Bacterium control method and system based on water chiller unit

Abstract

The application discloses a bacterium inhibition control method and system based on a water chilling unit, and belongs to the technical field of bacterium inhibition control, and the method specifically comprises the following steps: collecting operation parameters of cooling water, and constructing a micro-ecological state model of the cooling water based on the operation parameters; calculating operation adjustment parameters according to the micro-ecological state model, and controlling the water chilling unit to perform periodic or quasi-random non-steady operation adjustment on the cooling water; in the non-steady operation adjustment process, collecting local energy field operation data of the cooling water corresponding to the operation state of the water chilling unit, and generating state data of the local energy field; and dynamically adjusting the action time sequence of the operation adjustment parameters according to the state data, so as to form a non-steady energy distribution environment that is not conducive to the metabolism and adhesion of bacteria colonies. The application is targeted at different working condition segments and different heat exchange interface regions in the cooling water, and is beneficial to improving the adaptability and continuity of bacterium inhibition control, and is suitable for the water chilling unit system with complex operation conditions and frequent load changes.

Description

Technical Field

[0001] This invention belongs to the field of antibacterial control technology, specifically an antibacterial control method and system based on chiller units. Background Technology

[0002] In complex water operating environments, the temperature distribution, flow state, and heat exchange interface conditions of cooling water exhibit obvious spatiotemporal variation characteristics, which in turn provide objective conditions for the attachment, growth, and migration of microorganisms in cooling water. Especially in areas such as the surface of heat exchangers and the inner wall of pipes, microorganisms easily form an attachment layer together with impurities in the water.

[0003] Existing technologies typically employ the addition of chemical agents, physical sterilization devices, or combinations thereof for control. For example, the aquatic environment is altered by periodically or continuously adding antibacterial agents, or physical means such as ultraviolet light, ozone, and electromagnetic radiation are used to treat circulating water. However, as the scale expands and operating conditions become more complex, the reliance on external treatment methods has gradually revealed its lack of adaptability, making it difficult to perform precise control for different operating stages and different heat exchange zones. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes a method and system for antibacterial control based on chiller units. By introducing a non-steady-state regulation mechanism during the operation and adjustment of the chiller unit, and simultaneously coordinating the multi-dimensional energy field parameters induced by the unit operation in the cooling water, the cooling water exhibits a dynamically changing operating state in the heat exchange channel and related interface areas, thereby achieving integrated, process-oriented, and collaborative control of the antibacterial process of the cooling water.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] Antibacterial control methods based on chiller units include:

[0007] The operating parameters of the cooling water are collected, and a micro-ecological state model of the cooling water is constructed based on the operating parameters. The micro-ecological state model is used to characterize the potential attachment and reproduction trend of bacteria in the cooling water. The operating parameters include water temperature gradient, flow velocity pulsation characteristics, conductivity change rate and heat flux distribution on the heat exchanger surface.

[0008] Based on the micro-ecological state model, the operating adjustment parameters are calculated, and the chiller unit is controlled to perform periodic or quasi-random non-steady-state operation adjustment of the cooling water.

[0009] During the unsteady-state operation adjustment process, the local energy field operation data of the cooling water corresponding to the operating state of the chiller unit is collected, and the state data of the local energy field is generated. Based on the state data, the action sequence of the operation adjustment parameters is dynamically adjusted to form an unsteady energy distribution environment.

[0010] The operating feedback parameters of the cooling water are continuously monitored and compared with the micro-ecological state model. Based on the comparison results, the unsteady-state operation adjustment is dynamically corrected.

[0011] Specifically, the process of collecting operating parameters of the cooling water and constructing a micro-ecological state model of the cooling water based on these operating parameters includes:

[0012] Within a preset sampling period, the water temperature gradient, flow velocity pulsation characteristics, conductivity change rate, and heat flux distribution on the heat exchanger surface of the cooling water are collected from multiple sources, and the data from each source are time-aligned to form a sequence of operating parameters on the same time axis.

[0013] The sequence of operating parameters is divided into multiple operating condition segments, and the boundaries of the operating condition segments are determined by one or more triggering conditions.

[0014] For each working condition segment, a cross-parameter coupling feature set is constructed.

[0015] The cross-parameter coupling feature set is mapped to the interface region set corresponding to the heat exchanger surface to obtain the interface region-level operating feature sequence.

[0016] Based on the operational feature sequence at the interface region level, a micro-ecological state model is generated according to the hierarchical organization of operating condition segments, interface regions, and time sequence.

[0017] Specifically, the micro-ecological state model generated based on the interface region-level operational feature sequence, according to a hierarchical organization of working condition segments, interface regions, and time sequence, includes:

[0018] The operation feature sequence at the interface region level is reorganized. The reorganization process includes primary sorting of the operation feature sequence according to the occurrence order of the operation condition segments, and secondary sorting of the operation feature sequence according to the spatial identifier of the interface region within each operation condition segment.

[0019] For each operational feature item in the recombined operational feature sequence, construct operational condition segment index, interface region index, and time index respectively, and combine all indexes to form a multidimensional index set;

[0020] Based on the multidimensional index set, the recombined running feature sequence is divided into multiple state units, and a corresponding state identifier is assigned to each state unit.

[0021] Based on the state units and their corresponding state identifiers, a micro-ecological state model is constructed that includes a set of states and temporal relationships between states.

[0022] Specifically, based on the aforementioned micro-ecological state model, operational adjustment parameters are calculated, and the chiller unit is controlled to perform periodic or quasi-random non-steady-state operational adjustments on the cooling water, including:

[0023] Based on the micro-ecological state model, the state identifier corresponding to the current working condition segment is extracted, and the state identifier is mapped to one or more sets of operating adjustment parameters. The set of operating adjustment parameters includes time adjustment parameters and amplitude adjustment parameters related to the cooling water flow process.

[0024] The set of operating adjustment parameters is combined and processed to generate an adjustment sequence corresponding to the status identifier;

[0025] Periodic adjustment segments and quasi-random adjustment segments are introduced into the adjustment sequence, and the periodic adjustment segments and quasi-random adjustment segments are arranged alternately according to a preset segment nesting rule to form a composite adjustment sequence;

[0026] The composite adjustment sequence is decomposed into multiple time-continuous adjustment instruction units, and a corresponding execution window identifier is assigned to each adjustment instruction unit.

[0027] According to the execution window identifier of the adjustment instruction unit, the adjustment instruction unit is sequentially loaded into the operation control logic of the chiller unit, so that the cooling water forms a non-steady-state operation state corresponding to the adjustment sequence during operation.

[0028] Specifically, periodic adjustment segments and quasi-random adjustment segments are introduced into the adjustment sequence, and the periodic adjustment segments and quasi-random adjustment segments are interleaved according to a preset segment nesting rule to form a composite adjustment sequence, including:

[0029] Obtain the set of periodic adjustment segments and the set of quasi-random adjustment segments corresponding to the current state identifier, and assign a segment identifier and a segment length identifier to each adjustment segment to form a segment candidate set;

[0030] Based on the preset segment nesting rules, a nesting template is generated and the nesting template is bound to the working condition segment index. The nesting template includes the reference insertion position of the periodic adjustment segment, the intercalation position of the quasi-random adjustment segment, and the interval identifier between adjacent intercalations.

[0031] According to the nested template, a periodic adjustment segment is selected as the main sequence segment from the set of periodic adjustment segments, and an intercalation segment from the set of quasi-random adjustment segments is inserted at the reference insertion position of the main sequence segment. At the same time, the insertion order of the intercalation segments is advanced according to the interval identifier to generate a composite segment sequence.

[0032] Each adjustment segment in the composite segment sequence is written with its segment identifier, operating condition segment index, and its sequence position identifier in the composite segment sequence, and then encapsulated with an identifier set to form a composite adjustment sequence.

[0033] Specifically, the generation of quasi-randomized modulated segments includes:

[0034] Read the historical order of the status identifier corresponding to the current working condition segment in the status set, as well as the connection position of the status identifier in adjacent working condition segments, to form a status context set;

[0035] Based on the aforementioned state context set, a candidate set containing multiple candidate adjustment units is constructed;

[0036] According to the selection rules associated with the set of state contexts, one or more candidate adjustment units are selected from the candidate set as quasi-random adjustment segments.

