Air conditioning system database management method, system, medium and equipment

Abstract

The invention provides an air conditioning system database management method and system, a medium and equipment. A to-be-managed data file is obtained, and test data is extracted; identifying an abnormal value in the test data based on the distribution state of the test data, and predicting an estimated value corresponding to the abnormal value; determining a correction value of the abnormal value; storing the corrected test data into a corresponding structured list; responding to the data processing instruction, calling the test data in the structured list and performing corresponding processing; the method comprises the following steps: extracting test data in a test report, identifying an abnormal value in the test data, generating a corresponding estimated value, correcting the abnormal value, ensuring the accuracy of the test data, and storing the test data in a structured list to realize standardized storage of the test data. And when the user inputs the data processing instruction, the corresponding test data are quickly called and correspondingly processed, so that automatic, high-efficiency and high-accuracy air conditioner test data management is realized.

Description

Technical Field

[0001] This application relates to the field of database management technology, specifically to a method, system, medium, and device for managing an air conditioning system database. Background Technology

[0002] Testing of air conditioning systems and components is a crucial part of automotive product development and quality verification. With advancements in testing technology and increased testing precision, the testing process generates massive amounts of data, including but not limited to parameters such as temperature, pressure, flow rate, and power consumption. This data is not only diverse and inconsistent in format, but also typically originates from scattered sources and is stored in different test reports, data files, or temporary databases. The lack of effective system integration and long-term traceability mechanisms poses a significant challenge to subsequent data processing and analysis.

[0003] In actual R&D and quality management, especially when dealing with multiple suppliers for the same OEM, it is often necessary to compare current test parameters with those from previous tests under the same or similar conditions to verify consistency, analyze differences, or optimize the design. Traditional methods rely heavily on manual data searching, extraction, and comparison, which not only consumes significant human resources but also results in a high error rate due to data disorganization, format differences, and human error. In new product development, this historical data comparison and analysis is even more frequent, and the inefficiency and inaccuracy of data processing have become bottlenecks restricting R&D efficiency and quality improvement.

[0004] Therefore, there is an urgent need for a systematic and automated solution to achieve unified processing, long-term storage, and rapid traceability of multi-source heterogeneous data, thereby reducing manpower input, improving the accuracy and efficiency of data processing and comparison, and meeting the needs of the automotive air conditioning testing field for refined and intelligent data management. Summary of the Invention

[0005] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a method, system, medium, and device for managing an air conditioning system database.

[0006] According to one aspect of this application, a method for managing an air conditioning system database is provided, comprising: acquiring a data file to be managed; wherein the data file to be managed includes a test report; extracting test data from the data file to be managed; identifying outliers in the test data based on the distribution state of the test data, and predicting the estimated value corresponding to the outlier; determining a correction value for the outlier based on the estimated value; storing the corrected test data in a corresponding structured list; and responding to a data processing instruction to retrieve the test data in the structured list and perform corresponding processing.

[0007] In one embodiment, identifying outliers in the experimental data based on the distribution of the experimental data and predicting the estimated value corresponding to the outliers includes: arranging the experimental data in order of approximate size to obtain a distribution sequence of the experimental data; calculating the difference between the third quartile and the first quartile in the distribution sequence of the experimental data; calculating an outlier threshold for the experimental data based on the difference; determining the normal range of the experimental data based on the outlier threshold, the third quartile, and the first quartile; determining that if there is data in the experimental data that exceeds the normal range, the data is identified as an outlier; and fitting and estimating the estimated value of the outlier based on the normal values ​​in the experimental data.

[0008] In one embodiment, determining the correction value of the outlier based on the estimated value includes: receiving a user's estimated value confirmation instruction and using the estimated value as the correction value of the outlier, or using a value input by the user as the correction value of the outlier.

[0009] In one embodiment, responding to a data processing instruction, retrieving experimental data from the structured list and performing corresponding processing includes: if the data processing instruction is for data display, retrieving experimental data from the structured list and displaying the experimental data based on a selected display method.

