Intelligent building waterscape regulation and control system and method based on climate perception

Abstract

The invention relates to the technical field of regulation systems, and discloses an intelligent building waterscape regulation and control system and method based on climate awareness, and the system comprises a feature index fusion module, a multi-dimensional strategy matching module, a collaborative optimization module, a state regulation and control module, and a final regulation and control module. Performing real-time feature index fusion on the multi-source heterogeneous environmental parameters of the building waterscape to obtain a real-time climate index; based on the operation strategy of the building waterscape, performing multi-dimensional strategy matching on the real-time climate index to obtain a preliminary regulation and control strategy of the real-time climate index; based on the preliminary regulation and control strategy, performing overall collaborative optimization on the building waterscape to obtain a preliminary overall regulation and control instruction of the building waterscape; performing state real-time regulation and control on the initial overall regulation and control instruction to obtain an initial optimized operation state of the building waterscape; performing strategy optimization according to the preliminary optimization operation state to obtain a final regulation and control strategy of the building waterscape; according to the invention, the accuracy of intelligent building waterscape regulation and control can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of regulation system, and in particular to an intelligent building water scene regulation system and method based on climate perception. BACKGROUND

[0002] In the prior art, in the intelligent building water scene regulation, the processing of multi-source heterogeneous environmental parameters lacks a scientific fusion mechanism, only single or a few environmental parameters are simply collected, multi-modal standard parameter fusion and cross-domain feature analysis are not performed, the internal correlation between parameters cannot be mined to form a comprehensive climate index. At the same time, the synergistic response relationship between the landscape aesthetic demand and the water scene evaporation compensation demand is not considered, and the regulation basis is only formulated based on a single demand, resulting in a one-sided climate index that cannot accurately reflect the comprehensive influence of the actual climate on the water scene operation, and the reliability of the basic data provided for the subsequent regulation strategy formulation is insufficient.

[0003] At the same time, the prior art has obvious defects in the regulation strategy formulation and optimization link. On the one hand, the building water scene operation strategy is not analyzed in terms of time sequence characteristics in the strategy matching process, and the influence factors of the climate index are not deeply analyzed, only a preliminary regulation strategy is determined through a simple correspondence relationship, resulting in a low adaptation degree of the strategy to the real-time climate condition and the water scene operation demand; on the other hand, lacking a whole synergistic optimization and dynamic adjustment mechanism, after the preliminary regulation instruction is generated, the parameter weight is not dynamically optimized combined with the climate feature change trend, and the running state is not evaluated and strategy optimized in multiple dimensions, which cannot timely correct the regulation deviation, so that the water scene running state is difficult to balance the aesthetic effect and resource saving, and the regulation precision and stability are insufficient. SUMMARY

[0004] The present application provides an intelligent building water scene regulation system and method based on climate perception to solve the problems raised in the background art.

[0005] To achieve the above-mentioned purpose, the present application provides an intelligent building water scene regulation system based on climate perception, characterized in that the system comprises a feature index fusion module, a multi-dimensional strategy matching module, a synergistic optimization module, a state regulation module and a final regulation module, wherein:

[0006] The feature index fusion module is used for real-time feature index fusion of multi-source heterogeneous environmental parameters of the building water scene, to obtain a real-time climate index of the multi-source heterogeneous environmental parameters;

[0007] The multi-dimensional strategy matching module is used for multi-dimensional strategy matching of the real-time climate index based on the operation strategy of the building water scene, to obtain a preliminary regulation strategy of the real-time climate index;

[0008] The collaborative optimization module is used to perform overall collaborative optimization of the building water feature based on the preliminary control strategy, and obtain the preliminary overall control instructions for the building water feature.

[0009] The state control module is used to perform real-time state control on the preliminary overall control command to obtain the preliminary optimized operating state of the building water feature.

[0010] The final control module is used to perform strategy optimization based on the preliminary optimized operating status to obtain the final control strategy for the building water feature.

[0011] In a preferred embodiment, the real-time feature index fusion of the multi-source heterogeneous environmental parameters of the architectural water feature to obtain the real-time climate index of the multi-source heterogeneous environmental parameters is specifically used for:

[0012] Multimodal standard parameter fusion is performed on the multi-source heterogeneous environmental parameters to obtain the standardized environmental parameters of the multi-source heterogeneous environmental parameters;

[0013] Cross-domain feature analysis is performed on the standardized environmental parameters to obtain the associated feature vector of the standardized environmental parameters;

[0014] Based on the response relationship between landscape aesthetic needs and water feature evaporation compensation needs, the associated feature vectors are dynamically weighted and fused to obtain the initial climate index of the associated feature vectors.

[0015] The initial climate index is reconstructed in real time with priority to obtain the real-time climate index of the multi-source heterogeneous environmental parameters.

[0016] In a preferred embodiment, the dynamic weighted fusion of the associated feature vectors based on the response relationship between landscape aesthetic needs and water feature evaporation compensation needs to obtain an initial climate index for the associated feature vectors, specifically used for:

[0017] Extract landscape aesthetics-related features and water feature evaporation compensation-related features from the associated feature vectors;

[0018] Based on the synergistic response relationship, the features related to landscape aesthetics and the features related to water feature evaporation compensation are nonlinearly weighted and fused to obtain an initial climate index of the associated feature vector. The calculation formula for the initial climate index is as follows:

[0019]

[0020] In the formula, This refers to the initial climate index. Thermodynamic gradient response coefficient, For temperature gradient, The first of the standard environmental parameters Dynamic weights of environmental parameters, The first of the standard environmental parameters One standardized feature value, This represents the water surplus or deficit. For aesthetic-evaporation balance coefficient, As a landscape-climate synergistic factor, It is the hyperbolic tangent function. It is an exponential function.

[0021] In a preferred embodiment, the operation strategy based on the building water feature is used to perform multi-dimensional strategy matching on the real-time climate index to obtain a preliminary control strategy for the real-time climate index, specifically for:

[0022] The timing characteristics of the operation strategy are analyzed to obtain the timing adaptation strategy of the operation strategy;

[0023] The influence factors of the real-time climate index are obtained by performing characteristic influence factor analysis.

[0024] The matching degree between the influencing factors and the time-series adaptation strategy is evaluated to obtain candidate strategies for the real-time climate index.

[0025] Based on the candidate strategies, the real-time climate index is subjected to multi-objective coordinated regulation to obtain a preliminary regulation strategy for the real-time climate index.

[0026] In a preferred embodiment, the step of evaluating the strategy matching degree between the influencing factors and the time-series adaptation strategy to obtain candidate strategies for the real-time climate index is specifically used for:

[0027] A dynamic relationship mapping is performed between the impact factors and the time-series adaptation strategy to obtain a mapping relationship table between the impact factors and the time-series adaptation strategy;

[0028] Based on the mapping table, the influence factors are analyzed to obtain the appropriate strategies for the influence factors.

[0029] Based on the intensity level of the influencing factors, the adaptation strategies are prioritized to obtain a priority sequence of the adaptation strategies.

[0030] Based on the priority sequence, the real-time climate index is grouped by fit to obtain candidate strategies for the real-time climate index.

[0031] In a preferred embodiment, the step of performing overall coordinated optimization of the architectural water feature based on the preliminary control strategy to obtain preliminary overall control instructions for the architectural water feature is specifically used for:

[0032] According to the multi-dimensional target demand of the building water scene, a multi-target optimization evaluation system of the building water scene is established;

[0033] The preliminary control strategy is comprehensively evaluated to obtain a priority level of the preliminary control strategy;

[0034] According to the current climate characteristic change trend, the weight proportion of the demand in the multi-target optimization evaluation system is dynamically adjusted;

[0035] Based on the adjusted weight proportion, a parameter of the strategy with the highest priority level is refined to obtain a control parameter of the building water scene;

[0036] The control parameter is arranged in a logical instruction sequence to obtain a preliminary overall control instruction of the building water scene.

[0037] In a preferred embodiment, the parameter of the strategy with the highest priority level is refined based on the adjusted weight proportion to obtain the control parameter of the building water scene, which is specifically used for:

[0038] The control target contained in the strategy with the highest priority level is analyzed to obtain a core control parameter of the strategy with the highest priority level;

[0039] According to the adjusted weight proportion, a dynamic mapping relationship between different control target requirements is established;

[0040] Based on the dynamic mapping relationship, the core control parameter is converted into an executable logical control parameter;

[0041] The logical control parameter is coordinately optimized to obtain the control parameter of the building water scene.

[0042] In a preferred embodiment, a preliminary optimization running state of the building water scene is obtained by performing real-time state regulation on the preliminary overall control instruction, which is specifically used for:

[0043] The preliminary overall control instruction is dynamically analyzed to obtain a water scene running mode requirement and a parameter adjustment requirement in the preliminary overall control instruction;

[0044] According to the water scene running mode requirement, a control logic sequence of the water scene running mode is determined;

[0045] Based on the parameter adjustment requirement, an execution parameter combination of the parameter adjustment requirement is generated;

[0046] The control logic sequence and the execution parameter combination are dynamically coordinated to form an executable cooperative control scheme;

[0047] The multi-element cooperative optimization is performed on the cooperative control scheme, so as to obtain a preliminary optimized running state of the architectural waterscape.

