second primary coil LB; one end of the fourth secondary coil L4 is connected to one end of the normally open contact of the eighth AC contactor KM8, and the other end of the fourth secondary coil L4 is connected to the zero line through the normally closed contact of the tenth AC contactor KM10; one end of the normally open contact of the eleventh AC contactor KM11 is connected to one end of the fifth secondary coil L5, and the other end of the normally open contact of the eleventh AC contactor KM11 is connected to the other end of the fifth secondary coil L5.

[0098] Each of the soft switching circuits as Figure 7 shown further includes: a resistor R, one end of the resistor R is connected to one end of the normally open contact of the eleventh AC contactor KM11, and the other end of the resistor R is connected to one end of the fifth secondary coil L5.

[0099] The specific working principle is as follows: Three sensors in the sampling circuit respectively collect the voltages of each phase of the power supply line of the power supply, obtaining corresponding first data, second data, and third data. The sampling processor converts the first data, the second data, and the third data to obtain first analog quantity data. The PLC compares the first analog quantity data with a set value and controls the on / off states of the second AC contactor KM2, the third AC contactor KM3, the fourth AC contactor KM4, the fifth AC contactor KM5, the sixth AC contactor KM6, and the seventh AC contactor KM7 according to the comparison result.

[0100] When the sampling circuit detects that the voltage data of one or more phases of the power supply is higher than the set value, the PLC controls the seventh AC contactor KM7 to turn on, and at the same time controls any one of the third AC contactor KM3, the fourth AC contactor KM4, or the fifth AC contactor KM5 to close. At this time, the circuit state is a negative feedback step-down state, and the simplified connection relationship is as Figure 5 shown. The winding direction of the secondary coil is the same as that of the first primary coil LA. At this time, according to Lenz's law, it can be known that the voltage of the secondary coil increases, and the generated voltage affects the magnetic flux in the iron core, further affecting the first primary coil LA connected to it through the same iron core, and slowing down the rising speed of the voltage and / or current of the first primary coil LA or reducing the voltage of the first primary coil LA.

[0101] When the sampling circuit detects that the voltage data of one or more phases of the power supply is lower than the set value, the PLC controls the second AC contactor KM2 to turn on, and at the same time controls any one of the fourth AC contactor KM4, the fifth AC contactor KM5, or the sixth AC contactor KM6 to close. At this time, the circuit state is a positive feedback step-up state, and the simplified connection relationship is as Figure 6 shown. The winding direction of the secondary coil is opposite to that of the first primary coil LA. At this time, according to Lenz's law, it can be known that the voltage of the secondary coil increases, and the generated voltage affects the magnetic flux in the iron core, further affecting the first primary coil LA connected to it through the same iron core, and increasing the voltage and / or current of the first primary coil LA.

[0102] For the above data storage, security, and energy-saving machine, a fuse FU can be additionally added. One end of the fuse FU is connected to the COM port of the PLC, and the other end of the fuse FU is connected to the live wire of the power supply line.

[0103] Regarding the selection of the PLC, those skilled in the art can make a selection according to actual needs. Usually, the Mitsubishi FX series can meet the requirements.

[0104] Embodiment 3

[0105] As Figure 2As shown, in this embodiment, a specific structure of a power quality optimization mechanism is provided. The power quality optimization mechanism is a dedicated transformer for data storage and is disposed within a new high-quality power supply device. The dedicated transformer for data storage can optimize the power quality input from a multi-source distributed power grid to an automatic transfer switch.

[0106] The purpose of such a design is that in the hardware power quality optimization model, the dedicated transformer for data storage can, based on its internal circuit structure, use the multi-source distributed power grid supply network as the power input end, and filter and suppress the electrical pollution generated during use due to phase imbalance, insufficient power factor, frequent device startup, harmonic interference, overload, etc. within the system, thereby improving the power quality and increasing the power saving rate, providing a guarantee for the safe use of the data storage server.

