Cloud configuration-based energy consumption data acquisition system, equipment and medium

Abstract

The invention provides an energy consumption data acquisition system and equipment based on cloud configuration and a medium. The system comprises energy consumption data acquisition equipment, a cloud management server and a data receiving server, the energy consumption data acquisition device is in communication connection with data instruments of a plurality of energy consumption devices and is used for acquiring energy consumption data of each energy consumption device in real time; the cloud management server is in communication connection with the energy consumption data acquisition equipment and is used for sending a service instruction to the energy consumption data acquisition equipment according to the operation parameters of the energy consumption data acquisition equipment; the data receiving server is in communication connection with the energy consumption data acquisition equipment and is used for receiving and storing the energy consumption data acquired by the energy consumption data acquisition equipment; when a service instruction needs to be sent to the energy consumption data acquisition equipment, the cloud management server side sends corresponding service information to the energy consumption data acquisition equipment, so that remote service updating and data management of the energy consumption data acquisition equipment can be realized, and maintenance and management of the energy consumption data acquisition equipment are greatly facilitated.

Description

Technical Field

[0001] This invention relates to the field of data acquisition, and in particular to a cloud-based energy consumption data acquisition system, device, and medium. Background Technology

[0002] Energy consumption data acquisition equipment is usually deployed in computer rooms or power distribution rooms of buildings in various cities. It collects energy consumption data from electricity meters in real time and uploads it to the energy consumption data acquisition system. Since there is usually a lack of dedicated personnel to manage and maintain the energy consumption data acquisition equipment, it is necessary to require the energy consumption data acquisition equipment to have high stability and reliability.

[0003] Currently, there are various energy consumption data acquisition devices or equipment on the market. Since most of these devices are configured and managed locally, maintenance is quite cumbersome when they malfunction or go offline. Specialized technicians must be dispatched to the site to restore normal operation, resulting in high management and maintenance costs. In particular, when the domain name or IP address of the energy consumption data acquisition system changes, the data upload addresses of the energy consumption data acquisition devices deployed in each building must also be modified. This again requires dispatching specialists to the site to change the IP addresses of the energy consumption data acquisition devices. Therefore, the current data processing methods for energy consumption data acquisition systems are time-consuming and labor-intensive, leading to high management costs. Summary of the Invention

[0004] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows: According to one aspect of this application, a cloud-based energy consumption data acquisition system is provided, comprising: An energy consumption data acquisition device is connected to the data meters of several energy-consuming devices to collect energy consumption data of each energy-consuming device in real time. The cloud-based management server communicates with the energy consumption data acquisition equipment and sends service instructions to the equipment based on its operating parameters. These service instructions include version upgrade service instructions and parameter configuration service instructions. The data receiving server is connected to the energy consumption data acquisition device to receive and store the energy consumption data collected by the device.

[0005] In one exemplary embodiment of this application, the cloud management server includes: The upgrade module is used to send version upgrade packages to energy consumption data acquisition devices; The configuration module is used to configure the operating parameters of the energy consumption data acquisition device; The heartbeat module is used to receive heartbeat packets sent by the energy consumption data acquisition device at preset intervals, and to send warning information when the heartbeat information in the heartbeat packet is abnormal. The early warning module is used to send an early warning signal to the client when it receives early warning information.

