System and method for checking modeling parameters of integrated energy equipment based on hardware-in-the-loop

Abstract

The invention discloses a hardware-in-the-loop comprehensive energy equipment modeling parameter checking system and method, and relates to the technical field of energy system equipment modeling. A physical environment system of the checking system comprises coupling equipment and a measuring instrument, the measuring instrument is used for measuring parameter values of preset load parameters of the coupling equipment in a running state, and the preset load parameters are coupling network load parameters in an integrated parameter model of the coupling equipment and are used for performing parameter identification on the efficiency of the coupling equipment. The monitoring system is used for receiving measurement data output by the physical environment system and writing the measurement data into the operation database, and the digital system is used for obtaining target measurement data from the operation database, constructing a target training sample according to the target measurement data under the condition of determining that a preset model update triggering condition is met, and sending the target training sample to the physical environment system. And checking and updating the parameters in the integrated parameter model based on the target training sample. According to the scheme, the modeling precision of the comprehensive energy equipment and the accuracy of the model parameters can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system equipment modeling, and in particular to a verification system, method, device, equipment, storage medium and product for comprehensive energy equipment modeling parameters based on hardware-in-the-loop. Background Art

[0002] The diversification of energy consumption structure and load types, the intensified contradiction between energy supply and demand, and the increasingly severe environmental situation have put forward new requirements for the operation of energy systems. The multi-energy coupling and multi-energy complementary characteristics of integrated energy systems (IES) make them an inevitable choice for the development of the energy field. By efficiently optimizing the operation of integrated energy systems, they can have better energy efficiency and flexibility. The operation optimization of integrated energy systems is mainly divided into two parts: modeling and solving. The complexity of the model will directly affect the accuracy and performance of the solution, and the two often conflict with each other.

[0003] At present, the modeling accuracy and model parameter accuracy of integrated energy equipment still need to be improved. Summary of the invention

[0004] The present invention provides a hardware-in-the-loop based integrated energy equipment modeling parameter verification system, method, device, equipment, storage medium and product, which can solve the problem of low modeling accuracy and model parameter accuracy of integrated energy equipment.

[0005] According to one aspect of the present invention, there is provided a verification system for comprehensive energy equipment modeling parameters based on hardware-in-the-loop, the verification system comprising a physical environment system, a monitoring system and a digital system connected in communication;

[0006] The physical environment system includes a coupling device and a measuring instrument in the integrated energy system, wherein the measuring instrument includes an instrument for measuring a parameter value of a preset load parameter of the coupling device in operation, the preset load parameter is a coupling network load parameter in an integrated parameter model of the coupling device, and the preset load parameter is used to perform parameter identification on the efficiency of the coupling device;

[0007] The monitoring system is used to receive the measurement data output by the physical environment system and write the measurement data into the operation database;

[0008] The digital system is used to obtain target measurement data from the operation database, construct target training samples according to the target measurement data when it is determined that a preset model update trigger condition is met, and calibrate and update the parameters in the integrated parameter model based on the target training samples.

[0009] According to another aspect of the present invention, a verification method for integrated energy equipment modeling parameters based on hardware-in-the-loop is provided, which is applied to a digital system in a verification system according to any embodiment of the present invention, and the method comprises:

[0010] Obtain target measurement data from the operation database;

[0011] When it is determined that the preset model update trigger condition is met, constructing a target training sample according to the target measurement data;

[0012] The parameters in the integrated parameter model of the coupling devices in the integrated energy system are calibrated and updated based on the target training samples.

[0013] According to another aspect of the present invention, a verification device for integrated energy equipment modeling parameters based on hardware-in-the-loop is provided, characterized in that the digital system configured in the verification system described in any embodiment of the present invention comprises:

[0014] A target measurement data acquisition module is used to obtain target measurement data from the operation database;

[0015] A target training sample construction module is used to construct a target training sample according to the target measurement data when it is determined that a preset model update trigger condition is met;

[0016] The parameter verification module is used to verify and update the parameters in the integrated parameter model of the coupling equipment in the integrated energy system based on the target training samples.

[0017] According to another aspect of the present invention, an electronic device is provided, wherein the electronic device is configured as a digital system in the calibration system according to any embodiment of the present invention, and the electronic device comprises:

[0018] at least one processor; and

[0019] a memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for verifying the modeling parameters of the integrated energy equipment based on hardware in the loop as described in any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for verifying the modeling parameters of integrated energy equipment based on hardware-in-the-loop as described in any embodiment of the present invention when executed.

[0022] According to another aspect of the present invention, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, it implements the method for verifying the modeling parameters of the integrated energy equipment based on hardware-in-the-loop as described in any embodiment of the present invention.

[0023] An embodiment of the present invention provides a verification scheme for modeling parameters of integrated energy equipment based on hardware in the loop. The verification system includes a physical environment system, a monitoring system and a digital system that are communicatively connected. The physical environment system includes a coupling device and a measuring instrument in the integrated energy system, wherein the measuring instrument includes an instrument for measuring the parameter value of a preset load parameter of the coupling device in operation, the preset load parameter is a coupling network load parameter in an integrated parameter model of the coupling device, and the preset load parameter is used to perform parameter identification on the efficiency of the coupling device; the monitoring system is used to receive measurement data output by the physical environment system and write the measurement data into an operation database; the digital system is used to obtain target measurement data from the operation database, and when it is determined that the preset model update trigger condition is met, construct a target training sample according to the target measurement data, and calibrate and update the parameters in the integrated parameter model based on the target training sample. By adopting the above technical solution, a new strategy is provided for the modeling of complex coupled devices in integrated energy systems using data-driven methods. At the same time, the accuracy of the model is greatly improved through hardware-in-the-loop verification. It is universal for complex and diverse integrated energy equipment and can realize universal data-driven parameter identification, equipment modeling and hardware-in-the-loop real-time verification of model parameters for integrated energy equipment. It helps to build an integrated parameter model with higher accuracy and flexibility and real-time self-correction capability, which can improve the modeling accuracy of integrated energy equipment and the accuracy of model parameters, enhance the feasibility and accuracy of theoretical analysis, provide a theoretical basis and technical demonstration for the precise modeling of complex equipment, and promote the application of integrated energy systems.

