A zero-carbon park inter-industry information fusion operation and maintenance system

Abstract

The present invention discloses an information fusion operation and maintenance system for zero-carbon park industries, which specifically relates to the technical field of dynamic carbon emission control, and is used to solve the problem of carbon accounting distortion caused by the deviation between the existing static carbon emission model and the physical system state; an initial carbon emission model including energy efficiency benchmark parameters is established in the design stage, and multi-condition dynamic calibration is adopted in the construction stage to obtain the operating boundary characteristic spectrum and synchronously collect thermodynamic traces; topological vulnerable nodes are identified based on Lyapunov index stability analysis and optical flow method thermal gradient analysis; the carbon emission cascade conduction effect is quantified in combination with the equipment energy topology network, and dynamic carbon emission model correction instructions are generated to update energy efficiency parameters, and finally dual-instruction collaborative control is executed in the operation and maintenance stage; a full-cycle carbon data flow closed loop is realized, and the carbon emission intensity of the park is continuously reduced through multi-source information fusion and conduction chain modeling.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic carbon emission control, and more specifically, to a zero-carbon park inter-industry information fusion operation and maintenance system. Background Art

[0002] The construction and operation and maintenance of a zero-carbon park involves a multi-stage collaborative effort, encompassing design, construction, and operations. The industry currently employs an EPC (engineering, general contracting) model to integrate the entire supply chain, leveraging technologies like BIM modeling and IoT monitoring to manage carbon emissions. Existing technologies typically establish static carbon emission models (such as fixed equipment energy efficiency parameters) during the design phase, implement equipment installation according to blueprints during the construction phase, and perform carbon accounting based on pre-set models during the operation and maintenance phase, resulting in a one-way data flow.

[0003] Existing technologies have the defect of a broken carbon data flow throughout the EPC cycle: the static carbon model in the design phase cannot dynamically absorb changes in equipment parameters in the construction phase (such as energy efficiency upgrades or downgrades), resulting in the carbon accounting benchmark in the operation and maintenance phase continuing to deviate from the actual state of the physical system, causing systemic carbon emission distortion. The error accumulates and expands with equipment aging or modification, seriously restricting the accurate achievement of zero-carbon goals. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a zero-carbon park inter-industry information fusion operation and maintenance system to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A zero-carbon park inter-industry information fusion operation and maintenance system, including:

[0007] The initial modeling module is used to establish an initial carbon emission model during the design phase. The initial carbon emission model includes the energy efficiency benchmark parameters of the park equipment;

[0008] The dual-source acquisition module is used to obtain the operating boundary characteristic spectrum of the park equipment through dynamic calibration of multiple working conditions during the construction phase, and simultaneously collect the infrared thermal imaging time series of the equipment cooling system to generate thermodynamic traces;

[0009] A topology identification module is used to analyze the energy efficiency stability of the operating boundary characteristic spectrum based on the Lyapunov exponent, and to analyze the thermal gradient vector field of the thermodynamic trace using the optical flow method to identify the topologically vulnerable nodes of the campus equipment in the energy network.

[0010] A cascade assessment module is used to assess the carbon emission cascade conduction effect of parameter changes of topologically vulnerable nodes on associated devices when a topologically vulnerable node is identified;

[0011] The model update module is used to generate dynamic carbon emission model correction instructions based on the carbon emission cascade conduction effect, update the energy efficiency benchmark parameters and the associated device parameters of the topological vulnerable nodes, and form a dynamic carbon emission model;

[0012] The carbon emission control module is used to perform park carbon emission control operations based on the dynamic carbon emission model during the operation and maintenance phase.

[0013] Furthermore, an initial carbon emission model is established during the design phase. The initial carbon emission model includes the energy efficiency benchmark parameters of the park equipment, including:

[0014] Build an equipment energy topology network based on the park energy system design drawings;

[0015] Obtain the rated energy efficiency parameters of campus equipment from the equipment supplier database;

[0016] Bind the rated energy efficiency parameters to the corresponding nodes of the equipment energy topology network;

[0017] Calculate the energy efficiency conduction coefficient of related equipment through the park's cooling, heating and electricity coupling relationship;

[0018] Combine the equipment energy topology network, rated energy efficiency parameters and energy efficiency conduction coefficient to generate an initial carbon emission model;

[0019] The energy efficiency benchmark parameters include rated energy efficiency parameters and energy efficiency conduction coefficient.

[0020] Furthermore, during the construction phase, the operating boundary characteristic spectrum of the park equipment is obtained through dynamic calibration of multiple working conditions, and the infrared thermal imaging time series of the equipment cooling system is simultaneously collected to generate thermodynamic traces, including:

[0021] Apply step load changes to park equipment during the construction and commissioning phase;

[0022] The dynamic response of equipment energy efficiency parameters under different load rates is collected through frequency converters and power sensors;

[0023] Draw the curve of energy efficiency parameters changing with load rate according to the dynamic response of energy efficiency parameters;

[0024] The energy efficiency inflection point interval in the change curve is extracted as the operating boundary characteristic spectrum;

[0025] At the same time, an infrared thermal imager is used to continuously shoot the surface temperature field of the equipment's cooling system;

[0026] Perform pixel-level thermal radiation intensity analysis on the time-series images of the surface temperature field of the equipment cooling system;

[0027] Thermodynamic traces are generated based on the migration trajectory of thermal radiation intensity between consecutive frames.

[0028] Furthermore, the run boundary characteristic spectra are synchronously aligned with the thermodynamic trace timestamps.

[0029] Furthermore, based on the Lyapunov exponent analysis of the energy efficiency stability of the operating boundary characteristic spectrum, combined with the optical flow method to analyze the thermal gradient vector field of the thermodynamic trace, the topological vulnerable nodes of the campus equipment in the energy network are identified, including:

[0030] Calculate the Lyapunov index based on the energy efficiency parameter load rate variation curve based on the operating boundary characteristic spectrum;

[0031] When the Lyapunov exponent is greater than the preset stability threshold, it is marked as an energy efficiency mutation interval;

[0032] The thermal gradient vector field is obtained by performing optical flow vector field calculation on the thermodynamic trace;

[0033] Extract the spatial region where the heat flux intensity exceeds the thermal interaction threshold in the thermal gradient vector field;

[0034] Locate the overlap points between the energy efficiency mutation interval corresponding to the park equipment and the equipment in the spatial area where the heat flow intensity exceeds the thermal interaction threshold in the equipment energy topology network;

[0035] Device nodes that simultaneously meet the energy efficiency mutation range conditions and whose heat flux intensity exceeds the thermal interaction threshold are identified as topological vulnerable nodes.