[0006] In one exemplary embodiment of this application, the energy consumption data acquisition device is used to perform the following method: Step S100: Real-time acquisition of energy consumption data for each energy-consuming device. If the energy consumption data of any energy-consuming device is outside the normal energy consumption data range, the energy-consuming device is identified as the target energy-consuming device, the energy consumption data is identified as the target energy consumption data, and the acquisition time corresponding to the energy consumption data is identified as the target acquisition time. Step S200: Based on several energy consumption data of the target energy-consuming device within the first time period, determine the first abnormal result; the duration of the first time period is a preset duration, and the start time of the first time period is the next collection time after the target collection time. Step S300: If the first abnormal result indicates that the target energy-consuming equipment has an operational abnormality, proceed to step S600; if the first abnormal result indicates that the target energy-consuming equipment has not an operational abnormality, proceed to step S400. Step S400: Perform an operational anomaly analysis on several energy consumption data of the target energy-consuming device in the first time period and several energy consumption data of the target energy-consuming device in the second time period to determine the second anomaly result; the duration of the second time period is greater than or equal to the duration of the first time period, and the end time of the second time period is the previous acquisition time of the target acquisition time. Step S500: If the second abnormal result indicates that the target energy-consuming device has an operational abnormality, proceed to step S600; if the second abnormal result indicates that the target energy-consuming device has not an operational abnormality, add heartbeat information indicating normal information to the next heartbeat packet sent to the heartbeat module. Step S600: Add heartbeat information representing abnormal information to the next heartbeat packet sent to the heartbeat module; the abnormal information includes the device identifier corresponding to the target energy-consuming device and the target acquisition time.

[0007] In one exemplary embodiment of this application, step S200 includes: Step S210: Obtain several energy consumption data of the target energy-consuming device within the first time period, and obtain the first energy consumption data list A=(A1,A2,...,A...). i ,...,A j ); where i=1,2,...,j; j is the number of data collection moments within the first time period; A i The energy consumption data of the target energy-consuming device at the i-th data collection time within the first time period; Step S220: Traverse the first energy consumption data list A and identify the energy consumption data in the first energy consumption data list A that is not within the normal energy consumption data range as abnormal energy consumption data; Step S230: If the number of abnormal energy consumption data in the first energy consumption data list A accounts for a proportion greater than a preset proportion threshold, then the target energy consumption device is determined to have an operational abnormality as the first abnormal result; otherwise, the target energy consumption device is determined to have no operational abnormality as the first abnormal result.

[0008] In one exemplary embodiment of this application, step S400 includes: Step S410: Obtain several energy consumption data of the target energy-consuming device during the second time period, and obtain the second energy consumption data list B=(B1,B2,...,B m ,...,B n ); where m=1,2,...,n; n is the number of data collection moments within the second time period; B m The energy consumption data of the target energy-consuming device at the m-th data collection time point in the second time period; Step S420: Encode the features of several energy consumption data in the first energy consumption data list A and the second energy consumption data list B to obtain several energy consumption data features. Step S430: Input several energy consumption data features into a preset logistic regression model to obtain the confidence level of the logistic regression model output; the logistic regression model is trained based on several historical abnormal energy consumption data features of the target energy consumption device in a historical time period; the end time of the historical time period is earlier than the start time of the second time period. Step S440: If the confidence level is greater than or equal to the preset first confidence level threshold, then the abnormal operation of the target energy-consuming equipment is determined as the second abnormal result. If the confidence level is less than or equal to the preset second confidence level threshold, then the absence of operational abnormality in the target energy-consuming equipment is determined as the second abnormal result. The first confidence threshold is greater than the second confidence threshold.

[0009] In one exemplary embodiment of this application, step S440 further includes: Step S441: If the confidence level is less than the first confidence level threshold and greater than the second confidence level threshold, then determine the adjustment coefficient corresponding to the confidence level based on the proportion of the number of abnormal energy consumption data in the first energy consumption data list A in the first energy consumption data list A. Step S442: Determine the adjusted confidence level by multiplying the adjustment coefficient and the confidence level; Step S443: If the adjusted confidence level is greater than or equal to the first confidence level threshold, then the target energy-consuming equipment is identified as having an operational abnormality as the second abnormal result; otherwise, the target energy-consuming equipment is identified as not having an operational abnormality as the second abnormal result.

[0010] In one exemplary embodiment of this application, the adjustment coefficient corresponding to the confidence level is determined according to the following steps: Step S4411: Based on the proportion of the number of abnormal energy consumption data in the first energy consumption data list A to the first energy consumption data list A, query the corresponding adjustment coefficient from the preset coefficient mapping table; The coefficient mapping table stores the mapping relationship between several ratios and several adjustment coefficients; the several ratios and several adjustment coefficients stored in the coefficient mapping table are positively correlated.

