Unpowered solar energy and auxiliary heat source intelligent switching control system

Abstract

The application relates to the technical field of solar water heating systems, and particularly discloses an intelligent switching control system of a non-powered solar energy and auxiliary heat source, which comprises a heat storage system, a water storage system, a cold water supply system, a plurality of water use points and a central controller, solves the problems of low energy utilization rate, slow system response speed and poor user experience of the existing solar water heating system, introduces topology and matrix manifold theory into the system, the central controller collects multi-dimensional heat state data, constructs a heat state topological space, extracts topological features, constructs a heat state evaluation matrix, traces a matrix evolution track to generate a flow distribution vector field, realizes intelligent coordination start and stop among the heat storage system, the water storage system and the cold water supply system, the system automatically switches four operation modes of solar energy priority, auxiliary heat supplement, energy-saving heat storage and emergency water use, and the solar energy utilization rate is significantly improved.

Description

Technical Field

[0001] This invention relates to the field of solar water heating system technology, specifically to an intelligent switching control system for non-powered solar energy and auxiliary heat sources, applicable to hot water supply and thermal energy management in homes, commercial buildings, and special environments. Background Technology

[0002] With the increasing popularity of renewable energy utilization, solar water heating systems are widely used in residential and commercial sectors due to their environmentally friendly and economical characteristics. However, existing solar water heating systems have the following problems: First, the availability of solar energy resources is greatly affected by weather and time, resulting in poor system stability; second, traditional control methods are mainly based on simple temperature threshold judgments, which cannot fully utilize solar energy resources; in addition, the system has poor adaptability to users' water usage habits, resulting in low energy utilization efficiency.

[0003] In existing technologies, solar water heating systems typically employ a fixed threshold control strategy, meaning that when the outlet water temperature from the solar water heater falls below a set threshold, the auxiliary heat source is activated. This simple control method cannot adapt to complex environmental changes and varying user demands, nor can it maximize solar energy utilization. Furthermore, existing systems lack fine-grained control over heat distribution, often leading to energy waste or a poor user experience. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent switching control system for non-powered solar energy and auxiliary heat sources. By introducing topology and matrix manifold theory, it achieves accurate characterization and intelligent control of the heat transfer process, solving problems such as low energy utilization, slow system response speed, and poor user experience in existing technologies.

[0005] This invention proposes an intelligent switching control system for non-powered solar energy and auxiliary heat sources, comprising:

[0006] Thermal energy storage systems are used to collect and store solar thermal energy.

[0007] A water storage system, connected to the thermal storage system, is used to store hot water and supply it to users;

[0008] A cold water supply system, connected to the thermal storage system and the water storage system, is used to provide cold water;

[0009] Multiple water usage points are connected to the thermal storage system and the water storage system respectively via hot water pipes;

[0010] The central controller, connected to the thermal storage system, the water storage system, and the cold water supply system, is used for:

[0011] Collect multidimensional thermal state data, which includes water storage temperature data, flow rate data, liquid level data, and environmental data;

[0012] A thermal state topology space is constructed based on the multidimensional thermal state data;

[0013] Extract topological features from the thermal state topological space;

[0014] A thermal state evaluation matrix is ​​constructed based on the aforementioned topological features;

[0015] A flow distribution vector field is generated by tracking the evolution trajectory of the thermal state assessment matrix;

[0016] The coordinated start-up and shutdown sequence and water output between the thermal storage system, the water storage system, and the cold water supply system are determined based on the flow distribution vector field, thereby realizing the effective scheduling and operation of the thermal storage system.

[0017] Preferably, the thermal storage system includes:

[0018] Non-powered solar water heaters are used to collect solar energy;

[0019] A water storage tank, connected to the non-powered solar water heater, is used to store hot water;

[0020] An electric auxiliary heating system, connected to the water storage tank, is used to provide auxiliary heat energy when solar energy is insufficient;

[0021] A circulating water tank is connected to the non-powered solar water heater to form a thermal energy circulation system;

[0022] A heat storage system, connected to the circulating water tank, is used to store thermal energy;

[0023] A temperature control system, connected to the non-powered solar water heater, is used to monitor the water temperature and adjust the output water volume of the non-powered solar water heater.

[0024] Preferably, the circulating water tank includes a cold water tank and a hot water tank. The hot water tank and the cold water tank are connected by a water pump to form a return water loop. A heat exchanger is provided between the hot water tank and the cold water tank. The circulating water tank provides heat energy to the heat dissipation equipment of the power consumption point through the heat exchanger.

[0025] Preferably, the water storage tank is equipped with a first temperature sensor, a second temperature sensor, and a third temperature sensor. The first temperature sensor is used to monitor the temperature of the upper layer of the water storage tank, the second temperature sensor is used to monitor the temperature of the middle layer of the water storage tank, and the third temperature sensor is used to monitor the temperature of the lower layer of the water storage tank.

[0026] Preferably, the central controller includes:

[0027] The data acquisition module is used to acquire the multidimensional thermal state data;

[0028] The thermal state topology characterization module, connected to the data acquisition module, is used for:

[0029] A thermal state topology space is constructed based on the multidimensional thermal state data;

[0030] Extract the thermal state trajectory from the thermal state topology space;

[0031] Calculate the topological invariants of the thermal state trajectory;

[0032] The matrix manifold evaluation module, connected to the thermal state topology characterization module, is used for:

[0033] Construct a thermal state evaluation matrix based on the aforementioned topological invariants;

[0034] Track the evolution trajectory of the thermal state assessment matrix on the matrix manifold;

[0035] Generate the flow allocation vector field;

[0036] The flow control module, connected to the matrix manifold evaluation module, is used for:

[0037] Calculate the optimal pump frequency and valve opening based on the flow distribution vector field;

[0038] Control commands are generated and sent to the thermal storage system, the water storage system, and the cold water supply system;

[0039] The anomaly detection module, connected to the thermal state topology characterization module, is used for:

[0040] Monitor changes in the topological invariants;

[0041] Detect abnormal thermal conditions and generate early warning information.

