A multi-unit wind control dehumidification parameter self-adaptive configuration method based on swarm intelligence optimization

Abstract

The application discloses a kind of multi-unit wind control dehumidification parameter self-adaptive configuration methods optimized by swarm intelligence, it is related to the field of swarm intelligence technology, comprising: the corresponding direction proportion of cable threading hole in azimuth acquisition point is calculated;Determine the corresponding relationship of target space between cable threading hole and air conditioning control subarea in data center machine room, calculate jet connection strength according to target space corresponding relationship;According to the current operation data of air conditioning unit, construct initial particle swarm;Based on jet connection strength, the initial particle swarm is iteratively updated to determine the target particle;Get the leaf curtain routing burden amount corresponding to air conditioning unit;According to leaf curtain routing burden amount and target particle, the wind control dehumidification parameter of air conditioning unit is self-adaptively configured;The application improves the accuracy of machine room humidity propagation path analysis.

Description

Technical Field

[0001] This invention relates to the field of swarm intelligence technology, and in particular to an adaptive configuration method for multi-unit wind control and dehumidification parameters based on swarm intelligence optimization. Background Technology

[0002] Data center server rooms typically house numerous server racks, precision air conditioning units, raised floors, and cable management systems. The space beneath the raised floor not only accommodates power, communication, and monitoring cables but also serves to transport cool air. Cool air generated by the air conditioning units is delivered to the server rack area via this space to maintain the temperature and humidity required for server operation. As data centers continue to expand, multiple air conditioning units are often configured to regulate the temperature and humidity in different areas. Numerous cable routing holes are located on the raised floor to allow cables to pass through vertically. Although these holes are typically sealed with brush-type plugs to reduce air leakage, directional airflow can still occur due to cable distribution, gaps in the sealing structure, and changes in local pressure differentials. This causes airflow within the raised space to diffuse in specific directions and enter different areas, thereby altering local air transport paths, moisture migration processes, and humidity distribution in the areas corresponding to the air conditioning units.

[0003] Existing technologies typically adjust the operating parameters of air conditioning units based on temperature and humidity data collected within the air conditioning control area, focusing on the correspondence between the regional environmental state and the operating state of the air conditioning equipment. However, there is a lack of effective quantitative methods for the directional air transport phenomenon formed by cable penetrations in the raised floor. It is difficult to accurately assess the impact of air flow in different directions on humidity changes in each air conditioning control area, and it is also difficult to incorporate the differences in air transport paths into the optimization process of operating parameters for multiple air conditioning units. This leads to a discrepancy between the air conditioning operating parameter configuration results and the actual air flow state. When abnormal local humidity diffusion or humidity migration occurs in the computer room, it can easily cause problems such as unreasonable dehumidification load distribution for some air conditioning units, increased overall energy consumption, and decreased humidity control effect. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies that make it difficult to accurately assess the impact of airflow from different directions on humidity changes in each air conditioning control area, and to propose an adaptive configuration method for multi-unit air conditioning dehumidification parameters based on swarm intelligence optimization.

[0005] To address the problems existing in the prior art, the present invention adopts the following technical solution: An adaptive configuration method for multi-unit wind control and dehumidification parameters based on swarm intelligence optimization includes: S1. Calculate the directional percentage of the cable threading hole at the azimuth acquisition point; S2. Determine the target spatial correspondence between the cable threading hole and the air conditioning control sub-area in the data center computer room, and calculate the jet connection strength based on the target spatial correspondence. S3. Based on the current operating data of the air conditioning unit, construct an initial particle swarm; based on the jet connection strength, iteratively update the initial particle swarm to determine the target particles; S4. Obtain the foliage routing load corresponding to the air conditioning unit; S5. Based on the leaf curtain routing load and target particles, the air conditioning unit's air control and dehumidification parameters are adaptively configured.

[0006] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention constructs an azimuth observation mechanism around the cable hole and calculates the directional proportion and jet connection strength by combining local wind speed data. This enables the quantitative characterization of the directional air transport path under the raised floor, and can identify the degree of influence of the airflow escaping from the cable hole on different air conditioning control sub-zones. This transforms the traditionally imperceptible local airflow disturbance into calculable spatial correlation data, improving the accuracy of humidity propagation path analysis in the computer room.

[0007] 2. This invention constructs the influence of the foliage jet based on the jet connection strength, and forms a route-weighted humidity deviation by combining the humidity deviation data of the air conditioning control sub-zone. Then, it uses a swarm intelligence optimization process to collaboratively optimize the operating parameters of multiple air conditioning units, so that the operating parameter optimization process considers both humidity control requirements and air transport influence, effectively improving the matching degree between operating parameters and actual airflow distribution state, and improving the humidity regulation effect.

[0008] 3. This invention further maps the influence of the leaf curtain jet to the air conditioning unit to form the leaf curtain routing load, and dynamically corrects the target operating parameters based on the leaf curtain routing load, so that different air conditioning units can allocate dehumidification tasks according to the actual degree of air transport influence they undertake, realize load coordination and resource optimization among multiple air conditioning units, reduce overall operating energy consumption while ensuring the target humidity control effect, and improve the energy-saving operation level of data center computer rooms. Attached Figure Description

[0009] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating an adaptive configuration method for multi-unit wind control and dehumidification parameters based on swarm intelligence optimization, as provided in an embodiment of the present invention. Detailed Implementation

[0010] The technical solutions 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 only some embodiments of the present invention, and not all embodiments.

[0011] This embodiment provides a method for adaptive configuration of multi-unit wind control and dehumidification parameters based on swarm intelligence optimization. (See also...) Figure 1 Specifically, including: S1. Calculate the directional percentage of the cable threading hole at the azimuth acquisition point; In an embodiment of the present invention, calculating the pointing ratio includes: Determine the cable routing holes on the raised floor of the data center; A data center raised floor refers to a load-bearing structure installed above the floor of a data center server room and separated from the original ground. It is formed by splicing multiple floor panels and is used to provide spatial separation between cable laying areas, air supply areas, and equipment installation areas. The original ground and the floor panels form an elevated space to accommodate cables, airflow, and related infrastructure. A cable pass-through hole refers to a through-hole structure opened on the data center raised floor panel, used for cables to pass through the elevated space and connect to server racks, power distribution equipment, or communication equipment. This opening structure runs through the upper and lower surfaces of the floor panel, and its edges are equipped with brush-type sealing elements to restrict the outward flow of air in the elevated space through the opening structure.