[0011] In one exemplary embodiment of this application, the logistic regression model is determined according to the following steps: Step S431: Obtain several historical energy consumption data of the target energy-consuming device within a historical time period; Step S432: Identify historical energy consumption data that are outside the normal energy consumption data range from a number of historical energy consumption data as historical abnormal energy consumption data; Step S433: Identify any historical abnormal energy consumption data as the target historical abnormal data; Step S434: Perform feature encoding on several historical energy consumption data of the target energy consumption device in the first historical time period and several historical energy consumption data of the target energy consumption device in the second historical time period to obtain several historical energy consumption data features corresponding to the target historical abnormal data. The duration of the first historical time period is equal to the duration of the first time period, and the start time of the first historical time period is the next collection time after the collection time corresponding to the target historical abnormal data. The duration of the second historical time period is equal to the duration of the second time period, and the end time of the second historical time period is the time before the collection time corresponding to the target historical abnormal data. Step S435: Obtain the heartbeat information from the first heartbeat packet sent by the energy consumption data acquisition device after the acquisition time corresponding to the target historical abnormal data; Step S436: If the heartbeat information in the heartbeat packet represents normal information, then determine that the target value corresponding to the target historical abnormal data is 0; If the heartbeat information in the heartbeat packet is characterized as abnormal information, then the target value corresponding to the target historical abnormal data is determined to be 1; Step S437: Using the features of several historical energy consumption data corresponding to the target historical abnormal data as input samples and the target value corresponding to the target historical abnormal data as output labels, supervised training is performed on the preset initial logistic regression model to obtain the logistic regression model.

[0012] 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 therein, the at least one instruction or the at least one program being loaded and executed by a processor to implement the aforementioned method performed by the energy consumption data acquisition device.

[0013] According to another aspect of this application, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0014] The present invention has at least the following beneficial effects: The present invention discloses a cloud-based energy consumption data acquisition system, comprising an energy consumption data acquisition device, a cloud management server, and a data receiving server. The energy consumption data acquisition device is communicatively connected to data meters of several energy-consuming devices for real-time acquisition of energy consumption data from each device. The cloud management server is communicatively connected to the energy consumption data acquisition device and sends service instructions to it based on its operating parameters. These service instructions include version upgrade instructions and parameter configuration instructions. The data receiving server is communicatively connected to the energy consumption data acquisition device and receives and stores the energy consumption data collected by the device. By deploying the services required by the energy consumption data acquisition device in the cloud, when service instructions need to be sent to the device, there is no need to dispatch personnel to the site for service management. Instead, the cloud management server directly sends the corresponding service information to the device, enabling remote service updates and data management. This greatly facilitates the daily maintenance and management of the energy consumption data acquisition device. Attached Figure Description

[0015] 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.

[0016] Figure 1 A schematic diagram of module communication for a cloud-based energy consumption data acquisition system provided in an embodiment of the present invention; Figure 2A flowchart illustrating the method executed by the energy consumption data acquisition device in a cloud-based energy consumption data acquisition system provided in this embodiment of the invention. Detailed Implementation

[0017] 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.

[0018] This application proposes a cloud-based energy consumption data acquisition system, such as... Figure 1 As shown, the system includes an energy consumption data acquisition device, a cloud management server, and a data receiving server. The energy consumption data acquisition device communicates with data meters (such as electricity meters) of several energy-consuming devices (such as devices that consume electrical energy) to collect energy consumption data (such as electricity consumption) of each device in real time. The cloud management server communicates with the energy consumption data acquisition device to send service instructions to the energy consumption data acquisition device based on its operating parameters. These service instructions include version upgrade service instructions and parameter configuration service instructions. The data receiving server communicates with the energy consumption data acquisition device to receive and store the energy consumption data collected by the device.

[0019] The cloud management server is deployed on a cloud server and is mainly responsible for the remote configuration management of energy consumption data collection equipment. It includes an upgrade module, a configuration module, a heartbeat module, and an early warning module.

