A central air conditioner water chiller operation parameter optimization method based on modeling simulation

Abstract

The present application relates to the technical field of engineering system modeling simulation optimization, in particular to a central air conditioning chiller operation parameter optimization method based on modeling simulation, comprising the following steps: collecting data to generate a working condition matrix, calculating power response to divide monotonic intervals, screening and verifying parameter combinations to generate a matching set, and constructing an instruction generation optimization output group, in the present application, a time synchronization matrix is constructed by collecting multi-dimensional operation data to eliminate sampling frequency difference interference, the power response is deduced for the evaporation temperature, the inflection point is located by analyzing the differential sign flip characteristics, the nonlinear space is divided into monotonic intervals, the extreme value area is locked by using concave-convex characteristics to avoid blind traversal, the interval boundary and trend label are combined for double verification, the false extreme value interference is eliminated and the physical accessibility is ensured, the optimized parameters are reorganized into control instructions according to the register protocol, the adjustment response rate and stability under dynamic load are improved, and the system optimal energy efficiency operation is carried out.

Description

Technical Field

[0001] This invention relates to the field of engineering system modeling, simulation and optimization technology, and in particular to a method for optimizing the operating parameters of a central air conditioning chiller unit based on modeling and simulation. Background Technology

[0002] The field of engineering system modeling, simulation, and optimization technology refers to the technical field that focuses on the operating mechanism and state changes of complex engineering objects. Through mathematical model construction, parameter description, numerical calculation, and simulation analysis, it digitally expresses and calculates the operating process of engineering systems. This includes modeling the physical characteristics of engineering objects, mathematically representing the system's operating state, constructing simulation calculation processes, and parameter analysis methods based on calculation results. It employs deterministic mathematical models, mechanistic models, and numerical simulation methods to calculate, extrapolate, and analyze the operating state of engineering systems under different operating conditions. Specifically, the optimization of operating parameters for traditional central air conditioning chiller units refers to the combined calculation and comparative analysis of operating parameters such as evaporation temperature, condensation temperature, chilled water flow rate, cooling water flow rate, and compressor load during the operation of the chiller unit. Based on the unit's thermodynamic mechanism model and energy balance relationship, these parameters are calculated and compared to determine the parameter values ​​that meet operating constraints. Traditionally, this method relies on chiller unit performance curves, equipment nameplate parameters, and empirical operating condition data, using mechanistic modeling combined with simulation calculations to calculate and compare different parameter combinations one by one, thereby completing the optimization analysis of operating parameters.

[0003] Traditional methods rely excessively on equipment nameplate parameters and idealized thermodynamic models, neglecting factors such as scale buildup on heat exchanger surfaces, compressor mechanical wear, and physical degradation due to changes in refrigerant charge during long-term service. This leads to inherent discrepancies between theoretical calculation models and the actual physical characteristics of the equipment, distorting the calculation baseline and causing optimization results to deviate from the actual optimal energy efficiency point. The strategy of calculating and comparing values ​​for each combination of evaporation temperature, condensation temperature, and flow rate parameters is computationally burdensome and inefficient when dealing with high-dimensional data spaces with multiple operating conditions and variables, making it difficult to complete global optimization quickly and causing significant delays in control command issuance. Furthermore, simple numerical comparisons lack the ability to assess the monotonicity and rate of change characteristics of operating parameter functions, failing to identify the stability of operating points and easily selecting parameter combinations at the steep edges of performance curves. This results in severe power fluctuations or frequent start-ups and shutdowns of the unit due to minor disturbances during actual operation. When faced with scenarios involving drastic fluctuations in outdoor weather conditions or rapid migration of terminal loads, rigid model calculation methods cannot achieve adaptive dynamic tracking, causing the system to operate at high energy consumption under suboptimal conditions for extended periods, exacerbating equipment wear and reducing energy efficiency. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a method for optimizing the operating parameters of a central air conditioning chiller unit based on modeling and simulation, comprising the following steps:

[0005] S1: Collect inlet and outlet temperatures, flow rates, pressures, load rates, and power from the condenser cooling water circuit; combine the inlet and outlet temperatures, flow rates, pressures, load rates, and power to generate status data; and summarize the status data to generate a multi-parameter modeling input matrix for operating conditions.

[0006] S2: Based on the multi-parameter modeling input matrix of the operating conditions, construct a master control variable sequence with evaporation temperature as the main control variable. Under the condition of keeping condensation temperature, flow rate and load rate constant, calculate the power response corresponding to the master control variable sequence through the pre-constructed central air conditioning chiller unit operating power simulation model, and store the generated master control variable power response sequence structure.

[0007] S3: Perform differential calculation on the electric power response values ​​corresponding to adjacent master control variables in the master control variable power response sequence structure, identify the position index of the power difference result changing from positive to negative or from negative to positive, define the range with the corresponding master control variable value and assign direction labels, and generate the evaporation temperature monotonicity interval structure.

[0008] S4: Based on the monotonic interval structure of the evaporation temperature, candidate parameter combinations are screened. For parameter combinations in the candidate parameter combinations where the evaporation temperature term is within the corresponding monotonic interval range, the corresponding electric power response value is obtained. It is then determined whether the direction of the change of the electric power response with the evaporation temperature is consistent with the direction label of the corresponding interval in the monotonic interval structure of the evaporation temperature. Parameter combinations with consistent directions are retained, and a parameter combination matching sequence set is generated.

[0009] S5: Extract operating parameters from the parameter combination matching sequence set, construct instruction frames according to the preset control parameter register address order, organize the instruction frames to generate a control write dataset, and generate a central air conditioning unit operating parameter optimization output group based on the control write dataset.

[0010] As a further embodiment of the present invention, the multi-parameter modeling input matrix includes chilled water inlet and outlet temperatures, cooling water inlet and outlet temperatures, chilled water flow rate, cooling water flow rate, evaporation pressure, condensation pressure, compressor load rate, and electrical power output value. The main control variable power response sequence structure includes the main control variable temperature sequence, temperature sequence position index, corresponding electrical power response value, and temperature-power correspondence. The evaporation temperature monotonicity interval structure includes the interval boundary temperature, interval start and end range, and interval direction label. The parameter combination matching sequence set includes the evaporation temperature comparison target within the interval, the power response sorting sequence, and parameter combination entries with consistent direction. The central air conditioning unit operating parameter optimization output group includes the evaporation temperature setpoint, condensation temperature setpoint, chilled water flow rate setpoint, cooling water flow rate setpoint, and compressor load rate setpoint.

[0011] As a further aspect of the present invention, in the status data generated by the inlet and outlet temperatures, flow rates, pressures, load rates, and power, the sampling interval for the inlet and outlet temperatures is limited to equal-interval sampling within a fixed time window, and the flow rates and pressures are correlated using a synchronous timestamp alignment method.

