Data checking method, device and medium based on energyplus simulation results

Abstract

The application discloses a data checking method and device based on EnergyPlus simulation results and a medium. The method comprises the following steps: determining target static data based on the analysis result of a target simulation configuration file in EnergyPlus; wherein the target static data comprises a target object and the attributes of the target object; determining the target time sequence running data of the target object based on the analysis result of a target simulation result file corresponding to the target simulation configuration file; determining the target data item according to a target data checking task, and determining the target checking mode of the target data item; determining the to-be-tested object and the to-be-tested attribute of the to-be-tested object from the target static data according to the target data item, and determining the to-be-tested time sequence running data associated with the to-be-tested attribute of the to-be-tested object from the target time sequence running data; and performing data checking on the to-be-tested time sequence running data according to the target checking mode to obtain a data checking result. The technical scheme can greatly improve the checking efficiency and applicability of EnergyPlus simulation data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a data inspection method, device, and medium based on EnergyPlus simulation results. Background Technology

[0002] EnergyPlus is a widely used building simulation software worldwide, capable of comprehensively simulating building cooling, heating, lighting, ventilation, equipment operation, and energy consumption. In scenarios where actual data is insufficient, using EnergyPlus to quickly generate large amounts of data is an excellent way to supplement existing data.

[0003] Because EnergyPlus modeling is extremely complex, and various parameter settings rely heavily on the designer's experience, simulation results often fail to meet certain physical understandings or engineering experience. This necessitates a convenient method to examine the simulation results, guiding modifications to the EnergyPlus model or directly filtering the data to ensure its quality meets subsequent usage requirements.

[0004] Currently, there is a software called xEsoView on the market that can view the simulation results of EnergyPlus through line charts. However, it only supports viewing one indicator at a time, and its performance is poor, making it unsuitable for handling multi-scenario inspections of large amounts of data. Summary of the Invention

[0005] This invention provides a data inspection method, device, and medium based on EnergyPlus simulation results. It can perform rapid and effective data inspection on EnergyPlus simulation results by using an inspection method determined based on the data inspection task, which greatly improves the efficiency and applicability of data inspection.

[0006] According to one aspect of the present invention, a data inspection method based on EnergyPlus simulation results is provided, the method comprising:

[0007] The target static data is determined based on the parsing results of the target simulation configuration file in EnergyPlus; wherein, the target static data includes target object data and the association data between the target object data, and the target object data includes the target object, the object type to which the target object belongs, and the attributes of the target object;

[0008] Based on the parsing results of the target simulation result file corresponding to the target simulation configuration file, the target time-series running data of the target object is determined; wherein, the target time-series running data is represented by the running state time slice of the target object;

[0009] The target data items are determined based on the target data inspection task, and the target inspection method for the target data items is determined; wherein, the target data items include the target object type and the target attribute type of the target object type;

[0010] Based on the target data item, determine the object to be tested and the attribute to be tested of the object to be tested from the target static data, and determine the time series running data associated with the attribute to be tested of the object to be tested from the target time series running data;

[0011] The data inspection results are obtained by performing a data inspection on the time-series running data to be tested according to the target inspection method.

[0012] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0013] At least one processor; and,

[0014] A memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the data inspection method based on EnergyPlus simulation results as described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the data inspection method based on EnergyPlus simulation results as described in any embodiment of the present invention.

[0017] The technical solution of this invention determines target static data based on the parsing results of the target simulation configuration file in EnergyPlus. The target static data includes target object data and the relationship data between target object data. The target object data includes the target object, the object type to which the target object belongs, and the attributes of the target object. Based on the parsing results of the target simulation result file corresponding to the target simulation configuration file, the target time-series runtime data of the target object is determined. The target time-series runtime data is represented by time slices of the target object's runtime state. Target data items are determined according to the target data inspection task, and the target inspection method for the target data items is determined. The target data items include the target object type and the target attribute type of the target object type. The object to be tested and its test attributes are determined from the target static data based on the target data items, and the test time-series runtime data associated with the test attributes of the object to be tested is determined from the target time-series runtime data. Data inspection is performed on the test time-series runtime data according to the target inspection method to obtain the data inspection results. This technical solution enables rapid and effective data inspection of EnergyPlus simulation results through the inspection method determined based on the data inspection task, greatly improving the efficiency and applicability of data inspection.