[0012] Specifically, the installation location and range of the raised floor in the data center server room are obtained. The position of each floor panel is confirmed piece by piece along the surface of the raised floor. The appearance of each floor panel is inspected to identify the opening structure on the floor panel for cable passage. The opening boundary range of the identified opening structure is measured to confirm whether the opening structure penetrates the upper and lower surfaces of the floor panel. The opening structure penetrating the upper and lower surfaces of the floor panel is recorded. It is checked whether there are cables running inside the opening structure and whether brush-type sealing devices are installed at the edge of the opening structure. The opening structure that meets the conditions of both cable passage and brush-type sealing device installation is identified as cable passage hole. The spatial position, opening size and cable distribution of the corresponding cable passage hole in the raised floor are recorded to form cable passage hole position data.

[0013] An observation ring is installed around the outer periphery of the brush-type sealing component of the cable threading hole; The observation ring refers to the ring-shaped carrier structure that is laid out around the cable hole. It is used to provide fixed installation positions for multiple directional acquisition points, so that multiple directional acquisition points are continuously distributed around the cable hole, thereby obtaining airflow information in different directions around the cable hole. Specifically, the center position of the cable hole is determined based on the cable hole location data. Using the center position of the cable hole as a reference, a ring-shaped installation structure is arranged around the brush-type sealing component, ensuring that the ring-shaped installation structure is continuously distributed around the cable hole. The spatial interval between the ring-shaped installation structure and the brush-type sealing component is checked to ensure that the ring-shaped installation structure does not contact the brush bristle area of ​​the brush-type sealing component. Multiple sampling positions are divided circumferentially on the ring-shaped installation structure, and wind speed sampling devices are installed at each sampling position to form a complete circumferential distribution around the cable hole. The spatial orientation corresponding to each sampling position is corrected and the azimuth information of each sampling position is recorded. The installation and azimuth calibration of the observation ring are completed, resulting in multiple azimuth sampling points distributed around the cable hole.

[0014] Local wind speed data corresponding to cable threading holes were collected by using multiple directional acquisition points on the observation ring. Azimuth acquisition points refer to data acquisition locations set at different circumferential positions of the observation ring. Each azimuth acquisition point corresponds to a specific spatial direction and is used to acquire local wind speed data in that direction to characterize the flow state of air as it diffuses from the cable hole in different directions. Local wind speed data refers to the air flow velocity per unit time at the location of the azimuth acquisition point, which is used to reflect the flow strength of air around the cable hole in different directions. The locations of all azimuth acquisition points on the observation ring are determined, and the spatial orientation information corresponding to each azimuth acquisition point is recorded. The wind speed acquisition device is fixed at the corresponding azimuth acquisition point, and the sensing direction of the wind speed acquisition device is consistent with the spatial orientation of the corresponding azimuth acquisition point. The wind speed measurement value generated by the air flow at each azimuth acquisition point is continuously acquired while the data center air conditioning is running. The wind speed measurement value obtained by each azimuth acquisition point within the same acquisition cycle is processed for time correspondence to form a local wind speed data set corresponding to the cable hole, and the correlation between the local wind speed data and the corresponding azimuth acquisition point is established.

[0015] Based on multiple local wind speed data of the same cable threading hole, calculate the directional proportion of the cable threading hole at the azimuth acquisition point.

[0016] Directional ratio refers to the ratio of the local wind speed data at a certain location of the same cable hole to the total local wind speed data at all locations of the same cable hole. It is used to characterize the degree to which the airflow escaping from the cable hole gathers and diffuses in a specific direction.

[0017] Extract all local wind speed data corresponding to the same cable hole, summarize and calculate the total local wind speed, and calculate the ratio of the local wind speed data corresponding to each directional collection point to the total local wind speed to obtain the wind speed percentage data corresponding to each directional collection point. Then, associate the wind speed percentage data corresponding to each directional collection point with the spatial orientation information of the corresponding directional collection point to form the directional percentage corresponding to each directional collection point.

[0018] It should be noted that the local wind speed data measured at various sampling points around the cable hole reflects the strength of airflow as it travels from the cable hole to different spatial directions. Since the momentum carried by the airflow is transferred along the flow direction, a higher local wind speed indicates a stronger air transport capacity in that direction, and the airflow tends to diffuse in that direction. After summing all the local wind speed data corresponding to the same cable hole to obtain the total local wind speed, the ratio between the local wind speed data at each sampling point and the total local wind speed represents the share of air transport in that direction. This share reflects the degree to which the corresponding direction occupies the total airflow. When the proportion of local wind speed data in a certain direction to the total local wind speed is large, it indicates that the airflow is more concentrated in that direction. When the proportion of local wind speed data in a certain direction to the total local wind speed is small, it indicates that the airflow is less distributed in that direction. Therefore, the proportional relationship between the local wind speed data at each sampling point and the total local wind speed can characterize the distribution of airflow relative to different directions, thus obtaining the directional proportion corresponding to each sampling point.

[0019] S2. Determine the target spatial correspondence between the cable threading hole and the air conditioning control sub-area in the data center computer room, and calculate the jet connection strength based on the target spatial correspondence. In embodiments of the present invention, determining the target spatial correspondence between the cable threading hole and the air conditioning control sub-zone includes: The floor plan layout data of the data center is divided into zones to obtain the zone division results; the zone division results include multiple air conditioning control sub-zones; Floor plan layout data refers to a set of data used to characterize the spatial layout relationships within a data center server room. It includes the server room boundary locations, rack distribution locations, air conditioning equipment distribution locations, aisle distribution locations, and the coverage area of ​​the raised floor, used to describe the spatial positional relationships between various physical objects within the server room. Area division results refer to spatial partitioning data formed based on the floor plan layout data, used to characterize multiple independent areas within the server room and the spatial extent of each area. Air conditioning control sub-areas refer to local spatial areas within the data center server room covered by the same air conditioning operating range, where air temperature and humidity changes are strongly correlated. Specifically, the building boundary of the data center server room is determined, as are the installation locations and arrangement directions of the server racks, the installation locations and air supply directions of the air conditioning equipment, the location ranges of the cold aisle and hot aisle areas, the laying range of the raised floor, and the distribution locations of the air supply openings. Based on the server rack locations, air conditioning equipment locations, cold aisle area locations, hot aisle area locations, and air supply opening distribution locations, a server room floor plan is created. The air conditioning function range in the server room floor plan is continuously divided, and spatial areas that are mainly affected by the air supply of the same air conditioning equipment and have continuous air exchange paths are divided into the same air conditioning control sub-zone. All air conditioning control sub-zones are boundary-marked and numbered to obtain the area division result containing multiple air conditioning control sub-zones.

