Intelligent ventilation system for machine room

Abstract

The application relates to the field of general ventilation systems of server machine rooms, in particular to an intelligent ventilation system for a machine room, which comprises the following steps: a data acquisition module acquires temperature data of the sensor array, fan air duct data of a ventilation fan group, and transmits the temperature data and the fan air duct data to a calculation module; the calculation module calculates and obtains a ventilation strategy according to the temperature data and the fan air duct data of the ventilation fan group acquired by the data acquisition module, and sends the ventilation strategy to a control module; the control module generates a control signal according to the ventilation strategy, and controls the opening and closing and the wind speed of the ventilation fan group in the machine room according to the control signal. The application adjusts the fan wind speed in the ventilation equipment of the machine room through a simplified fluid mechanics model, and solves the problem that the fixed ventilation scheme of the server machine room in the prior art cannot effectively ventilate the machine room equipped with differentiated server equipment, thereby causing local heat accumulation.

Description

Technical Field

[0001] This invention relates to the field of ventilation systems for server rooms, and more specifically to intelligent ventilation systems for server rooms. Background Technology

[0002] With the rapid development of information technology and automation technology, the scale of various computer rooms (such as data centers, large computing centers, and precision instrument control rooms) is constantly expanding, and the density of internal equipment and operating intensity are also increasing dramatically. This has led to a significant increase in heat generation inside the computer room, and heat dissipation has become an increasingly critical challenge to ensure the stable operation of equipment and extend its service life.

[0003] Traditional airflow organization design methods typically rely on fluid dynamics simulations to predict airflow paths within the computer room. Combined with pre-defined equipment heat generation, this information is used to initially determine the installation locations and basic parameters of ventilation equipment (such as fans and supply / return air vents) to achieve basic heat dissipation. However, modern computer room applications are unique and complex. Computer rooms contain a wide variety of equipment with diverse functions. During actual operation, the actual power consumption (i.e., heat generation) of each device fluctuates frequently due to various dynamic factors such as business load fluctuations, task allocation, and equipment start-up and shutdown. These fluctuations are often difficult to predict accurately. Traditional ventilation systems are often designed based on full-load operation of all equipment or a peak load, determining the installation locations of ventilation equipment accordingly. However, in actual operation, because the load of each device varies at different times, this fixed ventilation scheme leads to uneven temperature distribution within the computer room, resulting in heat accumulation in some areas and over-ventilation in others. This uneven temperature distribution and heat accumulation not only significantly reduce the overall efficiency of the ventilation system and cause unnecessary energy consumption, but more importantly, it can adversely affect equipment performance, lifespan, and even reliability, impacting the stable operation of the entire system. Therefore, existing fixed ventilation solutions are no longer able to effectively meet the actual ventilation needs of high-density data centers with differentiated equipment and dynamically changing loads.

[0004] To overcome the limitations of traditional fixed ventilation solutions, there is an urgent need to develop a new type of intelligent ventilation system. This system should possess real-time sensing capabilities, accurately monitoring the real-time operating status and load changes of each device within the computer room, while simultaneously collecting real-time temperature distribution data at key points within the room. Based on this real-time data, the system can use intelligent algorithms to analyze and judge the data, dynamically and automatically adjusting the airflow and supply / return direction of ventilation equipment (such as variable frequency fans and adjustable angle dampers) to achieve precise control of the temperature field within the computer room. This ensures uniform temperature distribution, effectively prevents localized heat accumulation, and thus maximizes ventilation efficiency and reduces system energy consumption while guaranteeing the safe and stable operation of the equipment. Summary of the Invention

[0005] To address the aforementioned technical problems, the present invention aims to provide an intelligent ventilation system for computer rooms, the system comprising:

[0006] Temperature sensor array, a sensor array installed at the first preset location in the server room;

[0007] The data acquisition module is used to collect temperature data from the sensor array and fan duct data from the ventilation fan group, and transmit the temperature data and fan duct data to the computing module.

[0008] The calculation module is used to calculate and derive a ventilation strategy based on the temperature data collected by the data acquisition module and the fan duct data of the ventilation fan group, and send the ventilation strategy to the control module.

[0009] The control module is used to generate control signals according to the ventilation strategy, and to control the on / off state and fan speed of the ventilation fan group in the computer room according to the control signals.

[0010] The execution module includes multiple ventilation fan groups installed at a second preset location in the server room; wherein each ventilation fan group includes multiple fans.