[0018] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] 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.

[0020] Figure 1 This is a flowchart of a data inspection method based on EnergyPlus simulation results provided in Embodiment 1 of the present invention;

[0021] Figure 2 This is a schematic diagram of the structure of target object data provided in Embodiment 1 of the present invention;

[0022] Figure 3 This is a schematic diagram of the structure of target static data provided in Embodiment 1 of the present invention;

[0023] Figure 4 This is a schematic diagram illustrating the process of determining target runtime sequence data according to Embodiment 1 of the present invention;

[0024] Figure 5 This is a flowchart of a data inspection method based on EnergyPlus simulation results provided in Embodiment 2 of the present invention;

[0025] Figure 6 This is a schematic diagram of a data inspection device based on EnergyPlus simulation results provided in Embodiment 3 of the present invention;

[0026] Figure 7 This is a schematic diagram of the structure of an electronic device that implements a data inspection method based on EnergyPlus simulation results according to an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] Figure 1 This is a flowchart of a data inspection method based on EnergyPlus simulation results provided in Embodiment 1 of the present invention. This embodiment is applicable to the case of data inspection of EnergyPlus simulation results. The method can be executed by a data inspection device based on EnergyPlus simulation results. This data inspection device can be implemented in hardware and / or software and can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0031] S110, determine the target static data based on the parsing results of the target simulation configuration file in EnergyPlus.

[0032] The simulation configuration file (i.e., the IDF file) serves as input for EnergyPlus simulations. It describes the simulation objects (objects defined in EnergyPlus, such as a building structure or electromechanical equipment), the simulation control logic, and the simulation configuration parameters (including simulation data items and their corresponding parameter values). The target simulation configuration file refers to the IDF file to be used in the simulation. Target static data includes target object data and the relationships between target object data. Target object data includes the target object, its object type, and its attributes. The target object can be an object defined in the building's physical world, and its attributes can include both static and dynamic attributes. Figure 2 This is a schematic diagram of the structure of target object data provided in Embodiment 1 of the present invention. Information points represent attributes, and the object ID can be used to uniquely identify the target object. For example... Figure 2 As shown, the target object is Room 1 (with object ID SP01). Room 1 belongs to the object type "room". Room 1's static attributes include area and orientation, while its dynamic (i.e., sequential state) attributes include set temperature, temperature, and relative humidity. The relationships between target object data can be represented through these relationships. These relationships can include hierarchical relationships, connection relationships, and service relationships.

[0033] In this embodiment, the target simulation configuration file in EnergyPlus is first obtained, and then parsed. The parsing result is then converted to obtain the target static data. The specific process is as follows:

[0034] 1. Obtain the data dictionary definition file from EnergyPlus and parse it to obtain the class library structure. The data dictionary definition file (i.e., the idd file) describes the EnergyPlus class structure definition, i.e., the data dictionary parsing specification. The class library structure includes object types and their corresponding attribute types, which serve as the basis for subsequent simulation file parsing and data conversion. For example, taking "room" as the object type, its corresponding attribute types could include area, height, and orientation.

[0035] 2. Based on the EnergyPlus class library structure, the EnergyPlus input model parser parses the target simulation configuration file, identifies the types and attributes defined in each section of the IDF file, constructs the corresponding object instances (including simulation objects and their attributes), and calculates the dependency reference associations between object instances (i.e., the reference relationships between simulation objects).

[0036] 3. Using a hierarchical perspective of "Building -> Floor -> Space" and "Mechatronics System -> Mechatronics Equipment," the simulated objects and their attributes in EnergyPlus are converted into target objects and their attributes in the physical world of the building. It should be noted that EnergyPlus does not have the concepts of floors and mechatronics systems, and one piece of mechatronics equipment in the physical world of the building may correspond to one or more pieces of mechatronics equipment in EnergyPlus, and one mechatronics system in the physical world of the building may correspond to one or more loops in EnergyPlus.