[0012] As a further aspect of the present invention, during the construction of the master control variable sequence, the condensing temperature, the flow rate, and the load rate are set to constant values ​​within a preset stable range. The power response is the electric power response value calculated based on the input matrix of the multi-parameter modeling of the operating condition. The power response values ​​corresponding to adjacent sampling points are differentially calculated to form an ordered differential sequence for power change trend analysis.

[0013] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0014] S101: Based on the evaporator chilled water circuit and the condenser cooling water circuit, the inlet temperature and outlet temperature are collected, the equipment control unit is called to read the chilled water flow rate and cooling water flow rate, and the parameters are synchronized and integrated according to the timestamp to generate a synchronized heat exchange parameter group.

[0015] S102: Call the equipment control unit to read the evaporation pressure, condensation pressure, compressor load rate and power output value, and align and merge the parameters with the synchronous heat exchange parameter group according to the time index to establish a time-aligned operating parameter matrix;

[0016] S103: Based on the time-aligned running parameter matrix, structurally splice all parameters at different times and integrate them into a unified data structure with multiple state parameters to obtain the multi-parameter modeling input matrix for operating conditions.

[0017] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0018] S201: Based on the evaporation temperature recorded in the input matrix of the multi-parameter modeling of the working condition, set the upper limit of temperature and the step size, perform interval division and sequential arrangement operations on the evaporation temperature sequence, mark the position index of the temperature nodes, and organize the temperature values ​​into an ordered sequence structure according to the index order to generate the main control variable temperature sequence.

[0019] S202: Based on the temperature sequence of the main control variable, keep the condensing temperature, flow rate and load rate parameters taken from the current state value of the multi-parameter modeling input matrix of the operating condition, perform power calculation operation for the temperature node of the sequence, map and organize the calculated power value with the corresponding temperature index, and obtain the temperature power corresponding sequence.

[0020] S203: Based on the temperature-power correspondence sequence, the power values ​​are aggregated in a structured manner according to the temperature index order. The temperature index, temperature value, and power response data are uniformly encapsulated and processed to establish a sequence-level data organization form and generate a power response sequence structure of the main control variable.

[0021] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0022] S301: Based on the power response sequence structure of the master control variable, extract the power response values ​​of adjacent positions, perform subtraction operation to obtain a continuous power difference sequence, construct a symbol sequence for identifying direction changes according to the positive and negative change trend of the difference, and mark the position index of the symbol changing from positive to negative or from negative to positive, and generate a power direction change position index set.

[0023] S302: Based on the power direction change position index set, extract the corresponding master control variable value from the master control variable power response sequence structure, construct each continuous temperature interval in the order of the index, distinguish and mark the upper and lower boundary values ​​of the interval, record the start and end positions, and obtain the monotonic interval boundary set.

[0024] S303: For the set of monotonic interval boundaries, based on the power response change trend before and after the direction change position, assign an increasing or decreasing label to each interval, and aggregate the boundary value, index value and direction attribute into a unified structural unit to establish the monotonic interval structure of evaporation temperature.

[0025] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0026] S401: Based on the monotonic interval structure of the evaporation temperature, obtain the upper and lower limit boundary values ​​of the interval, perform position judgment on the evaporation temperature item in the candidate parameter combination, compare the temperature value with the upper and lower limits of the interval for interval assignment, index and record the temperature items within the interval range, and form a corresponding relationship with the interval number to generate a candidate temperature index set within the interval.

[0027] S402: Based on the candidate temperature index set in the interval, extract the corresponding power response value from the candidate parameter combination, construct a sorting sequence according to the temperature index order, perform difference direction judgment on adjacent power response values, and match the obtained direction result with the interval direction label in the monotonic interval structure of evaporation temperature one by one to obtain the direction consistency judgment sequence.

[0028] S403: For the direction consistency determination sequence, filter the parameter combinations whose direction determination results are consistent with the interval direction labels, perform structured aggregation of the corresponding evaporation temperature item, power response value and interval identifier, and complete the sequence merging process according to the interval number to generate a parameter combination matching sequence set.

[0029] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0030] S501: Based on the parameter combination matching sequence set, extract the corresponding evaporation temperature, condensation temperature, chilled water flow rate, cooling water flow rate and compressor load rate from the combination, perform index matching and arrangement according to the parameter register address order set by the controller, construct instruction content according to field number, and generate control instruction data frame sequence;

[0031] S502: According to the control instruction data frame sequence, perform a reorganization operation according to the parameter field correspondence, bind the control fields in the data frame with the set target values, and uniformly number the field indexes to complete the classification and assembly of the control instruction content and obtain the control writing dataset.

[0032] S503: For the control data set, filter and merge the data according to the field type, aggregate the control target values ​​to build a unified parameter group, and mark the control cycle identifier for this round. Integrate the dataset identifier, parameter fields and target values ​​into a unified structure to generate the central air conditioning unit operation parameter optimization output group.

[0033] As a further aspect of the present invention, the combinations retained in the parameter combination matching sequence set must satisfy the condition that the evaporation temperature is within the corresponding interval range, and the direction of the change of the electrical power response with the evaporation temperature is consistent with the direction label; the instruction frames in the control data set are arranged in ascending order of address to generate a unique output group.

[0034] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0035] In this invention, a time synchronization matrix is ​​constructed by collecting multi-dimensional operating data to eliminate sampling frequency difference interference. The power response is extrapolated based on the evaporation temperature, and the inflection point is located by analyzing the differential sign flip characteristics. The nonlinear space is divided into monotonic intervals, and the concavity and convexity are used to lock the extreme value region to avoid blind traversal. The dual verification of interval boundaries and trend labels is combined to eliminate pseudo-extreme value interference and ensure physical reachability. The optimal parameters are reorganized into control instructions according to the register protocol to improve the adjustment response rate and stability under dynamic load and achieve optimal energy efficiency operation of the system. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a schematic diagram of the steps of the present invention;

[0038] Figure 2 This is a detailed schematic diagram of S1 of the present invention;

[0039] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0040] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0041] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0042] Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0043] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0044] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0045] Please see Figure 1 This invention provides a method for optimizing the operating parameters of a central air conditioning chiller unit based on modeling and simulation, comprising the following steps:

[0046] S1: Collect inlet and outlet temperatures from the evaporator chilled water circuit and the condenser cooling water circuit. Call the equipment control unit to read chilled water flow rate, cooling water flow rate, evaporation pressure, condensation pressure, compressor load rate, and power output value. Combine the parameters into a single operating status data in a time synchronization manner, summarize them to form a multi-parameter input matrix, and generate a multi-parameter modeling input matrix for operating conditions.