[0011] Furthermore, the computing module is further used for:

[0012] Based on the principles of fluid mechanics, the air duct data of each fan is obtained, and the influence of the negative pressure caused by the fan on the surrounding air flow is analyzed. The first-level air duct influence weight is constructed based on the influence of each fan on the air in the server room.

[0013] The wind speed attenuation weight is determined based on the influence weight of the first-level air duct; wherein, the wind speed attenuation weight includes the influence of obstacles outside the fan air duct on the wind speed inside the fan air duct.

[0014] The remaining wind speed intensity is obtained based on the wind speed attenuation weight and the obstruction of obstacles in the server room. The Huygens wavefront is used as an approximate model of wind reflection on obstacles. The reflected wind influence intensity is constructed based on the remaining wind speed intensity to characterize the influence of wind on airflow in the server room under the influence of obstacles.

[0015] The final airflow influence weights of multiple fans in the server room on the airflow of the server room space are determined based on the influence weight of the first-level air duct, the residual wind speed intensity, and the influence intensity of the reflected wind.

[0016] Furthermore, the data acquisition module is further used for:

[0017] Obtain the locations of all non-obstruction points within the server room; each location has a temperature value that is currently empty.

[0018] Based on these locations, a thermal cloud map is constructed;

[0019] The temperature data of all temperature sensors acquired at time t are added to the corresponding spatial point positions in the empty thermal cloud map to form the process thermal cloud map.

[0020] Using the process thermal cloud map as input, a three-dimensional linear interpolation algorithm is used to calculate and output the actual thermal cloud map, which is then sent to the calculation module.

[0021] Furthermore, the computing module is further used for:

[0022] The difference between the temperature value of each spatial point in the actual thermal cloud map and the preset rated value is calculated; the difference is used to replace the temperature value of the spatial point in the actual thermal cloud map, and this is recorded as a heat offset cloud map.

[0023] The summation of all temperature values ​​in the heat offset cloud map is recorded as the overall temperature of the server room;

[0024] The standard deviation of all temperature values ​​in the heat offset cloud map is recorded as the temperature imbalance situation.

[0025] Furthermore, the computing module is further used for:

[0026] When the overall temperature of the server room reaches a preset level that meets the heat accumulation condition, a ventilation strategy is implemented.

[0027] The preset conditions include at least one or more of the following: the overall temperature of the server room is higher than the maximum temperature, the overall temperature of the server room is lower than the minimum temperature, and the temperature imbalance is greater than the rated value of the temperature imbalance.

[0028] Furthermore, the computing module is further used for:

[0029] The temperature values ​​of the heat offset cloud map are weighted and summed according to the final airflow influence weight of each fan to obtain the fan judgment value of each fan;

[0030] Calculate fan judgment values ​​for all fans in the current server room, and sort the fan judgment values;

[0031] Based on the sorting results, the final fan to resolve the current heat buildup situation is determined, and the final fan is sent to the control module as the ventilation strategy.

[0032] Furthermore, the ventilation strategy specifically includes:

[0033] If the overall temperature of the server room is higher than the maximum temperature, then the final fan speed will be increased.

[0034] If the overall temperature of the server room is lower than the minimum temperature, the final fan speed will be reduced.

[0035] When the temperature imbalance condition is lower than the rated value, stop adjusting the wind speed.

[0036] Furthermore, the maximum temperature is 25 degrees Celsius; the minimum temperature is 19 degrees Celsius; and the rated value for temperature imbalance is 3 degrees Celsius.

[0037] Furthermore, the first preset position is specifically:

[0038] One or more temperature sensors are installed in multiple corners of the server room to collect room temperature data.

[0039] One or more temperature sensors are installed on each rack in the server room to collect temperature data for each rack.

[0040] Furthermore, the second preset position specifically refers to: installing one or more fans on the side of each rack in the server room to form a ventilation fan group.

