Green clean energy comprehensive energy-saving management platform based on internet of things intelligent terminal

Abstract

The application discloses a green clean energy comprehensive energy-saving management platform based on an Internet of Things intelligent terminal and particularly relates to the technical field of intelligent monitoring and optimal control, and is used for solving the problem that the prior art has no solution or outputs a high-risk operation strategy due to neglecting the coupling relationship between subsystems when dealing with multiple security constraints; through the Internet of Things terminal, operation parameters and security constraint boundaries are acquired, when multiple constraints are close to being reached, the key coupling path of the connected constraint is identified; then, the change of the conflict correlation strength between the constraints under different relaxation schemes is simulated, a target scheme that can reduce the network structure tightness to the maximum extent is selected by constructing and analyzing the conflict relationship network, a group of coordinated constraint boundaries is generated; finally, the global energy efficiency optimization calculation is performed and the equipment set value is output based on the boundaries, so that the coordinated control of the safe and efficient operation of the complex energy system is realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring and optimization control technology, and in particular to a green and clean energy integrated energy-saving management platform based on Internet of Things (IoT) intelligent terminals. Background Technology

[0002] In the comprehensive energy conservation management of green and clean energy for industrial parks or large building complexes, existing technologies generally adopt IoT-based data acquisition and monitoring platforms to centrally monitor and independently control multiple energy subsystems such as air conditioning, lighting, photovoltaics, and energy storage. The goal is usually to achieve optimal overall energy efficiency. The operating setpoints of each device in the system are coordinated and calculated through optimization algorithms. In this process, the inherent safety constraints of each subsystem, such as the power limit of electrical circuits, the pressure limit of hydraulic networks, and the temperature protection threshold of refrigeration equipment, are all input as independent boundary conditions into the optimization model.

[0003] However, when dealing with the above-mentioned multiple security constraints, existing technologies usually treat them as simple superpositions of independent static boundaries. Since the energy subsystems are tightly coupled in terms of actual physical connection and energy flow, these discrete constraints may have potential interactions and conflicts when the system operating point is pushed toward the efficient boundary. This may cause the optimization model to fail to find a feasible solution or to calculate a setting strategy that approaches multiple constraint boundaries and exposes the system to operational risks. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a green and clean energy integrated energy-saving management platform based on Internet of Things (IoT) smart terminals.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A comprehensive green and clean energy management platform based on IoT smart terminals includes: The data acquisition module is used to acquire, in real time, the operating parameters of each sub-equipment in the integrated energy system and the current boundary values ​​of each safety constraint through IoT smart terminals; The boundary determination module is used to determine, based on the running parameters and the current boundary value, whether there are at least two different types of safety constraints whose real-time boundary values ​​are simultaneously approached. The path identification module is used to determine the minimum edge set corresponding to the node that is touched when the determination is true, and to identify the minimum edge set as the critical coupling path. The intensity simulation module is used to simulate the changes in the intensity of conflict correlations between various security constraints under different relaxation schemes, based on the critical coupling path. The scheme generation module is used to construct and analyze the changes in the influence centrality of nodes in the constrained conflict relationship network based on the changes in the conflict association strength, select the target relaxation scheme from different relaxation schemes that reduces the structural tightness of the constrained conflict relationship network, and generate a set of coordinated constraint boundary values. The optimization execution module is used to perform global energy efficiency optimization calculations and output device setting values, using the coordinated constraint boundary values ​​as optimization boundary conditions.

[0006] Furthermore, through IoT smart terminals, the operating parameters of each sub-device in the integrated energy system and the current boundary values ​​of each safety constraint are obtained in real time, including: Raw operational data and raw boundary settings are collected from IoT smart terminals deployed in various sub-devices of the integrated energy system; The collected raw operating data is validated and cleaned to obtain the operating parameters of each sub-device; The original boundary settings collected are converted and stored to obtain the current boundary values ​​of each security constraint.

[0007] Furthermore, based on the operating parameters and the current boundary value, it is determined whether at least two different categories of safety constraints have their real-time boundary values ​​simultaneously approached, including: For each safety constraint, calculate the real-time proximity based on its corresponding operating parameters and the current boundary value; All security constraints with a real-time proximity greater than a preset proximity threshold are selected as a set of security constraints that are close to the boundary. Based on the category attributes of the safety constraints, the safety constraints in the set of safety constraints near the boundary are classified. Determine whether there are security constraints belonging to at least two different categories whose corresponding real-time proximity values ​​are consistently greater than a preset proximity threshold within a preset time window.

[0008] Furthermore, the category attributes are divided according to the energy subsystems associated with the security constraints, including HVAC category, power supply category, water network category, and clean energy category; for each security constraint in the set of security constraints near the boundary, its predefined category attribute label is read, and security constraints with the same category attribute label are grouped into the same group, thereby completing the classification.

[0009] Furthermore, when the determination is yes, the minimum edge set corresponding to the node connected to the approached security constraint is determined, and the minimum edge set is identified as the critical coupling path, including: Based on the pre-defined network topology of the integrated energy system, each approaching security constraint is mapped to a corresponding node in the network topology. Using the preset weights of edges in the network topology as a metric, calculate the minimum set of edges that need to be removed to make the corresponding nodes no longer connected; The edges in the calculated minimum set are identified as the critical coupling paths connecting the approaching security constraints.

[0010] Furthermore, the network topology is constructed based on the actual connection relationships between various physical devices and pipelines in the integrated energy system; the nodes in the topology represent energy-consuming devices, energy conversion devices, or key measurement points, and the edges represent physical connection pipelines, power lines, or data transmission links between devices. The preset weights of the edges are set based on the design flow rate of the connecting pipelines, the rated capacity of the lines, or the data bandwidth of the links.

[0011] Furthermore, based on the critical coupling path, the changes in the conflict correlation strength between various security constraints under different relaxation schemes are simulated, including: Based on the nodes on the key coupling path, an initial correlation matrix reflecting the correlation between nodes is constructed; For each preset relaxation scheme, simulate the adjustment of the current boundary values ​​of the corresponding security constraints on the critical coupling path; Based on the boundary value adjustment results, calculate the changes in the influence factors between nodes on the critical coupling path and update the initial correlation matrix; Based on the updated correlation matrix, the changes in the conflict correlation strength among various security constraints are determined.

[0012] Furthermore, based on the changes in conflict correlation strength, the changes in the influence centrality of nodes in the constrained conflict relationship network are constructed and analyzed. A target relaxation scheme that reduces the structural tightness of the constrained conflict relationship network is selected from different relaxation schemes, and a set of coordinated constraint boundary values ​​is generated, including: Based on the changes in the conflict correlation strength among various security constraints, a constraint conflict relationship network is constructed with security constraints as nodes and conflict correlation strength as edge weights; For each different relaxation scheme, calculate the eigenvector centrality values ​​of all nodes in the constraint conflict relation network; Based on the distribution of eigenvector centrality values, the global efficiency index of the constraint conflict relationship network is calculated to characterize its structural tightness. Select the relaxation schemes that cause the largest decrease in the global efficiency index as the target relaxation schemes. Based on the target relaxation scheme, determine the specific boundary value adjustment amount of the safety constraint to be adjusted, and generate the coordinated constraint boundary value.

[0013] Furthermore, based on the calculated eigenvector centrality values ​​of all nodes, the importance distribution of nodes is constructed; the global efficiency index is obtained by calculating the average shortest path length of the inverse of the eigenvector centrality values ​​between all pairs of nodes in the constraint conflict relationship network; an increase in the average shortest path length is characterized by a decrease in the density of the network structure.