[0047] S2: Based on the multi-parameter modeling input matrix of the operating conditions, construct the master control variable sequence with evaporation temperature as the main control variable. Under the condition of keeping the condensing temperature, flow rate and load rate constant, calculate the power response corresponding to the master control variable sequence through the pre-constructed central air conditioning chiller unit operating power simulation model, and store the generated master control variable power response sequence structure.

[0048] S3: Based on the power response sequence structure of the master control variable, perform direction extraction on the power response values ​​of adjacent positions, identify the position index where the symbol changes from positive to negative or from negative to positive, use the corresponding master control variable value as the boundary temperature of the monotonicity interval, divide the start and end range of the interval, and assign an upward or downward direction label to generate the evaporation temperature monotonicity interval structure.

[0049] S4: Based on the monotonic interval structure of evaporation temperature, candidate parameter combinations are screened. For parameter combinations in the candidate parameter combinations whose evaporation temperature term is within the corresponding monotonic interval range, the corresponding electric power response value is obtained. It is then determined whether the direction of the change of electric power response with evaporation temperature is consistent with the direction label of the corresponding interval in the monotonic interval structure of evaporation temperature. Parameter combinations with consistent directions are retained, and a set of parameter combination matching sequences is generated.

[0050] S5: Based on the parameter combination matching sequence set, extract the evaporation temperature, condensation temperature, chilled water flow rate, cooling water flow rate and compressor load rate, construct control instruction data frames according to the address order of the controller setting register, organize and generate control write dataset, use as the target control parameters set for this round of operation, and generate the central air conditioning unit operation parameter optimization output group.

[0051] The multi-parameter modeling input matrix includes chilled water inlet and outlet temperatures, cooling water inlet and outlet temperatures, chilled water flow rate, cooling water flow rate, evaporation pressure, condensation pressure, compressor load rate, and electrical power output value. The main control variable power response sequence structure includes the main control variable temperature sequence, temperature sequence position index, corresponding electrical power response value, and temperature-power correspondence. The evaporation temperature monotonicity interval structure includes the interval boundary temperature, interval start and end range, and interval direction label. The parameter combination matching sequence set includes the evaporation temperature comparison target within the interval, power response sorting sequence, and parameter combination entries with consistent direction. The central air conditioning unit operating parameter optimization output group includes the evaporation temperature setpoint, condensation temperature setpoint, chilled water flow rate setpoint, cooling water flow rate setpoint, and compressor load rate setpoint.

[0052] Please see Figure 2 The specific steps of S1 are as follows:

[0053] S101: Based on the evaporator chilled water circuit and the condenser cooling water circuit, the inlet temperature and outlet temperature are collected, the equipment control unit is called to read the chilled water flow rate and cooling water flow rate, and the parameters are synchronized and integrated according to the timestamp to generate a synchronized heat exchange parameter group.

[0054] Table 1. Synchronous Heat Exchange Parameter Recording Table

[0055]

[0056] The inlet and outlet temperatures of the evaporator chilled water circuit and the condenser cooling water circuit are collected at the same sampling rate, with a sampling period of 1 second. The sampling time is triggered by a unified clock and written to a millisecond-level timestamp. Insertion-type platinum resistance temperature sensors are used for temperature acquisition, installed at least 10 times the pipe diameter downstream of the inlet and outlet flanges of the heat exchanger. The collected temperature values ​​are first validated, with the rule that the maximum and minimum values ​​of the most recent 10 sampling points should not exceed 2.0℃, and the single-point jump amplitude should not exceed 1.0℃. If these conditions are not met, the point is marked as abnormal and replaced with the previous valid point. Subsequently, the chilled water flow rate and cooling water flow rate are read through the equipment control unit. The reading method uses register polling with the same period as the temperature, with the polling order fixed as chilled water flow rate followed by cooling water flow rate. The returned values ​​are written to the same timestamp queue. To ensure temperature and flow rate alignment, the steps perform timestamp synchronization and integration for each parameter, setting the synchronization window width to 500 milliseconds. When multiple records exist within the window, the record with the timestamp closest to the center of the window is selected. If a record is missing within the window, the window is marked as missing and removed entirely in the subsequent alignment stage. After integration, the evaporator-side inlet temperature, evaporator-side outlet temperature, condenser-side inlet temperature, condenser-side outlet temperature, chilled water flow rate, and cooling water flow rate are written into the same data structure in ascending order of timestamp. The field order is fixed and carries a quality flag bit, generating a synchronous heat exchange parameter group. For example, during a certain operating period, the chilled water inlet temperature is 12.2℃, the chilled water outlet temperature is 6.8℃, the cooling water inlet temperature is 30.0℃, the cooling water outlet temperature is 35.1℃, the chilled water flow rate is 210.0 cubic meters per hour, and the cooling water flow rate is 260.0 cubic meters per hour. After integration according to the above synchronous window, a single valid record is formed and entered into the parameter group sequence. The sequence length continuously accumulates with sampling until the data segment encapsulation for this control cycle is completed.

[0057] S102: Call the equipment control unit to read the evaporation pressure, condensation pressure, compressor load rate and power output value, and align and merge the above parameters with the synchronous heat exchange parameter group according to the time index to establish a time-aligned operating parameter matrix;

[0058] The reading cycle is the same as in step S101, still 1 second, and the reading order is fixed as evaporation pressure, condensation pressure, compressor load rate, and electrical power output value to ensure that the timestamps of the same polling batch are traceable. The pressure acquisition point is connected to the control unit through a pressure transmitter. Range boundary judgment is performed on the pressure value. The judgment range for evaporation pressure is 150 kPa to 600 kPa, and the judgment range for condensation pressure is 600 kPa to 2200 kPa. If the value exceeds the range, the record is marked as abnormal and removed. After the compressor load rate is read, discretization and quantization processing is performed. The continuous percentage is rounded in steps of 1%, and the difference before and after rounding is recorded. When the difference exceeds 0.6%, the point is marked as a quantization deviation point and weighted in the subsequent modeling input. The weighting coefficient is 0.7. The coefficient setting is based on the contribution ratio of the quantization deviation point to the power fluctuation in the field playback experiment. The experiment uses 1000 samples to statistically obtain the mean absolute error of the power at the deviation point as 1.43 times that of the normal point. The coefficient is set as the reciprocal of this multiple and rounded to 0.7. After reading the power output value, smoothing and noise reduction are performed. A 5-point sliding window is used to average the current point with the two points before and after it. If there are outliers within the window, the nearest valid point is used instead. Subsequently, the above operating parameters are aligned and merged with the synchronous heat exchange parameter group according to the time index to establish a time-aligned operating parameter matrix. The alignment rule prioritizes complete consistency of timestamps. If there are inconsistencies, the nearest neighbor record is taken within a 500-millisecond synchronization window. If it still does not exist, the entire row is removed. For example, the evaporation pressure is 320 kPa, the condensation pressure is 1450 kPa, the compressor load rate is 72%, and the power output value is 180.0 kW at the same timestamp. This data is merged with the temperature and flow records at the same timestamp in Table 1 to form a matrix with 1 row. The column order of the matrix is ​​fixed as timestamp, 6 heat exchange parameters, 4 operating parameters, quality flag, and weighting coefficient. The matrix is ​​continuously expanded by appending rows until it covers the control cycle.