[0041] This invention offers the following advantages: The invention acquires temperature data from the sensor array and fan duct data from the ventilation fan group via a data acquisition module, and transmits these data to a calculation module. The calculation module calculates and derives a ventilation strategy based on the acquired temperature data and fan duct data, and sends the ventilation strategy to a control module. The control module generates a control signal based on the ventilation strategy and controls the on / off state and fan speed of the ventilation fans in the server room according to the control signal. This invention, through a data acquisition module, simplifies the airflow patterns in the air using fluid dynamics, constructs a final airflow influence weight characterizing the effect of fans on air at different locations in the space, and then selects and adjusts fan speeds to optimize the airflow direction organization scheme in the server room in real time. By differentially adjusting the fan speeds in the server room ventilation equipment, it solves the problem that existing fixed ventilation schemes for server rooms cannot effectively ventilate server rooms with diverse server equipment, leading to localized heat accumulation, and improves the ventilation and heat dissipation capacity of the server room. Attached Figure Description

[0042] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a schematic diagram of an intelligent ventilation system for a computer room provided in one embodiment of the present invention;

[0044] Figure 2 This is a schematic diagram of a three-dimensional environment model of a computer room provided in one embodiment of the present invention;

[0045] Figure 3 This is a schematic diagram of fan performance analysis provided in one embodiment of the present invention. Detailed Implementation

[0046] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an intelligent ventilation system for a computer room proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0048] The following description, in conjunction with the accompanying drawings, details a specific solution for an intelligent ventilation system for computer rooms provided by the present invention.

[0049] Please see Figure 1 This illustration shows an intelligent ventilation system for a computer room according to an embodiment of the present invention. The system includes a temperature sensor array 110, a data acquisition module 120, a calculation module 130, a control module 140, and an execution module 150.

[0050] Temperature sensor array 110 is a sensor array installed at a first preset location in the server room.

[0051] In this embodiment, to adjust the airflow organization scheme within the computer room in real time, it is necessary to acquire the heat distribution within the computer room and determine the current heat accumulation situation. This embodiment uses a temperature sensor array to acquire the heat inside the computer room; alternatively, an infrared thermal imager can be used as an alternative temperature sensor.

[0052] In one optional implementation, the first preset location specifically involves: installing one or more temperature sensors in multiple corners of the server room to collect server room temperature data; and installing one or more temperature sensors on each server rack in the server room to collect temperature data for each server rack.

[0053] In one alternative implementation, the second preset location is specifically: one or more fans are installed on the side of each rack in the server room to form a ventilation fan group.

[0054] Number all temperature sensors with the digit 'a', and record the position of the 'a'-th sensor as follows: Here, the geometric center of the computer room is chosen as the origin of the coordinate system, and the temperature of the temperature sensor at time t is denoted as... The sampling period of the temperature sensor is empirically set to 1 second.

[0055] In this embodiment, the computer room consists of 16 server racks arranged in 2 rows and 8 columns. The racks are arranged in a common way in general computer rooms. For example, when racks are arranged in the same row, the front of one rack faces the back of another rack. When racks are arranged in the same column, the right side of the left rack faces the left side of the right rack.

[0056] Ventilation is achieved by installing ventilation fan arrays on both sides of the server rack. Each ventilation fan array consists of multiple fan groups, and each fan group contains multiple fans. In this embodiment, each fan group has 16 fans, each fan being a square fan with a front dimensions of 0.5m x 0.5m. The fans are arranged in a 4x4 pattern to form a 2m x 2m fan group. These fan groups are installed on the opposite wall of the server rack, with one fan group installed every 1 meter. Behind each fan is a panel-type heatsink containing circulating coolant to ensure that the airflow from the fan is cool air.

[0057] The wind speed of each fan can be individually controlled via a control signal from the control module 140. The installed fans are numbered with the letter 'b', and the center position of each fan is recorded. The fan speed at time t is Simultaneously, the air duct data of each fan is acquired. In this implementation, the air duct data of the fan used is for a circular fan with a radius of 0.4 meters, which means that the air blown out by the fan forms an air duct with a cross-section of a cylinder in space; wherein, the central axis of the cylindrical air duct is parallel to the x-axis of the coordinate system.

[0058] The data acquisition module 120 acquires the temperature data of the sensor array and the fan duct data of the ventilation fan group, and transmits the temperature data and the fan duct data to the calculation module 130.

[0059] The calculation module 130 is used to calculate and derive a ventilation strategy based on the temperature data collected by the data acquisition module 120 and the fan duct data of the ventilation fan group, and then send the ventilation strategy to the control module 140.

[0060] The control module 140 is used to generate control signals according to the ventilation strategy, and to control the on / off state and fan speed of the ventilation fan group in the computer room according to the control signals.

[0061] The execution module 150 includes multiple ventilation fan groups installed at a second preset location in the server room; wherein each ventilation fan group includes multiple fans.