[0037] Specifically, during the unsteady-state operation adjustment process, local energy field operation data of the cooling water corresponding to the operating state of the chiller unit is collected, and state data of the local energy field is generated. Based on the state data, the timing of the operation adjustment parameters is dynamically adjusted to form an unsteady-state energy distribution environment, including:

[0038] During the unsteady-state operation adjustment process, the set of local energy field parameters associated with the operating state of the chiller unit is identified, and time indexes are established for each energy field parameter. The set of local energy field parameters includes energy distribution parameters corresponding to cooling water flow, heat exchange process and operating rhythm.

[0039] The time index of the local energy field parameter set is aligned with the adjustment command unit to determine the energy field parameter range corresponding to each adjustment command unit, thus forming a synchronous mapping relationship between operation adjustment and energy field parameters.

[0040] The energy field parameters within the same action range are deconstructed, splitting them into basic parameter components and superimposed parameter components, and then associated with different time sub-intervals of the adjustment command unit.

[0041] Based on the synchronous mapping relationship and the energy field parameter deconstruction results, the basic parameter components and superimposed parameter components of different energy field parameters are coordinated and arranged.

[0042] Based on the energy field parameters after collaborative arrangement and their corresponding operating range and adjustment command unit, the timing of the operation adjustment parameters is dynamically adjusted to form an unsteady energy distribution environment.

[0043] Specifically, the method of coordinating the basic parameter components and superimposed parameter components of different energy field parameters based on the synchronization mapping relationship and the energy field parameter deconstruction results includes:

[0044] Based on the energy field parameter deconstruction results, the basic parameter components and superimposed parameter components belonging to the same synchronous mapping relationship are respectively assigned to the corresponding component groups, and each component group is associated with the energy field parameter identifier and the action range identifier from which it originates.

[0045] Based on the component groups, a collaborative orchestration grammar is generated, which includes the precedence position of the basic parameter components, the insertion position of the superimposed parameter components, and the overlap markers between different component groups.

[0046] According to the collaborative orchestration syntax, the basic parameter components and superimposed parameter components are sequentially placed into the corresponding time sub-intervals, and the participation order of different component groups is adjusted according to the overlap mark during the operation to generate the collaboratively orchestrated energy field parameters.

[0047] Specifically, the dynamic adjustment of the timing of the operating adjustment parameters based on the collaboratively orchestrated energy field parameters and their corresponding operating ranges and adjustment command units to form a non-steady-state energy distribution environment includes:

[0048] The coordinated arrangement record of the energy field parameters after coordinated arrangement is aligned with the corresponding action range and regulation command unit to generate a ternary alignment table containing energy field parameters, action range and regulation command unit, and a time priority mark is assigned to each entry in the ternary alignment table.

[0049] Based on the timing priority marker, timing editing rules are generated, including the splitting boundary of the adjustment instruction unit, the rearrangement order of the split sub-units, and the corresponding maintenance rules with the effective interval.

[0050] According to the timing editing rules, one or more time window tokens are injected into the execution window of the adjustment instruction unit to divide the adjustment instruction unit into multiple time sub-windows, and the time sub-windows are respectively bound to the corresponding entries in the ternary alignment table;

[0051] The operating adjustment parameters are dynamically scheduled according to the order of the time sub-windows to form an unsteady energy distribution environment.

[0052] The antibacterial control system based on a chiller unit is used to implement the antibacterial control method based on a chiller unit, and includes: a parameter acquisition module, an operating parameter adjustment module, a dynamic adjustment module, and a correction module.

[0053] The parameter acquisition module is used to acquire the operating parameters of the cooling water and construct a micro-ecological state model of the cooling water based on the operating parameters. The micro-ecological state model is used to characterize the potential attachment and reproduction trend of bacteria in the cooling water. The operating parameters include water temperature gradient, flow velocity pulsation characteristics, conductivity change rate and heat flux distribution on the heat exchanger surface.

[0054] The operating parameter adjustment module is used to calculate the operating adjustment parameters according to the micro-ecological state model, and control the chiller unit to perform periodic or quasi-random non-steady-state operation adjustment of the cooling water.

[0055] The dynamic adjustment module is used to collect local energy field operation data of cooling water corresponding to the operating state of the chiller unit during the non-steady-state operation adjustment process, generate local energy field state data, and dynamically adjust the action sequence of the operation adjustment parameters according to the state data to form a non-steady-state energy distribution environment.

[0056] The correction module is used to continuously monitor the operating feedback parameters of the cooling water and compare them with the micro-ecological state model, and dynamically correct the unsteady-state operation adjustment based on the comparison results.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] This invention proposes a method and system for antibacterial control based on chiller units. By hierarchically organizing and modeling the operating parameters of the cooling water, and introducing the resulting micro-ecological state model into the chiller unit's operation and regulation process, antibacterial control is synergistically linked with the chiller unit's unsteady-state operation and energy field regulation processes. This achieves antibacterial control over the time progression of cooling water flow, heat exchange, and energy distribution. Compared to existing technologies, this method no longer relies on a single external treatment method but utilizes the dynamic operating environment formed by the chiller unit's own operation and regulation to perform targeted control on different operating segments and heat exchange interface regions within the cooling water. This improves the adaptability and continuity of antibacterial control and allows the antibacterial process to dynamically adjust according to changes in the chiller unit's operating state. It is suitable for chiller unit systems with complex operating conditions and frequent load changes. Attached Figure Description

[0059] Figure 1 The flowchart of the antibacterial control method based on a chiller unit provided by the present invention;

[0060] Figure 2 A schematic diagram of the micro-ecological state model provided by this invention;

[0061] Figure 3 A flowchart for generating an unsteady energy distribution environment provided by this invention;

[0062] Figure 4The architecture diagram of the antibacterial control system based on a chiller unit provided by the present invention. Detailed Implementation

[0063] The present application will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all fall within the protection scope of the present application.

[0064] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0065] It should be noted that, unless there is a conflict, the various features in the embodiments of this application can be combined with each other, all of which are within the protection scope of this application. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than the module division in the device or the order in the flowchart. In addition, the terms "first," "second," and "third" used in this application do not limit the data or execution order, but only distinguish identical or similar items with essentially the same function and effect.

[0066] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0067] Example 1:

[0068] Please see Figures 1-3 The present invention provides an embodiment of an antibacterial control method based on a chiller unit, comprising the following specific steps:

[0069] Step S1: Collect the operating parameters of the cooling water and construct a micro-ecological state model of the cooling water based on the operating parameters. The micro-ecological state model is used to characterize the potential attachment and reproduction trend of bacteria in the cooling water. The operating parameters include water temperature gradient, flow velocity pulsation characteristics, conductivity change rate and heat flux distribution on the heat exchanger surface.

[0070] like Figure 2 As shown, the specific steps of step S1 are as follows:

[0071] Step S101: Within a preset sampling period, the water temperature gradient, flow velocity pulsation characteristics, conductivity change rate, and heat flux distribution on the heat exchanger surface of the cooling water are collected from multiple sources, and the data from each source are time-aligned to form a sequence of operating parameters on the same time axis.

[0072] In this embodiment, by simultaneously collecting data on water temperature gradient, flow velocity pulsation characteristics, conductivity change rate, and heat flux distribution on the heat exchanger surface within a preset sampling period, operating parameters from different sources can cover the dynamic behavior of cooling water in three dimensions: flow, heat transfer, and water quality changes. In practice, although the sampling locations, sampling frequencies, and change rhythms of each operating parameter differ, their changes are all driven by the same chiller unit operating conditions. Therefore, the collected data can be aligned using a unified time reference, remapping the parameter changes originally dispersed across different time scales onto the same time axis, thus forming a continuous and comparable sequence of operating parameters. This method ensures that the spatial distribution of water temperature gradient, the periodic fluctuations of flow velocity pulsation, the phased adjustments of conductivity change rate, and the rearrangement of heat flux distribution on the heat exchanger surface remain consistent in the time dimension.

[0073] Step S102: Divide the operating parameter sequence into multiple operating condition segments. The boundaries of the operating condition segments are determined by one or more triggering conditions. The triggering conditions include any one or a combination of the following: morphological changes in flow velocity pulsation characteristics, spatial rearrangement of heat flux distribution, stratification switching of water temperature gradient, and sign reversal of conductivity rate of change.