[0020] Obtain the coordinates of the cable threading holes on the raised floor of the data center; The cable hole coordinates refer to the spatial location data of the cable hole on the raised floor of the data center, which is used to describe the positional relationship of the cable hole relative to the coordinate reference of the computer room. Specifically, the coordinate reference position of the data center raised floor is determined, a planar coordinate reference grid is established on the surface of the raised floor, the actual installation position of each cable hole in the raised floor is determined, the horizontal and vertical distances of the center position of the cable hole relative to the coordinate reference position are measured, the spatial coordinate values ​​corresponding to the cable hole are determined based on the measured horizontal and vertical distances, and the cable hole identifier is associated with the corresponding spatial coordinate values ​​and stored to obtain the hole position coordinates of the cable hole on the data center raised floor.

[0021] Obtain the boundary coordinates of the air conditioning control sub-zone within the data center server room; Retrieve the boundary information corresponding to each air conditioning control sub-zone from the area division results, determine all turning points and boundary connection points on the boundary of each air conditioning control sub-zone, extract the spatial coordinate data corresponding to each boundary position in sequence according to the continuous distribution direction of the boundary contour, associate the spatial coordinates corresponding to all boundary positions of the same air conditioning control sub-zone according to the boundary connection order, form a set of boundary coordinates that can characterize the coverage of the air conditioning control sub-zone, and associate and store the set of boundary coordinates with the corresponding air conditioning control sub-zone identifier to obtain the sub-zone boundary coordinates of the air conditioning control sub-zone in the data center computer room.

[0022] Based on the hole position coordinates and sub-area boundary coordinates, determine the target spatial correspondence between the cable threading hole and the air conditioning control sub-area.

[0023] Sub-area boundary coordinates refer to the location data used to describe the boundary outline of the air conditioning control sub-area, which is used to determine the coverage area of ​​the air conditioning control sub-area in the computer room space; target spatial correspondence refers to the relationship between the cable hole and the air conditioning control sub-area based on spatial location, which is used to characterize which air conditioning control sub-areas may be affected by the airflow corresponding to the cable hole.

[0024] Obtain the coordinates of the cable threading hole and the boundary coordinates of each air conditioning control sub-zone. Map the hole coordinates to the spatial region enclosed by the sub-zone boundary coordinates. Determine which air conditioning control sub-zone the hole coordinates are located within. Identify the air conditioning control sub-zone containing the hole coordinates as the target air conditioning control sub-zone corresponding to the cable threading hole. Associate the cable threading hole identifier with the target air conditioning control sub-zone identifier to obtain the target spatial correspondence between the cable threading hole and the air conditioning control sub-zone.

[0025] In an embodiment of the present invention, calculating the jet connection strength includes: Based on the spatial correspondence of the target, determine the azimuth coverage area corresponding to the azimuth acquisition point; Obtain the spatial orientation information corresponding to the azimuth acquisition point and the hole coordinates of the cable hole to which the azimuth acquisition point belongs. Establish the directional ray of the corresponding azimuth acquisition point with the hole coordinates as the starting position. Extend the directional ray along the corresponding spatial orientation in the layout of the data center server room. Obtain the spatial intersection between the directional ray and the boundary of each air conditioning control sub-area. Continuously record the spatial area passed by the directional ray. Based on the coverage trajectory of the directional ray in the server room plane, form the azimuth coverage range of the corresponding azimuth acquisition point.

[0026] The air conditioning control sub-area within the azimuth coverage area is identified as the target air conditioning control sub-area corresponding to the azimuth acquisition point; The azimuth coverage area refers to the spatial area of ​​action formed with the spatial orientation corresponding to the azimuth acquisition point as the center. This spatial area of ​​action is used to characterize the area that can be reached and affected when the air flow escaping from the cable hole spreads along the corresponding direction. The target air conditioning control sub-area refers to the air conditioning control sub-area located within the azimuth coverage area and established with the corresponding azimuth acquisition point. It is used to characterize the area object that the air flow in the corresponding direction may affect. Obtain the boundary coordinates of each air conditioning control sub-zone and the azimuth coverage of the corresponding azimuth acquisition point. Perform spatial overlap analysis between the azimuth coverage and the boundary range of each air conditioning control sub-zone to identify the air conditioning control sub-zones that overlap with the azimuth coverage. Statistically calculate the overlap relationship between each air conditioning control sub-zone and the azimuth coverage. Identify the air conditioning control sub-zones with spatial overlap as the target air conditioning control sub-zones of the corresponding azimuth acquisition points and establish the correspondence between the azimuth acquisition points and the target air conditioning control sub-zones.

[0027] The pointing ratio is determined as the initial jet correlation between the cable hole and the target air conditioning control sub-zone; The initial jet correlation quantity refers to the initial correlation degree data when air is transported from the cable hole to the target air conditioning control sub-area. It is characterized by the directional proportion of the corresponding sampling points and is used to reflect the relative proportion of air flow gathering and diffusion towards the target air conditioning control sub-area. The directional proportion reflects the distribution share of the airflow escaping from the same cable hole in various spatial directions. The target air conditioning control sub-zone is the airflow effect area determined based on the directional coverage area corresponding to the directional acquisition point. Therefore, when the airflow direction corresponding to a certain directional acquisition point covers a certain target air conditioning control sub-zone, the directional proportion corresponding to that directional acquisition point can characterize the relative proportion of the airflow escaping from the cable hole flowing towards that target air conditioning control sub-zone. Since the directional proportion is derived from the proportion of local wind speed data to the total local wind speed, it essentially reflects the degree to which the air transport capacity of the cable hole is distributed in a specific direction. The target air conditioning control sub-zone corresponds to the airflow receiving area in that direction. Therefore, using the directional proportion to characterize the degree of airflow correlation between the cable hole and the target air conditioning control sub-zone can reflect the initial impact share of the airflow escaping from the cable hole on the target air conditioning control sub-zone.