[0062] In an optional implementation, the calculation module 130 is further configured to: acquire the air duct data of each fan according to the principles of fluid mechanics, analyze the impact of the negative pressure caused by the fan on the surrounding air flow, construct a first-level air duct influence weight based on the impact of each fan on the air in the server room; determine the wind speed attenuation weight based on the first-level air duct influence weight; wherein the wind speed attenuation weight includes the influence of obstacles outside the fan air duct on the wind speed inside the fan air duct; obtain the remaining wind speed intensity based on the wind speed attenuation weight and the obstruction of obstacles in the server room, and use the Huygens wavefront as an approximate model of wind reflection on obstacles, construct the reflected wind influence intensity based on the remaining wind speed intensity, characterizing the influence of wind on the air flow in the server room under the influence of obstacles; and determine the final air flow influence weight of multiple fans in the server room on the airflow in the server room space based on the first-level air duct influence weight, the remaining wind speed intensity, and the reflected wind influence intensity.

[0063] In this embodiment, it is necessary to analyze the impact of a single fan on the airflow in the server room. Therefore, based on the principles of fluid mechanics, airflow data for each fan is acquired, and the negative pressure caused by the fan, which drives the surrounding airflow, is analyzed. A first-level airflow influence weight is constructed, and a wind speed attenuation weight is obtained based on this. The wind speed attenuation weight includes the influence of external obstacles on the wind speed within the airflow, making the calculation of airflow more accurate. Furthermore, the residual wind speed intensity is obtained based on the wind speed attenuation weight and the obstruction situation. A Huygens wavefront is used as an approximate model for wind reflection on obstacles. The reflected wind influence intensity is constructed based on the residual wind speed intensity, characterizing the impact of wind on the airflow in the server room under the influence of obstacles.

[0064] Finally, the influence weight of the first-level air duct, the remaining wind speed intensity, and the influence intensity of the reflected wind are used as the final airflow influence weight of the fans on the airflow in the computer room. This allows for the estimation of airflow in the computer room with minimal calculations. Combined with the thermal cloud map obtained by the data acquisition module, the most suitable fans for adjusting the current heat accumulation situation are selected, and the ventilation scheme of the computer room is adjusted. By making differentiated adjustments to the fan speeds in the computer room ventilation equipment, the problem of local heat accumulation caused by differentiated server equipment is solved, thereby improving the ventilation and heat dissipation capacity of the computer room.

[0065] In this embodiment, obstacles affecting airflow within the server room are modeled. Specifically, a 3D model is constructed using on-site measurements. Optionally, the 3D model of the server room can also be directly obtained from a system equipped with a Building Information Modeling (BIM) management platform. The resolution of this 3D model can be, but is not limited to, 0.5m × 0.5m × 0.5m. (See [link to relevant documentation]). Figure 2 , Figure 2 This is a schematic diagram of a three-dimensional environment model of a computer room provided in one embodiment of the present invention.

[0066] Furthermore, based on the Bernoulli effect in fluid mechanics, negative pressure is created in the fan duct due to the lower air velocity. This negative pressure draws air from the surrounding space into the duct, slowing down the wind speed and causing airflow in the surrounding area. Simultaneously, the fan's duct overlaps with obstacles, creating reflected airflow on the obstacle surface, which also contributes to airflow. Therefore, this embodiment calculates the influence weight of the b-th fan on different locations in space, using a single fan as the unit. Specifically, the location where the fan duct can directly blow air is designated as the first-level duct spatial location. This involves first obtaining the surface circle equation of the b-th fan, then selecting points in space whose y and z coordinates lie within this surface circle equation, and finally, identifying non-obstacle points and points that can be translated along the x-axis from these points to reach the fan surface circle as first-level duct spatial points. All first-level duct spatial points constitute the first-level duct spatial location.

[0067] As the negative pressure caused by the fan gradually decreases with airflow, points with the same x-coordinate equal to r in the first-level air duct spatial points are designated as the r-th type of first-level air duct spatial point set, and the geometric center of this set is the r-th type of first-level space center. Furthermore, other spatial points with x-coordinate equal to r are determined to be on the same plane as the r-th type of first-level air duct spatial point set. Here, r is a preset parameter.

[0068] Select other spatial points that are on the same plane as the set of spatial points of the first-level air duct of type r, and filter out spatial points that have no obstacles on the line connecting them to the center of the first-level air duct. Assign the influence weight of the first-level air duct of type r to these spatial points. The influence weight of the first-level air duct can be obtained by calculating the reciprocal of the Euclidean distance. Specifically, the closer the distance to the spatial point of the first-level air duct, the greater the influence weight of the first-level air duct. The influence weight of the first-level air duct represents the impact of the negative pressure caused by the b-th fan air duct on the surrounding air flow.