[0036] Furthermore, when a topologically vulnerable node is identified, the cascading effect of parameter changes of the topologically vulnerable node on the carbon emissions of associated devices is evaluated, including:

[0037] Determine directly associated devices of topologically vulnerable nodes based on the device energy topology network;

[0038] Calculate the primary carbon emission impact of topology vulnerable node parameter changes on directly associated devices based on the energy efficiency conduction coefficient in the initial carbon emission model;

[0039] Tracing indirectly related devices through energy transfer paths in the device energy topology network;

[0040] Combined with the energy efficiency conduction coefficient, chain calculation is used to calculate the secondary carbon emission impact value of indirectly related equipment;

[0041] The primary carbon emission impact value and the secondary carbon emission impact value are accumulated to generate a quantitative value of the carbon emission cascade conduction effect.

[0042] Furthermore, a dynamic carbon emission model correction instruction is generated based on the carbon emission cascade conduction effect, and the energy efficiency benchmark parameters and the associated device parameters of the topological vulnerable nodes are updated to form a dynamic carbon emission model, including:

[0043] Extract the energy efficiency parameter deviation of topological vulnerable nodes from the quantified value of carbon emission cascade conduction effect;

[0044] Calculate the parameter correction coefficient of the topology vulnerable node in the device energy topology network according to the energy efficiency parameter deviation;

[0045] Locate the directly associated devices of the topologically vulnerable nodes in the device energy topology network;

[0046] Calculate the parameter compensation of directly related equipment through the energy efficiency conduction coefficient;

[0047] Convert the energy efficiency parameter deviation and parameter compensation into dynamic carbon emission model correction instructions;

[0048] Update the energy efficiency benchmark parameters of the initial carbon emission model according to the dynamic carbon emission model correction instruction;

[0049] Synchronously update the energy efficiency conduction coefficients of topological vulnerable nodes and directly associated devices;

[0050] The updated energy efficiency benchmark parameters and energy efficiency conduction coefficient are combined to generate a dynamic carbon emission model.

[0051] Furthermore, during the operation and maintenance phase, the park’s carbon emission control operations are performed based on the dynamic carbon emission model, including:

[0052] Use IoT sensors to collect real-time data on the operating load rate and energy consumption of park equipment;

[0053] Input the operating load rate into the dynamic carbon emission model to obtain the theoretical energy efficiency benchmark parameters under the current working conditions;

[0054] Calculate the carbon emission deviation between actual energy consumption data and theoretical energy efficiency benchmark parameters;

[0055] When the carbon emission deviation value exceeds the preset tolerance threshold, an equipment energy efficiency optimization instruction is generated;

[0056] Determine the compensation control amount of related equipment based on the energy efficiency conduction coefficient in the dynamic carbon emission model;

[0057] Send energy efficiency optimization instructions to topology vulnerable node devices and send compensation control instructions to associated devices.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. Continuous evolution of the carbon emission model is achieved through a multi-stage dynamic closed-loop mechanism. The initial carbon emission model in the design phase undergoes dynamic calibration under multiple working conditions during the construction phase, and the energy efficiency benchmark parameters are corrected in real time. The multi-dimensional data of the operating boundary characteristic spectrum and thermodynamic trace are integrated to accurately capture the degradation of equipment energy efficiency stability and heat transfer anomalies, breaking through the blind spot of traditional single-dimensional monitoring for identifying hidden topological vulnerable nodes. The carbon emission conduction effect is quantified based on the equipment energy topology network, and a computable model of carbon flow correlation between multi-hop equipment is constructed to provide a scientific basis for dynamic correction. The dual-instruction collaborative control strategy in the operation and maintenance phase realizes a qualitative upgrade from single-point control to system collaborative optimization through the parallel execution of energy efficiency optimization instructions and compensation control instructions.

[0060] 2. Establish a closed loop of carbon data flow throughout the entire cycle: Dynamic calibration data during the construction phase reversely modifies the design model, and real-time carbon emission data during the operation and maintenance phase positively drives the model update, forming a cross-stage self-optimization mechanism; the introduction of the equipment energy topology network transforms discrete devices into a computable node network, enabling mathematical modeling of the carbon emission conduction path; multi-source information fusion technology based on the Lyapunov index and optical flow method locates vulnerable nodes from the dual dimensions of energy efficiency stability and thermodynamic characteristics, greatly improving identification reliability; parameter compensation instructions are generated through the quantification of the conduction effect to ensure the coordinated optimization of related equipment; and finally, dynamic matching of theoretical energy efficiency benchmark parameters with actual working conditions is achieved at the operation and maintenance end. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a structural diagram of an information fusion operation and maintenance system among industries in a zero-carbon park according to the present invention. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0063] Example: Figure 1 The present invention provides a structural diagram of a zero-carbon park inter-industry information fusion operation and maintenance system, which includes:

[0064] The initial modeling module is used to establish an initial carbon emission model during the design phase. The initial carbon emission model includes the energy efficiency benchmark parameters of the park equipment;

[0065] The dual-source acquisition module is used to obtain the operating boundary characteristic spectrum of the park equipment through dynamic calibration of multiple working conditions during the construction phase, and simultaneously collect the infrared thermal imaging time series of the equipment cooling system to generate thermodynamic traces;

[0066] A topology identification module is used to analyze the energy efficiency stability of the operating boundary characteristic spectrum based on the Lyapunov exponent, and to analyze the thermal gradient vector field of the thermodynamic trace using the optical flow method to identify the topologically vulnerable nodes of the campus equipment in the energy network.

[0067] A cascade assessment module is used to assess the carbon emission cascade conduction effect of parameter changes of topologically vulnerable nodes on associated devices when a topologically vulnerable node is identified;

[0068] The model update module is used to generate dynamic carbon emission model correction instructions based on the carbon emission cascade conduction effect, update the energy efficiency benchmark parameters and the associated device parameters of the topological vulnerable nodes, and form a dynamic carbon emission model;

[0069] The carbon emission control module is used to perform park carbon emission control operations based on the dynamic carbon emission model during the operation and maintenance phase.

[0070] The initial modeling module is used to establish an initial carbon emission model during the design phase. The initial carbon emission model includes the energy efficiency benchmark parameters of the park equipment. The specific implementation is as follows:

[0071] The specific process of constructing an equipment energy topology network based on the design drawings of the campus energy system is as follows: the equipment connection relationships in the design drawings are converted into a graph data structure, in which campus equipment such as refrigeration units, boilers, and photovoltaic inverters serve as nodes, and energy transmission media such as steam pipes, power lines, and cooling water pipes serve as connecting edges. Each node records the unique identification code and spatial location coordinates of the equipment, and the weight of each edge is determined by the specific value of the energy type. For example, the weight of the power line is the voltage level multiplied by the line length, and the weight of the thermal pipeline is the flow rate multiplied by the pipe diameter. The equipment energy topology network is stored in the form of an adjacency matrix. The matrix element values ​​represent the physical connection strength between devices. The matrix element values ​​between devices that are not directly connected are set to zero.

