Energy consumption management method and system

Abstract

The present invention provides an energy consumption management method and a system, which are characterized in that: after energy consumption index related information is parsed, structured management is carried out; after corresponding energy consumption analysis, an indication of an energy consumption anomaly can be provided to aid in energy consumption management; and further, a rule parser is invoked in parsing, which can automatically run a specific algorithm and extract variable information of a parsing rule correlation function so as to implement automated parsing configuration of the rule parser.

Description

Energy consumption management method and system Technical Field

[0001] The present invention relates to an energy consumption management method and system, and in particular to the analysis of energy consumption information in energy consumption management. Background Art

[0002] Energy consumption management is crucial for businesses. It's not only a necessary component of operating cost management but also crucial for fulfilling their commitments to energy conservation and emissions reduction. Many energy-consuming enterprises currently have a large number of machines and equipment, generating significant energy consumption in real time. This energy consumption may be distributed across various production processes and equipment, workshops, and locations. For energy-consuming enterprises with complex organizational structures, this energy consumption may also be distributed across diverse production locations in different regions. Managing this energy consumption in real time and automatically is a key issue for energy-consuming enterprises.

[0003] Although there are currently many types of smart instruments and meters on the market that can reflect the energy consumption of equipment, energy-consuming enterprises often have a variety of energy-consuming equipment. Different equipment may be equipped with smart instruments and meters from different manufacturers and using different communication protocols. After installing these instruments, it is impossible to form structured data management and energy waste risk warnings. Often, only manual meter reading and analysis are required, which is inefficient and prone to energy waste. In addition, in the absence of professional energy management analysts, energy-consuming enterprises are also concerned about how to easily identify energy waste points and provide countermeasures to help them reduce energy waste.

[0004] Summary of the Invention

[0005] The present invention provides an energy consumption management method and system, which aims to solve the defects of current energy consumption management being unintelligent and inefficient.

[0006] In order to achieve the above objectives, in a first aspect, the present invention provides an energy consumption management method, characterized in that it includes the following steps:

[0007] Step S100: receiving first energy consumption information for parsing, wherein the first energy consumption information is a data message, wherein a data portion of the first energy consumption information includes an indicator value string corresponding to a target energy consumption indicator, and a value of the target energy consumption indicator can be obtained based on the indicator value string corresponding to the target energy consumption indicator;

[0008] Step S200: performing the analysis on the first energy consumption information, the analysis comprising obtaining a value of the target energy consumption indicator based on the first energy consumption information;

[0009] Step S300: Based on the analysis, obtaining second energy consumption information, where the second energy consumption information is structured and includes the target energy consumption indicator and its value;

[0010] Step S400: performing energy consumption analysis based on the second energy consumption information;

[0011] Step S500: providing an indication of abnormal energy consumption based on the energy consumption analysis;

[0012] Among them, the optional items of the energy consumption analysis include benchmark comparison analysis, non-working time energy consumption analysis, average power factor analysis, energy consumption volatility analysis, energy consumption contribution change analysis, and energy consumption short-term peak and valley analysis.

[0013] In one embodiment, an energy consumption collection module is provided for energy-consuming equipment; based on the energy consumption collection module's monitoring of the target energy consumption indicator of the energy-consuming equipment, and based on a parsing rule variable table corresponding to the target energy consumption indicator in the energy consumption collection module and a communication protocol used by the energy consumption collection module, the energy consumption collection module generates the first energy consumption information; the parsing rule variable table is a table stored in a graphic format, and its text content includes the names and values ​​of parsing rule variables showing their corresponding relationships, which are used to indicate the conversion relationship between the target energy consumption indicator and the data portion of the first energy consumption information; the energy consumption management method further calls a rule parser, and the rule parser, after parsing the target energy consumption indicator for the energy consumption collection module, includes:

[0014] Step T200: Acquire the symbolic information of the communication protocol used by the energy consumption collection module and the parsing rule variable table corresponding to the target energy consumption indicator of the energy consumption collection module, wherein the communication protocol is used to indicate the correspondence between the data message using the communication protocol and the data portion thereof;

[0015] Step T300: determining the boundaries of each cell in the obtained parsing rule variable table based on the Faster RCNN algorithm;

[0016] Step T400: using a character recognition algorithm to identify characters within the boundaries of each cell;

[0017] Step T500: Based on the boundaries of each cell in the parsing rule variable table determined in step T300 and based on the characters within the boundaries of each cell identified in step T400, determining the correspondence between the parsing rule variables and their values ​​for the target energy consumption indicator;

[0018] Step T600: generating an energy consumption index parsing function for the target energy consumption index based on the correspondence between the parsing rule variables and their values ​​determined for the target energy consumption index and based on the acquired symbolic information of the communication protocol used by the energy consumption acquisition module;

[0019] The analysis in step S200 further includes:

[0020] Step S210: calling the rule parser configured for the energy consumption acquisition module according to the analysis of the target energy consumption index to obtain the energy consumption index parsing function corresponding to the target energy consumption index;

[0021] Step S220: running the energy consumption index parsing function corresponding to the target energy consumption index on the first energy consumption information to obtain the value of the target energy consumption index.

[0022] In one embodiment, for the target energy consumption indicator, the parsing rule variables include at least:

[0023] A start bit, used to indicate the starting position of the indicator value character string corresponding to the target energy consumption indicator in the data part of the first energy consumption information;

[0024] Character length, used to indicate the number of characters in the indicator value string corresponding to the target energy consumption indicator; and

[0025] The parsing format is used to indicate a conversion rule between the value of the target energy consumption indicator and the indicator value character string corresponding to the target energy consumption indicator.

[0026] In one embodiment, the parsing in step S200 further includes obtaining the execution time of the parsing, and the second energy consumption information in step S300 includes the execution time.

[0027] In one embodiment, the communication protocol is selected from one of MODBUS RTU, MODBUS TCP, OPC UA, and PLC.

[0028] In one embodiment, the communication protocol is MODBUS RTU, and the parsing rule variable further includes byte order.

[0029] In one embodiment, the symbolic information of the communication protocol used by the energy consumption collection module refers to the name of the communication protocol used by the energy consumption collection module, and the parsing configuration of the rule parser further includes: step T100 before step T200: obtaining the protocol of the energy consumption collection module, the protocol of the energy consumption collection module including a parsing rule variable table corresponding to the target energy consumption indicator of the energy consumption collection module and the name of the communication protocol used by the energy consumption collection module;

[0030] Correspondingly, step T200: according to the obtained protocol of the energy consumption collection module, based on the keyword matching algorithm, obtain the name of the communication protocol used by the energy consumption collection module, and based on the table detection algorithm, obtain the analysis rule variable table corresponding to the target energy consumption indicator of the energy consumption collection module.

[0031] In a second aspect, the present invention provides an energy consumption management system, characterized in that the energy consumption analysis system includes: an energy consumption collection module, an energy consumption information receiving module, a rule parser, an energy consumption information parsing module, a structuring module, an energy consumption information analysis module, and an energy consumption anomaly indication module; wherein the energy consumption collection module generates first energy consumption information for a set energy consuming device, the first energy consumption information being a data message, the data portion of the first energy consumption information including an indicator value string corresponding to a target energy consumption indicator, the value of the target energy consumption indicator being obtainable based on the indicator value string corresponding to the target energy consumption indicator, the first energy consumption information being generated based on the energy consumption collection module monitoring the target energy consumption indicator of the energy consuming device, and based on a parsing rule variable table corresponding to the target energy consumption indicator in the energy consumption collection module and a communication protocol used by the energy consumption collection module, the parsing rule variable table being a table stored in a graphic format, the text content of the parsing rule variable table including names and values ​​of parsing rule variables showing their corresponding relationships, and indicating a conversion relationship between the target energy consumption indicator and the data portion of the first energy consumption information;

[0032] For the target energy consumption indicator, the parsing rule variables include at least:

[0033] (a) a start bit, used to indicate the starting position of the indicator value character string corresponding to the target energy consumption indicator in the data portion of the first energy consumption information;

[0034] (b) character length, which is used to indicate the number of characters in the indicator value string corresponding to the target energy consumption indicator; and

[0035] (c) a parsing format for indicating a conversion rule between a value of the target energy consumption indicator and an indicator value character string corresponding to the target energy consumption indicator;

[0036] The energy consumption information receiving module is used to receive the first energy consumption information for analysis;

[0037] The rule parser, after parsing and configuring the energy consumption collection module corresponding to the target energy consumption indicator, includes:

[0038] Step T100: Acquire the protocol of the energy consumption collection module, wherein the protocol of the energy consumption collection module includes a parsing rule variable table corresponding to the target energy consumption indicator of the energy consumption collection module and the name of the communication protocol used by the energy consumption collection module;

[0039] Step T200: Based on the acquired protocol of the energy consumption collection module, a name of the communication protocol used by the energy consumption collection module is acquired based on a keyword matching algorithm, and a parsing rule variable table corresponding to the target energy consumption indicator of the energy consumption collection module is acquired based on a table detection algorithm, wherein the communication protocol is used to indicate a correspondence between a data message using the communication protocol and the data portion thereof;

[0040] Step T300: determining the boundaries of each cell in the obtained parsing rule variable table based on the Faster RCNN algorithm;

[0041] Step T400: using a character recognition algorithm to identify characters within the boundaries of each cell;

[0042] Step T500: Based on the boundaries of each cell in the parsing rule variable table determined in step T300 and based on the characters within the boundaries of each cell identified in step T400, determining the correspondence between the parsing rule variables and their values ​​for the target energy consumption indicator;

[0043] Step T600: generating an energy consumption index parsing function for the target energy consumption index based on the correspondence between the parsing rule variables and their values ​​of the target energy consumption index and based on the acquired name of the communication protocol used by the energy consumption acquisition module;

[0044] The energy consumption information parsing module is configured to perform the parsing on the first energy consumption information, wherein the parsing includes:

[0045] Step S210: calling the rule parser configured for the energy consumption acquisition module according to the analysis of the target energy consumption index to obtain the energy consumption index parsing function corresponding to the target energy consumption index; and

[0046] Step S220: running the energy consumption index parsing function corresponding to the target energy consumption index on the first energy consumption information to obtain the value of the target energy consumption index;

[0047] The structuring module is used to obtain second energy consumption information based on the analysis, where the second energy consumption information is structured and includes the target energy consumption index and the value of the target energy consumption index;

[0048] The energy consumption information analysis module is used to perform energy consumption analysis based on the second energy consumption information; optional items of the energy consumption analysis include benchmark comparison analysis, non-working time energy consumption analysis, average power factor analysis, energy consumption fluctuation analysis, energy consumption contribution change analysis, and energy consumption short-term peak and valley analysis;

[0049] The energy consumption anomaly indication module is used to provide an indication of energy consumption anomaly based on the energy consumption analysis.

[0050] In one embodiment, the parsing of the first energy consumption information by the energy consumption information parsing module further includes obtaining an execution time of the parsing, and the second energy consumption information obtained by the structuring module includes the execution time.