[0037] 4. Determine the relationships between target objects based on the reference relationships between simulated objects. It should be noted that the simulation configuration file in EnergyPlus contains rich attribute definitions and mutual references between instance objects. Therefore, by calculating some attributes and referring to the reference logic between instance objects, various types of "object relationships" required in the physical world can be constructed. Through the above data parsing and transformation process, the target static data can be obtained.

[0038] Figure 3 This is a schematic diagram of the structure of target static data provided in Embodiment 1 of the present invention. The EnergyPlus model represents the target simulation configuration file, and the static object data represents the target object data, specifically including the space enclosure structure and equipment (simplified representation here; for detailed structure, please refer to...). Figure 2 ); The relationship data between objects refers to the relationship data between the target object data. For example... Figure 3 As shown, by parsing and transforming the EnergyPlus model, target static data, including static object data and data on relationships between objects, can be obtained.

[0039] S120, based on the parsing results of the target simulation result file corresponding to the target simulation configuration file, determine the target timing operation data of the target object.

[0040] The simulation result file (i.e., the .eso file) can be used as an output item of the EnergyPlus simulation run, describing the simulation data items and their corresponding simulation runtime timing data. The simulation runtime timing data can be understood as the runtime timing data directly output by EnergyPlus. The target simulation result file can refer to the .eso file corresponding to the target simulation configuration file. The target timing runtime data is represented by time slices of the target object's runtime state.

[0041] In this embodiment, it is also necessary to obtain the target simulation result file corresponding to the target simulation configuration file in EnergyPlus, parse the target simulation result file, and then perform data transformation on the parsed result to obtain the target time-series runtime data of the target object. The specific process is as follows: 1. Obtain the data dictionary definition file in EnergyPlus and parse the data dictionary definition file to obtain the class library structure. 2. Based on the class library structure of EnergyPlus, parse the target simulation result file through the EnergyPlus output data parser to obtain the simulation data items and their corresponding simulation runtime sequence data. Among them, the simulation runtime sequence data is recorded one by one based on a single indicator item. 3. Aggregate the simulation runtime sequence data at the object dimension to construct the time-series data in the real-time state slice format of the object (i.e., the target runtime sequence data corresponding to the target object). Figure 4 This is a schematic diagram illustrating the process of determining target runtime sequence data according to Embodiment 1 of the present invention. Figure 4 As shown, by parsing and transforming the EnergyPlus simulation results, the target runtime sequence data of the target object can be obtained.

[0042] S130, determine the target data items based on the target data inspection task, and determine the target inspection method for the target data items.

[0043] The target data inspection task describes the main object to be inspected and the content to be inspected. Target data items include the target object type and the target attribute type of the target object type. It should be noted that the target data item mainly focuses on which data needs to be inspected, while the target inspection method mainly focuses on the execution logic and output results of the data inspection. For example, taking room environment as the target data item, its typical configuration is as follows: Target data item: Room environment, Target object type: Room, Target attribute type: [Temperature, Humidity, Set temperature].

[0044] In this embodiment, optionally, determining the target data item based on the target data inspection task includes: determining the data item to be tested based on the target data inspection task; determining the reference data item associated with the data item to be tested based on the correlation data between target object data; and determining the target data item based on the data item to be tested and the reference data item.

[0045] It should be noted that, since a data item has a correlation with other data items, a comparative check of multiple related data items can be used to improve the accuracy and comprehensiveness of data checking. For example, assuming the data item to be tested is the room environment, based on the correlation data between target object data, it is determined that the fan coil unit is associated with the room through a service relationship. In this case, the fan coil unit's operating status can be used as a reference data item for the room environment, thus making the room environment and its associated fan coil unit operating status the target data items. For example, a typical configuration for a target data item using the comparative check method is as follows: Data item to be tested: Room environment; Object type to be tested: Room; Attribute type to be tested: [Temperature, Humidity, Set Temperature]; Reference data item: Fan coil unit operating status; Reference object type: Fan coil unit; Reference attribute type: [Operating Power, Supply Air Temperature]; Associated data item: Room environment; Relationship type: Service relationship.