[0042] Preferably, the thermal state topology characterization module is used for:

[0043] Construct a multidimensional thermal state space that includes temperature, flow rate, and environmental dimensions;

[0044] The data in the multidimensional thermal state space are standardized.

[0045] Principal component analysis was used to extract the main directions of change and to construct a simplified thermal state space.

[0046] Track the thermal state trajectory in the simplified thermal state space;

[0047] Construct a simple complex body based on the aforementioned thermal state trajectory;

[0048] The Betti number and continuous barcode of the simple complex were calculated using continuous homology analysis;

[0049] Extract the thermal state topological stability index.

[0050] Preferably, the matrix manifold evaluation module is used for:

[0051] Construct a 3×3 thermal state assessment matrix, where the rows of the matrix represent thermal energy properties and the columns represent system components;

[0052] Calculate the diagonal elements of the matrix to represent the core performance indicators of each component;

[0053] Calculate the off-diagonal elements of the matrix to represent the coupling relationships between components;

[0054] The topological invariants are incorporated as weights into the thermal state evaluation matrix;

[0055] Define a metric tensor on a matrix manifold;

[0056] Solve the geodesic equations to predict the evolution trajectory of the thermal state assessment matrix;

[0057] Define a flow distribution vector field on the matrix manifold;

[0058] The flow allocation vector field is converted into specific flow adjustment instructions.

[0059] Preferably, the flow control module is used for:

[0060] Construct the hot-swapping network topology, including the set of nodes, the set of edges, and the weights;

[0061] Identify the key nodes, key paths, and bottleneck locations in the hot-swapping network topology;

[0062] The minimum spanning tree algorithm is used to extract the main paths of heat transfer.

[0063] Define the pump frequency parameter set, valve opening parameter set, and temperature setting parameter set;

[0064] Construct a comprehensive objective function that includes user comfort indicators, energy efficiency indicators, and system life indicators;

[0065] The parameter set is optimized based on the traffic allocation vector field;

[0066] Generate and send control commands to the corresponding devices.

[0067] Preferably, the anomaly detection module is used for:

[0068] Build an anomaly pattern library and collect the topological features of typical anomaly patterns;

[0069] Calculate the deviation between the current topological features and the normal pattern in real time;

[0070] Perform similarity matching between the current topological features and the anomaly pattern library;

[0071] Calculate the probability distribution of anomalies and assess the warning level;

[0072] Implement corresponding self-repair strategies based on the warning level;

[0073] Notify users and provide response suggestions in advanced abnormal situations.

[0074] Preferably, the central controller automatically switches between the following operating modes based on environmental conditions and user needs:

[0075] Solar priority mode: Activated when solar radiation intensity reaches the threshold and the water tank temperature is below the target value to maximize solar energy utilization;

[0076] Auxiliary heating supplement mode: This mode is activated when the outlet water temperature of the solar water heater is lower than the required temperature. It prioritizes using solar preheated water and then heats the water through the auxiliary heating system.

[0077] Energy-saving heat storage mode: It is activated during periods of low water usage and when the solar water heater has residual heat energy, using the residual heat of the solar water heater to store heat in the water tank.

[0078] Emergency water use mode: Activated when a sudden large flow of water demand is detected, the solar and auxiliary heating systems will operate at maximum power to ensure the outlet water temperature.

[0079] The beneficial effects of this invention include:

[0080] 1. Significantly improves solar energy utilization: Through thermal state topology analysis and precise flow control, the system can maximize the utilization of available solar energy, improving solar energy utilization by 15% to 25% compared to traditional systems;

[0081] 2. Improved system response speed and stability: Predictive control based on matrix manifold can detect system changes in advance and respond accordingly, reducing hot water supply response time by 40% to 60% and temperature fluctuation by more than 50%;

[0082] 3. Enhanced system anomaly detection and prevention capabilities: Topological invariants are extremely sensitive to changes in system structure, enabling the prediction of system anomalies 3-5 minutes in advance, thus improving preventative maintenance efficiency by 70%;

[0083] 4. Improve the system's adaptability to environmental changes: Under extreme weather conditions, the system's performance will decrease by no more than 10%, while traditional systems will decrease by 30% to 50%. Attached Figure Description

[0084] Figure 1 This is a schematic diagram of the overall system architecture of the present invention;

[0085] Figure 2 This is a schematic diagram of the thermal storage system structure of the present invention;

[0086] Figure 3 This is a schematic diagram of the circulating water tank structure of the present invention;

[0087] Figure 4 This is a schematic diagram of the arrangement of the temperature sensor in the water storage tank according to the present invention;

[0088] Figure 5 This is a schematic diagram of the central controller module of the present invention;

[0089] Figure 6 This is a schematic diagram of the thermal state topology characterization process of the present invention;

[0090] Figure 7 This is a schematic diagram of the matrix manifold evaluation process of the present invention;

[0091] Figure 8 This is a schematic diagram of the flow control process of the present invention;

[0092] Figure 9 This is a schematic diagram of the anomaly detection process of the present invention. Detailed Implementation

[0093] Please refer to Figures 1-9 The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art should understand that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0094] like Figure 1 As shown, the intelligent switching control system for non-powered solar energy and auxiliary heat source provided by the present invention includes a thermal storage system 1, a water storage system 2, a cold water supply system 3, multiple water use points 4, and a central controller 5.