[0051] In one embodiment, the communication protocol is selected from one of MODBUS RTU, MODBUS TCP, OPC UA, and PLC, and when the communication protocol is MODBUS RTU, the parsing rule variable further includes byte order.

[0052] Compared with the existing technology, the advantages of the present invention are that it can realize structured management of energy consumption data, improve the automation and intelligence level of energy consumption management, and provide energy waste risk warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] FIG1 is a flow chart of an energy consumption management method provided by an embodiment of the present invention;

[0055] FIG2 is a flowchart of a parsing configuration of a rule parser used in one embodiment of the present invention;

[0056] Figure 3: An energy consumption management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0057] Unless otherwise specified, all or some of the steps, devices, systems, equipment, and modules in the methods disclosed herein may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0058] Where appropriate, the component used to implement all or some steps or achieve specific functions may be a processor (such as a central processing unit, a digital signal processor or a microprocessor) that executes corresponding instructions, an integrated circuit (such as an application-specific integrated circuit) or other physical component.

[0059] Where appropriate, such software or instructions may be deployed on a computer-readable medium, which typically includes RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and which can be accessed by a computer.

[0060] Where appropriate or necessary, communication may be required within or between components implementing the methods disclosed herein, which may be accomplished via communication media. Such communication media typically include computer-readable signals, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0062] 1-3 , an embodiment of the present invention provides an energy consumption management method, which includes the following steps: Step S100: receiving first energy consumption information for parsing, wherein the first energy consumption information is a data message, and the data portion of the first energy consumption information includes an indicator value string corresponding to a target energy consumption indicator, and the value of the target energy consumption indicator can be obtained based on the indicator value string corresponding to the target energy consumption indicator; Step S200: performing the parsing on the first energy consumption information, wherein the parsing includes obtaining the value of the target energy consumption indicator based on the first energy consumption information; Step S300: obtaining second energy consumption information based on the parsing, wherein the second energy consumption information is structured and includes the target energy consumption indicator and its value; Step S400: performing energy consumption analysis based on the second energy consumption information; Step S500: providing an indication of energy consumption anomalies based on the energy consumption analysis; wherein the optional items of the energy consumption analysis include benchmark comparison analysis, non-working time energy consumption analysis, average power factor analysis, energy consumption volatility analysis, energy consumption contribution change analysis, and energy consumption short-term peak and valley analysis. The “optional items include” means that at least one of the energy consumption analysis items listed after the statement can be adapted to the second energy consumption information and obtain meaningful analysis results, but each energy consumption analysis item listed after the statement is also provided as an optional item.

[0063] As described in steps S100 to S500 above, an embodiment of the present invention provides the following energy consumption management method:

[0064] First, first energy consumption information for analysis is received. Energy consumption information (including first and second energy consumption information) refers to information that directly or indirectly includes the value of at least one energy consumption indicator reflecting the energy consumption of an energy-consuming device. Energy consumption may be direct energy consumption for energy sources such as electricity, water, gas, nuclear energy, and thermal fuels. It may also be derived from the consumption of these energy sources, such as the current production volume, material handling volume, and gas emissions of an energy-consuming device acting as a production facility. It may also be other conditions meaningful for analyzing the energy consumption of an energy-consuming device, such as the current time in the time zone where the energy-consuming device is located or the continuous operating time of the energy-consuming device. Accordingly, energy consumption indicators may include the current, voltage, power, power factor, power consumption, instantaneous flow rate, total flow rate, gas flow rate, length, volume, time, and temperature of the energy-consuming device. They may also be logical judgment conditions related to the energy consumption of the target energy-consuming device, such as whether the energy-consuming device is currently consuming energy, whether the instantaneous flow rate exceeds a specific value, or whether current is currently flowing through the energy-consuming device. In addition to those that can be obtained through direct collection, energy consumption indicators also include other indicators derived from directly collected energy consumption indicators, such as energy consumption conditions such as process data and result data generated in the energy consumption analysis described later. Accordingly, the value of an energy consumption indicator refers to a relative quantity (or its range) of an energy consumption indicator with or without a dimension, or a logical condition, that is directly understandable to humans. Depending on how the energy consumption indicator is set, the value of the energy consumption indicator may be selected from a single value with or without a dimension, a range of values, or logical conditions such as "yes", "no", "yes", or "no". Energy consumption information may directly include the value of an energy consumption indicator. For example, the energy consumption information may include a numerically measured energy consumption indicator in decimal format. For example, the energy consumption information may include the current of the energy-consuming device as 1A. Energy consumption information may also include the value of an energy consumption indicator indirectly. This means that the value of the energy consumption indicator can be obtained through calculations and conversions based on the energy consumption information. For example, the energy consumption information may include a byte-ordered binary format containing a numerically measured energy consumption indicator, which can be restored to the normal order of the numerically measured energy consumption indicator in decimal format through calculations and conversions. When an energy consumption indicator is used as an assessment target, it is recorded as a target energy consumption indicator. For multiple energy-consuming devices, if they are identical, similar, or related, some energy consumption indicators may be meaningful to all of them. However, sometimes, a specific energy consumption indicator may be meaningless to an energy-consuming device. For example, for an energy-consuming device powered by electricity, the energy consumption indicator of natural gas usage may be meaningless.

[0065] As a limitation, the first energy consumption information is a data message. A data message refers to the basic unit of transmission in a computer information network, which can be stored, processed, read and / or distributed by a computer-readable medium, and includes at least a header and a data part. The header mainly includes information related to the transmission, such as the destination IP, destination port, source address, source port, etc. The data part mainly includes information related to the transmission object, for example, information that can be decoded into human-readable characters. The combination and segmentation method of the header and data part is mainly related to the associated communication protocol; based on the communication protocol, at least its data part can be obtained from the data message. The data message is usually a character string in binary, decimal, or hexadecimal form.

[0066] The first energy consumption information is a data message, the data portion of which includes an indicator value string corresponding to the target energy consumption indicator. A string is a continuous sequence of symbols or numerical values. For an energy consumption indicator, the indicator value string corresponding to it is a string that can be obtained through operations and conversions that comply with specific rules.

[0067] For example, a smart meter communicates via MODBUS RTU format. The first energy consumption information it generates, indicating current, is specifically represented by a header and the hexadecimal data portion: "02 03 04 4121 999A 673E." According to the rules, "02" represents the device address 2; "03" indicates the "Read Multiple Registers" function; "04" is the hexadecimal data bit length; "4121999A" is the hexadecimal 32-bit floating-point value string corresponding to the current energy consumption indicator. After regular calculations and conversions, the decimal value of the current in A is 10.1. "673E" is a redundancy check code used to verify data accuracy and prevent data errors caused by interference during transmission.

[0068] The energy consumption collection module generates first energy consumption information for the energy-consuming device. For example, the first energy consumption information may be collected and generated by a smart meter associated with the energy-consuming device. A smart meter is an intelligent device, such as a smart electric meter or flow meter, that can collect the value of at least one energy consumption indicator of the energy-consuming device and convert such data into a data message. In addition to being integrated into the same device, the collection and conversion of the energy consumption indicator value may also be performed by a device that does not have the function of converting the energy consumption indicator value into a data format, and a device that can perform the function of converting the energy consumption indicator value into a data format. In this case, the combination of multiple such devices performing different functions can be considered a single smart meter. For example, the energy consumption indicator value is first collected by a common simple instrument such as an electric meter, milliammeter, voltmeter, flowmeter, or power meter installed on the energy-consuming device. Then, an intelligent image acquisition device reads the current value on the instrument (for example, the current value can be determined by reading the pointer position on the instrument panel or the number on the display screen) and converts the value into data in a computer-readable format. Depending on the actual situation, such intelligent image acquisition devices can be independently developed to read and transmit specific meter data, or they can be general-purpose intelligent devices with optical character recognition (OCR) and communication functions (such as general-purpose intelligent monitors with OCR and other functions). Intelligent meters can also be fully or partially integrated into energy-consuming devices.

[0069] The first energy consumption information received for analysis can be sent directly from the aforementioned smart meter with a communication component, or it can be sent from the aforementioned smart meter to an edge gateway and then sent by the edge gateway. An edge gateway is a gateway deployed at the edge of the network that can receive, process, and store data, and further send (also called "forward") the data to a cloud server or other device. The aforementioned edge gateway can connect and communicate with the aforementioned smart meter and other terminal devices via wired or wireless communication. Common edge gateways are Linux-based embedded devices with external interfaces such as network interfaces and serial interfaces. To communicate with or further control smart meters, they are typically built-in or configured with communication protocols commonly used by smart meters, such as MODBUS RTU and MODBUS TCP. They may also be built-in or configured with protocols such as OPC UA and PLC. The edge gateway can locally pre-process data and securely store data sent from smart meters and other terminal devices. It can also be used as a relay between smart meters and cloud servers or other devices. It can also coordinate real-time communication between multiple smart meters, and between terminal devices such as smart meters and their related accessories, as well as relay devices. Before being directly received for analysis, the first energy consumption information may also be forwarded by more than one edge gateway or other relay devices (such as a LoRa wireless gateway) continuously or discontinuously.

[0070] The first energy consumption information can also be generated by other types of energy consumption collection modules, that is, it can be collected and converted using methods other than smart meters. For example, the real-time data of a commonly used simple meter associated with the energy-consuming device can be manually copied and manually input into an electronic device with a communication function to complete the collection. At this time, the electronic device can convert it into a data message and send it through its available communication protocol. In this case, the first energy consumption information received for analysis can be sent directly by the aforementioned electronic device with a communication function, or it can be sent by the edge gateway after the aforementioned electronic device sends it to the edge gateway.

[0071] The reception for parsing is completed by the energy consumption information receiving module. The energy consumption information receiving module is a device with the ability to receive data packets, for example, it can be a local server, a cloud server or an edge gateway. A cloud server refers to a group of remote servers whose physical location may not be at the energy consumption site. The first energy consumption information directly used for parsing received by the cloud server as the energy consumption information receiving module may be sent by a terminal device such as a smart meter serving as an energy consumption collection module, may be forwarded by an edge gateway serving as a relay device from a terminal device such as a smart meter serving as an energy consumption collection module, or may be further forwarded by other relay devices from other edge gateways serving as relay devices. Similarly, when the edge gateway or the local server serves as the energy consumption information receiving module to receive the first energy consumption information, the first energy consumption information may be sent by a terminal device such as a smart meter serving as an energy consumption collection module, may be forwarded by other edge gateways serving as relay devices from a terminal device such as a smart meter serving as an energy consumption collection module, or may be further forwarded by other relay devices from other edge gateways serving as relay devices.