[0046] Since there are multiple rooms in the building, and each room has different fan coil units installed, a relationship setting can be added to the reference data item to clarify that the fan coil units need to be associated with the rooms through service relationships. This way, multiple sets of data can be obtained for inspection. Each set of data includes the environmental data of a room and the operating status data of the fan coil units in that room.

[0047] The target inspection method can include two approaches: qualitative inspection (such as manual analysis inspection) and quantitative inspection (such as formula / function inspection). The specific approach can be flexibly set according to actual needs, such as choosing one approach or combining both. Optionally, determining the target inspection method for the target data item includes: determining a target inspection function from a preset inspection function library based on the target data item; wherein the preset function inspection library includes inspection functions for multiple architectural scenarios, and the inspection functions include calculation logic and judgment rules; and determining the target inspection method for the target data item based on the target inspection function.

[0048] The preset function check library refers to a pre-defined database containing check functions for various building scenarios. These check functions serve as the basis for data checks and specifically include calculation logic and judgment rules. For example, the preset function check library includes check functions corresponding to building thermal processes, terminal equipment operation processes, and chilled water system operation processes. Specifically, check functions corresponding to building thermal processes may include reasonable room temperature ranges, building interior and exterior temperature responses, the impact of air conditioning terminals on room temperature, and the relationship between air conditioning set temperatures and total building load. Check functions corresponding to terminal equipment operation processes may include reasonable ranges for terminal supply / return air temperatures, the relationship between terminal supply / return air temperatures, reasonable ranges for terminal supply / return water temperatures, and the relationship between terminal supply / return water temperatures. Check functions corresponding to chilled water system operation processes may include the relationship between rated power and actual power of equipment, the relationship between rated water flow rate and actual water flow rate of equipment, reasonable ranges for chiller efficiency, reasonable ranges for water pump efficiency, reasonable ranges for cooling tower efficiency, reasonable ranges for chilled water supply / return water temperatures, the relationship between chilled water supply / return water temperatures, reasonable ranges for cooling water supply / return water temperatures, the relationship between cooling water supply / return water temperatures, and the consistency of operation of multiple devices.

[0049] In this embodiment, to address the complexity of building simulation, a pre-set function check library containing a series of check functions is required to cover various scenarios ranging from heat exchange processes to equipment operation. When determining the target check method for a target data item, one or more check functions matching the target data item can be found from the pre-set check function library as the target check function, and the target check function is determined as the target check method for the target data item. Each target check function can be configured with one or more parameters derived from the target data item configuration.

[0050] For example, taking the typical configuration of a target data item using the comparison check method described above, the target check function corresponding to this target data item can be set as follows: Check function 1: Reasonable range of room temperature, parameters: [room environment.temperature, room environment.set temperature]; Check function 2: Impact of air conditioning terminal on room temperature, parameters: [fan coil unit.operating power, room environment.temperature, room environment.set temperature]. These two check functions can be used to check the reasonableness of the room temperature and the relationship between the room temperature change trend and the fan coil unit.

[0051] This solution, through its configuration, enables rapid and quantitative checks on simulated data using function-based checks. It is applicable to massive data checks in various scenarios, effectively improving data checking efficiency.

[0052] S140, determine the object to be tested and the attribute to be tested of the object to be tested from the target static data based on the target data item, and determine the time series running data associated with the attribute to be tested of the object to be tested from the target time series running data.

[0053] In this embodiment, after determining the target data item, the object to be tested and its attributes to be tested can be determined from the target static data based on the target data item, and then the time-series runtime data associated with the attributes to be tested of the object to be tested can be determined from the target time-series runtime data. Optionally, determining the object to be tested and its attributes to be tested from the target static data based on the target data item includes: determining the object to be tested from the target objects based on the target object type; and determining the attributes to be tested from the attributes of the object to be tested based on the target attribute type of the target object type.

[0054] For example, assuming the target object type is a room, and the target attribute types include temperature, humidity, and set temperature, then all rooms in the target object (e.g., room 1, room 2... room n) or some rooms can be instantiated and identified as the objects to be tested. The temperature, humidity, and set temperature attributes of the objects to be tested can then be identified as the attributes to be tested. After identifying the objects to be tested and their attributes, all or part of the data associated with the attributes to be tested can be filtered from the target time-series runtime data as the time-series runtime data to be tested.