[0072] The rated energy efficiency parameters of campus equipment are obtained from the equipment supplier's database by accessing the cloud-based parameter library provided by the equipment manufacturer through an application programming interface (API). The performance parameters under rated operating conditions are retrieved using the equipment model as the search keyword. For chillers, the nominal value of the coefficient of performance (COP) is obtained, for example, a COP of 3.8. For gas boilers, the rated value of the thermal efficiency (η) is obtained, for example, a thermal efficiency of 92%. For photovoltaic arrays, the power generation efficiency per unit area under standard test conditions is obtained, for example, a power generation efficiency of 21.5%. These rated energy efficiency parameters are stored in a structured data table using a unified unit of measurement (kJ / kWh). Each record in the table contains four fields: the equipment number, the parameter name, the parameter value, and the unit of measurement.

[0073] The specific operation of binding the rated energy efficiency parameter to the corresponding node in the device energy topology network involves traversing each node in the device energy topology network and performing an exact match query in the device parameter table based on the node device number. When a chiller node with device number CH-01 is located, its COP value, for example, 3.8, is written into the energy efficiency attribute field of the node. When a gas boiler node with device number GB-05 is located, its thermal efficiency value, for example, 92%, is converted to a decimal value of 0.92 and stored in the corresponding field. The binding process is implemented using a hash table data structure, with the device number as the key and the energy efficiency parameter value as the value, ensuring fast data access with constant time complexity.

[0074] The specific method for calculating the energy efficiency transfer coefficient of associated equipment through the park's cold, heat, and electricity coupling relationship is as follows: First, identify the conversion nodes in the equipment energy topology network that are connected to multiple energy types at the same time. For example, a waste heat boiler is connected to the steam network and the generator set at the same time. According to the law of conservation of energy, the equipment energy balance equation is established. For the waste heat boiler node numbered WHB-12, its heat input comes from the exhaust heat of the gas turbine, and its output is the power generation and steam heat. The calculation formula for the energy efficiency transfer coefficient k is the total output energy divided by the input energy. Specifically, k is equal to the power generation plus steam heat divided by the exhaust heat of the gas turbine. The power generation is measured in megawatt-hours and the heat is measured in gigajoules. The dimensions are unified by the energy unit conversion factor of 3.6 gigajoules / megawatt-hour. The resulting relational data table contains three columns of data: the starting equipment number, the ending equipment number, and the energy efficiency transfer coefficient value.

[0075] The specific steps for generating an initial carbon emission model by combining the device energy topology network, rated energy efficiency parameters, and energy efficiency conduction coefficients include: A multi-source data fusion is performed on the device energy topology network in adjacency matrix format, the rated energy efficiency parameters stored in a hash table, and the energy efficiency conduction coefficient table in tabular format. The fusion operation uses the device number as the primary key, attaching the energy efficiency parameters to the node attributes of the device energy topology network through a database connection operation, and adding the energy efficiency conduction coefficient table as an additional attribute of the connecting edges. The generated initial carbon emission model is stored in a graph database, where node attributes include the device type and rated energy efficiency value, and edge attributes include the energy efficiency conduction coefficient value. For example, the energy efficiency conduction coefficient field for the edge between the chiller node with device number NODE_001 and the cooling tower node with device number NODE_005 is assigned a value of 0.85.

[0076] The energy efficiency benchmark parameters, which include rated energy efficiency parameters and energy efficiency transfer coefficients, are implemented as follows: Rated energy efficiency parameters are stored as static attributes in the graph database node attribute set. For example, the photovoltaic node with device number PV_Array_03 has a "rated power generation efficiency" attribute with a value of 21.5%. The energy efficiency transfer coefficient is stored as a dynamic attribute in edge attributes. For example, the edge from the combined heat and power unit with device number CHP_Unit_07 to the heat storage tank with device number Heat_Storage_09 has an "energy efficiency transfer coefficient" attribute with a value of 0.93. These two parameters together constitute the carbon emission calculation benchmark and are stored in a hierarchical data format in the model file. Node parameter blocks and edge parameter blocks are precisely mapped using device numbers and connection identifiers.

[0077] The dual-source acquisition module is used to obtain the operating boundary characteristic spectrum of the park equipment through dynamic calibration of multiple working conditions during the construction phase, and simultaneously collect the infrared thermal imaging time series of the equipment cooling system to generate thermodynamic traces. The specific implementation is as follows:

[0078] The specific implementation process for applying step load changes to park equipment during the construction and commissioning phase is as follows: A programmable logic controller sends step control signals to the equipment's drive motors, causing the equipment's load rate to gradually change according to a predetermined gradient. For example, the chiller's load rate starts at 30% and increases in 10% steps to 100%, with each load step maintaining stable operation for five minutes. The load change command is then sent to the inverter via an industrial communication protocol. The inverter adjusts the motor speed based on the received command value, achieving a precise step change in the equipment's output power.

[0079] The specific method for using a frequency converter and power sensor to collect the dynamic response of equipment energy efficiency parameters at different load rates is as follows: A three-phase power sensor is installed at the equipment power input to measure voltage, current, and power factor parameters in real time at a sampling frequency of 10 times per second. The frequency converter simultaneously records motor speed and output torque data. The raw measurement values ​​are received and real-time energy efficiency calculations are performed based on the equipment type. For refrigeration equipment, the real-time coefficient of performance (COP) is calculated by the ratio of cooling capacity to input power. The cooling capacity is calculated by multiplying the refrigerant flow rate by the enthalpy difference. For water pump equipment, the efficiency value is calculated by the ratio of flow rate to input power. Dynamic response data is stored in a time series format, with each data point containing three pieces of information: the load rate value, a millisecond timestamp, and the energy efficiency parameter value.

[0080] The steps for plotting a curve of energy efficiency parameters changing with load rate based on the dynamic response of energy efficiency parameters include: extracting data corresponding to load rate and energy efficiency parameters from a time-series database, locating discrete points in a two-dimensional coordinate system with the load rate percentage as the horizontal axis and the energy efficiency parameter as the vertical axis. A curve fitting algorithm is used to process the discrete points, such as using the least squares method to fit a cubic polynomial function to generate a continuous and smooth curve. Abnormal data is filtered during the curve plotting process. When the energy efficiency parameter values ​​of adjacent data points change by more than 20%, a data verification mechanism is triggered to confirm the actual operating conditions through the associated equipment operating status signals.