[0014] Furthermore, using the coordinated constraint boundary values ​​as optimization boundary conditions, global energy efficiency optimization calculations are performed and equipment setpoints are output, including: Global energy efficiency optimization calculations are performed using the coordinated constraint boundary values ​​as boundary conditions. Based on the global energy efficiency optimization calculation results, the optimal operating parameters of each sub-device are determined; Convert the optimal operating parameters of each sub-device into the corresponding device settings and output them.

[0015] The beneficial effects of this invention are: 1. By constructing a dynamic and coordinated relaxation mechanism for coupled constraints, the safety and optimization reliability of the integrated energy system in the high-efficiency operating range are effectively improved. When the system detects that multiple heterogeneous safety constraints are approaching the operating boundary at the same time, it not only identifies the apparent conflict, but also traces the source to the key coupling path in the physical connection through graph theory. This transforms the handling of constraints from isolated adjustment to targeted intervention in the inherent coupling relationship of the system. Furthermore, by simulating and analyzing the impact of different relaxation strategies on the network structure of conflict associations between constraints, the scheme that can reduce the density of the network to the greatest extent is selected. This ensures that the generated coordinated post-boundary not only alleviates immediate conflicts, but also guides the overall operating state of the system to evolve towards a more robust configuration with fewer conflicts. This constitutes a complete technical closed loop from conflict diagnosis, root cause analysis, strategy evaluation to decision generation.

[0016] 2. Compared with the traditional method of treating constraints as static independent boundaries, the collaborative relaxation and optimization control implemented in this scheme significantly enhances the decision quality and control robustness of the global energy efficiency management platform. It ensures that the optimization algorithm can search for the best within a safe and feasible domain that fully respects the physical reality of the multi-energy coupling of the system. This fundamentally avoids unsolvable or high-risk outputs caused by the distortion of the constraint model. It can safely and reliably pursue the optimal energy efficiency target within a range that is closer to the real physical limits, achieving a balance between operational efficiency and operational safety, and improving the level of automation and intelligent control of complex energy systems. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of the green and clean energy integrated energy-saving management platform based on Internet of Things smart terminals of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example: Figure 1 A schematic diagram of the structure of the green and clean energy integrated energy-saving management platform based on IoT smart terminals of the present invention is provided. The green and clean energy integrated energy-saving management platform based on IoT smart terminals includes: The data acquisition module is used to acquire, in real time, the operating parameters of each sub-equipment in the integrated energy system and the current boundary values ​​of each safety constraint through IoT smart terminals; The boundary determination module is used to determine, based on the running parameters and the current boundary value, whether there are at least two different types of safety constraints whose real-time boundary values ​​are simultaneously approached. The path identification module is used to determine the minimum edge set corresponding to the node that is touched when the determination is true, and to identify the minimum edge set as the critical coupling path. The intensity simulation module is used to simulate the changes in the intensity of conflict correlations between various security constraints under different relaxation schemes, based on the critical coupling path. The scheme generation module is used to construct and analyze the changes in the influence centrality of nodes in the constrained conflict relationship network based on the changes in the conflict association strength, select the target relaxation scheme from different relaxation schemes that reduces the structural tightness of the constrained conflict relationship network, and generate a set of coordinated constraint boundary values. The optimization execution module is used to perform global energy efficiency optimization calculations and output device setting values, using the coordinated constraint boundary values ​​as optimization boundary conditions.

[0020] Through IoT smart terminals, the operating parameters of each sub-device in the integrated energy system and the current boundary values ​​of each safety constraint are obtained in real time. Specifically, this is implemented as follows: Raw operational data and boundary settings are collected from IoT smart terminals deployed in various sub-devices of the integrated energy system. These IoT smart terminals include smart meters, smart water meters, gas flow meters, temperature sensors, pressure sensors, humidity sensors, and smart controllers. The IoT smart terminals transmit the monitored raw operational data to a data aggregation point via wired or wireless communication protocols. Transmission occurs according to a preset acquisition cycle, which is determined based on the system's real-time data requirements and network load capacity, such as 30 seconds or 1 minute. The raw operational data includes voltage, current, active power, and reactive power values ​​for electrical circuits; pressure, flow, and temperature values ​​for hydraulic networks; chilled water supply temperature, chilled water return temperature, and cooling water outlet temperature for air conditioning equipment; main unit operating frequency; compressor current; DC voltage, DC current, and AC power values ​​for photovoltaic arrays; and state of charge and charge / discharge power values ​​for energy storage batteries. Simultaneously, raw boundary setpoints are collected from the control systems or management interfaces corresponding to each sub-device. These raw boundary setpoints include manually set or system default upper limits for power, pressure, temperature, temperature, and flow. The raw boundary setpoints are stored numerically in the device controller or host computer software configuration file and can be read through the corresponding communication and data access interfaces. The collected raw operating data and raw boundary setpoints are timestamped and have device identifiers, and are temporarily stored in the raw data buffer.

[0021] The collected raw operational data undergoes validity verification and data cleaning to obtain the operating parameters of each sub-device. Validity verification includes range verification and logical verification. Range verification determines whether the value of each data point is within a reasonable physical range for that physical quantity. A reasonable physical range is determined based on the permissible operating range in the technical specifications provided by the equipment manufacturer or the safe operating range obtained from long-term system operation statistics. For example, the reasonable physical range for chilled water temperature is 0 to 20 degrees Celsius. Logical verification determines whether the logical relationship between multiple parameters of the same or related equipment is reasonable. The logical relationship is determined based on the equipment's working principle and physical laws. For example, according to the principle of energy conservation, the chilled water supply temperature collected at the same time should be higher than the chilled water return temperature. If the supply temperature is lower than the return temperature and the temperature difference exceeds a preset reasonable temperature difference threshold, the relevant data is marked as data to be verified. Data cleaning processes invalid and data to be verified. Data points determined as invalid by range verification are discarded, meaning they are not included in any subsequent calculations. For data to be verified with logical check marks, linear interpolation is used for repair. Linear interpolation involves taking the data value of the preceding valid timestamp and the data value of the following valid timestamp adjacent to the data point to be verified, and using the ratio of the difference between the timestamp of the data point to be verified and the preceding valid timestamp to the total difference between the two valid timestamps as a weight. The result is then used as the repaired data value. After data cleaning, the operating parameters of each sub-device are obtained. These operating parameters are standardized data that has been verified, cleaned, and formatted uniformly.

[0022] The process involves format conversion and storage of the collected raw boundary setpoints to obtain the current boundary values ​​of each safety constraint. Format conversion refers to converting the raw boundary setpoints from various heterogeneous formats to the system's unified floating-point format and standard units of measurement. Heterogeneous formats include device manufacturer-defined string formats and integer or floating-point formats without explicit units. The conversion process follows a predefined mapping rule table, which records the conversion relationship between the data format, unit information, and target standard units for each device or system parameter. For example, the raw string of temperature setting read from the chiller controller is used to identify its numerical and unit parts according to the mapping rule table and converted to a standard floating-point format in degrees Celsius. Storage involves storing the format-converted boundary setpoints along with their corresponding unique identifiers for safety constraints into the boundary value database. Each unique identifier for a safety constraint is associated with a specific device, parameter, and its boundary type. The boundary value database supports real-time querying and updating based on identifiers. This process ultimately yields the current boundary values ​​of each safety constraint. The boundary value database is continuously maintained during system operation. When the safe operating boundary of any sub-device is adjusted through manual operation or automatic policy, the corresponding current boundary value in the boundary value database will be updated accordingly.