[0048] In a preferred embodiment, a strategy optimization is performed according to the preliminary optimized running state, so as to obtain a final regulation and control strategy of the architectural waterscape, and the final regulation and control strategy is specifically used for:

[0049] A multi-dimensional index evaluation is performed on the preliminary optimized running state, so as to obtain a running index of the preliminary optimized running state.

[0050] A dimensional difference analysis is performed on the running index and an expected target value, so as to obtain a key dimension of the running index.

[0051] Based on the key dimension, a strategy optimization is performed on a strategy library of the architectural waterscape, so as to obtain a candidate optimized strategy of the strategy library.

[0052] Based on a current climate characteristic change trend, an applicability adaptation is performed on the candidate optimized strategy, so as to obtain an optimized strategy of the candidate optimized strategy.

[0053] The optimized strategy and the preliminary regulation and control strategy are fused and optimized, so as to obtain the final regulation and control strategy of the architectural waterscape.

[0054] In order to solve the above problems, the application further provides an intelligent architectural waterscape regulation and control method based on climate perception, and the method comprises the following steps:

[0055] S1. Real-time characteristic index fusion is performed on multi-source heterogeneous environment parameters of an architectural waterscape, so as to obtain a real-time climate index of the multi-source heterogeneous environment parameters.

[0056] S2. Multi-dimensional strategy matching is performed on the real-time climate index based on a running strategy of the architectural waterscape, so as to obtain a preliminary regulation and control strategy of the real-time climate index.

[0057] S3. Overall cooperative optimization is performed on the architectural waterscape based on the preliminary regulation and control strategy, so as to obtain a preliminary overall regulation and control instruction of the architectural waterscape.

[0058] S4. State real-time regulation and control is performed on the preliminary overall regulation and control instruction, so as to obtain a preliminary optimized running state of the architectural waterscape.

[0059] S5. Strategy optimization is performed according to the preliminary optimized running state, so as to obtain a final regulation and control strategy of the architectural waterscape.

[0060] Compared with the prior art, the application has the following beneficial effects:

[0061] 1.The application fuses multi-modal standard parameters of multi-source heterogeneous environmental parameters of building water landscape through a feature index fusion module, analyzes cross-domain features, dynamically weights the synergistic response relationship between landscape aesthetics demand and water landscape evaporation compensation demand, and further reconfigures the initial climate index in real time, which can accurately integrate multi-dimensional environmental data, generate real-time climate index that is comprehensive and meets actual demand, provide high-quality data support for subsequent control strategy formulation, and effectively improve the accuracy of control decision.

[0062] 2.The application realizes accurate matching of real-time climate index and operation strategy through a multi-dimensional strategy matching module to obtain a preliminary control strategy, dynamically adjusts weights and refines parameters through a synergistic optimization module combined with a multi-objective optimization evaluation system and climate feature change trend, generates a preliminary optimized operation state through a state control module, and finally generates a final control strategy based on multi-dimensional index evaluation and strategy optimization through a final control module, which realizes dynamic optimization and accurate execution of the control strategy throughout the process, significantly improves the intelligent level and stability of building water landscape control, and ensures that water landscape operation takes into account landscape aesthetics and efficient resource utilization. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 A system architecture diagram of an intelligent building water landscape control system based on climate perception is provided for an embodiment of the application. Figure 2 A flowchart of an intelligent building water landscape control method based on climate perception is provided for an embodiment of the application.

[0064] 100.An intelligent building water landscape control system based on climate perception; 101.A feature index fusion module; 102.A multi-dimensional strategy matching module; 103.A synergistic optimization module; 104.A state control module; and 105.A final control module.

[0065] The implementation, functional features and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0066] To make the purpose, technical solutions and advantages of the embodiments of the application clearer, the technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings of the embodiments of the application. Obviously, the described embodiments belong to some of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the application.

[0067] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0068] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0069] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0070] In practice, the server-side equipment deployed in a climate-sensing-based intelligent building water feature control system may consist of one or more devices. This climate-sensing-based intelligent building water feature control system can be implemented as: a business instance, a virtual machine, and hardware devices. For example, this climate-sensing-based intelligent building water feature control system can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, this climate-sensing-based intelligent building water feature control system can be understood as software deployed on a cloud node, used to provide a climate-sensing-based intelligent building water feature control system to various user terminals. Alternatively, this climate-sensing-based intelligent building water feature control system can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Alternatively, this climate-sensing-based intelligent building water feature control system can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more hardware devices configured to provide a climate-sensing-based intelligent building water feature control system to various user terminals.

[0071] In terms of implementation, an intelligent building water feature control system based on climate perception and the user terminal are mutually compatible. That is, if the intelligent building water feature control system based on climate perception is implemented as an application installed on a cloud service platform, then the user terminal is a client that establishes a communication connection with the application; or if the intelligent building water feature control system based on climate perception is implemented as a website, then the user terminal is implemented as a webpage; or if the intelligent building water feature control system based on climate perception is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0072] likeFigure 1 The diagram shown is a system architecture diagram of an intelligent building water feature control system based on climate perception, provided by an embodiment of the present invention.

[0073] The intelligent building water feature control system 100 based on climate perception described in this invention can be installed on a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed on the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed as a website. Depending on the functions implemented, the intelligent building water feature control system 100 based on climate perception may include a feature index fusion module 101, a multi-dimensional strategy matching module 102, a collaborative optimization module 103, a state control module 104, and a final control module 105. The modules described in this invention can also be called units, referring to a series of computer program segments that can be executed by an electronic device's processor and perform a fixed function, stored in the electronic device's memory.

[0074] In this embodiment of the invention, in a climate-sensing-based intelligent building water feature control system, each of the above-mentioned modules can be implemented independently and can call other modules. Here, "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. The climate-sensing-based intelligent building water feature control system provided by this embodiment of the invention allows for adjustment of the applicable scope of the system architecture without modifying the program code. This is achieved by adding modules and directly calling them, enabling cluster-based horizontal expansion and flexibly expanding the climate-sensing-based intelligent building water feature control system. In practical applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in a cloud server.

[0075] The following describes, with reference to specific embodiments, the various components and specific workflows of an intelligent building water feature control system based on climate sensing:

[0076] The feature index fusion module 101 is used to perform real-time feature index fusion on the multi-source heterogeneous environmental parameters of the building water feature to obtain the real-time climate index of the multi-source heterogeneous environmental parameters.

[0077] In this embodiment of the invention, the real-time feature index fusion of the multi-source heterogeneous environmental parameters of the architectural water feature to obtain the real-time climate index of the multi-source heterogeneous environmental parameters is specifically used for:

[0078] Multimodal standard parameter fusion is performed on the multi-source heterogeneous environmental parameters to obtain the standardized environmental parameters of the multi-source heterogeneous environmental parameters;

[0079] Cross-domain feature analysis is performed on the standardized environmental parameters to obtain the associated feature vector of the standardized environmental parameters;

[0080] Based on the response relationship between landscape aesthetic needs and water feature evaporation compensation needs, the associated feature vectors are dynamically weighted and fused to obtain the initial climate index of the associated feature vectors.

[0081] The initial climate index is reconstructed in real time with priority to obtain the real-time climate index of the multi-source heterogeneous environmental parameters.

[0082] Based on the response relationship between landscape aesthetic needs and water feature evaporation compensation needs, the associated feature vectors are dynamically weighted and fused to obtain an initial climate index for the associated feature vectors, specifically used for:

[0083] Extract landscape aesthetics-related features and water feature evaporation compensation-related features from the associated feature vectors;

[0084] Based on the synergistic response relationship, the features related to landscape aesthetics and the features related to water feature evaporation compensation are nonlinearly weighted and fused to obtain an initial climate index of the associated feature vector. The calculation formula for the initial climate index is as follows:

[0085]

[0086] In the formula, This refers to the initial climate index. Thermodynamic gradient response coefficient, For temperature gradient, The first of the standard environmental parameters Dynamic weights of environmental parameters, The first of the standard environmental parameters One standardized feature value, This represents the water surplus or deficit. For aesthetic-evaporation balance coefficient, As a landscape-climate synergistic factor, It is the hyperbolic tangent function. It is an exponential function.

[0087] Specifically, when performing multimodal standard parameter fusion on multi-source heterogeneous environmental parameters, first collect multi-source heterogeneous environmental parameters around the building's water feature. These parameters cover different types of data such as temperature, humidity, wind speed, solar radiation intensity, and water turbidity, and the units and numerical ranges of each parameter are different.

[0088] Furthermore, a unified standard parameter system was subsequently established, which sets clear standard units and standard value ranges for each environmental parameter.

[0089] Furthermore, the parameter mapping transformation method is then used to process each type of multi-source heterogeneous environmental parameter. Specifically, the original values ​​of each type of parameter are transformed into the corresponding standard value range according to the preset mapping rules, and at the same time converted into standard units. After completing the transformation of all multi-source heterogeneous environmental parameters, the standardized environmental parameters of multi-source heterogeneous environmental parameters are integrated to ensure that all parameters are under a unified standard system and to eliminate the differences in multimodal data.

