CN112130492A - Electric energy efficiency management system and control method suitable for big data center - Google Patents
Electric energy efficiency management system and control method suitable for big data center Download PDFInfo
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- CN112130492A CN112130492A CN202010982542.9A CN202010982542A CN112130492A CN 112130492 A CN112130492 A CN 112130492A CN 202010982542 A CN202010982542 A CN 202010982542A CN 112130492 A CN112130492 A CN 112130492A
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
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Abstract
The invention discloses an electric energy efficiency management system and a control method suitable for a big data center, wherein the electric energy efficiency management system comprises a temperature control pipeline, a temperature measurement system and a temperature control system; the temperature control pipeline is based on computational fluid mechanics, a finite element method and existing building design parameters; the temperature measurement system focuses on system optimization configuration and relates to quantitative indexes of global temperature conductivity coefficient, local temperature conductivity coefficient, temperature control equipment sensitivity table and HVAC temperature element adjusting capacity; the control mode of the temperature control system is hierarchical and sectional control and is combined with predictive control and optimal control. According to the invention, the heat energy generated by the large data center is effectively diffused by the way of arranging the temperature control pipeline system, the energy consumption of the HVAC system is greatly reduced, the purpose of green energy conservation is achieved, the singleness of the temperature control way of the traditional HVAC system of the large data center is overcome, the temperature control efficiency is improved, and the accurate control of the flow speed of the cooling medium and the optimal control of uniform temperature distribution are effectively realized.
Description
Technical Field
The invention relates to the field of electric energy efficiency management, in particular to an electric energy efficiency management system and a control method suitable for a large data center.
Background
A large data center, that is, an infrastructure for providing computation, storage, communication, power, and heat dissipation services for a large amount of data, includes various IT devices (servers, switches, computers, workstations, etc.) and their supporting facilities (power supply, lighting, air conditioning, etc.). With the global high popularity of big data, internet + and cloud computing industries, the demand of big data centers is increasing to improve the experience and reliability of data center service users. Due to the fact that a large amount of data are calculated at a high speed, the energy consumption level of a large data center is high, and with the development of the calculation capacity of IT equipment, the heat dissipation density of the large data center is also increased remarkably (the heat productivity of a single cabinet exceeds 10kW, and the peak value of the heat density of a CPU is close to 106W/m), so that the energy consumption of a cooling system is increased; in addition, with the increase of energy-saving consciousness and technical level of the data center, the reduction of the occupation ratio of non-IT infrastructures becomes a research focus at home and abroad. Therefore, in terms of further improving the performance of the data center or reducing the energy consumption of the data center, the heat dissipation research of the data center is one of the key problems to be solved urgently, and the method has important significance and a wide application prospect.
From the change of the whole cooling technology, the cooling mode develops from air cooling to liquid cooling gradually, develops from a machine room level to a cabinet level, and the cold source selection develops from mechanical refrigeration to a natural cold source gradually. Regarding the cooling technology research results and engineering practical experience in the aspect of controlling the temperature control pipeline system, the following schemes are adopted: 1) a water-cooling multi-connected heat pipe system is systematized; 2) the distributed heat dissipation scheme is characterized in that a two-stage heat pipe loop of an internal cooling frame and a serial multi-cold source combined circulating water path are designed, and the load distribution can be dynamically adjusted according to the change of server load and outdoor conditions; 3) cooling the data center by adopting high-temperature hot water, and then recycling waste heat; 4) the absorption refrigeration system is used for recovering waste heat of data center cooling, and heat of a condenser of the cooling system is used as a driving heat source of the lithium bromide absorption system; 5) the heat pipe natural cooling technology is one natural cooling technology for transferring outdoor cold via heat pipe. The method provides a plurality of beneficial methods for a big data cooling technology, but the design scheme of the whole big data cooling system, the design scheme of a big data center partition hierarchical control system, the design scheme of a big data center heat conduction sensitivity coefficient generation method based on historical temperature data and the like are not deeply researched.
The existing large data center refrigeration center mainly faces the problems of solving the global optimization problem of the heat dissipation process of the data center, solving the heat dissipation problem of the high-density data center, solving the safety problem of the energy-saving technology and solving the flexible and mobile systematic arrangement and management technical problem. The main reason why the resource utilization rate of the data center is low most of the time due to the data center resource configuration strategy driven by service performance and reliability is that the flexible energy efficiency control mechanism is mainly lacked due to the large energy consumption
Disclosure of Invention
In order to solve the above mentioned drawbacks in the background art, the present invention provides an electric energy efficiency management system and a control method for a big data center, which effectively dissipate heat energy generated by the big data center by laying out a temperature control pipeline system, reduce energy consumption of an HVAC system, and achieve the purpose of green and energy saving.