[0023] The preset reasonable temperature difference threshold is obtained by analyzing historical normal operating data of the system during steady-state operation. Specifically, the historical values ​​of the temperature difference between the chilled water supply and return water are statistically analyzed, the average value and standard deviation of the temperature difference data are calculated, and the value obtained by adding twice the standard deviation to the average value is set as the preset reasonable temperature difference threshold to cover most normal fluctuations and effectively identify anomalies.

[0024] Based on the operating parameters and the current boundary value, determine whether at least two different categories of safety constraints have their real-time boundary values ​​simultaneously approached. Specifically, the implementation is as follows: For each safety constraint, a real-time proximity score is calculated based on its corresponding operating parameters and the current boundary value. The method for calculating the real-time proximity score is to take the absolute difference between the actual value of the operating parameter and the current boundary value, then divide this absolute difference by the current boundary value; the complement of the quotient is the real-time proximity score. For a current boundary value with an upper limit, the real-time proximity score is equal to 1 minus the difference between the actual value of the operating parameter and the current boundary value divided by the current boundary value. For a current boundary value with a lower limit, the real-time proximity score is equal to 1 minus the difference between the current boundary value and the actual value of the operating parameter divided by the current boundary value. The operating parameters are derived from the operating parameters of each sub-device obtained after data cleaning, and the current boundary value is derived from the current boundary values ​​of each safety constraint stored in the boundary value database. The calculated real-time proximity score is a dimensionless value between 0 and 1; the closer the value is to 1, the closer the operating parameter is to the current boundary value.

[0025] All security constraints with real-time proximity scores greater than a preset proximity threshold are filtered into a set of security constraints approaching the boundary. The preset proximity threshold is a pre-defined value used to determine whether a security constraint is in a state requiring attention and is approaching the boundary. The preset proximity threshold is set based on statistical analysis of historical system operation data. Specifically, the real-time proximity time-series data of each security constraint during normal and stable system operation is extracted from the historical database. The maximum real-time proximity score of each security constraint during normal operation is calculated, and then the statistical average of the normal maximum real-time proximity scores of all security constraints is calculated. This statistical average is multiplied by an adjustment factor greater than 1 to obtain the preset proximity threshold. The adjustment factor is set according to the system's requirements for early warning sensitivity; for example, it can be set to 1.05. The preset proximity threshold is, for example, 0.9. The filtering process involves iterating through all security constraints, comparing the calculated real-time proximity score of each security constraint with the preset proximity threshold, and adding security constraints with real-time proximity scores greater than the preset proximity threshold to a temporary data set, which is the set of security constraints approaching the boundary. Each element in the set of security constraints approaching the boundary records a unique identifier for the security constraint and its corresponding real-time proximity score value.

[0026] Based on the category attributes of the safety constraints, the safety constraints in the set of safety constraints near the boundary are classified. The category attributes of the safety constraints are divided according to the energy subsystems associated with the safety constraints. In this embodiment, the category attributes include HVAC category, power supply category, hydraulic network category, and clean energy category. The HVAC category covers safety constraints related to air conditioning units, water pumps, fans, cooling towers, and related temperature and pressure parameters. The power supply category covers safety constraints related to power distribution circuits, transformers, power of electrical equipment, and voltage and current parameters. The hydraulic network category covers safety constraints related to water pumps, valves, pipeline pressure and flow parameters. The clean energy category covers safety constraints related to photovoltaic inverters, energy storage converters, ground source heat pump units, and related power state of charge parameters. Each safety constraint is assigned a predefined category attribute label during system initialization configuration. The classification process involves reading the unique identifier of each safety constraint in the set of safety constraints near the boundary, querying its predefined category attribute label based on the unique identifier, and then grouping safety constraints with the same category attribute label into the same group. After classification, several groups are obtained. Each group contains all the security constraints of the proximity boundary belonging to the same category and their corresponding real-time proximity values.

[0027] The system determines whether there exist safety constraints belonging to at least two different categories whose corresponding real-time proximity values ​​consistently exceed a preset proximity threshold within a preset time window. The preset time window is a continuous time length used to determine whether the proximity state is persistent rather than fluctuating instantaneously. The preset time window length is determined based on the inertial time constant of the system's main equipment and the control system's adjustment cycle. The inertial time constant refers to the time required for equipment to change from one operating state to another; for example, the inertial time constant of a chiller unit is approximately several minutes. The control system adjustment cycle refers to the time interval between one closed-loop adjustment by the automatic control system. The preset time window length is set to be greater than the inertial time constant of the main equipment and covers multiple control system adjustment cycles, such as 5 minutes or 10 minutes. The determination process first selects the safety constraint with the highest real-time proximity value within each group after classification as the representative of that category. Then, it checks whether the real-time proximity values ​​of these representative safety constraints from different categories are greater than the preset proximity threshold at each sampling time within a preset time window period. The sampling time is determined by the data acquisition cycle. This check requires querying the historical database to record all real-time proximity history of these security constraints within a preset time window. If it is found that at least two representative security constraints of different categories satisfy the condition that their corresponding real-time proximity values ​​are consistently greater than a preset proximity threshold within the preset time window, it is determined that the real-time boundary values ​​of at least two different categories of security constraints are simultaneously approaching. The determination result is recorded and triggers the subsequent path recognition module operation.

[0028] When the determination is yes, the minimum edge set corresponding to the node connected to the approached safety constraint is determined, and the minimum edge set is identified as the critical coupling path. Specifically, the implementation is as follows: The network topology is constructed based on the actual connection relationships between various physical devices and pipelines in the integrated energy system. The construction process utilizes the integrated energy system's engineering design drawings, equipment installation lists, and on-site survey information to identify all energy-consuming devices, all energy conversion devices, and all critical measurement points within the system. Energy-consuming devices include fans, pumps, and lighting distribution boxes. Energy conversion devices include chillers, boilers, photovoltaic inverters, and energy storage converters. Critical measurement points refer to physical locations within the system where sensors are installed to monitor system status, and whose monitoring data is directly used as the basis for judging one or more safety constraints. Examples include the pressure sensor installation point on the main pipeline, the multi-function energy meter installation point on the main power supply circuit, and the outlet temperature sensor installation point of the chiller evaporator. Each independent energy-consuming device, energy conversion device, or critical measurement point is abstracted as a network topology node, and each node is assigned a unique node identifier. The node identifier uses string encoding, which includes device type and location information.

[0029] Then, based on the actual physical connections between the devices, an edge is established between two nodes with a direct physical connection. Physical connections include liquid or gas pipelines connecting equipment, power cables connecting electrical equipment, and data communication cables connecting control equipment. Each edge is assigned a preset weight. The preset weight is set based on the inherent properties or design parameters of the physical connection represented by the edge. For an edge representing a water pipe or air duct, its preset weight is set based on the pipe's maximum design flow rate. The maximum design flow rate is obtained from the system's hydraulic calculations, pipeline design drawings, or equipment technical specifications. The preset weight is calculated by dividing the pipe's maximum design flow rate by a selected system flow rate benchmark. The system flow rate benchmark can be the average of all pipes' maximum design flow rates in the system, or the design flow rate of the largest main pipeline in the system. For an edge representing a power line, its preset weight is set based on the line's rated current carrying capacity or the rated capacity of the transformer connected to it. The rated value is obtained from electrical design drawings or equipment nameplate parameters. The preset weight is calculated by dividing the rated value by a selected system power benchmark. The system power baseline can be the average rated current carrying capacity of all power lines in the system, or the rated capacity of the total distribution transformer. For edges representing data transmission links, their preset weights can be set based on their communication bandwidth, for example, by dividing the bandwidth value by a baseline bandwidth value, or, for simplicity, by uniformly setting the preset weights of such edges to a fixed constant, such as 1. The final network topology is stored in computer memory or a database as a graph data structure, where the set of nodes, the set of edges, and the preset weight of each edge are explicitly recorded. The network topology is constructed during the initial system deployment and updated when the system's physical structure changes.