[0055] S150, according to the target inspection method, perform data inspection on the timing data to be tested and obtain the data inspection results.

[0056] In this embodiment, a function-based check method can be used to perform quantitative data checks on the timing data under test. Optionally, the data check results are obtained by checking the timing data under test according to the target check method, including: performing data verification on each timing data under test according to the target check method to obtain a verification result for the timing data under test; wherein, the verification result is either a pass or a fail; determining the verification pass rate of the timing data under test based on the verification result, and determining the data check result of the timing data under test based on the verification pass rate.

[0057] Specifically, firstly, the corresponding target check function is executed for each time series data to be tested. The target check function determines whether each time series data to be tested passes data validation, obtaining the validation result for each time series data to be tested. Then, the ratio of the amount of time series data to be tested that passed validation to the total amount of time series data to be tested is calculated, and this ratio is used as the validation pass rate of the time series data to be tested. For example, if a building has 100 rooms, and 10 "room temperature reasonable range" checks are performed for each room, for a total of 1000 function checks, assuming 5 checks fail, that is, 995 checks pass, then the validation pass rate is 99.5%. Furthermore, a pass rate threshold (e.g., 90%) can be preset according to actual needs. When the validation pass rate of the time series data to be tested is greater than the pass rate threshold, the data quality of the time series data to be tested can be considered high. During the execution of the target check function, error messages of failed validations can be recorded in the log, and the validation pass rate is statistically analyzed and recorded in the log. By reviewing the log, the data check results can be quickly and intuitively obtained. It should be noted that in this embodiment, for multiple target data items, parallel execution can be used to check the time-series running data to be tested corresponding to the target data items, thereby effectively improving the data checking efficiency.

[0058] The technical solution of this invention determines target static data based on the parsing results of the target simulation configuration file in EnergyPlus. The target static data includes target object data and the relationship data between target object data. The target object data includes the target object, the object type to which the target object belongs, and the attributes of the target object. Based on the parsing results of the target simulation result file corresponding to the target simulation configuration file, the target time-series runtime data of the target object is determined. The target time-series runtime data is represented by time slices of the target object's runtime state. Target data items are determined according to the target data inspection task, and the target inspection method for the target data items is determined. The target data items include the target object type and the target attribute type of the target object type. The object to be tested and its test attributes are determined from the target static data based on the target data items, and the test time-series runtime data associated with the test attributes of the object to be tested is determined from the target time-series runtime data. Data inspection is performed on the test time-series runtime data according to the target inspection method to obtain the data inspection results. This technical solution enables rapid and effective data inspection of EnergyPlus simulation results through the inspection method determined based on the data inspection task, greatly improving the efficiency and applicability of data inspection.

[0059] In this embodiment, optionally, after determining the data inspection result of the timing data to be tested based on the verification pass rate, the method further includes: analyzing the changing trend of the timing data to be tested to obtain the analysis result; and supplementing the data inspection result of the timing data to be tested based on the analysis result.

[0060] It should be noted that quantitative inspection methods may have certain limitations in practical use. For example, the inspection function may only be set for certain inspection items in certain scenarios, and may not cover all inspection items. Therefore, to ensure the comprehensiveness and completeness of data inspection, qualitative inspection methods can be combined with quantitative inspection methods for a comprehensive data inspection. Specifically, after determining the time series data to be tested, a visualization chart (such as a line chart) can be generated based on the time series data. Then, through manual or automated inspection methods, the changing trend of the time series data under test is analyzed based on the visualization chart to obtain the analysis results. These analysis results are then used to supplement the data inspection results of the time series data under test, resulting in a more comprehensive data inspection outcome.

[0061] This solution, through its setup, can simultaneously perform quantitative and qualitative data checks using either a fully automated approach or a combination of automated and manual checks. This improves the comprehensiveness and completeness of data checks, thereby enhancing their accuracy.