[0107] As Figure 3 shown, a specific composition of a dedicated transformer for data storage is provided. The dedicated transformer for data storage includes a primary coil and a secondary coil. The secondary coil includes a step-down coil L1 and a reactance filtering coil L2. Both the step-down coil and the reactance filtering coil are connected to the power supply line of the multi-source distributed power grid, and the reactance filtering coil is connected between the step-down coil and the power supply line.

[0108] The secondary coil further includes a voltage regulating coil. The voltage regulating coil is connected in series with the reactance filtering coil and is connected between the output end of the step-down coil and the power supply line. The voltage regulating coil is not shown in the figure.

[0109] An adjustable contact K is provided between the output end of the step-down coil and the load. Through the adjustable contact K, the step-down coil is directly connected to the power supply line or indirectly connected to the power supply line through the reactance filtering coil.

[0110] The secondary coil further includes a closed-loop control coil. One end of the closed-loop control coil is connected to the output end of the step-down coil, and the other end of the closed-loop control coil is connected to the step-down coil.

[0111] At the same time, the secondary coil further includes a current sharing coil / current equalizing coil connected to the power supply line.

[0112] Optional contacts K1, K2, and K3 are provided between the current equalizing coil and the load. The optional contacts K1, K2, and K3 are respectively connected to three phase lines.

[0113] The reactance filtering coil is connected in a Z shape. The primary coil and the reactance filtering coil form three coil groups. Each coil group includes a primary coil and at least one reactance filtering coil. There are three magnetic cores, and the three magnetic cores are parallel to each other and distributed in a triangular shape. The primary coil and the reactance filtering coil of each coil group are respectively wound around adjacent two magnetic cores, and each magnetic core is sleeved with a primary coil and at least one reactance filtering coil, and the primary coil and the reactance filtering coil do not come from the same coil group.

[0114] It should be noted that in this embodiment, the structures of the dedicated transformer for data storage and the energy-saving and secure data storage machine are described separately. Due to the particularity of the circuit structure, different markings are inevitably used for the same type of component in the description. Since the structures of the dedicated transformer for data storage and the energy-saving and secure data storage machine are independent of each other in the description, those skilled in the art can understand their circuit structures.

[0115] It should be noted that in the above embodiment, a power management chip such as the TPS259XX series can be set in the controller.

[0116] In this embodiment, two relays are provided on the automatic transfer switch connected to the multi-source distributed power grid, that is, to control the connection of the multi-source distributed power grid. This control method is that the controller receives the control request sent by the disaster recovery switching module in the data storage server. When the power quality index of the multi-source distributed power grid does not meet the expectation, after the data storage module completes data disaster recovery, it actively disconnects the connection with the multi-source distributed power grid and quickly uses a disaster recovery power supply structure with more controllable and better power quality for power supply. After the power quality index of the multi-source distributed power grid is restored, the disaster recovery switching module sends a control request again. After the data storage module completes data disaster recovery, it actively disconnects the connection with the disaster recovery power supply structure and is powered by the multi-source distributed power grid again. Such an operation realizes a control of an active high-definition and high-cost-effective data storage model.

[0117] Embodiment 4

[0118] As Figure 8 shown, in this embodiment, a data security storage method in a multi-source distributed power grid power supply scenario is provided based on a high-definition and high-cost-effective data storage model in a multi-source grid-connected power supply scenario. For a new type of high-quality power supply device, the method includes the following steps:

[0119] S1. The new type of high-quality power supply device obtains the power provided by the multi-source distributed power grid, and the power quality optimization mechanism performs power optimization processing;

[0120] S2. The new type of high-quality power supply device supplies power to the data storage server;

[0121] S3. When the controller receives the data transmitted by the disaster recovery switching module, it disconnects the connection between the multi-source distributed power grid and the automatic transfer switch;

[0122] S4. The generator supplies power to the data storage module;

[0123] S5. When the controller receives the data transmitted by the disaster recovery switching module again, it disconnects the connection between the generator and the automatic transfer switch and turns on the connection between the multi-source distributed power grid and the automatic transfer switch;

[0124] S6. The multi-source distributed power grid supplies power to the data storage server.