[0081] The specific operation of extracting the energy efficiency inflection point interval in the change curve as the operating boundary characteristic spectrum is as follows: calculate the first-order derivative function of the change curve and identify the load interval where the absolute value change rate of the derivative exceeds the set threshold. For example, when the derivative value change rate of the refrigeration unit COP curve suddenly increases from 0.05 / 1% to 0.15 / 1% at a load rate of 60%, the load range of 55% to 65% is determined to be the energy efficiency inflection point interval. Each inflection point interval records three characteristic parameters: the starting load rate value, the ending load rate value, and the maximum energy efficiency change rate, for example, a starting load rate of 55%, an ending load rate of 65%, and a maximum energy efficiency change rate of 0.15. The characteristic parameter set of multiple inflection point intervals constitutes the operating boundary characteristic spectrum, which is stored as a structured data table.

[0082] The technology used to continuously capture the surface temperature field of the equipment's cooling system using an infrared thermal imager involves placing fiducial markers on the surface of the equipment's heat sink, and using an infrared thermal imager mounted on a fixed bracket to capture thermal images at a rate of 5 frames per second. The thermal imager operates in the long-wave infrared range, with a temperature resolution of 0.1 degrees Celsius and a spatial resolution of 640 by 480 pixels. Each frame is timestamped with millisecond-level accuracy, and the time base is maintained consistent with the power acquisition system through a clock synchronization protocol. The acquisition process fully covers the entire step load variation process, generating a thermal imaging dataset with strict chronological order.

[0083] The specific process for performing pixel-level thermal radiation intensity analysis on time-series images of the surface temperature field of the device's cooling system is as follows: The thermal imaging sequence is converted into a temperature matrix sequence, where each matrix element represents the temperature measurement at the corresponding pixel. For each pixel coordinate, the temperature variation between consecutive frames is analyzed. For example, for pixel coordinates row 120 and column 80, the temperature variation between two consecutive frames is 3.2 degrees Celsius. Based on the physical laws of thermal radiation, the temperature values ​​are converted into thermal radiation intensity values, which are proportional to the fourth power of the absolute temperature. The analysis results generate a dataset of the spatiotemporal variations in thermal radiation intensity.

[0084] The method for generating a thermodynamic trace based on the migration trajectory of thermal radiation intensity between consecutive frames is as follows: in the thermal radiation intensity field of adjacent time frames, the motion trajectory of a specific high-temperature area is identified by an image feature tracking algorithm. For high-temperature spots on the heat sink, the displacement vector of its centroid pixel coordinates between consecutive frames is calculated. For example, if the coordinates of the high-temperature area at time t1 are row 100 and column 200, and at time t2 are row 102 and column 198, then the displacement vector is 2 pixels in the row direction and 2 pixels in the column direction. The displacement vectors between all consecutive frames are connected in chronological order to form a migration trajectory path of thermal radiation intensity. Each thermodynamic trace contains the starting coordinates of the trajectory, the displacement vector sequence, and the total time length information, and is stored in a structured data format.

[0085] The mechanism for ensuring the synchronous alignment of the operating boundary characteristic spectrum and the thermodynamic trace timestamps is to deploy a time synchronization server within the data acquisition network to provide a unified time reference for the power sensor, inverter, and infrared thermal imager. All data records are timestamped with Coordinated Universal Time, with a time synchronization error of less than 10 milliseconds. During the data processing phase, a timestamp matching algorithm is used to spatially align the operating boundary characteristic data with the thermodynamic trace data at the same time point. For example, a mapping relationship is established between the energy efficiency inflection point characteristic parameters at 14:35:20:05 and a corresponding thermal radiation migration trajectory.

[0086] The topology identification module is used to analyze the energy efficiency stability of the operating boundary characteristic spectrum based on the Lyapunov exponent, and to analyze the thermal gradient vector field of the thermodynamic trace using the optical flow method to identify the topologically vulnerable nodes of the campus equipment in the energy network. The specific implementation is as follows:

[0087] The specific implementation process for calculating the Lyapunov exponent based on the energy efficiency parameter versus load factor curve of the operating boundary characteristic spectrum is as follows: The energy efficiency parameter-load factor data sequence stored in the operating boundary characteristic spectrum is obtained, and the load factor is used as a time-equivalent variable to construct a dynamic system model. For the energy efficiency parameter sequence of the refrigeration unit in the load factor range of 40% to 90%, the phase space is reconstructed using a time delay embedding method. The embedding dimension parameter is set to 3, and the time delay parameter is determined based on the first zero crossing point of the sequence autocorrelation function. The exponential divergence rate of adjacent phase space orbits is calculated, and the Lyapunov exponent λ is solved using the orbit evolution relationship. Specifically, when the distance between adjacent orbits evolves from the initial value d0 to the distance dt after time t, the relationship dt satisfies the equation dt = d0 multiplied by the natural constant e raised to the power of λ multiplied by t. The calculated Lyapunov exponent for each load interval is stored as a numerical feature vector.

[0088] When the Lyapunov index exceeds the preset stability threshold, the decision logic for marking it as an energy efficiency mutation interval is as follows: the preset stability threshold is set according to the equipment type, for example, 0.25 for cooling equipment and 0.30 for heating equipment. All load intervals are traversed. If the Lyapunov index calculation result for a particular interval exceeds the corresponding equipment type threshold, for example, the lambda value of a cooling unit is 0.28 between 55% and 65% load factor, then that interval is marked as an energy efficiency mutation interval. The marking result records three attributes: the starting load factor value, the ending load factor value, and the maximum Lyapunov index value, forming a structured mutation interval feature data table.

[0089] The specific method for calculating the thermal gradient vector field by performing optical flow calculation on the thermodynamic trace is as follows: read the displacement vector sequence in the thermodynamic trace data set, and establish a two-dimensional coordinate system on the heat dissipation surface of the device. Decompose the displacement vector of each pixel into a horizontal component u and a vertical component v, and solve the motion vector field between consecutive frames by the optical flow algorithm. In the vector field calculation, the smoothing coefficient parameter is set to 0.2, the number of iterations is set to 100 times, and the convergence condition is set to the change in adjacent iterations less than 0.01 pixels per frame. The final output thermal gradient vector field is a matrix data structure, and each element of the matrix contains the vector amplitude and direction angle. For example, the vector amplitude at coordinate row 150 and column 200 is 3.5 pixels per frame, and the direction angle is 120 degrees.