[0074] In this embodiment, based on the time continuity, the changing trends of each parameter in the obtained operating parameter sequence are first jointly observed. When the flow velocity pulsation characteristics change from a stable period to asymmetric fluctuations or rhythmic changes, or when the heat flux distribution on the heat exchanger surface shows a spatial rearrangement from concentrated to dispersed, or from single-zone to multi-zone, or when the water temperature gradient in the cooling water body shows a switching from a single temperature difference layer to a multi-layer temperature difference structure, or when the rate of change of conductivity reverses in direction on the time axis, it is determined that the cooling water has entered a new operating stage. Based on the occurrence location of one or more of the above triggering conditions, the operating parameter sequence is divided at the corresponding time points, so that the operating parameters in each operating segment remain relatively consistent in terms of change rhythm and spatial characteristics, thereby decomposing the continuous operation process into multiple operating segment with clear boundaries.

[0075] Step S103: Based on each working condition segment, construct a cross-parameter coupling feature set. The cross-parameter coupling feature set includes the correspondence between water temperature gradient and heat flux distribution, the phase correlation between flow velocity pulsation feature and water temperature gradient, the synchronous correlation between conductivity change rate and flow velocity pulsation feature, and the spatial-temporal registration feature between heat flux distribution and flow velocity pulsation feature.

[0076] In this embodiment, within the same operating condition segment, the operating parameters of the cooling water do not change in isolation, but rather exhibit a traceable coordinated change relationship due to the constraints of common operating conditions. In practice, for each segment of the operating conditions, the distribution contour of the water temperature gradient over time and space is first extracted within that segment and compared with the heat flux distribution on the heat exchanger surface at the corresponding time position. This constructs a feature description reflecting the correspondence between the water temperature difference structure and the energy distribution at the heat exchange interface. Subsequently, the flow velocity pulsation characteristics and the water temperature gradient change process are compared over time within the same segment to identify the relative relationship between the two in terms of the starting point, rhythm, and delay of the change, forming a feature characterizing the phase correlation between the two. Furthermore, the time series of the conductivity change rate is synchronized with the change nodes of the flow velocity pulsation characteristics, recording the linkage or misalignment between the two within the same time interval to reflect the synchronous relationship between the electrical changes of the water and the flow state. Finally, the spatial variation region of the heat flux distribution on the heat exchanger surface is registered with the temporal fluctuation process of the flow velocity pulsation characteristics, so that the change in the heat distribution location can correspond to a specific flow rhythm, thus forming a spatial-temporal integrated registration feature.

[0077] Step S104: Map the cross-parameter coupling feature set to the interface region set corresponding to the heat exchanger surface to obtain the interface region-level operating feature sequence. The interface region set is determined by the partitioning results of the heat flux distribution on the heat exchanger surface, and each interface region is associated with the corresponding flow velocity pulsation feature and water temperature gradient feature.

[0078] In this embodiment, based on the characteristic of uneven energy distribution and spatial stability of the heat exchanger surface during cooling water operation, the heat flux distribution on the heat exchanger surface is partitioned into several interface regions with clear boundaries. Specifically, firstly, the spatial range of each interface region is determined according to the relative distribution of heat flux on the heat exchanger surface, ensuring that each interface region corresponds to relatively consistent heat exchange characteristics within the operating condition segment. Subsequently, the cross-parameter coupling feature set constructed in step S103 is mapped to the corresponding interface region according to its spatial location, so that the water temperature gradient change, water flow rhythm, and their temporal correlation characteristics related to that interface region are centrally merged. Based on this, each interface region is associated with its corresponding flow velocity pulsation characteristics and water temperature gradient characteristics, and these characteristics are arranged in chronological order to form an interface region-level operating feature sequence.

[0079] Step S105: Based on the operational feature sequence at the interface region level, generate a micro-ecological state model according to the hierarchical organization of working condition segments, interface regions, and time sequence.

[0080] The specific steps of step S105 are as follows:

[0081] Step S1051: Reorganize the operation feature sequence at the interface region level. The reorganization process includes primary sorting of the operation feature sequence according to the occurrence order of the operating condition segments, and secondary sorting of the operation feature sequence according to the spatial identifier of the interface region within each operating condition segment.

[0082] In this embodiment, based on the fact that interface region-level operating characteristics are simultaneously sortable in both time and space dimensions, the original operating characteristic sequence is hierarchically reorganized to give it a clear hierarchical structure. Specifically, firstly, the sequence of occurrence of operating condition segments is used as the main thread to perform a primary sorting of the interface region-level operating characteristic sequence, forming continuous segments on the time axis for operating characteristics belonging to the same operating condition segment, thus maintaining the sequential relationship between cooling water operation stages. Subsequently, within each sorted operating condition segment, spatial identifiers of interface regions are introduced as a secondary arrangement basis, merging and arranging operating characteristics from different interface regions according to their spatial positions on the heat exchanger surface, so that the operating characteristics of the same interface region within that operating condition segment form a locally continuous sequence. Through the above reorganization process, the interface region-level operating characteristic sequence is transformed into a hierarchical sequence structure that simultaneously reflects the evolution sequence of operating conditions and the spatial distribution of the heat exchange interface, unifying the time and space dimensions within the same data organization framework.

[0083] Step S1052: Construct operating condition segment index, interface area index and time index for each operating feature item in the recombined operating feature sequence, and combine all indexes to form a multi-dimensional index set to limit the unique position of each operating feature item in the hierarchical organizational structure.

[0084] In this embodiment, based on the fact that the recombined operational feature sequence already possesses both operational stage sequence and interface region spatial arrangement structural information, a multi-dimensional index is introduced for each operational feature to further eliminate ambiguity between feature items and establish a traceable hierarchical positioning relationship. Specifically, each operational feature is first assigned a corresponding operational segment index to indicate the operational stage to which it belongs; then, an interface region index is constructed based on the heat exchanger surface interface region from which the operational feature originates, to clarify its spatial attribution; simultaneously, a time index is generated based on the arrangement order of the operational feature in the recombined sequence to reflect its chronological position during the time progression. Furthermore, the aforementioned operational segment index, interface region index, and time index are combined to form a multi-dimensional index set, ensuring that each operational feature corresponds to a unique index combination within the hierarchical organizational structure. In this way, even if different operational feature items are similar in value or feature type, they can be clearly distinguished in the three dimensions of operational condition, space, and time.

[0085] Step S1053: Based on the multidimensional index set, the recombined running feature sequence is divided into multiple state units, and a corresponding state identifier is assigned to each state unit. The state unit consists of continuous time running feature items of at least one interface region within the same working condition segment.

[0086] In this embodiment, based on the multi-dimensional index set, each operational feature item has been clearly defined with a unique position in the operating condition stage, interface region, and time series, thus enabling ordered segmentation. Specifically, the operating condition segment index is first used as a primary constraint for segmentation, ensuring that the operational feature sequence participates in subsequent grouping only within the same operating condition segment. Subsequently, interface region indexes are introduced within the operating condition segment as spatial constraints, aggregating operational feature items belonging to the same interface region. Based on this, the aggregated operational feature items are sequentially checked according to the continuity of the time index. When adjacent operational feature items remain continuous in the time index, they are grouped into the same state unit. When the time index is interrupted or the interface region index changes, the boundary division of the state unit is completed at the corresponding position. After segmentation, a corresponding state identifier is assigned to each state unit, enabling the state identifier to represent the combined features of the state unit in the operating condition segment, interface region, and time dimension. In this way, the original continuous sequence of operating characteristics is transformed into a discrete structure composed of multiple state units, so that the operating state of cooling water under different operating conditions and different heat exchange interfaces can be clearly characterized in the form of state units.

[0087] Step S1054: Based on the state unit and its corresponding state identifier, construct a micro-ecological state model that includes a set of states and temporal relationships between states. The temporal relationships between states are determined by the arrangement order of state units during the switching of working condition segments and the advancement of time.

[0088] In this embodiment, based on the fact that the state units have been segmented and have unique state identifiers, the arrangement relationships of the state units in the time and operating condition dimensions are systematically organized to form a micro-ecological state model that can be used to describe the evolution of cooling water operation. Specifically, all state units are first grouped according to their state identifiers, with state units having the same identifier grouped into the same state category, thus constructing a state set containing multiple state categories. Subsequently, based on the arrangement order of the state units in the reorganized operating characteristic sequence, the sequential relationship between adjacent state units is recorded. When state units appear consecutively within the same operating condition segment, a temporal correlation within the segment is established; when state units appear across the boundary of an operating condition segment, a temporal correlation reflecting the switching of operating conditions is established. By unifying and organizing these two types of temporal correlations, a complete temporal chain is formed between the state set and the states, thereby constructing a micro-ecological state model that simultaneously includes the state set and the temporal correlation between states.