[0095] Thermal storage system 1 is used to collect and store solar thermal energy. Water storage system 2 is connected to thermal storage system 1 and is used to store hot water and supply it to users. Cold water supply system 3 is connected to thermal storage system 1 and water storage system 2 to provide cold water. Multiple water usage points 4 are connected to thermal storage system 1 and water storage system 2 respectively via hot water pipes. Central controller 5 is connected to thermal storage system 1, water storage system 2, and cold water supply system 3 for intelligent system control.

[0096] The central controller 5 collects multi-dimensional thermal state data, including water storage temperature, flow rate, liquid level, and environmental data. Based on this data, the central controller 5 constructs a thermal state topology space and extracts topological features from it. Subsequently, the central controller 5 constructs a thermal state evaluation matrix based on the extracted topological features and generates a flow distribution vector field by tracking the evolution trajectory of this matrix. Finally, the central controller 5 determines the coordinated start-up and shutdown sequence and water output between the thermal storage system 1, the water storage system 2, and the cold water supply system 3 according to the flow distribution vector field, thereby achieving effective scheduling and operation of the thermal storage system 1.

[0097] In one embodiment of the present invention, temperature data is collected at a frequency of 5 minutes / time, flow rate data at a frequency of 1 minute / time, and environmental data at a frequency of 10 minutes / time. When the system detects a large change in temperature or flow rate (e.g., a temperature change rate exceeding 2°C / minute or a flow rate change rate exceeding 5L / minute), the collection frequency is automatically increased to 30 seconds / time to ensure accurate capture of rapid changes.

[0098] like Figure 2 As shown, the thermal storage system 1 includes a non-powered solar water heater 11, a water storage tank 12, an electric auxiliary heating system 13, a circulating water tank 14, a thermal storage system 15, and a temperature control system 16.

[0099] The non-powered solar water heater 11 collects solar energy using vacuum tube heat collection technology, achieving a heat collection efficiency of over 70%. The storage tank 12, connected to the non-powered solar water heater 11, employs a multi-layer insulation structure, controlling the heat loss rate to within 0.5℃ / hour. The electric auxiliary heating system 13, connected to the storage tank 12, includes an adjustable-power electric heater with a power range of 1kW-5kW, dynamically adjustable according to demand. The circulating water tank 14, connected to the non-powered solar water heater 11, forms a thermal energy circulation system. The heat storage system 15, connected to the circulating water tank 14, utilizes phase change material heat storage technology, achieving a heat storage density of up to 300MJ / m³. The temperature control system 16, connected to the non-powered solar water heater 11, monitors the water temperature and adjusts the output water flow of the non-powered solar water heater 11.

[0100] Preferably, the collector area of ​​the non-powered solar water heater 11 is 4-20 square meters, configured according to the actual application scenario. Smaller collector areas are suitable for residential users, while larger collector areas are suitable for commercial buildings or residential buildings. The capacity of the storage tank 12 is typically 50-75 liters per square meter of the collector area, ensuring sufficient hot water storage capacity.

[0101] like Figure 3As shown, the circulating water tank 14 includes a cold water tank 141 and a hot water tank 142. A water pump 143 circulates between the hot water tank 142 and the cold water tank 141 to form a return water loop. A heat exchanger 144 is installed between the hot water tank 142 and the cold water tank 141, and the circulating water tank 14 provides heat energy to the heat dissipation equipment at the power consumption point through the heat exchanger 144.

[0102] In a preferred embodiment of the present invention, the water pump 143 employs variable frequency control technology, with a flow rate range of 0.5–10 L / min, and can automatically adjust the flow rate according to heat load requirements. The heat exchanger 144 adopts a plate heat exchanger design, with a heat exchange efficiency of over 85%. Both the cold water tank 141 and the hot water tank 142 are covered with insulation material to reduce heat loss.

[0103] Furthermore, the return water loop design enables the system to effectively recover waste heat from the water usage points, further improving the overall energy efficiency of the system. In practical applications, when the return water temperature at the water usage point is detected to be higher than a preset threshold (usually 35°C), the system will guide this warm water to the cold water tank 141 for preheating, rather than discharging it directly, thereby achieving secondary utilization of thermal energy.

[0104] like Figure 4 As shown, the water storage tank 12 is equipped with a first temperature sensor 121, a second temperature sensor 122, and a third temperature sensor 123. The first temperature sensor 121 is used to monitor the temperature of the upper layer of the water storage tank 12, the second temperature sensor 122 is used to monitor the temperature of the middle layer of the water storage tank 12, and the third temperature sensor 123 is used to monitor the temperature of the lower layer of the water storage tank 12.

[0105] In practical applications, the water temperature in the storage tank 12 exhibits natural stratification, with the upper layer having the highest temperature and the lower layer the lowest. By monitoring these three temperature layers, the system can obtain more accurate temperature distribution information, thereby achieving more refined thermal energy management. Preferably, the three temperature sensors are PT100 platinum resistance temperature sensors, with an accuracy of ±0.1℃ and a measurement range of 0-100℃, meeting the accuracy requirements of the system's temperature monitoring.

[0106] In addition, the first temperature sensor 121 is installed 10-15 cm from the top of the water storage tank 12, the second temperature sensor 122 is installed in the middle of the height of the water storage tank 12, and the third temperature sensor 123 is installed 10-15 cm from the bottom of the water storage tank 12. This arrangement can accurately capture the vertical distribution of water temperature, providing a reliable data basis for thermal energy management.

[0107] like Figure 5 As shown, the central controller 5 includes a data acquisition module 51, a thermal state topology characterization module 52, a matrix manifold evaluation module 53, a flow control module 54, and an anomaly detection module 55.

[0108] The data acquisition module 51 is used to acquire multidimensional thermal state data, including data from temperature sensors, flow meters, liquid level sensors, and environmental sensors. In a preferred embodiment of the present invention, the data acquisition module 51 employs a 24-bit high-precision ADC (analog-to-digital converter) with a sampling rate of up to 100Hz, ensuring the accuracy and real-time performance of data acquisition.