The purpose of the invention can be realized by the following technical scheme:
a power energy efficiency management system suitable for a big data center comprises a temperature control pipeline, a temperature measurement system and a temperature control system;
the temperature control pipeline carries out three-dimensional modeling on the equipment based on computational fluid mechanics, a finite element method and the existing building design parameters;
the temperature measurement system focuses on system optimization configuration and relates to quantitative indexes of global temperature conductivity coefficient, local temperature conductivity coefficient, temperature control equipment sensitivity table and HVAC temperature element adjusting capacity;
the control mode of the temperature control system is hierarchical and sectional control and is combined with predictive control and optimal control.
Further preferably, the temperature control pipeline carries out three-dimensional modeling on the equipment based on computational fluid mechanics, a finite element method and existing building design parameters, analyzes the temperature control effect of the equipment under different heat load distribution conditions, and obtains an optimal temperature control pipeline design scheme through comparison.
Further preferably, the temperature measurement system is composed of a temperature sensor, a temperature control device, a local controller, a communication network and the like, the temperature measurement system accurately simulates the power required by cooling a certain area by utilizing a temperature conduction coefficient and a sensitivity table of the temperature control device, and real-time power distribution optimization is carried out by combining with a quantitative index of HVAC temperature regulation capacity, so that the optimal control of uniform temperature distribution is realized.
Further preferably, the temperature conduction coefficient comprises a global heat transfer coefficient and a local heat transfer coefficient, and according to historical data of the big data center, the temperature conduction coefficient carries out data cleaning, data feature extraction and new feature generation on the historical data to finally form the global heat transfer coefficient and the local heat transfer coefficient.
Further preferably, the sensitivity table of the temperature control device is combined with the global heat transfer coefficient and the local heat transfer coefficient, and the quantized index of the adjustment capability of the HVDC temperature control device, to form a sensitivity table of the temperature control unit system, wherein the sensitivity table includes the temperature control sensitivity of the temperature control unit system itself, and 1-order and 2-order temperature control sensitivity tables of adjacent devices.
Preferably, the HVAC temperature element adjusting capacity quantitative index gives a model of electric energy and refrigerating capacity according to design parameters and physical mechanisms of electric energy and refrigerating capacity of an HVAC system, model parameter identification is carried out based on actual operation historical data of HVDC, and finally the HVAC temperature element adjusting capacity quantitative index is formed.
Further preferably, the temperature control system implements hierarchical, partitioned and hierarchical control, and implements predictive control and optimal control according to different regions and importance;
the predictive control is realized by applying an intelligent control algorithm and state operation data according to a temperature control sensitivity table and the temperature design requirements of a big data center building and the optimal performance requirements of a big data operation platform such as a CPU (central processing unit), and is mainly used for local single load elements;
the optimization control is divided into global and local conditions, the temperature control sensitivity table and the temperature requirement of the large data center building under the condition of optimal temperature control distribution, the temperature requirement of the server performance, the real-time operation condition of the server and the like, and the local and global optimal control is realized by virtue of a temperature control system platform.
A power energy efficiency control method suitable for a big data center specifically comprises the following steps:
s1, three-dimensional modeling is carried out on the big data center and the IT equipment thereof based on the computational fluid mechanics and finite element method,
s2, analyzing the temperature propagation speed to form a region temperature conductivity coefficient and a global temperature conductivity coefficient;
s3, forming a temperature control equipment sensitivity table based on historical data of temperature distribution of the big data center and configuration data of temperature control elements corresponding to HVAC and the like by means of a deep learning mining technology;
s4, obtaining an optimal control scheme with uniform regional temperature distribution by combining quantization indexes based on local temperature conduction coefficients, a temperature control equipment sensitivity table and HVAC temperature regulation capacity; obtaining an optimal control scheme with uniform global temperature distribution by combining quantization indexes based on the global temperature conduction coefficient, the temperature control equipment sensitivity table and the HVAC temperature regulation capacity;
s5, obtaining a single load element to execute a prediction type local control scheme;
and S6, building a distributed control system by combining the optimization control scheme and the prediction control scheme, and realizing a hierarchical and partition control system such as centralized control, regional centralized control and local control.