[0072] Secondly, the first energy consumption information is parsed, and the parsing includes obtaining the value of the target energy consumption indicator based on the first energy consumption information. Since the data message in the first energy consumption information includes a data portion, and the data portion includes an indicator value string corresponding to the target energy consumption indicator, and the value of the target energy consumption indicator can be obtained based on the indicator value string corresponding to the target energy consumption indicator, the value of the target energy consumption indicator can be obtained based on the first energy consumption information. Specifically, for example, the parsing performed on the first energy consumption information may include first extracting the data portion of the data message of the first energy consumption information based on the communication protocol applicable to the first energy consumption information, then extracting the indicator value string corresponding to the target energy consumption indicator from the data portion, and finally obtaining the value of the target energy consumption indicator based on the conversion rule of the indicator value string.

[0073] In the aforementioned example, the first energy consumption information generated by a smart meter in MODBUS RTU format, indicating current as an energy consumption indicator, is specifically represented by a header and a data portion called "02 03 04 4121 999A 673E." During parsing, the data portion "02 03 04 4121 999A 673E" is first extracted from the first energy consumption information data packet based on the smart meter's communication protocol. Then, based on the calculation and conversion rules for the indicator value string, the decimal value of the current (unit A) is calculated and converted to 10.1.

[0074] The energy consumption information parsing module is used to perform the parsing of the first energy consumption information. If the energy consumption information receiving module has the ability to perform parsing calculations, it can also serve as the energy consumption information parsing module, for example, a local server, cloud server, or edge gateway serving as the energy consumption information receiving module. The energy consumption information parsing module can also be completed by other devices with parsing calculation capabilities, for example, other local servers, cloud servers, or edge gateways, or registers with parsing calculation capabilities. Where appropriate, the energy consumption information parsing module can also be composed of multiple different or identical devices with parsing calculation capabilities, each of which performs all or part of the parsing calculations.

[0075] In a specific embodiment, the energy consumption information parsing module needs to call a rule parser when performing the parsing, wherein the rule parser can be used to provide the energy consumption information parsing module with extraction, calculation and conversion rules for obtaining the value of the target energy consumption indicator from the first energy consumption information.

[0076] Thirdly, based on the analysis, second energy consumption information is obtained, where the second energy consumption information is structured and includes the target energy consumption indicator and its value.

[0077] The second energy consumption information is obtained by the structuring module based on the analysis.

[0078] Structured means that the data can be efficiently accessed by programs and has a standardized format.

[0079] Referring to the previous example, a smart meter generates first energy consumption information indicating current, an energy consumption indicator. After the energy consumption information parsing module performs parsing, the decimal value of the current in A is 10.1. The structuring module then generates structured second energy consumption information based on this information, for example, represented by the following data table:

[0080] If it has the ability to process structured information, the energy consumption information parsing module can also serve as a structured module, for example, a local server, cloud server or edge gateway serving as the energy consumption information parsing module. Considering the necessity for some energy consumption analysis projects, the structured information module preferably also has a device that can store structured information, such as a local server or cloud server. The structured module can also be other devices that have the ability to process structured information, such as other local servers, cloud servers, or caches or flash memory devices. Where appropriate, the structured module can also be composed of multiple different or identical devices that have the ability to process structured information, each of which generates all or part of the structured information.

[0081] Structured data is more intuitive, easier to manipulate, and analyze. Parsing unstructured primary energy consumption information into structured target energy consumption indicator data can reduce the difficulty of accessing target energy consumption indicator data. This significantly reduces the amount of computation required, particularly in scenarios where multiple target energy consumption indicator calls are required, facilitating further analysis and processing of the target energy consumption indicator. The parsed target energy consumption indicator data also takes up less storage space, facilitating full utilization of storage space.

[0082] The energy consumption collection module may also record the time of collecting the value of an energy consumption indicator as another energy consumption indicator, or when the energy consumption collection module cannot collect the information, the energy consumption information parsing module provides the execution time of the corresponding parsing instead as the time when the value of the energy consumption indicator is collected. Therefore, the time of collecting the value of an energy consumption indicator as another energy consumption indicator can also be used as part of the structured second energy consumption information.

[0083] Next, energy consumption analysis is performed based on the second energy consumption information. Specifically, the energy consumption analysis may be performed based on the data provided in the second energy consumption information to perform at least one of the following analyses, including:

[0084] (a) Benchmark comparison analysis. Benchmark comparison analysis refers to the degree of deviation of a specific energy consumption indicator from its historical average data. For example, suppose the total energy consumption of energy-consuming equipment in a certain time period is A, and the average energy consumption of the same time period in the previous n days is A. avg , then the energy consumption excess ratio X can be calculated according to the following formula:

[0085] The "to-be-inspected time period" refers to the period before the time of the inspection. For example, if the inspection time is 14:00, the "to-be-inspected time period" can be one hour before that, that is, from 13:00 to 14:00 on the current day. Correspondingly, the "n days prior" time period refers to from 13:00 to 14:00 on the previous n days, where n can be set manually, for example, 7. The aforementioned inspection time can be the current time or a time in the past.

[0086] The total energy consumption A of the time period to be investigated can be directly collected as an energy consumption indicator (for example, electricity consumption as an energy consumption indicator), or it can be calculated through other energy consumption indicators, for example, by adding the product of instantaneous power and time length collected multiple times during the time period to be investigated. The average energy consumption of this time period in the previous n days is A avg The energy consumption index value of the corresponding time period in the previous n days can be retrieved from the time series database, and the energy consumption of the time period each day can be obtained by averaging the obtained energy consumption.

[0087] In addition, the excess ratio of the current value at a certain time point to be examined compared to the average value of the current at the time point in the previous n days can also be examined. The implementation method is similar to the above.

[0088] The excess ratio X may be directly displayed as the analysis result of the "benchmark comparison analysis" project, or an abnormal warning value (eg, 10%) may be set to provide an indication of abnormal energy consumption when the excess ratio X exceeds the abnormal warning value.

[0089] After setting the corresponding parameters, the analysis project can be automatically repeated regularly; when the analysis start time is further set to the time point to be investigated, it is equivalent to realizing dynamic monitoring of the analysis project.

[0090] (b) Analysis of energy consumption during non-working hours. "Analysis of energy consumption during non-working hours" refers to determining whether energy-consuming equipment generates energy consumption that should not be generated during non-working hours, that is, whether there is energy waste. The non-working hours can be set manually, and can be set to be the same or different within each time period (for example, daily, weekly, monthly, etc.). When determining whether energy consumption that should not be generated is generated, at least one energy consumption indicator to be examined can be compared to see whether it exceeds a baseline value, where the baseline value can also be set manually.

[0091] For example, suppose the non-operating hours for energy-consuming equipment are set to 00:00-05:00 daily, and the energy consumption indicator to be examined is power, with a baseline value of 5W. First, the power value at the time of examination is retrieved from the time series database to determine whether it is greater than the baseline value of 5W. If the return result is yes, the next step is to determine whether the time of examination is during non-operating hours. If the return result is yes, the energy-consuming equipment is considered to be consuming energy that should not be generated during non-operating hours, indicating that energy waste has occurred.

[0092] There may be more than one energy consumption index to be examined. In this case, the exceeding of any one, multiple or all of the energy consumption indexes to be examined may be used as a criterion for judging energy waste.

[0093] The time point to be investigated can be the time when the analysis is started, or before that. Similarly, after setting the corresponding parameters, the analysis project can be automatically repeated regularly, and real-time dynamic monitoring can be achieved.

[0094] (c) Average power factor analysis. In general circuits, the transmitted electric power contains both active and reactive components. The power factor PF is the ratio of the active power P of the electrical equipment to the apparent power S. The lower the power factor, the greater the reactive power, which means more energy is wasted. "Average power factor analysis" refers to calculating the average value of the power factor PF of the energy-consuming equipment during the time period to be examined (i.e., the average power factor, PF avg ) to determine the energy waste of energy-consuming devices during the time period under investigation. The active power P and apparent power S used to calculate the power factor PF of energy-consuming devices can be directly collected energy consumption indicators or calculated from other energy consumption indicators (such as voltage and current). The calculation methods are well known. For example, one method for calculating apparent power S based on energy consumption indicators is to calculate the product of voltage and current in the circuit where the energy-consuming device is located.

[0095] For example, by retrieving the energy consumption index value stored in the time series database, the m time points or sub-periods t in the time period to be investigated can be calculated. i The power factor PF of each (i=1,2,…,m) ti At the m time points or subdivided time periods t i Compared with the case where the time period to be investigated is uniformly sampled, the m power factors PF can be calculated by ti The arithmetic mean of the average power factor PF avg ,Right now

[0096] The time period to be examined may be the time when the analysis starts and a period of time before that (which can be set manually), for example, the time when the analysis starts and 5 minutes before that. The time period to be examined may also be a period of time before the analysis starts.

[0097] It can be imagined that, as needed, the average power factor PF avg It can also be the power factor PF during the time period to be investigated ti The geometric mean, weighted mean, square mean, etc.

[0098] Average power factor PF avg It can be directly displayed as the analysis result of the "Average Power Factor Analysis" project, or it can be displayed by setting an abnormal warning value (such as 90%) in the average power factor PF avg When the energy consumption is lower than the abnormal warning value, an indication of abnormal energy consumption is provided.

[0099] Similarly, after setting the corresponding parameters, the analysis project can be automatically repeated regularly and can achieve real-time dynamic monitoring.

[0100] (d) Energy consumption fluctuation analysis. "Energy consumption fluctuation analysis" refers to calculating the fluctuations in the energy consumption indicators of energy-consuming equipment during the time period under investigation. Large fluctuations in the values ​​may indicate abnormal energy consumption.

[0101] A common method for assessing fluctuations in values ​​is to calculate the coefficient of variation. The coefficient of variation, also known as the coefficient of dispersion, is a relative statistic that measures the degree of variation in data. It is used to compare the degree of variation in two or more samples with different means and is the ratio of the standard deviation to its mean.

[0102] For example, the energy consumption indicator to be investigated has been set to power. By retrieving the value of the energy consumption indicator stored in the time series database, the average sampling of m time points or sub-periods t in the time period to be investigated can be obtained. i The power P of each (i=1,2,…,m) ti , and then calculate the m power P ti The ratio of the standard deviation σ to its mean μ is used to obtain its power variation coefficient V σ , as a reflection of its energy consumption volatility, that is

[0103] in,

[0104] The time period to be examined may be the time when the analysis starts and a period of time before that (which can be set manually), for example, the time when the analysis starts and 5 minutes before that. The time period to be examined may also be a period of time before the analysis starts.

[0105] It is conceivable that the energy consumption indicator to be examined can also be set to other energy consumption indicators, such as current, voltage, etc.; the fluctuation of the value can also be evaluated by other methods, such as range, quartile difference, variance, standard deviation, etc.

[0106] Power variation coefficient V σ It can be directly displayed as the analysis result of the "Energy Consumption Fluctuation Analysis" project, or it can be displayed by setting an abnormal warning value (for example, 20%) in the power variation coefficient V σ When the energy consumption exceeds the abnormal warning value, an indication of abnormal energy consumption is provided. There may be more than one energy consumption indicator to be examined. In this case, the exceeding of any one, multiple or all energy consumption indicators to be examined may be used as a criterion for judging abnormal energy consumption.