[0109] The thermal state topology characterization module 52 is connected to the data acquisition module 51 and is used to construct a thermal state topology space based on multidimensional thermal state data, extract thermal state trajectories from it, and calculate the topological invariants of the thermal state trajectories. This innovative technology elevates traditional thermal energy management to the topological level, enabling the capture of hidden thermal energy transfer structures and patterns in the system.

[0110] The matrix manifold evaluation module 53 is connected to the thermal state topology characterization module 52. It is used to construct a thermal state evaluation matrix based on topological invariants, track the evolution trajectory of this matrix on the matrix manifold, and generate a flow distribution vector field. This technique represents the thermal state as points on the matrix manifold, and the system evolution corresponds to geodesics on the manifold, thereby simplifying the expression and calculation of the thermal dynamic system while preserving the nonlinear characteristics and topological structure of the system.

[0111] The flow control module 54 is connected to the matrix manifold evaluation module 53. It is used to calculate the optimal pump frequency and valve opening based on the flow distribution vector field, generate control commands, and send them to the thermal storage system 1, the water storage system 2, and the cold water supply system 3. This module is the executor of system decisions, transforming the abstract mathematical model into concrete control actions.

[0112] The anomaly detection module 55 is connected to the thermal state topology characterization module 52 and is used to monitor changes in topological invariants, detect thermal state anomalies, and generate early warning information. Topological invariants are extremely sensitive to changes in system structure and can capture minute anomalies in the system before conventional parameters such as temperature change significantly.

[0113] like Figure 6 As shown, the thermal state topology characterization module 52 is used to construct a multidimensional thermal state space containing temperature, flow, and environmental dimensions; to standardize the data in the multidimensional thermal state space; to extract the main change directions using principal component analysis and construct a simplified thermal state space; to track the thermal state trajectory in the simplified thermal state space; to construct a simple complex based on the thermal state trajectory; to calculate the Betti number and continuous barcode of the simple complex through continuous homology analysis; and to extract the thermal state topology stability index.

[0114] When constructing the multidimensional thermal state space, the temperature dimension includes the temperatures of the upper, middle, and lower layers of the water storage tank 12. The flow rate dimension includes the inlet and outlet flow rates of the solar water heater. Auxiliary heating system inlet and outlet flow rates and water usage point flow rate The environmental dimension includes ambient temperature. Solar radiation intensity and timestamp .

[0115] Standardization of the collected data is necessary to eliminate the influence of different units of measurement. Temperature data is standardized using the following formula:

[0116] ,

[0117] in, The standardized temperature value. This is the original temperature value. and These are the minimum and maximum temperatures, respectively, and are usually taken as... , .

[0118] Traffic data is standardized using a percentage relative to the maximum traffic volume:

[0119] ,

[0120] in, The standardized flow rate value. The original flow rate value. The maximum flow rate designed for the system is usually taken as... .

[0121] The normalization of solar radiation intensity is expressed as a percentage relative to the theoretical maximum value:

[0122] ,

[0123] in, The standardized solar radiation intensity, The original solar radiation intensity, This is the theoretical maximum value, usually taken as... .

[0124] After standardization, the original data forms a high-dimensional space, which requires significant computation for direct processing. Therefore, Principal Component Analysis (PCA) is applied to extract the main directions of change and reduce the data dimensionality. Retaining the principal components that explain 90% of the variability typically reduces the dimensionality to 3-5 dimensions, significantly improving computational efficiency. The simplified thermal state space can be represented as:

[0125] ,

[0126] in, To simplify the thermal state space, For the first One principal component.

[0127] In the simplified thermal state space, the system state changes over time, forming a thermal state trajectory. To extract trajectory features, the trajectory is first divided into segments. This is based on several factors: time windows (every 2 hours); events (such as user water usage and system mode switching); and state changes (when the temperature gradient exceeds a threshold, typically 5°C / hour).

[0128] For the segmented trajectory, geometric features (such as trajectory length, curvature, and twist), statistical features (such as mean, variance, and kurtosis of each dimension), and topological features (such as the number of connected components, the number of cyclic structures, and the number of holes) are extracted. The trajectory features are further enriched by identifying key points (such as inflection points, stable points, and bifurcation points).

[0129] A distance matrix is ​​constructed based on the distance between hot state points. ,in Point and points The Euclidean distance between them. Set a distance threshold. Connection distance less than Point pairs are used to construct a simple complex. This is achieved by gradually increasing... The system constructs nested complex sequences, tracks the appearance and disappearance of topological features (connected components, cycles, holes), and records the duration of the features.

[0130] Finally, calculate the Betti number for each dimension. ,in Indicates the number of connected components. Indicates the number of loop structures. Represents the number of holes. A persistence barcode and persistence diagram are constructed, and topological features are extracted. Thermal topological stability index. Defined as the proportion of features with long durations (durations exceeding three times the system's characteristic timescale, typically 30 minutes), with values ​​ranging from [0,1]. The closer to 1, the more stable the system.

[0131] like Figure 7As shown, the matrix manifold evaluation module 53 is used to construct a 3×3 thermal state evaluation matrix, where the matrix rows represent thermal energy properties and the matrix columns represent system components; calculate the diagonal elements of the matrix to represent the core performance indicators of each component; calculate the off-diagonal elements of the matrix to represent the coupling relationships between components; incorporate topological invariants as weights into the thermal state evaluation matrix; define a metric tensor on the matrix manifold; solve the geodesic equation to predict the evolution trajectory of the thermal state evaluation matrix; define a flow distribution vector field on the matrix manifold; and convert the flow distribution vector field into specific flow adjustment commands.