[0090] The operation of extracting the spatial area where the heat flux intensity exceeds the thermal interaction threshold in the thermal gradient vector field includes: calculating the heat flux intensity value of each position point in the thermal gradient vector field, where the heat flux intensity is equal to the vector amplitude multiplied by the thermal conductivity coefficient of the material. The thermal interaction threshold is set according to the material characteristics of the device heat sink. For example, the threshold for an aluminum alloy heat sink is set to 200 watts per square meter. Scan the entire vector field matrix data and mark all pixels whose heat flux intensity values ​​exceed the corresponding material threshold. For example, when the heat flux intensity value at the coordinate row 120 and column 80 is 250 watts per square meter, it is marked. Perform regional clustering processing on consecutive adjacent marked points to form a set of areas with excessive heat flux intensity. The boundary coordinate data and the maximum heat flux intensity value are recorded for each area.

[0091] The steps for locating the overlap points between campus equipment corresponding to energy efficiency mutation intervals and spatial areas where heat flux intensity exceeds the thermal interaction threshold in the equipment energy topology network are as follows: Establish a device spatial coordinate mapping relationship table containing three data items: the unique device number, the center coordinate value in the thermal image, and the node number in the device energy topology network. For example, device number CH-01 has coordinates of row 300 and column 400 in the thermal image, and is node N15 in the topology network. Traverse the list of devices corresponding to all energy efficiency mutation intervals and locate their projected areas in the thermal image using the coordinate mapping relationship table. Simultaneously, obtain the spatial location information of the areas where heat flux intensity exceeds the standard, and calculate the spatial intersection area between the device's projected area and the area where heat flux intensity exceeds the standard. When the ratio of the overlap area between the device's projected area in the thermal image and the area where heat flux intensity exceeds the standard exceeds the 50% threshold, it is determined to be a device overlap point, and the device number and the set of overlapping area coordinates are recorded.

[0092] The specific process for identifying device nodes that simultaneously meet the energy efficiency mutation interval conditions and whose heat flux intensity exceeds the thermal interaction threshold as topologically vulnerable nodes is as follows: Filter the corresponding device nodes in the device overlap point set and verify whether they exist in the energy efficiency mutation interval feature data table. For example, if device number P-07 appears in both the device overlap point list and the mutation device list with a load rate of 60% to 70%, the device node is determined to meet both conditions. The topologically vulnerable nodes finally identified record the device's unique number, the associated energy efficiency mutation interval range, the maximum heat flux intensity value, and the node number in the device energy topology network to form a topologically vulnerable node list. This list is stored in association with the network topology structure through the node number in the device energy topology network.

[0093] The cascade assessment module is used to assess the cascade conduction effect of parameter changes on the carbon emissions of associated devices when a topology vulnerable node is identified. The specific implementation is as follows:

[0094] The specific implementation process for determining the directly associated devices of a topologically vulnerable node based on the device energy topology network is as follows: The data processing system reads the unique device number from the topologically vulnerable node list and performs node location retrieval within the adjacency matrix data structure of the device energy topology network. The directly connected device relationships are determined based on the positions of non-zero elements in the adjacency matrix. For example, when the topologically vulnerable node is numbered N15, the column number corresponding to the non-zero element in row 15 of the adjacency matrix is ​​scanned, and device numbers N08 and N22 indicated by the corresponding column numbers are identified as directly associated devices. The determined set of directly associated devices records three attribute data: the device unique number, the energy transmission type, and the connection weight value within the device energy topology network.

[0095] The specific method for calculating the primary carbon emission impact of topologically vulnerable node parameter changes on directly associated devices based on the energy efficiency conduction coefficient in the initial carbon emission model is as follows: The energy efficiency conduction coefficient values ​​between the topologically vulnerable node and each directly associated device are extracted from the initial carbon emission model database. The parameter change for the topologically vulnerable node is expressed as a percentage. The primary carbon emission impact for a specific directly associated device is calculated using the following relationship: the primary carbon emission impact equals the parameter change multiplied by the energy efficiency conduction coefficient, then multiplied by the device's baseline carbon emissions. The baseline carbon emissions are calculated by multiplying the device's rated power by the standard coal consumption coefficient, then by the annual operating hours. For example, if device N08 has a rated power of 500 kilowatts, a standard coal consumption coefficient of 0.8 kilograms of carbon dioxide per kilowatt-hour, and an annual operating hours of 2,000 hours, the baseline carbon emissions are calculated as: 500 × 2,000 × 0.8 = 800,000, expressed in kilograms of carbon dioxide. The calculated primary carbon emission impact values ​​for all directly associated devices form a primary impact data list.

[0096] The process of tracing indirectly connected devices through energy transfer paths in the device energy topology network involves executing a breadth-first search algorithm within the device energy topology network, starting with a topologically vulnerable node. The search depth parameter is set to three layers, and each layer's expansion follows the non-backtracking principle: starting from the current node, traversing unvisited device nodes along the connection edges. For example, starting the search from node N15, the first layer directly reaches devices N08 and N22; the second layer connects to N33 via N08 and to N41 via N22; and the third layer connects to N57 via N33. During the search, the device path sequence and path comprehensive weight are fully recorded. The path comprehensive weight is equal to the product of the energy efficiency conduction coefficients of each connection edge on the path. The resulting indirect device data table contains three fields: the device unique number, the path hop count, and the path comprehensive weight.

[0097] The steps for calculating the secondary carbon impact of indirectly connected devices using the energy efficiency transmission coefficient chain are as follows: For each indirectly connected device, extract the energy efficiency transmission coefficient sequence along its device path. For example, the path sequence for device N57 is N15 to N08 to N33 to N57. The transmission coefficient chain consists of three coefficients: 0.85 for N15 to N08, 0.72 for N08 to N33, and 0.68 for N33 to N57. Multiplying these three coefficients yields the transmission coefficient chain product of 0.85 × 0.72 × 0.68 = 0.416. The secondary carbon impact is calculated using the following relationship: the secondary carbon impact is equal to the parameter change multiplied by the transmission coefficient chain product, then multiplied by the device's baseline carbon emissions. The parameter change refers to the parameter change of the topologically vulnerable node. The baseline carbon emissions calculation method is the same as for directly connected devices. The secondary carbon impact calculation results for all indirectly connected devices form a secondary impact data list.

[0098] The process for accumulating primary and secondary carbon impact values ​​to generate a quantitative value for the carbon cascade transmission effect is as follows: Initialize the accumulator value to 0. First, traverse the primary impact data list and arithmetically add the impact values ​​of each directly associated device. For example, if the impact value of device N08 is 12,000 kg CO2 and the impact value of device N22 is 8,000 kg CO2, the primary cumulative sum is calculated as: 12,000 + 8,000 = 20,000, in kg CO2. Then traverse the secondary impact data list and arithmetically add the impact values ​​of each indirectly associated device. For example, if the impact value of device N33 is 5,000 kg CO2, the impact value of device N41 is 3,000 kg CO2, and the impact value of device N57 is 2,000 kg CO2, the secondary cumulative sum is calculated as: 5,000 + 3,000 + 2,000 = 10,000, in kg CO2. The final quantitative value of the carbon emission cascade effect is equal to the primary cumulative sum plus the secondary cumulative sum, calculated as: 20,000 + 10,000 = 30,000, in kilograms of CO2. This quantitative value is output as the cascade effect assessment result to downstream processing.

