Metering and charging method and device for energy system

Abstract

The invention discloses an energy system metering and charging method and device, which is applied to the technical field of comprehensive energy, and comprises the steps: determining delay measurement point data and normal measurement point data of a to-be-detected index of to-be-detected equipment of a target enterprise according to the obtained target enterprise identifier, to-be-detected equipment identifier, to-be-detected index name and time granularity, wherein the delay measuring point data is data of which the data receiving time is later than the expected time in the data acquisition or transmission process, adding the delay measuring point data and the normal measuring point data to obtain the measuring point data of the to-be-detected equipment under the time granularity, and finally calculating the energy consumption cost of the to-be-detected index by utilizing the measuring point data. According to the method and the device, the condition that the scheduling period is missed on the delayed measuring point is considered, the final measuring point data is calculated by combining the delayed measuring point data and the normal measuring point data, the calculated measuring point data is more accurate, and the energy consumption cost of an enterprise can be effectively calculated according to the calculated measuring point data.

Description

Technical Field

[0001] The present application relates to the field of integrated energy technology, and in particular to a method and device for energy system metering and billing. Background Art

[0002] In the energy industry, companies need to measure and bill the energy they consume to ensure the comparability, controllability and rationality of energy consumption.

[0003] Generally, an enterprise reports its energy consumption once a period. Billing is based on the measurement results and the prescribed energy price to calculate the energy consumption costs for the enterprise. By testing and analyzing the measurement and billing results, the enterprise can discover potential problems with energy equipment and take effective measures to reduce energy consumption and costs.

[0004] Therefore, how to effectively calculate the energy consumption costs of an enterprise is a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0005] The present application provides a method and device for energy system metering and billing to effectively calculate the energy costs consumed by an enterprise and provide supporting data for the enterprise to analyze potential problems of energy equipment.

[0006] In a first aspect, the present application provides a method for energy system metering and billing, comprising:

[0007] Obtain the target enterprise ID, the device ID to be tested, the name of the indicator to be tested, and the time granularity;

[0008] Determine the delayed measurement point data and normal measurement point data of the target enterprise's ...

[0009] Adding the delayed measurement point data and the normal measurement point data to obtain the measurement point data of the device to be detected at the time granularity;

[0010] The energy consumption cost of the indicator to be detected is calculated using the measuring point data.

[0011] Optionally, the calculating the energy consumption cost of the indicator to be detected by using the measuring point data includes:

[0012] The producer process outputs the measurement point data to the kafka queue;

[0013] The consumer process receives the measurement point data from the kafka queue, and uses the measurement point data to calculate the energy consumption cost of the indicator to be detected.

[0014] Optionally, the calculating the energy consumption cost of the indicator to be detected by using the measuring point data includes:

[0015] Get indicator configuration information;

[0016] If the indicator configuration information indicates that the billing type is time-sharing billing, then determining the energy consumption of each time period in the measurement point data;

[0017] The product of the energy consumption in each time period and the corresponding charging price is determined, and the energy consumption fee is determined according to the sum of the products.

[0018] If the indicator configuration information indicates that the billing type is standard billing, determining the total amount of energy consumption in the measurement point data;

[0019] The energy consumption fee is determined based on the product of the total energy consumption and the corresponding billing price.

[0020] Optionally, according to the target enterprise identification, the identification of the device to be detected, the name of the indicator to be detected and the time granularity, a window is opened with a preset minute dimension to determine the delayed measurement point data and normal measurement point data of the indicator to be detected under each window.

[0021] Optionally, the adding of the delayed measurement point data and the normal measurement point data to obtain the measurement point data of the device to be detected at the time granularity includes:

[0022] Adding the delayed measurement point data and the normal measurement point data of the indicator to be detected under the same window to obtain the measurement point data of the indicator to be detected under the window;

[0023] The measurement point data corresponding to each window at the time granularity are added together to obtain the measurement point data of the device to be detected at the time granularity.