[0090] Furthermore, when conducting cross-domain feature analysis on standardized environmental parameters, the domain to which different types of parameters belong is first identified. Then, for the standardized environmental parameters within each domain, their core features are extracted: for example, the temperature change trend feature within a day; for humidity parameters, the feature of the duration of sustained humidity stability; for wind speed parameters, the feature of the time period when wind speed peaks occur; and for solar radiation intensity parameters, the feature of the cumulative duration of solar radiation intensity.

[0091] Furthermore, for the turbidity parameter in the water quality domain, the turbidity variation amplitude characteristics are extracted. Then, the correlation between parameter characteristics in different domains is analyzed, as well as the correlation between the peak wind speed period and the cumulative duration of sunshine intensity, and the relationship between meteorological parameter characteristics and water turbidity variation amplitude characteristics. These correlated features are organized into vector form according to the correlation logic, with each vector containing multiple interrelated feature information, finally obtaining the correlation feature vector of standardized environmental parameters.

[0092] Furthermore, based on the response relationship between landscape aesthetic needs and water feature evaporation compensation needs, when dynamically weighting and fusing the associated feature vectors, the specific content of landscape aesthetic needs should be clarified first, including the visual appeal requirements of architectural water features, as well as the specific content of water feature evaporation compensation needs, namely, the need to replenish the water lost due to evaporation of water features according to changes in environmental parameters, so as to ensure the stability of water feature water level to maintain landscape effect and normal function.

[0093] Furthermore, the response relationship between the two is then analyzed to determine the direction and extent of the change in landscape aesthetic demand and water feature evaporation compensation demand when a certain type of feature in the associated feature vector changes. Dynamic weights need to be assigned to each feature in the associated feature vector based on this response relationship.

[0094] For example, when the solar intensity-related features in the associated feature vector are enhanced, the evaporation of the water feature will increase. At this time, the demand for evaporation compensation for the water feature increases. Meanwhile, stronger sunlight may enhance the shimmering effect on the water surface, thus satisfying the aesthetic needs of the landscape to a certain extent.

[0095] Furthermore, during the weight allocation process, if a certain feature has a greater impact on the aesthetic needs of the landscape, it will be given a higher weight; if it has a greater impact on the needs of water feature evaporation compensation, the corresponding weight will also be adjusted, and the weight will be adjusted according to the real-time changes in the response relationship between the two.

[0096] Furthermore, each feature in the associated feature vector is multiplied by its corresponding dynamic weight, and then all the multiplication results are summed to obtain the initial climate index of the associated feature vector.

[0097] Furthermore, when reconstructing the initial climate index in real time, the key scenario requirements affecting the operation of the building's water feature should be identified first, including daily landscape display scenarios, extreme weather response scenarios, and special event landscape protection scenarios. Different scenarios have different focuses on the real-time climate index and correspondingly different priority settings.

[0098] Next, the actual scene information of the current building water feature is collected to determine the current scene type. Then, according to the preset scene-priority correspondence rules, the elements included in the initial climate index are prioritized and sorted. Elements with a high degree of matching with the core needs of the current scene are ranked with higher priority, and elements with a low degree of matching are ranked with lower priority.

[0099] Furthermore, based on the priority ranking results, the elements of the initial climate index are re-integrated, with high-priority elements occupying a more prominent position in the real-time climate index, ensuring that the real-time climate index can prioritize reflecting the core needs under the current scenario, and finally obtaining a real-time climate index with multi-source heterogeneous environmental parameters.

[0100] Specifically, when extracting landscape aesthetics-related features and water feature evaporation compensation-related features from the associated feature vectors, we first clarify the specific types of landscape aesthetics-related features, including light intensity features, color contrast features, and dynamic effect features. Among them, light intensity features reflect the influence of natural light on the visual effect of water features, color contrast features reflect the color matching coordination between water features and the surrounding environment, and dynamic effect features describe the presentation state of dynamic landscapes such as water flow and ripples. At the same time, we clarify the specific types of water feature evaporation compensation-related features, including temperature features, humidity features, and wind speed features. Among them, temperature features directly affect the evaporation rate of water bodies, humidity features reflect the air's ability to hold water vapor and affect evaporation efficiency, and wind speed features are a key factor in accelerating the loss of water from the water surface.

[0101] Furthermore, the system then iterates through all the feature elements in the associated feature vector, identifies and filters each feature element according to the above type definition, and categorizes the feature elements belonging to light intensity, color contrast, and dynamic effects into the feature set related to landscape aesthetics, and the feature elements belonging to temperature, humidity, and wind speed into the feature set related to water feature evaporation compensation.

[0102] Furthermore, the two sets are checked to ensure that they cover all relevant feature types without omissions or misclassifications. The resulting two feature sets are the landscape aesthetics-related features and water feature evaporation compensation-related features extracted from the associated feature vectors.

[0103] Furthermore, based on the synergistic response relationship, when nonlinearly weighted and fused the features related to landscape aesthetics and those related to evaporation compensation to obtain the initial climate index of the associated feature vector, the synergistic response relationship between landscape aesthetic needs and water feature evaporation compensation needs is first determined. That is, when the evaporation compensation demand increases due to rising temperature and decreasing humidity, water replenishment needs to be increased to maintain water volume. This process may affect the dynamic effect of the water feature, and the weights of dynamic effect features related to landscape aesthetics need to be adjusted synchronously. When the light intensity increases and enhances the landscape aesthetic effect, it may be accompanied by rising temperature and exacerbate evaporation, and the weights of temperature features related to evaporation compensation need to be increased synchronously.

[0104] Furthermore, based on this synergistic relationship, dynamically changing weights are assigned to different features. For example, in high-temperature and low-humidity environments, the weights of temperature and humidity features related to evaporation compensation are increased, while the weights of dynamic effect features related to landscape aesthetics are adjusted accordingly to adapt to water level changes. When there is sufficient sunlight but suitable temperature, the weights of light intensity features related to landscape aesthetics are increased, while the weights of features related to evaporation compensation remain at a basic level. Subsequently, according to the set dynamic weights, the features related to landscape aesthetics and the features related to evaporation compensation are fused. The values ​​of each feature are adjusted according to their weights and then summarized to form a single index value that comprehensively reflects the synergistic effect of the two. This index value is the initial climate index of the associated feature vector.

[0105] Specifically, the thermodynamic gradient response coefficient is derived from analyzing the correlation between historical temperature and evaporation data in the area where the building's water feature is located, and the value is determined by the ratio of the magnitude of change in evaporation to the gradient of temperature change.

[0106] Furthermore, the temperature gradient is derived from real-time acquisition of ambient temperature, calculating the temperature difference between two adjacent acquisitions. The dynamic weights of the corresponding environmental parameters in the standard environmental parameters are determined by considering the synergistic relationship between landscape aesthetics and evaporation compensation requirements, based on the parameter's contribution to the demand and the priority of the demand.

[0107] Furthermore, the standardized characteristic values ​​of the corresponding environmental parameters in the standard environmental parameters are obtained by mapping and transforming the original environmental parameters according to a preset standard. The water surplus / deficit is obtained by calculating the actual water volume using a water level sensor and then subtracting it from the design standard water volume.

[0108] Furthermore, the aesthetic-evaporation balance coefficient is determined by considering the impact of water balance on aesthetics and evaporation efficiency, based on the water feature design objectives. The landscape-climate synergy factor is determined by statistically analyzing the contribution of climatic conditions to the aesthetic presentation of the landscape.

[0109] Furthermore, the formula's significance is reflected in three parts, which together yield the initial climate index. The first part quantifies the impact of temperature change on the landscape-climate synergy. The hyperbolic tangent function limits the range of results; as temperature rises, this part increases in value, enhancing the synergy; as temperature falls, the value decreases, weakening the synergy.

[0110] Furthermore, the second part weights and sums the standardized environmental parameters to reflect the basic contribution of each parameter to the initial climate index, with the core parameters having a more significant impact.

[0111] Furthermore, the third part quantifies the corrective effect of water balance; the exponential function limits the range of results, and this part decreases when water imbalance intensifies, thus lowering the initial climate index. The product of the three parts is the initial climate index, which comprehensively reflects the impact of multiple environmental parameters on architectural water features.

[0112] Furthermore, when the temperature change gradient increases, the initial climate index generally increases; when the temperature change gradient decreases, the initial climate index generally decreases. When the absolute value of water surplus / deficit increases, the initial climate index generally decreases; when the absolute value of water surplus / deficit decreases, the initial climate index generally increases. When the sum of the products of the dynamic weights and standardized eigenvalues ​​of most environmental parameters increases, the initial climate index generally increases; when this sum decreases, the initial climate index generally decreases. The combined effects of these three factors form a comprehensive trend.

[0113] In summary, the fusion of multimodal standard parameters to obtain standardized environmental parameters from multi-source heterogeneous environmental parameters can eliminate differences in units and numerical ranges, unify data standards, avoid subsequent analysis biases, provide a standardized data foundation for cross-domain feature analysis, and solve the problem of existing technologies not being able to perform multimodal fusion.

[0114] In summary, cross-domain feature analysis of standardized environmental parameters yields associated feature vectors, which can uncover the intrinsic relationships between parameters from different domains, integrate scattered parameters, break down domain barriers, and more comprehensively reflect the combined impact of environmental parameters on water features compared to existing technologies, providing rich feature support for dynamic weighted fusion.