[0107] Similarly, after setting the corresponding parameters, the analysis project can be automatically repeated regularly and can achieve real-time dynamic monitoring.

[0108] (e) Analysis of energy consumption contribution fluctuations. Energy consumption contribution refers to the ratio of the target energy-consuming device's energy consumption to the total energy consumption of multiple energy-consuming devices, including the target device. Energy consumption contribution fluctuation refers to the difference between the target device's energy consumption contribution and its baseline energy consumption contribution during the time period under investigation. The greater the fluctuation in energy consumption contribution, the more likely the target device is experiencing energy consumption anomalies.

[0109] Among them, one method of representing the benchmark value of the energy consumption contribution of the target energy-consuming equipment is the arithmetic mean of the energy consumption contribution of the target energy-consuming equipment in the past n days corresponding to the time period to be examined.

[0110] For example, the energy consumption to be investigated is represented by the energy consumption indicator of electricity consumption E, the time period to be investigated is 13:00-14:00 on the same day, and the energy consumption contribution benchmark value of the target energy-consuming equipment is the average energy consumption contribution of the energy-consuming equipment corresponding to the time period to be investigated in the past n days, where n is 7; by calling the value of the energy consumption indicator in the time series database, it can be obtained that the electricity consumption of the target energy-consuming equipment in the time period to be investigated on the same day (i.e. 13:00-14:00 on the same day) is E target , the electricity consumption during the time period to be investigated (i.e., 13:00-14:00 of the previous i-th day) on the previous i-th day (i=1,2,…,7) is E target-i The multiple energy-consuming devices including the energy-consuming devices are set as all the energy-consuming devices in the workshop where the target energy-consuming device is located, and their power consumption during the inspection period on the same day (i.e., 13:00 to 14:00 on the same day) is E all , the electricity consumption during the time period to be investigated on the previous i-th day (i.e. 13:00~14:00 on the previous i-th day) is E all-i ; Then the energy consumption contribution M of the target energy-consuming equipment in the time period to be investigated is

[0111] The benchmark value L of energy consumption contribution of target energy-consuming equipment is

[0112] Where n = 7; then the energy consumption contribution change Δ is

[0113] It is easy to imagine that the energy consumption to be investigated can also be represented by other energy consumption indicators, such as water consumption, gas consumption, coal consumption, etc.; the multiple energy-consuming equipment including the target energy-consuming equipment can also be set to all or part of the factory where the target energy-consuming equipment is located, or energy-consuming equipment of the same or different types as the target energy-consuming equipment; the energy consumption contribution baseline value of the target energy-consuming equipment can also be represented by other methods, such as the geometric mean, weighted mean, square mean, etc. of the energy consumption contribution of the target energy-consuming equipment corresponding to the time period to be investigated in the past n days, or an empirical value set manually; the change in energy consumption contribution can also be evaluated by other methods, such as range, interquartile range, variance, standard deviation, etc.

[0114] The energy consumption contribution change Δ can be directly displayed as the analysis result of the "Energy Consumption Contribution Change Analysis" project, or an abnormal warning value (e.g., 10%) can be set to provide an indication of energy consumption anomaly when the energy consumption contribution change Δ exceeds the abnormal warning value. There can be more than one energy consumption indicator to be examined. In this case, exceeding the standard for any one, multiple, or all of the energy consumption indicators to be examined can be used as the basis for determining energy consumption anomaly.

[0115] Similarly, after setting the corresponding parameters, the analysis project can be automatically repeated regularly and can achieve real-time dynamic monitoring.

[0116] (f) Short-term energy consumption peak and valley analysis. This analysis determines whether energy consumption of energy-consuming equipment experiences extreme peaks or valleys within a short period of time. Extreme peaks or valleys in energy consumption within a short period of time indicate a possible defect in the equipment that affects energy consumption.

[0117] The extreme peaks and valleys can be determined based on the time derivative changes of energy consumption. For example, the energy consumption indicator to be investigated has been set to power; by calling the energy consumption indicator information in the time series database, the power P(t) of the energy-consuming device at time point t can be obtained, and the time derivative of the power P(t) at this time point can be calculated as P'(t), wherein the calculation method of the time derivative is well known. Intercept a time point t1 and t2 before and after the time point to be investigated, wherein, due to the limitations of the collection and analysis frequency of the energy consumption indicator, the time points t1 and t2 can be set to 5 seconds before and 5 seconds after t. Determine whether the mathematical signs of P'(t1) and P'(t2) are opposite (i.e., one is positive and the other is negative). If the signs are opposite, it is considered that the power of the energy-consuming device has an extreme peak or valley. In addition, judgment conditions can be added. For example, it is further required that only when one or both of |P'(t1)| and |P'(t2)| are higher than a set value (such as 30%), it is considered that the power of the energy-consuming device has an extreme peak or valley.

[0118] In addition, the extreme peak and valley can be determined based on the magnitude of other change rates of energy consumption. For example, similarly, the energy consumption equipment at time point t can be obtained.i-1 , t i and t i+1 The power is P(t i-1 ), P(t i ) and P(t i-1 ), where, due to the limitations of energy consumption index collection and analysis frequency, t i-1 , t i and t i+1 If the interval is 5 seconds, determine whether the following two equations have opposite signs and their absolute values ​​are both greater than the set value (such as 30%). If so, it is considered that the power of the energy-consuming equipment has an extreme peak or valley:

[0119] as well as

[0120] The extreme peaks and valleys can also be determined by expressing the energy consumption changes in other mathematical ways.

[0121] The energy consumption to be investigated can also be characterized by other energy consumption indicators. The presence or absence of short-term energy consumption peaks and valleys can be directly displayed as the analysis results of the "Short-term Energy Consumption Peak and Valley Analysis" project. Alternatively, abnormal warning values ​​can be set, and an indication of energy consumption anomalies can be provided when the abnormal warning values ​​are met. For example, the abnormal warning value can be that short-term energy consumption peaks and valleys occur more than n times during the time period to be investigated, or that short-term energy consumption peaks and valleys occur at least n times per day within m consecutive days.

[0122] Similarly, after setting the corresponding parameters, the analysis project can be automatically repeated regularly and can achieve real-time dynamic monitoring.

[0123] Depending on practical circumstances, each of the above energy consumption analysis items can be real-time or non-real-time. Real-time analysis refers to the energy consumption data of the current or a short period of time (e.g., the last 5 minutes, the last hour). Non-real-time analysis refers to the energy consumption data of an even earlier period (e.g., one day, one week, one month ago, etc.), i.e., retrospective analysis.

[0124] In one embodiment, the energy consumption analysis includes executing each of the following items and obtaining meaningful analysis results: baseline comparison analysis, non-operating time energy usage analysis, average power factor analysis, energy consumption volatility analysis, energy consumption contribution variation analysis, and short-term energy consumption peak and valley analysis. Each item can be executed simultaneously or at different times and can be based on a single or the same energy consumption indicator or second energy consumption information, or based on multiple or different energy consumption indicators or second energy consumption information.

[0125] In one embodiment, in addition to the above energy consumption analysis items, the energy consumption analysis performed may also include other analyses on or based on energy consumption indicators, such as the following conventional energy consumption analysis items: time ratio analysis, analogy analysis, and trend analysis.

[0126] It can be understood that one energy consumption analysis project is to directly compare the energy consumption indicators directly obtained through collection with the corresponding abnormal warning values.

[0127] The energy consumption information analysis module performs the energy consumption analysis. When more than one energy consumption analysis project is involved, each energy consumption analysis project may be performed simultaneously (including synchronization achieved through multiplexing technology, such as time division multiplexing), or sequentially and / or separately, subject to the capabilities of the energy consumption information analysis module.

[0128] The energy consumption information analysis module is a device with analytical computing capabilities, such as a cloud server, local server, edge gateway, or a register with computing capabilities. Due to its computing capabilities, the energy consumption information parsing module may also serve as the energy consumption information analysis module; however, the energy consumption information analysis module can also be another device with analytical computing capabilities.

[0129] Under appropriate circumstances, the energy consumption information parsing module and the energy consumption information analysis module may be the same device, different devices, or multiple identical devices.

[0130] Finally, based on the energy consumption analysis, an indication of energy consumption anomalies is provided.

[0131] An energy consumption anomaly occurs when at least one, partial, or complete energy consumption metric of an energy-consuming device or its combination meets, fails to meet, exceeds, or falls below an abnormal warning value. Depending on the specific project being analyzed, the energy consumption metric under investigation can be the target energy consumption metric from the aforementioned analysis, with its value derived based on that analysis, or it can be an energy consumption metric other than the target energy consumption metric, with its value derived based on other current or past energy consumption analyses.

[0132] According to the type of each specific energy consumption indicator, its abnormal warning value may be selected from a single value with or without dimension, a value range, or logical conditions such as "yes", "no", "yes", "no", etc.

[0133] There are various ways to determine the abnormal warning value. The abnormal warning value can be obvious, set manually based on experience, or set according to regulations (such as environmental regulations, energy regulations). The abnormal warning value can also be derived based on the energy consumption index obtained by direct collection, such as the process data and result data generated in the current or past energy consumption analysis, obtained with or without specific calculation rules. The previous description of the energy consumption analysis project has given examples of abnormal warning values ​​that can be set under multiple energy consumption analysis projects, which will not be repeated here.

[0134] The energy consumption abnormality indication module is used to provide an indication of energy consumption abnormality, and is a device capable of providing abnormality indication. Based on the form of the indication of energy consumption abnormality, the optional range of the specific form of the energy consumption abnormality indication module can be determined.

[0135] The indication of abnormal energy consumption can be selected from a variety of forms. The indication can be human-oriented, such as visual, auditory, tactile indication, etc. Common visual indications include adjusting or maintaining the color, size, shape, flicker, etc. of visible objects; common auditory indications include adjusting or maintaining the volume, frequency, spatial position, etc. of sounds; common tactile stimulations include adjusting or maintaining the vibration frequency, intensity, direction, etc. of touchable objects. The indication can also be other perceptions caused by stimulating human nerves through light, sound, electricity, touch, etc. Correspondingly, the energy consumption abnormality indication module may be a corresponding device that can provide the above indications, such as visual devices such as display screens and display lights, auditory devices such as alarms and speakers, and tactile devices such as vibration generators and vibrating seats.

[0136] The energy consumption anomaly indication module can be provided by the devices involved in the previous steps, or by other devices independent of these devices. For example, the indication of energy consumption anomaly is executed by a local server and its display that communicates with the cloud server, and the indication is in the form of a color prompt. After the cloud server performs energy consumption analysis and finds that a specific energy consumption indicator has an energy consumption anomaly, it can generate a message about the energy consumption anomaly, and when it receives a request to retrieve data, it sends an instruction to the local server, and the local server controls the display to form a flashing red background prompt in the display area of ​​the corresponding energy consumption indicator value. For another example, the indication of energy consumption anomaly is executed by a warning light or warning bell deployed on the corresponding energy-consuming device. For another example, the indication of energy consumption anomaly is executed by a vibration device deployed next to the energy consumption management personnel.