[0089] Specifically, the micro-ecological state model is not an abstract mathematical model or a general artificial intelligence model detached from application scenarios. Instead, it is a state expression model constructed specifically for the operating characteristics of the cooling water system of a chiller unit. In terms of model structure, the micro-ecological state model is constructed using a hierarchical state organization method, which includes at least: an operating condition segment layer, an interface region layer, and a state unit layer. Among them, the operating condition segment layer is used to distinguish the overall state of the cooling water system in different operating stages; the interface region layer is used to distinguish the differences in operating characteristics corresponding to different areas of the heat exchanger surface; the state unit layer is used to characterize the operating state of the cooling water system in the same operating condition segment and the same interface region within a continuous time interval, and each state unit corresponds to a unique state identifier. The model takes the operating parameters of the cooling water as input and outputs the changes in the micro-ecological state of the cooling water under different operating stages, different heat exchange interface regions, and different time conditions.

[0090] In the model generation process, the operational feature sequence at the interface region level is first reorganized according to the order of occurrence of working condition segments and the spatial identifier of the interface region. For each operational feature item, a working condition segment index, an interface region index, and a time index are constructed, and its unique position in the model is determined by a multi-dimensional index set. Subsequently, based on the multi-dimensional index set, the operational feature sequence is divided into multiple state units, and the temporal correlation between state units is established according to the arrangement order of state units in the working condition segment switching and time progression process, thereby forming a micro-ecological state model that includes a set of states and the temporal correlation between states.

[0091] like Figure 2 As shown, multiple operating condition segments are set under the micro-ecological state model, such as operating condition segments 1 to n shown in the figure. Each operating condition segment is used to characterize the state differentiation results of the cooling water system under different operating stages. The operating condition segments are obtained by dividing the operating parameter sequence according to preset trigger conditions. Under each operating condition segment, multiple interface regions are further set, such as interface regions 11 to 1r corresponding to operating condition segment 1, and interface regions n1 to nq corresponding to operating condition segment n. The interface regions are determined by the partitioning results of the heat flux distribution on the heat exchanger surface, and are used to distinguish the differences in operating characteristics of different regions of the heat exchanger surface within the same operating condition segment. Through the above hierarchical structure, Figure 2 The micro-ecological state model shown can organize the operating state of the cooling water system into layers according to operating condition segments and interface regions, so that the state information corresponding to different operating stages and different heat exchange interface regions has a clear belonging relationship in the model.

[0092] Step S2: Based on the micro-ecological state model, calculate the operation adjustment parameters and control the chiller unit to perform periodic or quasi-random unsteady-state operation adjustment of the cooling water.

[0093] like Figure 3 As shown, the specific steps of step S2 are as follows:

[0094] Step S201: Based on the micro-ecological state model, extract the state identifier corresponding to the current working condition segment, and map the state identifier to one or more sets of operating adjustment parameters. The set of operating adjustment parameters includes time adjustment parameters and amplitude adjustment parameters related to the cooling water flow process.

[0095] In this embodiment, a correspondence between state identifiers and cooling water operating characteristics has been established based on the micro-ecological state model. By transforming abstract state information into a set of parameters that can be used for operational adjustment, the transition from model to control logic is achieved. Specifically, firstly, based on the location result of the current operating time in the micro-ecological state model, a state identifier corresponding to the current operating condition segment is extracted. This state identifier reflects the comprehensive operating state of the cooling water within a specific interface region and time range. Subsequently, according to a pre-established state identifier-adjustment parameter mapping rule, the state identifier is mapped to one or more sets of operational adjustment parameters. Each set of adjustment parameters includes a time adjustment parameter for limiting the adjustment rhythm and an amplitude adjustment parameter for limiting the adjustment magnitude. The above mapping process is not a simple one-to-one correspondence, but rather combines the order of appearance of state identifiers in the micro-ecological state model, their duration intervals, and their temporal relationship with adjacent states to select or combine the adjustment parameter sets. This ensures that the generated set of operational adjustment parameters maintains consistency with the state characteristics of the current operating condition segment in terms of timing and amplitude configuration. In this way, the state information in the micro-ecological state model is concretized into operational adjustment parameters that directly participate in the regulation of the cooling water flow process.

[0096] Step S202: Combine the set of operating adjustment parameters to generate an adjustment sequence corresponding to the status identifier, wherein a differentiated arrangement rule is set between the adjustment parameters corresponding to adjacent status identifiers to form an adjustment sequence with non-repetitive characteristics.

[0097] In this embodiment, based on the premise that different state identifiers represent different operational contexts in the micro-ecological state model, the operational adjustment parameter sets are ordered and combined to enable the adjustment behavior to exhibit distinctiveness as the state changes. Specifically, firstly, using the order of appearance of state identifiers as the main thread, the operational adjustment parameter sets obtained in step S201 are initially arranged according to the chronological order of their corresponding state identifiers. Subsequently, a differentiated arrangement rule is introduced between the operational adjustment parameter sets corresponding to adjacent state identifiers. This rule limits the different arrangements of adjacent operational adjustment parameter sets in terms of the order of time adjustment parameters, the range of amplitude adjustment parameters, or the combination of both. Through this rule processing, even if the operational adjustment parameter sets mapped from adjacent state identifiers have similar components, their arrangement structure in the adjustment sequence remains distinct, thereby avoiding repetitive structures in the adjustment sequence during continuous state switching. After the combination process is completed, an adjustment sequence is formed that corresponds one-to-one with the state identifier sequence, enabling the adjustment sequence to reflect the differences in adjustment rhythm and amplitude arrangement brought about by state changes as time progresses.

[0098] Step S203: Introduce periodic adjustment segments and quasi-random adjustment segments into the adjustment sequence, and arrange the periodic adjustment segments and quasi-random adjustment segments alternately according to a preset segment nesting rule to form a composite adjustment sequence.

[0099] The specific steps of step S203 are as follows:

[0100] Step S2031: Obtain the set of periodic adjustment segments and the set of quasi-random adjustment segments corresponding to the current state identifier, and assign a segment identifier and a segment length identifier to each adjustment segment to form a segment candidate set.

[0101] In this embodiment, based on the status identifier already used to characterize the current cooling water operating context, the concept of regulation segments is introduced to provide optional units for the subsequent arrangement of regulation sequences. Specifically, firstly, based on the correspondence between the current status identifier and the micro-ecological state model, the set of periodic regulation segments and the set of quasi-random regulation segments associated with that status identifier are retrieved. The periodic regulation segments represent the repeatable regulation rhythm structure under that state, while the quasi-random regulation segments represent the variable regulation arrangement under that state. Subsequently, each regulation segment is assigned a unique segment identifier to distinguish different segment sources and their identity in the segment candidate set. Simultaneously, a segment length identifier is determined for each regulation segment, limiting its occupancy range on the time axis. By uniformly organizing the type information, segment identifier, and segment length identifier of the regulation segments, a segment candidate set corresponding to the current status identifier is formed.

[0102] Step S2032: Generate a nested template according to the preset segment nesting rules, and bind the nested template to the working condition segment index. The nested template includes the reference insertion position of the periodic adjustment segment, the intercalation position of the quasi-random adjustment segment, and the interval identifier between adjacent intercalations.

[0103] In this embodiment, considering that adjustment segments need to be arranged according to specific organizational logic within different operating condition segments, the insertion relationship of adjustment segments is formally described through preset segment nesting rules. Specifically, firstly, a nested template describing the interleaving relationship of adjustment segments is generated according to the preset segment nesting rules. The nested template uses periodic adjustment segments as the sequence skeleton, and the reference insertion positions of the periodic adjustment segments are pre-marked in this skeleton to define their basic arrangement structure in the overall adjustment sequence. Subsequently, intercalation positions of quasi-random adjustment segments are set between the reference insertion positions, and the relative spacing of different quasi-random adjustment segments during the time progression is defined by setting interval markers for adjacent intercalations. Furthermore, the generated nested template is bound to the corresponding operating condition segment index, so that the nested template can be clearly applied to a specific operating stage.