[0028] The initial jet correlation quantities corresponding to the same air conditioning control sub-zone are merged to obtain the jet connection strength.

[0029] Jet connection strength refers to the data on the degree of airflow correlation between the cable hole and the air conditioning control sub-zone. It is formed by merging multiple initial jet correlation quantities corresponding to the same air conditioning control sub-zone and is used to characterize the overall impact of the airflow escaping from the cable hole on the air conditioning control sub-zone.

[0030] Extract all initial jet correlation quantities corresponding to the same air conditioning control sub-zone, classify all initial jet correlation quantities according to the corresponding air conditioning control sub-zone, accumulate the initial jet correlation quantities belonging to the same air conditioning control sub-zone to obtain the total correlation quantity value corresponding to the air conditioning control sub-zone, determine the total correlation quantity value as the jet connection strength of the corresponding air conditioning control sub-zone, and establish the jet connection relationship data between the cable hole and the air conditioning control sub-zone.

[0031] It should be noted that the initial jet correlation quantity characterizes the relative transport share of the airflow escaping from the cable hole towards the corresponding air conditioning control sub-area along a specific direction. The same air conditioning control sub-area may simultaneously fall within the azimuth coverage area of ​​multiple azimuth sampling points. Therefore, the airflow influence received by this air conditioning control sub-area does not originate from a single direction, but rather from the superposition of air transport contributions from multiple directions. According to the principle of fluid continuity and the law of flow superposition, when airflow from multiple directions acts on the same spatial area, the overall airflow influence experienced by that spatial area is equal to the cumulative result of the airflow contributions from each direction. Therefore, merging all the initial jet correlation quantities corresponding to the same air conditioning control sub-area yields a result that characterizes the overall contribution of the airflow escaping from the cable hole to transport air to that air conditioning control sub-area. As the initial jet correlation quantities involved in the merging increase, it indicates that a larger share of airflow is transported to the air conditioning control sub-area, corresponding to a simultaneous enhancement in air momentum transfer and air mass transport capacity. Therefore, the merged result reflects the strength of the airflow connection between the cable hole and the air conditioning control sub-area, and is thus determined as the jet connection strength.

[0032] S3. Based on the current operating data of the air conditioning unit, construct an initial particle swarm; based on the jet connection strength, iteratively update the initial particle swarm to determine the target particles; In an embodiment of the present invention, constructing an initial particle swarm includes: Acquire the current operating data of the air conditioning unit; the current operating data includes supply air temperature command, fan speed command, coil control parameters, reheat control parameters, and power data; An air conditioning unit refers to an air handling unit capable of independently performing air delivery, cooling, dehumidification, and reheat regulation. It achieves environmental control of a corresponding area by adjusting the air state. Supply air temperature command refers to control data used to control the temperature of the air delivered by the air conditioning unit, corresponding to the target temperature state of the air at the air outlet. Fan speed command refers to control data used to control the rotation speed of the internal fan of the air conditioning unit, corresponding to the airflow delivery capacity during the air delivery process. Coil control parameters refer to control data used to control the working state of the heat exchange coils of the air conditioning unit, corresponding to the degree of heat exchange between the air and the heat exchange medium. Reheat control parameters refer to control data used to control the working state of the reheat components of the air conditioning unit, corresponding to the degree of heating of the air after cooling and dehumidification. Power data refers to data representing the electrical energy consumed per unit time during the operation of the air conditioning unit, reflecting the current operating load level of the air conditioning unit. Identify all air conditioning units involved in wind control and dehumidification regulation, establish the association between air conditioning unit identifiers and corresponding controllers, extract the current effective supply air temperature command, fan speed command, coil control quantity, and reheat control quantity from the corresponding controllers, obtain the power data corresponding to the current time, and classify the supply air temperature command, fan speed command, coil control quantity, reheat control quantity, and power data according to the air conditioning unit identifier to form the current operating data set corresponding to the air conditioning unit.

[0033] Based on the current operating data of the air conditioning unit, define the particles in the particle swarm optimization algorithm; A particle refers to a candidate solution in the particle swarm optimization algorithm. It is formed by combining the supply air temperature command, fan speed command, coil control quantity and reheat control quantity corresponding to the air conditioning unit, and is used to characterize an executable operating parameter configuration scheme. Extract the supply air temperature command, fan speed command, coil control quantity, and reheat control quantity corresponding to each air conditioning unit. Arrange the supply air temperature commands corresponding to all air conditioning units in the order of air conditioning unit identification, arrange the fan speed commands corresponding to all air conditioning units in the same order, arrange the coil control quantities corresponding to all air conditioning units in the same order, and arrange the reheat control quantities corresponding to all air conditioning units in the same order. Combine the above-arranged parameters to form a multi-air conditioning unit operating parameter combination, and define this multi-air conditioning unit operating parameter combination as a particle in the particle swarm algorithm.

[0034] The initial position of each particle is determined based on the current operating data of the air conditioning unit; The initial position of the corresponding particle is determined by the combination of operating parameters of multiple air conditioning units constituting the particle, the supply air temperature command corresponding to each air conditioning unit is determined as the supply air temperature position parameter in the initial position, the fan speed command corresponding to each air conditioning unit is determined as the fan speed position parameter in the initial position, the coil control quantity corresponding to each air conditioning unit is determined as the coil control position parameter in the initial position, and the reheat control quantity corresponding to each air conditioning unit is determined as the reheat control position parameter in the initial position. All position parameters are associated and combined to obtain the initial position data of the corresponding particle.