[0115] In summary, the initial climate index is obtained by dynamically weighting and fusing the associated feature vectors based on the two types of demand response relationships. This approach can take into account the synergistic effects of landscape aesthetics and evaporation compensation needs. The generated index comprehensively reflects the combined effect of climate on water features, solves the problem of the one-sidedness of existing climate indices, and provides reliable basic data for regulation strategies.

[0116] In summary, the real-time climate index is obtained by reconstructing the initial climate index in real time. The priority of elements can be adjusted according to the core needs of the current scenario, so that the index fits the actual scenario. Compared with the fixed index of existing technology, it provides a precise basis for multi-dimensional strategy matching.

[0117] In summary, extracting two types of features can accurately screen core elements, eliminate irrelevant interference, focus on key needs of water features, solve the problem of ambiguous control basis of existing technologies, and lay a precise foundation for weighted fusion.

[0118] In summary, by combining nonlinear weighted fusion of collaborative response relationships, weights can be dynamically allocated, avoiding biases caused by a single demand, and ensuring that the results align with the actual operational needs of the water feature.

[0119] In summary, the initial climate index calculated using the formula can comprehensively quantify the impact of multiple parameters on water features, making it more comprehensive and accurate than existing technologies. This provides high-quality data support for subsequent processes and enhances the scientific nature of regulation.

[0120] The multi-dimensional strategy matching module 102 is used to perform multi-dimensional strategy matching on the real-time climate index based on the operation strategy of the building water feature, so as to obtain a preliminary control strategy for the real-time climate index.

[0121] In this embodiment of the invention, the operation strategy based on the building water feature is used to perform multi-dimensional strategy matching on the real-time climate index to obtain a preliminary control strategy for the real-time climate index, specifically for:

[0122] The timing characteristics of the operation strategy are analyzed to obtain the timing adaptation strategy of the operation strategy;

[0123] The influence factors of the real-time climate index are obtained by performing characteristic influence factor analysis.

[0124] The matching degree between the influencing factors and the time-series adaptation strategy is evaluated to obtain candidate strategies for the real-time climate index.

[0125] Based on the candidate strategies, the real-time climate index is subjected to multi-objective coordinated regulation to obtain a preliminary regulation strategy for the real-time climate index.

[0126] The step of evaluating the strategy matching degree between the influencing factors and the time-series adaptation strategy to obtain candidate strategies for the real-time climate index is specifically used for:

[0127] A dynamic relationship mapping is performed between the impact factors and the time-series adaptation strategy to obtain a mapping relationship table between the impact factors and the time-series adaptation strategy;

[0128] Based on the mapping table, the influence factors are analyzed to obtain the appropriate strategies for the influence factors.

[0129] Based on the intensity level of the influencing factors, the adaptation strategies are prioritized to obtain a priority sequence of the adaptation strategies.

[0130] Based on the priority sequence, the real-time climate index is grouped by fit to obtain candidate strategies for the real-time climate index.

[0131] Specifically, when performing time-series feature analysis on the operation strategy, we first collect the existing complete operation strategies of the building water feature. These strategies cover the water feature operation parameter settings for different time periods, including water replenishment frequency, light on duration, water circulation rate, etc.

[0132] Furthermore, the operational strategy was then broken down according to the time dimension, dividing the day into multiple fixed time periods and each season into multiple cycles. Core parameters of the operational strategy within each time period were extracted; for example, the water replenishment frequency in the morning is once every two hours, and the lighting duration in the midday period is four hours. Next, the changing patterns of the operational strategy parameters in different time periods were analyzed to determine the adaptation logic for each parameter's changes over time.

[0133] Furthermore, based on the adaptation logic of these time dimensions, detailed rules for operation strategies for different time periods are compiled. These rules are the time-series adaptation strategies for operation strategies, ensuring that the time-series adaptation strategies can accurately correspond to the operation requirements of different time nodes.

[0134] Furthermore, when conducting characteristic influencing factor analysis on real-time climate indices, it is necessary to first identify the core characteristics contained in real-time climate indices. These characteristics include temperature-related characteristics, humidity-related characteristics, water balance-related characteristics, and landscape aesthetics-related characteristics.

[0135] Furthermore, the specific impact of each feature on the operation of the architectural water feature was analyzed one by one. For example, the temperature-related feature directly affects the evaporation rate of the water feature. The higher the value of the temperature-related feature, the faster the evaporation rate, and the water replenishment strategy needs to be adjusted. The lower the value of the humidity-related feature, the higher the air dryness, and the faster the water surface of the water feature loses moisture, and the water circulation rate needs to be adjusted. The water balance-related feature directly reflects the difference between the actual water volume and the standard water volume of the water feature. When the value is negative, water replenishment needs to be increased. The lower the value of the landscape aesthetics-related feature, the worse the visual effect of the water feature, and the lighting or water quality maintenance strategy needs to be adjusted.

[0136] Furthermore, each feature and its corresponding impact are correlated to identify each feature as a key factor affecting the operation of the water feature. For example, temperature is identified as the factor affecting water replenishment frequency, humidity as the factor affecting water circulation rate, water balance as the factor affecting water replenishment amount, and landscape aesthetics as the factor affecting lighting settings. Finally, these key factors and their corresponding impact objects and influence logic are organized to form the influencing factors of the real-time climate index, ensuring that each influencing factor clearly corresponds to a specific control direction for the operation of the water feature.

[0137] Furthermore, when evaluating the matching degree between influencing factors and time-series adaptation strategies, the influencing factors are first classified according to their control direction, into factors related to water replenishment control, water circulation control, lighting control, and water quality maintenance. Then, corresponding to the operational parameter categories for different time periods within the time-series adaptation strategy, the strategy is further divided into water replenishment strategy units, water circulation strategy units, lighting strategy units, and water quality maintenance strategy units. Each strategy unit contains specific operational parameter standards for the corresponding time period.

[0138] Furthermore, for each type of influencing factor, a matching evaluation is then performed with the corresponding time-series adaptation strategy unit. Specifically, it is determined whether the control requirements indicated by the influencing factor are consistent with the parameter standards of the time-series adaptation strategy unit, and the degree of fit between the actual adjustment range and the strategy standard is calculated. If the deviation between the water replenishment frequency indicated by the influencing factor and the strategy standard is within the preset range, the matching degree is high; otherwise, the matching degree is low.

[0139] Furthermore, the matching degree between all influencing factors and their corresponding time-series adaptation strategy units is quantitatively determined. The operational strategy parameters corresponding to combinations with high matching degrees are extracted and integrated to form multiple strategy schemes that meet the current needs of influencing factors. These schemes are the candidate strategies for real-time climate indices, ensuring that each candidate strategy can simultaneously adapt to the regulatory needs of influencing factors and the temporal characteristics of time-series adaptation strategies.

[0140] Furthermore, when conducting multi-objective coordinated regulation of real-time climate indices based on candidate strategies, the specific content of the multiple objectives is first clarified. These objectives include meeting the evaporation compensation requirements of water features, maintaining the aesthetic effect of the landscape, ensuring reasonable operational energy consumption, and complying with time-series operational specifications. Subsequently, the degree to which each candidate strategy meets the multiple objectives is analyzed one by one.

[0141] Furthermore, a multi-objective trade-off is then performed on each candidate strategy. If a candidate strategy satisfies the first three objectives while having a small deviation from the timing adaptation strategy and an increase in energy consumption within a preset range, it is adopted as the priority control scheme. If a candidate strategy fails to meet one objective, the parameters in that strategy are adjusted, such as appropriately reducing the water replenishment frequency or shortening the lighting duration, until all objectives are met.

[0142] Furthermore, the adjusted candidate strategies are integrated to determine the combination of control parameters that simultaneously meet the needs of multiple objectives. For example, the water replenishment frequency for the current time period is determined to be once per hour, the water replenishment amount is a fixed value, the light-on duration is three hours, and the water circulation rate is a certain fixed value.

[0143] Furthermore, these control parameters are combined and organized to form a complete control scheme, which is the preliminary control strategy for the real-time climate index, ensuring that the preliminary control strategy can synergistically meet the various needs of water feature operation.

[0144] Specifically, when dynamically mapping the relationship between influencing factors and time-series adaptation strategies, the influencing factors are first classified into categories such as water replenishment control factors, water circulation control factors, lighting control factors, and water quality maintenance factors according to the control direction. At the same time, the time-series adaptation strategies are divided into morning strategy units, midday strategy units, evening strategy units, and nighttime strategy units according to time periods. Each strategy unit contains specific operating parameter standards for each control direction within the corresponding time period. For example, the standard for water replenishment control in the morning strategy unit is to replenish water once at a fixed interval and the amount of water replenished each time is a fixed value.

[0145] Furthermore, for each type of influencing factor, the corresponding control direction strategy unit in each time period of the time series adaptation strategy is matched one by one, and the correspondence between the numerical changes of the influencing factors and the parameter adjustments of the time series strategy unit is analyzed.

[0146] Furthermore, the correspondence between all influencing factors and time-series strategy units for each time period, as well as the parameter adjustment rules, are then compiled into a table. The table clearly records the category of influencing factor, the time period of the time-series strategy unit, the corresponding control direction, the range of changes in the value of the influencing factor, and the adjustment method of the time-series strategy parameters. This table is the mapping relationship table between influencing factors and time-series adaptation strategies.