[0010] In one embodiment, the response data processing instruction to retrieve experimental data from the structured list and perform corresponding processing includes: if the data processing instruction is a parallel comparison between multiple experimental data, then retrieving the multiple experimental data; calculating multiple evaluation indicators for each of the experimental data; and determining the parallel comparison result of the multiple experimental data based on the multiple evaluation indicators.

[0011] In one embodiment, the calculation of multiple evaluation indicators for each of the test data includes: calculating the mean variance of the temperature parameter and / or pressure parameter for each of the test data to obtain a first evaluation indicator; calculating the comprehensive cooling / heating per unit volume for each of the test data to obtain a second evaluation indicator; and calculating the comprehensive energy consumption per unit volume for each of the test data to obtain a third evaluation indicator.

[0012] In one embodiment, determining the parallel comparison results of the multiple test data based on the multiple evaluation indicators includes: weighting the multiple evaluation indicators to obtain the comprehensive energy efficiency level of each test data; and determining the parallel comparison results of the multiple test data based on the comprehensive energy efficiency level.

[0013] According to another aspect of this application, a management system for an air conditioning system database is provided, comprising: a data file acquisition module for acquiring a data file to be managed; wherein the data file to be managed includes a test report; a test data extraction module for extracting test data from the data file to be managed; an anomaly data processing module for identifying outliers in the test data based on the distribution state of the test data and predicting the estimated value corresponding to the outlier; a correction data determination module for determining the correction value of the outlier based on the estimated value; a test data storage module for storing the corrected test data into a corresponding structured list; and a processing instruction response module for responding to a data processing instruction, retrieving the test data from the structured list and performing corresponding processing.

[0014] According to another aspect of this application, a computer-readable storage medium is provided, the storage medium storing a computer program for performing any of the methods described above.

[0015] According to another aspect of this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; the processor being configured to perform any of the methods described above.

[0016] This application provides a method, system, medium, and device for managing an air conditioning system database. The method involves acquiring a data file to be managed, including a test report; extracting test data from the data file; identifying outliers based on the distribution of the test data and predicting the corresponding estimated values; determining correction values ​​for the outliers based on the estimated values; storing the corrected test data in a corresponding structured list; and responding to data processing commands by retrieving the test data from the structured list and performing corresponding processing. Specifically, by extracting test data from the test report, identifying outliers, generating corresponding estimated values ​​to correct the outliers, ensuring the accuracy of the test data, and storing the test data in a structured list to achieve standardized storage of the test data, the method enables rapid retrieval and processing of the corresponding test data when the user inputs data processing commands, thereby achieving automated, efficient, and highly accurate management of air conditioning test data. Attached Figure Description

[0017] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0018] Figure 1 This is a flowchart illustrating an exemplary embodiment of the air conditioning system database management method provided in this application.

[0019] Figure 2 This is a schematic diagram of the structure of the management system for the air conditioning system database provided in an exemplary embodiment of this application.

[0020] Figure 3 This is a structural diagram of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0021] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0022] Figure 1 This is a flowchart illustrating an exemplary embodiment of an air conditioning system database management method provided in this application. Figure 1 As shown, the management method for the air conditioning system database includes the following steps: Step 110: Obtain the data file to be managed.

[0023] The data files to be managed include test reports. This application obtains the corresponding test reports and other data files to be managed by receiving test reports input by the user or retrieving them from a user-specified address. Specific file formats may include, but are not limited to, Excel, PDF, CSV, TXT, and images.

[0024] Step 120: Extract the test data from the data file to be managed.

[0025] This application uses the corresponding text extraction method according to the file format to extract the experimental data from the data file to be managed, thereby obtaining the important data that needs to be saved and managed.

[0026] Step 130: Based on the distribution of the experimental data, identify outliers in the experimental data and predict the estimated values ​​corresponding to the outliers.

[0027] This application identifies outliers in test data based on the distribution of test data. That is, it judges outliers based on single or multiple consecutive test data of a single air conditioner, or it can judge outliers based on test data of all air conditioners of the same model or category. When an outlier is determined, the outlier is predicted to predict the correct or reasonable data for the outlier.