[0137] The indication of energy consumption anomaly may also be a data message about energy consumption anomaly generated according to or without a request. The data message may or may not be in a portable document format (PDF), rich text format, or other document format that can be read by humans directly or through a reading program. For example, after performing energy consumption analysis and discovering that a specific energy consumption indicator has an energy consumption anomaly, the cloud server that is a component of the energy consumption information analysis module and the energy consumption anomaly indication module summarizes various information about the energy consumption anomaly and generates a file in PDF format to be sent to the local device that is another component of the energy consumption anomaly indication module.

[0138] The indication of abnormal energy consumption and the device serving as the abnormal energy consumption indication module may also be any combination of the above forms.

[0139] Due to practical limitations, each directly collected energy consumption indicator can be collected for a single energy-consuming device, and the first energy consumption information and the second energy consumption information can also be associated with the energy-consuming device. Therefore, even when managing multiple energy-consuming devices simultaneously, the indication of abnormal energy consumption can be made for the abnormal energy consumption of a single (or each) energy-consuming device or a specific combination thereof.

[0140] Since the energy consumption analysis can be real-time or non-real-time compared to the energy consumption being analyzed, the indication of energy consumption anomalies can also be real-time or non-real-time compared to the corresponding energy consumption anomalies. The non-real-time energy consumption analysis and the indication of energy consumption anomalies can enable retrospective analysis of energy consumption anomalies and provide energy consumption anomaly warnings, helping energy-consuming enterprises understand the energy consumption anomalies of energy-consuming equipment in previous specific or unspecified time periods, making it easier for energy-consuming enterprises to discover the location and cause of energy consumption anomalies and resolve them accordingly.

[0141] Furthermore, the indication of abnormal energy consumption may be in real time or in non-real time (i.e., delayed) compared to the corresponding energy consumption analysis. In certain circumstances, an indication of abnormal energy consumption that lags behind the energy consumption analysis may be desirable, possibly because the period during which relevant responsible personnel in the energy-consuming enterprise expect to receive such an indication is short and / or fixed.

[0142] Based on the foregoing embodiments or part of the embodiments, another embodiment of the present invention provides an energy consumption management method, wherein first energy consumption information is generated by an energy consumption collection module set for energy-consuming equipment, and the generation of the first energy consumption information is based on the monitoring of the target energy consumption indicators of the energy-consuming equipment by the energy consumption collection module.

[0143] Different types of energy consumption indicators may need to be collected by different types of energy consumption collection modules. The first energy consumption information includes information related to the target energy consumption indicator of the energy-consuming device. Therefore, in order to generate the first energy consumption information, it can be understood that the energy consumption collection module set for the energy-consuming device can monitor the target energy consumption indicator of the energy-consuming device and generate the first energy consumption information accordingly. For example, when voltage is used as the target energy consumption indicator, an intelligent voltmeter that can generate the first energy consumption information can be set as an energy consumption collection module for the electrical device. The energy consumption collection module can monitor the target energy consumption indicator of the energy-consuming device directly or indirectly. Direct monitoring means that the energy consumption collection module can directly collect the value of the target energy consumption indicator and generate the first energy consumption information based on it; for example, when voltage is used as the target energy consumption indicator as mentioned above, an intelligent voltmeter that can generate the first energy consumption information is set. Indirect monitoring means that the energy consumption collection module directly collects the values ​​of other energy consumption indicators, and converts the values ​​of other directly collected energy consumption indicators into the values ​​of target energy consumption indicators through regular conversion to generate the first energy consumption information; for example, when instantaneous power is used as the target energy consumption indicator, an intelligent voltmeter, an intelligent ammeter, and a computing and communication device are set up for the energy-consuming equipment as an energy consumption collection module, wherein the intelligent voltmeter and the intelligent ammeter respectively collect the voltage and current data of the energy-consuming equipment, and the computing and communication device obtains the data from the intelligent voltmeter and the intelligent ammeter, calculates the instantaneous power by itself, and generates the first energy consumption information.

[0144] The generation of the first energy consumption information is also based on the energy consumption collection module's parsing rule variable table corresponding to the target energy consumption indicator. A parsing rule variable is a variable used to indicate the conversion relationship between the energy consumption indicator collected by the energy consumption collection module and the data portion of a data message sent by the energy consumption collection module, including an indicator value string corresponding to the energy consumption indicator. The parsing rule variable table includes the textually recorded names of the parsing rule variables and the values ​​corresponding to the variables.

[0145] For example, a smart meter using the MODBUS RTU communication protocol generates first energy consumption information indicating the energy consumption indicator of current, where the data portion is "02 03 04 4121 999A 673E". This smart meter uses the following parsing rule variable table for the energy consumption indicator of current:

[0146] The dimension, parsing format, start bit, and character length are parsing rule variables and have the aforementioned values, indicating that in the data portion of the first energy consumption information data message, the four characters starting from the fourth bit are encoded as Float32 (32-bit floating point numbers) and record the value of the monitored current energy consumption indicator with A as the dimension. Based on these conversion relationships, the energy consumption collection module can convert the corresponding energy consumption indicator value into the data portion of the aforementioned data message.

[0147] Because different energy consumption collection modules may use different communication protocols and data formats, and their manufacturers configure different data interpretation methods, the variables used to represent the conversion relationship between energy consumption indicators and the data portion of the data message generated by the energy consumption collection module will also vary, resulting in different parsing rule variables. For example, the end bit may be used as a parsing rule variable, used together with the start bit to determine the character bit range in the data message that indicates the indicator value string corresponding to the energy consumption indicator. For another example, the parsing rule variables applicable to the DL / T645 communication protocol may include the address field, data field, and data field length.

[0148] The names of parsing rule variables can be their Chinese names, as shown above, or English names (for example, dimension, numeration, start position, and length, respectively, in the previous example), or other understandable character expressions. The values ​​of parsing rule variables have a wider range of options than energy consumption indicator values ​​and can be a variety of characters depending on their meaning.

[0149] The parsing rule variable table is a table that includes the names and values ​​of the parsing rule variables that display their corresponding relationships. A table refers to a combination of cells that are formed by the intersection of at least one row and at least one column to form at least one rule. In terms of their corresponding relationship, the name of each parsing rule variable is located in a different cell in a row or a column of this table, and the value of the parsing rule variable is located in the corresponding cell in the other row or other column of the column where the cell where the name of the parsing rule variable is located, thereby displaying their corresponding relationship. As in the previous example, for a smart meter that uses the MODBUS RTU communication protocol, the parsing rule variable table can be a combination of cells with 5 rows and 2 columns, with the names of the parsing rule variables located in 4 different cells in the first column, and the values ​​of the parsing rule variables located in the same cell in the second column outside the first column where the name of the corresponding parsing rule variable is located. Similarly, the parsing rule variable table can also be a 2-row, 5-column table that is an inverted table compared to the previous example. In addition, the header of the parsing rule variable table may also be other expressions besides the previous example "name" and "value", such as item and value; or other forms. For example, when one table corresponds to multiple energy consumption indicators, the parsing rule variable table has more columns, and the expression "value" is removed and the name of each energy consumption indicator is directly used to indicate that the column is filled with the value of the energy consumption indicator corresponding to the parsing rule variable.

[0150] In one embodiment, the parsing rule variable table is stored in a picture format. The parsing rule variable table stored in a picture format can maintain its original form in different transmission environments, and remain stable when other text in the same file undergoes changes in font, paragraph, and other formats. The structure of the parsing rule variable table can still remain stable, and the correspondence between rows and columns in the original content is presented in a highly readable manner, which is conducive to dissemination. In addition, the parsing rule variable table in tabular form is the most intuitive way to reflect the correspondence between parsing rule variables and their values. Its design is simple and does not require complex data structure design. It is easy for parsing rule designers to provide to the audience, and it is also easy for the audience to understand. In general, the way to reflect the correspondence between parsing rule variables and their values ​​in a tabular manner and store it in a picture manner is an intuitive, stable, easy to produce, easy to disseminate, and easy to understand way.

[0151] It should be understood that "storing in image format" does not require that the parsing rule variable table be in image format from the moment it is generated. Rather, it only requires that it be stored and processed in image format during operations generated based on its image format in the corresponding embodiments. For example, the parsing rule variable table may have been in a common Excel file format, but may have been converted to an image format using an image conversion tool, and then stored in that image format when it is processed in the corresponding embodiments.

[0152] The generation of the first energy consumption information is also based on the communication protocol used by the energy consumption collection module. A communication protocol, also known as a communication protocol, refers to an agreement between two communicating parties regarding the control of data transmission. Specifically, the communication protocol specifies the location, length, and representation of information such as the destination address and format in the header of transmitted data messages. Furthermore, based on the rules of the communication protocol, it is possible to determine at least how to distinguish between the header and data portion of a data message, thereby indicating the correspondence between a data message using the communication protocol and its data portion.

[0153] The energy consumption management method involves a rule parser, which is configured to store an energy consumption index parsing function corresponding to the target energy consumption index after parsing the energy consumption acquisition module corresponding to the target energy consumption index.

[0154] The aforementioned parsing configuration of the rule parser includes at least step T200, step T300, step T400, step T500, and step T600.

[0155] Step T200 includes obtaining the symbolic information of the communication protocol used by the energy consumption acquisition module, and the parsing rule variable table corresponding to the target energy consumption index of the energy consumption acquisition module. The parsing rule variable table and the communication protocol are as described above. The obtained parsing rule variable table is a table in the form of a picture. The symbolic information of the communication protocol obtained refers to information that can be used to directly or indirectly obtain the entire substantive content of the communication protocol. For common communication protocols or other communication protocols that have been published in full text in public channels, the entire substantive content of the communication protocol can be obtained by the name of the communication protocol, so its name can be used as symbolic information; but for communication protocols that are not widely used or have the same name, its symbolic information may be its entire substantive content. The symbolic information of a communication protocol may also be in the form of abbreviations, names in other languages, logos, etc. For example, for a smart meter that serves as an energy consumption collection module, the full text of the MODBUS RTU protocol used, or just the protocol name "MODBUS RTU," can be obtained as identifiers of the communication protocol. Furthermore, the smart meter's parsing rule variable table can be obtained, as exemplified by the smart meter that generates the first energy consumption information, indicating current, when introducing the parsing rule variable table. The communication protocol identifiers and parsing rule variable table can be obtained manually or from other components, or automatically extracted based on such uploaded or sent information.

[0156] Step T300 includes determining the boundaries of each cell in the obtained parsing rule variable table based on the Faster RCNN algorithm. Cell boundaries are the divisions between rows and columns, which can be reflected as cell boundary lines or, if no lines are drawn, as locations that separate rows and columns.