[0069] Secondly, the influence weights of the first-level wind duct of the r-th class are summed and used as the wind speed attenuation weight of the b-th fan wind duct at the spatial point set of the first-level wind duct of the r-th class. The calculated wind speed attenuation weight includes the influence of external obstacles on the wind speed inside the wind duct, which can make the calculation of airflow more accurate.

[0070] Next, obtain the number of spatial points in the first-level air duct spatial point set of the r-th class, and calculate the ratio of this number to the number of spatial points contained in the fan surface circle. This ratio is denoted as the air duct obstruction rate, which characterizes the impact of obstacles on the air duct.

[0071] Finally, assuming the b-th fan has R wind speed attenuation weights, calculate the residual intensity of the r-th type of wind speed. The higher the wind duct obstacle rate at the r-th spatial location, and the greater the sum of the wind speed attenuation weights of other types between the r-th spatial location and the fan surface circle, the smaller the residual intensity of the r-th type of wind speed. This represents the influence of the Bernoulli effect when the wind passes through the obstacle, and the smaller the wind speed when it reaches the set of first-level wind duct spatial points of the r-th type.

[0072] Since wind is reflected when it reaches an obstacle, and the reflected wind also drives the flow of nearby air and thus dissipates heat, the reflection of the wind duct needs to be taken into account in the model. It should be noted that this embodiment only considers the first reflection of the wind, and the effect of the wind after multiple reflections is negligible. According to the Huygens wavefront model, the reflection of the wind at the obstacle can be regarded as a new wind emission point, and the wind emitted from the reflection point spreads outward in a hemispherical shape.

[0073] Therefore, to obtain the reflection position corresponding to the b-th fan, we only need to find the spatial point in the first-stage air duct spatial position. The next spatial point or If a point is not located within the first-level wind tunnel, the next point becomes the reflection point. The initial reflection intensity of this point is equal to the remaining intensity of the x-th wind speed. Other unobstructed points on the line connecting this reflection point are selected, and these points are assigned a reflection wind influence intensity. This intensity is directly proportional to the remaining intensity of the x-th wind speed and inversely proportional to the Euclidean distance from the reflection point. , or These are the three-dimensional coordinates of a point in space.

[0074] Finally, it was determined that the b-th fan has three characteristic values ​​for each spatial point: the influence weight of the first-level air duct, the remaining intensity of the wind speed, and the influence intensity of the reflected wind. If the c-th spatial point does not have a corresponding characteristic value, then its corresponding characteristic value is set to 0.

[0075] The final airflow influence weight of the b-th fan on the c-th spatial point is obtained by summing the influence weights of the first-level air duct, wind speed attenuation, and reflected wind intensity. The greater the influence weight of the first-level air duct, wind speed attenuation, or reflected wind intensity, the greater the airflow influence weight, indicating a greater impact of the b-th fan's wind speed on the airflow and heat dissipation of the c-th spatial point. This final airflow influence weight can also be stored locally in the control center's computing module, reducing computational load when estimating airflow in the computer room compared to fluid dynamics simulation software.

[0076] In an optional implementation, the data acquisition module 120 is further configured to: acquire the locations of all non-obstruction spatial points in the server room; wherein each location has a null temperature value; construct an empty thermal cloud map based on these locations; add the temperature data of all temperature sensors acquired at time t to the corresponding spatial point locations in the empty thermal cloud map to construct a process thermal cloud map; use the process thermal cloud map as input, calculate using a three-dimensional linear interpolation algorithm, output the actual thermal cloud map, and send it to the calculation module.

[0077] Among them, non-barrier-free space points refer to unobstructed, traversable empty spaces within the computer room, excluding server racks and other equipment, furniture, and household items; the three-dimensional linear interpolation algorithm is a well-known technology in this field. The actual heat map represents the heat accumulation within the computer room at time t.

[0078] In an optional implementation, the calculation module 130 is further configured to: calculate the difference between the temperature value of each spatial point in the actual thermal cloud map and the preset rated value; replace the temperature value of the spatial point in the actual thermal cloud map with the difference, and record it as a heat offset cloud map; sum all the temperature values ​​in the heat offset cloud map and record it as the overall temperature of the server room; and calculate the standard deviation of all the temperature values ​​in the heat offset cloud map and record it as the temperature imbalance situation.