[0028] Step 140: Based on the estimated value, determine the correction value for the outlier.

[0029] After predicting the estimated value of outliers, the correction value of the outliers is determined based on the estimated value to obtain accurate experimental data, thereby improving the accuracy of subsequent data management.

[0030] Step 150: Store the corrected test data in the corresponding structured list.

[0031] This application stores the revised test data in a structured list to achieve structured storage of test data. For example, the header of the structured list is set according to key information such as sample parameters from different manufacturers, refrigerant type, operating conditions, temperature performance, and pressure performance, thereby improving the standardization and normalization of test data storage and facilitating subsequent data management.

[0032] Step 160: Respond to the data processing command, retrieve the experimental data from the structured list and process it accordingly.

[0033] After storing the test data, this application can also respond to the user's data processing instructions, retrieve the corresponding test data and perform corresponding processing to achieve automated data processing and display.

[0034] This application provides a method for managing an air conditioning system database, which involves: acquiring a data file to be managed, including a test report; extracting test data from the data file; identifying outliers in the test data based on its distribution and predicting the corresponding estimated values; determining correction values ​​for the outliers based on the predicted values; storing the corrected test data in a corresponding structured list; responding to data processing commands, retrieving the test data from the structured list and performing corresponding processing; that is, by extracting test data from the test report, identifying outliers, generating corresponding estimated values ​​to correct the outliers, ensuring the accuracy of the test data, and storing the test data in a structured list to achieve standardized storage of the test data; and quickly retrieving and processing the corresponding test data when the user inputs data processing commands to achieve automated, efficient, and highly accurate management of air conditioning test data.

[0035] In one embodiment, step 130 can be implemented as follows: arranging the experimental data in order of approximate size to obtain a distribution sequence of the experimental data; calculating the difference between the third quartile and the first quartile in the distribution sequence of the experimental data; calculating the outlier threshold of the experimental data based on the difference; determining the normal range of the experimental data based on the outlier threshold, the third quartile, and the first quartile; determining that the data is an outlier if there is data in the experimental data that exceeds the normal range; and fitting and estimating the estimated value of the outlier based on the normal values ​​in the experimental data.

[0036] Specifically, this application arranges the experimental data in order of approximate size to obtain a distribution sequence of the experimental data. It calculates the difference between the third quartile (75th percentile) and the first quartile (25th percentile) in the distribution sequence (i.e., the difference between the third quartile and the first quartile). After calculating this difference, it calculates an outlier threshold for the experimental data (e.g., set to 1.5 times or 3 times the difference). After determining the outlier threshold, it determines the normal range of the experimental data based on the outlier threshold, the third quartile, and the first quartile (e.g., the lower limit of the normal range is the first quartile minus the outlier threshold, and the upper limit is the third quartile plus the outlier threshold). If there is data in the experimental data that exceeds the normal range, it is determined to be an outlier. After determining the outlier, it fits and estimates the estimated value of the outlier based on the normal values ​​in the experimental data, and displays the corresponding outlier and estimated value to the user, for example, marking the outlier in red and the estimated value in yellow.

[0037] In one embodiment, step 140 can be implemented by receiving a user's estimated value confirmation instruction and using the estimated value as a correction value for an outlier, or by using the value input by the user as a correction value for an outlier.

[0038] After marking and displaying outliers and estimated values ​​to the user, this application receives correction instructions from the user. Specifically, if the user determines that the estimated value is accurate, they input a confirmation instruction, at which point the estimated value is used as the correction value for the outlier. If the user determines that the estimated value is inaccurate, they manually input or adjust the outlier or estimated value to obtain the user-input correction value. Preferably, the user-input correction value can be further verified. If the user-input correction value is within a reasonable range (e.g., the normal range defined above), the verification passes. If the user-input correction value exceeds a reasonable range (e.g., due to user error), the verification fails and the user is prompted to confirm again.