[0024] Optionally, the method further includes:

[0025] The calculated energy consumption cost is stored in a time series database.

[0026] In a second aspect, the present application further provides a device for energy system metering and billing, the device comprising:

[0027] An acquisition unit, used to acquire the target enterprise ID, the device ID to be detected, the name of the indicator to be detected, and the time granularity;

[0028] A determination unit, configured to determine delayed measurement point data and normal measurement point data of the target enterprise's ...

[0029] An obtaining unit, configured to add the delayed measuring point data and the normal measuring point data to obtain the measuring point data of the device to be detected at the time granularity;

[0030] A calculation unit is used to calculate the energy consumption cost of the indicator to be detected by using the measurement point data.

[0031] Optionally, the computing unit is specifically used to:

[0032] The producer process outputs the measurement point data to the kafka queue;

[0033] The consumer process receives the measurement point data from the kafka queue, and uses the measurement point data to calculate the energy consumption cost of the indicator to be detected.

[0034] Optionally, the computing unit is specifically used to:

[0035] Get indicator configuration information;

[0036] If the indicator configuration information indicates that the billing type is time-sharing billing, then determining the energy consumption of each time period in the measurement point data;

[0037] The product of the energy consumption in each time period and the corresponding charging price is determined, and the energy consumption fee is determined according to the sum of the products.

[0038] If the indicator configuration information indicates that the billing type is standard billing, determining the total amount of energy consumption in the measurement point data;

[0039] The energy consumption fee is determined based on the product of the total energy consumption and the corresponding billing price.

[0040] Optionally, the determining unit is specifically configured to:

[0041] According to the target enterprise identification, the identification of the equipment to be detected, the name of the indicator to be detected and the time granularity, a window is opened with a preset minute dimension to determine the delayed measurement point data and the normal measurement point data of the indicator to be detected under each window.

[0042] Optionally, obtain a unit, specifically for:

[0043] Adding the delayed measurement point data and the normal measurement point data of the indicator to be detected under the same window to obtain the measurement point data of the indicator to be detected under the window;

[0044] The measurement point data corresponding to each window at the time granularity are added together to obtain the measurement point data of the device to be detected at the time granularity.

[0045] Optionally, the device further comprises:

[0046] The storage module is used to store the calculated energy consumption cost in a time series database.

[0047] In a third aspect, an embodiment of the present application provides a device, comprising a memory and a processor, wherein the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device executes any method described in the first aspect.

[0048] In a fourth aspect, an embodiment of the present application provides a computer storage medium, in which a code is stored. When the code is executed, a device executing the code implements the method described in any one of the first aspects above.

[0049] Compared with the prior art, this application has the following beneficial effects:

[0050] In this application, the target enterprise identification, the identification of the equipment to be detected, the name of the indicator to be detected and the time granularity are obtained, and the delayed measurement point data and normal measurement point data of the indicator to be detected of the equipment to be detected of the target enterprise are determined according to the target enterprise identification, the identification of the equipment to be detected, the name of the indicator to be detected and the time granularity, wherein the delayed measurement point data is data received later than the expected time during the data collection or transmission process, the delayed measurement point data and the normal measurement point data are added to obtain the measurement point data of the equipment to be detected at the time granularity, and finally the energy consumption cost of the indicator to be detected is calculated using the measurement point data. In this application, the situation where the delayed measurement point misses the scheduling cycle is taken into account, and then the final measurement point data is calculated by combining the delayed measurement point data and the normal measurement point data, so that the calculated measurement point data will be more accurate, so that the energy consumption cost of the enterprise can be effectively calculated according to the calculated measurement point data, thereby providing support data for enterprises to analyze potential problems of energy equipment and helping energy enterprises optimize operational efficiency and profitability. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1A flowchart of a method for energy system metering and billing provided in an embodiment of the present application;

[0053] Figure 2 A flowchart of a method for charging energy consumption fees provided in an embodiment of the present application;

[0054] Figure 3 A schematic diagram of the structure of a specific implementation of the energy billing device provided in this application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] It should be noted that the energy system metering and billing method and device provided in this application are used in the field of integrated energy technology. The above is only an example and does not limit the application field of the method and device provided in this application.