[0099] The model update module is used to generate dynamic carbon emission model correction instructions based on the carbon emission cascade conduction effect, update the energy efficiency benchmark parameters and the associated device parameters of the topological vulnerable nodes, and form a dynamic carbon emission model. The specific implementation is as follows:

[0100] The specific implementation process for extracting the energy efficiency parameter deviation of topologically vulnerable nodes from the quantified value of the carbon emission cascade transmission effect is as follows: The carbon emission cascade transmission effect assessment results, which include the quantified value data and associated topologically vulnerable node information, are received. The deviation is determined by comparing the quantified value with a preset baseline value, which is set in the initial carbon emission model based on the device type. For example, for a topologically vulnerable node in a refrigeration unit, if the quantified value is 30,000 kg CO2 and the baseline value is 25,000 kg CO2, the energy efficiency parameter deviation is calculated as: (30,000 - 25,000) / 25,000 = 0.2, which is a 20% deviation. The deviation is calculated as a relative percentage to ensure comparability across different types of devices. The extracted energy efficiency parameter deviation records three attributes: the device number, the deviation value, and the calculation timestamp.

[0101] Calculating the parameter correction coefficient for a topologically vulnerable node in the device energy topology network based on the energy efficiency parameter deviation involves establishing a correction coefficient mapping function that takes into account three factors: equipment type, operating age, and current load factor. For example, for refrigeration equipment with an operating age of more than five years, the correction coefficient is equal to the energy efficiency parameter deviation multiplied by the age attenuation factor, which is determined based on the equipment aging curve and has a typical value of 0.9. For equipment with a load factor below 40%, the correction coefficient is equal to the energy efficiency parameter deviation multiplied by the load compensation factor, which is determined based on the equipment's load characteristics and has a typical value of 1.2. The final value range of the parameter correction coefficient is limited to -50% to +50%, with the boundary value applied if it exceeds the range. The calculated parameter correction coefficient is then bound to the topologically vulnerable node.

[0102] The steps for locating the directly associated devices of a topologically vulnerable node in the device energy topology network are as follows: Retrieve the row data corresponding to the topologically vulnerable node in the device energy topology network's adjacency matrix. Scan the columns containing non-zero elements in that row; each non-zero element corresponds to a directly associated device node. For example, if topologically vulnerable node N15 is in row 15 of the adjacency matrix, device nodes N08 and N22, corresponding to column positions 8 and 22, are identified as directly associated devices. The locating results form a list of directly associated devices, including device numbers, connection types, and connection weights.

[0103] The specific method for calculating parameter compensation for directly associated devices using the energy efficiency transfer coefficient is as follows: The energy efficiency transfer coefficient values ​​between topologically vulnerable nodes and each directly associated device are extracted from the initial carbon emission model database. The parameter compensation calculation relationship is: the parameter compensation amount is equal to the parameter correction coefficient of the topologically vulnerable node multiplied by the energy efficiency transfer coefficient, then multiplied by the device sensitivity factor. The device sensitivity factor is set based on the device type. For example, the sensitivity factor for variable-frequency devices is typically 0.8, while that for fixed-frequency devices is typically 1.0. The parameter compensation calculation results for all directly associated devices are compiled into a compensation data table.

[0104] The process of converting energy efficiency parameter deviations and parameter compensations into dynamic carbon emission model correction instructions is as follows: Create a structured instruction object, which contains an instruction type field, a target device field, and a parameter value field. Generate a parameter update instruction for the topologically vulnerable node, set the instruction type to the node parameter correction type, and fill the parameter value field with the energy efficiency parameter deviation value. Generate a parameter compensation instruction for each directly associated device, set the instruction type to the associated device compensation type, and fill the parameter value field with the corresponding parameter compensation value. For example, the compensation instruction for device N08 is: the instruction type is the associated device compensation type, the target device is N08, and the parameter value is -0.15. All instructions constitute an instruction set.

[0105] The operation of updating the energy efficiency benchmark parameters of the initial carbon emission model based on the dynamic carbon emission model correction instructions includes: loading the initial carbon emission model database and executing the update in the order of the instruction set. For parameter update instructions, the energy efficiency benchmark parameter values ​​of the target device are directly modified. For parameter compensation instructions, the compensation value is added to the current energy efficiency benchmark parameters of the target device. For example, if the original energy efficiency benchmark parameter of device N08 is 3.5 and the compensation value is -0.15, the new energy efficiency benchmark parameter is 3.5 + (-0.15) = 3.35. A version control mechanism is set during the update process, and a new model version identifier is generated for each update.

[0106] The steps for synchronously updating the energy efficiency transfer coefficients of topologically vulnerable nodes and directly connected devices are as follows: Analyze the changes in energy transfer relationships between devices after parameter changes and recalculate the energy efficiency transfer coefficients. For connections between topologically vulnerable nodes and directly connected devices, the new energy efficiency transfer coefficients are equal to the original coefficients multiplied by a correction factor, where the correction factor = 1 + parameter change rate. For example, if the original coefficient is 0.85 and the parameter change rate is -0.1, the correction factor = 1 + (-0.1) = 0.9, resulting in a new coefficient = 0.85 × 0.9 = 0.765. The updated energy efficiency transfer coefficients overwrite the corresponding values ​​in the initial carbon emission model.

[0107] The process for combining the updated energy efficiency baseline parameters with the energy efficiency conduction coefficient to generate a dynamic carbon emission model is as follows: A new model data structure is created, which contains a device energy efficiency parameter table and a device conduction relationship table. The energy efficiency baseline parameters for all devices are extracted from the updated initial carbon emission model and populated into the device energy efficiency parameter table. The energy efficiency conduction coefficients for all device connections are extracted from the updated conduction coefficient data and populated into the conduction relationship table. For example, the device energy efficiency parameter table records the energy efficiency baseline parameter of device N15 as 2.8, while the conduction relationship table records the energy efficiency conduction coefficient from starting device N15 to ending device N08 as 0.765. The resulting dynamic carbon emission model is then stored in the model database with a timestamp and version number.