[0020] The upgrade module is responsible for managing the system program version upgrades of the energy consumption data acquisition equipment. When a version upgrade package is available, the cloud management server will proactively send the version upgrade package to the energy consumption data acquisition equipment. After receiving the version upgrade package, the energy consumption data acquisition equipment will automatically update its local program version and restart.

[0021] The configuration module is responsible for setting various configuration parameters of the energy consumption data acquisition device. It is used to configure the operating parameters of the energy consumption data acquisition device, including the instrument acquisition address, the data upload energy consumption data acquisition system address, the data acquisition and upload frequency, the data upload format, the encryption method, and other parameter settings.

[0022] The heartbeat module is used to receive heartbeat packets sent by the energy consumption data acquisition device at preset intervals, and to send warning information to the warning module when the heartbeat information in the heartbeat packet is abnormal or no heartbeat packet is received.

[0023] The early warning module is used to send an early warning signal to the client when it receives an early warning message from the heartbeat module. This signal is used to send an equipment malfunction warning to the management personnel and notify them to go to the site where the energy consumption data acquisition equipment is located to perform equipment maintenance.

[0024] On the other hand, energy consumption data acquisition equipment is also used to determine whether the energy consumption equipment has experienced operational abnormalities based on the collected energy consumption data, and to generate corresponding heartbeat packets. Specifically, for example... Figure 2 As shown, it performs the following method: Step S100: Real-time acquisition of energy consumption data for each energy-consuming device. If the energy consumption data of any energy-consuming device is outside the normal energy consumption data range, the energy-consuming device is identified as the target energy-consuming device, the energy consumption data is identified as the target energy consumption data, and the acquisition time corresponding to the energy consumption data is identified as the target acquisition time. If the energy consumption data of an energy-consuming device is outside the normal range, it indicates an anomaly in the device's energy consumption data, such as a sudden increase in electricity consumption. In this case, it is necessary to notify the management personnel to check the device. However, in actual application scenarios, the authenticity of energy consumption data is often questionable due to external factors. Therefore, in order to verify the authenticity of the energy consumption data, further verification is required to reduce the possibility of anomalies in the energy consumption device, reduce the number of times management personnel need to check, and further reduce personnel management costs.

[0025] Step S200: Based on several energy consumption data of the target energy-consuming device in the first time period, determine the first abnormal result; The duration of the first time period is the preset duration, and the start time of the first time period is the next collection time after the target collection time.

[0026] Furthermore, step S200 includes steps S210-S230: Step S210: Obtain several energy consumption data of the target energy-consuming device within the first time period, and obtain the first energy consumption data list A=(A1,A2,...,A...). i ,...,A j ); where i=1,2,...,j; j is the number of data collection moments within the first time period; A i The energy consumption data of the target energy-consuming device at the i-th data collection time within the first time period; Step S220: Traverse the first energy consumption data list A and identify the energy consumption data in the first energy consumption data list A that is not within the normal energy consumption data range as abnormal energy consumption data; Step S230: If the number of abnormal energy consumption data in the first energy consumption data list A accounts for a proportion greater than a preset proportion threshold, then the target energy consumption device is determined to have an operational abnormality as the first abnormal result; otherwise, the target energy consumption device is determined to have no operational abnormality as the first abnormal result.

[0027] When the number of abnormal energy consumption data in the first energy consumption data list A accounts for a proportion greater than the preset proportion threshold, it indicates that the target energy consumption device has a large number of abnormal energy consumption data after the target acquisition time. This means that the target energy consumption device did indeed experience an operational abnormality at the target acquisition time. Therefore, the operational abnormality of the target energy consumption device is determined as the first abnormal result. There is no need to perform the following steps of judgment. The abnormal information is directly added to the next heartbeat packet.