[0147] Furthermore, based on the mapping table, when performing strategy analysis on the influencing factors, all influencing factors corresponding to the current real-time climate index are first extracted to determine the category, current value, and time series period of each influencing factor.

[0148] Next, the entry corresponding to each influencing factor category and the current time period is found in the mapping table. Based on the current value of the influencing factor, the range of value change corresponding to the mapping table is determined, and then the adjustment method of the time series strategy parameters within this range is obtained. For example, if the current water replenishment control factor value is in the "high" range and the current time period is noon, the corresponding entry is found in the mapping table, and the strategy adjustment method for the water replenishment control direction is determined to be "shortening the water replenishment interval from a fixed duration to a shorter duration, and increasing the water replenishment amount by a fixed percentage each time".

[0149] Furthermore, the aforementioned search and extraction operations were then performed on each influencing factor, integrating the time-series strategy parameter adjustment methods corresponding to each influencing factor to ensure coverage of all control directions involved by all influencing factors, such as parameter adjustments for control directions like water replenishment, water circulation, and lighting. Finally, all the integrated parameter adjustment methods were compiled into a complete strategy plan, which serves as the adaptation strategy for the influencing factors. The adaptation strategy clearly records the specific operational parameter adjustment requirements for each control direction, ensuring it can directly guide the adjustment of the water feature's operational parameters.

[0150] Furthermore, when prioritizing adaptation strategies based on the intensity level of influencing factors, the criteria for classifying the intensity level of influencing factors are first determined. These criteria are set according to the degree of influence of influencing factors on the operation of the water feature, and the current intensity level of each influencing factor is determined based on these criteria.

[0151] Furthermore, the parameter adjustments for each control direction in the adaptation strategy are associated with the intensity level priority weight of the corresponding influencing factors. For example, the adjustment of the water replenishment control direction corresponds to a "higher" priority weight, while the adjustment of the lighting control direction corresponds to a "medium" priority weight. Finally, the parameter adjustments for all control directions in the adaptation strategy are sorted in descending order of priority weight to form an ordered strategy adjustment sequence, which is the priority sequence of the adaptation strategy.

[0152] Furthermore, when grouping real-time climate indices by fit based on the priority sequence, the criteria for fit grouping are first determined. These criteria are set according to the feasibility of the adjustments made in each control direction in the priority sequence. Groups where the adjustments made in the first two high-priority control directions can meet the execution conditions are classified as "high fit group". Groups where the adjustments made in the first high-priority control direction can meet the execution conditions but the adjustments made in the second high-priority control direction require slight adjustments are classified as "medium fit group". Groups where the adjustments made in the first two high-priority control directions require significant adjustments are classified as "low fit group".

[0153] Next, check whether the current operating conditions of the building's water feature meet the execution requirements of the adjustment content of each control direction in the priority sequence. For example, check whether the water replenishment equipment is operating normally to meet the adjustment requirements of high-priority water replenishment control, and whether the lighting equipment is in an adjustable state to meet the adjustment requirements of the second-highest priority lighting control.

[0154] Furthermore, based on the inspection results, the adaptation strategies corresponding to the real-time climate index are categorized into different adaptation groups according to the aforementioned grouping criteria. Each adaptation group contains complete adaptation strategy adjustment content. Finally, from each adaptation group, the adaptation strategy that best fits the current operating conditions is selected. If a suitable strategy exists in the "high adaptation group," that strategy is selected first; if no suitable strategy exists in the "high adaptation group," the strategy from the "medium adaptation group" is selected. All selected strategies together constitute the candidate strategies for the real-time climate index. The candidate strategies include feasible strategy schemes from different adaptation groups, ensuring that the optimal scheme can be selected based on the actual situation in the future.

[0155] In summary, analyzing the temporal characteristics of the operation strategy yields a temporal adaptation strategy, which can break down the time dimension parameters of the operation strategy, clarify the adaptation rules for each time period, avoid the rigidity of the strategy caused by the neglect of time differences in existing technologies, make the strategy more in line with the operation needs of the water feature at different times, and provide accurate time dimension basis for subsequent matching.

[0156] In summary, the analysis of real-time climate indices reveals influencing factors, which can pinpoint key elements affecting water feature operation within the climate indices. This addresses the issue that existing technologies have not delved deeply into the logic of climate index influence, clarifying the direction of regulation for strategy matching and improving the targeting of matching.

[0157] In summary, assessing the matching degree between influencing factors and time-series adaptation strategies to obtain candidate strategies can determine the degree of fit between key influencing factors and strategies for each time period, screen out feasible strategies that adapt to real-time climate and time requirements, avoid the disconnect between strategies and actual conditions, and provide a high-quality strategy foundation for subsequent regulation.

[0158] In summary, the preliminary control strategy obtained based on the multi-objective synergistic regulation of candidate strategies can take into account objectives such as evaporation compensation, landscape aesthetics, and energy consumption control. By adjusting conflicting parameters in the candidate strategies, the one-sidedness of single-objective regulation in existing technologies can be resolved, ensuring that the preliminary strategy meets the multi-dimensional operational needs of water features and improving the rationality of regulation.

[0159] In summary, dynamic relationship mapping yields a mapping table, clearly defining the correspondence between influencing factors and time-series adaptation strategies. This transforms abstract logic into a structured table, avoiding matching confusion caused by ambiguous associations, providing a clear basis for subsequent analysis, and improving matching efficiency.

[0160] In summary, by analyzing influencing factors based on mapping relationship tables, the scheme can be adjusted according to factor categories and current time period positioning strategies, solving the problem of existing technical analysis lacking clear basis, ensuring that the adaptation strategy accurately matches the needs, and reducing deviations.

[0161] In summary, ranking the adaptation strategies by the intensity level of the impact factors distinguishes their importance, avoids confusion between primary and secondary tasks during implementation, and allows the adaptation groups to focus on key needs, thereby improving the targeting of strategy implementation.

[0162] In summary, candidate strategies are obtained by grouping based on priority sequences, and feasible strategies with different degrees of adaptability are screened, covering multiple climate scenarios. This solves the problem of limited candidate strategies in existing technologies, provides a wide range of options for subsequent regulation, and ensures adaptability.

[0163] The collaborative optimization module 103 is used to perform overall collaborative optimization of the building water feature based on the preliminary control strategy, and obtain the preliminary overall control instructions for the building water feature.

[0164] In this embodiment of the invention, the step of performing overall coordinated optimization of the architectural water feature based on the preliminary control strategy to obtain preliminary overall control instructions for the architectural water feature is specifically used for:

[0165] Based on the multi-dimensional target requirements of the architectural water feature, a multi-objective optimization evaluation system for the architectural water feature is established;

[0166] A comprehensive effectiveness evaluation of the preliminary control strategy is conducted to determine its priority level.

[0167] Based on the current climate change trends, the weight ratio of demand in the multi-objective optimization evaluation system is dynamically adjusted.

[0168] Based on the adjusted weight ratios, the parameters of the highest priority strategy are refined to obtain the control parameters of the architectural water feature.

[0169] The control parameters are arranged into a logical instruction sequence to obtain the preliminary overall control instructions for the building water feature.

[0170] Based on the adjusted weight ratios, the highest priority strategy is refined to obtain the control parameters for the architectural water feature, specifically used for:

[0171] The control objectives contained in the highest priority strategy are analyzed to obtain the core control parameters of the highest priority strategy;

[0172] Based on the adjusted weight ratios, a dynamic mapping relationship is established between different regulatory objectives.

[0173] Based on the dynamic mapping relationship, the core control parameters are transformed into executable logical control parameters;

[0174] The control parameters of the building water feature are obtained by coordinating and optimizing the logic control parameters.

[0175] Specifically, when establishing a multi-objective optimization evaluation system for architectural water features based on the multi-dimensional target requirements, the multi-dimensional target requirements are first clarified, including evaporation compensation, landscape aesthetics, energy conservation, and ecological maintenance requirements. Then, evaluation indicators are set for each requirement, such as "water volume deviation rate" for evaporation compensation and "visual effect compliance rate" for landscape aesthetics. The compliance standards for each indicator are then determined. Finally, the requirements, indicators, and compliance standards are organized into a structured document, which is the multi-objective optimization evaluation system for architectural water features.

[0176] Furthermore, a comprehensive effectiveness evaluation of the preliminary control strategies is conducted to determine their priority levels. Each preliminary control strategy is then substituted into a multi-objective optimization evaluation system to calculate its performance value on the evaluation indicators. Scoring rules are set according to the achievement of the targets, and the total comprehensive effectiveness score is calculated. The strategies are then ranked from highest to lowest based on their total scores, and the ranking result is the priority level of the preliminary control strategies.

[0177] Furthermore, when dynamically adjusting the weight ratio of demand in the multi-objective optimization evaluation system based on the current climate characteristic change trend, we first analyze the current climate characteristic change trend to determine its impact on each demand. For example, the importance of evaporation compensation demand increases. Then, we adjust the weight of each demand according to the degree of impact to ensure that the total weight is fixed. The adjustment result is the dynamically adjusted weight ratio.