[0057] In the energy industry, companies need to measure and bill the energy they consume to ensure the comparability, controllability and rationality of energy consumption.

[0058] Generally, an enterprise reports its energy consumption once a period. Billing is based on the measurement results and the prescribed energy price to calculate the energy consumption costs for the enterprise. By testing and analyzing the measurement and billing results, the enterprise can discover potential problems with energy equipment and take effective measures to reduce energy consumption and costs.

[0059] Therefore, how to effectively calculate the energy consumption costs of an enterprise is a technical problem that technical personnel in this field urgently need to solve.

[0060] In view of this, the present application proposes a method for energy system metering and billing, including: obtaining the target enterprise identification, the identification of the equipment to be detected, the name of the indicator to be detected and the time granularity in the present application, determining the delayed measurement point data and normal measurement point data of the indicator to be detected of the target enterprise's equipment to be detected according to the target enterprise identification, the identification of the equipment to be detected, the name of the indicator to be detected and the time granularity, wherein the delayed measurement point data is data received later than the expected time during the data collection or transmission process, adding the delayed measurement point data and the normal measurement point data to obtain the measurement point data of the equipment to be detected at the time granularity, and finally using the measurement point data to calculate the energy consumption cost of the indicator to be detected. In the present application, the situation that the delayed measurement point misses the scheduling cycle is taken into account, and then the final measurement point data is calculated by combining the delayed measurement point data and the normal measurement point data, so that the calculated measurement point data will be more accurate, so that the energy consumption cost of the enterprise can be effectively calculated according to the calculated measurement point data, thereby providing support data for enterprises to analyze potential problems of energy equipment and helping energy enterprises optimize operational efficiency and profitability.

[0061] The method provided in the embodiment of the present application can be executed by software on a computing device. The computing device can be, for example, a mobile phone, a tablet computer, a computer, a server, etc. The software can be, for example, system software.

[0062] In order to make the technical personnel in this field better understand the present application, the present application is further described in detail below in combination with the drawings and specific implementation methods. The following is an example of the method provided in the present application embodiment being executed by a computing device.

[0063] Figure 1 A flow chart of a method for energy system metering and billing provided in an embodiment of the present application. Figure 1 As shown, the method includes:

[0064] S101: Obtain the target enterprise ID, the device ID to be detected, the name of the indicator to be detected, and the time granularity.

[0065] The computing device obtains the target enterprise ID, the device ID to be detected, the name of the indicator to be detected, and the time granularity, wherein the target enterprise refers to the enterprise for which the energy consumption cost needs to be calculated, and the target enterprise ID is used to uniquely identify the enterprise. The device to be detected refers to the equipment in the target enterprise for which the energy consumption cost needs to be calculated, and the device ID to be detected is used to uniquely identify the equipment. The name of the indicator to be detected refers to the indicator of the device to be detected for which the energy consumption cost needs to be calculated, such as the enterprise's electricity consumption, cumulative steam flow, etc. The time granularity can be year, quarter, month, week, day, hour, minute, etc.

[0066] S102: Determine the delayed measurement point data and the normal measurement point data of the target enterprise's equipment to be detected according to the target enterprise identifier, the equipment to be detected identifier, the name of the indicator to be detected and the time granularity.

[0067] The computing device can determine the delayed measurement point data and normal measurement point data of the target enterprise's ...

[0068] For example, if the time granularity is day, the delayed measurement point data and normal measurement point data of the target enterprise's equipment to be tested indicators on that day or a specified day are determined.

[0069] S103: Add the delayed measurement point data and the normal measurement point data to obtain the measurement point data of the device to be detected at the time granularity.

[0070] After determining the delayed measurement point data and the normal measurement point data, the computing device may add the delayed measurement point data and the normal measurement point data to obtain the measurement point data of the device to be detected at the time granularity.