[0024] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1It is a structural schematic diagram of a verification system for comprehensive energy equipment modeling parameters based on hardware-in-the-loop provided according to an embodiment of the present invention;

[0027] Figure 2 It is a structural schematic diagram of another verification system of integrated energy equipment modeling parameters based on hardware-in-the-loop provided according to an embodiment of the present invention;

[0028] Figure 3 is a schematic diagram of an update cycle triggering method provided according to an embodiment of the present invention;

[0029] Figure 4 is a schematic diagram of a target training sample construction process provided according to an embodiment of the present invention;

[0030] Figure 5 is a structural schematic diagram of a digital system provided according to an embodiment of the present invention;

[0031] Figure 6 It is a schematic diagram of a verification scheme of comprehensive energy equipment modeling parameters based on hardware-in-the-loop provided according to an embodiment of the present invention;

[0032] Figure 7 is a schematic diagram of a comparison result provided according to an embodiment of the present invention;

[0033] Figure 8 It is a flow chart of a method for verifying parameters of integrated energy equipment modeling based on hardware-in-the-loop according to an embodiment of the present invention;

[0034] Fig. 9 It is a structural schematic diagram of a device for verifying parameters of integrated energy equipment modeling based on hardware-in-the-loop according to an embodiment of the present invention;

[0035] Fig.10 It is a structural schematic diagram of an electronic device for implementing a verification method of comprehensive energy equipment modeling parameters based on hardware-in-the-loop according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 creative work should fall within the scope of protection of the present invention.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] In order to better understand the technical solutions of the embodiments of the present invention, the relevant technologies are introduced below. Currently, the commonly used modeling methods are mainly divided into mechanism-driven and data-driven. Data-driven is different from mechanism-driven. It is not committed to describing the complete operating rules of the equipment, but to construct equipment models through data fitting or other machine learning methods. With the increase of strong nonlinear characteristic devices on the source, grid, and load sides of the power system, the original mechanism modeling method is difficult to apply, and the simplicity and effectiveness of the data-driven method make it a powerful tool for modeling. For integrated energy systems with more complex and diverse equipment, the advantages of data-driven methods are more obvious. Under the premise of the increasingly improved current energy management systems and operation monitoring systems, the accumulation of a large amount of operation data has also expanded the application scenarios of data-driven methods. In the prior art, research on data-driven modeling methods has been carried out on power systems or local industrial scenarios, but it has not been extended to the modeling of integrated energy systems and used for operation optimization problems. The integrated energy system can be regarded as a deep integration of the power system and various industrial scenarios. Therefore, in the embodiment of the present invention, the data-driven modeling method is applied to the integrated energy system. The machine learning method used in data-driven modeling can be adopted. At the same time, the explicit analytical equation group constructed by the data-driven method can be used. The constructed explicit analytical equation group can be directly used to construct operating constraints. Therefore, it is theoretically feasible to use it for operation optimization problems.

[0039] Parameter identification can be a method of constructing or verifying the mathematical model of the equipment through the measurement data of hardware in the loop. Although hardware-in-the-loop systems are commonly used in industrial testing, they are less used in modeling and parameter verification. The hardware-in-the-loop method is a method of mapping the physical environment and the digital system to each other, and carrying out upper-level applications based on the interaction of data and control signals. The application research of hardware-in-the-loop methods in the industrial field is relatively in-depth, especially in heavy industrial scenarios such as aviation, automobiles, and ships. Common applications are divided into two directions: parameter identification and simulation testing. The hardware-in-the-loop method is an important means for modeling and simulation testing in the above-mentioned industrial scenarios, but it has not been applied in integrated energy systems.

[0040] The integrated energy equipment modeling parameter identification and hardware-in-the-loop real-time verification scheme proposed in the embodiment of the present invention is universal for complex and diverse integrated energy equipment, and can realize universal data-driven parameter identification, equipment modeling and hardware-in-the-loop real-time verification of model parameters for integrated energy equipment. It helps to build an integrated parameter model with higher accuracy and flexibility and real-time self-correction capability, which can improve the modeling accuracy of integrated energy equipment and the accuracy of model parameters, enhance the feasibility and accuracy of theoretical analysis, provide a theoretical basis and technical demonstration for the precise modeling of complex equipment, and promote the application of integrated energy systems.

[0041] Figure 1 1 is a schematic diagram of a structure of a verification system for modeling parameters of integrated energy equipment based on hardware in the loop according to an embodiment of the present invention. This embodiment is applicable to modeling coupling devices in an integrated energy system and to verify modeling parameters in real time. Figure 1 As shown, the verification system 100 for the modeling parameters of the integrated energy equipment based on hardware in the loop includes a physical environment system 101, a monitoring system 102 and a digital system 103 which are connected in communication. The physical environment system 101 includes coupling devices and measuring instruments in the integrated energy system, wherein the measuring instruments include instruments for measuring the parameter values ​​of the preset load parameters of the coupling devices in operation, wherein the preset load parameters are the coupling network load parameters in the integrated parameter model of the coupling devices, and the preset load parameters are used to perform parameter identification on the efficiency of the coupling devices; the monitoring system 102 is used to receive the measurement data output by the physical environment system and write the measurement data into the operation database; the digital system 103 is used to obtain the target measurement data from the operation database, and when it is determined that the preset model update trigger condition is met, construct the target training samples according to the target measurement data, and verify and update the parameters in the integrated parameter model based on the target training samples.