[0039] In one embodiment, step 160 can be implemented as follows: if the data processing instruction is data display, then retrieve the test data from the structured list and display the test data based on the selected display method.

[0040] After receiving the user's data processing instruction, determine the type of the instruction. If the instruction is for data display, retrieve the corresponding experimental data from the structured list and display the experimental data based on the selected display method (e.g., bar chart, scatter plot, histogram, etc.).

[0041] In one embodiment, the specific implementation of step 160 above may be as follows: if the data processing instruction is a parallel comparison between multiple experimental data, then multiple experimental data are retrieved; multiple evaluation indicators for each experimental data are calculated respectively; and based on the multiple evaluation indicators, the parallel comparison results of the multiple experimental data are determined.

[0042] If the user's data processing instruction is a parallel comparison between multiple experimental data sets, then the corresponding multiple experimental data sets are retrieved, and multiple evaluation indicators are calculated for each experimental data set. Based on these multiple evaluation indicators, the parallel comparison results of the multiple experimental data sets are determined. Preferably, after retrieving the corresponding multiple experimental data sets, this application first performs time or condition alignment operations on the experimental data sets. If a parameter is missing in a certain experimental data set, the parameter name is marked and the comparison of that parameter is skipped to improve the accuracy of the comparison.

[0043] In one embodiment, step 160 can be implemented as follows: calculate the mean variance of the temperature and / or pressure parameters for each test data to obtain a first evaluation index; calculate the comprehensive cooling / heating per unit volume for each test data to obtain a second evaluation index; and calculate the comprehensive energy consumption per unit volume for each test data to obtain a third evaluation index.

[0044] For temperature and pressure-related parameters, stability is evaluated. Specifically, the variance of the corresponding data is calculated; a larger value indicates greater data volatility, resulting in a lower evaluation score. Finally, the mean of the variances of the temperature-pressure and pressure-related parameters is calculated. SQ As the primary evaluation indicator, the evaluation of cooling capacity and energy consumption parameters is conducted using the comprehensive cooling / heating capacity per unit volume and the comprehensive energy consumption per unit volume. The comprehensive cooling / heating capacity per unit volume is used for evaluation. IQ The calculation formula is as follows: ; Comprehensive energy consumption per unit volume VAE The calculation formula is as follows: ; in, The comprehensive average cooling / heating capacity per unit volume of the air conditioning system within a specific temperature range. Where n is the number of different operating conditions under a specific temperature range. For cooling / heating under different operating conditions within a specific temperature range, The net volume within the area where the system or component is located. This refers to the comprehensive energy consumption per unit volume of an air conditioning system within a specific temperature zone. , For air conditioner i Operating time in each temperature zone For the first i Cooling / heating efficiency ratio under different operating conditions in each temperature zone.

[0045] In one embodiment, step 160 can be implemented by weighting multiple evaluation indicators to obtain the comprehensive energy efficiency level of each test data; and determining the parallel comparison results of multiple test data based on the comprehensive energy efficiency level.

[0046] Specifically, after calculating each evaluation index, this application assigns scores to the test data (air conditioning performance) based on each evaluation index, as shown in the table below: Table 1 Air Conditioning Performance Scoring Table

[0047] After obtaining the scores for each indicator, a weighted sum is calculated to obtain the overall energy efficiency score of the air conditioning system or component. The overall energy efficiency level is then determined based on this score. Specifically, the overall equivalent score P = 0.2SQ + 0.4IQ + 0.4VAE. The correspondence between the overall energy efficiency score and the corresponding overall energy efficiency level is shown in the table below: Table 2 Comprehensive Energy Efficiency Rating Table

[0048] Ultimately, the relative merits of the data are determined by the overall energy efficiency rating corresponding to multiple different test data. If multiple different test data correspond to the same overall energy efficiency rating, then the relative merits are determined based on the overall energy efficiency score.