[0059] S103: Based on the time-aligned running parameter matrix, all parameters at different times are structurally spliced ​​together and integrated into a unified data structure with multiple state parameters to obtain the multi-parameter modeling input matrix of the working condition;

[0060] During the concatenation process, the matrix is ​​first sorted and validated to ensure that the timestamps are monotonically increasing. If a reverse order is found, the data is reordered by timestamp, and the number of differences before and after sorting is recorded. When the number of differences exceeds 1% of the total number of rows, the data segment is marked as a time-series outlier and removed. Subsequently, a unified field mapping table is generated for each row of records. The field mapping table is stored with fixed numbers, without any abbreviations. The numbers start from 1 and correspond sequentially to chilled water inlet temperature, chilled water outlet temperature, cooling water inlet temperature, cooling water outlet temperature, chilled water flow rate, cooling water flow rate, evaporation pressure, condensation pressure, compressor load rate, electrical power output value, quality flag, and quantization weighting coefficient. Missing measurements and outliers are handled uniformly. Rows with missing or outlier quality flags are directly removed, while rows with quantization weighting coefficients lower than 1.0 are retained but participate in subsequent weighting calculations. After cleaning, multiple rows of records from adjacent time points are concatenated into a two-dimensional input matrix. The number of rows in the matrix equals the number of valid sampling points, and the number of columns equals the number of fields. To ensure the stability of subsequent temperature sequence partitioning, the steps perform consistent construction on evaporation temperature-related fields. The evaporation temperature is taken from the saturation temperature conversion table corresponding to the evaporator-side outlet temperature and evaporation pressure. This conversion table is offline embedded in the control unit using a refrigerant property table. During conversion, linear interpolation is performed between adjacent table nodes based on the evaporation pressure, and a new evaporation temperature field is written. This new field replaces the original evaporator-side outlet temperature in the subsequent construction of the main control variables. For example, an evaporation pressure of 320 kPa corresponds to a saturation temperature of approximately 2.6°C. When the table nodes are 300 kPa corresponding to 2.0°C and 330 kPa corresponding to 3.0°C, the interpolation yields 2.6°C, which is then added to a new column in the matrix, forming the input matrix for multi-parameter modeling of operating conditions. The steps encapsulate this input matrix into a unified data structure, writing data segment identifiers, start and end timestamps, the number of valid rows, the number of rows removed due to anomalies, and the field mapping table version number, and outputting it for use in the next stage.

[0061] Please see Figure 3 The specific steps of S2 are as follows:

[0062] S201: Based on multi-parameter modeling of operating conditions, the evaporation temperature is recorded in the input matrix. The upper limit of the temperature and the step size are set. The interval division and sequential arrangement operations are performed on the evaporation temperature sequence. The temperature nodes are marked with position indexes, and the temperature values ​​are organized into an ordered sequence structure according to the index order to generate the main control variable temperature sequence.

[0063] First, the evaporation temperature sequence was validated for its range, with an allowable range of 1.0℃ to 12.0℃. This range was set based on the commonly used supply and return water ranges of 6.7℃ and 12.2℃, and the heat exchanger temperature difference of approximately 3.0℃ to 6.0℃. Points below 1.0℃ were identified as freezing risk zones and removed, while points above 12.0℃ were identified as low-load, high-temperature zones and marked separately. Next, an upper temperature limit and step size were set. The upper temperature limit was set to 12.0℃, and the step size to 0.5℃. Experimental verification of the upper temperature limit and step size was performed. Under the same unit, condensing temperature, and flow rate conditions, step sizes of 0.2℃, 0.5℃, and 1.0℃ were used for playback calculations. The number of power sequence direction changes was compared. At 0.2℃, the average number of changes was 7, with a high number of noise points; at 1.0℃, the average number of changes was 2, and the range was too coarse; at 0.5℃, the average number of changes was 4, and the range boundary was stable. 0.5℃ was chosen as the implementation value. Next, interval partitioning and sequential arrangement operations are performed on the evaporation temperature sequence. Discrete temperature nodes are formed in increments of 0.5℃ from 1.0℃ to 12.0℃, and each temperature node is indexed, starting from 1 and incrementing. The node sequence is then ordered, arranged from low to high temperature, generating the main control variable temperature sequence. For example, discrete nodes are taken as 2.0℃, 2.5℃, 3.0℃ up to 12.0℃, where the index of the 3.0℃ node is 5 and the index of the 8.0℃ node is 15. The steps involve writing the temperature nodes, indices, and node validity flags into the sequence structure, and recording the coverage of the temperature nodes and the original samples. Coverage is calculated as the number of samples falling within the neighborhood of a node, with a neighborhood width of 0.25℃. When the coverage is less than 30 samples, the node is marked as a low-coverage node, and a neighboring node completion strategy is used in subsequent power calculations. The completion strategy uses a weighted average of the power results of the two nearest high-coverage nodes, with weights allocated proportionally to the coverage. The weights are obtained by normalizing the coverage and written into the sequence structure.

[0064] S202: Based on the temperature sequence of the main control variable, keep the condensing temperature, flow rate and load rate parameters taken from the current state value of the input matrix of the multi-parameter modeling of the operating condition, perform the power calculation operation for the temperature node of the sequence, map and organize the calculated power value with the corresponding temperature index, and obtain the temperature power corresponding sequence.