[0030] Based on the pre-defined network topology of the integrated energy system, each approaching safety constraint is mapped to a corresponding node in the network topology. The mapping relationship is predefined and stored in a mapping table during system initialization. Each safety constraint's unique identifier is associated with a specific physical device parameter or a monitoring parameter of a key measurement point. The mapping table records the correspondence between the unique identifier of the safety constraint and the identifier of the network topology node. For example, a safety constraint concerning the upper limit of the cooling water pump outlet pressure is associated with a pressure sensor at the cooling water pump outlet. This pressure sensor is defined as a key measurement point node with a node identifier during network topology construction; therefore, a correspondence between the unique identifier of this safety constraint and the node identifier is established in the mapping table. The mapping process is completed by querying the mapping table. For each safety constraint identified as approaching, a query operation is performed in the mapping table using its unique identifier; the result of the query operation is the node identifier corresponding to that safety constraint in the network topology.

[0031] Using the preset weights of edges in the network topology as a metric, this algorithm calculates the minimum set of edges that need to be removed to disconnect the corresponding node. The corresponding node refers to the set of nodes represented by all node identifiers obtained in the previous step. The problem of calculating the minimum set is called finding the minimum cut set connecting a specific set of nodes in graph theory. This embodiment uses the Stoer-Wagner algorithm to find the global minimum cut and uses the edge set of this minimum cut as the desired minimum set. This algorithm iteratively merges vertices and calculates cut values ​​to find the edge set with the minimum sum of edge weights required to disconnect the specified set of nodes. This edge set represents the key physical coupling path connecting the conflicting constraints. The calculation process uses the network topology as the input graph, where each edge has its preset weight. The algorithm initializes an empty vertex set A and randomly adds a corresponding node to vertex set A. It then iterates, finding a vertex that is not in vertex set A but has the highest connectivity to vertex set A in each iteration and adding it to vertex set A. The connectivity of a vertex is the sum of the preset weights of the edges between that vertex and all vertices in vertex set A. The iterative process records the cut value generated in each round when the last vertex is added to vertex set A, which is the sum of the preset weights of all edges between that vertex and other vertices in vertex set A. This iteration is repeated until all vertices are added to vertex set A. Throughout the process, the iteration that produces the minimum cut value is recorded. The cut formed by the last added vertex and the previous set of vertices in that iteration is a global minimum cut. By backtracking the edges involved in that iteration, the set of edges constituting the minimum cut can be obtained. This set of edges is the minimum set of edges that need to be removed to disconnect the corresponding nodes. The total preset weight of the edges in the minimum set is the smallest among all possible partitioning schemes. The calculation process is executed by a computer program. After the algorithm terminates, it outputs a set containing several edge identifiers, i.e., the minimum set.

[0032] The edges in the calculated minimal set are identified as critical coupling paths connecting the approaching safety constraints. The identification process involves converting the abstract identifier of each edge in the minimal set into a description of the actual physical connection or logical dependency it represents. By querying the network topology construction information, the identifiers of the two specific nodes connected to each edge can be obtained, and then the actual device names or measurement point names represented by these two nodes can be traced based on the node identifiers. For example, if an edge in the minimal set connects a node representing a chiller unit and a node representing a cooling tower, then this edge corresponds to the actual physical connection between the chiller unit and the cooling tower, i.e., the cooling water circulation pipe. The physical connections corresponding to all edges in the minimal set are organized according to their connection order in the network to form one or more actual paths connecting the devices involved in the approaching safety constraints; these paths are marked as critical coupling paths. The information of the critical coupling paths is stored in a structured manner, including the path number, the sequence of edges contained in the path, the name of the specific device connected to each edge, and the total coupling weight of the path. The total coupling weight is the sum of the preset weights of all edges in the minimal set. This critical coupling path information will be provided to the subsequent intensity simulation module.

[0033] Based on the critical coupling path, the variation in the conflict correlation strength among various security constraints under different relaxation schemes is simulated. Specifically, the implementation is as follows: Based on the nodes on the critical coupling path, an initial correlation degree matrix reflecting the correlation between nodes is constructed. The critical coupling path is provided by the path identification module, which contains a series of node identifiers constituting the path and edge information connecting these nodes. The first step in constructing the initial correlation degree matrix is ​​to extract all unique node identifiers on the critical coupling path, forming a node list. Assuming the node list contains N nodes, where N is a positive integer representing the number of nodes, the initial correlation degree matrix is ​​an N x N square matrix. The value of the element in the i-th row and j-th column of the matrix represents the degree of influence of the i-th node on the j-th node in the node list; this value is called the correlation degree. The diagonal elements, i.e., the cases where i equals j, represent the influence of the node on itself, and their correlation degree is set to 1. The initial correlation degree of the off-diagonal elements is set based on the tightness of the actual physical connection between the nodes and the statistical correlation of historical operating data. For any two nodes directly connected by an edge on the critical coupling path, their initial correlation degree directly adopts the preset weight of the connecting edge. This preset weight has been normalized according to the design parameters and is therefore a dimensionless value between 0 and 1. For any two nodes that are not directly connected on the critical coupling path but are indirectly connected through other nodes, their initial association degree is set by multiplying the sum of the preset weights of all connected paths between them by a decay coefficient. The decay coefficient decreases as the number of edges in the path increases, and the specific relationship is expressed by the formula: Decay coefficient = Aa nWhere n represents the number of edges in the connected path, and Aa is a positive constant less than 1, for example, Aa can be 0.8. All correlation degree values ​​are dimensionless scalars, ranging from 0 to 1. The basic assignment rules for elements in the initial correlation degree matrix are as follows. If two nodes have a direct physical connection on the critical coupling path, their correlation degree is calculated using the following formula: ;in, This represents the basic association degree between node i and node j; This represents the preset normalized weight of the direct physical connection. If the two nodes are indirectly connected, the correlation degree is calculated using the following formula: ;in, This represents the product of the weights of all edges on the k-th connected path; This represents the topological hop count of the k-th path; This represents the spatial decay constant. The initial correlation matrix is ​​stored in memory for subsequent calculations after construction.

[0034] For each pre-defined relaxation scheme, the current boundary value adjustment of the corresponding safety constraint on the critical coupling path is simulated. The pre-defined relaxation schemes are a predefined set, each describing a specific way to relax the current boundary values ​​of one or more specific safety constraints involved. Relaxation schemes are pre-defined during the system configuration phase, based on expert analysis of system coupling relationships, summaries of constraint conflict events that occurred during historical operation, and assessments of the adjustment potential of different devices. Each relaxation scheme includes the following information: scheme number, a list of unique identifiers for the adjusted safety constraints, the direction of boundary value adjustment for each safety constraint, and the relative magnitude of boundary value adjustment for each safety constraint. The boundary value adjustment direction is divided into positive and negative relaxation. For upper limit constraints, positive relaxation means increasing the upper limit value; for lower limit constraints, positive relaxation means decreasing the lower limit value. The relative magnitude of boundary value adjustment is a percentage value, representing the proportion of the adjustment amount to the original current boundary value of the safety constraint, such as 5% or 10%. The specific value of the relative magnitude adjustment is set according to the adjustability margin of the corresponding device, which is calculated from the allowable operating range in the device's technical manual or the actual safety range determined during on-site commissioning. The simulation process iterates through each preset relaxation scheme sequentially. For the currently processed relaxation scheme, it first identifies which security constraints lie on the critical coupling path based on the list of unique identifiers for security constraints within the scheme. Then, for each security constraint on the critical coupling path, it calculates the new boundary value after the simulation adjustment based on its adjustment direction and relative adjustment magnitude. The calculation method is as follows: for the upper limit, the new boundary value = the original current boundary value × (1 + relative adjustment magnitude); for the lower limit, the new boundary value = the original current boundary value × (1 - relative adjustment magnitude). This simulation calculation only changes the boundary value parameters used for subsequent impact factor calculations and does not actually change the current boundary values ​​stored in the boundary value database.