[0125] The purpose of such a design is that the new high-quality power supply device forms a high-quality power supply based on the unique power quality optimization mechanism in the above embodiments. It supplies power to the data storage module, which can eliminate the blocking effect of the superposition of internal and external power pollution, and also has the protection effects of reducing noise, vibration, temperature rise and lightning strike hazards. Moreover, it can control overvoltage, low voltage, low power, etc.

[0126] For the controller part, it receives the control request sent by the disaster recovery switching module in the data storage server. When the power quality index of the multi-source distributed power grid does not meet the expectation, after the data storage module completes data disaster recovery, it actively disconnects from the multi-source distributed power grid and quickly uses the disaster recovery power supply structure with more controllable and better power quality index for power supply. After the power quality index of the multi-source distributed power grid is restored, the disaster recovery switching module sends the control request again. After the data storage module completes data disaster recovery, it actively disconnects from the disaster recovery power supply structure and is powered by the multi-source distributed power grid again.

[0127] The high-definition and high cost-effective data storage model based on the multi-source grid-connected power supply scenario in this embodiment provides a data security storage method in the multi-source distributed power grid power supply scenario. For the data storage server, it includes the following steps:

[0128] A1. The power quality acquisition module acquires the power quality indexes input by the multi-source distributed power grid and the power quality indexes output by the new high-quality power supply device to the data storage server within a set time series, and generates an evaluation data set for each power quality index.

[0129] A2. The power quality early warning module analyzes the power quality indexes input by the multi-source distributed power grid and the power quality indexes output by the new high-quality power supply device to the data storage server from the evaluation data sets of each power quality index generated by the power quality acquisition module, and transmits the results to the disaster recovery switching module.

[0130] A3. The disaster recovery switching module processes according to the results sent by the power quality early warning module.

[0131] In a specific usage scenario, step A1 further includes the following steps:

[0132] B1. Select the power quality indicators input from the multi-source distributed power grid. The voltage deviation, frequency deviation, harmonic content, three-phase unbalance parameter, and voltage fluctuation and flicker parameter are respectively marked as VD1, FD1, HD1, TI1, and VFF1. Select the power quality indicators output from the new high-quality power supply device to the data storage server. The voltage deviation, frequency deviation, harmonic content, three-phase unbalance parameter, and voltage fluctuation and flicker parameter are respectively marked as VD2, FD2, HD2, TI2, and VFF2;

[0133] B2. Establish an evaluation data set of power quality indicators with respect to date and time interval, which are respectively expressed as VD1[d][t], FD1[d][t], HD1[d][t], TI1[d][t], VFF1[d][t], VD2[d][t], FD2[d][t], HD2[d][t], TI2[d][t], and VFF2[d][t], where d represents the date and t represents the time interval point.

[0134] At the same time, step A2 also includes the following steps:

[0135] C1. Select three consecutive dates in the power quality indicator evaluation data set, and the same power quality indicator at the same time interval point, to obtain the mean value µ and standard deviation σ of the power quality indicator;

[0136] C2. Select the fourth date in the above power quality indicator evaluation data set, and judge through the mean value µ and standard deviation σ obtained from the three dates, configure the confidence level, and select the filtering coefficient α;

[0137] C3. Through a first-order low-pass IIR filter, select the filtering coefficient to update the mean value µ to obtain the updated mean value µ';

[0138] C4. Based on the updated mean value µ', establish the time mean value sequence U1 of the power quality indicators output from the multi-source distributed power grid and the time mean value sequence U2 of the power quality indicators output from the new high-quality power supply device to the data storage server;

[0139] C5. Select the time mean value sequence U1 and the current time sequence D1 to obtain the deviation ERR1, where the current time sequence D1 includes the current values of the voltage deviation, frequency deviation, harmonic content, three-phase unbalance parameter, and voltage fluctuation and flicker parameter of the power quality indicators input from the multi-source distributed power grid. Select the time mean value sequence U2 and the current time sequence D2 to obtain the deviation ERR2, where the current time sequence D2 includes the current values of the voltage deviation, frequency deviation, harmonic content, three-phase unbalance parameter, and voltage fluctuation and flicker parameter of the power quality indicators output from the new high-quality power supply device to the data storage server;

[0140] Judge the deviation ERR1 or deviation ERR2, and transmit the result to the disaster recovery switching module.