[0079] The preset rated value is the indoor ambient temperature set based on experience, such as 22 degrees Celsius.

[0080] In an optional implementation, the calculation module 130 is further configured to: construct a ventilation strategy when the overall temperature of the server room meets the preset conditions for heat accumulation; wherein the preset conditions include at least one or more of the following: the overall temperature of the server room is higher than the maximum temperature, the overall temperature of the server room is lower than the minimum temperature, and the temperature imbalance is greater than the rated value of the temperature imbalance.

[0081] In an optional implementation, the calculation module 130 is further configured to: perform a weighted summation of the temperature values ​​of the heat offset cloud map based on the final airflow influence weight of each fan to obtain a fan judgment value for each fan; the larger the fan judgment value, the better the fan can solve the current heat accumulation problem under low energy consumption; calculate the fan judgment value for all fans in the current server room and sort the fan judgment values; determine the final fan that solves the current heat accumulation situation based on the sorting result, and send the final fan as a ventilation strategy to the control module.

[0082] In one alternative implementation, the ventilation strategy is as follows: if the overall temperature of the server room is higher than the maximum temperature, the final fan speed is increased; if the overall temperature of the server room is lower than the minimum temperature, the final fan speed is decreased; and when the temperature imbalance is lower than the rated value for temperature imbalance, the fan speed adjustment is stopped.

[0083] In one alternative implementation, the highest temperature is 25 degrees Celsius; the lowest temperature is 19 degrees Celsius; the temperature imbalance is the standard deviation of all temperatures, without units, representing only a numerical characteristic, and the temperature imbalance condition is rated at 3.

[0084] For example, using the final airflow impact weight of the b-th fan on the c-th spatial point as the weight, the temperature values ​​in the heat offset cloud map are weighted and summed according to the final airflow impact weight to obtain the fan judgment value. The larger the value, the better the b-th fan can solve the current heat accumulation problem with low energy consumption. The fan judgment value is calculated for all fans, and the fan with the largest judgment value is selected. (See [reference]). Figure 3 , Figure 3 This is a schematic diagram of fan performance analysis provided in one embodiment of the present invention. The fan with the highest judgment value, i.e., fan number 1, is selected. It should be noted that for large computer rooms, one fan can be selected from each ventilation fan group.

[0085] If the overall temperature of the computer room is higher than the preset rated value of 22 degrees Celsius, the selected fan speed will be gradually increased; otherwise, the selected fan speed will be decreased. When the preset rated value of the computer room is between 21 and 23 degrees Celsius, and the temperature imbalance condition is 2, but lower than the rated value of 3, the fan speed adjustment will stop. When the selected fan cannot continue to increase or decrease its speed, the fan with the closest equilateral distance to the selected fan will be selected as the replacement. If multiple fans have the same equilateral distance, all of them will be selected as replacement fans.

[0086] The final control module 140 sends the control signal to the execution module 150, which then executes the ventilation strategy to adjust the airflow direction in the computer room.

[0087] The system in this embodiment collects temperature data from the sensor array and fan duct data from the ventilation fan group via a data acquisition module, and transmits the temperature data and fan duct data to a calculation module. The calculation module calculates and derives a ventilation strategy based on the temperature data and fan duct data collected by the data acquisition module, and sends the ventilation strategy to a control module. The control module generates a control signal based on the ventilation strategy and controls the on / off state and fan speed of the ventilation fan group in the computer room according to the control signal. This embodiment uses the data acquisition module to simplify the airflow law in the air based on fluid mechanics, constructs a final airflow influence weight characterizing the effect of fans on the air at different locations in the space, and then selects fans to adjust their speeds, optimizing the airflow direction organization scheme in the computer room in real time. By differentially adjusting the fan speeds in the computer room ventilation equipment, it solves the problem that the fixed ventilation scheme of the server room in the prior art cannot effectively ventilate the computer room with differentiated server equipment, thus causing local heat accumulation, and improves the ventilation and heat dissipation capacity of the computer room.