[0028] When the number of abnormal energy consumption data in the first energy consumption data list A is less than or equal to the preset percentage threshold, it indicates that the number of abnormal energy consumption data of the target energy consumption device after the target collection time is small. This suggests that the target energy consumption data of the target energy consumption device at the target collection time may be erroneous data caused by external factors, and therefore, it needs to be judged again.

[0029] Step S300: If the first abnormal result indicates that the target energy-consuming equipment has an operational abnormality, proceed to step S600; if the first abnormal result indicates that the target energy-consuming equipment has not an operational abnormality, proceed to step S400. Step S400: Perform an operational anomaly analysis on several energy consumption data of the target energy-consuming device in the first time period and several energy consumption data of the target energy-consuming device in the second time period to determine the second anomaly result. The duration of the second time period is greater than or equal to the duration of the first time period, and the end time of the second time period is the time preceding the target acquisition time.

[0030] Furthermore, step S400 includes steps S410-S443: Step S410: Obtain several energy consumption data of the target energy-consuming device during the second time period, and obtain the second energy consumption data list B=(B1,B2,...,B m ,...,B n ); where m=1,2,...,n; n is the number of data collection moments within the second time period; B m The energy consumption data of the target energy-consuming device at the m-th data collection time point in the second time period; Step S420: Encode the features of several energy consumption data in the first energy consumption data list A and the second energy consumption data list B to obtain several energy consumption data features. The existing data encoding methods can be used to encode the features of energy consumption data.

[0031] Step S430: Input several energy consumption data features into a preset logistic regression model to obtain the confidence level of the logistic regression model output; The logistic regression model is trained based on the characteristics of several historical abnormal energy consumption data of the target energy-consuming device within a historical time period.

[0032] The confidence level indicates the degree of credibility of the obtained results. Since the training samples of the logistic regression model are several historical abnormal energy consumption data within a historical time period, the obtained results are the detection results of the target energy consumption equipment malfunctioning. Therefore, the higher the confidence level, the greater the probability that the target energy consumption equipment is malfunctioning.

[0033] Step S440: If the confidence level is greater than or equal to the preset first confidence level threshold, then the abnormal operation of the target energy-consuming equipment is determined as the second abnormal result. If the confidence level is less than or equal to the preset second confidence level threshold, then the absence of operational abnormality in the target energy-consuming equipment is determined as the second abnormal result. The first confidence threshold is greater than the second confidence threshold.

[0034] When the confidence level is greater than or equal to the preset first confidence level threshold, it indicates that the detection result of the target energy consumption device having an operational abnormality is highly reliable, and it is determined that the target energy consumption device has an operational abnormality; when the confidence level is less than or equal to the preset second confidence level threshold, it indicates that the detection result of the target energy consumption device having an operational abnormality is less reliable, and it is determined that the target energy consumption device has not an operational abnormality.

[0035] When the confidence level is less than the first confidence threshold but greater than the second confidence threshold, it indicates that the detection result of the target energy-consuming equipment malfunctioning is of medium confidence and cannot clearly indicate that the target energy-consuming equipment malfunctions. Therefore, further judgment is needed to improve the accuracy of the detection result.

[0036] Step S441: If the confidence level is less than the first confidence level threshold and greater than the second confidence level threshold, then determine the adjustment coefficient corresponding to the confidence level based on the proportion of the number of abnormal energy consumption data in the first energy consumption data list A in the first energy consumption data list A. The adjustment coefficient corresponding to the confidence level is determined according to step S4411: Step S4411: Based on the proportion of the number of abnormal energy consumption data in the first energy consumption data list A to the first energy consumption data list A, query the corresponding adjustment coefficient from the preset coefficient mapping table; The coefficient mapping table stores the mapping relationship between several ratios and several adjustment coefficients; the several ratios and several adjustment coefficients stored in the coefficient mapping table are positively correlated.

[0037] In the coefficient mapping table, when the ratio is one-half, the corresponding adjustment coefficient is 1, so that the adjusted confidence level can be increased or decreased in a timely manner.