[0042] Exemplarily, the integrated energy system is a system that integrates multiple energy subsystems, and may involve the coordinated complementarity of multiple links such as the generation, transmission, storage, use, and conversion of at least two energy forms among multiple energy forms such as electricity, heat, cold, and natural gas. Generally, with the power system as the core, multiple energy subsystems such as heat, cold, and natural gas are coupled, and the organic coordination and optimized operation of each link of "source-grid-load-storage" are carried out from the physical level during the planning, construction, and operation process, so as to form an integrated system of production, supply, and consumption. In the embodiment of the present invention, the specific composition of the integrated energy system is not limited, and the coupling device can be any one or more coupling devices in the integrated energy system.

[0043] In an embodiment of the present invention, a data-driven integrated parameter modeling method is used to model the coupling device in the integrated energy system. Optionally, the integrated parameter model in the embodiment of the present invention is determined by: determining the preset load parameter based on the network coupling characteristics of the coupling device; constructing a fitting function of the efficiency of the coupling device and the preset load parameter based on historical operation data to obtain the device efficiency equation; using the load power equation to describe the input power and output power of the network at both ends of the coupling device, and using the device efficiency equation to connect the input power and the output power to obtain the integrated parameter model.

[0044] Among them, the network coupling characteristics can include network load type and device coupling type. In the modeling process, the network load type and device coupling type of the integrated energy are systematically analyzed, the load power equation for the integrated energy system network is constructed, and a method for identifying the efficiency parameters of the coupled equipment based on the operation data fitting is proposed. The efficiency equation is used to connect the input and output load powers to form an integrated parameter model of the coupled equipment.

[0045] For example, since various energy networks use different loads as energy carriers, these loads drive the transmission of energy through flow. When the flowing load is connected to the device, its energy becomes the input or output energy of the device, and its general formula is set as:

[0046] W=M×ρ=m×t×ρ

[0047] Where W represents energy, M represents the amount of load, ρ represents the energy per unit load, m represents flow rate, and t represents time. For different loads, M, m, and ρ can be different physical quantities with different units and dimensions.

[0048] It is considered that the network load factor is the core factor affecting the efficiency of the coupling device. The efficiency parameters of the coupling device are identified by fitting method. The selected coupling network load parameters are shown in Table 1:

[0049] Table 1. List of coupling network load parameters

[0050]

[0051] Among them, P represents power; U represents voltage; I represents current; m represents mass flow; p represents pressure; T represents temperature; and v represents volume flow.

[0052] Efficiency (energy conversion efficiency of the coupling device) is the core parameter that links the import load power (input power) and the export load energy (output power). In the embodiment of the present invention, the fitting function between the efficiency and the device operation parameters is constructed using the historical operation data (historical measurement data). This is the key to characterizing the coupling device model using the data-driven method. The corresponding network load parameters (preset load parameters) can be selected to perform parameter identification on the efficiency of the coupling device, and a polynomial equation for the device energy conversion efficiency with respect to the coupled network load characteristics on both sides of the device is constructed.

[0053] Exemplarily, a linear fitting method such as LinearRegression is used to fit the equipment efficiency, and a polynomial equation (referred to as the equipment efficiency equation) is constructed, the general term of which is:

[0054] η=f(X1,X2)X 1, X2∈{EL,HT,CL,ST,GP}

[0055] Wherein, η represents efficiency; represents the polynomial fitting function; X1 represents input power related data; X2 represents output power related data.

[0056] Exemplarily, the load power equation is used to describe the input power and output power of the network at both ends of the coupled energy device, and the device efficiency equation is used to connect the input and output power to form an integrated parameter model of the device. The general formula of the model is:

[0057]

[0058] Among them, P in Indicates input power; m in,1 represents the first input flow; ρ in,1 Represents the energy of the first input unit load; α in Indicates the input coefficient; m in,2 represents the second input flow; ρ in,2 Represents the energy of the second input unit load; P out represents output power; η represents efficiency; X in Indicates input power related data; X out Represents output power related data; ∪ represents a union; m out,1 represents the first output flow; ρ out,1 Represents the energy of the first output unit load; α out Indicates the output coefficient; m out,2 represents the second output flow; ρ out,2 represents the energy of the second output unit load; α represents the input coefficient and output coefficient.

[0059] Figure 2FIG. 1 is a schematic diagram of another structure of a verification system for integrated energy equipment modeling parameters based on hardware in the loop according to an embodiment of the present invention. Figure 2 The verification system of comprehensive energy equipment modeling parameters based on hardware-in-the-loop is shown.

[0060] Exemplarily, a physical environment, that is, a physical environment system, is built. The physical environment system includes the physical device body of the coupling device and various data measuring instruments. Measuring instruments such as flow meters, pressure gauges, and temperature meters can be installed on the equipment according to the load parameters (preset load parameters) involved in the integrated parameter model. Power meters and heat meters for monitoring energy can also be installed.