[0065] The current state value is taken from the most recent valid record within the control cycle. The condensing temperature is taken from the conversion result of the saturation temperature corresponding to the condenser outlet temperature and condensing pressure, and the conversion method is the same as that for the evaporating temperature, still using offline solidified property table interpolation. The chilled water flow rate and cooling water flow rate are directly taken from the matrix field values, and the compressor load rate is taken as the quantized percentage value. Subsequently, the power calculation operation is performed for the sequence temperature nodes. The calculation logic is described by textual operation. First, the power output value in the current state is taken as the reference power. Then, the difference between the evaporating temperature node and the current state evaporating temperature is taken. The power correction amount is generated according to the direction and amplitude of the difference. The correction amount is obtained by superimposing three parts. The first part is the temperature difference multiplied by the temperature power slope coefficient. The slope coefficient is obtained by statistical analysis through playback experiments under the same condensing temperature and flow conditions. Taking 100 sets of samples, the average change in power for every 1.0℃ increase in evaporating temperature is negative 12. The first part is 0 kW, with the slope coefficient set to 12.0 kW for every 1.0℃. The second part is the load power coefficient multiplied by the deviation of the compressor load rate from 70%. The coefficient is obtained through segmented statistics. The average power increase is 2.1 kW for every 1% increase in load rate within the 50% to 90% range. 2.1 kW is taken as the implementation value. The third part is the flow power coefficient multiplied by the deviation of the cooling water flow rate from 260.0 cubic meters per hour. The coefficient is obtained through on-site variable frequency pump adjustment experiments. The average power change is 0.8 kW for every 10.0 cubic meters per hour increase in flow rate. For example, given the current state with an evaporation temperature of 2.6℃, a condensation temperature of 40.0℃, a chilled water flow rate of 210.0 cubic meters per hour, a cooling water flow rate of 260.0 cubic meters per hour, a compressor load rate of 72%, and a base power of 180.0 kilowatts, when the temperature node is set to 3.0℃, by incorporating the textual calculation logic of temperature difference and slope coefficient, load deviation and load power coefficient, and flow deviation and flow power coefficient, the calculated power for this node is 174.0 kilowatts. The next step involves mapping and organizing the calculated power value with the corresponding temperature index, and generating the power of low-coverage nodes using a padding strategy to form a temperature-power correspondence sequence.

[0066] S203: Based on the temperature-power correspondence sequence, the power values ​​are aggregated in a structured manner according to the temperature index order. The three types of data, namely temperature index, temperature value, and electric power response, are uniformly encapsulated and processed to establish a sequence-level data organization form and generate the power response sequence structure of the main control variable.

[0067] During aggregation, the continuity of the index is first checked. If an index is missing, a placeholder record is inserted based on the missing index. The power of the placeholder record is the weighted average of the power of the two adjacent indexes, with the weight taken as the coverage ratio of the adjacent indexes and written into the placeholder identifier. Subsequently, each record is written into a sequence-level data organization format. The sequence header records the sequence length, index start point, index end point, temperature step size, current state condensing temperature, and current state flow rate and load rate snapshot values. The snapshot values ​​are used to ensure that subsequent interval division and candidate combination matching refer to the same set of boundary conditions. The step performs a consistency check on the power response values. The check rule is that the absolute value of the power difference between two adjacent indexes does not exceed 40.0 kilowatts. If it exceeds this, it is judged as an abnormal inflection point, and the power at that point is replaced with the median of the three points before and after it.

[0068] Table 2 Power Response Sequence Table of Main Control Variables

[0069]

[0070] The example uses Table 2 as a fragment. The power values ​​for indices 5 to 7 are 174.0 kW, 168.0 kW, and 162.0 kW respectively, with differences all within 40.0 kW. These values ​​are directly written into the structure. Each record in the structure includes a data source marker, indicating whether it was calculated, padded, or substituted due to anomalies. It also includes the aggregation result of quantized weighting coefficients, with the aggregation rule taking the average of the coefficients corresponding to the samples covered by that temperature node. Upon completion, the output is a power response sequence structure for the main control variable, which can be used for subsequent power direction transformation position index identification.

[0071] Please see Figure 4 The specific steps of S3 are as follows:

[0072] S301: Based on the power response sequence structure of the master control variable, extract the power response values ​​of adjacent positions, perform subtraction operation to obtain a continuous power difference sequence, construct a symbol sequence for identifying direction changes according to the positive and negative change trend of the difference, and mark the position index of the symbol changing from positive to negative or from negative to positive, and generate a power direction change position index set.

[0073] The textual rule for subtraction is as follows: subtract the power response of the previous index from the power response of the next index to obtain the power change for that segment, and write each change into the difference sequence. Then, a symbol sequence is constructed based on the positive or negative trend of the difference. The determination rule is: a change greater than 0.5 kWh is marked as positive, a change less than -0.5 kWh is marked as negative, and a change between -0.5 kWh and 0.5 kWh is marked as zero. The threshold of 0.5 kWh is determined by a noise experiment. The experiment collected power for 600 seconds under constant load and constant flow conditions, and the standard deviation of adjacent sampling differences was calculated to be 0.18 kWh. The threshold is three times the standard deviation and rounded to obtain 0.5 kWh. After completing the symbol sequence, the position index where the step marker symbol changes from positive to negative or from negative to positive is used. The zero direction is considered a continuation of the previous non-zero direction and does not trigger a transformation independently. The example uses indices 5 to 7, with power values ​​of 174.0 kW, 168.0 kW, and 162.0 kW respectively. The adjacent differences are -6.0 kW and -6.0 kW, both negative, and do not result in a direction change. If a subsequent segment changes from negative to positive, a change marker is written at the index of the segment after the change. The process involves deduplicating all change marker indices and sorting them in ascending order to form a power direction change position index set. Simultaneously, the forward and backward directions of each change point are recorded, and these records are written into the index set structure for direct reference during interval boundary construction.

[0074] S302: Based on the power direction change position index set, extract the corresponding master control variable values ​​from the master control variable power response sequence structure, construct each continuous temperature interval in the order of the index, distinguish and mark the upper and lower boundary values ​​of the interval, record the start and end positions, and obtain the monotonic interval boundary set.

[0075] The construction method is as follows: Starting with index 1, the first segment begins; the first direction change index minus 1 becomes the first segment end; the temperature corresponding to this end is recorded as the upper or lower boundary in index order. Then, the next segment begins with the same direction change index, ends with the second direction change index minus 1, and so on until the index end. If the distance between adjacent direction change indices is less than three indices, the change is considered a short-range jitter. The jitter threshold of three indices is determined by the relationship between the step size of 0.5℃ and the minimum effective interval width of 1.5℃. When jitter is detected, the change index is merged with the previous change index, and the intermediate boundary is deleted. Each interval is distinguished and its upper and lower boundaries are marked. The upper and lower boundaries are determined by temperature values; the lower temperature is recorded as the lower boundary, and the higher temperature as the upper boundary. The start and end positions are recorded, with the interval number, start index, end index, lower boundary temperature, and upper boundary temperature written in the record fields. For example, assuming the direction transformation index set is 10 and 18, then interval 1 is indices 1 to 9, interval 2 is indices 10 to 17, and interval 3 is indices 18 to the endpoint. If the lower bound of interval 2 is 5.5℃ and the upper bound is 8.5℃, then write this boundary set and output the monotonic interval boundary set.