[0157] The Faster RCNN algorithm is a deep learning model for object detection, part of the R-CNN family of models. It aims to address two key challenges in object detection: detection accuracy and processing speed. For the implementation of the Faster RCNN algorithm, reference can be made to publicly available information in the prior art, such as those available on Github and Gitee. More specifically, a Python project on Github is available at https: / / github.com / rbgirshick / py-faster-rcnn. Before using it for table detection and cell boundary determination, the general Faster RCNN algorithm must be adaptively trained using a training dataset. For training datasets, reference can be made to publicly available datasets in the prior art, such as those available on Github and Gitee. More specifically, a dataset available at the following domain is https: / / github.com / doc-analysis / TableBank. Custom training datasets can also be created based on the target task (e.g., table detection and cell boundary delineation).

[0158] Continuing with the above example, the Faster RCNN algorithm is applied to the obtained parsing rule variable table of the smart meter that generates the first energy consumption information of the energy consumption indicator indicating current, and the frame lines of each cell in the table are determined.

[0159] Compared to other algorithm models selected in this step, the Faster RCNN algorithm has various advantages, including high accuracy, easy to improve accuracy, data exploration, low time overhead, and high real-time performance, high flexibility, and high target diversity. Specifically, several of these advantages are mentioned. First, the detection and recognition method based on the Faster RCNN algorithm can be applied to datasets with marked table element locations. Compared to the detection and recognition method based on machine learning, which mainly targets datasets with marked table locations, the variety of datasets that the Faster RCNN algorithm can apply to allows it to describe tables in documents in more detail, thereby improving its accuracy and ability to effectively resist interference. Second, because the table detection and recognition method based on the Faster RCNN algorithm is more dependent on the type of dataset it is applied to, the accuracy of the model can be improved to a higher level when there is a more detailed data description, and its accuracy is relatively easy to improve. This is different from the difficult method of improving the accuracy of table detection and recognition under machine learning algorithms, which mainly adopts the improvement of the model itself. Thirdly, the Faster RCNN algorithm-based detection and recognition method can explore detailed table information within complete text. Unlike machine learning-based table detection and recognition methods, which primarily train models on existing data, the Faster RCNN algorithm-based detection and recognition method can obtain more useful content, thus offering advantages in data exploration. Finally, machine learning-based table detection and recognition methods are often susceptible to factors such as training data and model size, while the Faster RCNN algorithm-based table detection and recognition method is much easier to identify and detect, significantly shortening the time required and reducing time overhead.

[0160] Step T400 includes using a character recognition algorithm to identify characters within the boundaries of each cell in the obtained parsing rule variable table. Exemplarily, conventional character recognition algorithms include using a DBNet algorithm for text detection and a CRNN algorithm for text recognition. For DBNet and CRNN algorithms, reference can be made to publicly available information in the prior art, such as content published on popular code disclosure platforms like Github and Gitee.

[0161] Continuing with the above example, the obtained parsing rule variable table of the smart meter generating the first energy consumption information indicating the current energy consumption indicator and the determined frame line are applied to the content within the frame line to obtain the characters in each cell in the table.

[0162] Step T500 includes determining the correspondence between the parsing rule variables and their values ​​for the target energy consumption indicator based on the determination of the boundaries of each cell in the parsing rule variable table and the identification of the characters within the boundaries of each cell. Determining the boundaries of each cell in the parsing rule variable table allows for determining the correspondence between rows and columns in the parsing rule variable table, i.e., determining which parsing rule variables and which values ​​are in the same row or column; identifying the characters within the boundaries of each cell allows for determining the substance of each parsing rule variable and its value; and combining these two methods allows for determining the correspondence between the parsing rule variables and their values.

[0163] Continuing with the above example, the frame lines and contents within the frame lines of the parsing rule variable table of the smart meter that obtains the first energy consumption information that generates the energy consumption indicator indicating current are clearly identified with the starting position, character length, parsing format and respective values ​​of the rule parsing variables, and based on the fact that the starting position parsing rule variable and the cell containing the value "4" have frame lines that are extension lines of each other, their peer relationship is confirmed, and then their corresponding relationship is confirmed, and the corresponding relationship between other parsing rule variables and their values ​​is correspondingly confirmed.

[0164] Different from the usual manual recognition method, the correspondence between energy consumption parsing rule variables and their values ​​obtained by determining cell boundaries through the Faster RCNN algorithm and determining cell contents through the character recognition algorithm is a more automated method that reduces the repetitive work of developers, improves efficiency when configuring multiple energy consumption indicators of the same energy consumption parsing module at the same time, and avoids errors caused by memory errors when manually transcribing and identifying such correspondences.

[0165] Step T600 involves generating an energy consumption indicator parsing function for the target energy consumption indicator based on the correspondence between the parsing rule variables and their values, and based on the signature information of the communication protocol used by the energy consumption collection module. The energy consumption indicator parsing function for a specific energy consumption indicator converts the first energy consumption information containing the indicator value string for that energy consumption indicator into the energy consumption indicator value.

[0166] First, once the communication protocol used by the energy consumption collection module has been determined, a program (represented as a function, called a data extraction function) can be identified to extract the data portion from the data packets generated using that communication protocol. Data extraction functions are readily available. If the communication protocol used is common, their source may be public information channels or a library of data extraction functions compiled by developers based on common communication protocols for energy consumption collection in their energy consumption management scenarios. If the communication protocol used is not common, the data extraction function can be written by the developer based on the content of the communication protocol.

[0167] Secondly, based on the data part extraction function, an atomic function with a parsing rule variable can also be generated. After the parsing rule variable is assigned a value, the atomic function can be used to convert the value of the energy consumption indicator corresponding to the value from the data message generated using the communication protocol. Specifically, for different communication protocols, it can usually obtain the content of the indicator value string corresponding to the determined target energy consumption indicator from the data part based on the values ​​of the two conventional parsing rule variables, the start bit and the character length, and further obtain the value of the determined target energy consumption indicator from the indicator value string based on the value of the conventional parsing rule variable, the parsing format. Therefore, given that the data part extraction function can already extract the data part from the data message generated using the communication protocol, a corresponding program can be further generated, and the program is represented as a function (i.e., an atomic function) with the corresponding parsing rule variable as the function variable. Therefore, when the variable of the atomic function is assigned the value of the parsing rule variable corresponding to the target energy consumption indicator, the atomic function can convert the value of the target energy consumption indicator. The atomic function using a specific parsing rule variable can be extracted from an atomic function library. The atomic functions in the atomic function library can be from public channels or written in advance by developers based on conventional parsing rule variables. Conventional parsing rule variables are mainly as described above, and include various parsing rule variables (defined as above) of energy consumption collection modules commonly used in the field of energy consumption management. Even if the communication protocol used is conventional, the developer of the energy consumption parsing module may still set unconventional parsing rule variables. For example, two parsing rule variables, the middle position of the indicator value string and the character length, are set at the same time to determine the position of the indicator value string corresponding to the target energy consumption indicator in the data part and then determine its content. In this case, the developer can also write atomic functions for the energy consumption parsing module in advance based on the unconventional parsing rule variable settings; the generation of atomic functions may also be automatic or semi-automatic, including using common natural language processing and understanding tools to understand the meaning of the parsing rule variables, and using common parsing program writing tools to generate functions with such parsing rule variables as function variables based on this understanding. For an energy consumption collection module that collects multiple energy consumption indicators, the obtained atomic function can be universally applicable to various energy consumption indicators that apply such communication protocols and use such parsing rule variables, and used to obtain their values.

[0168] Finally, for the atomic function obtained above that is applicable to the communication protocols and uses the parsing rule variables, the value of the parsing rule variable obtained based on the parsing rule variable table of the target energy consumption indicator is assigned to it, and the generated function is the energy consumption indicator parsing function corresponding to the target energy consumption indicator.

[0169] For example, for an energy consumption acquisition module of a smart meter that uses the MODBUS RTU protocol to collect current, an energy consumption indicator, the data portion extraction function of the conventional protocol can be obtained based on the content disclosed on common code disclosure platforms Github and Gitee. Developers write the corresponding atomic function parse(data, format, start, length) based on the data portion extraction function and the parsing rule variables used by the smart meter (i.e., start bit, character length, and parsing format). The atomic function is assigned a value based on the value of the parsing rule variable in the parsing rule variable table corresponding to the current to generate an energy consumption indicator parsing function corresponding to the current, parse(data, Float32, 4, 4), or written as parseFloat32(data, 4, 4).

[0170] The above-mentioned parsing configuration process of the rule parser may be completed only by using built-in programs or with the help of external equipment, devices, modules, algorithms, and programs.

[0171] The above parsing configuration of the rule parser can achieve the efficient and convenient generation of energy consumption index parsing functions for target energy consumption indicators based on the knowledge of the parsing rule variable table and communication protocol, with minimal human intervention. In appropriate cases, the above parsing configuration of the rule parser can even be completed automatically. Furthermore, the above-mentioned parsing configuration of the rule parser provides a universal, stable and consistent configuration method for generating target energy consumption index parsing functions for different energy consumption parsing modules using different communication protocols: once the atomic function is set in advance, all energy consumption indicators that apply the same communication protocol and use the same parsing rule variables can apply the atomic function. Therefore, when it is necessary to configure an energy consumption acquisition module that simultaneously collects multiple energy consumption indicators and includes indicator value strings corresponding to multiple energy consumption indicators in the data message, it can quickly generate its energy consumption index parsing functions in batches based on the configuration information of each energy consumption indicator for subsequent energy consumption analysis; the energy consumption index parsing functions generated for multiple energy consumption indicators only differ in variable assignments, and their function content (i.e., program content) is highly similar. Therefore, the energy consumption index parsing function, the energy consumption index value obtained using the function, and the corresponding energy consumption analysis results all have high and consistent reliability, and can also avoid the errors that may occur in the previous manual configuration of the energy consumption index parsing function. In addition, the generated energy consumption index parsing function corresponding to the target energy consumption indicator can also be called repeatedly, which is conducive to extracting the value of the target energy consumption indicator multiple times based on the multiple first energy consumption information generated by the energy consumption acquisition module. In addition, for an energy consumption collection module that collects multiple energy consumption indicators at the same time and includes indicator value strings corresponding to multiple energy consumption indicators in the data message, unlike the past situation where all collected energy consumption indicators and their values ​​need to be parsed before the value of the target energy consumption indicator can be filtered out, this configuration method provides a method of configuring the energy consumption indicator parsing function for only one of the target energy consumption indicators, reducing the necessary amount of calculations and improving efficiency.

[0172] The analysis in step S200 further includes step S210: calling the rule parser that has been configured to analyze the target energy consumption indicator for the energy consumption collection module to obtain an energy consumption indicator analysis function corresponding to the target energy consumption indicator; and step S220: running the energy consumption indicator analysis function corresponding to the target energy consumption indicator on the first energy consumption information to obtain the value of the target energy consumption indicator.