[0178] Furthermore, based on the adjusted weight ratio, the parameters of the highest priority strategy are refined to obtain the control parameters of the building water feature. The highest priority strategy is extracted, and the control direction is determined according to the adjusted weight to refine the priority. First, the parameters of the high priority direction are refined, and then the parameters of the regular priority direction are refined. The refined parameters are summarized to form the control parameters of the building water feature.

[0179] Furthermore, when arranging the control parameters into a logical instruction sequence to obtain the preliminary overall control instructions for the building water feature, the equipment operation type corresponding to each control parameter is clarified, the logical relationship of equipment operation is analyzed to determine the sequence and linkage mechanism, the parameters are converted into operation instructions with execution conditions and duration, and arranged into an instruction sequence according to logic. This sequence is the preliminary overall control instruction for the building water feature.

[0180] Specifically, when analyzing the control objectives contained in the highest priority strategy to obtain the core control parameters of the strategy, the text of the highest priority strategy is first decomposed to identify control objectives such as "maintaining stable water level", "optimizing nighttime landscape", "controlling energy consumption" and "maintaining clear water quality". Then, relevant parameter descriptions are extracted for each objective. For example, the water level objective corresponds to "water replenishment cycle range" and "reference value of single water replenishment", and the landscape objective corresponds to "lighting on time interval". After removing ambiguous content, the parameters are classified and organized according to the objectives to form the core control parameters of the highest priority strategy.

[0181] Furthermore, when establishing a dynamic mapping relationship between different control target requirements based on the adjusted weight ratios, the weight ratio of each target is first clarified, such as water level stability having the highest weight and landscape having the second highest. Then, the specific requirements of each target are sorted out, such as water level needing to be kept within the standard range and lighting needing to cover peak periods of pedestrian traffic. Based on the weights, the priority of the targets is determined, and the correlation constraints between the targets are analyzed, such as extending the lighting duration potentially exceeding the energy consumption limit. Then, coordination rules such as "ensuring the core period of landscape and compressing energy consumption during non-core periods" are formulated and organized into a dynamic mapping relationship.

[0182] Furthermore, based on the dynamic mapping relationship, when the core control parameters are transformed into executable logical control parameters, the mapping rules of the core parameters and the corresponding targets are matched. For example, the "water replenishment cycle range" is combined with the "priority water level maintenance" rule, the specific water replenishment duration is determined with reference to the evaporation rate, and the execution condition of "triggering water replenishment when the water level is lower than the lower limit by a fixed proportion" is set. The parameters are transformed into operation parameters that the equipment can recognize, such as the "running time" and "start-up threshold" of the water replenishment equipment, and integrated to form logical control parameters.

[0183] Furthermore, the logic control parameters are optimized for coordination. When obtaining the control parameters of the building water feature, the parameters are classified by equipment, and conflicts are checked. For example, if the start time of water replenishment and water circulation equipment coincides, it may lead to insufficient pressure. Adjustments are made according to the target priority, and the start time of water circulation is postponed to after water replenishment. Verify whether the optimized parameters meet all targets, and organize the parameters that do not conflict and meet the standards to form the control parameters of the building water feature.

[0184] In summary, establishing a multi-objective optimization evaluation system that covers core needs and clarifies evaluation criteria solves the problem of singular regulatory objectives and provides a comprehensive objective framework for subsequent actions.

[0185] In summary, the initial strategies are evaluated and prioritized, performance differences are quantified to select the best strategies, and the direction for parameter refinement is clarified to improve the targeting of optimization.

[0186] In summary, the demand weights are adjusted according to climate trends to adapt to real-time climate impacts, avoid the problem of fixed weights, and ensure that the parameters are refined to meet current core needs.

[0187] In summary, refining high-priority strategy parameters transforms macro-level strategies into specific control parameters, resolving parameter ambiguity issues and providing a precise basis for instruction orchestration.

[0188] In summary, the arrangement of control parameters is a logical instruction that organizes the sequence of equipment operations and their linkages, avoids execution conflicts, and ensures coordinated operation of the equipment.

[0189] In summary, analyzing high-priority strategy objectives yields core control parameters, accurately identifies key objectives and core parameters, avoids interference, resolves the problem of ambiguous strategy objectives, and provides a focused basis for parameter transformation.

[0190] In summary, the dynamic mapping relationship of the reconstructed objectives based on the adjusted weights clarifies the coordination rules between high-weight objectives and other objectives, avoids regulatory contradictions, and solves the problem of lack of coordination among objectives.

[0191] In summary, by using mapping relationships to transform core parameters into logical control parameters, abstract parameters are transformed into operational parameters that can be recognized by the device, thus solving the problem that parameters cannot directly guide device operation.

[0192] In summary, optimizing the control parameters of the logic control parameters, troubleshooting and adjusting equipment operation conflicts, avoiding poor coordination issues, and improving the stability and accuracy of water feature control are all important.

[0193] The state control module 104 is used to perform real-time state control on the preliminary overall control command to obtain the preliminary optimized operating state of the building water feature.

[0194] In this embodiment of the invention, the preliminary overall control command is subjected to real-time state control to obtain the preliminary optimized operating state of the architectural water feature, specifically for:

[0195] The preliminary overall control command is dynamically analyzed to obtain the water feature operation mode requirements and parameter adjustment requirements in the preliminary overall control command;

[0196] Based on the requirements of the water feature operation mode, determine the control logic sequence of the water feature operation mode;

[0197] Based on the parameter adjustment requirements, generate the execution parameter combination required by the parameter adjustment requirements;

[0198] The control logic sequence and execution parameters are dynamically coordinated to form an executable collaborative control scheme;

[0199] The collaborative control scheme is optimized through multi-factor collaborative optimization to obtain the preliminary optimized operating state of the building water feature.

[0200] Specifically, when the preliminary overall control command is dynamically analyzed to obtain the water feature operation mode requirements and parameter adjustment requirements in the command, the preliminary overall control command is first split according to the command type, and the description of the water feature operation mode is identified, such as "daytime landscape enhancement mode", "nighttime energy-saving operation mode", "nighttime maintenance mode", etc. These clear mode names and corresponding operation scenario descriptions are the water feature operation mode requirements.

[0201] Furthermore, the instructions involving adjustments to equipment operating parameters are extracted, such as "the water replenishment equipment starts at fixed intervals, with each run lasting a fixed duration," "the lighting equipment operates at a fixed brightness during fixed time periods," and "the water circulation equipment's speed is adjusted to a fixed level." These specific descriptions of parameter adjustments constitute the parameter adjustment requirements. Finally, the extracted operating mode requirements and parameter adjustment requirements are organized into structured documents to ensure that the two types of requirements are clearly distinguished and that the content is complete.

[0202] Furthermore, when determining the control logic sequence of the water feature operation mode according to the requirements of the water feature operation mode, the core equipment and operation rules corresponding to each operation mode should be clearly defined first, and each piece of equipment should be operated synchronously during the daytime. The "energy-saving operation mode" should reduce the operating frequency of non-essential equipment and retain only the basic water circulation and necessary water replenishment functions.

[0203] Furthermore, the startup sequence, runtime correlation, and shutdown conditions of each device in this mode are then analyzed, with all devices stopping in reverse order at sunset. Subsequently, the sequence of device operations, their correlations, and triggering conditions are arranged in a step-by-step manner to form a coherent logical flow, which constitutes the control logic sequence of the water feature operation mode.

[0204] Furthermore, when generating the execution parameter combination based on the parameter adjustment requirements, the parameter adjustment requirements are first categorized by equipment type, such as parameters for water replenishment equipment, lighting equipment, and water circulation equipment, ensuring that the adjustment requirements for each piece of equipment are listed separately. For the parameter adjustment requirements of each piece of equipment, combined with the equipment's technical specifications and operational capabilities, the vague expressions in the requirements are transformed into specific executable parameters. Subsequently, the specific parameters of all equipment are integrated to form a set of parameters covering all equipment requiring adjustment. This set is the execution parameter combination for the parameter adjustment requirements, ensuring that each parameter can be directly recognized and executed by the equipment.

[0205] Furthermore, when dynamically coordinating the control logic sequence with the execution parameter combination to form an executable collaborative control scheme, each step in the control logic sequence is first matched with the parameters of the corresponding device in the execution parameter combination. For example, the step of "starting the water replenishment equipment" in the control logic sequence is matched with parameters such as the start interval and single run duration of the water replenishment equipment in the execution parameter combination. The process of matching these two parameters is checked for conflicts. For instance, if the control logic sequence requires "the water replenishment equipment to start once at fixed intervals during the day," but the start interval of the water replenishment equipment in the execution parameter combination is too short, it may overlap with the running time of the water circulation equipment, leading to unstable water pressure.

[0206] Furthermore, adjustments are made to address conflicts based on the core objectives of the operating mode. For example, the start-up interval of the water replenishment equipment can be extended to ensure that its operation time is staggered with that of the water circulation equipment, while still meeting basic water replenishment needs. Finally, the coordinated logical steps and parameter combinations are integrated to form a complete scheme that includes the equipment operation sequence, specific operating parameters, and conflict handling rules. This scheme is the executable collaborative control scheme.