[0104] It should be noted that the preset segment nesting rules are used to limit the organization of periodic adjustment segments and quasi-random adjustment segments in the adjustment sequence. These rules are not arbitrarily set, but rather a set of pre-determined arrangement rules based on the coordinated requirements of the continuity and variability of the adjustment rhythm during cooling water operation. These rules include: 1. Skeleton rules, used to determine the basic arrangement of periodic adjustment segments in the adjustment sequence. These rules stipulate that periodic adjustment segments serve as the main sequence units of the adjustment sequence, arranged sequentially according to a predetermined order to form a time skeleton structure covering the entire operating condition segment; 2. Interpolation rules, used to limit the insertion position of quasi-random adjustment segments relative to periodic adjustment segments. These rules stipulate that quasi-random adjustment segments can only be inserted into preset interpolation positions between adjacent periodic adjustment segments without changing the original order between periodic adjustment segments; 3. Interval rules, used to limit the minimum time interval or minimum number of segments between adjacent quasi-random adjustment segments to avoid excessive concentration of quasi-random adjustment segments on the time axis; 4. Stage binding rules, used to limit the nesting template type used for different operating condition segments. These rules stipulate that nesting templates must be bound to operating condition segment indexes, ensuring that the same nesting template is only effective within the corresponding operating condition segment.

[0105] Step S2033: According to the nested template, select a periodic adjustment segment as the main sequence segment from the set of periodic adjustment segments, and insert an intercalation segment from the set of quasi-random adjustment segments at the reference insertion position of the main sequence segment. At the same time, advance the insertion order of the intercalation segments according to the interval identifier to generate an interleaved composite segment sequence.

[0106] In this embodiment, the nested template clearly defines the organization of adjustment segments in the time series. By performing segment selection and insertion operations according to the template, a composite segment sequence is constructed. Specifically, firstly, a periodic adjustment segment matching the current operating condition segment index is selected from the set of periodic adjustment segments, and this segment is arranged sequentially along the time axis as the main sequence segment, forming the basic skeleton of the adjustment sequence. Subsequently, according to the preset benchmark insertion position in the nested template, an intercalation segment from the quasi-random adjustment segment set is selected at the corresponding position of the main sequence segment and inserted, embedding the quasi-random adjustment segment into the periodic structure. After a single insertion, the insertion position of the intercalation segment is advanced according to the interval marker set in the nested template, determining the time interval and sequential position of the next intercalation segment relative to the previous intercalation segment, thereby sequentially completing the staggered insertion of multiple intercalation segments. Through the above operations, periodic adjustment segments and quasi-random adjustment segments form a staggered composite segment sequence on the time axis, enabling the adjustment sequence to maintain a phased structure while possessing diverse segment combination forms.

[0107] Step S2034: Write the segment identifier, operating condition segment index and its sequence position identifier in the composite segment sequence to each adjustment segment in the composite segment sequence, and encapsulate them into a composite adjustment sequence using an identifier set.

[0108] In this embodiment, based on the already completed interleaving of periodic and quasi-random adjustment segments in the composite segment sequence, a unified identification and encapsulation mechanism is introduced for each adjustment segment to ensure that the sequence can be accurately identified and invoked by subsequent operation scheduling steps. Specifically, firstly, for each adjustment segment in the composite segment sequence, its corresponding segment identifier is written to distinguish the source and type of different adjustment segments; then, the applicable operating condition segment index is associated with it, enabling the adjustment segment to clearly correspond to a specific operating stage; simultaneously, a sequence identifier is generated based on the arrangement position of the adjustment segment in the composite segment sequence to reflect its chronological order in the time progression. Further, the above segment identifiers, operating condition segment indexes, and sequence identifiers are combined to form an identifier set corresponding one-to-one with each adjustment segment, and this identifier set is used to encapsulate the composite segment sequence, thereby obtaining a structured composite adjustment sequence.

[0109] The generation of the quasi-random adjustment segment includes:

[0110] Read the historical occurrence order of the status identifier corresponding to the current working condition segment in the status set, as well as the connection position of the status identifier in adjacent working condition segments, to form a set of state contexts used to limit the generation range of quasi-random adjustment segments;

[0111] Based on the state context set, a candidate set containing multiple candidate adjustment units is constructed. The candidate adjustment units are different from each other in terms of time length, arrangement order and adjacent difference rules, and each candidate adjustment unit corresponds to at least one combination of running adjustment parameters.

[0112] According to the selection rules associated with the set of state contexts, one or more candidate control units are selected from the candidate set as quasi-random control segments, wherein the selection rules restrict the selected candidate control units from using the same arrangement order repeatedly in continuous operating condition segments.

[0113] In this embodiment, the generation of quasi-random adjustment segments is based on the historical context information carried by state identifiers in the micro-ecological state model. By introducing the historical occurrence order of states and the connection relationship across operating condition segments into the adjustment segment construction process, the generation process has constrained boundaries rather than being completely random. In specific implementation, firstly, the historical occurrence order of the state identifier corresponding to the current operating condition segment in the state set is read, and combined with the preceding and following connection positions of the state identifier in adjacent operating condition segments, a state context set that reflects the position of the state in the time evolution process is formed. Subsequently, based on the state context set, multiple candidate adjustment units are generated under preset adjustment unit construction rules, so that different candidate adjustment units differ in time length arrangement, internal arrangement order, and adjacent adjustment difference rules. At least one combination of operating adjustment parameters is configured for each candidate adjustment unit, thereby forming a candidate set. On this basis, according to the selection rules associated with the state context set, one or more candidate adjustment units are selected from the candidate set as quasi-random adjustment segments. The selection rules constrain the same state identifier to not repeat the same arrangement order in consecutive operating condition segments, so that the adjustment segment maintains a changing structure between different operating condition stages. In this way, the generated quasi-random adjustment segments are both constrained by the state evolution context and have diversity in specific arrangement forms, thus providing intercalation units with state-aware characteristics for composite adjustment sequences.

[0114] It should be noted that the generation of quasi-random adjustment fragments does not rely on mathematical expressions or codes at all, but is a nondeterministic choice under state context constraints, possessing the control characteristics of being traceable but unpredictable.

[0115] Step S204: Decompose the composite adjustment sequence into multiple time-continuous adjustment instruction units, and assign a corresponding execution window identifier to each adjustment instruction unit to limit the execution order of the adjustment instruction units in the time dimension.

[0116] In this embodiment, based on the fact that the composite adjustment sequence already possesses clear segment identifiers and sequence information, further refinement of the time organization enables the adjustment behavior to be triggered sequentially during operation. Specifically, firstly, based on the segment length identifier and sequence identifier of each adjustment segment in the composite adjustment sequence, the composite adjustment sequence is divided along the time axis, dividing portions with consistent adjustment parameter configurations within continuous time intervals into independent adjustment instruction units. Subsequently, a corresponding execution window identifier is assigned to each adjustment instruction unit. This execution window identifier is used to define the starting position and duration interval of the adjustment instruction unit on the overall time axis, thereby clarifying the sequential relationship between different adjustment instruction units. By further lowering the adjustment segment level to time-continuous adjustment instruction units and defining their execution order using execution window identifiers, the composite adjustment sequence can be transformed into an instruction sequence that can be called sequentially according to time progression.

[0117] Step S205: According to the execution window identifier of the adjustment instruction unit, the adjustment instruction unit is sequentially loaded into the operation control logic of the chiller unit, so that the cooling water forms a non-steady-state operation state corresponding to the adjustment sequence during operation.

[0118] In this embodiment, based on the completion of time segmentation and the clear identification of execution windows for the adjustment command units, the phased switching during operation is achieved by embedding the adjustment behavior into the chiller unit's operation control logic in chronological order. Specifically, the adjustment command units are sorted by time according to their corresponding execution window identifiers, and during chiller unit operation, they are loaded into the operation control logic sequentially according to the sorting result, so that different adjustment command units take effect within their respective execution windows. When switching execution windows, the previous adjustment command unit ends, and its corresponding adjustment parameter configuration is replaced by the adjustment parameter configuration of the next adjustment command unit, thus forming continuous but parameter-structured operation phases as time progresses. In this way, the composite adjustment sequence is transformed into control commands that can act on the chiller unit's operation segment by segment as time progresses, causing the cooling water to exhibit a non-steady-state operation consistent with the adjustment sequence during operation.

[0119] Step S3: During the non-steady-state operation adjustment process, collect the local energy field operation data of the cooling water corresponding to the operating state of the chiller unit, and generate the state data of the local energy field. Based on the state data, dynamically adjust the action sequence of the operation adjustment parameters to form a non-steady-state energy distribution environment that is unfavorable to the metabolism and attachment of microbial communities.

[0120] like Figure 3 As shown, the specific steps of step S3 are as follows:

[0121] Step S301: During the unsteady-state operation adjustment process, identify the set of local energy field parameters associated with the operating state of the chiller unit, and establish time indexes for each energy field parameter. The set of local energy field parameters includes energy distribution parameters corresponding to cooling water flow, heat exchange process and operating rhythm.