[0038] Step S442: Determine the adjusted confidence level by multiplying the adjustment coefficient and the confidence level; Step S443: If the adjusted confidence level is greater than or equal to the first confidence level threshold, then the target energy-consuming equipment is identified as having an operational abnormality as the second abnormal result; otherwise, the target energy-consuming equipment is identified as not having an operational abnormality as the second abnormal result.

[0039] The larger the proportion of abnormal energy consumption data in the first energy consumption data list A, the greater the probability that the target energy consumption equipment is malfunctioning. Therefore, the corresponding adjustment coefficient is increased, thereby increasing the adjusted confidence level. Then, the adjusted confidence level is compared with the first confidence threshold. If the adjusted confidence level is greater than or equal to the first confidence threshold, it is determined that the target energy consumption equipment is malfunctioning. If the adjusted confidence level is still less than the first confidence threshold, it means that the confidence level of the detection result output by the logistic regression model is reliable, and it is determined that the target energy consumption equipment is not malfunctioning. By adjusting the confidence level, the accuracy of the obtained detection result is improved.

[0040] The logistic regression model is determined according to steps S431-S437: Step S431: Obtain several historical energy consumption data of the target energy-consuming device within a historical time period; The end of the first historical period is earlier than the beginning of the second historical period.

[0041] Step S432: Identify historical energy consumption data that are outside the normal energy consumption data range from a number of historical energy consumption data as historical abnormal energy consumption data; Step S433: Identify any historical abnormal energy consumption data as the target historical abnormal data; Step S434: Perform feature encoding on several historical energy consumption data of the target energy consumption device in the first historical time period and several historical energy consumption data of the target energy consumption device in the second historical time period to obtain several historical energy consumption data features corresponding to the target historical abnormal data. The duration of the first historical time period is equal to the duration of the first time period, and the start time of the first historical time period is the next collection time after the collection time corresponding to the target historical abnormal data. The duration of the second historical time period is equal to the duration of the second time period, and the end time of the second historical time period is the time before the collection time corresponding to the target historical abnormal data. Step S435: Obtain the heartbeat information from the first heartbeat packet sent by the energy consumption data acquisition device after the acquisition time corresponding to the target historical abnormal data; Step S436: If the heartbeat information in the heartbeat packet represents normal information, then determine that the target value corresponding to the target historical abnormal data is 0; If the heartbeat information in the heartbeat packet is characterized as abnormal information, then the target value corresponding to the target historical abnormal data is determined to be 1; Step S437: Using the features of several historical energy consumption data corresponding to the target historical abnormal data as input samples and the target value corresponding to the target historical abnormal data as output labels, supervised training is performed on the preset initial logistic regression model to obtain the logistic regression model.

[0042] Step S500: If the second abnormal result indicates that the target energy-consuming device has an operational abnormality, proceed to step S600; if the second abnormal result indicates that the target energy-consuming device has not an operational abnormality, add heartbeat information indicating normal information to the next heartbeat packet sent to the heartbeat module. Step S600: Add heartbeat information representing abnormal information to the next heartbeat packet sent to the heartbeat module; the abnormal information includes the device identifier corresponding to the target energy-consuming device and the target acquisition time.

[0043] The present invention discloses a cloud-based energy consumption data acquisition system, comprising an energy consumption data acquisition device, a cloud management server, and a data receiving server. The energy consumption data acquisition device is communicatively connected to data meters of several energy-consuming devices for real-time acquisition of energy consumption data from each device. The cloud management server is communicatively connected to the energy consumption data acquisition device and sends service instructions to it based on its operating parameters. These service instructions include version upgrade instructions and parameter configuration instructions. The data receiving server is communicatively connected to the energy consumption data acquisition device and receives and stores the energy consumption data collected by the device. By deploying the services required by the energy consumption data acquisition device in the cloud, when service instructions need to be sent to the device, there is no need to dispatch personnel to the site for service management. Instead, the cloud management server directly sends the corresponding service information to the device, enabling remote service updates and data management. This greatly facilitates the daily maintenance and management of the energy consumption data acquisition device.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0048] 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.”

[0049] 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.