[0061] Exemplarily, a monitoring system is built, the monitoring system includes a communication module and an operation database, the monitoring system can receive the measurement data of the physical environment system, and cache, display and send the measurement data. Optionally, the measurement system is connected to a computer through an electrical control cabinet, the computer can be configured as a digital system, and a verification algorithm can be embedded in the digital system, specifically, the relevant algorithm of the verification method of the comprehensive energy equipment modeling parameters based on hardware in the loop provided by the embodiment of the present invention can be embedded.

[0062] Exemplarily, a digital system is built, and the digital system may have the capabilities of receiving data, calculation, sample processing, and model updating. For example, the digital system may include upper-level application algorithms such as data monitoring, update trigger judgment, sample pool, model update algorithm, and optimization solution, which can be specifically implemented through software or scripts and other programs.

[0063] Exemplarily, the physical environment system, the monitoring system and the digital system can be connected via a connection line such as a network cable, following a point-to-point interface protocol (PPI) or a Modbus-TCP protocol, to achieve synchronous or asynchronous data transmission.

[0064] Exemplarily, during the operation of the physical device body of the coupling device, its operating status is measured by a measuring instrument and transmitted to the monitoring system via a communication line. The monitoring system receives the data through a communication module and writes it into the operation log database. The database accepts on-demand reading from the digital system, and the reading and writing of the operation database can be performed asynchronously. The digital system monitors the real-time data asynchronously read from the operation database and incorporates it into the historical data. When the model update trigger condition is met, the latest batch of real-time data and historical data samples can be combined into a new training sample pool to update the integrated parameters and formulate a new optimization strategy. That is, during the operation of the equipment, real-time data is continuously used to participate in the re-fitting of model parameters, and then the integrated parameter model is updated to realize the verification of the historical data fitting model.

[0065] In some embodiments, the digital system is used to obtain real-time data samples from the operation database in batches according to sample batches, and the target measurement data includes the real-time data samples, and the real-time measurement data in the real-time data samples is determined according to a preset data volume.

[0066] Exemplarily, the operation status of the coupled device is monitored and a model update trigger condition is formulated, which is recorded as a preset model update trigger condition. Real-time data is continuously read into the digital system in batches, but the model does not need to be updated for each batch of new data. Instead, the new samples are cached in the real-time sample pool. The model is updated only when the real-time data meets certain trigger conditions, and the real-time sample pool is merged into the historical sample pool.

[0067] Optionally, the preset model update trigger condition includes a periodic update trigger condition and a follow-up update trigger condition. Thus, an update condition that is jointly triggered by periodic update and follow-up update is formulated, which can trigger the update of the model more flexibly, ensure the accuracy of the model update timing, and thus improve the accuracy and timeliness of the model.

[0068] Figure 3 This is a schematic diagram of an update cycle triggering method provided according to an embodiment of the present invention, which can be referred to Figure 3 Understand the preset model update trigger conditions. T represents time and N represents sample size.

[0069] The Cyclical Update (CU) triggering condition is determined based on a preset time period or a preset sample batch period.

[0070] For example, a time period condition for periodic update is constructed, and during the data collection process, a fixed time period T is set. up d (recorded as the preset time period) or sample batch (or sample size) period N upd (recorded as the preset sample batch period), when the sampling time or sampling batch of new data reaches the corresponding period, all new sample data within the period are used to participate in the model update.

[0071] The Followed Update (FU) triggering condition includes: the difference between the real-time data samples of the current batch and the real-time data samples of the previous batch is greater than the preset difference threshold, and / or the data change amplitude within the real-time data samples of the current batch is greater than the preset amplitude threshold. The data change amplitude can specifically be the difference between the maximum value and the minimum value of the data. The preset difference threshold and the preset amplitude threshold can be preset according to actual needs.

[0072] Exemplarily, a monitoring trigger condition for follow-up update (follow-up update trigger condition) is constructed. When the real-time data of a certain batch is significantly different from the previous batch, or the data in the same batch changes significantly, the samples of this batch are used to participate in the immediate update of the model, so that the model changes immediately with the changes in the environment or working conditions. When using some features in the data as the basis for judgment, the follow-up update trigger condition can be expressed as:

[0073]

[0074] Among them, i represents the sample number in a single sample batch; Γ represents the number of samples in a single sample batch; n represents the current batch; n-1 represents the previous batch; A represents the data feature; min represents the minimum value of the data feature; max represents the maximum value of the data feature; thr represents the threshold of the data feature.

[0075] In some embodiments, the target training sample is constructed according to the target measurement data, including: for the target real-time data sample, a target number of continuous target historical data samples are selected from the historical data samples; wherein the target number is consistent with the number of the target real-time data samples; the target historical data samples and the target real-time data samples meet the preset time series similarity requirements; for the case where the update is triggered by satisfying the periodic update trigger condition, the target real-time data sample includes the real-time data sample within the current preset time period or the current preset sample batch period; for the case where the update is triggered by satisfying the follow-up update trigger condition, the target real-time data sample includes the real-time data sample of the current batch; obtain the preset number of additional historical data samples after the target historical data sample, and merge the target historical data sample and the additional historical data sample into a playback data sample; construct the target training sample according to the target real-time data sample and the playback data sample. Thus, based on the sampling playback of the time series similarity samples, and the model update, the samples in the historical data with the time series similarity to the real-time data are sampled and played back, and the integrated parameters are refitted using the playback samples and the real-time samples, which can improve the accuracy of parameter verification.

[0076] There may be multiple groups of target historical data samples, that is, there may be multiple playback data samples.