[0035] The initial velocity of the particles is determined based on the parameter changes between the initial position and the current operating data of each air conditioning unit. The parameter change refers to the difference between the parameter values ​​at the initial position and the current operating data of the air conditioning unit. It is used to characterize the adjustment range of the candidate operating parameter configuration scheme relative to the current operating state. The initial position of the particle and the current operating data of each air conditioning unit are obtained. The supply air temperature position parameter in the initial position is compared with the supply air temperature command of the corresponding air conditioning unit to obtain the change in supply air temperature parameter. The fan speed position parameter in the initial position is compared with the fan speed command of the corresponding air conditioning unit to obtain the change in fan speed parameter. The coil control position parameter in the initial position is compared with the coil control quantity of the corresponding air conditioning unit to obtain the change in coil control parameter. The reheat control position parameter in the initial position is compared with the reheat control quantity of the corresponding air conditioning unit to obtain the change in reheat control parameter. The changes in supply air temperature parameter, fan speed parameter, coil control parameter, and reheat control parameter are combined according to the parameter arrangement order in the particle to form the initial velocity of the corresponding particle.

[0036] An initial particle swarm is constructed based on the initial position and initial velocity of the particles.

[0037] Acquire the initial position and initial velocity data of all particles, associate the initial position of each particle with the corresponding initial velocity to form a particle movement record, summarize all particle movement records to form a particle set, number each particle in the particle set and establish a particle index relationship, and combine all particle movement records containing particle number, initial position and initial velocity to form an initial particle swarm.

[0038] In embodiments of the present invention, determining the target particle includes: By merging the jet connection intensities pointing to the same air conditioning control sub-zone, the influence of the shroud jet corresponding to the air conditioning control sub-zone is obtained. The influence of the blade jet refers to the comprehensive characterization data of the airflow escaping from the cable hole in the air conditioning control sub-zone. It is used to reflect the degree of influence of the directional airflow formed by the brush-type sealing device on the air transport process in the air conditioning control sub-zone. Obtain the jet connection strength between all cable holes and the air conditioning control sub-zone. Classify the jet connection strength according to the air conditioning control sub-zone identifier. Extract all jet connection strengths belonging to the same air conditioning control sub-zone and sum them up to obtain the total jet connection strength of the corresponding air conditioning control sub-zone.

[0039] Acquire relative humidity data and target relative humidity data for the air conditioning control sub-zone; Relative humidity data refers to the ratio of the actual water vapor content in the air within the air conditioning control sub-zone to the saturated water vapor content under the same temperature conditions. It is used to characterize the current air humidity state in the air conditioning control sub-zone. Target relative humidity data refers to the data corresponding to the air humidity state that the air conditioning control sub-zone is expected to achieve. It is used to characterize the target level of wind control dehumidification regulation. Humidity acquisition devices are deployed within the air conditioning control sub-zone to obtain relative humidity data under the current air conditions of the corresponding air conditioning control sub-zone. Target relative humidity data associated with the corresponding air conditioning control sub-zone is extracted, and the relative humidity data and target relative humidity data are associated according to the air conditioning control sub-zone identifier to form a humidity status data set corresponding to the air conditioning control sub-zone. A correspondence between the air conditioning control sub-zone, relative humidity data, and target relative humidity data is established.

[0040] Based on the influence of the leaf curtain jet, the relative humidity data of the air conditioning control sub-area, and the target relative humidity data, calculate the route-weighted humidity deviation corresponding to the air conditioning control sub-area; The route-weighted humidity deviation refers to the comprehensive evaluation data formed by combining the influence of the foliage jet and the humidity deviation data. It is used to characterize the degree of humidity deviation in the air conditioning control sub-zone under the influence of directional airflow. The difference between the relative humidity data and the target relative humidity data is calculated to obtain the humidity deviation of the corresponding air conditioning control sub-zone. The influence of the louver jet is used as a weighting coefficient for the humidity deviation. The influence of the louver jet is multiplied by the humidity deviation to obtain the route-weighted humidity deviation of the corresponding air conditioning control sub-zone. The larger the influence of the louver jet, the stronger the airflow escaping from the cable hole on the air transport of the corresponding air conditioning control sub-zone, and the higher the influence weight of the humidity deviation in subsequent dehumidification adjustment. The smaller the influence of the louver jet, the weaker the influence of the directional airflow on the corresponding air conditioning control sub-zone, and the lower the influence weight of the humidity deviation in subsequent dehumidification adjustment. The product of the influence of the louver jet and the humidity deviation is used to characterize the actual humidity deviation of the air conditioning control sub-zone under the influence of the directional airflow route, thus obtaining the route-weighted humidity deviation of the corresponding air conditioning control sub-zone.

[0041] It should be noted that the difference between the relative humidity data and the target relative humidity data represents the degree of deviation of the current air humidity state in the air conditioning control sub-zone from the target humidity state, while the influence of the louver jet represents the overall contribution of the airflow escaping from the cable hole to the air transported to the corresponding air conditioning control sub-zone. According to the air mass transport law, the more concentrated the airflow is in a certain air conditioning control sub-zone, the more easily the water vapor migration process in that sub-zone is affected by the airflow escaping from the cable hole. Therefore, the same humidity deviation has different importance to subsequent dehumidification adjustment under different louver jet influence conditions; when the louver jet influence is large, it means that more air is transported to the corresponding air conditioning control sub-zone, and the humidity deviation of that air conditioning control sub-zone will increase accordingly. The air transport process is further amplified and propagated to a wider range. When the influence of the foliage jet is small, the corresponding humidity deviation has a relatively weak impact on the overall air condition. Therefore, the humidity deviation is weighted by the influence of the foliage jet, which essentially introduces the air transport capacity into the humidity deviation evaluation process. This allows the humidity deviation to not only reflect the degree of deviation from the humid state but also the ability of this deviation to be transmitted outward through directional airflow. By multiplying the humidity deviation by the influence of the foliage jet, the result contains both the degree of humidity deviation and the contribution of air transport. This result can characterize the actual humidity influence level of the corresponding air conditioning control sub-zone under the directional airflow routing. Therefore, this result is determined as the route-weighted humidity deviation.

[0042] The fitness is calculated based on the route-weighted humidity deviation and the power data of the air conditioning unit; The total route-weighted humidity deviation is obtained by summing the route-weighted humidity deviations of all air conditioning control sub-areas. The total power value is obtained by summing the power data of all air conditioning units. The total route-weighted humidity deviation represents the overall humidity deviation of all air conditioning control sub-areas under the current operating parameter configuration scheme. The total power value represents the overall energy consumption level of all air conditioning units under the current operating parameter configuration scheme. The adaptability of the corresponding operating parameter configuration scheme is obtained by dividing the total route-weighted humidity deviation by the total power value.

