Energy consumption monitoring data verification method, electronic equipment and storage medium
By designing a data verification method in the energy consumption monitoring system and utilizing a verification rule base and data encapsulation technology, the problem of low data quality in energy consumption monitoring was solved, data stability and integrity were achieved, and building energy consumption analysis was supported.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-06
- Publication Date
- 2026-05-19
AI Technical Summary
Existing energy consumption monitoring systems suffer from low-quality data, are difficult to manage, cannot be used for building energy consumption analysis, and lack automated verification methods.
A method for verifying energy consumption monitoring data is designed. By determining the target verification rules from a preset verification rule base, data encapsulation and verification are performed to generate data verification results, ensuring the stability and continuity of the data.
It achieves stability and continuity of energy consumption monitoring data, ensures data integrity, and supports building energy consumption analysis.
Smart Images
Figure CN122064670A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data verification, and in particular to a method, electronic device and storage medium for verifying energy consumption monitoring data. Background Technology
[0002] As energy consumption monitoring of office buildings and large public buildings continues to advance, the number of instruments connected to the monitoring system is increasing, making management more difficult. Currently, most energy consumption monitoring systems focus on data access, neglecting the quality of the data and the quality of construction. This leads to increasing management challenges in the project's progress. Inadequate management or insufficient oversight in the early stages results in low data quality, with only partial data collected, making it unusable for building energy consumption analysis. Therefore, it is necessary to design a method for automated verification of energy consumption monitoring data. Summary of the Invention
[0003] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows: According to one aspect of this application, a method for verifying energy consumption monitoring data is provided, comprising: Based on the verification requirements corresponding to the data to be verified, several target verification rules corresponding to the data to be verified are determined from the preset verification rule library. By encapsulating several target verification rules, a corresponding data verification scheme can be obtained; The data verification scheme verifies the data to be verified in order to obtain the corresponding data verification results.
[0004] In one exemplary embodiment of this application, the verification rule base is determined according to the following steps: Retrieve the data validation fields corresponding to several verified data; Semantic recognition is performed on the data verification field of each verified data to obtain the semantic features corresponding to each data verification field; Obtain the verification requirements and verification item types corresponding to each data verification field; According to the preset rule generation method, the semantic features of several data verification fields with the same verification requirements and verification item types are integrated and processed to obtain the verification rules corresponding to the verification requirements and verification item types. Several verification rules are stored in a preset database to obtain a verification rule library.
[0005] In one exemplary embodiment of this application, the rule generation method includes: Among several data validation fields, the data validation field that has the same verification requirement and verification item type is determined as the target validation field corresponding to the verification requirement and the verification item type. Cluster the semantic features of the verification requirement and the target verification field corresponding to the verification project type to obtain several clusters of semantic features; Based on the preset rule definition strategy, a corresponding rule is defined for each cluster of semantic features to obtain the verification rule for each cluster of semantic features.
[0006] In one exemplary embodiment of this application, based on the verification requirements corresponding to the data to be verified, several target verification rules corresponding to the data to be verified are determined from a preset verification rule base, including: Obtain the verification requirements and verification item types corresponding to the data to be verified; Semantic features are extracted from the verification requirements corresponding to the data to be verified in order to obtain the corresponding features of the verification requirements. Retrieve the verification item type corresponding to each verification rule in the verification rule base; Among several verification rules in the verification rule base, the verification rules whose verification item type is the same as the verification item type corresponding to the data to be verified are identified as key verification rules. Obtain the verification requirements corresponding to each key verification rule; Semantic features are extracted for the verification requirements corresponding to each key verification rule to obtain the rule requirement features corresponding to each key verification rule; Feature matching is performed on the feature to be verified and each rule feature separately to obtain the feature matching degree corresponding to each rule feature. The key verification rules corresponding to the rule requirements features whose feature matching degree is greater than the preset matching degree threshold are determined as the target verification rules for the data to be verified.
[0007] In one exemplary embodiment of this application, a corresponding data verification scheme is obtained by encapsulating several target verification rules, including: Based on the preset data encapsulation method, several target verification rules corresponding to the data to be verified are encapsulated to obtain the data verification scheme corresponding to the data to be verified.
[0008] In one exemplary embodiment of this application, the data encapsulation method is any one of object-oriented encapsulation, functional encapsulation, modular encapsulation, and service-oriented encapsulation.