[0141] It should be noted that, as described in the background art, the current overall power supply development trend is to combine multi-source power generation such as wind power generation, photovoltaic power generation, and tidal power generation with power stations for grid-connected power supply. The addition of these power supply structures will have a certain impact on the power quality. Therefore, at some important nodes such as data storage centers, standby UPS power supplies or rectifier-inverter modules are set up to perform dual-output of mains power / UPS power. When the mains power is abnormal, it can protect the data storage server. However, most of these are passive protections and not intelligent switching based on power quality indicators. Moreover, with the grid-connected power supply of multi-source power generation methods, problems rarely occur in the power supply itself, but large fluctuations occur in some power quality indicators. This passive protection method cannot provide better safety protection.

[0142] Therefore, the above solution is provided in this embodiment. In the specific implementation, since the main realization is the active disaster recovery switching after the deterioration of power quality indicators, five indicators, namely voltage deviation, frequency deviation, harmonic content, three-phase unbalance parameter, and voltage fluctuation and flicker parameter, are selected to evaluate the power quality indicators. Of course, some parameters such as power outage time and power supply reliability are not used in the display of this embodiment.

[0143] Since in this embodiment, it is necessary to analyze the electric energy input from the multi-source distributed power grid and the electric energy output from the new high-quality power supply device to the data storage server. The former is used to judge whether it can be switched back to the multi-source distributed power grid power supply after disaster recovery switching, and the latter is used to judge when to perform disaster recovery switching and supply power through the disaster recovery power supply structure.

[0144] Based on the above content, establish an evaluation data set of power quality indicators with respect to date and time interval, which are respectively represented as VD1[d][t], FD1[d][t], HD1[d][t], TI1[d][t], VFF1[d][t], VD2[d][t], FD2[d][t], HD2[d][t], TI2[d][t] and VFF2[d][t]. Where d represents the date and t represents the time interval point. Taking voltage fluctuation and flicker (Voltage Fluctuation and Flicker) as an example, the following data table can be obtained:

[0145]

[0146] Among them, the horizontal rows represent 8 collection points within a day, with a time interval of 3 hours between each collection point, and the vertical columns represent the collection dates. It should be noted that the number of collection points can be adjusted according to actual needs. The more points are selected, the higher the overall accuracy. At the same time, since VFF usually includes three indicators: voltage fluctuation (ΔU), short-term flicker (Pst), and long-term flicker (Plt), and since short-term flicker (Pst) is positively correlated with voltage fluctuation (ΔU), and long-term flicker (Plt) is based on the statistical value of short-term flicker (Pst), voltage fluctuation (ΔU) is selected for display in this embodiment, as Figure 9 shown. The collected data of a data port in a certain area is selected. This data port is located in a scenario where wind power generation and photovoltaic power generation are connected to the grid for power supply. In this scenario, although there are certain fluctuations in wind power generation and photovoltaic power generation, these fluctuations can be divided into transient fluctuations and normal fluctuations. Transient fluctuations have the characteristics of occasionality, such as short-term changes in wind power and short-term changes in sunlight. These transient fluctuations can be optimized based on the power structure of the power supply network after grid connection, or can be optimized through the high-quality power supply hardware structure part of the technical solution provided in this application. When optimization is not possible or the optimization situation is lower than expected, disaster tolerance switching can be carried out. For normal fluctuations, such as the impact of connection to the grid at sunrise and sunset, the impact of other large enterprises and large equipment startups, etc., the main purpose of this method is to find these continuously normal fluctuations within a short period of time. Here, a short period of time means that it will not change within a cycle, such as several days, several weeks, or even several months. This kind of fluctuation may be caused by the startup and shutdown of other large equipment or defects in the power grid itself. Usually, this kind of fluctuation has little impact on general equipment, but has a greater impact on the safe storage of data storage servers, such as significantly increasing the PUE value. Therefore, active disaster tolerance switching needs to be carried out for this kind of fluctuation, such as first supplying power through a UPS, then switching to a controllable generator for power supply, and then switching back to multi-source distributed grid power supply after this continuously normal fluctuation within a short period of time has passed.