[0122] In this embodiment, based on the objective fact that multiple energy exchange behaviors are introduced simultaneously during the unsteady-state operation and adjustment of the chiller unit, local energy field parameters are identified and organized by analyzing the relationship between the operating state and the cooling water response. Specifically, during the unsteady-state operation and adjustment process, the changes in the chiller unit's operating rhythm, the adjustment of the cooling water flow state, and the energy transfer characteristics during the heat exchange process are tracked synchronously. Parameters reflecting changes in energy distribution are extracted and summarized into a set of local energy field parameters. Subsequently, based on the time position of each energy field parameter during operation, time indexes are established for each energy field parameter, ensuring that each parameter corresponds to a clear order of occurrence and duration on the time axis. Through the above identification and indexing process, energy distribution parameters dispersed in different operating stages are uniformly incorporated into the same time frame for description, thereby forming a set of local energy field parameters that can be used for subsequent alignment and coordinated arrangement with the operation and adjustment sequence.

[0123] Step S302: Align the time index of the local energy field parameter set with the adjustment command unit, determine the effective range of the energy field parameter corresponding to each adjustment command unit, and form a synchronous mapping relationship between operation adjustment and energy field parameters.

[0124] In this embodiment, based on the alignability between local energy field parameters and operational adjustment commands during the time progression, a unified time reference is used to establish their correspondence. Specifically, firstly, the timestamps corresponding to each energy field parameter in the local energy field parameter set are read and mapped to the time interval where the execution window identifier of the adjustment command unit is located. Then, within the execution window of each adjustment command unit, energy field parameters whose timestamps fall within that window interval are selected, and the selected energy field parameters are merged into the energy field parameter effective range corresponding to that adjustment command unit. By performing the above alignment process on all adjustment command units, each adjustment command unit is associated with a clear energy field parameter effective range, thereby establishing a one-to-one synchronous mapping relationship between the operational adjustment sequence and the local energy field parameter set.

[0125] Step S303: Deconstruct the energy field parameters within the same action range, splitting them into basic parameter components and superimposed parameter components, and associate them with different time sub-intervals of the adjustment command unit to limit the order of change of the energy field parameters during the time progression.

[0126] In this embodiment, based on the fact that energy field parameters typically exhibit a temporal characteristic consisting of both stable and changing components within the same effective range, these parameters are deconstructed to clarify their participation order in the time dimension. Specifically, within the effective range of each adjustment command unit, each energy field parameter is analyzed item by item. The components that persist throughout the entire effective range and exhibit a relatively consistent rate of change are identified as basic parameter components, while the components that undergo phased changes as the adjustment progresses are identified as superimposed parameter components. Subsequently, according to the time division structure within the adjustment command unit, the basic parameter components are associated with time sub-intervals covering the entire execution window, and the superimposed parameter components are associated with different time sub-intervals located within the execution window, establishing a sequence for each time sub-interval. Through this deconstruction and association process, the originally holistic energy field parameters are broken down into parameter components with clearly defined temporal participation positions, thereby limiting the order of change of energy field parameters during the execution of the adjustment command unit.

[0127] Step S304: Based on the synchronization mapping relationship and the energy field parameter deconstruction results, the basic parameter components and superimposed parameter components of different energy field parameters are coordinated and arranged.

[0128] The specific steps of step S304 are as follows:

[0129] Step S3041: Based on the energy field parameter deconstruction results, the basic parameter components and superimposed parameter components belonging to the same synchronization mapping relationship are respectively assigned to the corresponding component groups, and the energy field parameter identifier and the action range identifier of each component group are associated with its source.

[0130] In this embodiment, based on the premise that the energy field parameters have been deconstructed and the synchronization mapping relationship has been clearly established, the parameter components are orderly merged to give the subsequent collaborative arrangement a clear organizational boundary. Specifically, firstly, according to the synchronization mapping relationship formed in step S302, the energy field parameter components within the same regulatory command unit's operating range are screened, and the basic parameter components and superimposed parameter components originating from the same energy field parameter and corresponding to the same operating range are extracted respectively. Then, according to the affiliation of the synchronization mapping relationship, the basic parameter components are grouped into basic component groups, and the superimposed parameter components are grouped into superimposed component groups, thus distinguishing different types of parameter components in terms of organizational structure. While completing the grouping, each component group is associated with its source energy field parameter identifier and corresponding operating range identifier, thereby clarifying the source and applicable scope of the component group in the overall operation regulation and energy field action. Through the above processing, the energy field parameter components are transformed from discrete deconstruction results into component group structures with clear identifiers and boundaries.

[0131] Step S3042: Based on the component groups, generate a collaborative orchestration grammar, which includes the precedence position of the basic parameter components, the insertion position of the superimposed parameter components, and the overlap markers between different component groups.

[0132] In this embodiment, based on the premise that the component groups have clearly distinguished between basic parameter components and superimposed parameter components, and have source and action interval identifiers, a cooperative orchestration syntax is introduced to standardize the description of the participation order and parallel relationship of different components. Specifically, firstly, according to the time sub-interval division rules within the adjustment instruction unit, the preceding position of the basic parameter component on the time axis is determined, so that the basic parameter component corresponds to the initial segment of the entire or main execution interval. Subsequently, according to the time sub-interval position associated with the superimposed parameter component, the insertion position of the superimposed parameter component is marked after the preceding position to limit the participation time point of the superimposed parameter component in the time progression process. Further, when there are multiple component groups from different energy field parameters, an overlap marker is set to indicate the parallel or interleaved participation relationship of different component groups within the same time sub-interval. By uniformly organizing the above-mentioned preceding position, insertion position, and overlap marker, a cooperative orchestration syntax for describing the timing and superimposed relationship of parameter components is formed.

[0133] Step S3043: According to the cooperative orchestration syntax, the basic parameter components and superimposed parameter components are sequentially placed into the corresponding time sub-intervals, and the participation order of different component groups is adjusted according to the overlap mark during the operation to generate the cooperatively orchestrated energy field parameters.

[0134] In this embodiment, based on the clearly defined participation order and parallel relationship of parameter components in the cooperative orchestration syntax, the temporal organization of energy field parameters is completed by transforming abstract orchestration rules into specific time delivery operations. Specifically, firstly, according to the prior positions determined in the cooperative orchestration syntax, each basic parameter component is delivered to its corresponding time sub-interval, allowing it to participate in the initial execution phase of the adjustment instruction unit. Subsequently, according to the insertion positions marked in the cooperative orchestration syntax, the superimposed parameter components are sequentially delivered to their corresponding time sub-intervals. During the delivery process, when there are overlap markers in the cooperative orchestration syntax, the participation order of parameter components from different component groups within the same time sub-interval is adjusted according to the rules defined by the overlap markers, so that each component group participates in the operation in a preset interleaved or parallel manner during the time progression. Through the above sequential delivery and order adjustment processes, the basic parameter components and superimposed parameter components form a structured arrangement in the time dimension, thereby generating cooperatively orchestrated energy field parameters with a clear temporal structure.

[0135] Step S305: Based on the energy field parameters after collaborative arrangement and their corresponding action range and adjustment command unit, dynamically adjust the action sequence of the operation adjustment parameters to form an unsteady energy distribution environment that is unfavorable to bacterial metabolism and attachment.

[0136] The specific steps of step S305 are as follows:

[0137] Step S3051: Align the coordinated arrangement record of the energy field parameters with the corresponding action range and adjustment command unit to generate a ternary alignment table containing energy field parameters, action range and adjustment command unit, and assign a timing priority mark to each entry in the ternary alignment table.

[0138] In this embodiment, the energy field parameters, based on the collaborative orchestration, already possess a clear time deployment order and source identifier. By further aligning the operational adjustment dimensions, the energy field parameters can be incorporated into a unified scheduling framework. Specifically, the time sub-interval information corresponding to each parameter component in the energy field parameter collaborative orchestration record is first read, and then matched with the execution window of the adjustment command unit and its corresponding action interval. This establishes a connection between energy field parameters, action intervals, and adjustment command units within the same time range. Subsequently, the above association results are organized into a ternary alignment table containing energy field parameters, action intervals, and adjustment command units, ensuring that each entry corresponds to a clear time mapping relationship. Simultaneously with generating the ternary alignment table, a time priority marker is assigned to each entry based on its relative position on the time axis, indicating the order in which different entries participate in subsequent time-series adjustments. Through this alignment and marking process, the participation order of energy field parameters can be clearly identified at the adjustment command level.