[0009] In one exemplary embodiment of this application, a data verification scheme is used to verify the data to be verified in order to obtain the data verification result corresponding to the data to be verified, including: The data verification scheme is decapsulated to obtain several target verification rules in the data verification scheme; According to each target verification rule, the data to be verified is verified separately to obtain the expected value of the data verification for each target verification rule. If the expected value corresponding to any target verification rule is greater than the preset expected value threshold, then the verification is passed and determined as the data verification result corresponding to that target verification rule; otherwise, the verification is failed and determined as the data verification result corresponding to that target verification rule.
[0010] In one exemplary embodiment of this application, the data to be verified is document data and / or database data.
[0011] According to another aspect of this application, a non-transitory computer-readable storage medium is provided, wherein at least one instruction or at least one program is stored in the storage medium, the at least one instruction or the at least one program being loaded and executed by a processor to implement the aforementioned energy consumption monitoring data verification method.
[0012] According to another aspect of this application, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.
[0013] The present invention has at least the following beneficial effects: The energy consumption monitoring data verification method of the present invention first determines several target verification rules corresponding to the data to be verified from a preset verification rule library according to the verification requirements corresponding to the data to be verified. Then, the several target verification rules are encapsulated to obtain a data verification scheme corresponding to the data to be verified. The data to be verified is verified through the data verification scheme to obtain the data verification result corresponding to the data to be verified, thereby realizing the verification of the data to be verified. By using this method to verify the energy consumption monitoring data to be verified, the stability and continuity of the energy consumption monitoring data upload can be guaranteed, ensuring the continuity and integrity of the collected energy consumption monitoring data and enabling statistical analysis. Attached Figure Description
[0014] 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.
[0015] Figure 1 A flowchart of the energy consumption monitoring data verification method provided in the embodiments of the present invention. Detailed Implementation
[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.
[0017] This application proposes a method for verifying energy consumption monitoring data, such as... Figure 1 As shown, it includes: Step S100: Based on the verification requirements corresponding to the data to be verified, determine several target verification rules corresponding to the data to be verified from the preset verification rule library. The data to be verified is document data and / or database data, which is data that requires energy consumption monitoring and verification.
[0018] Verification requirements refer to the specific aspects of the data to be verified.
[0019] The verification rule base is determined according to steps S001-S005: Step S001: Obtain the data verification fields corresponding to several verified data; The verified data refers to the energy consumption monitoring and verification data that has been conducted within the historical period. Therefore, the verification results and the rules for conducting the verification are known. The data verification field is the verification field of the verification rules corresponding to the verified data.
[0020] Step S002: Perform semantic recognition on the data verification field of each verified data to obtain the semantic features corresponding to each data verification field; Step S003: Obtain the verification requirements and verification item types corresponding to each data verification field; The verification item type is the type of the corresponding verification item, such as public buildings.
[0021] Step S004: According to the preset rule generation method, integrate the semantic features of several data verification fields with the same verification requirements and verification item types to obtain the verification rules corresponding to the verification requirements and verification item types. The rule generation method includes steps S0041-S0043: Step S0041: Among several data validation fields, the data validation field that has the same verification requirement and verification item type is determined as the target validation field corresponding to the verification requirement and the verification item type. Step S0042: Cluster the semantic features of the verification requirement and the target verification field corresponding to the verification item type to obtain several clusters of semantic features; K-means clustering can be used as a clustering method.
[0022] Step S0043: Define corresponding rules for each cluster of semantic features according to the preset rule definition strategy, so as to obtain the verification rules corresponding to each cluster of semantic features.
[0023] The preset rule definition strategy is a strategy customized by the user according to the verification requirements.
[0024] Step S005: Store several verification rules in a preset database to obtain a verification rule library.
[0025] The verification rule base stores several verification rules. Users can maintain these verification rules according to their actual needs, such as adding, modifying, or deleting verification rules, which facilitates the management of verification rules.
[0026] Each verification rule in the verification rule base is an independent entity. Each verification rule represents a certain function and role of energy consumption monitoring verification. In addition, the verification rule needs to clearly define and encapsulate the operation and processing process of verification data. The data processing methods and processes of each verification rule are different to ensure the independence of each verification rule.