[0132] Thermal state assessment matrix It is a 3×3 matrix, where the rows represent thermal energy properties (transfer, storage, response) and the columns represent system components (solar water heater, water tank, auxiliary heat source):

[0133] ,

[0134] The diagonal elements of the matrix represent the core performance indicators of each component:

[0135] This represents the heat transfer efficiency of a solar water heater, typically ranging from [0.6 to 0.9].

[0136] This indicates the thermal energy storage capacity of the water tank, and its value typically ranges from [0.7 to 0.95].

[0137] This represents the thermal response rate of the auxiliary heat source, and its value typically ranges from [0.8 to 0.98].

[0138] The off-diagonal elements of the matrix represent the coupling relationships between components:

[0139] This represents the heat transfer efficiency from the solar water heater to the storage tank, with a value range of [0.5, 0.85].

[0140] This represents the synergistic efficiency between the solar water heater and the auxiliary heat source, with a value range of [0.3, 0.7].

[0141] This represents the heat feedback from the water storage tank to the solar water heater, with a value range of [0.1, 0.4].

[0142] This represents the heat exchange efficiency between the water storage tank and the auxiliary heat source, with a value range of [0.4, 0.8].

[0143] This represents the impact of the auxiliary heat source on the solar water heater, with a value range of [0.1, 0.3].

[0144] This represents the heat transfer efficiency from the auxiliary heat source to the water storage tank, with a value range of [0.6, 0.9].

[0145] Topological invariants are incorporated as weights into the thermal state evaluation matrix, enhancing the matrix's sensitivity to the system's topology.

[0146] ,

[0147] in, For the adjusted matrix elements, For the original matrix elements, The weighting coefficient is usually taken as... This reflects the importance of topological features in different dimensions. The Betti number is defined in Example 6.

[0148] To ensure the matrix satisfies physical constraints, regularization is required. The conservation of heat energy requires the sum of the matrix's rows and columns to remain balanced; positive definiteness ensures the overall stability of the system. The regularized matrix... The calculation is as follows:

[0149] ,

[0150] in, for The transpose of the matrix, This is the matrix after adjustment for topological invariants.

[0151] Treating the thermal state assessment matrix as a point on a Riemannian manifold, the Frobenius norm is defined as the distance metric between matrices:

[0152] ,

[0153] in, For matrix and The distance between them For matrix The Line 1 Column elements, For matrix The Line 1 Column elements.

[0154] Define a metric tensor on a matrix manifold , is represented as:

[0155] ,

[0156] in, Describes the differential of a matrix. This indicates the inner product operation.

[0157] Based on this metric tensor, solve the geodesic equations:

[0158] ,

[0159] in, Representation matrix Regarding time The second derivative, Representation matrix Regarding time The first derivative, The symbol is Christoffel, representing a connection on a manifold. By solving this equation, the evolution trajectory of the thermal state assessment matrix can be predicted, thus anticipating future state changes of the system.

[0160] Define a flow distribution vector field on the matrix manifold This indicates the optimal flow rate adjustment direction under the current thermal state:

[0161] ,

[0162] in, For the objective function The gradient on a manifold, with a negative sign indicating that the optimal solution is sought along the gradient descent direction. Objective function Weighted sum of user comfort and energy efficiency:

[0163] ,

[0164] in, and The weighting coefficient is usually taken as... This reflects the relative importance of comfort and energy efficiency. For the comfort objective function, This is the energy efficiency objective function.

[0165] Finally, the flow distribution vector field is converted into specific flow adjustment commands. (Pump frequency adjustment amount) The calculation is as follows:

[0166] ,

[0167] in, This is the proportionality coefficient, usually taken as... Unit vector length, For vector fields The norm of the vector represents its magnitude. Valve opening adjustment amount. The calculation is as follows:

[0168] ,

[0169] in, This is the proportionality coefficient, usually taken as... / unit vector length, Represents a vector field Unit vector of valve direction dot product, This represents the projection of the vector field onto the valve direction.

[0170] like Figure 8 As shown, the flow control module 54 is used to construct the heat exchange network topology, including the node set, edge set, and weights; identify the key nodes, key paths, and bottleneck locations in the heat exchange network topology; extract the main paths of heat energy transfer using the minimum spanning tree algorithm; define the pump frequency parameter set, valve opening parameter set, and temperature setting parameter set; construct a comprehensive objective function, including user comfort index, energy efficiency index, and system life index; optimize the parameter set based on the flow distribution vector field; and generate and send control commands to the corresponding devices.

[0171] When constructing the heat exchange network topology, nodes are defined as heat source points (such as solar water heaters, auxiliary heat sources), storage points (such as water storage tanks, circulating water tanks), and water usage points. Edges are defined as connections between hot water pipes and cold water pipes. Weights are defined based on heat transfer efficiency and flow rate, calculated as follows:

[0172] ,

[0173] in, For the edge weights, For the edge Traffic, For the edge Temperature difference at both ends The maximum system flow rate is usually taken as... / minute, The maximum temperature difference of the system is usually taken as... .

[0174] Centrality measures, such as degree centrality, betweenness centrality, and proximity centrality, are used to identify key nodes. Degree centrality... The calculation is as follows:

[0175] ,

[0176] in, For nodes Degree centrality, For nodes The degree (the number of edges directly connected to it). This represents the total number of nodes in the network. Degree centrality reflects the direct connectivity of nodes; a higher value indicates a more important node.

[0177] Betweenness centrality The calculation is as follows:

[0178] ,

[0179] in, For nodes betweenness centrality, For the node To the node The number of shortest paths, For the nodes from arrive The number of shortest paths. The summation range is all distinct pairs of nodes in the network. ,and Betweenness centrality reflects the mediating role of a node in a network; a larger value indicates a more critical node.