[0071] In some possible implementations, based on the target enterprise identification, the identification of the equipment to be detected, the name of the indicator to be detected and the time granularity, a window is opened with the preset minute as the dimension to determine the delayed measurement point data and the normal measurement point data of the indicator to be detected under each window, wherein windowing refers to applying a fixed-size window to the time series data so as to analyze or calculate the data in the window. The preset minutes can be set according to needs, for example, it can be 15 minutes, etc., and of course it can also be other times, which is not limited in this application.

[0072] For example, if the preset number of minutes is 15 minutes, the window will be opened every 15 minutes. That is, when the time granularity is higher than 15 minutes, such as hours, days, months, seasons and years, the indicators are uniformly triggered and calculated every 15 minutes, which ensures the accuracy of the calculation results and solves the problem of unequal indicator values ​​calculated at the time granularity of days, months and years.

[0073] After determining the delayed measurement point data and the normal measurement point data of the indicator to be detected under each window, the delayed measurement point data and the normal measurement point data of the indicator to be detected under the same window can be added to obtain the measurement point data of the indicator to be detected under the window;

[0074] The measurement point data corresponding to each window at the time granularity are added together to obtain the measurement point data of the device to be detected at the time granularity. For example, the time granularity is taken as a day and a window is taken as 15 minutes. The computing device can add the measurement point data of the window corresponding to every 15 minutes on that day to obtain the measurement point data of the device to be detected on that day.

[0075] In some possible implementations, the computing device may also directly add the delayed measurement point data and the normal measurement point data determined at the time granularity to obtain the measurement point data of the device to be detected at the time granularity.

[0076] After obtaining the measurement point data, the computing device can also store the measurement point data in the time series database. The time series database can support millions of concurrent writes and tens of thousands of concurrent reads, and still have high performance when a large number of aggregate queries are performed. It can also support cluster deployment, horizontal expansion, and no single point of failure, providing a basis for the stable operation of the production environment. The time series database has low requirements for hardware resources and a high data compression rate, saving at least 70% of hardware resources on average.

[0077] In some possible implementations, the producer process of the computing device can output the calculated measurement point data to a Kafka queue, where Kafka is a distributed stream processing platform that is mainly used to build real-time data pipelines and streaming applications. The core of Kafka is a message queue system that can efficiently process high-throughput data.

[0078] In some possible implementations, the present application can also obtain the start time and end time of energy statistics. For example, the start time is August 1, 2024, and the end time is August 15, 2024. The time granularity is taken as day. The computing device can obtain the measurement point data of each day from the start time August 1, 2024 to the end time August 15, 2024, and then add the measurement point data of each day to obtain the measurement point data from the start time to the end time.

[0079] S104: Calculate the energy consumption cost of the indicator to be detected using the measurement point data.

[0080] After determining the measurement point data of the device to be detected, the computing device can use the measurement point data to calculate the energy consumption cost of the indicator to be detected.

[0081] In some possible implementations, a consumer process of a computing device may receive measurement point data from a Kafka queue, and use the measurement point data to calculate the energy consumption cost of the indicator to be detected.

[0082] The following is a detailed description of the process of calculating energy consumption costs. Figure 2 As shown, the computing device may execute step S201 to obtain the indicator configuration information, and then execute step 202 to determine the billing type indicated by the indicator configuration information. If the indicator configuration information indicates that the billing type is time-sharing billing, then execute step S203 to determine the energy consumption of each time period in the measurement point data, and then execute step S204 to determine the product of the energy consumption of each time period and the corresponding billing price, and determine the energy consumption fee according to the sum of the products.

[0083] If the indicator configuration information indicates that the billing type is standard billing, step S205 is executed to determine the total energy consumption in the measurement point data, and then step S206 is executed to determine the energy consumption fee according to the product of the total energy consumption and the corresponding billing price.

[0084] By way of example, the indicator to be detected is taken as an example of the electricity consumption of an enterprise.