[0035] Based on the boundary value adjustment results, the changes in the inter-node influence factors on the critical coupling path are calculated, and the initial correlation matrix is ​​updated. The inter-node influence factor is a quantified coefficient representing the impact of a change in the operating state of one node on the operating state of another node. The calculation of the influence factor depends on the system model. This embodiment uses a simplified linearized model based on equipment characteristic curves and the hydraulic-thermal balance equations of the pipe network to approximate the calculation of the influence factors. Equipment characteristic curves describe the relationship between key output parameters and key input parameters, such as the head-flow rate curve of a water pump and the cooling capacity-power consumption curve of a chiller unit. These curves are provided by the equipment manufacturer or obtained through field performance testing. At typical operating points of the system, a first-order Taylor expansion is performed on the equipment characteristic curves to obtain their linearized approximation. The hydraulic-thermal balance equations of the pipe network describe the conservation relationships of parameters such as flow rate, pressure, and temperature in the system, and are linearized near the steady-state operating point. By combining the linearized models of all equipment and the linearized balance equations of the pipe network, a linear state-space model or transfer function matrix of the entire system involved in the critical coupling path can be established. From this linear model, the steady-state gain between any two node parameters can be extracted, and this steady-state gain serves as the initial value of the influence factor Fij between these two nodes. Under the simulated relaxation scheme, the current boundary value of the safety constraint associated with node j changes by ΔBj. According to linear system theory, when a constraint boundary value changes, the system will tend towards a new steady-state operating point. The change ΔXj of the operating parameter Xj of node j in the new steady state can be obtained by solving the linear model, specifically: ΔXj = Sj × ΔBj. Here, Sj is the sensitivity coefficient, which is a coefficient in the linear model reflecting the sensitivity of the node j parameter to its own boundary changes. Furthermore, the change ΔXi of the operating parameter Xi of node i in the new steady state is calculated as: ΔXi = Fij × ΔXj. Since the coefficient matrix of the linear model itself is a function of the operating point, and changes in boundary values ​​will cause the operating point to shift, strictly speaking, the influence factor Fij will also change. To simplify calculations, this embodiment uses a first-order approximation to estimate the change in the influence factor, assuming the new influence factor Fijnew equals the original influence factor Fij multiplied by a correction coefficient Kij. The correction coefficient Kij is positively correlated with the relative magnitude of boundary value adjustment and the original coupling strength between nodes, specifically expressed by the formula: Kij = 1 + Rj × Aij × c. Here, Rj represents the relative magnitude of boundary value adjustment of the safety constraint associated with node j, Aij represents the element value in the i-th row and j-th column of the initial correlation matrix, and c is a model empirical coefficient. The model empirical coefficient c is obtained by fitting the linear model using the least squares method after performing numerical perturbation analysis at multiple virtual operating points. After calculating the correction coefficient Kij between each pair of nodes on the critical coupling path, the initial correlation matrix can be updated.The update rule is to multiply the value of the element in the i-th row and j-th column of the initial correlation matrix by the corresponding correction coefficient Kij to obtain the new element value at the corresponding position in the updated correlation matrix. This update process is performed on the currently being processed relaxation scheme and produces an updated correlation matrix corresponding to that relaxation scheme.

[0036] Based on the updated correlation matrix, the changes in the conflict correlation strength between each security constraint are determined; the formula for calculating the conflict correlation strength between two security constraints is: ;in, This indicates the strength of the conflict between security constraints p and q; and Let represent the correlation degree between node i and node j, and between node j and node i, respectively, in the updated correlation degree matrix. The conflict correlation strength is used to quantify the likelihood of mutual constraint or conflict between any two security constraints. A mapping relationship exists between security constraints and nodes on the critical coupling path, determined by the path identification module. Assume that all nodes on the critical coupling path are mapped to M different security constraints, where M is a positive integer. The conflict correlation strength matrix is ​​defined as an M-row, M-column square matrix. The element value in the p-th row and q-th column of the conflict correlation strength matrix represents the conflict correlation strength between the p-th and q-th security constraints, denoted as Cpq. This element value is calculated based on the updated correlation degree matrix. Specifically, firstly, based on the mapping relationship between security constraints and nodes, find node i corresponding to the p-th security constraint and node j corresponding to the q-th security constraint. Then, extract the correlation degree value Aij between node i and node j, and the correlation degree value Aji between node j and node i from the updated correlation degree matrix. Since the correlation degree matrix is ​​usually asymmetric, Aij is not necessarily equal to Aji, reflecting the strength of the unidirectional influence. The conflict correlation strength Cpq is defined as the geometric mean of Aij and Aji, expressed by the formula: This definition reflects the reciprocity of conflict, meaning that the degree to which two constraints influence each other jointly determines the intensity of their conflict. For each preset relaxation scheme, a complete conflict correlation strength matrix is ​​calculated using the updated correlation matrix and the method described above. The conflict correlation strength matrix calculated under the current relaxation scheme is compared with the baseline conflict correlation strength matrix calculated based on the initial correlation matrix. The purpose of the comparison is to quantify the overall change in the conflict correlation strength between various safety constraints caused by the relaxation scheme. The quantification method uses the sum of the absolute values ​​of the differences between corresponding elements of the two matrices, expressed by the formula: Overall change V = Σ|Cpqcurrent - Cpqbase|. Where Σ represents the cyclic summation over all p from 1 to M and q from 1 to M, Cpqcurrent is the element of the conflict correlation strength matrix under the current relaxation scheme, and Cpqbase is the corresponding element of the baseline conflict correlation strength matrix. The overall change V is a comprehensive measure of the change in the conflict correlation strength between various safety constraints under different relaxation schemes. Each relaxation scheme ultimately outputs a value of change V, which is passed to the subsequent scheme generation module for decision-making. Once all the preset relaxation schemes have been simulated, the task of the intensity simulation module is complete.