[0049] Figure 2 This is a schematic diagram of the structure of a management system for an air conditioning system database provided in an exemplary embodiment of this application. For example... Figure 2 As shown, the management system 20 of the air conditioning system database includes: a data file acquisition module 21, used to acquire data files to be managed; wherein, the data files to be managed include test reports; a test data extraction module 22, used to extract test data from the data files to be managed; an anomaly data processing module 23, used to identify outliers in the test data based on the distribution of the test data, and predict the estimated value corresponding to the outliers; a correction data determination module 24, used to determine the correction value of the outliers based on the estimated value; a test data storage module 25, used to store the corrected test data into the corresponding structured list; and a processing instruction response module 26, used to respond to data processing instructions, retrieve the test data in the structured list, and perform corresponding processing.

[0050] This application provides a management system for an air conditioning system database. The system uses a data file acquisition module 21 to acquire a data file to be managed, including a test report. A test data extraction module 22 extracts test data from the data file. An anomaly data processing module 23 identifies outliers in the test data based on its distribution and predicts the corresponding estimated values. A correction data determination module 24 determines the correction values ​​for the outliers based on the predicted values. A test data storage module 25 stores the corrected test data in a corresponding structured list. A processing instruction response module 26 responds to data processing instructions, retrieves the test data from the structured list, and performs corresponding processing. Specifically, by extracting test data from the test report, identifying outliers, generating corresponding estimated values, and correcting the outliers, the system ensures the accuracy of the test data. The system stores the test data in a structured list to achieve standardized storage of the test data. When the user inputs a data processing instruction, the system quickly retrieves the corresponding test data and performs the appropriate processing, thereby achieving automated, efficient, and highly accurate management of air conditioning test data.

[0051] In one embodiment, the above-mentioned abnormal data processing module 23 can be further configured to: arrange the experimental data in order of approximate size to obtain a distribution sequence of the experimental data; calculate the difference between the third quartile and the first quartile in the distribution sequence of the experimental data; calculate the abnormal threshold of the experimental data based on the difference; determine the normal range of the experimental data based on the abnormal threshold, the third quartile, and the first quartile; if there is data in the experimental data that exceeds the normal range, determine that the data is an abnormal value; and fit and estimate the estimated value of the abnormal value based on the normal value in the experimental data.

[0052] In one embodiment, the above-mentioned corrected data determination module 24 may be further configured to: receive the user's estimated value confirmation instruction and use the estimated value as the corrected value of the outlier, or use the value input by the user as the corrected value of the outlier.

[0053] In one embodiment, the above-mentioned processing instruction response module 26 can be further configured to: if the data processing instruction is data display, retrieve the test data from the structured list and display the test data based on the selected display method.

[0054] In one embodiment, the above-mentioned processing instruction response module 26 can be further configured to: if the data processing instruction is a parallel comparison between multiple experimental data, then retrieve multiple experimental data; calculate multiple evaluation indicators for each experimental data; and determine the parallel comparison results of the multiple experimental data based on the multiple evaluation indicators.

[0055] In one embodiment, the above-mentioned processing instruction response module 26 may be further configured to: calculate the mean variance of the temperature parameter and / or pressure parameter of each test data to obtain a first evaluation index; calculate the comprehensive cooling / heating per unit volume of each test data to obtain a second evaluation index; and calculate the comprehensive energy consumption per unit volume of each test data to obtain a third evaluation index.

[0056] In one embodiment, the above-mentioned processing instruction response module 26 can be further configured to: weight multiple evaluation indicators to obtain the comprehensive energy efficiency level of each test data; and determine the parallel comparison results of multiple test data based on the comprehensive energy efficiency level.

[0057] Below, for reference Figure 3 This application describes an electronic device according to embodiments thereof. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

[0058] Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0059] like Figure 3 As shown, the electronic device 10 includes one or more processors 11 and memory 12.

[0060] The processor 11 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0061] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the methods of the various embodiments of this application described above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0062] In one example, the electronic device 10 may also include an input device 13 and an output device 14, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0063] When the electronic device is a standalone device, the input device 13 can be a communication network connector for receiving the collected input signals from the first device and the second device.