[0085] The computing device obtains the indicator configuration information. For example, the indicator configuration information indicates that the billing type is time-of-use billing. The calculation rules of electricity charges in different intervals can be as shown in Table 1:

[0086] Table 1

[0087] type Time interval Electricity price (yuan) tip 14:00-18:00 1.3 peak 10:00-14:00 0.9 flat 08:00-10:00 18:00-22:00 0.6 valley 00:00-08:00 22:00-24:00 0.3

[0088] For example, if the enterprise electricity consumption of the equipment to be tested is counted with daily time granularity, the energy consumption cost of that day is equal to the electricity consumption of the equipment to be tested in the peak time period of that day*1.3+the electricity consumption of the equipment to be tested in the peak time period of that day*0.9+the electricity consumption of the equipment to be tested in the normal time period of that day*0.6+the electricity consumption of the equipment to be tested in the valley time period of that day*0.3. It should be noted that the unit of electricity consumption here is degree.

[0089] For example, if the indicator configuration information indicates that the billing type is standard billing and the price of one kilowatt-hour is 0.8, then the total power consumption of the device to be tested on that day can be multiplied by 0.8.

[0090] In this application, the target enterprise identification, the identification of the equipment to be detected, the name of the indicator to be detected and the time granularity are obtained, and the delayed measurement point data and normal measurement point data of the indicator to be detected of the equipment to be detected of the target enterprise are determined according to the target enterprise identification, the identification of the equipment to be detected, the name of the indicator to be detected and the time granularity, wherein the delayed measurement point data is data received later than the expected time during the data collection or transmission process, the delayed measurement point data and the normal measurement point data are added to obtain the measurement point data of the equipment to be detected at the time granularity, and finally the energy consumption cost of the indicator to be detected is calculated using the measurement point data. In this application, the situation where the delayed measurement point misses the scheduling cycle is taken into account, and then the final measurement point data is calculated by combining the delayed measurement point data and the normal measurement point data, so that the calculated measurement point data will be more accurate, so that the energy consumption cost of the enterprise can be effectively calculated according to the calculated measurement point data, thereby providing support data for enterprises to analyze potential problems of energy equipment and helping energy enterprises optimize operational efficiency and profitability.

[0091] The above are some specific implementations of the energy billing method provided in the embodiments of the present application. Based on this, the present application also provides a corresponding device. The device provided in the embodiments of the present application will be introduced from the perspective of functional modularization. The device and the energy billing method described above can be referred to in correspondence with each other.

[0092] Figure 3 The structural block diagram provided for the embodiment of the present invention is referred to as the specific implementation mode 2, and the Figure 3 The device may include:

[0093] An acquisition unit 300 is used to acquire a target enterprise identifier, a device identifier to be detected, a name of an indicator to be detected, and a time granularity;

[0094] A determination unit 310 is used to determine delayed measurement point data and normal measurement point data of the target enterprise's ...

[0095] An obtaining unit 320 is used to add the delayed measurement point data and the normal measurement point data to obtain the measurement point data of the device to be detected at the time granularity;

[0096] The calculation unit 330 is used to calculate the energy consumption cost of the indicator to be detected by using the measurement point data.

[0097] Optionally, the computing unit is specifically used to:

[0098] The producer process outputs the measurement point data to the kafka queue;

[0099] The consumer process receives the measurement point data from the kafka queue, and uses the measurement point data to calculate the energy consumption cost of the indicator to be detected.

[0100] Optionally, the computing unit is specifically used to:

[0101] Get indicator configuration information;

[0102] If the indicator configuration information indicates that the billing type is time-sharing billing, then determining the energy consumption of each time period in the measurement point data;

[0103] The product of the energy consumption in each time period and the corresponding charging price is determined, and the energy consumption fee is determined according to the sum of the products.

[0104] If the indicator configuration information indicates that the billing type is standard billing, determining the total amount of energy consumption in the measurement point data;

[0105] The energy consumption fee is determined based on the product of the total energy consumption and the corresponding billing price.