[0147] The power quality acquisition module acquires the power quality indicators input via the multi-source distributed grid. According to the requirements of the GB / T12326-2008 standard, the normal value range of voltage fluctuation (ΔU) should be less than or equal to 2%, and temporarily allowed to be less than or equal to 3% in the short term.

[0148] From Figure 9It can be obtained that within three consecutive days, there are short-term exceedances of the standard requirements at 7 am and 7 pm, which are exactly the times when photovoltaic power generation is connected and disconnected. At the same time, there are large fluctuations in single-day values at 8 am, 10 am, and 4 pm. After manual verification, there are wind force changes at 8 am and 4 pm on a certain day within three consecutive days, causing large fluctuations at a certain point. Therefore, the obvious abnormal points (photovoltaic power connection and disconnection) and accidental abnormal points (wind force mutation) in the above values are deleted, and the mean value of each collection point of voltage fluctuation (ΔU) within three days is obtained. And the standard deviation , and form the mean value array µ[i] and standard deviation array σ[i] with respect to time, where i = 1, 2, 3... n, and x is the sample value;

[0149] Then as Figure 10 shown, the value of the fourth consecutive day is predicted through the 3σ criterion, that is, configure the interval confidence level, where is the sample value of the fourth day.

[0150] The following table is obtained:

[0151]

[0152] When performing subsequent processing, the FIR low-pass filtering method can be selected for processing, or the IIR filtering method can be selected for processing. The difference lies in the number of input samples. In some newly deployed high-definition and high-cost-performance data storage model scenarios, the FIR low-pass filtering method requires a large amount of collected data and has a slow convergence speed, while the IIR filtering method is jointly determined by the current input value and historical values. After manually removing the obvious abnormal points and accidental abnormal point annotations, the data adjustment time can be reduced.

[0153] Therefore, in this embodiment, the IIR filtering method is used for processing, and the filtering coefficient is obtained by taking 1 / 2 of the interval probability, and the mean value array µ[i] is updated to obtain the updated mean value array µ’[i], and there is the following formula:

[0154]

[0155] Thus, the mean value array µ VFF1 ’[i] of VFF1 can be indirectly obtained, and by analogy, µ FD1 ’[i], µ HD1 ’[i], µ VD1 ’[i], and µ TI1 ’[i] are obtained, and a time mean sequence U1 of the power quality index of the multi-source distributed power grid output is established, where U1 = , where i is the sampling time point, and at the same time, u[i] = (µ VFF1’[i], μ FD1 ’[i], μ VD1 ’[i], μ HD1 ’[i], μ TI1 ’[i]).

[0156] At the same time, select the high-dimensional time series of the current data as D1, where D1 = , where i is the sampling time point, and at the same time there is p[i] = (p VFF1 [i], p FD1 [i], p VD1 [i], p HD1 [i], p TI1 [i]), where p VFF1 [i], p FD1 [i], p VD1 [i], p HD1 [i], p TI1 [i] respectively represent the current values of five selected power quality indicators.

[0157] Then compare D1 with U1 to obtain the deviation ERR1 of the five power quality indicators, and ERR1 = , where i is the sampling time point.

[0158] The power quality warning module compares the values in ERR1. When it meets the set threshold, such as less than 2 abnormal indicators appear at consecutive multiple acquisition time points, it can be judged that the power quality indicators are restored, and the power supply can be switched back from the disaster recovery power supply agency to the multi-source distributed power grid supply.