The invention has the beneficial effects that:
the energy efficiency management system enables heat energy generated by the large data center to be effectively diffused in a mode of arranging the temperature control pipeline system, and finally the temperature is controlled to be about 20 ℃. Compared with the traditional large data center temperature control pipeline system, the energy consumption of the HVAC system is greatly reduced, and the purpose of green and energy saving is achieved.
The invention adopts a systematic design method, takes multiple considerations into consideration, effectively integrates, and overcomes the blindness and the dispersity of the traditional large data center building process.
According to the invention, the large data center and the IT equipment thereof are subjected to three-dimensional modeling by a computational fluid mechanics and finite element method, so that the scientific arrangement of the cooling aisle is realized, the temperature control effect is improved, and the investment waste is avoided.
Different from the singleness of the temperature control mode of the traditional large data center HVAC system, the invention provides three control modes (hierarchical and sectional control, prediction type local control and uniform and optimal control of regional temperature distribution), improves the temperature control efficiency and increases the flexibility of distribution control.
The invention effectively realizes the accurate control of the flow rate of the cooling medium and the optimal control of uniform temperature distribution by combining the temperature conductivity coefficient, the sensitivity of the temperature control equipment and the quantitative index of the HVAC temperature adjusting capacity.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a flow chart of the design of a temperature controlled pipeline based on fluid mechanics according to the present invention;
FIG. 2 is a schematic diagram of a heat dissipation system of the present invention;
FIG. 3 is a schematic view of a liquid cooling system of a big data cabinet of the present invention
FIG. 4 is a schematic view of the temperature control system optimization process of the present invention;
FIG. 5 is a schematic diagram of the overall structure of the temperature control system of the present invention;
FIG. 6 is a schematic diagram of the present invention for in-place control using a sliding mode algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "opening," "upper," "lower," "thickness," "top," "middle," "length," "inner," "peripheral," and the like are used in an orientation or positional relationship that is merely for convenience in describing and simplifying the description, and do not indicate or imply that the referenced component or element must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be considered as limiting the present invention.
As shown in fig. 1 to 5, an electric energy efficiency management system suitable for a large data center includes a temperature control pipeline, a temperature measurement system, and a temperature control system;
the temperature control pipeline carries out three-dimensional modeling on the equipment based on computational fluid mechanics, a finite element method and the existing building design parameters;
the temperature measurement system focuses on system optimization configuration and relates to quantitative indexes of global temperature conductivity coefficient, local temperature conductivity coefficient, temperature control equipment sensitivity table and HVAC temperature element adjusting capacity;
the control mode of the temperature control system is hierarchical and sectional control and is combined with predictive control and optimal control.
The temperature control pipeline carries out three-dimensional modeling on the equipment based on computational fluid mechanics, a finite element method and the existing building design parameters, analyzes the temperature control effect of the equipment under different heat load distribution conditions, and obtains an optimal temperature control pipeline design scheme through comparison.
The temperature measurement system is composed of a temperature sensor, temperature control equipment, a local controller, a communication network and the like, accurately simulates the power required by cooling a certain area by utilizing a temperature conductivity coefficient and a sensitivity meter of the temperature control equipment, and performs real-time power distribution optimization by combining with an HVAC temperature regulation capacity quantization index to realize the optimal control of uniform temperature distribution.
The temperature conduction coefficient comprises a global heat transfer coefficient and a local heat transfer coefficient, and according to historical data of the big data center, the temperature conduction coefficient carries out data cleaning, data feature extraction and new feature generation on the historical data to finally form the global heat transfer coefficient and the local heat transfer coefficient.
Wherein the temperature conductivity coefficient can be expressed by a one-dimensional heat conduction equation of temperature versus space and time:
where D represents the distance between the temperature sensor and the heat flow Q, x represents the distance between the temperature sensor and the heat flow Q (between 0 and D), c is a constant, k represents the heat transfer coefficient which is a function of the temperature T, and can be set
The sensitivity meter of the temperature control equipment combines the global heat transfer coefficient and the local heat transfer coefficient, and the adjustment capacity quantization index of the HVDC temperature control equipment, so as to form the sensitivity meter of the temperature control unit system, wherein the sensitivity meter comprises the temperature control sensitivity of the temperature control unit system, 1-order and 2-order temperature control sensitivity meters of adjacent equipment and the like.
The sensitivity equation satisfying the control variable U can be obtained by deriving the parameters a, B, C by a heat conduction equation:
after the sensitivity equation is obtained, the parameter identification of the parameters A, B and C can be converted into the vector solving
And (3) a nonlinear optimization problem of achieving a minimum value in the objective function F.