[0062] Example 2

[0063] Figure 5 This is a flowchart of a data inspection method based on EnergyPlus simulation results provided in Embodiment 2 of the present invention. This embodiment is an optimization based on the above embodiment.

[0064] like Figure 5 As shown, the method in this embodiment specifically includes the following steps:

[0065] S210, determine the target static data based on the parsing results of the target simulation configuration file in EnergyPlus.

[0066] Among them, the target static data includes target object data and the relationship data between target object data. The target object data includes the target object, the object type to which the target object belongs, and the attributes of the target object.

[0067] S220, based on the parsing results of the target simulation result file corresponding to the target simulation configuration file, determines the target timing operation data of the target object.

[0068] The target time-series runtime data is represented by time slices of the target object's runtime state.

[0069] S230, determine the target data items based on the target data inspection task, and determine the target inspection method for the target data items.

[0070] The target data item includes the target object type and the target attribute type of the target object type. Furthermore, the specific implementation methods of S210-S230 can be found in the relevant description in Embodiment 1, and will not be repeated here.

[0071] S240, the target object corresponding to the target object type is identified as a candidate object.

[0072] It should be noted that since simulated data is often measured in years and typically includes a large number of rooms and devices, performing a full data check would consume excessive time and computing resources. Furthermore, in scenarios requiring manual qualitative analysis, excessive data can hinder inspectors' effective review and analysis. Therefore, this embodiment adopts a data sampling inspection strategy. Through data sampling configuration, several time points and a subset of objects can be selected within a specified time period to perform inspection operations, obtaining representative data inspection results. Besides reducing the amount of data, data sampling also provides a more focused perspective for the data inspection task, improving the relevance and effectiveness of inspection rules (including target data items and target inspection functions). For example, assuming the target object type is a room, all rooms can be instantiated (e.g., room 1, room 2, ..., room n) to identify candidate objects.

[0073] S250, a preset number of candidate objects are randomly selected as the test objects.

[0074] The preset number can refer to the number of candidate objects pre-set according to actual needs, such as 15. In this embodiment, after determining the candidate objects, a preset number of objects can be selected as the test objects by random sampling.

[0075] S260, Determine the attribute to be tested from the attributes of the object to be tested based on the target attribute type of the target object type.

[0076] S270, the target time series running data associated with the test attribute of the test object is determined as candidate time series running data.

[0077] In this embodiment, after determining the object to be tested and the attribute to be tested of the object to be tested, data associated with the attribute to be tested of the object to be tested can be selected from the target time series running data as candidate time series running data.

[0078] S280 determines the timing data to be tested from the candidate timing data based on the preset sampling period and preset sampling quantity.

[0079] Here, the preset sampling period and preset sampling number can refer to the sampling period and sampling number preset according to actual needs, respectively. In this embodiment, after determining the candidate time series running data, data can be extracted from the candidate time series running data at equal intervals according to the preset sampling period and preset sampling number, thereby obtaining the time series running data to be tested.

[0080] For example, a typical configuration for a data inspection task is as follows: directory of simulated data to be inspected: / sim_data_001, inspection rules: room environment inspection through inspection function, preset sampling period: cooling season, preset number of samples: 5, number of days per sampling point: 3, daily time period: air conditioning running time, number of objects sampled in the room environment (i.e., preset number): 15.

[0081] Specifically, in the example above, the location of the time-series data to be tested (i.e., the directory of the simulated data to be checked) and the inspection rules to be used (including the target data items and the target inspection function) are first specified. Then, the sampling configuration for the time portion is set, with the preset sampling period set to the air conditioning cooling season; the preset sampling quantity and the number of days per sampling point indicate that 5 samples will be taken throughout the entire cooling season, each containing 3 days, with the same date interval between multiple samples, and only data from the air conditioning on-time will be selected for each day. Finally, the sampling settings for the data objects are configured, allowing for the setting of filter conditions for the target data items in the inspection rules, such as selecting a maximum of 15 rooms for inspection.

[0082] S290, according to the target inspection method, perform data inspection on the timing data to be tested and obtain the data inspection results.