[0050] 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).

[0051] 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.

[0052] 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).

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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).

[0061] 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.

[0062] 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.

[0063] 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 cloud-based energy consumption data acquisition system, characterized in that, include: An energy consumption data acquisition device is communicatively connected to the data meters of several energy consumption devices, and is used to collect the energy consumption data of each of the energy consumption devices in real time. A cloud-based management server is communicatively connected to the energy consumption data acquisition device and is used to send service instructions to the energy consumption data acquisition device according to the operating parameters of the energy consumption data acquisition device; the service instructions include version upgrade service instructions and parameter configuration service instructions. The data receiving server is communicatively connected to the energy consumption data acquisition device and is used to receive and store the energy consumption data collected by the energy consumption data acquisition device.

2. The cloud-based energy consumption data acquisition system according to claim 1, characterized in that, The cloud management server includes: The upgrade module is used to send a version upgrade package to the energy consumption data acquisition device; The configuration module is used to configure the operating parameters of the energy consumption data acquisition device; The heartbeat module is used to receive heartbeat packets sent by the energy consumption data acquisition device at preset intervals, and to send warning information when the heartbeat information in the heartbeat packet is abnormal. The early warning module is used to send an early warning signal to the client when it receives the early warning information.

3. The cloud-based energy consumption data acquisition system according to claim 2, characterized in that, The energy consumption data acquisition device is used to perform the following method: Step S100: Real-time acquisition of energy consumption data for each of the energy-consuming devices. If the energy consumption data of any of the energy-consuming devices is outside the normal energy consumption data range, the energy-consuming device is identified as the target energy-consuming device, the energy consumption data is identified as the target energy consumption data, and the acquisition time corresponding to the energy consumption data is identified as the target acquisition time. Step S200: Based on several energy consumption data of the target energy-consuming device within a first time period, determine the first abnormal result; the duration of the first time period is a preset duration, and the start time of the first time period is the next collection time after the target collection time. Step S300: If the first abnormal result indicates that the target energy-consuming device has an operational abnormality, proceed to step S600; if the first abnormal result indicates that the target energy-consuming device has not an operational abnormality, proceed to step S400. Step S400: Perform an operational anomaly analysis on several energy consumption data of the target energy-consuming device in the first time period and several energy consumption data of the target energy-consuming device in the second time period to determine a second anomaly result; the duration of the second time period is greater than or equal to the duration of the first time period, and the end time of the second time period is the previous acquisition time of the target acquisition time. Step S500: If the second abnormal result indicates that the target energy-consuming device has an operational abnormality, proceed to step S600; if the second abnormal result indicates that the target energy-consuming device has not an operational abnormality, add heartbeat information indicating normal information to the next heartbeat packet sent to the heartbeat module. Step S600: Add heartbeat information representing abnormal information to the next heartbeat packet sent to the heartbeat module; the abnormal information includes the device identifier corresponding to the target energy-consuming device and the target acquisition time.

4. The cloud-based energy consumption data acquisition system according to claim 3, characterized in that, Step S200 includes: Step S210: Obtain several energy consumption data of the target energy-consuming device within a first time period to obtain a first energy consumption data list A=(A1,A2,...,A...). i ,...,A j ); where i = 1, 2, ..., j; j is the number of collection times within the first time period; A i The energy consumption data of the target energy-consuming device at the i-th collection time within the first time period; Step S220: Traverse the first energy consumption data list A and determine the energy consumption data in the first energy consumption data list A that is not within the normal energy consumption data range as abnormal energy consumption data; Step S230: If the proportion of the number of abnormal energy consumption data in the first energy consumption data list A is greater than a preset proportion threshold, then the target energy consumption device is determined to have an operational abnormality as the first abnormal result; otherwise, the target energy consumption device is determined to have no operational abnormality as the first abnormal result.