[0207] Furthermore, when the preliminary optimized operation status of the building water feature is obtained by multi-dimensional collaborative optimization of the collaborative control scheme, multi-dimensional optimization objectives are first set, including equipment operation stability, landscape effect compliance rate, energy consumption control, and water quality maintenance.

[0208] Furthermore, the collaborative control scheme was incorporated into the actual operational simulation environment to monitor the achievement of each objective. For example, during the simulation, it was found that the lighting equipment operated at continuously high brightness under the "daytime landscape enhancement mode," leading to excessive energy consumption. At the same time, the excessive rotation speed of the water circulation equipment caused water fluctuations, affecting the landscape effect. Parameters were fine-tuned for the objectives that were not met.

[0209] Furthermore, the adjusted scheme is simulated and verified again until all optimization objectives are met. At this point, the operating state of the building water feature in the simulation environment is the preliminary optimized operating state, in which all equipment operates in coordination and all indicators meet the preset requirements.

[0210] In summary, analyzing the operational mode and parameter requirements of the initial overall control instructions, breaking down the core content to avoid execution deviations, and providing a clear basis for subsequent steps.

[0211] In general, the control logic sequence is determined according to the operating mode, the equipment operation sequence and linkage rules are clarified, disorderly operation is avoided, and the standard procedures are followed.

[0212] In summary, the system generates a combination of execution parameters based on the parameter requirements, transforming abstract requirements into specific and identifiable parameters, thus providing accurate data support for equipment operation.

[0213] In summary, the coordination logic and parameters form a collaborative solution, conflicts are identified and adjusted, operational contradictions are avoided, and the solution can be executed smoothly.

[0214] In summary, the optimization of the coordination scheme has achieved initial optimization, taking into account multiple objectives, addressing the one-sidedness of single-control measures, and improving the quality of control.

[0215] The final control module 105 is used to perform strategy optimization based on the preliminary optimized operating state to obtain the final control strategy of the building water feature.

[0216] In this embodiment of the invention, the step of performing strategy optimization based on the preliminary optimized operating state to obtain the final control strategy for the architectural water feature is specifically used for:

[0217] The preliminary optimized operating state is evaluated using multi-dimensional indicators to obtain the operating indicators of the preliminary optimized operating state;

[0218] By performing a dimensional difference analysis between the operational indicators and the expected target values, the key dimensions of the operational indicators are obtained.

[0219] Based on the aforementioned key dimensions, strategy optimization is performed on the strategy library of the architectural water feature to obtain candidate optimization strategies from the strategy library.

[0220] Based on the current climate characteristic change trend, the candidate optimization strategy is adapted to suit the applicability of the candidate optimization strategy to obtain the optimized strategy of the candidate optimization strategy;

[0221] The optimization strategy is integrated with the preliminary control strategy to obtain the final control strategy for the architectural water feature.

[0222] Specifically, a multi-dimensional indicator evaluation is conducted on the preliminary optimized operation status. When the operation indicators of this status are obtained, the specific content of the multi-dimensional evaluation indicators is first determined, including equipment operation stability indicators, landscape presentation effect indicators, energy consumption control indicators, water quality maintenance indicators, and water balance indicators.

[0223] Furthermore, various data are collected in real time by sensors installed in the water feature system to initially optimize the operation. For example, fault detectors are used to record equipment malfunctions, high-definition cameras are used to analyze images to judge the landscape presentation effect, smart meters are used to count equipment energy consumption, water quality detectors are used to measure water indicators, and water level sensors are used to monitor water level changes.

[0224] Furthermore, the collected data is converted and quantified according to the definition of each indicator. For example, the continuous fault-free operation time of the equipment is directly recorded as a specific value of the stability indicator, and the visual integrity of the water surface is converted into a compliance percentage through image analysis. Finally, a set of specific values ​​covering all evaluation dimensions is formed, which are the operation indicators for preliminary optimization of the operating status.

[0225] Furthermore, a dimensional difference analysis is conducted between the operational indicators and the expected target values. When the key dimensions of the operational indicators are obtained, the preset expected target values ​​for each dimension are retrieved first. For example, the continuous fault-free operation time of the equipment must reach a fixed duration or more, the compliance of the landscape presentation effect must reach a fixed percentage or more, the energy consumption per unit time must be controlled below a fixed value, the water transparency must reach a fixed level or more, and the water level deviation must be controlled within the specified range.

[0226] Furthermore, the operational metrics for each dimension are compared with their corresponding expected target values, and the degree of difference between the two is calculated. Based on the degree of difference, a key dimension judgment standard is set, and dimensions with a degree of difference exceeding a fixed threshold are judged as key dimensions.

[0227] For example, if the difference between equipment operation stability and landscape presentation effect exceeds the threshold, while the difference in energy consumption does not reach the threshold, then the dimensions of equipment operation stability and landscape presentation effect are identified as key dimensions. These identified dimensions are the key dimensions of the operation indicators.

[0228] Furthermore, based on key dimensions, strategy optimization is performed on the strategy library of architectural water features. When obtaining candidate optimization strategies from the strategy library, the types of strategies stored in the strategy library are first clarified. These strategies are classified according to the control dimension, including optimization strategies specifically for equipment operation stability, optimization strategies for landscape presentation effects, and optimization strategies for energy consumption, etc.

[0229] Furthermore, based on key dimensions, the corresponding strategy type is retrieved from the strategy library. For example, if the key dimensions are device operation stability and landscape presentation effect, all strategies related to these two dimensions are retrieved from the strategy library.

[0230] Furthermore, the retrieved strategies are evaluated for their effectiveness. The evaluation criterion is the potential for improvement in key performance indicators after implementation. For example, a strategy for equipment stability records "shortening the equipment maintenance cycle from a fixed duration to a shorter duration," which, after implementation, would extend the continuous fault-free operation time of the equipment by a fixed amount, matching the current need for improvement in equipment stability indicators. A strategy for landscape effect records "adjusting the light projection angle to a fixed range," which, after implementation, would increase the landscape conformity by a fixed percentage, matching the current need for improvement in landscape effect indicators. The top few strategies with the highest matching degree are selected as candidate optimization strategies in the strategy library.

[0231] Furthermore, based on the current climate characteristic change trend, the candidate optimization strategy is adapted for applicability. When obtaining the optimization strategy of the candidate optimization strategy, the specific content of the current climate characteristic change trend is first obtained, such as the temperature continues to rise, the humidity continues to decrease, and the wind force gradually increases.

[0232] Furthermore, the feasibility and potential impact of each candidate optimization strategy under this climate trend are analyzed. For example, a candidate optimization strategy is "increasing the frequency of equipment maintenance". In high-temperature environments, equipment is more prone to failure due to overheating. This strategy is feasible and can effectively improve stability. Another candidate optimization strategy is "extending the duration of light operation to enhance the landscape effect". In low humidity and high wind environments, extending the duration will not cause additional burden on the equipment, but the increased line load that may be caused by high temperature needs to be considered.

[0233] Furthermore, adjustments were made to strategies with potential impacts, extending the duration during the slightly cooler evening hours to ensure both aesthetic appeal and avoid overloading the lines. After adapting all candidate optimization strategies to their suitability, the resulting new strategy becomes the optimized strategy of the candidate strategies, ensuring that the optimized strategy is compatible with current climate change trends.

[0234] Furthermore, when integrating and optimizing the optimization strategy with the preliminary control strategy to obtain the final control strategy for the building water feature, we first compare the control direction and specific measures of the optimization strategy and the preliminary control strategy to identify the similarities and differences between the two. For example, the two have the same measures in the direction of water replenishment control, both requiring water replenishment at fixed intervals, but the measures in the direction of equipment maintenance and lighting control are different. The optimization strategy requires shortening the maintenance cycle and adjusting the lighting duration, while the preliminary control strategy does not involve these details.

[0235] Furthermore, retain the aspects where the regulatory direction is consistent and the measures are effective, such as retaining common water replenishment and regulation measures. For the differences, integrate them according to the weighting ratio of the multi-objective optimization evaluation system, prioritizing the retention of measures corresponding to objectives with higher weights.

[0236] For example, if the stability of equipment operation is given higher weight than the landscape effect, measures to shorten the equipment maintenance cycle in the optimization strategy should be retained first. Then, the lighting adjustment measures for the landscape effect should be combined with the basic lighting parameters in the preliminary control strategy to form a more complete lighting control scheme.

[0237] Furthermore, after integration, check whether there are any conflicts in the overall strategy, such as whether the equipment maintenance period and the water replenishment period overlap. If there are conflicts, adjust the schedule to ensure that all measures can be implemented in a coordinated manner.

[0238] Furthermore, the final strategy that encompasses all effective control measures, is conflict-free, and meets multiple objective requirements is the final control strategy for architectural water features.

[0239] In summary, multi-dimensional evaluation of operational indicators in the initial optimization state quantifies the operational status, avoids subjective judgment bias, and provides objective data for difference analysis.

[0240] In summary, the key dimensions of the analysis of the difference between the indicators and the expected values ​​are to identify the core areas that have not met the standards, clarify the direction of optimization, and improve the targeting.

[0241] In summary, candidate strategies are selected from the strategy library based on key dimensions, and mature strategies that are suitable for the core problem are screened to avoid inefficient construction.