The target F can be represented by the following formula, wherein T1、T2Representing the above two expressions, n represents the total number of measurements:
the iterative process can be represented by the following formula, where k represents the iterative process, r represents the sub-relaxation factor, and M is an information matrix:
and the quantitative index of the adjusting capacity of the HVAC temperature element provides a model of the electric energy and the refrigerating capacity according to the design parameters and the physical mechanism of the electric energy and the refrigerating capacity of the HVAC system, and the model parameter identification is carried out based on the actual operation historical data of the HVDC, so that the quantitative index of the adjusting capacity of the HVAC temperature element is finally formed.
Specifically, the method comprises the steps of setting a volume V of a large data center, a heat conductivity coefficient K, an air specific heat capacity C and an air density rho, and determining an adjustment range of indoor temperature; changing the indoor temperature to zero at the optimal temperature, and determining an HVAC power value for maintaining the current indoor temperature; evaluating a range of adjustments that can be made to the HVAC system power; the time t during which the optimum power can be maintained is evaluated.
The temperature control system implements hierarchical, subarea and hierarchical control, and implements predictive control and optimal control according to different areas and importance;
the predictive control is realized by applying an intelligent control algorithm and state operation data according to a temperature control sensitivity table and the temperature design requirements of a big data center building and the optimal performance requirements of a big data operation platform such as a CPU (central processing unit), and is mainly used for local single load elements;
the optimization control is divided into global and local conditions, the temperature control sensitivity table and the temperature requirement of the large data center building under the condition of optimal temperature control distribution, the temperature requirement of the server performance, the real-time operation condition of the server and the like, and the local and global optimal control is realized by virtue of a temperature control system platform.
A power energy efficiency control method suitable for a big data center specifically comprises the following steps:
s1, three-dimensional modeling is carried out on the big data center and the IT equipment thereof based on the computational fluid mechanics and finite element method,
s2, analyzing the temperature propagation speed to form a region temperature conductivity coefficient and a global temperature conductivity coefficient;
s3, forming a temperature control equipment sensitivity table based on historical data of temperature distribution of the big data center and configuration data of temperature control elements corresponding to HVAC and the like by means of a deep learning mining technology;
s4, obtaining an optimal control scheme with uniform regional temperature distribution by combining quantization indexes based on local temperature conduction coefficients, a temperature control equipment sensitivity table and HVAC temperature regulation capacity; obtaining an optimal control scheme with uniform global temperature distribution by combining quantization indexes based on the global temperature conduction coefficient, the temperature control equipment sensitivity table and the HVAC temperature regulation capacity;
s5, obtaining a single load element to execute a prediction type local control scheme;
and S6, building a distributed control system by combining the optimization control scheme and the prediction control scheme, and realizing a hierarchical and partition control system such as centralized control, regional centralized control and local control.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (8)
1. The electric energy efficiency management system suitable for the big data center is characterized by comprising a temperature control pipeline, a temperature measurement system and a temperature control system;
the temperature control pipeline carries out three-dimensional modeling on the equipment based on computational fluid mechanics, a finite element method and the existing building design parameters;
the temperature measurement system emphasizes system optimization configuration and relates to quantitative indexes of global temperature conductivity, local temperature conductivity, a temperature control equipment sensitivity table and HVAC temperature element adjusting capacity;
the control mode of the temperature control system is hierarchical and sectional control and is combined with predictive control and optimal control.
2. The electric energy efficiency management system suitable for the big data center according to claim 1, wherein the temperature control pipeline performs three-dimensional modeling on equipment based on computational fluid dynamics, a finite element method and existing building design parameters, analyzes temperature control effects of the equipment under different heat load distribution conditions, and obtains an optimal temperature control pipeline design scheme through comparison.
3. The electric power energy efficiency management system suitable for the big data center according to claim 1, wherein the temperature measurement system is composed of a temperature sensor, a temperature control device, a local controller, a communication network and the like, the temperature measurement system accurately simulates power required by cooling a certain area by using a temperature conductivity coefficient and a sensitivity table of the temperature control device, and performs real-time power distribution optimization by combining with a quantitative index of HVAC temperature regulation capability, so as to realize optimal control of uniform temperature distribution.
4. The electric energy efficiency management system suitable for the big data center according to claim 3, wherein the temperature conduction coefficient comprises a global heat transfer coefficient and a local heat transfer coefficient, and the temperature conduction coefficient performs data cleaning, data feature extraction and new feature generation on historical data according to the historical data of the big data center to finally form the global heat transfer coefficient and the local heat transfer coefficient.