[0180] Critical path identification is based on heat transfer efficiency, employing an improved Dijkstra's algorithm that considers edge weights and node importance. Bottleneck location identification is based on flow rate and temperature gradient; a potential bottleneck is identified when the flow rate is below 80% of the system demand or the temperature gradient exceeds 15°C / meter.

[0181] The minimum spanning tree algorithm is applied to extract the main paths of heat transfer, implemented using either Kruskal's or Prim's algorithm. The minimum spanning tree ensures network connectivity while minimizing the total edge weight, thereby optimizing the heat transfer paths.

[0182] Define the pump frequency parameter set ,in The frequency of the solar-powered circulating water pump is within the range of [10Hz, 50Hz]. The frequency of the auxiliary heating circulating water pump is within the range of [10Hz, 50Hz]. The frequency of the water circulation pump is defined, with a range of [15Hz, 60Hz]. Valve opening parameter set. This indicates the valve opening degree of each pipeline, with a value range of [0%, 100%]. Temperature setting parameter set. , representing the target temperature of each layer in the water storage tank, with values ​​ranging from [45°C, 60°C], [40°C, 55°C], and [35°C, 50°C], respectively.

[0183] Constructing a comprehensive objective function Including user comfort metrics Energy efficiency indicators and system life indicators :

[0184] ,

[0185] in, For the comprehensive objective function, The weighting coefficient is usually taken as... This reflects the relative importance of the three indicators.

[0186] User comfort index Including temperature stability and response speed:

[0187] ,

[0188] in, This is a user comfort index, with a value range of [0,1]. A larger value indicates higher comfort. The weighting coefficient is usually taken as... This indicates the relative importance of temperature stability. The standard deviation of the outlet water temperature. and These are the highest and lowest acceptable temperatures, typically taken as... , , This refers to the system response time, in seconds. To achieve the maximum acceptable response time, it is typically taken as... Second.

[0189] Energy efficiency indicators Includes solar energy utilization rate and auxiliary heating energy consumption:

[0190] ,

[0191] in, This is an energy efficiency index, with a value range of [0,1]. A higher value indicates higher energy efficiency. The weighting coefficient is usually taken as... =0.6, indicating the relative importance of solar energy utilization. The heat provided by solar energy, measured in kWh. Total heat demand, in kWh. The energy consumption of the auxiliary heat source is expressed in kWh. This represents the maximum permissible energy consumption, expressed in kWh.

[0192] The system lifespan metric $J_{lifetime}$ includes equipment workload and start / stop frequency:

[0193] ,

[0194] in, This is a system lifespan indicator, with a value range of [0,1]. A larger value indicates a smaller impact on system lifespan. The weighting coefficient is usually taken as... , The working load of equipment is usually expressed as the ratio of the equipment's operating power to its rated power. The maximum allowable load is usually taken as , This refers to the start / stop frequency, measured in times per hour. The maximum permissible start-stop frequency is usually taken as... times / hour.

[0195] The parameter set is optimized based on the flow distribution vector field, and optimization algorithms such as gradient descent or simulated annealing are employed. The initial parameters are based on historical optimal configurations, the parameters are adjusted based on the direction of the flow distribution vector field, the parameters are verified by simulating and predicting the performance of the adjusted system, and the parameters are applied to the system after confirming that they meet the constraints.

[0196] The optimized parameters are converted into specific control commands and sent to the corresponding equipment. Examples include pump frequency control commands, valve opening control commands, and electric auxiliary heating power control commands. Before executing the control, a safety check is performed to ensure that the control commands are within safe limits, do not violate physical constraints, and prevent equipment overload and improper operation.

[0197] like Figure 9 As shown, the anomaly detection module 55 is used to build an anomaly pattern library, collect the topological features of typical anomaly patterns, calculate the deviation between the current topological features and the normal pattern in real time, perform similarity matching between the current topological features and the anomaly pattern library, calculate the anomaly probability distribution and evaluate the warning level, execute the corresponding self-repair strategy according to the warning level, and notify the user and provide coping suggestions in the case of advanced anomalies.

[0198] When constructing the anomaly pattern library, the topological features of typical anomaly patterns are collected, and anomaly types are classified, such as equipment failure, external interference, and abnormal user water usage. A mapping relationship between anomaly features and their causes is established, forming anomaly feature vectors. It includes topological invariants, trajectory features, and statistical features.

[0199] Real-time calculation of the deviation D between the current topological features and the normal pattern:

[0200] ,

[0201] Where D represents the deviation between the current topological features and the normal pattern. This is the current topological feature vector. For normal mode feature vectors, The vector norm is usually represented by Euclidean distance or Mahalanobis distance.

[0202] The current topological features are compared with the anomaly pattern library to calculate the similarity score. :

[0203] ,

[0204] in, For the current feature and the first The similarity of the abnormal patterns, For the first Feature vectors of various abnormal patterns Represents the dot product of two vectors. and Let these represent the norms of the two vectors. Similarity. The value ranges from [-1, 1], and the closer it is to 1, the more similar it is.

[0205] Calculate the probability distribution of anomalies :

[0206] ,

[0207] in, For the first The probability of such anomalies, For temperature parameters, it is usually taken as... To control the steepness of the probability distribution, Represents an exponential function. This represents the exponential sum of all anomalous patterns, used to normalize the probability distribution.

[0208] The warning levels are assessed and categorized into low-level warning, medium-level warning, high-level warning, and emergency warning.

[0209] Low-level warning: Minor abnormalities may resolve on their own.