[0076] S303: For the set of monotonic interval boundaries, based on the power response change trend before and after the direction change position, each interval is assigned an increasing or decreasing label, and the boundary value, index value and direction attribute are aggregated into a unified structural unit to establish the monotonic interval structure of evaporation temperature.

[0077] The assignment rule compares the starting index power and ending index power of the interval. A power difference of more than 0.5 kW between the ending and ending power is marked as an increase, and a power difference of more than 0.5 kW between the ending and ending power is marked as a decrease. Differences within a threshold range are marked as stable and inherited according to the direction of adjacent intervals. Boundary values, index values, and direction attributes are then aggregated into a unified structural unit. The field order of this structural unit is fixed as interval number, starting index, ending index, lower boundary temperature, upper boundary temperature, direction label, and average weighting coefficient within the interval. The average weighting coefficient within the interval is obtained by averaging the weighting coefficients of each temperature node covered by the interval. During averaging, a reduction factor of 0.8 is assigned to low-coverage nodes. This reduction factor is determined by coverage experiments. Nodes with coverage less than 30 samples have a mean absolute power error 25% higher than high-coverage nodes in playback. The reduction factor is calculated as 1 minus 0.25, resulting in 0.75. Based on a conservative principle, this reduction factor is not increased, and 0.75 is used as the reduction factor for low-coverage nodes. For example, a certain interval has a lower boundary of 5.5℃ and an upper boundary of 8.5℃, an initial power of 160.0 kW and an ending power of 190.0 kW. If the comparison result exceeds the threshold, it is marked as an increase. After marking all intervals, the monotonicity interval structure of the evaporation temperature is output. The structure is stored in ascending order of interval number and carries the construction timestamp and threshold configuration version number.

[0078] Please see Figure 5 The specific steps of S4 are as follows:

[0079] S401: Based on the monotonicity interval structure of evaporation temperature, obtain the upper and lower limit boundary values ​​of the interval, perform position judgment on the evaporation temperature item in the candidate parameter combination, compare the temperature value with the upper and lower limits of the interval to determine the interval affiliation, index and record the temperature items within the interval range, form a correspondence with the interval number, and generate a candidate temperature index set within the interval.

[0080] The candidate parameter combinations are derived from multiple sets of trial values ​​within the same control cycle. These trial values ​​are generated by taking four nodes before and after the current evaporation temperature in 0.5℃ increments, while maintaining the current state values ​​for condensing temperature, chilled water flow rate, cooling water flow rate, and compressor load rate, thus forming combination entries. The process involves comparing the evaporation temperature value of each combination entry with the lower and upper boundary temperatures of the interval. The comparison rule is that if the evaporation temperature is not less than the lower boundary and not greater than the upper boundary, it is considered to fall within that interval. If the evaporation temperature is exactly equal to the boundary values ​​of two adjacent intervals, it is assigned to the interval with the smaller interval number. Evaporation temperature items within the interval are indexed and recorded. The index record fields include the combination number, temperature index, and assigned interval number, forming a correspondence with the interval numbers, thus generating a candidate temperature index set within the interval. In the example, the current evaporation temperature is 6.0℃, and the trial nodes are from 4.0℃ to 8.0℃. If the monotonic interval structure contains an interval with a lower boundary of 5.5℃ and an upper boundary of 8.5℃, then the combinations corresponding to 5.5℃ to 8.0℃ are all written into the candidate index set of that interval. 4.0℃ and 4.5℃ are written into the candidate index set of their respective adjacent intervals. The step performs a duplicate check on the candidate index set; if the same combination number is written twice, the first write is retained and a conflict flag is recorded.

[0081] S402: Based on the candidate temperature index set within the interval, extract the corresponding power response value from the candidate parameter combination, construct a sorting sequence according to the temperature index order, perform difference direction judgment on adjacent power response values, and match the obtained direction results with the interval direction labels in the monotonic interval structure of evaporation temperature one by one to obtain the direction consistency judgment sequence.

[0082] The power response value is obtained by prioritizing the mapping value in the temperature-power correspondence sequence calculated in step S202. If the candidate temperature node is a completion node, the completion identifier and coverage information are read simultaneously. Then, a difference direction judgment is performed on adjacent power response values. The judgment rule follows the threshold of 0.5 kW from step S301. The change is obtained by subtracting the previous node's power from the subsequent node's power. If the change exceeds the threshold, it is determined to be an upward direction; if the change is below the negative threshold, it is determined to be a downward direction; if it is within the threshold range, it is determined to be a stationary direction and inherits the previous non-stationary direction. The obtained direction results are matched one-to-one with the interval direction labels in the monotonicity interval structure of the evaporation temperature to construct a direction consistency judgment sequence. The judgment fields include the candidate combination number, interval number, power direction judgment result, interval direction label, and consistency flag. The consistency flag is either consistent, inconsistent, or pending judgment. When pending judgment is used for a stationary direction and the interval label is upward or downward, the processing rule for pending judgment is to read the direction results of the two more distant nodes within the interval for supplementary judgment. If the supplementary judgment still cannot clarify the situation, it is treated as inconsistent.

[0083] S403: For the direction consistency determination sequence, filter the parameter combinations whose direction determination results are consistent with the interval direction labels, perform structured aggregation of the corresponding evaporation temperature item, power response value and interval identifier, and complete the sequence merging process according to the interval number to generate a parameter combination matching sequence set.

[0084] During the filtering process, the data is first grouped by interval number. Within each group, combinations marked as consistent are extracted. The corresponding evaporation temperature item, power response value, and interval identifier are then read and written into the matching entries. The matching entry fields are fixed as interval number, combination number, evaporation temperature node, calculated power, coverage flag, completion flag, and average weighting coefficient. Subsequently, sequence merging is performed by interval number, and matching entries within the same interval are sorted from low to high evaporation temperature to form a set of parameter combination matching sequences. If no consistent entries exist within an interval, that interval is marked as an empty set, and the reason is recorded as inconsistency in direction or missing data. For example, if combination numbers 8 and 9 are consistent within interval number 2, then the matching sequence for interval 2 contains 2 records and is output to the set. The set is then sorted in ascending order by interval number.

[0085] Please see Figure 6 The specific steps of S5 are as follows:

[0086] S501: Based on the parameter combination matching sequence set, extract the corresponding evaporation temperature, condensation temperature, chilled water flow rate, cooling water flow rate and compressor load rate in the combination, perform index matching and arrangement according to the parameter register address order set by the controller, construct the instruction content according to the field number of the extracted data, and generate a control instruction data frame sequence.