[0159] Similarly, for the power quality indicators output from the new high-quality power supply device to the data storage server, when the data in the obtained ERR2 does not meet the set threshold, such as more than 2 abnormal indicators appear at consecutive multiple acquisition time points, it indicates that the power quality indicators input by the multi-source distributed power grid deteriorate, and there is a possibility of exceeding the processing capacity of the data storage dedicated transformer and the data storage safety and energy-saving machine. At this time, through the disaster recovery switching module, the disaster recovery switching module will issue a data disaster recovery backup requirement to the data storage module according to the warning issued by the power quality warning module; the disaster recovery switching module will give an external alarm according to the warning issued by the power quality warning module; the disaster recovery switching module will obtain the result transmitted by the power quality warning module to control the controller in the new high-quality power supply device, turn on / off the disaster recovery power supply structure, and disconnect / turn on the multi-source distributed power grid. It should be noted that this switching method takes less than 10 ms and will not cause the data storage server to flash.

[0160] The purpose of such a design is to change the existing passive switching method and perform a combined active and passive switching for the power supply in the multi-source distributed power grid scenario of the area where it is located. In the above example, on the one hand, due to the power quality index fluctuations at fixed times, a disaster recovery switching strategy can be adopted during this period to ensure the normal power supply of the data storage server. On the other hand, the accidental abnormal points in the entire collection points are manually checked and deleted to reduce the impact of accidental abnormal points on the collected data. On the third hand, the power supply situation is predicted and evaluated using five-dimensional power quality indicators, which can greatly improve the accuracy of prediction and evaluation. On the fourth hand, in the form of generating a warning detection table, the power quality index fluctuations occurring at the same time point on consecutive dates are marked as outlier abnormal points. After manual checking, disaster recovery switching can be carried out in advance at this time point to reduce the impact of power quality index fluctuations on the data storage server, thereby reducing the PUE value and achieving true energy conservation and emission reduction of the data storage server.

[0161] Example 5

[0162] Based on the above high-definition and high-cost-performance data storage model in the multi-source grid-connected power supply scenario, five categories of data storage servers or data centers or data ports are provided in terms of scale, use, configuration, and site.

[0163] The first category: Miniature data port

[0164] The number of data storage cabinets is between 1 and 9 (including new high-quality power supplies, air conditioners, lighting, etc.), the data storage capacity is 3456TB to 31104TB, the required site space is 5 square meters to 90 square meters, and the power supply power is 6Kw to 54Kw.

[0165] The second category: Small data port

[0166] The number of data storage cabinets is between 1 and 50 groups (10 cabinets in a group, including new high-quality power supplies, air conditioners, lighting, etc.), the data storage capacity is 3456TB to 172800TB, the required site space is 10 square meters to 500 square meters, and the power supply power is 6Kw to 300Kw.

[0167] The third category: Medium-sized data port

[0168] The number of data storage cabinets is between 51 and 100 groups (including new high-quality power supplies, air conditioners, lighting, etc.), the data storage capacity is 176256TB to 345600TB, the required site space is 510 square meters to 1000 square meters, and the power supply power is 306Kw to 600Kw.

[0169] The fourth category: Large data port

[0170] The data storage cabinets are between 101 and 1000 groups (including new high-quality power supplies, air conditioners, lighting, etc.), the data storage capacity is 349056 TB to 3456 PB, the required site space is 1010 square meters to 10000 square meters, and the power supply power is between 606 Kw and 6000 Kw.

[0171] Category 5: Ultra-large data ports

[0172] The data storage cabinets are between 1001 and 5000 groups (including new high-quality power supplies, air conditioners, lighting, etc.), the data storage capacity is 3453.4 PB to 17.25 EB, the required site space is 10010 square meters to 50000 square meters, and the power supply power is between 6006 Kw and 30000 Kw.

[0173] Among these five data port models of different scales constructed by data storage servers, based on the hardware and software in the above embodiments for optimizing power supply quality, the PUE value is only 1.10 to 1.15, far lower than the current situation where the PUE value of data centers and computing power centers is ≥ 1.25. Compared with centralized storage, it can further save power consumption and reduce carbon emissions.

[0174] At the same time, it should be noted that in this embodiment, the high cost performance is achieved by reducing the PUE value, while the high definition is achieved by providing a stable new high-quality power supply device and disaster recovery switching logic, so that the transmitted and stored text and images have more stable and higher clarity.