[0108] The carbon emission control module is used to perform carbon emission control operations in the park based on the dynamic carbon emission model during the operation and maintenance phase. The specific implementation is as follows:

[0109] The specific implementation process for using IoT sensors to collect real-time operating load and energy consumption data for campus equipment is as follows: Current sensors, voltage sensors, and power meters are deployed at key equipment nodes in the park. These sensors collect electrical parameters using the Modbus-RTU protocol at a sampling rate of once per second. The operating load factor is calculated by dividing the real-time power by the equipment's rated power. For example, if a chiller unit has a rated power of 500 kilowatts and the real-time collected power is 350 kilowatt-hours, the operating load factor is 70%. Energy consumption data is calculated by integrating power over time, with energy consumption values ​​accumulated over 15-minute periods in kilowatt-hours. The collected data is pre-processed by the edge computing gateway and uploaded to the central data processing system.

[0110] The operation of inputting the operating load rate into the dynamic carbon emission model to obtain the theoretical energy efficiency benchmark parameters under the current operating conditions includes: the central data processing system receives real-time operating load rate data and retrieves the corresponding record in the equipment energy efficiency parameter table of the dynamic carbon emission model based on the equipment number. The operating condition matching algorithm is called, which matches the preset energy efficiency curve according to the load rate range. For example, when the load rate is between 60% and 80%, a quadratic polynomial fitting curve is used to calculate the theoretical value. The specific implementation is: input the load rate value into the fitting function, and output the theoretical energy efficiency benchmark parameters. For example, the theoretical energy efficiency benchmark parameter corresponding to a load rate of 70% is 3.2 kg of carbon dioxide per kilowatt-hour.

[0111] The specific method for calculating the carbon emissions deviation between actual energy consumption data and the theoretical energy efficiency benchmark is as follows: Convert the actual energy consumption during a period into carbon emissions by multiplying the actual energy consumption by the theoretical energy efficiency benchmark. For example, if the actual energy consumption during a 15-minute period is 87.5 kWh and the theoretical energy efficiency benchmark is 3.2 kg CO2 / kWh, the theoretical carbon emissions are 87.5 x 3.2 = 280 kg CO2. The actual carbon emissions during the same period, as measured by a flue gas analyzer, are 315 kg CO2. The carbon emissions deviation is calculated as: (315 - 280) / 280 x 100% = 12.5%. This calculation is performed every 30 minutes.

[0112] The steps for generating a device energy efficiency optimization instruction when the carbon emissions deviation exceeds the preset tolerance threshold are as follows: The preset tolerance threshold is set based on the device type, for example, 10% for refrigeration equipment. The relationship between the carbon emissions deviation and the threshold is determined in real time. When the deviation reaches 12.5% ​​and exceeds the 10% threshold, an energy efficiency optimization instruction is generated, including the device number, deviation amount, and timestamp. The instruction specifies the direction of energy efficiency parameter adjustment, for example, a positive deviation generates a downward adjustment instruction, and a negative deviation generates an upward adjustment instruction. The instruction is encapsulated in JSON format and transmitted to the control center.

[0113] The process for determining the compensation control amount for associated devices based on the energy efficiency conduction coefficient in the dynamic carbon emission model is as follows: The associated device records for the target device are retrieved from the dynamic carbon emission model's conduction relationship table, and the corresponding energy efficiency conduction coefficient values ​​are extracted. The compensation control amount is calculated as follows: the carbon emission deviation multiplied by the energy efficiency conduction coefficient, then multiplied by the control factor. The control factor is set as a floating point number between 0.6 and 1.2 based on the device's response characteristics, with a default value of 0.8. For example, if the conduction coefficient between target device N15 and associated device N08 is 0.75 and the carbon emission deviation is 12.5%, the compensation control amount = 12.5% ​​× 0.75 × 0.8 = 7.5%. The calculated results form the compensation instruction data set.

[0114] The final steps involved in sending energy efficiency optimization instructions to vulnerable nodes and compensation control instructions to associated devices include: the control center interprets the energy efficiency optimization instructions and sends parameter adjustment commands to the controllers of vulnerable nodes via Industrial Ethernet. For example, a command to reduce the energy efficiency parameters by 3% is sent to device N15. Simultaneously, compensation control instructions are sent to associated devices via the OPC UA protocol, for example, a command to compensate device N08 by 7.5%. After all instructions are executed, the system records the operation log and updates the device status database.

[0115] This system achieves precise management and control of zero-carbon parks through closed-loop data flows across six modules. The initial modeling module constructs an initial carbon emission model containing equipment energy efficiency benchmark parameters during the design phase, providing the system's foundational framework. The dual-source acquisition module simultaneously acquires operational boundary characteristic spectra and thermodynamic traces during the construction phase, forming a multidimensional dynamic database. The topology identification module integrates Lyapunov exponent stability analysis and optical flow thermal gradient analysis to locate topologically vulnerable nodes from the perspective of the energy network. The cascade assessment module calculates the carbon emission cascade effects of parameter changes based on the equipment energy topology network and quantifies the scope of the transmission impact. The model update module generates dynamic correction instructions based on the assessment results, updating the energy efficiency benchmark parameters and associated equipment compensation amounts in real time. During the operation and maintenance phase, the carbon emission control module implements dual-instruction collaborative control based on the dynamic model, enabling the parallel issuance of energy efficiency optimization instructions and compensation control instructions. Each module is seamlessly connected via a data bus.

[0116] This embodiment proposes a mechanism for evaluating and dynamically controlling carbon emission cascade effects. By establishing a coupled model of the device energy topology network and energy efficiency conduction coefficients, a computable representation of the carbon emission transmission path is achieved. To quantify the cascade effect, a breadth-first search algorithm combined with chain multiplication of conduction coefficients is used to calculate indirect effects, addressing the challenge of quantifying carbon emission correlations between multi-hop devices. A dynamic model update mechanism generates compensation instructions based on parameter deviations of vulnerable topological nodes, enabling a transition from single-point control to system collaborative optimization. A dual-instruction collaborative control strategy (parallel execution of energy efficiency optimization instructions and compensation control instructions) during the operation and maintenance phase forms a closed-loop feedback loop, achieving reduced carbon emission intensity in industrial parks through field measurements. By integrating multi-source heterogeneous data from device operating boundary characteristic spectra and heat dissipation thermodynamic traces, a cross-industry device energy topology network is established, enabling dynamic coupled modeling and collaborative control of the carbon emission transmission chain between devices, precisely driving overall carbon emission optimization within the park.

[0117] The calculations involved in the embodiments are all dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to actual conditions.

[0118] It should be noted that the present invention can be deployed on the device itself to implement embedded applications, and can also be run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0119] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the 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 methods include infrared, microwave, etc. The computer-readable storage medium can be any available medium accessible by a computer, or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0120] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0122] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0123] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0124] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0125] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0126] Finally: The above description is only a preferred embodiment of the present invention and is 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 in the scope of protection of the present invention.