[0087] The register address sequence is preset in the control unit as 5 consecutive address segments, corresponding sequentially to the evaporator temperature setpoint, condenser temperature setpoint, chilled water flow rate setpoint, cooling water flow rate setpoint, and compressor load rate setpoint. The process involves formatting each matching entry, converting temperature values ​​to integers with a resolution of 0.1℃, flow rates to integers with a resolution of 0.1 cubic meters per hour, and load rates to 1% resolution. Boundary clipping is performed during the conversion: the evaporator temperature setpoint range is 2.0℃ to 10.0℃, the condenser temperature setpoint range is 30.0℃ to 45.0℃, the chilled water flow rate setpoint range is 120.0 to 320.0 cubic meters per hour, the cooling water flow rate setpoint range is 150.0 to 400.0 cubic meters per hour, and the compressor load rate setpoint range is 20% to 100%. Values ​​outside the range are replaced with boundary values, and a clipping flag is recorded. Subsequently, the extracted data is used to construct instruction content according to field numbers, written sequentially from 1 to 5, with each instruction accompanied by a target range number and a combination number, generating a control instruction data frame sequence. Example: Select combination number 9 within interval 2, set evaporation temperature to 6.5℃, condensation temperature to 40.0℃, chilled water flow rate to 210.0 cubic meters per hour, cooling water flow rate to 260.0 cubic meters per hour, and compressor load rate to 72%. After formatting, write the data frame and add it to the sequence.

[0088] S502: Based on the control instruction data frame sequence, perform a reorganization operation according to the correspondence of parameter fields, bind the control fields in the data frame with the set target values, and uniformly number the field indices to complete the classification and assembly of the control instruction content and obtain the control data to be written into the dataset.

[0089] The control fields in the data frame are bound to the set target values. During binding, the field number and register address mapping table is read first, binding field number 1 to the evaporation temperature register address, field number 2 to the condensation temperature register address, and so on, completing the binding of all five fields sequentially. Then, the field indices are uniformly numbered, and the interval number and combination number are concatenated to generate a batch number. The batch number is represented as a decimal string and written to the data frame header for control cycle tracking. Multiple data frames within the same batch are categorized and assembled. The categorization rule prioritizes the entry with the lowest calculated power as the primary write frame, while retaining the second and third lowest as candidate frames. Candidate frames are marked as not being written, and switching only occurs if the primary write frame fails to write or if the readback verification is inconsistent. Write failure is determined by comparing the control unit's readback register value with the target value. The comparison rules are: temperature allowable deviation 0.2℃, flow allowable deviation 2.0 cubic meters per hour, and load rate allowable deviation 1%. Exceeding these limits indicates inconsistency and triggers switching. After the classification and assembly are completed, the control write dataset is obtained. The dataset contains the main write frame and the alternative frame, as well as their batch number, interval number, combination number, pruning flag and consistency check threshold configuration.

[0090] S503: For the control write dataset, filter and merge the structure according to the field type, aggregate the control target values ​​to build a unified parameter group, and mark the control cycle identifier of this round. Integrate the dataset identifier, parameter fields and target values ​​into a unified structure to generate the central air conditioning unit operation parameter optimization output group.

[0091] First, the main write frame is extracted according to write priority, and its corresponding batch number, interval number, combination number, pruning flag, and generation timestamp are read. Then, the target values ​​in the main write frame are divided into temperature, flow rate, and load rate categories according to field attributes, and written to a unified parameter group in register address order. Simultaneously, the node coverage flag and average weighting coefficient are written as supplementary information. After grouping, a consistency check is performed based on the current state snapshot value, which is taken from the most recent valid record in the multi-parameter modeling input matrix. The check rules are: the difference between the target evaporation temperature and the current value does not exceed 4.0℃, the difference in condensation temperature does not exceed 6.0℃, the difference in chilled water flow rate does not exceed 80.0 cubic meters per hour, the difference in cooling water flow rate does not exceed 100.0 cubic meters per hour, and the difference in compressor load rate does not exceed 30%. If any field exceeds the above range or the pruning flag is true, the main write frame is downgraded to a candidate, and the next candidate frame is extracted and the check process is repeated, with the number of switches accumulated synchronously. If the check conditions are still not met after three consecutive switches, the target value is frozen to the current state and a freeze flag is written. After verification and switching, the process aggregates the five target values ​​of the selected write frame to generate a central air conditioning unit operating parameter optimization output group. This output group includes the control cycle identifier, batch number, interval number, combination number, switching count, trimming flag, and freeze flag. Subsequently, a readback verification is performed on the target values ​​of the output group. The allowable deviation is set to 0.2℃ for temperature, 2.0 cubic meters per hour for flow rate, and 1% for load rate. If the readback result exceeds the allowable deviation, inconsistencies are recorded, triggering a switch to the alternative frame. This completes the parameter optimization output and confirmation for the current control cycle.

[0092] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing the operating parameters of a central air conditioning chiller unit based on modeling and simulation, characterized in that, Includes the following steps: S1: Collect inlet and outlet temperatures, flow rates, pressures, load rates, and power from the condenser cooling water circuit; combine the inlet and outlet temperatures, flow rates, pressures, load rates, and power to generate status data; and summarize the status data to generate a multi-parameter modeling input matrix for operating conditions. S2: Based on the multi-parameter modeling input matrix of the operating conditions, construct a master control variable sequence with evaporation temperature as the main control variable. Under the condition of keeping condensation temperature, flow rate and load rate constant, calculate the power response corresponding to the master control variable sequence through the pre-constructed central air conditioning chiller unit operating power simulation model, and store the generated master control variable power response sequence structure. S3: Perform differential calculation on the electric power response values ​​corresponding to adjacent master control variables in the master control variable power response sequence structure, identify the position index of the power difference result changing from positive to negative or from negative to positive, define the range with the corresponding master control variable value and assign direction labels, and generate the evaporation temperature monotonicity interval structure. S4: Based on the monotonic interval structure of the evaporation temperature, candidate parameter combinations are screened. For parameter combinations in the candidate parameter combinations where the evaporation temperature term is within the corresponding monotonic interval range, the corresponding electric power response value is obtained. It is then determined whether the direction of the change of the electric power response with the evaporation temperature is consistent with the direction label of the corresponding interval in the monotonic interval structure of the evaporation temperature. Parameter combinations with consistent directions are retained, and a parameter combination matching sequence set is generated. S5: Extract operating parameters from the parameter combination matching sequence set, construct instruction frames according to the preset control parameter register address order, organize the instruction frames to generate a control write dataset, and generate a central air conditioning unit operating parameter optimization output group based on the control write dataset.