[0088] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0089] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. An intelligent ventilation system for computer rooms, characterized in that, The system includes: Temperature sensor array, a sensor array installed at the first preset location in the server room; The data acquisition module is used to collect temperature data from the sensor array and fan duct data from the ventilation fan group, and transmit the temperature data and fan duct data to the computing module. The calculation module is used to calculate and derive a ventilation strategy based on the temperature data collected by the data acquisition module and the fan duct data of the ventilation fan group, and send the ventilation strategy to the control module. The control module is used to generate control signals according to the ventilation strategy, and to control the on / off state and fan speed of the ventilation fan group in the computer room according to the control signals. The execution module includes multiple ventilation fan groups installed at a second preset location in the server room; wherein each ventilation fan group includes multiple fans; The computing module is further used for: Based on the principles of fluid mechanics, the air duct data of each fan is obtained, and the influence of the negative pressure caused by the fan on the surrounding air flow is analyzed. The first-level air duct influence weight is constructed based on the influence of each fan on the air in the server room. The wind speed attenuation weight is determined based on the influence weight of the first-level air duct; wherein, the wind speed attenuation weight includes the influence of obstacles outside the fan air duct on the wind speed inside the fan air duct. The remaining wind speed intensity is obtained based on the wind speed attenuation weight and the obstruction of obstacles in the server room. The Huygens wavefront is used as an approximate model of wind reflection on obstacles. The reflected wind influence intensity is constructed based on the remaining wind speed intensity to characterize the influence of wind on airflow in the server room under the influence of obstacles. The final airflow influence weights of multiple fans in the server room on the airflow of the server room space are determined based on the influence weight of the first-level air duct, the residual wind speed intensity, and the influence intensity of the reflected wind.

2. The intelligent ventilation system for a computer room according to claim 1, characterized in that, The data acquisition module is further used for: Obtain the locations of all non-obstruction points within the server room; each location has a temperature value that is currently empty. A thermal cloud map is constructed based on these locations; The temperature data of all temperature sensors acquired at time t are added to the corresponding spatial point positions in the empty thermal cloud map to form the process thermal cloud map. Using the process thermal cloud map as input, a three-dimensional linear interpolation algorithm is used to calculate and output the actual thermal cloud map, which is then sent to the calculation module.

3. The intelligent ventilation system for a computer room according to claim 2, characterized in that, The computing module is further used for: The difference between the temperature value of each spatial point in the actual thermal cloud map and the preset rated value is calculated; the difference is used to replace the temperature value of the spatial point in the actual thermal cloud map, and this is recorded as a heat offset cloud map. The summation of all temperature values ​​in the heat offset cloud map is recorded as the overall temperature of the server room; The standard deviation of all temperature values ​​in the heat offset cloud map is recorded as the temperature imbalance situation.

4. The intelligent ventilation system for a computer room according to claim 3, characterized in that, The computing module is further used for: When the overall temperature of the server room reaches a preset level that meets the heat accumulation condition, a ventilation strategy is implemented. The preset conditions include at least one or more of the following: the overall temperature of the server room is higher than the maximum temperature, the overall temperature of the server room is lower than the minimum temperature, and the temperature imbalance is greater than the rated value of the temperature imbalance.

5. The intelligent ventilation system for a computer room according to claim 4, characterized in that, The computing module is further used for: The temperature values ​​of the heat offset cloud map are weighted and summed according to the final airflow influence weight of each fan to obtain the fan judgment value of each fan; Calculate fan judgment values ​​for all fans in the current server room, and sort the fan judgment values; Based on the sorting results, the final fan to resolve the current heat buildup situation is determined, and the final fan is sent to the control module as the ventilation strategy.

6. The intelligent ventilation system for a computer room according to claim 5, characterized in that, The ventilation strategy is as follows: If the overall temperature of the server room is higher than the maximum temperature, then the final fan speed will be increased. If the overall temperature of the server room is lower than the minimum temperature, the final fan speed will be reduced. When the temperature imbalance condition is lower than the rated value, stop adjusting the wind speed.

7. The intelligent ventilation system for a computer room according to any one of claims 4-6, characterized in that, The maximum temperature is 25 degrees Celsius; the minimum temperature is 19 degrees Celsius; and the rated value for temperature imbalance is 3.

8. The intelligent ventilation system for a computer room according to claim 1, characterized in that, The first preset position is specifically: One or more temperature sensors are installed in multiple corners of the server room to collect room temperature data. One or more temperature sensors are installed on each rack in the server room to collect temperature data for each rack.

9. The intelligent ventilation system for a computer room according to claim 1, characterized in that, The second preset location is specifically: one or more fans are installed on the side of each rack in the server room to form a ventilation fan group.

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