[0175] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A high-definition and cost-effective data storage model for multi-source grid-connected power supply scenarios, applicable to power supply scenarios under the grid connection conditions of photovoltaic power, wind power, and distributed power grids, capable of optimizing the power quality of data storage, characterized in that, It includes a new high-quality power supply optimization model and a data storage server optimization model. The new high-quality power supply optimization model includes a new high-quality power supply device. The input end of the new high-quality power supply device is connected to a multi-source distributed power grid, and the output end of the new high-quality power supply device is connected to the data storage server. A generator, an automatic transfer switch, a power quality optimization mechanism, and a controller are arranged in the new high-quality power supply device; The controller can receive the control of the data storage server, is signal-connected to the automatic transfer switch, and controls the start and stop of the generator; The generator can supply power to the data storage server via the automatic transfer switch; The multi-source distributed power grid can supply power to the data storage server via the automatic transfer switch; The power quality optimization mechanism can optimize the power quality for the data storage server; The data storage server optimization model is deployed at the data storage server end, and is internally provided with a power quality acquisition module, a power quality early warning module, a disaster recovery switching module, an energy consumption grid module, and a data storage module; The power quality acquisition module is used to acquire the power quality indexes at the output end of the multi-source distributed power grid and the output end of the new high-quality power supply device. The power quality indexes at the output end of the multi-source distributed power grid are used to judge whether it can be switched back to the multi-source distributed power grid for power supply after disaster recovery switching, and the power quality indexes at the output end of the new high-quality power supply device are used to judge when to perform disaster recovery switching and supply power through the disaster recovery power supply structure; The power quality early warning module is used to analyze and give early warning to the power quality indexes acquired by the power quality acquisition module, and provide a judgment on the switching timing of the disaster recovery switching module; The disaster recovery switching module is used to feedback the result obtained by the new high-quality power supply device processing the power quality early warning module. For the result that meets the threshold requirements set by the power quality early warning module, switch back to the multi-source distributed power grid for power supply. For the result that does not meet the threshold requirements set by the power quality early warning module, switch to be powered by the new high-quality power supply device; The energy consumption grid module is used to display the energy consumption under the current power quality scenario.

2. The high-definition and high cost-effective data storage model in the multi-source grid-connected power supply scenario according to claim 1, wherein The power quality optimization mechanism is a data storage safety and energy-saving machine, and is arranged in the new high-quality power supply device. The data storage safety and energy-saving machine is arranged between the sampling points of the automatic transfer switch and the controller, and can optimize the new power quality output to the data storage server.

3. The high-definition and high cost-effective data storage model in the multi-source grid-connected power supply scenario according to claim 1, wherein The power quality optimization mechanism is a special transformer for data storage, and is arranged in the new high-quality power supply device. The special transformer for data storage can optimize the power quality input from the multi-source distributed power grid to the automatic transfer switch.

4. A high-definition and cost-effective data storage method in a multi-source distributed power grid power supply scenario, which is implemented based on the high-definition and cost-effective data storage model in the multi-source grid-connected power supply scenario described in claim 1, characterized in that, For the new high-quality power supply device end, it includes the following steps: S1. The new high-quality power supply device obtains the power provided by the multi-source distributed power grid, and the new high-quality power supply device performs power optimization processing; S2. The new high-quality power supply device supplies power to the data storage server.

5. The high-definition and high cost-performance data storage method in the multi-source distributed power grid power supply scenario according to claim 4, characterized in that It also includes the following steps: S3. When the controller receives the data transmitted by the disaster recovery switching module, disconnect the connection between the multi-source distributed power grid and the automatic transfer switch; S4. The generator supplies power to the data storage module; S5. After the controller receives the data transmitted by the disaster recovery switching module again, disconnect the generator from the automatic transfer switch and connect the multi-source distributed power grid to the automatic transfer switch; S6. The multi-source distributed power grid supplies power to the data storage server.