[0083] The technical solution of this invention adopts a data sampling inspection strategy (including time sampling and object sampling). Through data sampling configuration, several time points and a portion of objects can be extracted within a specified time period to perform inspection operations, obtaining representative data inspection results. This setting effectively reduces the amount of data, improves data inspection efficiency, and provides a more focused perspective for data inspection tasks, enhancing the relevance and effectiveness of inspection rules (including target data items and target inspection functions).

[0084] Example 3

[0085] Figure 6 This is a schematic diagram of a data inspection device based on EnergyPlus simulation results provided in Embodiment 3 of the present invention. This device can execute the data inspection method based on EnergyPlus simulation results provided in any embodiment of the present invention, and possesses the corresponding functional modules and beneficial effects for executing the method. For example... Figure 6 As shown, the device includes:

[0086] The target static data determination module 310 is used to determine target static data based on the parsing results of the target simulation configuration file in EnergyPlus; wherein, the target static data includes target object data and the association data between the target object data, and the target object data includes the target object, the object type to which the target object belongs, and the attributes of the target object;

[0087] The target time-series running data determination module 320 is used to determine the target time-series running data of the target object based on the parsing results of the target simulation result file corresponding to the target simulation configuration file; wherein, the target time-series running data is characterized by time slices of the running state of the target object;

[0088] The target inspection parameter determination module 330 is used to determine target data items based on the target data inspection task, and to determine the target inspection method for the target data items; wherein, the target data item includes a target object type and a target attribute type of the target object type;

[0089] The test timing data determination module 340 is used to determine the test object and the test attribute of the test object from the target static data based on the target data item, and to determine the test timing data associated with the test attribute of the test object from the target timing data;

[0090] The data inspection result determination module 350 is used to perform data inspection on the time series running data to be tested according to the target inspection method to obtain the data inspection result.

[0091] Optionally, the target inspection parameter determination module 330 is used for:

[0092] Based on the target data inspection task, determine the data items to be tested;

[0093] Based on the correlation data between the target object data, determine the reference data items associated with the data items to be tested;

[0094] The target data item is determined based on the data item to be tested and the reference data item.

[0095] Optionally, the test timing data determination module 340 includes:

[0096] The test object determination unit is used to determine the test object from the target objects according to the target object type;

[0097] The attribute determination unit is used to determine the attribute to be tested from the attributes of the object to be tested based on the target attribute type of the target object type.

[0098] Optionally, the object determination unit is used for:

[0099] The target object corresponding to the target object type is identified as a candidate object;

[0100] A predetermined number of candidate objects are randomly selected as the test objects.

[0101] Optionally, the test timing data determination module 340 is used for:

[0102] The target time-series running data associated with the test attribute of the test object are determined as candidate time-series running data;

[0103] The timing data to be tested is determined from the candidate timing data based on a preset sampling period and a preset sampling quantity.

[0104] Optionally, the target inspection parameter determination module 330 is further configured to:

[0105] The target inspection function is determined from the preset inspection function library based on the target data item; wherein, the preset inspection function library includes inspection functions for multiple architectural scenarios, and the inspection function includes calculation logic and judgment rules;

[0106] The target inspection method for the target data item is determined based on the target inspection function.

[0107] Optionally, the data inspection result determination module 350 is used for:

[0108] According to the target inspection method, each of the time series running data to be tested is subjected to data verification to obtain the verification result of the time series running data to be tested; wherein, the verification result is either pass verification or fail verification;

[0109] Based on the verification results, the verification pass rate of the timing data to be tested is determined, and the data inspection result of the timing data to be tested is determined based on the verification pass rate.

[0110] Optionally, the device further includes: a data inspection result supplementation module, used for:

[0111] After determining the data inspection results of the time series running data to be tested based on the verification pass rate, the changing trend of the time series running data to be tested is analyzed to obtain the analysis results;

[0112] The data inspection results of the time series running data to be tested are supplemented based on the analysis results.

[0113] The data inspection device based on EnergyPlus simulation results provided in this embodiment of the invention can execute the data inspection method based on EnergyPlus simulation results provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0114] Example 4

[0115] Figure 7 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, 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 processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0116] like Figure 7 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0117] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0118] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as data inspection methods based on EnergyPlus simulation results.