[0064] In addition, the input device 13 may also include, for example, a keyboard, a mouse, etc.

[0065] The output device 14 can output various information to the outside, including determined distance information, direction information, etc. The output device 14 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0066] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device 10 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 10 may include any other suitable components depending on the specific application.

[0067] In addition to the methods and apparatus described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0068] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0069] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0070] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0071] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0072] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0073] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0074] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0075] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for managing an air conditioning system database, characterized in that, include: Obtain the data file to be managed; wherein, the data file to be managed includes a test report; Extract the test data from the data file to be managed; Based on the distribution of the experimental data, outliers in the experimental data are identified, and the estimated values ​​corresponding to the outliers are predicted. Based on the estimated value, determine the correction value for the outlier; The revised experimental data is stored in the corresponding structured list; In response to data processing instructions, the test data in the structured list is retrieved and processed accordingly.

2. The method for managing an air conditioning system database according to claim 1, characterized in that, The step of identifying outliers in the experimental data based on the distribution of the experimental data and predicting the estimated value corresponding to the outliers includes: The experimental data are arranged in order of approximate size to obtain the distribution sequence of the experimental data; Calculate the difference between the third quartile and the first quartile in the distribution sequence of the experimental data; Based on the difference, calculate the anomaly threshold of the test data; Based on the abnormal threshold, the third quartile, and the first quartile, the normal range of the test data is determined; If any data in the test data exceeds the normal range, then the data is determined to be an outlier. Based on the normal values ​​in the experimental data, the estimated value of the outlier is obtained by fitting and predicting.

3. The method for managing an air conditioning system database according to claim 1, characterized in that, The process of determining the correction value for the outlier based on the estimated value includes: Receive the user's estimated value confirmation instruction and use the estimated value as the correction value for the outlier, or use the value input by the user as the correction value for the outlier.

4. The method for managing an air conditioning system database according to claim 1, characterized in that, The response data processing instruction, which retrieves the test data from the structured list and performs corresponding processing, includes: If the data processing instruction is data display, then the test data in the structured list is retrieved and the test data is displayed based on the selected display method.

5. The method for managing an air conditioning system database according to claim 1, characterized in that, The response data processing instruction, which retrieves the test data from the structured list and performs corresponding processing, includes: If the data processing instruction is a parallel comparison between multiple experimental data, then the multiple experimental data are retrieved; Calculate multiple evaluation indicators for each of the test data; Based on the aforementioned evaluation indicators, the parallel comparison results of the aforementioned experimental data are determined.

6. The method for managing an air conditioning system database according to claim 5, characterized in that, The multiple evaluation metrics calculated for each of the experimental data include: Calculate the mean variance of the temperature and / or pressure parameters for each of the test data to obtain the first evaluation index; Calculate the comprehensive cooling / heating per unit volume for each of the test data to obtain the second evaluation index; The comprehensive energy consumption per unit volume for each of the aforementioned test data is calculated to obtain the third evaluation index.

7. The method for managing an air conditioning system database according to claim 5, characterized in that, The determination of the parallel comparison results of the multiple experimental data based on the multiple evaluation indicators includes: By weighting the multiple evaluation indicators, the comprehensive energy efficiency level of each of the test data is obtained; Based on the comprehensive energy efficiency rating, the parallel comparison results of the multiple test data are determined.

8. A management system for an air conditioning system database, characterized in that, include: A data file acquisition module is used to acquire data files to be managed; wherein, the data files to be managed include test reports; The test data extraction module is used to extract test data from the data file to be managed; An anomaly data processing module is used to identify outliers in the test data based on the distribution of the test data, and to predict the estimated value corresponding to the outliers. The data correction determination module is used to determine the correction value of the outlier based on the estimated value; The test data storage module is used to store the corrected test data into the corresponding structured list; The instruction response module is used to respond to data processing instructions, retrieve the test data from the structured list, and perform corresponding processing.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-7.

10. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is used to execute the method described in any one of claims 1-7.

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