[0210] Intermediate warning: The system needs to automatically adjust the parameters;

[0211] Advanced warning: Manual intervention may be required;

[0212] Emergency Warning: This could lead to equipment damage or safety risks;

[0213] The corresponding self-repair strategy is executed according to the warning level. For low-level anomalies, control parameters are adjusted to restore normal status; for medium-level anomalies, the operating mode is switched to avoid abnormal conditions; for high-level anomalies, the abnormal components are isolated and backup paths are used; for emergency anomalies, the relevant equipment is safely shut down and the user is notified.

[0214] In a preferred embodiment of the present invention, the anomaly detection module 55 employs an incremental learning mechanism to continuously update the anomaly pattern library, thereby improving the accuracy and adaptability of anomaly detection. The system records detailed information for each anomaly event, including topological features, occurrence time, environmental conditions, etc., forming an anomaly event log. By analyzing the anomaly event log, the system can discover new anomaly patterns and add them to the anomaly pattern library, thereby continuously enhancing its anomaly detection capabilities.

[0215] The central controller 5 automatically switches between the following operating modes based on environmental conditions and user needs: solar priority mode, auxiliary heat supplementation mode, energy-saving heat storage mode, and emergency water use mode.

[0216] The solar priority mode activates when solar radiation intensity reaches a threshold (typically 800 MJ / m²) and the water tank temperature is below the target value. In this mode, the system maximizes the water flow from the solar water heater, selects the optimal heat transfer path based on the topology, and the auxiliary heating system remains in standby mode, activating only when necessary. The heat distribution strategy prioritizes storing solar-heated water in the upper layer of the water tank, while cold water is preheated by the solar water heater, adjusting the circulating water volume according to predicted water demand. The system exits this mode when solar radiation intensity drops below the threshold, the water tank temperature reaches the target value, or the user manually switches the mode.

[0217] The auxiliary heating supplement mode is activated when the solar water heater outlet temperature is lower than the required temperature, the predicted water demand exceeds the heat that solar energy can provide, or the storage tank temperature is lower than a set threshold. In this mode, the system calculates the required auxiliary heating power based on the thermal state assessment matrix, prioritizes using solar preheating water, and then heats the water through the auxiliary heating system, dynamically adjusting the auxiliary heating power to avoid energy waste. The heat energy distribution strategy prioritizes supplying water from the auxiliary heating system to high-demand areas, and prioritizes preheating low-temperature water through the solar water heater, optimizing the heat transfer path based on the topology. When the solar water heater outlet temperature returns to the required level, the storage tank temperature reaches the target value, or the user's hot water demand decreases to a level that solar energy can meet, the system exits this mode.

[0218] The energy-saving thermal storage mode is activated during off-peak water usage periods (such as late at night), when the solar water heater has residual heat energy, and when peak water demand is predicted. In this mode, the system utilizes the waste heat from the solar water heater to store heat in the storage tank, storing hot water in layers. The upper layer is kept at a high temperature, while the lower layer is moderately heated, optimizing the circulation path and reducing heat loss. The heat energy distribution strategy adjusts the storage water temperature based on peak water usage predictions, maximizes thermal energy storage using the thermal topology, and adjusts the circulation pump frequency to minimize energy consumption. The system exits this mode when the storage tank temperature reaches the preset thermal storage target, the predicted peak water usage period begins, or the system switches to another higher-priority mode.

[0219] Emergency water demand mode is activated when a sudden surge in water usage is detected, the user manually activates the emergency mode, or the system predicts a large water demand within a short period. In this mode, the system simultaneously operates both the solar and auxiliary heating systems at maximum power, prioritizing outlet water temperature, followed by energy efficiency, temporarily ignoring non-critical topology optimization, and focusing solely on hot water supply. The heat distribution strategy directly delivers hot water to the point of use, reducing intermediate steps, allocating hot water resources according to the priority of the usage points, and using stored hot water from the storage tank directly when necessary. The system exits this mode when the emergency water demand ends, the user cancels the emergency mode, or the system returns to normal operation.

[0220] In a preferred embodiment of the present invention, the system mode switching employs a smooth transition mechanism to avoid system instability caused by sudden switching. The smooth transition is achieved through gradual adjustment of parameters. For example, when transitioning from a solar priority mode to an auxiliary heating supplement mode, the auxiliary heating power does not suddenly increase to the calculated value, but rather starts from the minimum value and gradually increases to the target value at a preset rate (typically 20% / minute). This smooth transition mechanism reduces system fluctuations and improves operational stability and user experience.

[0221] The above description is merely a preferred embodiment of the present invention and does not limit the scope of the patent. Any equivalent structural transformations made using the contents of the specification and drawings of the present invention under the concept of the present invention, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of the present invention.

Claims

1. A non-powered solar energy and auxiliary heat source intelligent switching control system, characterized in that, include: A thermal energy storage system for collecting and storing solar thermal energy, the thermal energy storage system including a non-powered solar water heater; A water storage system, connected to the thermal storage system, is used to store hot water and supply it to users; A cold water supply system, connected to the thermal storage system and the water storage system, is used to provide cold water; Multiple water usage points are connected to the thermal storage system and the water storage system respectively via hot water pipes; The central controller, connected to the thermal storage system, the water storage system, and the cold water supply system, is used for: Collect multidimensional thermal state data, which includes water storage temperature data, flow rate data, liquid level data, and environmental data; Based on the multidimensional thermal state data, a multidimensional thermal state space including temperature, flow rate and environmental dimensions is constructed. The data in the multidimensional thermal state space is standardized, principal component analysis is applied to construct a simplified thermal state space, and the thermal state trajectory is tracked in the simplified thermal state space. A simple complex is constructed based on the aforementioned thermal trajectory, and the Betti number of the simple complex is calculated through continuous homology analysis. ,in Indicates the number of connected components, Represents the number of loop structures, Indicates the number of holes; A 3×3 thermal state assessment matrix is ​​constructed. The rows of the thermal state assessment matrix represent three thermal energy attributes: transfer, storage, and response. The columns of the thermal state assessment matrix represent three system components: solar water heater, water storage tank, and auxiliary heat source. The diagonal elements of the thermal state assessment matrix represent the core performance indicators of each component, and the off-diagonal elements represent the coupling relationship between components. The Betti number is incorporated as a weight into the thermal state evaluation matrix to obtain the adjusted matrix elements. ,in For the original matrix elements, These are the weighting coefficients; The thermal state assessment matrix is ​​used as a point on the matrix manifold. A metric tensor is defined on the matrix manifold and the geodesic equation is solved to track the evolution trajectory of the thermal state assessment matrix on the matrix manifold, and a flow distribution vector field is generated based on the evolution trajectory. The coordinated start-up and shutdown sequence and water output between the thermal storage system, the water storage system, and the cold water supply system are determined based on the flow distribution vector field, thereby realizing the effective scheduling and operation of the thermal storage system.