[0027] Furthermore, in step S100, based on the verification requirements corresponding to the data to be verified, several target verification rules corresponding to the data to be verified are determined from the preset verification rule base, including steps S110-S180: Step S110: Obtain the verification requirements and verification item types corresponding to the data to be verified; Step S120: Extract semantic features from the verification requirements corresponding to the data to be verified to obtain the corresponding features of the verification requirements. Step S130: Obtain the verification item type corresponding to each verification rule in the verification rule base; Step S140: Among several verification rules in the verification rule base, the verification rules whose verification item type is the same as the verification item type corresponding to the data to be verified are identified as key verification rules; Step S150: Obtain the verification requirements corresponding to each key verification rule; Step S160: Extract semantic features from the verification requirements corresponding to each key verification rule to obtain the rule requirement features corresponding to each key verification rule. Step S170: Perform feature matching on the feature to be verified and each rule requirement feature to obtain the feature matching degree corresponding to each rule requirement feature. Step S180: Determine the key verification rules corresponding to the rule requirement features whose feature matching degree is greater than the preset matching degree threshold as the target verification rules corresponding to the data to be verified.
[0028] Step S200: Encapsulate several target verification rules to obtain the corresponding data verification scheme; Furthermore, in step S200, several target verification rules are encapsulated to obtain a corresponding data verification scheme, including: Step S210: According to the preset data encapsulation method, encapsulate several target verification rules corresponding to the data to be verified to obtain the data verification scheme corresponding to the data to be verified.
[0029] The data encapsulation method can be any one of the following: object-oriented encapsulation, functional encapsulation, modular encapsulation, or service-oriented encapsulation.
[0030] Each verification rule in the data verification scheme exists independently, with no sequential or relational relationship between them. The functions and roles of each verification rule are also different. Furthermore, the verification rules in the data verification scheme must come from the verification rule library. Verification rules cannot be added to the verification scheme independently to ensure the integrity of the data verification scheme and data security, thereby improving the security and accuracy of data verification.
[0031] Step S300: Verify the data to be verified using the data verification scheme to obtain the data verification result corresponding to the data to be verified; Furthermore, in step S300, the data to be verified is verified using a data verification scheme to obtain the data verification result corresponding to the data to be verified, including steps S310-S330: Step S310: Decapsulate the data verification scheme to obtain several target verification rules in the data verification scheme; Step S320: According to each target verification rule, the data to be verified is verified separately to obtain the expected value of each target verification rule for the data to be verified. Step S330: If the expected value corresponding to any target verification rule is greater than the preset expected value threshold, then the verification is passed and determined as the data verification result corresponding to the target verification rule; otherwise, the verification is failed and determined as the data verification result corresponding to the target verification rule.
[0032] Each verification rule specifies the expected result to be obtained after the verification of the data to be verified. If the verification result meets the expected value, the verification rule is marked as verified and marked in green to indicate that the verification rule has passed; otherwise, the verification rule is marked in red and marked as verified as failed.
[0033] The energy consumption monitoring data verification method of the present invention first determines several target verification rules corresponding to the data to be verified from a preset verification rule library according to the verification requirements corresponding to the data to be verified. Then, the several target verification rules are encapsulated to obtain a data verification scheme corresponding to the data to be verified. The data to be verified is verified through the data verification scheme to obtain the data verification result corresponding to the data to be verified, thereby realizing the verification of the data to be verified. By using this method to verify the energy consumption monitoring data to be verified, the stability and continuity of the energy consumption monitoring data upload can be guaranteed, ensuring the continuity and integrity of the collected energy consumption monitoring data and enabling statistical analysis.
[0034] Embodiments of the present invention also provide a computer program product including program code, which, when the program product is run on an electronic device, causes the electronic device to perform the steps of the methods described above in various exemplary embodiments of the present invention.
[0035] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.
[0036] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.
[0037] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.
[0038] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely in hardware, entirely in software (including firmware, microcode, etc.), or in a combination of hardware and software, collectively referred to herein as “circuit,” “module,” or “system.”
[0039] An electronic device according to this embodiment of the invention. The electronic device is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the invention.
[0040] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and buses connecting different system components (including memory and processor).
[0041] The storage device stores program code that can be executed by the processor to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of the present invention.