5. The cloud-based energy consumption data acquisition system according to claim 4, characterized in that, Step S400 includes: Step S410: Obtain several energy consumption data of the target energy-consuming device during the second time period to obtain a second energy consumption data list B=(B1,B2,...,B m ,...,B n ); where m=1,2,...,n; n is the number of data collection moments within the second time period; B m The energy consumption data of the target energy-consuming device at the m-th collection time within the second time period; Step S420: Encode the features of several energy consumption data in the first energy consumption data list A and the second energy consumption data list B to obtain several energy consumption data features; Step S430: Input several energy consumption data features into a preset logistic regression model to obtain the confidence level output by the logistic regression model; the logistic regression model is trained based on several historical abnormal energy consumption data features of the target energy consumption device in a historical time period; the end time of the historical time period is earlier than the start time of the second time period; Step S440: If the confidence level is greater than or equal to the preset first confidence level threshold, then the abnormal operation of the target energy-consuming device is determined as the second abnormal result. If the confidence level is less than or equal to the preset second confidence level threshold, then the absence of operational abnormality in the target energy-consuming device is determined as the second abnormal result. Wherein, the first confidence threshold is greater than the second confidence threshold.

6. The cloud-based energy consumption data acquisition system according to claim 5, characterized in that, Step S440 further includes: Step S441: If the confidence level is less than the first confidence level threshold and greater than the second confidence level threshold, then determine the adjustment coefficient corresponding to the confidence level based on the proportion of the number of abnormal energy consumption data in the first energy consumption data list A to the first energy consumption data list A. Step S442: The product of the adjustment coefficient and the confidence level is determined as the adjusted confidence level; Step S443: If the adjusted confidence level is greater than or equal to the first confidence level threshold, then the target energy-consuming device is determined to have an operational abnormality as the second abnormal result; otherwise, the target energy-consuming device is determined to have not had an operational abnormality as the second abnormal result.

7. The cloud-based energy consumption data acquisition system according to claim 6, characterized in that, The adjustment coefficient corresponding to the confidence level is determined according to the following steps: Step S4411: Based on the proportion of the number of abnormal energy consumption data in the first energy consumption data list A to the first energy consumption data list A, query the corresponding adjustment coefficient from the preset coefficient mapping table; The coefficient mapping table stores the mapping relationship between several ratios and several adjustment coefficients; the several ratios and several adjustment coefficients stored in the coefficient mapping table are positively correlated.

8. The cloud-based energy consumption data acquisition system according to claim 7, characterized in that, The logistic regression model is determined according to the following steps: Step S431: Obtain several historical energy consumption data of the target energy-consuming device within the historical time period; Step S432: Determine the historical energy consumption data that is not within the range of normal energy consumption data from the aforementioned historical energy consumption data as historical abnormal energy consumption data; Step S433: Identify any historical abnormal energy consumption data as the target historical abnormal data; Step S434: Perform feature encoding on several historical energy consumption data of the target energy-consuming device in the first historical time period and several historical energy consumption data of the target energy-consuming device in the second historical time period to obtain several historical energy consumption data features corresponding to the target historical abnormal data. The duration of the first historical time period is equal to the duration of the first time period, and the start time of the first historical time period is the next collection time after the collection time corresponding to the target historical abnormal data. The duration of the second historical time period is equal to the duration of the second time period, and the end time of the second historical time period is the time before the collection time corresponding to the target historical abnormal data. Step S435: Obtain the heartbeat information from the first heartbeat packet sent by the energy consumption data acquisition device after the acquisition time corresponding to the target historical abnormal data; Step S436: If the heartbeat information in the heartbeat packet represents normal information, then the target value corresponding to the target historical abnormal data is determined to be 0; If the heartbeat information in the heartbeat packet is characterized as abnormal information, then the target value corresponding to the target historical abnormal data is determined to be 1; Step S437: Using the historical energy consumption data features corresponding to the target historical abnormal data as input samples and the target value corresponding to the target historical abnormal data as output labels, supervised training is performed on the preset initial logistic regression model to obtain the logistic regression model.

9. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the method performed by the energy consumption data acquisition device as described in any one of claims 3-8.

10. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 9.

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