[0242] In summary, the optimization strategy, which combines climate trend adaptation candidate strategies, ensures alignment with real-time climate and improves strategy feasibility.

[0243] In summary, the ultimate strategy that integrates the two types of strategies combines advantages to resolve conflicts, takes into account multiple needs, and improves the accuracy and stability of regulation.

[0244] Reference Figure 2 The diagram shown is a flowchart illustrating an intelligent building water feature control method based on climate sensing, according to an embodiment of the present invention. In this embodiment, the intelligent building water feature control method based on climate sensing includes:

[0245] S1. Real-time feature index fusion of multi-source heterogeneous environmental parameters of building water features to obtain the real-time climate index of the multi-source heterogeneous environmental parameters;

[0246] S2. Based on the operation strategy of the building water feature, perform multi-dimensional strategy matching on the real-time climate index to obtain a preliminary control strategy for the real-time climate index;

[0247] S3. Based on the preliminary control strategy, the architectural water feature is optimized in an overall manner to obtain the preliminary overall control instructions for the architectural water feature;

[0248] S4. Perform real-time state control on the preliminary overall control command to obtain the preliminary optimized operating state of the building water feature;

[0249] S5. Based on the preliminary optimized operating status, perform strategy optimization to obtain the final control strategy for the building water feature.

[0250] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0251] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0252] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent building water feature control system based on climate sensing, characterized in that, The system includes a feature index fusion module, a multi-dimensional strategy matching module, a collaborative optimization module, a state control module, and a final control module, wherein: The feature index fusion module is used to perform real-time feature index fusion on the multi-source heterogeneous environmental parameters of the building water feature to obtain the real-time climate index of the multi-source heterogeneous environmental parameters. The multi-dimensional strategy matching module is used to perform multi-dimensional strategy matching on the real-time climate index based on the operation strategy of the building water feature, so as to obtain the preliminary control strategy of the real-time climate index. The collaborative optimization module is used to perform overall collaborative optimization of the building water feature based on the preliminary control strategy, and obtain the preliminary overall control instructions for the building water feature. The state control module is used to perform real-time state control on the preliminary overall control command to obtain the preliminary optimized operating state of the building water feature. The final control module is used to perform strategy optimization based on the preliminary optimized operating status to obtain the final control strategy for the building water feature.

2. The intelligent building water feature control system based on climate sensing as described in claim 1, characterized in that, The real-time feature index fusion of the multi-source heterogeneous environmental parameters of the architectural water feature to obtain the real-time climate index of the multi-source heterogeneous environmental parameters is specifically used for: Multimodal standard parameter fusion is performed on the multi-source heterogeneous environmental parameters to obtain the standardized environmental parameters of the multi-source heterogeneous environmental parameters; Cross-domain feature analysis is performed on the standardized environmental parameters to obtain the associated feature vector of the standardized environmental parameters; Based on the response relationship between landscape aesthetic needs and water feature evaporation compensation needs, the associated feature vectors are dynamically weighted and fused to obtain the initial climate index of the associated feature vectors. The initial climate index is reconstructed in real time with priority to obtain the real-time climate index of the multi-source heterogeneous environmental parameters.

3. The intelligent building water feature control system based on climate sensing as described in claim 2, characterized in that, Based on the response relationship between landscape aesthetic needs and water feature evaporation compensation needs, the associated feature vectors are dynamically weighted and fused to obtain an initial climate index for the associated feature vectors, specifically used for: Extract landscape aesthetics-related features and water feature evaporation compensation-related features from the associated feature vectors; Based on the synergistic response relationship, the features related to landscape aesthetics and the features related to water feature evaporation compensation are nonlinearly weighted and fused to obtain an initial climate index of the associated feature vector. The calculation formula for the initial climate index is as follows: ； In the formula, This refers to the initial climate index. Thermodynamic gradient response coefficient, For temperature gradient, The first of the standard environmental parameters Dynamic weights of environmental parameters, The first of the standard environmental parameters One standardized feature value, This represents the water surplus or deficit. For aesthetic-evaporation balance coefficient, As a landscape-climate synergistic factor, It is the hyperbolic tangent function. It is an exponential function.

4. The intelligent building water feature control system based on climate sensing as described in claim 1, characterized in that, The operational strategy based on the building's water feature performs multi-dimensional strategy matching on the real-time climate index to obtain a preliminary control strategy for the real-time climate index, specifically used for: The timing characteristics of the operation strategy are analyzed to obtain the timing adaptation strategy of the operation strategy; The influence factors of the real-time climate index are obtained by performing characteristic influence factor analysis. The matching degree between the influencing factors and the time-series adaptation strategy is evaluated to obtain candidate strategies for the real-time climate index. Based on the candidate strategies, the real-time climate index is subjected to multi-objective coordinated regulation to obtain a preliminary regulation strategy for the real-time climate index.

5. The intelligent building water feature control system based on climate sensing as described in claim 4, characterized in that, The step of evaluating the strategy matching degree between the influencing factors and the time-series adaptation strategy to obtain candidate strategies for the real-time climate index is specifically used for: A dynamic relationship mapping is performed between the impact factors and the time-series adaptation strategy to obtain a mapping relationship table between the impact factors and the time-series adaptation strategy; Based on the mapping table, the influence factors are analyzed to obtain the appropriate strategies for the influence factors. Based on the intensity level of the influencing factors, the adaptation strategies are prioritized to obtain a priority sequence of the adaptation strategies. Based on the priority sequence, the real-time climate index is grouped by fit to obtain candidate strategies for the real-time climate index.

6. The intelligent building water feature control system based on climate sensing as described in claim 1, characterized in that, Based on the preliminary control strategy, the architectural water feature is optimized holistically to obtain preliminary overall control instructions for the architectural water feature, specifically used for: Based on the multi-dimensional target requirements of the architectural water feature, a multi-objective optimization evaluation system for the architectural water feature is established; A comprehensive effectiveness evaluation of the preliminary control strategy is conducted to determine its priority level. Based on the current climate change trends, the weight ratio of demand in the multi-objective optimization evaluation system is dynamically adjusted. Based on the adjusted weight ratios, the parameters of the highest priority strategy are refined to obtain the control parameters of the architectural water feature. The control parameters are arranged into a logical instruction sequence to obtain the preliminary overall control instructions for the building water feature.

7. The intelligent building water feature control system based on climate sensing as described in claim 6, characterized in that, Based on the adjusted weight ratios, the highest priority strategy is refined to obtain the control parameters for the architectural water feature, specifically used for: The control objectives contained in the highest priority strategy are analyzed to obtain the core control parameters of the highest priority strategy; Based on the adjusted weight ratios, a dynamic mapping relationship is established between different regulatory objectives. Based on the dynamic mapping relationship, the core control parameters are transformed into executable logical control parameters; The control parameters of the building water feature are obtained by coordinating and optimizing the logic control parameters.

8. The intelligent building water feature control system based on climate sensing as described in claim 1, characterized in that, The initial overall control command is used for real-time state adjustment to obtain the initial optimized operating state of the building water feature, specifically for: The preliminary overall control command is dynamically analyzed to obtain the water feature operation mode requirements and parameter adjustment requirements in the preliminary overall control command; Based on the requirements of the water feature operation mode, determine the control logic sequence of the water feature operation mode; Based on the parameter adjustment requirements, generate the execution parameter combination required by the parameter adjustment requirements; The control logic sequence and execution parameters are dynamically coordinated to form an executable collaborative control scheme; The collaborative control scheme is optimized through multi-factor collaborative optimization to obtain the preliminary optimized operating state of the building water feature.

9. The intelligent building water feature control system based on climate sensing as described in claim 1, characterized in that, The final control strategy for the architectural water feature is obtained by performing strategy optimization based on the preliminary optimized operating state, specifically used for: The preliminary optimized operating state is evaluated using multi-dimensional indicators to obtain the operating indicators of the preliminary optimized operating state; By performing a dimensional difference analysis between the operational indicators and the expected target values, the key dimensions of the operational indicators are obtained. Based on the aforementioned key dimensions, strategy optimization is performed on the strategy library of the architectural water feature to obtain candidate optimization strategies from the strategy library. Based on the current climate characteristic change trend, the candidate optimization strategy is adapted to suit the applicability of the candidate optimization strategy to obtain the optimized strategy of the candidate optimization strategy; The optimization strategy is integrated with the preliminary control strategy to obtain the final control strategy for the architectural water feature.

10. A method for intelligent building water feature control based on climate sensing, characterized in that, The method includes: S1. Real-time feature index fusion of multi-source heterogeneous environmental parameters of building water features to obtain the real-time climate index of the multi-source heterogeneous environmental parameters; S2. Based on the operation strategy of the building water feature, perform multi-dimensional strategy matching on the real-time climate index to obtain a preliminary control strategy for the real-time climate index; S3. Based on the preliminary control strategy, the architectural water feature is optimized in an overall manner to obtain the preliminary overall control instructions for the architectural water feature; S4. Perform real-time state control on the preliminary overall control command to obtain the preliminary optimized operating state of the building water feature; S5. Based on the preliminary optimized operating status, perform strategy optimization to obtain the final control strategy for the building water feature.

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