[0037] Based on the changes in conflict correlation strength, the influence centrality changes of nodes in the constrained conflict relationship network are constructed and analyzed. A target relaxation scheme that reduces the structural tightness of the constrained conflict relationship network is selected from different relaxation schemes, and a set of coordinated constraint boundary values ​​is generated. The specific implementation is as follows: Based on the changes in the conflict correlation strength among various security constraints, a constraint conflict relationship network is constructed, with security constraints as nodes and conflict correlation strength as edge weights. The construction of the constraint conflict relationship network is performed independently for each simulated relaxation scheme. For a given relaxation scheme, its input data is the conflict correlation strength matrix calculated under that scheme, output by the strength simulation module. The conflict correlation strength matrix is ​​a square matrix whose number of rows and columns equals the total number of security constraints mapped on the current critical coupling path. The first step in constructing the network is to define the set of nodes. The node set contains multiple nodes, each uniquely corresponding to a security constraint, and the node label uses the unique identifier of that security constraint. The second step is to define the edges and edge weights of the network. The constraint conflict relationship network is a complete graph, meaning that there is an undirected edge between any two distinct nodes. For any two nodes, the edge weight of the undirected edge between them is directly taken from the element values ​​at the corresponding row and column positions in the conflict correlation strength matrix. Since the conflict correlation strength matrix is ​​obtained by calculating the geometric mean of the correlation degree, this edge weight value has symmetry, making it suitable for constructing undirected networks. All edge weights are dimensionless scalars, ranging from 0 to 1. The third step is to store the network structure in a computer-processable data format, using an adjacency matrix. A matrix is ​​constructed with the number of rows and columns equal to the total number of security constraints, such that the element at each position in the matrix equals the corresponding edge weight. This matrix is ​​the weighted adjacency matrix of the constraint conflict network, which fully characterizes the strength of the conflict between security constraints under a specific relaxation scheme. For each relaxation scheme being evaluated, the above construction process is repeated to obtain a series of weighted adjacency matrices, each corresponding to the network state under a given scheme.

[0038] For each different relaxation scheme, the eigenvector centrality values ​​of all nodes in the constraint conflict relationship network are calculated. Eigenvector centrality is an indicator that measures the influence of nodes in the network. The calculation process is based on the weighted adjacency matrix corresponding to each relaxation scheme. The calculation is performed using the power iteration method, a numerical method that iteratively solves for the principal eigenvectors of a matrix. Initialization is performed first: each node in the network corresponding to the weighted adjacency matrix is ​​assigned an initial centrality score, all set to 1. Then, iterative calculation begins. The centrality score of a node after one iteration is defined as equal to the sum of the edge weights from all other nodes to that node multiplied by the centrality scores of those other nodes after the previous iteration. After updating all nodes, all new scores are normalized. Normalization involves calculating the square root of the sum of the squares of all new scores to obtain the vector magnitude, and then dividing each new score by this magnitude. The purpose of normalization is to prevent the values ​​from increasing or decreasing infinitely during the iteration process. The iterative process continues until the Euclidean distance between the score vectors obtained from two consecutive iterations is less than a preset convergence threshold. This threshold is a very small positive number, set according to the required numerical precision, for example, 0.0001. The Euclidean distance is calculated by squared the difference in score between each node in two iterations, summing the squares of all differences, and then taking the square root. The normalized score vector obtained after iteration stops represents the eigenvector centrality value of the corresponding node. The sum of the eigenvector centrality values ​​of all nodes is 1. This calculation is performed independently once for the weighted adjacency matrix corresponding to each relaxation scheme, thereby generating a distribution vector of eigenvector centrality values ​​for each scheme.

[0039] Based on the distribution of eigenvector centrality values, a global efficiency index is calculated for constrained conflict networks to characterize their structural compactness. The global efficiency index is a measure in complex network theory used to quantify the overall efficiency of network information transmission. Its calculation consists of two main steps. The first step is to calculate the shortest path length between all pairs of nodes in the network. In a weighted network, the distance on a path is defined as the reciprocal of the edge weight of each edge on that path. The reciprocal of the edge weight reflects that the higher the conflict intensity, the shorter the "distance," indicating a greater direct impact of the conflict. For any two nodes, the length of a path connecting them is equal to the sum of the reciprocals of the edge weights of all edges on that path. The shortest path length between two nodes is the smallest path length among all possible paths connecting these two nodes. The Floyd algorithm is used to calculate the shortest path length between all pairs of nodes. The Floyd algorithm solves for the shortest distance between all pairs of nodes using a triple-loop dynamic programming approach. The algorithm initializes a distance matrix, where each element is initially the reciprocal of the edge weight between the corresponding two nodes; if they are the same node, the distance is 0. Then, multiple iterations are performed. In each iteration, for each pair of nodes, it is checked whether a shorter path length can be generated through another intermediate node. If the condition is met, the shortest path length between the two nodes is updated. The number of iterations equals the total number of nodes. After the iterations are complete, the values ​​stored in the distance matrix are the shortest path lengths between all node pairs. The second step is to calculate the global efficiency index using the obtained shortest path length matrix. The global efficiency is defined as the average efficiency between all node pairs in the network. The efficiency between any two nodes is defined as the reciprocal of the shortest path length between them. Since this network is a complete graph and the edge weights are positive, any two points are always connected, and the shortest path length is a finite positive value. The global efficiency is calculated by first calculating the sum of the efficiency values ​​between all unordered node pairs, and then dividing this sum by the total number of unordered node pairs. The total number of unordered node pairs is equal to the total number of nodes multiplied by the total number of nodes minus 1, and then divided by 2. The global efficiency value ranges from 0 to 1. A higher global efficiency value indicates a shorter overall network distance, making information or conflict propagation easier and the network structure more compact. Conversely, a lower global efficiency value indicates a longer overall network distance and a less compact structure. For each relaxation scheme, a corresponding global efficiency index is calculated based on the shortest path length matrix derived from its weighted adjacency matrix, using the method described above.

[0040] The global efficiency index is obtained by calculating the average shortest path length of the reciprocals of the eigenvector centrality values between all node pairs in the constraint conflict relationship network. First, the conflict intensity is converted into a graph theory distance, and the calculation method is as follows: Take the reciprocal of the conflict association intensity Cpq as the graph theory distance dpq between the corresponding two nodes, that is, dpq = 1 / Cpq. Here, dpq represents the graph theory distance between safety constraint p and safety constraint q; Cpq represents the conflict association intensity between safety constraint p and q. Subsequently, calculate the global efficiency index E of the network, and its formula is defined as: The global efficiency E is equal to the arithmetic mean of the efficiency values between all different node pairs in the network. Among them, the efficiency value between any two nodes is defined as the reciprocal of the shortest path distance D shortest(p,q) between them. The specific calculation formula of the global efficiency E is: E = [2 / (NS(NS - 1))] × Σ(1 / D shortest(p,q)), and the summation range covers all node pairs that satisfy p < q. Here, E represents the global efficiency of the constraint conflict relationship network; NS represents the total number of safety constraint nodes in the network; D shortest(p,q) represents the shortest path distance between safety constraint p and safety constraint q.

[0041] Select the different relaxation schemes that cause the most significant decrease in the global efficiency index as the target relaxation scheme. This step is carried out after calculating the global efficiency indices of all preset relaxation schemes. First, determine a comparison benchmark. Denote the global efficiency index of the network constructed based on the benchmark conflict association intensity matrix calculated from the initial association degree matrix without applying any relaxation scheme as the basic global efficiency index. Then, for each relaxation scheme, calculate its corresponding global efficiency index. Next, calculate the efficiency change amount of each relaxation scheme relative to the benchmark, and this change amount is equal to the global efficiency index of this scheme minus the basic global efficiency index. Since the goal is to reduce the structural compactness, that is, it is hoped that the global efficiency index of the scheme is smaller than the basic global efficiency index, so it is expected that this change amount is negative. The more negative it is, the more significant the effect of this relaxation scheme on reducing the network structural compactness; this judgment is based on complex network theory. The global efficiency index can be used to measure the network structural compactness, and the decrease in its value means that the distance between nodes in the conflict association network increases, the interaction weakens, and the overall operation state of the system is more stable. Therefore, taking the change amount as negative and having the largest absolute value as the objective technical criterion for scheme selection. The selection process is to traverse the efficiency change amounts of all relaxation schemes and find the one with the smallest numerical value, that is, the one with the most negative value. The relaxation scheme corresponding to this efficiency change amount is selected as the target relaxation scheme. If there are multiple relaxation schemes with the same efficiency change amount and all are the minimum values, then select the one with the smallest scheme number among them as the target relaxation scheme. The number of the target relaxation scheme and its complete definition information are output for subsequent steps.