[0139] Step S3052: Based on the timing priority mark, generate timing clipping rules, which include adjusting the split boundary of the instruction unit, rearranging the order of the split sub-units, and maintaining the corresponding interval.

[0140] In this embodiment, based on the established temporal priority relationships between energy field parameter entries in the ternary alignment table, the temporal structure of the adjustment command units is reorganized to ensure that the execution adjustment order aligns with the participation order of the energy field parameters. Specifically, firstly, the execution window of the adjustment command unit is analyzed according to the temporal priority markers of each entry in the ternary alignment table. The splitting boundary of the adjustment command unit is determined at the location where the temporal priority markers change, thus dividing the originally continuous adjustment command units into multiple time sub-units. Subsequently, the split sub-units are reordered according to the arrangement order of the temporal priority markers, ensuring they are arranged sequentially according to the participation order of the energy field parameters during time progression. Simultaneously, a correspondence-maintaining rule is introduced during the reordering process to ensure that each sub-unit remains associated with its original action interval before and after the reordering. The temporal editing rules generated in this manner allow the temporal structure of the adjustment command units to be adjusted sequentially without disrupting the correspondence between action intervals.

[0141] Step S3053: According to the timing editing rules, inject one or more time window tokens into the execution window of the adjustment instruction unit, divide the adjustment instruction unit into multiple time sub-windows, and bind the time sub-windows to the corresponding entries in the ternary alignment table respectively.

[0142] In this embodiment, based on the time-series editing rules which clearly define the splitting method and rearrangement logic of the adjustment command unit in the time dimension, a fine-grained time segmentation of the adjustment command unit is achieved by introducing time window tokens. Specifically, firstly, within the original execution window of the adjustment command unit, one or more time window tokens are injected according to the splitting boundary positions determined in the time-series editing rules to mark the splitting points of the adjustment command unit on the time axis. Then, using the time window tokens as boundaries, the adjustment command unit is divided into multiple time-continuous sub-windows, each corresponding to a clearly defined start and end interval. After segmentation, each time sub-window is bound to entries in the ternary alignment table that have the same time range and time priority marker, so that each time sub-window is associated with the corresponding energy field parameter and its effective range. Through the above injection, segmentation, and binding processes, the original operation and adjustment process, which was originally granular at the adjustment command unit level, is refined into multiple independently schedulable time sub-windows, providing a direct time carrier for subsequent dynamic scheduling according to the participation order of energy field parameters.

[0143] Step S3054: Dynamically schedule the operation adjustment parameters according to the order of the time sub-windows to form an unsteady energy distribution environment that is not conducive to bacterial metabolism and attachment.

[0144] In this embodiment, based on the completion of time sub-window segmentation and binding to the ternary alignment table, the operating adjustment parameters are scheduled according to the order of the time sub-windows to achieve dynamic switching of operating parameters in the time dimension. Specifically, the arrangement order of the time sub-windows is first read, and the operating adjustment parameters bound to the first time sub-window are loaded into the operating control logic, allowing this set of parameters to participate in cooling water operation within the corresponding time sub-window. Subsequently, as time progresses to the next time sub-window, the operating adjustment parameters bound to that time sub-window are replaced, and the above process is repeated until all time sub-windows are scheduled. During this scheduling process, the operating adjustment parameters corresponding to different time sub-windows differ in the combination structure of time adjustment parameters and amplitude adjustment parameters, causing the cooling water to exhibit a changing operating state over a continuous time interval. Through this dynamic scheduling method, the time participation order of the operating adjustment parameters and energy field parameters remains consistent, enabling the cooling water to form an energy distribution state with time-level changing characteristics during operation, thereby completing the operating adjustment implementation consistent with the previous collaborative scheduling results.

[0145] Step S4: Continuously monitor the operating feedback parameters of the cooling water and compare them with the micro-ecological state model. Based on the comparison results, dynamically correct the unsteady-state operation adjustment.

[0146] In this embodiment, based on the understanding that the operating state of cooling water has a continuously evolving characteristic, a feedback comparison mechanism is introduced to enable unsteady-state operation regulation to be corrected as the operating state changes. Specifically, during the operation of the chiller unit, operating feedback parameters reflecting the cooling water flow, heat exchange, and water characteristics are continuously collected, and these parameters are organized according to a time base consistent with the aforementioned operating parameters. Subsequently, the organized operating feedback parameters are mapped to the corresponding state identifiers or state evolution paths in the micro-ecological state model. The current operating state is compared with the state described in the model to identify deviations in state sequence, duration, or correlation. After the comparison is completed, the adjustment sequence, adjustment command units, or time sub-window arrangements used in unsteady-state operation regulation are adjusted according to the identified deviations, so that subsequent operation regulation can re-align with the state evolution reflected in the micro-ecological state model. Through the above continuous monitoring and dynamic correction process, unsteady-state operation regulation is no longer fixed to a preset mode but can be iteratively updated under the constraints of operating feedback, thereby forming a closed-loop regulation mechanism based on model comparison.

[0147] Example 2:

[0148] Please see Figure 4 Another embodiment of the present invention provides: an antibacterial control system based on a chiller unit, comprising: a parameter acquisition module, an operating parameter adjustment module, a dynamic adjustment module, and a correction module;

[0149] The parameter acquisition module is used to acquire the operating parameters of the cooling water and construct a micro-ecological state model of the cooling water based on the operating parameters. The micro-ecological state model is used to characterize the potential attachment and reproduction trend of bacteria in the cooling water. The operating parameters include water temperature gradient, flow velocity pulsation characteristics, conductivity change rate and heat flux distribution on the heat exchanger surface.

[0150] The operating parameter adjustment module is used to calculate the operating adjustment parameters according to the micro-ecological state model, and control the chiller unit to perform periodic or quasi-random non-steady-state operation adjustment of the cooling water.

[0151] The dynamic adjustment module is used to collect local energy field operation data of cooling water corresponding to the operating state of the chiller unit during the non-steady-state operation adjustment process, and generate local energy field state data. Based on the state data, the timing of the operation adjustment parameters is dynamically adjusted to form a non-steady-state energy distribution environment that is not conducive to bacterial metabolism and attachment.

[0152] The correction module is used to continuously monitor the operating feedback parameters of the cooling water and compare them with the micro-ecological state model, and dynamically correct the unsteady-state operation adjustment based on the comparison results.

[0153] In addition, the parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of the corresponding technical solutions in the prior art have not been described in detail, so as to avoid excessive elaboration.

[0154] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for inhibiting bacteria in water chillers, characterized in that, include: The operating parameters of the cooling water are collected, and a micro-ecological state model of the cooling water is constructed based on the operating parameters. The micro-ecological state model is used to characterize the potential attachment and reproduction trend of bacteria in the cooling water. The operating parameters include water temperature gradient, flow velocity pulsation characteristics, conductivity change rate and heat flux distribution on the heat exchanger surface. Based on the micro-ecological state model, the operating adjustment parameters are calculated, and the chiller unit is controlled to perform periodic or quasi-random non-steady-state operation adjustment of the cooling water. During the unsteady-state operation adjustment process, the local energy field operation data of the cooling water corresponding to the operating state of the chiller unit is collected, and the state data of the local energy field is generated. Based on the state data, the action sequence of the operation adjustment parameters is dynamically adjusted to form an unsteady energy distribution environment. The operating feedback parameters of the cooling water are continuously monitored and compared with the micro-ecological state model. Based on the comparison results, the unsteady-state operation adjustment is dynamically corrected.

2. The antibacterial control method based on a chiller unit as described in claim 1, characterized in that, The process of collecting operating parameters of the cooling water and constructing a micro-ecological state model of the cooling water based on these parameters includes: Within a preset sampling period, the water temperature gradient, flow velocity pulsation characteristics, conductivity change rate, and heat flux distribution on the heat exchanger surface of the cooling water are collected from multiple sources, and the data from each source are time-aligned to form a sequence of operating parameters on the same time axis. The sequence of operating parameters is divided into multiple operating condition segments, and the boundaries of the operating condition segments are determined by one or more triggering conditions. For each working condition segment, a cross-parameter coupling feature set is constructed. The cross-parameter coupling feature set is mapped to the interface region set corresponding to the heat exchanger surface to obtain the interface region-level operating feature sequence. Based on the operational feature sequence at the interface region level, a micro-ecological state model is generated according to the hierarchical organization of operating condition segments, interface regions, and time sequence.