[0077] For example, Figure 4 Schematic diagram of a target training sample construction process provided according to an embodiment of the present invention. A The real-time sample A (target real-time data sample) is selected from the historical sample Z (historical data sample) to meet its top n AThe m groups of non-overlapping continuous samples A′ (target historical data samples) with the highest time series similarity between samples (target number) and A constitute the playback sample sequence X = [A′1, A′2, A′3, …, A′ m-1 ,A′ m ], the amount of data for each playback sample (playback data sample) is n A′ . The relationship is:

[0078] n A′ =n A′ +n f

[0079] Among them, n f is the additional sample size (preset number). The first n in A′ A samples are used to characterize that the corresponding working conditions and external environment of this group of playback samples are close to the real-time working conditions, and the following n f The playback samples and real-time samples constitute the new training samples X′ (target training samples). The construction process of the new training samples (target training samples) is as follows: Figure 4 shown.

[0080] The similarity of sample time series can be determined by using methods such as Euclidean distance or Dynamic Time Warping (DTW).

[0081] Optionally, in order to avoid overlap of different groups of playback samples in the same verification, the selected playback samples need to be excluded from the search scope of the next group of similar samples. Exemplarily, after merging the target historical data sample and the additional historical data sample into the playback data sample, it may also include: deleting the playback data sample from the historical sample pool.

[0082] In some embodiments, the preset number corresponding to the periodic update trigger condition is different from the preset number corresponding to the follow-up update trigger condition. Thus, for different model update trigger conditions, different numbers of additional historical data samples are set, which can take into account both the model update efficiency and the model update accuracy.

[0083] Exemplarily, the digital system can be deployed using a client (Client) and server (Server) architecture. Figure 5 is a schematic diagram of a digital system according to an embodiment of the present invention. Figure 5As shown, the client reads data from the PLC programmable logic controller (PLC), caches the data, reports the data to the server after forming batches, and clears the cache. The server updates the real-time sample pool and determines whether the update conditions are triggered. If triggered, the model is updated, the optimization strategy is updated, and finally the historical sample pool is expanded.

[0084] Figure 6 is a schematic diagram of a verification scheme for integrated energy equipment modeling parameters based on hardware-in-the-loop according to an embodiment of the present invention, such as Figure 6 As shown, in the parameter identification and modeling stage, the load energy equation is constructed through load characteristic analysis, the coupling equipment efficiency equation is constructed through coupling characteristic analysis, the coupling energy equation is transformed and combined with the coupling equipment efficiency equation, and the parameter identification model is constructed to obtain the polynomial integrated parameter model. The block constant integrated parameter model is obtained by simplifying the high-order polynomial mixed integer. In the hardware-in-the-loop system architecture, including the physical environment, monitoring system and digital system, the polynomial integrated parameter model and the block constant integrated parameter model are applied to the integrated parameter real-time verification method. The model update timing is determined according to the model update trigger condition, and the model is updated by the sample playback method to realize the in-loop parameter verification. The verification results are verified by the hardware system and the digital system to realize the application of the verification results.

[0085] In order to better understand the embodiments of the present invention, the following is an explanation with reference to specific examples. Taking the integrated energy system as an electrothermal coupling system as an example, a small experimental platform is built. In the electrothermal coupling system, the establishment process of the dynamic model experimental platform of the power system is relatively clear, and the optimization strategy verification is relatively convenient. The core of the optimization strategy verification of the integrated energy system is the comparison between the working conditions of the theoretical analysis of the thermal system and the experimental conditions, so the working conditions of the thermal system need to be analyzed and compared on the experimental platform.

[0086] Specifically, the in-loop electric heating air conditioning system used in the embodiment of the present invention is composed of electric heaters (Electric Heaters, EH) and fan coils (Fan Coil, FC), and the integrated parameters of the electric heaters are calibrated in real time.

[0087] The electric heater loss is mainly the heating loss of the heating plate. Since the water tank adopts double-layer insulation and has good thermal insulation performance, the heat loss of the water tank is not taken into account in the experiment. Under normal operating conditions, the water supply flow rate remains unchanged, so the total power of the system, the water tank temperature (i.e., the water supply temperature) and the return water temperature are measured, the electric heating efficiency in the historical data is calculated, and the power and water supply temperature are selected to fit the equipment efficiency to obtain the integrated parameter model:

[0088] PH,EH =P E,EH μ EH (P E,EH ,T S1 )

[0089] Among them, P H,EH Indicates the heating power of the electric heater, in kW; P E,EH Indicates the electric power of the electric heater, in kW; μ EH Indicates the heating efficiency of the electric heater; T S1 Indicates the water supply temperature of the water tank in °C.

[0090] The electric power and the supply and return water temperatures are directly measured by the instrument, and the thermal power and efficiency can be calculated. Considering the slow change of the equipment operation status and the measurement error, a historical data sample is created using the data within a certain period of time. The system uses a fan to simulate the load, so the thermal power of the electric heater is manifested as the heating of the water in the water tank and the external heat supply through the coil and the fan. The average thermal power within a certain period of time is calculated as follows:

[0091]

[0092]

[0093] in, It represents the average electric heating power during this period, in kW; M represents the mass of water in the water tank, which is 300kg; Indicates the interval time between two computing nodes, in seconds; It indicates the average power of the water tank heating the fan coil during this period, in kW; and They represent the average supply and return water temperatures of the water tank during the period, in °C; c represents the specific heat capacity; and m represents the mass flow rate.

[0094] The average efficiency over a certain period of time is:

[0095]

[0096] The other data are averaged within the period to create historical data samples for model construction and real-time parameter calibration.