5. The electric energy efficiency management system suitable for the big data center according to claim 3, wherein the sensitivity table of the temperature control device is formed by combining a global heat transfer coefficient and a local heat transfer coefficient, and a quantized index of the adjustment capability of the HVDC temperature control device, and the sensitivity table of the temperature control unit system comprises a temperature control sensitivity of the temperature control unit system to itself, a 1-order and 2-order temperature control sensitivity table of adjacent devices, and the like.
6. The electric energy efficiency management system suitable for the big data center according to claim 3, wherein the HVAC temperature element adjusting capacity quantitative index gives a model of electric energy and refrigerating capacity according to design parameters and physical mechanisms of electric energy and refrigerating capacity of the HVAC system, and model parameter identification is performed based on actual operation historical data of HVDC to finally form the HVAC temperature element adjusting capacity quantitative index.
7. The electric energy efficiency management system suitable for the big data center according to claim 1, wherein the temperature control system implements hierarchical, zoning and grading control, and implements predictive control and optimal control according to different regions and importance;
the predictive control is realized by applying an intelligent control algorithm and state operation data according to a temperature control sensitivity table and the temperature design requirements of a big data center building and the optimal performance requirements of a big data operation platform such as a CPU (Central processing Unit), and is mainly used for local single load elements;
the optimization control is divided into global and local conditions, temperature control sensitivity tables and the temperature requirements of a large data center building under the condition of optimal temperature control distribution, the temperature requirements of the server performance, the real-time operation conditions of the server and the like, and the local and global optimal control is realized by virtue of a temperature control system platform.
8. The control method of the electric energy efficiency management system suitable for the big data center based on any one of claims 1 to 7 is characterized by specifically comprising the following steps:
s1, three-dimensional modeling is carried out on the big data center and the IT equipment thereof based on the computational fluid mechanics and finite element method,
s2, analyzing the temperature propagation speed to form a region temperature conductivity coefficient and a global temperature conductivity coefficient;
s3, forming a temperature control equipment sensitivity table based on historical data of temperature distribution of the big data center and configuration data of temperature control elements corresponding to HVAC and the like by means of a deep learning mining technology;
s4, obtaining an optimal control scheme with uniform regional temperature distribution by combining quantization indexes based on local temperature conduction coefficients, a temperature control equipment sensitivity table and HVAC temperature regulation capacity; obtaining an optimal control scheme with uniform global temperature distribution by combining quantization indexes based on the global temperature conduction coefficient, the temperature control equipment sensitivity table and the HVAC temperature regulation capacity;
s5, obtaining a single load element to execute a prediction type local control scheme;
and S6, building a distributed control system by combining the optimization control scheme and the prediction control scheme, and realizing a hierarchical and partition control system such as centralized control, regional centralized control and local control.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102937799A (en) * | 2012-09-10 | 2013-02-20 | 山东省计算中心 | Data center energy saving system and method |
| CN103529075A (en) * | 2013-10-28 | 2014-01-22 | 厦门大学 | Vacuum thermal insulation board thermal conduction coefficient measuring device and measuring method |
| CN103699744A (en) * | 2013-12-25 | 2014-04-02 | 国电南京自动化股份有限公司 | Wind power master control cabinet heat management analysis and optimization method based on finite element method |
| CN111365828A (en) * | 2020-03-06 | 2020-07-03 | 上海外高桥万国数据科技发展有限公司 | Model prediction control method for realizing energy-saving temperature control of data center by combining machine learning |
| CN111445065A (en) * | 2020-03-23 | 2020-07-24 | 清华大学 | Energy consumption optimization method and system for refrigeration group control of data center |
-
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- 2020-09-17 CN CN202010982542.9A patent/CN112130492A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102937799A (en) * | 2012-09-10 | 2013-02-20 | 山东省计算中心 | Data center energy saving system and method |
| CN103529075A (en) * | 2013-10-28 | 2014-01-22 | 厦门大学 | Vacuum thermal insulation board thermal conduction coefficient measuring device and measuring method |
| CN103699744A (en) * | 2013-12-25 | 2014-04-02 | 国电南京自动化股份有限公司 | Wind power master control cabinet heat management analysis and optimization method based on finite element method |
| CN111365828A (en) * | 2020-03-06 | 2020-07-03 | 上海外高桥万国数据科技发展有限公司 | Model prediction control method for realizing energy-saving temperature control of data center by combining machine learning |
| CN111445065A (en) * | 2020-03-23 | 2020-07-24 | 清华大学 | Energy consumption optimization method and system for refrigeration group control of data center |
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