3. The antibacterial control method based on a chiller unit as described in claim 2, characterized in that, The micro-ecological state model, based on the interface region-level operational feature sequence, is generated according to a hierarchical organization of operational segments, interface regions, and time sequence, including: The operation feature sequence at the interface region level is reorganized. The reorganization process includes primary sorting of the operation feature sequence according to the occurrence order of the operation condition segments, and secondary sorting of the operation feature sequence according to the spatial identifier of the interface region within each operation condition segment. For each operational feature item in the recombined operational feature sequence, construct operational condition segment index, interface region index, and time index respectively, and combine all indexes to form a multidimensional index set; Based on the multidimensional index set, the recombined running feature sequence is divided into multiple state units, and a corresponding state identifier is assigned to each state unit. Based on the state units and their corresponding state identifiers, a micro-ecological state model is constructed that includes a set of states and temporal relationships between states.

4. The antibacterial control method based on a chiller unit as described in claim 3, characterized in that, Based on the aforementioned micro-ecological state model, operational adjustment parameters are calculated, and the chiller unit is controlled to perform periodic or quasi-random non-steady-state operational adjustments to the cooling water, including: Based on the micro-ecological state model, the state identifier corresponding to the current working condition segment is extracted, and the state identifier is mapped to one or more sets of operating adjustment parameters. The set of operating adjustment parameters includes time adjustment parameters and amplitude adjustment parameters related to the cooling water flow process. The set of operating adjustment parameters is combined and processed to generate an adjustment sequence corresponding to the status identifier; Periodic adjustment segments and quasi-random adjustment segments are introduced into the adjustment sequence, and the periodic adjustment segments and quasi-random adjustment segments are arranged alternately according to a preset segment nesting rule to form a composite adjustment sequence; The composite adjustment sequence is decomposed into multiple time-continuous adjustment instruction units, and a corresponding execution window identifier is assigned to each adjustment instruction unit. According to the execution window identifier of the adjustment instruction unit, the adjustment instruction unit is sequentially loaded into the operation control logic of the chiller unit, so that the cooling water forms a non-steady-state operation state corresponding to the adjustment sequence during operation.

5. The antibacterial control method based on a chiller unit as described in claim 4, characterized in that, Periodic adjustment segments and quasi-random adjustment segments are introduced into the adjustment sequence, and the periodic adjustment segments and quasi-random adjustment segments are interleaved according to a preset segment nesting rule to form a composite adjustment sequence, including: Obtain the set of periodic adjustment segments and the set of quasi-random adjustment segments corresponding to the current state identifier, and assign a segment identifier and a segment length identifier to each adjustment segment to form a segment candidate set; Based on the preset segment nesting rules, a nesting template is generated and the nesting template is bound to the working condition segment index. The nesting template includes the reference insertion position of the periodic adjustment segment, the intercalation position of the quasi-random adjustment segment, and the interval identifier between adjacent intercalations. According to the nested template, a periodic adjustment segment is selected as the main sequence segment from the set of periodic adjustment segments, and an intercalation segment from the set of quasi-random adjustment segments is inserted at the reference insertion position of the main sequence segment. At the same time, the insertion order of the intercalation segments is advanced according to the interval identifier to generate a composite segment sequence. Each adjustment segment in the composite segment sequence is written with its segment identifier, operating condition segment index, and its sequence position identifier in the composite segment sequence, and then encapsulated with an identifier set to form a composite adjustment sequence.

6. The antibacterial control method based on a chiller unit as described in claim 5, characterized in that, The generation of quasi-randomized modulated segments includes: Read the historical order of the status identifiers corresponding to the current working condition segment in the status set, as well as the connection position of the corresponding status identifiers in adjacent working condition segments, to form a status context set; Based on the aforementioned state context set, a candidate set containing multiple candidate adjustment units is constructed; According to the selection rules associated with the set of state contexts, one or more candidate adjustment units are selected from the candidate set as quasi-random adjustment segments.

7. The antibacterial control method based on a chiller unit as described in claim 6, characterized in that, During the unsteady-state operation adjustment process, local energy field operation data of the cooling water corresponding to the operating state of the chiller unit is collected, and state data of the local energy field is generated. Based on the state data, the timing of the operation adjustment parameters is dynamically adjusted to form an unsteady energy distribution environment, including: During the unsteady-state operation adjustment process, the set of local energy field parameters associated with the operating state of the chiller unit is identified, and time indexes are established for each energy field parameter. The set of local energy field parameters includes energy distribution parameters corresponding to cooling water flow, heat exchange process and operating rhythm. The time index of the local energy field parameter set is aligned with the adjustment command unit to determine the energy field parameter range corresponding to each adjustment command unit, thus forming a synchronous mapping relationship between operation adjustment and energy field parameters. The energy field parameters within the same action range are deconstructed, splitting them into basic parameter components and superimposed parameter components, and then associated with different time sub-intervals of the adjustment command unit. Based on the synchronous mapping relationship and the energy field parameter deconstruction results, the basic parameter components and superimposed parameter components of different energy field parameters are coordinated and arranged. Based on the energy field parameters after collaborative arrangement and their corresponding operating range and adjustment command unit, the timing of the operation adjustment parameters is dynamically adjusted to form an unsteady energy distribution environment.

8. The antibacterial control method based on a chiller unit as described in claim 7, characterized in that, The method, based on the synchronization mapping relationship and the energy field parameter deconstruction results, involves the coordinated arrangement of the fundamental parameter components and superimposed parameter components of different energy field parameters, including: Based on the energy field parameter deconstruction results, the basic parameter components and superimposed parameter components belonging to the same synchronous mapping relationship are respectively assigned to the corresponding component groups, and each component group is associated with the energy field parameter identifier and the action range identifier from which it originates. Based on the component groups, a collaborative orchestration grammar is generated, which includes the precedence position of the basic parameter components, the insertion position of the superimposed parameter components, and the overlap markers between different component groups. According to the collaborative orchestration syntax, the basic parameter components and superimposed parameter components are sequentially placed into the corresponding time sub-intervals, and the participation order of different component groups is adjusted according to the overlap mark during the operation to generate the collaboratively orchestrated energy field parameters.

9. The antibacterial control method based on a chiller unit as described in claim 8, characterized in that, The dynamic adjustment of the timing of the operating adjustment parameters, based on the coordinated energy field parameters and their corresponding operating ranges and adjustment command units, forms a non-steady-state energy distribution environment, including: The coordinated arrangement record of the energy field parameters after coordinated arrangement is aligned with the corresponding action range and regulation command unit to generate a ternary alignment table containing energy field parameters, action range and regulation command unit, and a time priority mark is assigned to each entry in the ternary alignment table. Based on the timing priority marker, timing editing rules are generated, including the splitting boundary of the adjustment instruction unit, the rearrangement order of the split sub-units, and the corresponding maintenance rules with the effective interval. According to the timing editing rules, one or more time window tokens are injected into the execution window of the adjustment instruction unit to divide the adjustment instruction unit into multiple time sub-windows, and the time sub-windows are respectively bound to the corresponding entries in the ternary alignment table; The operating adjustment parameters are dynamically scheduled according to the order of the time sub-windows to form an unsteady energy distribution environment.

10. A chiller-based antibacterial control system, used to implement the chiller-based antibacterial control method according to any one of claims 1-9, characterized in that, include: Parameter acquisition module, operating parameter adjustment module, dynamic adjustment module, and correction module; The parameter acquisition module is used to acquire the operating parameters of the cooling water and construct a micro-ecological state model of the cooling water based on the operating parameters. The micro-ecological state model is used to characterize the potential attachment and reproduction trend of bacteria in the cooling water. The operating parameters include water temperature gradient, flow velocity pulsation characteristics, conductivity change rate and heat flux distribution on the heat exchanger surface. The operating parameter adjustment module is used to calculate the operating adjustment parameters according to the micro-ecological state model, and control the chiller unit to perform periodic or quasi-random non-steady-state operation adjustment of the cooling water. The dynamic adjustment module is used to collect local energy field operation data of cooling water corresponding to the operating state of the chiller unit during the non-steady-state operation adjustment process, generate local energy field state data, and dynamically adjust the action sequence of the operation adjustment parameters according to the state data to form a non-steady-state energy distribution environment. The correction module is used to continuously monitor the operating feedback parameters of the cooling water and compare them with the micro-ecological state model, and dynamically correct the unsteady-state operation adjustment based on the comparison results.

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