2. The method for optimizing the operating parameters of a central air conditioning chiller unit based on modeling and simulation according to claim 1, characterized in that, The multi-parameter modeling input matrix includes chilled water inlet and outlet temperatures, cooling water inlet and outlet temperatures, chilled water flow rate, cooling water flow rate, evaporation pressure, condensation pressure, compressor load rate, and electrical power output value. The master control variable power response sequence structure includes the master control variable temperature sequence, temperature sequence position index, corresponding electrical power response value, and temperature-power correspondence. The evaporation temperature monotonicity interval structure includes interval boundary temperature, interval start and end range, and interval direction label. The parameter combination matching sequence set includes evaporation temperature comparison targets within the interval, power response sorting sequence, and parameter combination entries with consistent direction. The central air conditioning unit operating parameter optimization output group includes evaporation temperature setpoint, condensation temperature setpoint, chilled water flow rate setpoint, cooling water flow rate setpoint, and compressor load rate setpoint.

3. The method for optimizing the operating parameters of a central air conditioning chiller unit based on modeling and simulation as described in claim 1, characterized in that: In the status data generated from the inlet and outlet temperatures, flow rates, pressures, load rates, and power, the sampling interval for the inlet and outlet temperatures is limited to equal-interval sampling within a fixed time window, and the flow rates and pressures are correlated using a synchronous timestamp alignment method.

4. The method for optimizing the operating parameters of a central air conditioning chiller unit based on modeling and simulation according to claim 1, characterized in that: In the process of constructing the master control variable sequence, the condensing temperature, the flow rate and the load rate are set to constant values ​​within a preset stable range. The power response is the electric power response value calculated based on the input matrix of the multi-parameter modeling of the operating condition. The power response values ​​corresponding to adjacent sampling points are differentially calculated to form an ordered differential sequence for power change trend analysis.

5. The method for optimizing the operating parameters of a central air conditioning chiller unit based on modeling and simulation according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Based on the evaporator chilled water circuit and the condenser cooling water circuit, the inlet temperature and outlet temperature are collected, the equipment control unit is called to read the chilled water flow rate and cooling water flow rate, and the parameters are synchronized and integrated according to the timestamp to generate a synchronized heat exchange parameter group. S102: Call the equipment control unit to read the evaporation pressure, condensation pressure, compressor load rate and power output value, and align and merge the parameters with the synchronous heat exchange parameter group according to the time index to establish a time-aligned operating parameter matrix; S103: Based on the time-aligned running parameter matrix, structurally splice all parameters at different times and integrate them into a unified data structure with multiple state parameters to obtain the multi-parameter modeling input matrix for operating conditions.

6. The method for optimizing the operating parameters of a central air conditioning chiller unit based on modeling and simulation according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the evaporation temperature recorded in the multi-parameter modeling input matrix of the working condition, set the upper limit of temperature and the step size, perform interval division and sequential arrangement operations on the evaporation temperature sequence, mark the position index of the temperature node, and organize the temperature value into an ordered sequence structure according to the index order to generate the main control variable temperature sequence. S202: Based on the temperature sequence of the main control variable, keep the condensing temperature, flow rate and load rate parameters taken from the current state value of the multi-parameter modeling input matrix of the operating condition, perform power calculation operation for the temperature node of the sequence, map and organize the calculated power value with the corresponding temperature index, and obtain the temperature power corresponding sequence. S203: Based on the temperature-power correspondence sequence, the power values ​​are aggregated in a structured manner according to the temperature index order. The temperature index, temperature value, and power response data are uniformly encapsulated and processed to establish a sequence-level data organization form and generate a power response sequence structure of the main control variable.

7. The method for optimizing the operating parameters of a central air conditioning chiller unit based on modeling and simulation according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Based on the power response sequence structure of the master control variable, extract the power response values ​​of adjacent positions, perform subtraction operation to obtain a continuous power difference sequence, construct a symbol sequence for identifying direction changes according to the positive and negative change trend of the difference, and mark the position index of the symbol changing from positive to negative or from negative to positive, and generate a power direction change position index set. S302: Based on the power direction change position index set, extract the corresponding master control variable value from the master control variable power response sequence structure, construct each continuous temperature interval in the order of the index, distinguish and mark the upper and lower boundary values ​​of the interval, record the start and end positions, and obtain the monotonic interval boundary set. S303: For the set of monotonic interval boundaries, based on the power response change trend before and after the direction change position, assign rising or falling labels to each interval, aggregate the boundary values, index values ​​and direction attributes into a unified structural unit, and establish the monotonic interval structure of evaporation temperature.

8. The method for optimizing the operating parameters of a central air conditioning chiller unit based on modeling and simulation according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Based on the monotonic interval structure of the evaporation temperature, obtain the upper and lower limit boundary values ​​of the interval, perform position judgment on the evaporation temperature item in the candidate parameter combination, compare the temperature value with the upper and lower limits of the interval for interval assignment, index and record the temperature items within the interval range, and form a corresponding relationship with the interval number to generate a candidate temperature index set within the interval. S402: Based on the candidate temperature index set in the interval, extract the corresponding power response value from the candidate parameter combination, construct a sorting sequence according to the temperature index order, perform difference direction judgment on adjacent power response values, and match the obtained direction result with the interval direction label in the monotonic interval structure of evaporation temperature one by one to obtain the direction consistency judgment sequence. S403: For the direction consistency determination sequence, filter the parameter combinations whose direction determination results are consistent with the interval direction labels, perform structured aggregation of the corresponding evaporation temperature item, power response value and interval identifier, and complete the sequence merging process according to the interval number to generate a parameter combination matching sequence set.

9. The method for optimizing the operating parameters of a central air conditioning chiller unit based on modeling and simulation according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the parameter combination matching sequence set, extract the corresponding evaporation temperature, condensation temperature, chilled water flow rate, cooling water flow rate and compressor load rate from the combination, perform index matching and arrangement according to the parameter register address order set by the controller, construct instruction content according to field number, and generate control instruction data frame sequence; S502: According to the control instruction data frame sequence, perform a reorganization operation according to the parameter field correspondence, bind the control fields in the data frame with the set target values, and uniformly number the field indexes to complete the classification and assembly of the control instruction content and obtain the control writing dataset. S503: For the control data set, filter and merge the data according to the field type, aggregate the control target values ​​to build a unified parameter group, and mark the control cycle identifier for this round. Integrate the dataset identifier, parameter fields and target values ​​into a unified structure to generate the central air conditioning unit operation parameter optimization output group.

10. The method for optimizing the operating parameters of a central air conditioning chiller unit based on modeling and simulation according to claim 1, characterized in that: The combinations retained in the parameter combination matching sequence set must satisfy the condition that the evaporation temperature is within the corresponding range, and the direction of the change of the electrical power response with the evaporation temperature is consistent with the direction label; the instruction frames written to the control dataset are arranged in ascending order of address to generate a unique output group.

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