[0042] The specific boundary value adjustment amount of the adjusted safety constraints is determined based on the target relaxation scheme, generating harmonized constraint boundary values. The definition information of the target relaxation scheme includes a list of unique identifiers for the adjusted safety constraints and the relative magnitude of the boundary value adjustment for each safety constraint. The process of generating harmonized constraint boundary values ​​is as follows: First, the current boundary value of each adjusted safety constraint is read from the boundary value database. Then, based on the boundary value adjustment direction and relative magnitude specified for that safety constraint in the target relaxation scheme, its adjusted new boundary value is calculated. The relative magnitude of the boundary value adjustment is a decimal value, for example, 5% corresponds to 0.05. Specifically, for upper limit constraints, the new boundary value equals the current boundary value multiplied by 1 plus the relative magnitude of the adjustment; for lower limit constraints, the new boundary value equals the current boundary value multiplied by 1 minus the relative magnitude of the adjustment. The calculated new boundary value is the harmonized constraint boundary value for that safety constraint. For all other safety constraints not selected by the target relaxation scheme, their harmonized constraint boundary values ​​remain the same as their current boundary values. Finally, the harmonized constraint boundary values ​​of all safety constraints are organized into a complete dataset according to the unique identifiers of the safety constraints. This dataset constitutes the final set of coordinated constraint boundary values. These boundary values ​​represent the optimal boundary conditions that best mitigate the detected multi-constraint conflicts while reducing the overall density of the system's conflict network structure. These coordinated constraint boundary values ​​will be passed to the optimization execution module as new boundary condition inputs for its global energy efficiency optimization calculations.

[0043] Using the coordinated constraint boundary values ​​as optimization boundary conditions, global energy efficiency optimization calculations are performed and equipment setpoints are output. The specific implementation is as follows: Using the coordinated constraint boundary values ​​as boundary conditions, a global energy efficiency optimization calculation is performed. This optimization calculation incorporates the coordinated constraint boundary values ​​as new safe operating boundary conditions into a mathematical model aimed at minimizing the total system operating cost. Mathematical programming methods are used to solve the problem, ultimately obtaining the optimal operating settings for each device that satisfy the new boundary conditions. The global energy efficiency optimization calculation seeks a set of operating parameters for each sub-device that minimizes the total operating cost of the entire integrated energy system under a given system structure and operating conditions. The calculation process receives a set of coordinated constraint boundary values ​​generated by the scheme generation module. This set of data contains the new boundary values ​​of all safety constraints after coordinated relaxation. Data preparation before optimization includes obtaining the current system external conditions and load demand. System external conditions include ambient dry-bulb temperature, ambient wet-bulb temperature, and real-time grid electricity price. Load demand includes cooling load demand, heating load demand, and electricity load demand for each area of ​​the building. Ambient dry-bulb temperature and ambient wet-bulb temperature are collected through IoT temperature and humidity sensors deployed outdoors. Real-time grid electricity price is obtained by accessing the electricity market information platform or the electricity price curve published by the grid company. Cooling and heating load demands are calculated using building energy simulation software combined with real-time indoor and outdoor temperature and humidity, or estimated by monitoring supply and return water temperatures and flow rates using IoT sensors installed at the air conditioning terminals. Electricity load demand is obtained by monitoring total power consumption using smart meters.

[0044] A global energy efficiency optimization mathematical model is constructed. The model's objective function is to minimize the total operating cost of the integrated energy system within one optimization cycle. The total operating cost includes the cost of purchasing electricity from the grid, the cost of consuming natural gas, and the cost calculated based on equipment operating losses. The cost calculated based on equipment operating losses is based on the ratio of the actual operating power of the equipment to its rated efficiency, calculating the equivalent additional energy consumption, and is included in the total cost according to the real-time electricity price. The mathematical expression of the objective function is to find the minimum sum of various energy consumption costs across all time steps. The decision variables of the model are the operating parameters of each sub-equipment at each time step within a future optimization cycle. These operating parameters include the cooling capacity of the chiller unit, the flow rate of the water pump, the frequency of the fan, the output power of the photovoltaic inverter, and the charging and discharging power of the energy storage system. The model's constraints are divided into three categories. The first category is the physical operating constraints of the equipment, including the upper and lower limits of output for each piece of equipment, the limit on the number of start-ups and shutdowns of the equipment, and the limit on the continuous operating time of the equipment. For decisions involving equipment start-ups and shutdowns, binary integer variables are used to represent the start-up and shutdown states. The second category is system energy balance constraints, which include the requirement that total cooling capacity equals total cooling load plus network cooling losses, total heating capacity equals total heat load plus network heat losses, and total power supply equals total electrical load plus network losses. The third category is safety constraints, which directly use a set of coordinated constraint boundary values ​​as the safe operating boundaries in the model. All constraints are expressed in the form of linear or nonlinear inequalities or equations.

[0045] A mathematical optimization solver is used to solve the constructed model. The solution algorithm is selected based on the model's properties. If the model is a linear programming or mixed-integer linear programming model, the simplex method or branch and bound method is used. If the model contains nonlinear relationships, sequential linear programming or a genetic algorithm is used. Sequential linear programming approximates the nonlinear constraints near the current solution and iterates until convergence. The solution process is executed on a computer. The solver automatically adjusts the values ​​of decision variables based on the objective function and constraints, finding the set of decision variable values ​​that minimizes the total operating cost while satisfying all constraints. If the solution fails, the system uses the optimization results from the previous optimization cycle or activates a rule-based empirical control strategy. After the solution is completed, the optimal operating parameters of each sub-device at each time step within the optimization cycle are obtained. For example, the optimal cooling capacity of the chiller unit at 14:00 is 1000 kW, and the optimal flow rate of the No. 1 water pump at 14:00 is 50 cubic meters per hour. These results constitute the global energy efficiency optimization calculation results.

[0046] Based on the global energy efficiency optimization calculation results, the optimal operating parameters for each sub-device are determined. From the time-series data of the global energy efficiency optimization calculation results, the operating parameter values ​​of all sub-devices corresponding to the current time or the next control time point are extracted. The control time point is determined by the system's control cycle, for example, 15 minutes. If the current time is 14:00, the parameter values ​​for each device corresponding to 14:00 in the optimization results are extracted. These extracted parameter values ​​are determined as the optimal operating parameters for each sub-device within the current control cycle. These optimal operating parameters are a set of values, each explicitly associated with a specific physical sub-device and its controlled variable.

[0047] The optimal operating parameters of each sub-device are converted into corresponding device setpoints and output. Device setpoints are instruction formats that the local controller of each sub-device can directly recognize and execute. The conversion process is based on the communication protocol and control interface characteristics of each sub-device. According to the device type and communication protocol, the values ​​of the optimal operating parameters are converted into the corresponding data frame format. For variable frequency pumps supporting the Modbus RTU protocol, the setpoint is the frequency value. The conversion process uses the pump's flow rate value from the optimal operating parameters, and then calculates the corresponding frequency setpoint, for example, 35 Hz, using the pump's flow-frequency characteristic curve. The flow-frequency characteristic curve is provided by the pump manufacturer or obtained by fitting data from on-site commissioning. Then, according to the Modbus protocol specification, the frequency value is encapsulated into a data packet containing the device address, function code, register address, and specific value. For devices requiring complex instructions, such as chillers, the setpoint includes the outlet water temperature setpoint and the load percentage setpoint. The conversion process takes the cooling capacity value in the optimal operating parameters, calculates it into the outlet water temperature setpoint and the load percentage setpoint through the unit's performance model under the current operating conditions, and packages it into unit-specific communication protocol instructions.