[0173] As shown in the previous example, for the energy consumption collection module of the smart meter that uses MODBUS RTU as the communication protocol to collect the energy consumption indicator of current, the rule parser that has been configured to parse the energy consumption indicator corresponding to current of the smart meter is called, and the energy consumption indicator parsing function parseFloat32(data,4,4) corresponding to the energy consumption indicator of current is obtained from it; the energy consumption indicator parsing function is run on the first energy consumption information generated by the smart meter, which includes the indicator value string corresponding to the energy consumption indicator of current, to obtain the current value of 10.1.

[0174] In another embodiment of the present invention based on the aforementioned embodiment, for the target energy consumption indicator, the parsing rule variables include at least: a start bit, which indicates the starting position of the indicator value string corresponding to the target energy consumption indicator in the data portion of the first energy consumption information; a character length, which indicates the number of characters in the indicator value string corresponding to the target energy consumption indicator; and a parsing format, which indicates the conversion rules between the target energy consumption indicator value and the indicator value string corresponding to the target energy consumption indicator. The start bit and character length parsing rule variables are used to determine the content of the indicator value string in the data portion; the parsing format parsing rule variable determines how to convert the indicator value string into the corresponding energy consumption indicator value.

[0175] As in the previous example, a smart meter generates first energy consumption information indicating the energy consumption indicator current. For this energy consumption indicator, its parsing rule variables include at least the start bit, character length, and parsing format. When the start bit is 4, the character length is 4, and the parsing format is a 32-bit floating-point number, for energy consumption indicator information with the data portion "02 03 04 4121 999A 673E," the indicator value string corresponding to this energy consumption indicator is "4121999A," which is the hexadecimal format of the current value in 32-bit floating-point format. The decimal value of current should be 10.1. Using the start bit, character length, and parsing format as essential parsing rule variables is applicable to determining the values ​​of most energy consumption indicators represented numerically (such as voltage, usage, time, and other common energy consumption indicators) or binary values ​​(such as those in logical judgment conditions). Therefore, using these essential parsing rule variables can reduce the range of variables in atomic functions and reduce the complexity of the functions and their execution.

[0176] In another embodiment of the present invention based on the aforementioned embodiment, the parsing in step S200 further includes obtaining the execution time of the parsing, and the second energy consumption information in step S300 includes the execution time. The execution time of the parsing refers to the time when the energy consumption information parsing module calls the rule parser to parse the first energy consumption information, which can be obtained by using a program listener or other methods. Based on its usage scenario, this time may be the standard time of a certain time zone (for example, the time zone where the energy consumption site is located), or it may be the system time determined based on the prior settings of the energy consumption information parsing module. Since the energy consumption index range collected by some energy consumption collection modules does not include the energy consumption index of the index value collection time, the first energy consumption information generated by it also does not include the energy consumption index of the index value collection time and its corresponding index value string, and thus it is impossible to obtain this energy consumption index from the first energy consumption information. Therefore, it is considered to use the execution time of the adjacent parsing step instead of the index value collection time. Acquiring the execution time in the parsing step and managing the parsed execution time as structured data in the second energy consumption information allows for convenient use of the parsed execution time in the subsequent energy consumption analysis step instead of the indicator value collection time to perform time series or other time-related analyses on the collected energy consumption indicators. Examples of time-related (time series) analyses can be found in the previous description of the energy consumption analysis project.

[0177] In another embodiment of the present invention based on the aforementioned embodiment, the communication protocol is selected from one of MODBUS RTU, MODBUS TCP, OPCUA, and PLC. The preferred communication protocol is MODBUS RTU. These protocols, especially the MODBUS RTU communication protocol, are conventional communication protocols used by intelligent energy consumption collection equipment in the field of energy consumption management. The actual contents of these protocols are easy to obtain, and the data part extraction protocols and atomic protocols based on these protocols are common on public channels such as public code platforms. Therefore, in the parsing configuration of the rule parser, in order to realize the acquisition of the energy consumption index parsing function, the atomic protocols developed based on these communication protocols can be automatically collected or directly retrieved from public channels, thereby further improving the degree of automation and processing efficiency of the parsing configuration of the rule parser.

[0178] In a preferred embodiment based on the aforementioned embodiment using MODBUS RTU as the communication protocol, the parsing rule variable also includes byte order, which is used to indicate the relative order of byte storage bits and address bits in the memory. Since the MODBUS RTU communication protocol supports adjustment of the byte order of the data portion in the data message, when using MODBUS RTU as the communication protocol, byte order is added as a parsing rule variable to accommodate the possibility of the energy consumption acquisition module adopting an unconventional byte order and to generate a correct energy consumption index parsing function. For the byte order parsing rule variable, its value may be big-endian (or represented by code 0, taking the four bytes ABCD as an example, its byte order is ABCD, the same below), single-word reversal (big-endian byte swap, or represented by code 1, byte order is BADC), double-word reversal (little-endian byte swap or represented by code 2, byte order is BADC), or little-endian (or represented by code 3, byte order is BADC).

[0179] In another embodiment of the present invention based on the above-mentioned embodiment, the landmark information of the communication protocol used by the energy consumption collection module refers to the name of the communication protocol used by the energy consumption collection module, and the parsing configuration of the rule parser further includes: step T100 before step T200, step T100 includes obtaining the protocol of the energy consumption collection module, and the protocol of the energy consumption collection module includes the parsing rule variable table corresponding to the target energy consumption index of the energy consumption collection module and the name of the communication protocol used by the energy consumption collection module; accordingly, step T200 includes obtaining the name of the communication protocol used by the energy consumption collection module based on the keyword matching algorithm according to the acquired protocol of the energy consumption collection module, and obtaining the parsing rule variable table corresponding to the target energy consumption index of the energy consumption collection module through a table-based detection algorithm. Among them, the keyword matching algorithm can adopt a conventional algorithm such as a naive matching algorithm or a KMP algorithm to detect and extract the name of the communication protocol included in the protocol of the energy consumption acquisition module; the table detection algorithm can adopt an algorithm such as the aforementioned Faster RCNN algorithm, and when the parsing rule variable table is stored in the form of an image, various conventional image detection and / or extraction algorithms and methods can also be used as table detection algorithms to detect and extract the parsing rule variable table in the protocol of the energy consumption acquisition module. The naive matching algorithm, KMP algorithm, Faster RCNN algorithm, image detection and / or extraction algorithms and methods can be referred to in the above text or in reference to public information in the prior art (for example, based on content disclosed on common code disclosure platforms Github and Gitee).

[0180] For example, when configuring the rule parser, if the target energy consumption metric is current, the protocol of a corresponding smart meter, serving as an energy consumption acquisition module, is obtained through either developer upload or transmission from other components or programs. The protocol includes a table of parsing rule variables corresponding to current, stored as an image, and a statement that the communication protocol used by the smart meter is "MODBUS RTU." A keyword matching algorithm, such as a naive matching algorithm or a KMP algorithm, can be used on the smart meter's protocol to obtain the name of the communication protocol used by the smart meter, "MODBUS RTU." Furthermore, a table detection algorithm, such as the Faster RCNN algorithm, can be used on the smart meter's protocol to obtain the table of parsing rule variables corresponding to current, stored as an image.

[0181] Steps T300 to T600 are implemented accordingly as in the previous example. In step T600, based on the acquired name of the communication protocol used by the smart meter, "MODBUS RTU," various atomic functions applicable to the MODBUS RTU protocol can be quickly and automatically called from public channels such as code disclosure platforms or from an atomic function library of common communication protocols pre-established by developers based on protocol names. Subsequently, the energy consumption indicator parsing function applicable to the smart meter for the target energy consumption indicator of current, as described in the previous example, can be obtained by combining the correspondence between the parsing rule variables and their values ​​based on the target energy consumption indicator of current.

[0182] The advantage of the technical means used in this embodiment is that the process of obtaining the parsing rule variable table corresponding to the target energy consumption index of the energy consumption acquisition module can be further automated, thereby improving efficiency. In addition, when there is an atomic function of a specific energy consumption acquisition module in an open channel such as a code disclosure platform or an atomic function library of a common communication protocol based on the protocol name and established in advance by the developer, the automation efficiency of the parsing configuration of the rule parser can be improved by quickly extracting the simple information of the communication protocol name and quickly calling the applicable atomic function based on it. In addition, under this technical solution, except that the uploading of the protocol of the energy consumption acquisition module and the generation of the atomic function may require manual assistance (but may also be completed by automation as described in the previous example), the parsing rule configuration process of the rule parser can achieve all automated processing, further improve efficiency, reduce the possibility of manual processing errors, and indirectly improve the reliability and consistency level of the corresponding generated energy consumption index parsing function; and then the energy consumption management method in the present invention can achieve higher efficiency, automation, reliability and consistency level of analysis results.

[0183] Based on the same inventive concept, embodiments of the present invention further provide an energy consumption management system. The implementation solution provided by this energy consumption management system is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations of one or more energy consumption management system embodiments provided below can be found in the above-mentioned limitations of the energy consumption management method and will not be further elaborated here.

[0184] In one embodiment, referring to Figures 1-3, an energy consumption management system is provided, characterized in that the energy consumption analysis system includes: an energy consumption collection module, an energy consumption information receiving module, a rule parser, an energy consumption information parsing module, a structuring module, an energy consumption information analysis module, and an energy consumption anomaly indication module; wherein the energy consumption collection module generates first energy consumption information for a set energy consuming device, the first energy consumption information being a data message, the data portion of the first energy consumption information including an indicator value string corresponding to a target energy consumption indicator, the value of the target energy consumption indicator being obtainable based on the indicator value string corresponding to the target energy consumption indicator, the first energy consumption information being generated based on the energy consumption collection module monitoring the target energy consumption indicator of the energy consuming device, and based on a parsing rule variable table corresponding to the target energy consumption indicator in the energy consumption collection module and a communication protocol used by the energy consumption collection module, the parsing rule variable table being a table stored in a graphic format, the text content of the parsing rule variable table including names and values ​​of parsing rule variables indicating their corresponding relationships, for indicating a conversion relationship between the target energy consumption indicator and the data portion of the first energy consumption information; and for the target energy consumption indicator, the parsing rule variables at least include:

[0185] (a) a start bit, used to indicate the starting position of the indicator value character string corresponding to the target energy consumption indicator in the data portion of the first energy consumption information;

[0186] (b) character length, which is used to indicate the number of characters in the indicator value string corresponding to the target energy consumption indicator; and

[0187] (c) a parsing format for indicating a conversion rule between a value of the target energy consumption indicator and an indicator value character string corresponding to the target energy consumption indicator;

[0188] The energy consumption information receiving module is used to receive the first energy consumption information for analysis; the rule parser is configured to parse the energy consumption acquisition module corresponding to the target energy consumption indicator, including:

[0189] Step T100: Acquire the protocol of the energy consumption collection module, wherein the protocol of the energy consumption collection module includes a parsing rule variable table corresponding to the target energy consumption indicator of the energy consumption collection module and the name of the communication protocol used by the energy consumption collection module;