[0043] It should be noted that the route-weighted humidity deviation represents the degree of humidity deviation of each air conditioning control sub-zone under the action of the leaf jet. The larger the value, the more significant the deviation between the overall air humidity state of the computer room and the target state under the current operating parameter configuration. Power data represents the energy level consumed by the air conditioning unit to maintain the current operating parameter configuration. According to the energy utilization law in the air conditioning process, achieving a smaller humidity deviation under the same energy consumption condition indicates a better humidity control effect per unit of energy; conversely, consuming more energy under the same humidity control effect indicates lower operating efficiency. Therefore, the routes corresponding to all air conditioning control sub-zones... The overall humidity deviation is obtained by accumulating the weighted humidity deviations, and the overall energy consumption level is obtained by accumulating the power data of all air conditioning units. The evaluation result is then constructed using the ratio between the overall humidity deviation and the overall energy consumption level, which reflects the degree of humidity deviation per unit of energy consumption. When the ratio decreases, it indicates that the system achieves a good humidity control effect with lower energy consumption. When the ratio increases, it indicates that the system consumes more energy but achieves a poor humidity control effect. Therefore, this ratio can simultaneously measure the dehumidification control quality and energy consumption cost, and serve as a comprehensive evaluation result of the merits of the operating parameter configuration scheme. Based on this, it is determined as the fitness level.

[0044] Based on fitness, determine the individual optimal position and the global optimal position; Obtain the fitness and position of each particle in the current iteration round. Compare the fitness of the current iteration round with the fitness of the corresponding particle saved in the historical iterations. When the current fitness is better than the historical fitness, update the current particle position to the individual optimal position of the corresponding particle and update the corresponding historical fitness record simultaneously. Compare the fitness of the individual optimal positions of all particles to determine the particle position with the best fitness. Set this particle position as the global optimal position for the current iteration round and establish the correspondence between the particle, the individual optimal position, and the global optimal position.

[0045] The initial particle swarm is iteratively updated using the individual optimal position and the global optimal position to obtain the particle swarm after the iteration. The target particle is determined based on the particle swarm after the iteration.

[0046] The target particle refers to the particle with the best fitness in the particle swarm after the iteration. It is used to characterize the operating parameter configuration scheme that meets the requirements of wind control and dehumidification and has better energy consumption performance.

[0047] Obtain the current position, current velocity, individual optimal position, and global optimal position of each particle in the initial particle swarm. Determine the individual guidance direction of the particle based on the positional difference between the individual optimal position and the current position, and determine the group guidance direction of the particle based on the positional difference between the global optimal position and the current position. Apply the individual guidance direction and the group guidance direction together to the current velocity to obtain the updated velocity. Adjust the current position based on the updated velocity to obtain the updated position. Recalculate the fitness using the running parameter configuration scheme corresponding to the updated position, and redetermine the individual optimal position and global optimal position of the corresponding particle based on the updated fitness.

[0048] The process of updating position, velocity, fitness, and individual and global optimal positions is repeated for all particles to form a new particle swarm. The new particle swarm is then used as the particle swarm for the next iteration to continue the update calculation until all iterations are completed, resulting in the particle swarm after the iteration ends. The fitness of all particles in the particle swarm after the iteration ends is obtained, and the particle with the best fitness is determined as the target particle. The operating parameter configuration scheme corresponding to the target particle is then used as the target operating parameter configuration scheme for subsequent wind control and dehumidification parameter configuration.

[0049] S4. Obtain the foliage routing load corresponding to the air conditioning unit; In an embodiment of the present invention, obtaining the foliage routing load corresponding to the air conditioning unit includes: Obtain the control correspondence between the air conditioning unit and the air conditioning control sub-zone; Control correspondence refers to the air conditioning effect relationship between the air conditioning unit and the air conditioning control sub-zone. It is used to characterize which air conditioning control sub-zones are mainly affected by the cooling capacity, heating capacity and air flow output by the air conditioning unit, and which air conditioning unit is responsible for regulating the temperature and humidity status of the corresponding air conditioning control sub-zone. Obtain the air supply coverage area corresponding to each air conditioning unit and the spatial boundary range corresponding to each air conditioning control sub-zone. Spatially match the air supply coverage area of ​​the air conditioning unit with the spatial boundary range of the air conditioning control sub-zone to determine the air conditioning control sub-zone where the air supplied by the air conditioning unit can continuously operate. Associate the air conditioning unit identifier with the corresponding air conditioning control sub-zone identifier to form a control correspondence between the air conditioning unit and the air conditioning control sub-zone, and establish a mapping data set between the air conditioning unit and the corresponding air conditioning control sub-zone.

[0050] Based on the control correspondence, the influence of the foliage jet corresponding to the air conditioning control sub-zone is merged into the corresponding air conditioning unit to obtain the foliage routing load corresponding to the air conditioning unit.

[0051] The leaf curtain route load refers to the data formed by the convergence of the leaf curtain jet influence of the air conditioning control sub-zone corresponding to the air conditioning unit. It is used to characterize the overall degree of influence of the airflow escaping from the cable hole on the area regulated by the air conditioning unit. When the leaf curtain route load corresponding to the air conditioning unit increases, it indicates that there is more directional air transport effect formed by the yaw of the brush-type sealing component in the area regulated by the air conditioning unit, and it needs to undertake a greater task of correcting the air humidity state.

[0052] Obtain the control correspondence and the blade jet influence of each air conditioning control sub-zone. Extract all air conditioning control sub-zones associated with the same air conditioning unit based on the control correspondence. Collect the blade jet influence of the corresponding air conditioning control sub-zones and calculate the accumulated blade jet influence item by item to obtain the blade routing load of the corresponding air conditioning unit. Associate the blade routing load with the corresponding air conditioning unit and store it to form the blade routing load data corresponding to the air conditioning unit.

[0053] S5. Based on the leaf curtain routing load and target particles, the air conditioning unit's air control and dehumidification parameters are adaptively configured.