2. The intelligent switching control system for non-powered solar energy and auxiliary heat source according to claim 1, characterized in that, The thermal storage system also includes: A water storage tank, connected to the non-powered solar water heater, is used to store hot water; An electric auxiliary heating system, connected to the water storage tank, is used to provide auxiliary heat energy when solar energy is insufficient; A circulating water tank is connected to the non-powered solar water heater to form a thermal energy circulation system; A heat storage system, connected to the circulating water tank, is used to store thermal energy; A temperature control system, connected to the non-powered solar water heater, is used to monitor the water temperature and adjust the output water volume of the non-powered solar water heater.

3. The intelligent switching control system for non-powered solar energy and auxiliary heat source according to claim 2, characterized in that, The circulating water tank includes a cold water tank and a hot water tank. The hot water tank and the cold water tank are connected by a water pump to form a return water loop. A heat exchanger is installed between the hot water tank and the cold water tank. The circulating water tank provides heat energy to the heat dissipation equipment of the power consumption point through the heat exchanger.

4. The intelligent switching control system for non-powered solar energy and auxiliary heat source according to claim 1, characterized in that, The water storage tank is equipped with a first temperature sensor, a second temperature sensor, and a third temperature sensor. The first temperature sensor is used to monitor the temperature of the upper layer of the water storage tank, the second temperature sensor is used to monitor the temperature of the middle layer of the water storage tank, and the third temperature sensor is used to monitor the temperature of the lower layer of the water storage tank.

5. The intelligent switching control system for non-powered solar energy and auxiliary heat source according to claim 1, characterized in that, The central controller includes: The data acquisition module is used to acquire the multidimensional thermal state data; A thermal state topology characterization module, connected to the data acquisition module, is used to construct a thermal state topology space based on the multidimensional thermal state data, extract thermal state trajectories from the thermal state topology space, and calculate the topological invariants of the thermal state trajectories. The matrix manifold evaluation module, connected to the thermal state topology characterization module, is used to construct the thermal state evaluation matrix based on the topological invariants, track the evolution trajectory of the thermal state evaluation matrix on the matrix manifold, and generate the flow distribution vector field. The flow control module, connected to the matrix manifold evaluation module, is used to calculate the optimal pump frequency and valve opening based on the flow distribution vector field, generate control commands, and send them to the thermal storage system, the water storage system, and the cold water supply system. An anomaly detection module, connected to the thermal state topology characterization module, is used to monitor changes in the topological invariants, detect thermal state anomalies, and generate early warning information.

6. The intelligent switching control system for non-powered solar energy and auxiliary heat source according to claim 1, characterized in that, The central controller is also used to: calculate the persistent barcode of the simple complex through the persistent homology analysis, and extract the thermal state topological stability index based on the persistent barcode. The thermal state topological stability index is the proportion of features whose duration exceeds three times the system feature time scale among all features, and its value range is [0,1].

7. The intelligent switching control system for non-powered solar energy and auxiliary heat source according to claim 5, characterized in that, The flow control module is used for: Construct the hot-swapping network topology, including the set of nodes, the set of edges, and the weights; Identify the key nodes, key paths, and bottleneck locations in the hot-swapping network topology; The minimum spanning tree algorithm is used to extract the main paths of heat transfer. Define the pump frequency parameter set, valve opening parameter set, and temperature setting parameter set; Construct a comprehensive objective function that includes user comfort indicators, energy efficiency indicators, and system life indicators; The parameter set is optimized based on the traffic allocation vector field; control commands are generated and sent to the corresponding devices.

8. The intelligent switching control system for non-powered solar energy and auxiliary heat source according to claim 5, characterized in that, The anomaly detection module is used for: Build an anomaly pattern library and collect the topological features of typical anomaly patterns; Calculate the deviation between the current topological features and the normal pattern in real time; perform similarity matching between the current topological features and the abnormal pattern library; Calculate the probability distribution of anomalies and assess the warning level; execute corresponding self-healing strategies based on the warning level; notify users and provide coping suggestions in the event of advanced anomalies.

9. The intelligent switching control system for non-powered solar energy and auxiliary heat source according to claim 1, characterized in that, The central controller automatically switches between the following operating modes based on environmental conditions and user needs: Solar priority mode: Activated when solar radiation intensity reaches the threshold and the water tank temperature is below the target value to maximize solar energy utilization; Auxiliary heating supplement mode: This mode is activated when the outlet water temperature of the solar water heater is lower than the required temperature. It prioritizes using solar preheated water and then heats the water through the auxiliary heating system. Energy-saving heat storage mode: It is activated during periods of low water usage and when the solar water heater has residual heat energy, using the residual heat of the solar water heater to store heat in the water tank. Emergency water use mode: Activated when a sudden large flow of water demand is detected, the solar and auxiliary heating systems will operate at maximum power to ensure the outlet water temperature.

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