[0097] The verification scheme includes operation control scheme, data acquisition scheme, model triggering mechanism and model updating mechanism.

[0098] Operation control scheme: For the electric heating air conditioning system, the automatic valve opening and closing is controlled to form a single-circuit dual-pipe heating system with electric heaters and fan coils. The water supply flow rate is kept constant at 1.2t / h by fixing the water pump frequency; the electric power is adjusted by the electric heater thyristor stepless voltage regulation; the wind speed is controlled by controlling the fan gear to adjust the heating amount to simulate different external environments.

[0099] Data collection scheme: In this experiment, the system electric power is measured by a multi-function electric meter, and the supply and return water temperatures on the water tank side are measured by two heat meters. The PLC module is used to sample the data of the electric meter and the heat meter. When collecting historical data, the electric power, water tank (initial) temperature and fan gear are controlled to switch according to the normal heating law within the operating range shown in Table 2, and the switching cycle is greater than 30 minutes. In the experiment, the water supply flow rate is kept unchanged, and the electric power and water supply temperature are directly and indirectly adjusted by the power controller. The historical data sampling cycle is 10 seconds, and the average of 18 groups of measurement data every 3 minutes is taken and the efficiency is calculated as a sample. After completion, it is downloaded to the computer through the touch screen USB port at one time. When collecting real-time data, the sampling frequency remains unchanged. The real-time data is read by the digital system process one through the PLC IO module and sent to the process two in batches, with 20 samples per batch and 3 batches for the update cycle. The real-time verification time is 9 hours, covering 3 batch cycles. During this period, the fan gear is switched according to the normal control cycle to simulate the changes in the external environment. During data collection, the laboratory environment changes naturally, and the behavior of people indoors and the opening and closing of doors and windows occur naturally.

[0100] Table 2. Operating range when collecting historical data

[0101]

[0102] Update trigger mechanism: Adopt periodic update and follow-up update joint trigger. Periodic update is 3 batches per cycle, and the data volume per cycle is 60. The follow-up update trigger condition is that the maximum and minimum values ​​of the sample features in the batch exceed a certain range. According to the historical data characteristics, the follow-up update trigger conditions used in the experiment are:

[0103] (P E,EH,max ≥0.5P E,EH,min )∨(P H,FC2,max ≥0.5P H,FC2,min )

[0104] Among them, P H,FC2 Indicates the fan coil power.

[0105] Model update mechanism: The model update adopts the combined update of sampling playback samples and real-time samples. When the batch cycle is updated, n A is 60, n f Fixed to 60, take 3 sets of playback samples, n X′Fixed to 420; when following the update, n A is 20, and each group of playback samples is appended with the number of samples remaining in the update cycle of the frequency, that is:

[0106] n f =60-20i

[0107] n X′ =260-60i

[0108] Here, i indicates that the real-time samples that trigger the follow-up update are the i-th batch in this cycle.

[0109] A total of 1,800 valid historical data were collected in the experiment. The function of electric heating efficiency with respect to electric power and water supply temperature obtained by fitting the historical data is as follows. The coefficients are shown in the first column of Table 4.

[0110]

[0111] Among them, μ EH Indicates the heating efficiency of the electric heater; a0, a i , a j,k represents the fitting coefficient of the corresponding X; i, j and k represent the element numbers in the set; X represents the set consisting of electric power and water supply temperature; P E,EAC Indicates electrical power; T S Indicates the supply water temperature.

[0112] In the real-time verification experiment, the trigger follow-up update was performed twice and the periodic update was performed twice, and the model was updated four times. The trigger process and the composition of the new training samples for each update are shown in Table 3.

[0113] Table 3. Trigger types and new training sample composition in real-time verification

[0114]

[0115] The polynomial parameter calibration process in the experiment is shown in Table 4.

[0116] Table 4. Polynomial coefficient verification process

[0117]

[0118] Each time an update is made, the n f The accuracy of the model was verified by using four samples, and the efficiency parameters after four updates were compared with the actual measured data. Figure 7 As shown in the figure, the excellent effect of in-loop verification is demonstrated. The model fitted with historical data can better describe the operation law of the equipment, but there is a relatively obvious error. The real-time verification significantly reduces this error and improves the accuracy of the model. For each verification cycle, the root mean square error of the calculated model is:

[0119]

[0120] Among them, ER represents the model error; n represents the number of validation samples; i represents the validation sample number; μ mea,i and μ mol,i They represent the measurement efficiency and model calculation efficiency of the i-th validation sample respectively.

[0121] The errors of the historical data fitting model and the real-time calibration model are compared, as shown in Table 5.

[0122] Table 5. Error comparison between historical data fitting model and real-time calibration model

[0123]

[0124] In the verification of four groups of real-time samples, the calibrated model reduced the error of the historical data fitting model by 85.1%, 95.9%, 85.8% and 83.63% respectively, proving that the hardware-in-the-loop method can perform a relatively accurate real-time verification of the model, and its application value is significant. It should be noted that this good real-time verification effect is the result of repeatedly adjusting parameters such as sampling period, batch sample size, update period, and playback sample composition in the experiment. When performing real-time parameter verification on different types of equipment, it is also necessary to continuously adjust these parameters through experiments and tests to ensure that the method achieves good practical results.