[0042] The storage may include readable media in the form of volatile storage, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).
[0043] The storage may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more applications, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.
[0044] A bus can represent one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus architectures.
[0045] Electronic devices can also communicate with one or more external devices (such as keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable users to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (such as routers, modems, etc.). This communication can be performed through input / output (I / O) interfaces. Furthermore, electronic devices can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapters.
[0046] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.
[0047] The program product may employ 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, but not limited to, 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 devices, magnetic storage devices, or any suitable combination thereof.
[0048] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.
[0049] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.
[0050] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java and C++, and conventional procedural programming languages such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's 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. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).
[0051] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.
[0052] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.
[0053] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A method for verifying energy consumption monitoring data, characterized in that, include: Based on the verification requirements corresponding to the data to be verified, several target verification rules corresponding to the data to be verified are determined from the preset verification rule library. The corresponding data verification scheme is obtained by encapsulating several of the target verification rules. The data to be verified is verified using the data verification scheme to obtain the data verification result corresponding to the data to be verified.
2. The method according to claim 1, characterized in that, The verification rule base is determined according to the following steps: Retrieve the data validation fields corresponding to several verified data; Semantic recognition is performed on the data verification field of each of the verified data to obtain the semantic features corresponding to each data verification field; Obtain the verification requirements and verification item types corresponding to each of the data verification fields; According to the preset rule generation method, the semantic features of several data verification fields with the same verification requirements and verification item types are integrated and processed to obtain the verification rules corresponding to the verification requirements and verification item types. Several verification rules are stored in a preset database to obtain the verification rule library.
3. The method according to claim 2, characterized in that, The rule generation method includes: Among several data validation fields, the data validation field that has the same verification requirement and verification item type is determined as the target validation field corresponding to the verification requirement and the verification item type. Cluster the semantic features of the verification requirement and the target verification field corresponding to the verification project type to obtain several clusters of semantic features; Based on the preset rule definition strategy, a corresponding rule is defined for each cluster of semantic features to obtain the verification rule for each cluster of semantic features.
4. The method according to claim 3, characterized in that, The step of determining several target verification rules corresponding to the data to be verified from a preset verification rule base based on the verification requirements corresponding to the data to be verified includes: Obtain the verification requirements and verification item types corresponding to the data to be verified; Semantic features are extracted from the verification requirements corresponding to the data to be verified in order to obtain the corresponding features of the verification requirements. Obtain the verification item type corresponding to each verification rule in the verification rule base; Among the verification rules in the verification rule base, the verification rules whose verification item type is the same as the verification item type corresponding to the data to be verified are identified as key verification rules. Obtain the verification requirements corresponding to each of the key verification rules; Semantic features are extracted for each verification requirement corresponding to the key verification rule to obtain the rule requirement features corresponding to each key verification rule; Feature matching is performed on the requirement feature to be verified and each of the rule requirement features to obtain the feature matching degree corresponding to each rule requirement feature; The key verification rules corresponding to the rule requirement features whose feature matching degree is greater than the preset matching degree threshold are determined as the target verification rules corresponding to the data to be verified.
5. The method according to claim 4, characterized in that, The step of encapsulating several target verification rules to obtain a corresponding data verification scheme includes: According to a preset data encapsulation method, several target verification rules corresponding to the data to be verified are encapsulated to obtain a data verification scheme corresponding to the data to be verified.
6. The method according to claim 5, characterized in that, The data encapsulation method can be any one of object-oriented encapsulation, functional encapsulation, modular encapsulation, or service-oriented encapsulation.
7. The method according to claim 6, characterized in that, The step of verifying the data to be verified using the data verification scheme to obtain the data verification result corresponding to the data to be verified includes: The data verification scheme is decapsulated to obtain several target verification rules in the data verification scheme; According to each of the target verification rules, the data to be verified is verified respectively to obtain the expected value of the data verification of the data to be verified by each of the target verification rules; If the expected value corresponding to any of the target verification rules is greater than the preset expected value threshold, then the verification is passed and determined as the data verification result corresponding to the target verification rule; otherwise, the verification is failed and determined as the data verification result corresponding to the target verification rule.
8. The method according to claim 1, characterized in that, The data to be verified is document data and / or database data.
9. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the method as described in any one of claims 1-8.
10. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 9.