[0054] In an embodiment of the present invention, adaptive configuration of the air conditioning unit's dehumidification parameters includes: Based on the target particles, determine the target supply air temperature command, target fan speed command, target coil control quantity, and target reheat control quantity corresponding to the air conditioning unit; The target particle refers to the particle with the optimal fitness obtained after the particle swarm optimization algorithm iterations. It contains a set of parameter configuration results for air conditioning operation adjustment. The target supply air temperature command refers to the supply air temperature control data determined by the target particle, which is used to characterize the target temperature state that the air outlet air of the air conditioning unit should reach. The target fan speed command refers to the fan operation control data determined by the target particle, which is used to characterize the air delivery capacity of the corresponding air conditioning unit. The target coil control quantity refers to the heat exchange coil adjustment data determined by the target particle, which is used to characterize the cooling output level of the corresponding air conditioning unit. The target reheat control quantity refers to the reheat adjustment data determined by the target particle, which is used to characterize the degree of air reheating of the corresponding air of the air conditioning unit. Obtain the position data corresponding to the target particle, and extract the supply air temperature parameters, fan speed parameters, coil control parameters, and reheat control parameters corresponding to each air conditioning unit based on the position data of the target particle. Determine the supply air temperature parameter as the target supply air temperature command for the corresponding air conditioning unit, the fan speed parameter as the target fan speed command for the corresponding air conditioning unit, the coil control parameter as the target coil control quantity for the corresponding air conditioning unit, and the reheat control parameter as the target reheat control quantity for the corresponding air conditioning unit. Establish the correspondence between the air conditioning unit and the target supply air temperature command, the target fan speed command, the target coil control quantity, and the target reheat control quantity.

[0055] The target supply air temperature command, target fan speed command, target coil control quantity and target reheat control quantity corresponding to the air conditioning unit are combined to obtain the unit energy-saving operation parameter template set; Obtain the target supply air temperature command, target fan speed command, target coil control quantity, and target reheat control quantity corresponding to each air conditioning unit. Associate and combine the target supply air temperature command, target fan speed command, target coil control quantity, and target reheat control quantity corresponding to the same air conditioning unit to form a unit operation parameter template. Collect all the unit operation parameter templates corresponding to all air conditioning units according to the air conditioning unit identifier to form a data set containing all air conditioning unit operation parameter templates, and determine this data set as the unit energy-saving operation parameter template set.

[0056] Based on the foliage routing load of the air conditioning unit, the energy-saving operation parameter template set of the unit is route-mapped and synthesized to obtain the air supply temperature execution value, fan speed execution value, coil control execution value and reheat control execution value of the air conditioning unit. The unit energy-saving operation parameter template set refers to a data set formed by combining the target supply air temperature command, target fan speed command, target coil control quantity, and target reheat control quantity corresponding to all air conditioning units. It is used to characterize the multi-air conditioning unit operation parameter scheme that meets the energy-saving optimization target. The supply air temperature execution value refers to the supply air temperature control data actually sent to the air conditioning unit for execution after routing mapping synthesis. The fan speed execution value refers to the fan speed control data actually sent to the air conditioning unit for execution after routing mapping synthesis. The coil control execution value refers to the coil adjustment data actually sent to the air conditioning unit for execution after routing mapping synthesis. The reheat control execution value refers to the reheat adjustment data actually sent to the air conditioning unit for execution after routing mapping synthesis.

[0057] Specifically, the following steps are taken: First, obtain the foliage routing load corresponding to each air conditioning unit. Then, sum the foliage routing loads of all air conditioning units to obtain the total foliage routing load. Next, divide the foliage routing load of each air conditioning unit by the total foliage routing load to obtain the routing mapping weight for that unit. Then, obtain the target supply air temperature command, target fan speed command, target coil control value, and target reheat control value for the corresponding air conditioning unit from the unit's energy-saving operation parameter template set. Finally, multiply the target fan speed command by the ratio between the routing mapping weight and the average routing mapping weight of all air conditioning units to obtain the fan speed execution value. Then, multiply the target coil control value by the ratio between the routing mapping weight and the average routing mapping weight of all air conditioning units to obtain the coil control execution value. Finally, subtract the deviation weight between the target supply air temperature command and the average routing mapping weight from the target supply air temperature command. The product of the differences yields the supply air temperature execution value. The reheat control execution value is obtained by subtracting the product of the target reheat control value and the difference between the route mapping weight and the average weight. When the foliage route load corresponding to the air conditioning unit increases, the corresponding route mapping weight increases, the fan speed execution value and the coil control execution value increase simultaneously, and the supply air temperature execution value and the reheat control execution value decrease simultaneously, thereby enhancing the air delivery capacity and dehumidification capacity of the corresponding air conditioning unit. When the foliage route load corresponding to the air conditioning unit decreases, the corresponding route mapping weight decreases, the fan speed execution value and the coil control execution value decrease simultaneously, and the supply air temperature execution value and the reheat control execution value increase simultaneously, thereby reducing the dehumidification load of the corresponding air conditioning unit. Thus, the supply air temperature execution value, fan speed execution value, coil control execution value, and reheat control execution value corresponding to the air conditioning unit are obtained.

[0058] The execution values ​​of supply air temperature, fan speed, coil control and reheat control for each air conditioning unit are combined to obtain a set of multi-unit operating parameters; Obtain the supply air temperature execution value, fan speed execution value, coil control execution value, and reheat control execution value corresponding to all air conditioning units. Associate and combine the supply air temperature execution value, fan speed execution value, coil control execution value, and reheat control execution value corresponding to the same air conditioning unit to form a unit execution parameter group. Collect all the unit execution parameter groups corresponding to all air conditioning units according to the air conditioning unit identifier to form a data set containing all air conditioning unit execution parameter groups, and determine this data set as the multi-unit operating parameter set.

[0059] Based on the set of multi-unit operating parameters, a configuration instruction package is generated; Obtain all unit execution parameter groups from the multi-unit operating parameter set. Encode the air conditioning unit identifier, supply air temperature execution value, fan speed execution value, coil control execution value, and reheat control execution value corresponding to each unit execution parameter group according to the control message format. Encapsulate the encoded execution parameter data with the corresponding air conditioning unit identifier to form the configuration instruction data of the corresponding air conditioning unit. Summarize and combine the configuration instruction data corresponding to all air conditioning units to obtain the configuration instruction package.

[0060] Based on the configuration instruction package, the air control and dehumidification parameters of the air conditioning unit are adaptively configured.