[0048] Through the existing control network or IoT gateway of the integrated energy system, all generated device setpoint commands are sent to the corresponding sub-device local controllers. The command transmission process must ensure correct timing and reliable communication; for critical equipment, a communication method with a request-response confirmation mechanism is adopted. Upon receiving the setpoint command, each sub-device controller parses the command and drives the actuator, ensuring the equipment operates at the optimal operating point determined by global optimization calculations. The system records all issued device setpoints and enters the next monitoring and optimization cycle, achieving efficient and safe operation control based on coordinated safety boundaries.

[0049] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0050] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0051] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. Computer-readable storage media can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0052] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0053] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0054] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0055] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0056] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0057] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0058] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A green and clean energy integrated energy-saving management platform based on Internet of Things (IoT) smart terminals, characterized in that: include: The data acquisition module is used to acquire, in real time, the operating parameters of each sub-equipment in the integrated energy system and the current boundary values ​​of each safety constraint through IoT smart terminals; The boundary determination module is used to determine, based on the running parameters and the current boundary value, whether there are at least two different types of safety constraints whose real-time boundary values ​​are simultaneously approached. The path identification module is used to determine the minimum edge set corresponding to the node that is touched when the determination is true, and to identify the minimum edge set as the critical coupling path. The intensity simulation module is used to simulate the changes in the intensity of conflict correlations between various security constraints under different relaxation schemes, based on the critical coupling path. The scheme generation module is used to construct and analyze the changes in the influence centrality of nodes in the constrained conflict relationship network based on the changes in the conflict association strength, select the target relaxation scheme from different relaxation schemes that reduces the structural tightness of the constrained conflict relationship network, and generate a set of coordinated constraint boundary values. The optimization execution module is used to perform global energy efficiency optimization calculations and output device setting values, using the coordinated constraint boundary values ​​as optimization boundary conditions.

2. The integrated energy-saving management platform for green and clean energy based on IoT smart terminals according to claim 1, characterized in that, Through IoT smart terminals, the operating parameters of each sub-device in the integrated energy system and the current boundary values ​​of each safety constraint are obtained in real time, including: Raw operational data and raw boundary settings are collected from IoT smart terminals deployed in various sub-devices of the integrated energy system; The collected raw operating data is validated and cleaned to obtain the operating parameters of each sub-device; The original boundary settings collected are converted and stored to obtain the current boundary values ​​of each security constraint.

3. The integrated energy-saving management platform for green and clean energy based on IoT smart terminals according to claim 1, characterized in that, Based on the operating parameters and the current boundary value, determine whether at least two different categories of safety constraints have their real-time boundary values ​​simultaneously approached, including: For each safety constraint, calculate the real-time proximity based on its corresponding operating parameters and the current boundary value; All security constraints with a real-time proximity greater than a preset proximity threshold are selected as a set of security constraints that are close to the boundary. Based on the category attributes of the safety constraints, the safety constraints in the set of safety constraints near the boundary are classified. Determine whether there are security constraints belonging to at least two different categories whose corresponding real-time proximity values ​​are consistently greater than a preset proximity threshold within a preset time window.

4. The integrated energy-saving management platform for green and clean energy based on IoT smart terminals according to claim 3, characterized in that, Category attributes are divided according to the energy subsystems associated with the safety constraints, including HVAC category, power supply category, water supply network category, and clean energy category; For each security constraint in the set of security constraints near the boundary, its predefined category attribute label is read, and security constraints with the same category attribute label are grouped into the same group, thus completing the classification.

5. The integrated energy-saving management platform for green and clean energy based on IoT smart terminals according to claim 1, characterized in that, When the determination is yes, the minimum edge set corresponding to the node connected to the approached safety constraint is determined, and the minimum edge set is identified as the critical coupling path, including: Based on the pre-defined network topology of the integrated energy system, each approaching security constraint is mapped to a corresponding node in the network topology. Using the preset weights of edges in the network topology as a metric, calculate the minimum set of edges that need to be removed to make the corresponding nodes no longer connected; The edges in the calculated minimum set are identified as the critical coupling paths connecting the approaching security constraints.

6. The integrated energy-saving management platform for green and clean energy based on IoT smart terminals according to claim 5, characterized in that, The network topology is constructed based on the actual connection relationships between various physical devices and pipelines in the integrated energy system. Nodes in the topology represent energy-consuming devices, energy conversion devices, or key measurement points, while edges represent physical connection pipelines, power lines, or data transmission links between devices. The preset weights of the edges are set based on the design flow rate of the connecting pipelines, the rated capacity of the lines, or the data bandwidth of the links.

7. The integrated energy-saving management platform for green and clean energy based on IoT smart terminals according to claim 1, characterized in that, Based on the critical coupling path, the variation in the conflict correlation strength among various security constraints under different relaxation schemes is simulated, including: Based on the nodes on the key coupling path, an initial correlation matrix reflecting the correlation between nodes is constructed; For each preset relaxation scheme, simulate the adjustment of the current boundary values ​​of the corresponding security constraints on the critical coupling path; Based on the boundary value adjustment results, calculate the changes in the influence factors between nodes on the critical coupling path and update the initial correlation matrix; Based on the updated correlation matrix, the changes in the conflict correlation strength among various security constraints are determined.

8. The integrated energy-saving management platform for green and clean energy based on IoT smart terminals according to claim 1, characterized in that, Based on the changes in conflict correlation strength, the influence centrality changes of nodes in the constrained conflict relationship network are constructed and analyzed. From different relaxation schemes, a target relaxation scheme that reduces the structural tightness of the constrained conflict relationship network is selected, and a set of coordinated constraint boundary values ​​is generated, including: Based on the changes in the conflict correlation strength among various security constraints, a constraint conflict relationship network is constructed with security constraints as nodes and conflict correlation strength as edge weights; For each different relaxation scheme, calculate the eigenvector centrality values ​​of all nodes in the constraint conflict relation network; Based on the distribution of eigenvector centrality values, the global efficiency index of the constraint conflict relationship network is calculated to characterize its structural tightness. Select the relaxation schemes that cause the largest decrease in the global efficiency index as the target relaxation schemes. Based on the target relaxation scheme, determine the specific boundary value adjustment amount of the safety constraint to be adjusted, and generate the coordinated constraint boundary value.

9. The integrated energy-saving management platform for green and clean energy based on IoT smart terminals as described in claim 8, characterized in that, The importance distribution of nodes is constructed based on the calculated eigenvector centrality values ​​of all nodes. The global efficiency index is obtained by calculating the average shortest path length of the inverse of the eigenvector centrality values ​​between all pairs of nodes in the constraint conflict relationship network. An increase in the average shortest path length is characterized by a decrease in the density of the network structure.

10. The integrated energy-saving management platform for green and clean energy based on IoT smart terminals according to claim 1, characterized in that, Using the coordinated constraint boundary values ​​as optimization boundary conditions, perform global energy efficiency optimization calculations and output equipment setpoints, including: Global energy efficiency optimization calculations are performed using the coordinated constraint boundary values ​​as boundary conditions. Based on the global energy efficiency optimization calculation results, the optimal operating parameters of each sub-device are determined; Convert the optimal operating parameters of each sub-device into the corresponding device settings and output them.

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