[0125] Figure 8 This is a flowchart of a method for verifying the modeling parameters of an integrated energy device based on hardware in the loop according to an embodiment of the present invention. This embodiment is applicable to the case of modeling coupled devices in an integrated energy system and verifying the modeling parameters in real time. The method can be executed by a verification device for the modeling parameters of an integrated energy device based on hardware in the loop. The verification device for the modeling parameters of an integrated energy device based on hardware in the loop can be implemented in the form of hardware and / or software. The verification device for the modeling parameters of an integrated energy device based on hardware in the loop can be configured in an electronic device such as a computer. The electronic device is configured as a digital system in the embodiment of the present invention. Figure 8 As shown, the method includes:

[0126] Step 801: Obtain target measurement data from the operation database.

[0127] Wherein, the monitoring system receives the measurement data output by the physical environment system (measured by the measuring instrument) and writes the measurement data into the operation database, and the digital system can obtain the real-time measurement data from the operation database in batches as the target measurement data. Optionally, the digital system is used to obtain real-time data samples from the operation database in batches according to sample batches, and the target measurement data includes the real-time data samples, and the real-time measurement data in the real-time data samples is determined according to the real-time measurement data of the preset data volume. The physical environment system includes coupling devices and measuring instruments in the integrated energy system, wherein the measuring instruments include instruments for measuring the parameter values ​​of preset load parameters of the coupling device in the operation state, the preset load parameters are the coupling network load parameters in the integrated parameter model of the coupling device, and the preset load parameters are used to perform parameter identification on the efficiency of the coupling device.

[0128] Step 802: When it is determined that the preset model update trigger condition is met, construct a target training sample according to the target measurement data.

[0129] Optionally, the preset model update trigger condition includes a periodic update trigger condition and a follow-up update trigger condition. The periodic update trigger condition is determined based on a preset time period or a preset sample batch period. The follow-up update trigger condition includes: when the difference between the real-time data samples of the current batch and the real-time data samples of the previous batch is greater than a preset difference threshold, and / or the data change amplitude within the real-time data samples of the current batch is greater than a preset amplitude threshold.

[0130] Optionally, for the target real-time data sample, a target number of continuous target historical data samples are screened from the historical data samples; wherein the target number is consistent with the number of the target real-time data samples; the target historical data samples and the target real-time data samples meet a preset timing similarity requirement; for the case where the update is triggered due to the satisfaction of the periodic update trigger condition, the target real-time data samples include real-time data samples within the current preset time period or the current preset sample batch period; for the case where the update is triggered due to the satisfaction of the follow-up update trigger condition, the target real-time data samples include real-time data samples of the current batch; a preset number of additional historical data samples after the target historical data sample are obtained, and the target historical data sample and the additional historical data sample are merged into a playback data sample; and a target training sample is constructed based on the target real-time data sample and the playback data sample.

[0131] Step 803: calibrate and update the parameters in the integrated parameter model of the coupling devices in the integrated energy system based on the target training samples.

[0132] The hardware-in-the-loop based integrated energy equipment modeling parameter verification method provided in the embodiment of the present invention uses a data-driven method to provide a new strategy for the modeling of complex coupled devices in the integrated energy system. At the same time, the accuracy of the model is greatly improved through hardware-in-the-loop verification. It is universal for complex and diverse integrated energy equipment and can realize universal data-driven parameter identification, equipment modeling and hardware-in-the-loop real-time verification of model parameters for integrated energy equipment. It helps to build an integrated parameter model with higher accuracy and flexibility and real-time self-correction capability, which can improve the modeling accuracy of integrated energy equipment and the accuracy of model parameters, enhance the feasibility and accuracy of theoretical analysis, provide a theoretical basis and technical demonstration for the precise modeling of complex equipment, and promote the application of integrated energy systems.

[0133] Fig. 9 1 is a schematic diagram of a structure of a verification device for integrated energy equipment modeling parameters based on hardware in the loop according to an embodiment of the present invention, and the device is configured in a digital system of a verification system described in any embodiment of the present invention. Fig. 9 As shown, the device comprises:

[0134] The target measurement data acquisition module 901 is used to acquire the target measurement data from the operation database;

[0135] A target training sample construction module 902 is used to construct a target training sample according to the target measurement data when it is determined that a preset model update trigger condition is met;

[0136] The parameter verification module 903 is used to verify and update the parameters in the integrated parameter model of the coupling equipment in the integrated energy system based on the target training samples.

[0137] The hardware-in-the-loop based integrated energy equipment modeling parameter verification device provided in the embodiment of the present invention uses a data-driven method to provide a new strategy for the modeling of complex coupled equipment in the integrated energy system. At the same time, the accuracy of the model is greatly improved through hardware-in-the-loop verification. It is universal for complex and diverse integrated energy equipment and can realize universal data-driven parameter identification, equipment modeling and hardware-in-the-loop real-time verification of model parameters for integrated energy equipment. It is helpful to build an integrated parameter model with higher accuracy and flexibility and real-time self-correction capability, which can improve the modeling accuracy of integrated energy equipment and the accuracy of model parameters, enhance the feasibility and accuracy of theoretical analysis, provide a theoretical basis and technical demonstration for the precise modeling of complex equipment, and promote the application of integrated energy systems.

[0138] Optionally, the target training sample construction module includes:

[0139] A target historical data sample screening unit is used to screen a target number of continuous target historical data samples from historical data samples for target real-time data samples when it is determined that a preset model update trigger condition is met; wherein the target number is consistent with the number of target real-time data samples; the target historical data samples and the target real-time data samples meet a preset time series similarity requirement; for a case where an update is triggered due to meeting the periodic update trigger condition, the target real-time data samples include real-time data samples within a current preset time period or within a current preset sample batch period; for a case where an update is triggered due to meeting the follow-up update trigger condition, the target real-time data samples include real-time data samples of the current batch;

[0140] a playback data sample determination unit, configured to obtain a preset number of additional historical data samples after the target historical data sample, and merge the target historical data sample and the additional historical data sample into a playback data sample;

[0141] The target training sample construction unit is used to construct a target training sample according to the target real-time data sample and the playback data sample.