[0061] The system parses the corresponding air conditioning unit identifier and its corresponding supply air temperature execution value, fan speed execution value, coil control execution value, and reheat control execution value. It then writes the supply air temperature execution value, fan speed execution value, coil control execution value, and reheat control execution value into the corresponding air conditioning unit's supply air temperature control register area, fan speed execution value, coil control execution value, and reheat control execution value. This enables the corresponding air conditioning unit to update its operating parameters according to the execution parameters in the configuration instruction package, thereby completing the adaptive configuration of the air conditioning unit's air control and dehumidification parameters.

[0062] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for adaptive configuration of multi-unit wind control and dehumidification parameters based on swarm intelligence optimization, characterized in that, Includes the following steps: S1. Calculate the directional percentage of the cable threading hole at the azimuth acquisition point; S2. Determine the target spatial correspondence between the cable threading hole and the air conditioning control sub-area in the data center computer room, and calculate the jet connection strength based on the target spatial correspondence. S3. Based on the current operating data of the air conditioning unit, construct an initial particle swarm; based on the jet connection strength, iteratively update the initial particle swarm to determine the target particles; S4. Obtain the foliage routing load corresponding to the air conditioning unit; S5. Based on the leaf curtain routing load and target particles, the air conditioning unit's air control and dehumidification parameters are adaptively configured.

2. The adaptive configuration method for multi-unit wind control and dehumidification parameters based on swarm intelligence optimization according to claim 1, characterized in that, The calculation of the percentage of points to the target includes: Determine the cable routing holes on the raised floor of the data center; An observation ring is installed around the outer periphery of the brush-type sealing component of the cable threading hole; Local wind speed data corresponding to cable threading holes were collected by using multiple directional acquisition points on the observation ring. Based on multiple local wind speed data of the same cable threading hole, calculate the directional proportion of the cable threading hole at the azimuth acquisition point.

3. The adaptive configuration method for multi-unit wind control and dehumidification parameters based on swarm intelligence optimization according to claim 1, characterized in that, Determine the target spatial correspondence between the cable entry holes and the air conditioning control sub-zone, including: The floor plan layout data of the data center is divided into zones to obtain the zone division results; the zone division results include multiple air conditioning control sub-zones; Obtain the coordinates of the cable threading holes on the raised floor of the data center; Obtain the boundary coordinates of the air conditioning control sub-zone within the data center server room; Based on the hole position coordinates and sub-area boundary coordinates, determine the target spatial correspondence between the cable threading hole and the air conditioning control sub-area.

4. The adaptive configuration method for multi-unit wind control and dehumidification parameters based on swarm intelligence optimization according to claim 2, characterized in that, Calculating the jet connection strength includes: Based on the spatial correspondence of the target, determine the azimuth coverage area corresponding to the azimuth acquisition point; The air conditioning control sub-area within the azimuth coverage area is identified as the target air conditioning control sub-area corresponding to the azimuth acquisition point; The pointing ratio is determined as the initial jet correlation between the cable hole and the target air conditioning control sub-zone; The initial jet correlation quantities corresponding to the same air conditioning control sub-zone are merged to obtain the jet connection strength.

5. The adaptive configuration method for multi-unit wind control and dehumidification parameters based on swarm intelligence optimization according to claim 1, characterized in that, Constructing the initial particle swarm includes: Acquire the current operating data of the air conditioning unit; the current operating data includes supply air temperature command, fan speed command, coil control parameters, reheat control parameters, and power data; Based on the current operating data of the air conditioning unit, define the particles in the particle swarm optimization algorithm; The initial position of each particle is determined based on the current operating data of the air conditioning unit; The initial velocity of the particles is determined based on the parameter changes between the initial position and the current operating data of each air conditioning unit. An initial particle swarm is constructed based on the initial position and initial velocity of the particles.

6. The adaptive configuration method for multi-unit wind control and dehumidification parameters based on swarm intelligence optimization according to claim 1, characterized in that, Identify the target particle, including: By merging the jet connection intensities pointing to the same air conditioning control sub-zone, the influence of the shroud jet corresponding to the air conditioning control sub-zone is obtained. Acquire relative humidity data and target relative humidity data for the air conditioning control sub-zone; Based on the influence of the leaf curtain jet, the relative humidity data of the air conditioning control sub-area, and the target relative humidity data, calculate the route-weighted humidity deviation corresponding to the air conditioning control sub-area; The fitness is calculated based on the route-weighted humidity deviation and the power data of the air conditioning unit; Based on fitness, determine the individual optimal position and the global optimal position; The initial particle swarm is iteratively updated using the individual optimal position and the global optimal position to obtain the particle swarm after the iteration. The target particle is determined based on the particle swarm after the iteration.

7. The adaptive configuration method for multi-unit wind control and dehumidification parameters based on swarm intelligence optimization according to claim 1, characterized in that, The load of the air conditioning unit's corresponding foliage routing is obtained, including: Obtain the control correspondence between the air conditioning unit and the air conditioning control sub-zone; Based on the control correspondence, the influence of the foliage jet corresponding to the air conditioning control sub-zone is merged into the corresponding air conditioning unit to obtain the foliage routing load corresponding to the air conditioning unit.

8. The adaptive configuration method for multi-unit wind control and dehumidification parameters based on swarm intelligence optimization according to claim 1, characterized in that, Adaptive configuration of air conditioning unit dehumidification and airflow control parameters, including: Based on the target particles, determine the target supply air temperature command, target fan speed command, target coil control quantity, and target reheat control quantity corresponding to the air conditioning unit; The target supply air temperature command, target fan speed command, target coil control quantity and target reheat control quantity corresponding to the air conditioning unit are combined to obtain the unit energy-saving operation parameter template set; Based on the foliage routing load of the air conditioning unit, the energy-saving operation parameter template set of the unit is route-mapped and synthesized to obtain the air supply temperature execution value, fan speed execution value, coil control execution value and reheat control execution value of the air conditioning unit. The execution values ​​of supply air temperature, fan speed, coil control and reheat control for each air conditioning unit are combined to obtain a set of multi-unit operating parameters; Based on the set of multi-unit operating parameters, a configuration instruction package is generated; Based on the configuration instruction package, the air control and dehumidification parameters of the air conditioning unit are adaptively configured.

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