[0190] Step T200: Based on the acquired protocol of the energy consumption collection module, a name of the communication protocol used by the energy consumption collection module is acquired based on a keyword matching algorithm, and a parsing rule variable table corresponding to the target energy consumption indicator of the energy consumption collection module is acquired based on a table detection algorithm, wherein the communication protocol is used to indicate a correspondence between a data message using the communication protocol and the data portion thereof;

[0191] Step T300: determining the boundaries of each cell in the obtained parsing rule variable table based on the Faster RCNN algorithm;

[0192] Step T400: using a character recognition algorithm to identify characters within the boundaries of each cell;

[0193] Step T500: Based on the determination of the boundaries of each cell in the parsing rule variable table and the recognition of the characters within the boundaries of each cell, determining the correspondence between the parsing rule variables and their values ​​for the target energy consumption indicator;

[0194] Step T600: generating an energy consumption index parsing function for the target energy consumption index based on the correspondence between the parsing rule variables and their values ​​of the target energy consumption index and based on the acquired name of the communication protocol used by the energy consumption acquisition module;

[0195] The energy consumption information parsing module is configured to perform the parsing on the first energy consumption information, wherein the parsing includes:

[0196] Step S210: calling the rule parser configured for the energy consumption acquisition module and corresponding to the target energy consumption index to obtain the energy consumption index parsing function corresponding to the target energy consumption index; and

[0197] Step S220: running an energy consumption index parsing function corresponding to the target energy consumption index on the first energy consumption information to obtain a value of the target energy consumption index;

[0198] The structuring module is used to obtain second energy consumption information based on the analysis, and the second energy consumption information is structured and includes the target energy consumption index and its value; the energy consumption information analysis module is used to perform energy consumption analysis based on the second energy consumption information; the optional items of the energy consumption analysis include benchmark comparison analysis, non-working time energy consumption analysis, average power factor analysis, energy consumption volatility analysis, energy consumption contribution change analysis, and energy consumption short-term peak and valley analysis; the energy consumption anomaly indication module is used to provide indications about energy consumption anomalies based on the energy consumption analysis.

[0199] In one embodiment, the parsing of the first energy consumption information by the energy consumption information parsing module further includes obtaining an execution time of the parsing, and the second energy consumption information obtained by the structuring module includes the execution time.

[0200] In one embodiment, the communication protocol is selected from one of MODBUS RTU, MODBUS TCP, OPC UA, and PLC, and when the communication protocol is MODBUS RTU, the parsing rule variable further includes byte order.

[0201] The present invention has been introduced in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The above implementation description is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. Changes and improvements to the present invention will be possible without exceeding the concept and scope specified in the appended claims. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. An energy consumption management method, characterized in that: The following steps are involved: Step S100: receiving first energy consumption information for parsing, wherein the first energy consumption information is a data message, and a data portion of the first energy consumption information includes an indicator value string corresponding to a target energy consumption indicator; Step S200: performing the analysis on the first energy consumption information, wherein the analysis includes obtaining a value of the target energy consumption indicator based on the first energy consumption information; Step S300: Based on the analysis, obtaining second energy consumption information, wherein the second energy consumption information is structured and includes the target energy consumption index and the value of the target energy consumption index; Step S400: performing energy consumption analysis based on the second energy consumption information; Step S500: providing an indication of abnormal energy consumption based on the energy consumption analysis; Among them, the optional items of the energy consumption analysis include benchmark comparison analysis, non-working time energy consumption analysis, average power factor analysis, energy consumption volatility analysis, energy consumption contribution change analysis, and energy consumption short-term peak and valley analysis.

2. The energy consumption management method according to claim 1, characterized in that: An energy consumption collection module is provided for energy-consuming equipment; Based on the monitoring of the target energy consumption index of the energy consuming device by the energy consumption collection module, and based on the parsing rule variable table corresponding to the target energy consumption index of the energy consumption collection module and the communication protocol used by the energy consumption collection module, the energy consumption collection module generates the first energy consumption information; The parsing rule variable table is a table stored in a picture format, and its text content includes the names and values ​​of the parsing rule variables showing their corresponding relationships; The energy consumption management method calls a rule parser, and the rule parser is configured to parse the energy consumption acquisition module corresponding to the target energy consumption index, including: Step T200: acquiring the symbolic information of the communication protocol used by the energy consumption collection module and the parsing rule variable table corresponding to the target energy consumption index of the energy consumption collection module; Step T300: Based on the Faster RCNN algorithm, determine the boundaries of each cell in the obtained parsing rule variable table; Step T400: using a character recognition algorithm to identify characters within the boundaries of each cell; Step T500: Based on the boundaries of each cell in the parsing rule variable table determined in step T300 and based on the characters within the boundaries of each cell identified in step T400, for the target energy consumption indicator, determining the corresponding relationship between the parsing rule variables and their values; Step T600: Based on the correspondence between the analysis rule variables and their values ​​determined for the target energy consumption index, and based on the acquired symbolic information of the communication protocol used by the energy consumption acquisition module, an energy consumption index analysis function for the target energy consumption index is generated; The analysis in step S200 further includes: Step S210: calling the rule parser configured for the energy consumption collection module corresponding to the target energy consumption index to obtain the energy consumption index parsing function corresponding to the target energy consumption index; Step S220: running the energy consumption index analysis function corresponding to the target energy consumption index on the first energy consumption information to obtain the value of the target energy consumption index.

3. The energy consumption management method according to claim 2, characterized in that: For the target energy consumption index, the parsing rule variables include at least: (a) a start bit, used to indicate the start position of the index value character string corresponding to the target energy consumption index in the data part of the first energy consumption information; (b) character length, used to indicate the number of characters in the indicator value string corresponding to the target energy consumption indicator; and (c) a parsing format for indicating the value of the target energy consumption index and the index value corresponding to the target energy consumption index Rules for converting between strings.

4. The energy consumption management method according to claim 2, characterized in that: The analysis in step S200 further includes obtaining the execution time of the analysis, and the second energy consumption information in step S300 includes the execution time.

5. The energy consumption management method according to claim 3, characterized in that: The communication protocol is selected from one of MODBUS RTU, MODBUS TCP, OPC UA, and PLC.

6. The energy consumption management method according to claim 5, characterized in that: The communication protocol is MODBUS RTU, and the parsing rule variables also include byte order.

7. The energy consumption management method according to claim 2, characterized in that: The symbolic information of the communication protocol used by the energy consumption collection module refers to the name of the communication protocol used by the energy consumption collection module. The parsing configuration of the rule parser further includes: Step T100 before step T200: obtaining a protocol of the energy consumption collection module, wherein the protocol of the energy consumption collection module includes a parsing rule variable table corresponding to the target energy consumption index of the energy consumption collection module and a name of a communication protocol used by the energy consumption collection module; Correspondingly, the step T200: according to the acquired protocol of the energy consumption collection module, based on the keyword matching algorithm, obtain the name of the communication protocol used by the energy consumption collection module, and based on the table detection algorithm, obtain the analysis rule variable table corresponding to the target energy consumption indicator of the energy consumption collection module.

8. An energy consumption management system, characterized in that: The energy consumption analysis system includes: an energy consumption acquisition module, an energy consumption information receiving module, a rule parser, an energy consumption information parsing module, a structuring module, an energy consumption information analysis module, and an energy consumption abnormality indication module; wherein, The energy consumption collection module generates first energy consumption information for the set energy consuming equipment, The first energy consumption information is a data message, and the data part of the first energy consumption information includes an indicator value string corresponding to the target energy consumption indicator. The generation of the first energy consumption information is based on the monitoring of the target energy consumption index of the energy consuming device by the energy consumption collection module and based on the parsing rule variable table corresponding to the target energy consumption index of the energy consumption collection module and the communication protocol used by the energy consumption collection module. The parsing rule variable table is a table stored in a picture format, and its text content includes the names and values ​​of the parsing rule variables showing their corresponding relationships; For the target energy consumption index, the parsing rule variables include at least: (a) a start bit, used to indicate the start position of the index value character string corresponding to the target energy consumption index in the data part of the first energy consumption information; (b) character length, used to indicate the number of characters in the indicator value string corresponding to the target energy consumption indicator; and (c) a parsing format for indicating a conversion rule between a value of a target energy consumption indicator and an indicator value string corresponding to the target energy consumption indicator; The energy consumption information receiving module is used to receive the first energy consumption information for analysis; The rule parser, after parsing and configuring the energy consumption collection module corresponding to the target energy consumption index, includes: Step T100: Acquire the protocol of the energy consumption collection module, wherein the protocol of the energy consumption collection module includes a parsing rule variable table corresponding to the target energy consumption index of the energy consumption collection module and the name of the communication protocol used by the energy consumption collection module; Step T200: According to the obtained protocol of the energy consumption collection module, based on the keyword matching algorithm, the energy consumption collection module is obtained. The name of the communication protocol used by the energy consumption collection module, and based on the table detection algorithm, obtain the analysis rule variable table corresponding to the target energy consumption index of the energy consumption collection module; Step T300: Based on the Faster RCNN algorithm, determine the boundaries of each cell in the obtained parsing rule variable table; Step T400: using a character recognition algorithm to identify characters within the boundaries of each cell; Step T500: Based on the boundaries of each cell in the parsing rule variable table determined in step T300 and based on the characters within the boundaries of each cell identified in step T400, determining the corresponding relationship between the parsing rule variables and their values ​​for the target energy consumption indicator; Step T600: Based on the correspondence between the parsing rule variable of the target energy consumption indicator and its value, and based on the acquired name of the communication protocol used by the energy consumption acquisition module, an energy consumption indicator parsing function for the target energy consumption indicator is generated; The energy consumption information parsing module is used to perform the parsing on the first energy consumption information, and the parsing includes: Step S210: calling the rule parser configured for the energy consumption acquisition module corresponding to the target energy consumption index to obtain the energy consumption index parsing function corresponding to the target energy consumption index; and Step S220: running the energy consumption index analysis function corresponding to the target energy consumption index on the first energy consumption information to obtain the value of the target energy consumption index; The structuring module is used to obtain second energy consumption information based on the analysis, where the second energy consumption information is structured and includes the target energy consumption index and the value of the target energy consumption index; The energy consumption information analysis module is used to perform energy consumption analysis based on the second energy consumption information; the optional items of the energy consumption analysis include benchmark comparison analysis, non-working time energy analysis, average power factor analysis, energy consumption volatility analysis, energy consumption contribution change analysis, and energy consumption short-term peak and valley analysis; The energy consumption anomaly indication module is used to provide an indication of energy consumption anomaly based on the energy consumption analysis.

9. The energy consumption management system according to claim 7, characterized in that: The analysis of the first energy consumption information by the energy consumption information analysis module further includes obtaining the execution time of the analysis, The second energy consumption information obtained by the structured module includes the execution time.

10. The energy consumption management system according to claim 7, characterized in that: The communication protocol is selected from one of MODBUS RTU, MODBUS TCP, OPC UA, and PLC, and when the communication protocol is MODBUS RTU, the parsing rule variable also includes byte order.

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