Claims

1. A zero-carbon park inter-industry information fusion operation and maintenance system, characterized by: include: The initial modeling module is used to establish an initial carbon emission model during the design phase. The initial carbon emission model includes the energy efficiency benchmark parameters of the park equipment; The dual-source acquisition module is used to obtain the operating boundary characteristic spectrum of the park equipment through dynamic calibration of multiple working conditions during the construction phase, and simultaneously collect the infrared thermal imaging time series of the equipment cooling system to generate thermodynamic traces; A topology identification module is used to analyze the energy efficiency stability of the operating boundary characteristic spectrum based on the Lyapunov exponent, and to analyze the thermal gradient vector field of the thermodynamic trace using the optical flow method to identify the topologically vulnerable nodes of the campus equipment in the energy network. The cascade assessment module is used to assess the carbon emission cascade conduction effect of parameter changes on associated devices when a topology vulnerable node is identified, including: Determine directly associated devices of topologically vulnerable nodes based on the device energy topology network; Calculate the primary carbon emission impact of topology vulnerable node parameter changes on directly associated devices based on the energy efficiency conduction coefficient in the initial carbon emission model; Tracing indirectly related devices through energy transfer paths in the device energy topology network; Combined with the energy efficiency conduction coefficient, chain calculation is used to calculate the secondary carbon emission impact value of indirectly related equipment; The primary carbon emission impact value and the secondary carbon emission impact value are accumulated to generate the quantitative value of the carbon emission cascade transmission effect; The model update module is used to generate dynamic carbon emission model correction instructions based on the carbon emission cascade conduction effect, update the energy efficiency benchmark parameters and the associated device parameters of the topological vulnerable nodes, and form a dynamic carbon emission model; The carbon emission control module is used to perform carbon emission control operations in the park based on the dynamic carbon emission model during the operation and maintenance phase, including: Use IoT sensors to collect real-time data on the operating load rate and energy consumption of park equipment; Input the operating load rate into the dynamic carbon emission model to obtain the theoretical energy efficiency benchmark parameters under the current working conditions; Calculate the carbon emission deviation between actual energy consumption data and theoretical energy efficiency benchmark parameters; When the carbon emission deviation value exceeds the preset tolerance threshold, an equipment energy efficiency optimization instruction is generated; Determine the compensation control amount of related equipment based on the energy efficiency conduction coefficient in the dynamic carbon emission model; Send energy efficiency optimization instructions to topology vulnerable node devices and send compensation control instructions to associated devices.

2. The zero-carbon park inter-industry information fusion operation and maintenance system according to claim 1 is characterized in that: During the design phase, an initial carbon emission model is established. This model includes the energy efficiency benchmark parameters of the park's equipment, including: Build an equipment energy topology network based on the park energy system design drawings; Obtain the rated energy efficiency parameters of campus equipment from the equipment supplier database; Bind the rated energy efficiency parameters to the corresponding nodes of the equipment energy topology network; Calculate the energy efficiency conduction coefficient of related equipment through the park's cooling, heating and electricity coupling relationship; Combine the equipment energy topology network, rated energy efficiency parameters and energy efficiency conduction coefficient to generate an initial carbon emission model; The energy efficiency benchmark parameters include rated energy efficiency parameters and energy efficiency conduction coefficient.

3. The zero-carbon park inter-industry information fusion operation and maintenance system according to claim 2 is characterized in that: During the construction phase, dynamic calibration of multiple working conditions is performed to obtain the operational boundary characteristic spectrum of the park equipment. Infrared thermal imaging time series of the equipment cooling system are simultaneously collected to generate thermodynamic traces, including: Apply step load changes to park equipment during the construction and commissioning phase; The dynamic response of equipment energy efficiency parameters under different load rates is collected through frequency converters and power sensors; Draw the curve of energy efficiency parameters changing with load rate according to the dynamic response of energy efficiency parameters; The energy efficiency inflection point interval in the change curve is extracted as the operating boundary characteristic spectrum; At the same time, an infrared thermal imager is used to continuously shoot the surface temperature field of the equipment's cooling system; Perform pixel-level thermal radiation intensity analysis on the time-series images of the surface temperature field of the equipment cooling system; Thermodynamic traces are generated based on the migration trajectory of thermal radiation intensity between consecutive frames.

4. The zero-carbon park inter-industry information fusion operation and maintenance system according to claim 3 is characterized in that: The run boundary characteristic spectra are synchronously aligned with the thermodynamic trace timestamps.

5. The zero-carbon park inter-industry information fusion operation and maintenance system according to claim 3 is characterized in that: The energy efficiency stability of the operating boundary characteristic spectrum is analyzed based on the Lyapunov exponent. The thermal gradient vector field of the thermodynamic trace is analyzed using the optical flow method to identify the topologically vulnerable nodes of the campus equipment in the energy network. This includes: Calculate the Lyapunov index based on the energy efficiency parameter load rate variation curve based on the operating boundary characteristic spectrum; When the Lyapunov exponent is greater than the preset stability threshold, it is marked as an energy efficiency mutation interval; The thermal gradient vector field is obtained by performing optical flow vector field calculation on the thermodynamic trace; Extract the spatial region where the heat flux intensity exceeds the thermal interaction threshold in the thermal gradient vector field; Locate the overlap points between the energy efficiency mutation interval corresponding to the park equipment and the equipment in the spatial area where the heat flow intensity exceeds the thermal interaction threshold in the equipment energy topology network; Device nodes that simultaneously meet the energy efficiency mutation range conditions and whose heat flux intensity exceeds the thermal interaction threshold are identified as topological vulnerable nodes.

6. The zero-carbon park inter-industry information fusion operation and maintenance system according to claim 5 is characterized in that: Generate dynamic carbon emission model correction instructions based on the carbon emission cascade conduction effect, update energy efficiency benchmark parameters and associated device parameters of topological vulnerable nodes, and form a dynamic carbon emission model, including: Extract the energy efficiency parameter deviation of topological vulnerable nodes from the quantified value of carbon emission cascade conduction effect; Calculate the parameter correction coefficient of the topology vulnerable node in the device energy topology network according to the energy efficiency parameter deviation; Locate the directly associated devices of the topologically vulnerable nodes in the device energy topology network; Calculate the parameter compensation of directly related equipment through the energy efficiency conduction coefficient; Convert the energy efficiency parameter deviation and parameter compensation into dynamic carbon emission model correction instructions; Update the energy efficiency benchmark parameters of the initial carbon emission model according to the dynamic carbon emission model correction instruction; Synchronously update the energy efficiency conduction coefficients of topological vulnerable nodes and directly associated devices; The updated energy efficiency benchmark parameters and energy efficiency conduction coefficient are combined to generate a dynamic carbon emission model.

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