6. A high-definition and cost-effective data storage method in a multi-source distributed power grid power supply scenario, which is implemented based on the high-definition and cost-effective data storage model in the multi-source grid-connected power supply scenario described in claim 1, characterized in that, For the data storage server side, the following steps are included: A1. The power quality acquisition module acquires the power quality indicators at the output end of the multi-source distributed power grid and the power quality indicators output from the new high-quality power supply device to the data storage server within the set time series, and generates an evaluation data set for each power quality indicator; A2. The power quality early warning module analyzes the power quality indicators at the output end of the multi-source distributed power grid and the power quality indicators output from the new high-quality power supply device to the data storage server from the evaluation data sets of each power quality indicator generated by the power quality acquisition module, and transmits the results to the disaster recovery switching module; A3. The disaster recovery switching module processes according to the results sent by the power quality early warning module.

7. The high-definition and high cost-performance data storage method in the multi-source distributed power grid power supply scenario according to claim 6, wherein For A1, the following steps are further included: B1. Select the power quality indicators input from the multi-source distributed power grid. The voltage deviation, frequency deviation, harmonic content, three-phase unbalance parameter, and voltage fluctuation and flicker parameter are respectively marked as VD1, FD1, HD1, TI1, and VFF1. Select the power quality indicators output from the new high-quality power supply device to the data storage server. The voltage deviation, frequency deviation, harmonic content, three-phase unbalance parameter, and voltage fluctuation and flicker parameter are respectively marked as VD2, FD2, HD2, TI2, and VFF2; B2. Establish evaluation data sets of power quality indicators with respect to date and time interval, which are respectively expressed as VD1[d][t], FD1[d][t], HD1[d][t], TI1[d][t], VFF1[d][t], VD2[d][t], FD2[d][t], HD2[d][t], TI2[d][t], and VFF2[d][t], where d represents the date and t represents the time interval point.

8. The high-definition and high cost-effective data storage method in the multi-source distributed power grid power supply scenario according to claim 7, characterized in that For A2, the following steps are further included: C1. Select three consecutive dates in the power quality indicator evaluation data set, and the same power quality indicator at the same time interval point to obtain the mean value µ and standard deviation σ of the power quality indicator; C2. Select the fourth date in the above power quality indicator evaluation data set, and judge through the mean value µ and standard deviation σ obtained from the three dates, configure the confidence level, and select the filtering coefficient α; C3. Through a first-order low-pass IIR filter, select the filtering coefficient to update the mean value µ to obtain the updated mean value µ'; C4. Based on the updated mean value µ', establish the time mean value sequence U1 of the power quality indicators at the output end of the multi-source distributed power grid and the time mean value sequence U2 of the power quality indicators output from the new high-quality power supply device to the data storage server; C5. Select the time mean sequence U1 and the current time sequence D1 to obtain the deviation ERR1, where the current time sequence D1 includes the current values of power quality indexes such as voltage deviation, frequency deviation, harmonic content, three-phase unbalance parameters, and voltage fluctuation and flicker parameters input by the multi-source distributed power grid. Select the time mean sequence U2 and the current time sequence D2 to obtain the deviation ERR2, where the current time sequence D2 includes the current values of power quality indexes such as voltage deviation, frequency deviation, harmonic content, three-phase unbalance parameters, and voltage fluctuation and flicker parameters output by the new high-quality power supply device to the data storage server; C6. Judge the deviation ERR1 or the deviation ERR2, and transfer the result to the disaster tolerance switching module.

9. The high-definition and high cost-effective data storage method in the multi-source distributed power grid power supply scenario according to claim 8, characterized in that, In A3, the following steps are included: L1. The disaster tolerance switching module issues a data disaster tolerance backup requirement to the data storage module according to the warning issued by the power quality warning module; L2. The disaster tolerance switching module issues an alarm externally according to the warning issued by the power quality warning module; L3. The disaster tolerance switching module obtains the result transmitted by the power quality warning module to control the controller in the new high-quality power supply device, turn on / off the generator, and disconnect / turn on the multi-source distributed power grid.

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