[0119] In some embodiments, the data inspection method based on EnergyPlus simulation results may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the data inspection method based on EnergyPlus simulation results described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the data inspection method based on EnergyPlus simulation results by any other suitable means (e.g., by means of firmware).

[0120] Various embodiments 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-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0121] Computer programs used to implement 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, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0122] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0123] To provide interaction with a user, the systems and techniques described herein can 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 pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0124] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0125] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0126] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0127] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A data inspection method based on EnergyPlus simulation results, characterized in that, The method includes: The target static data is determined based on the parsing results of the target simulation configuration file in EnergyPlus. The target static data includes target object data and the relationship data between the target object data. The target object data includes the target object, the object type to which the target object belongs, and the attributes of the target object. The relationships between the target objects include membership, connection, and service relationships. The target simulation configuration file is parsed to identify the type and attributes defined in each section, constructing corresponding object instances and calculating the dependency references between object instances. The simulation objects and their attributes in EnergyPlus are converted into target objects and their attributes in the building physics world. The relationships between target objects are determined based on the reference relationships between simulation objects, thus obtaining the target static data. Based on the parsing results of the target simulation result file corresponding to the target simulation configuration file, the target time-series running data of the target object is determined; wherein, the target time-series running data is represented by the running state time slice of the target object; wherein, the target simulation result file is parsed to obtain simulation data items and their corresponding simulation running time sequence data, and the simulation running time sequence data is aggregated at the object dimension to construct time-series data in the real-time state slice format of the object, which serves as the target time-series running data of the target object; Target data items are determined based on the target data inspection task, and the target inspection method for the target data items is determined; wherein, the target data item includes a target object type and a target attribute type of the target object type; wherein, determining the target data item based on the target data inspection task includes: determining the data item to be tested based on the target data inspection task; determining a reference data item associated with the data item to be tested based on the correlation data between the target object data; and determining the target data item based on the data item to be tested and the reference data item. Based on the target data item, determine the object to be tested and the attribute to be tested of the object to be tested from the target static data, and determine the time series running data associated with the attribute to be tested of the object to be tested from the target time series running data; The data inspection results are obtained by performing data inspection on the time series running data to be tested according to the target inspection method, including: performing data verification on each of the time series running data to be tested according to the target inspection method to obtain the verification result of the time series running data to be tested; wherein, the verification result is either passing or failing the verification. Based on the verification results, the verification pass rate of the timing data to be tested is determined, and the data inspection result of the timing data to be tested is determined based on the verification pass rate.

2. The method according to claim 1, characterized in that, Determining the object to be tested and its attributes to be tested from the target static data based on the target data item includes: The object to be tested is determined from the target objects based on the target object type; Based on the target attribute type of the target object type, determine the attribute to be tested from the attributes of the object to be tested.

3. The method according to claim 2, characterized in that, Determining the object to be tested from the target objects based on the target object type includes: The target object corresponding to the target object type is identified as a candidate object; A predetermined number of candidate objects are randomly selected as the test objects.

4. The method according to claim 3, characterized in that, Determining the test time series running data associated with the test attribute of the test object from the target time series running data includes: The target time-series running data associated with the test attribute of the test object are determined as candidate time-series running data; The timing data to be tested is determined from the candidate timing data based on a preset sampling period and a preset sampling quantity.

5. The method according to any one of claims 1-4, characterized in that, Determining the target inspection method for the target data item includes: The target inspection function is determined from the preset inspection function library based on the target data item; wherein, the preset inspection function library includes inspection functions for multiple architectural scenarios, and the inspection function includes calculation logic and judgment rules; The target inspection method for the target data item is determined based on the target inspection function.

6. The method according to claim 1, characterized in that, After determining the data inspection result of the timing data to be tested based on the verification pass rate, the method further includes: The changing trend of the time series data to be tested is analyzed to obtain the analysis results; The data inspection results of the time series running data to be tested are supplemented based on the analysis results.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the data inspection method based on EnergyPlus simulation results as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the data inspection method based on EnergyPlus simulation results as described in any one of claims 1-6.

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