[0142] The device for verifying the modeling parameters of integrated energy equipment based on hardware-in-the-loop provided in an embodiment of the present invention can execute the method for verifying the modeling parameters of integrated energy equipment based on hardware-in-the-loop provided in any embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method.

[0143] Fig.10 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is configured as a digital system in an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0144] like Fig.10As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0145] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0146] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a verification method for comprehensive energy equipment modeling parameters based on hardware in the loop.

[0147] In some embodiments, the verification method of the integrated energy device modeling parameters based on hardware in the loop can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the verification method of the integrated energy device modeling parameters based on hardware in the loop described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the verification method of the integrated energy device modeling parameters based on hardware in the loop by any other appropriate means (for example, by means of firmware).

[0148] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0149] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0150] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0151] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0152] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0153] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0154] The embodiments of the present disclosure provide a computer program product, including a computer program, which, when executed by a processor, implements the method for verifying the modeling parameters of integrated energy equipment based on hardware-in-the-loop provided in the above embodiments.

[0155] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0156] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A verification system for comprehensive energy equipment modeling parameters based on hardware in the loop, characterized in that: The verification system includes a physical environment system, a monitoring system and a digital system connected in communication; The physical environment system includes a coupling device and a measuring instrument in the integrated energy system, wherein the measuring instrument includes an instrument for measuring a parameter value of a preset load parameter of the coupling device in operation, the preset load parameter is a coupling network load parameter in an integrated parameter model of the coupling device, and the preset load parameter is used to perform parameter identification on the efficiency of the coupling device; The monitoring system is used to receive the measurement data output by the physical environment system and write the measurement data into the operation database; The digital system is used to obtain target measurement data from the operation database, construct target training samples according to the target measurement data when it is determined that a preset model update trigger condition is met, and calibrate and update the parameters in the integrated parameter model based on the target training samples.

2. The calibration system according to claim 1, characterized in that: The integrated parameter model is determined by: Determining a preset load parameter based on a network coupling characteristic of the coupling device; Constructing a fitting function of the efficiency of the coupling device and the preset load parameter based on historical operation data to obtain a device efficiency equation; The load power equation is used to describe the input power and output power of the network at both ends of the coupling device, and the device efficiency equation is used to connect the input power and the output power to obtain the integrated parameter model.

3. The calibration system according to claim 2, characterized in that: The digital system is used to obtain real-time data samples from the operation database in batches according to sample batches, the target measurement data includes the real-time data samples, and the real-time data samples are determined according to the real-time measurement data of a preset data volume; Wherein, the preset model update trigger condition includes a periodic update trigger condition and a follow-up update trigger condition; Wherein, the periodic update trigger condition is determined based on a preset time period or a preset sample batch period; Among them, the follow-up update trigger conditions include: monitoring that the difference between the real-time data samples of the current batch and the real-time data samples of the previous batch is greater than a preset difference threshold, and / or the data change amplitude within the real-time data samples of the current batch is greater than a preset amplitude threshold.

4. The calibration system according to claim 3, characterized in that: The step of constructing a target training sample according to the target measurement data includes: For the target real-time data samples, a target number of continuous target historical data samples are screened from the historical data samples; wherein the target number is consistent with the number of the target real-time data samples; the target historical data samples and the target real-time data samples meet the preset time series similarity requirement; for the case where the update is triggered due to the satisfaction of the periodic update trigger condition, the target real-time data samples include real-time data samples within the current preset time period or within the current preset sample batch period; for the case where the update is triggered due to the satisfaction of the follow-up update trigger condition, the target real-time data samples include the real-time data samples of the current batch; Acquire a preset number of additional historical data samples after the target historical data sample, and merge the target historical data sample and the additional historical data sample into a playback data sample; A target training sample is constructed according to the target real-time data sample and the playback data sample.

5. The calibration system according to claim 4, characterized in that: The preset number corresponding to the periodic update trigger condition is different from the preset number corresponding to the follow-up update trigger condition.

6. A method for verifying parameters of integrated energy equipment modeling based on hardware in the loop, characterized in that: A digital system applied to a calibration system as claimed in any one of claims 1 to 5, the method comprising: Obtain target measurement data from the operation database; When it is determined that the preset model update trigger condition is met, constructing a target training sample according to the target measurement data; The parameters in the integrated parameter model of the coupling devices in the integrated energy system are calibrated and updated based on the target training samples.

7. A verification device for comprehensive energy equipment modeling parameters based on hardware in the loop, characterized in that: A digital system configured in a calibration system as claimed in any one of claims 1 to 5, wherein the device comprises: A target measurement data acquisition module is used to obtain target measurement data from the operation database; A target training sample construction module is used to construct a target training sample according to the target measurement data when it is determined that a preset model update trigger condition is met; The parameter verification module is used to verify and update the parameters in the integrated parameter model of the coupling equipment in the integrated energy system based on the target training samples.

8. An electronic device, characterized in that: The electronic device is configured as a digital system in the calibration system according to any one of claims 1 to 5, and the electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for verifying the modeling parameters of the integrated energy equipment based on hardware-in-the-loop as described in any one of claim 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for verifying the modeling parameters of integrated energy equipment based on hardware-in-the-loop as described in claim 6 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the method for verifying parameters of integrated energy equipment modeling based on hardware-in-the-loop according to claim 6.

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