[0106] Optionally, the determining unit is specifically configured to:

[0107] According to the target enterprise identification, the identification of the equipment to be detected, the name of the indicator to be detected and the time granularity, a window is opened with a dimension of 15 minutes to determine the delayed measurement point data and the normal measurement point data of the indicator to be detected in each window.

[0108] Optionally, the obtaining unit is specifically used to:

[0109] Adding the delayed measurement point data and the normal measurement point data of the indicator to be detected under the same window to obtain the measurement point data of the indicator to be detected under the window;

[0110] The measurement point data corresponding to each window at the time granularity are added together to obtain the measurement point data of the device to be detected at the time granularity.

[0111] Optionally, the device further comprises:

[0112] The storage module is used to store the calculated energy consumption cost in a time series database.

[0113] The embodiments of the present application also provide corresponding devices and computer storage media for implementing the solutions provided by the embodiments of the present application.

[0114] The device includes a memory and a processor, the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device executes the method described in any embodiment of the present application.

[0115] The computer storage medium stores codes, and when the codes are executed, a device executing the codes implements the method described in any embodiment of the present application.

[0116] The "first" and "second" in the names such as "first" and "second" (if any) mentioned in the embodiments of the present application are only used as name identifiers and do not represent the first or second in order.

[0117] Through the description of the above implementation methods, it can be known that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a read-only memory (ROM) / RAM, a magnetic disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0118] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0119] The above description is merely an exemplary embodiment of the present application and is not intended to limit the protection scope of the present application.

Claims

1. A method for energy system metering and billing, characterized in that: include: Obtain the target enterprise ID, the device ID to be tested, the name of the indicator to be tested, and the time granularity; Determine the delayed measurement point data and normal measurement point data of the target enterprise's ... Adding the delayed measurement point data and the normal measurement point data to obtain the measurement point data of the device to be detected at the time granularity; The energy consumption cost of the indicator to be detected is calculated using the measuring point data.

2. The method according to claim 1, characterized in that The step of calculating the energy consumption cost of the indicator to be detected by using the measurement point data includes: The producer process outputs the measurement point data to the kafka queue; The consumer process receives the measurement point data from the kafka queue, and uses the measurement point data to calculate the energy consumption cost of the indicator to be detected.

3. The method according to claim 1 or 2, characterized in that: The step of calculating the energy consumption cost of the indicator to be detected by using the measurement point data includes: Get indicator configuration information; If the indicator configuration information indicates that the billing type is time-based billing, then determine the energy consumption corresponding to each time period of different billing prices in the measurement point data; Determine the product of the energy consumption in each time period and the corresponding billing price, and determine the energy consumption fee according to the sum of the products; If the indicator configuration information indicates that the billing type is standard billing, determining the total amount of energy consumption in the measurement point data; The energy consumption fee is determined based on the product of the total energy consumption and the corresponding billing price.

4. The method according to claim 1, characterized in that: According to the target enterprise identification, the identification of the equipment to be detected, the name of the indicator to be detected and the time granularity, a window is opened with a preset minute dimension to determine the delayed measurement point data and the normal measurement point data of the indicator to be detected under each window.

5. The method according to claim 4, characterized in that The step of adding the delayed measurement point data and the normal measurement point data to obtain the measurement point data of the device to be detected at the time granularity includes: Adding the delayed measurement point data and the normal measurement point data of the indicator to be detected under the same window to obtain the measurement point data of the indicator to be detected under the window; The measurement point data corresponding to each window at the time granularity are added together to obtain the measurement point data of the device to be detected at the time granularity.

6. The method according to claim 1, characterized in that The method further comprises: The calculated energy consumption cost is stored in a time series database.

7. A device for energy system metering and billing, characterized in that: include: An acquisition unit, used to acquire the target enterprise ID, the device ID to be detected, the name of the indicator to be detected, and the time granularity; A determination unit, configured to determine delayed measurement point data and normal measurement point data of the target enterprise's ... An obtaining unit, configured to add the delayed measuring point data and the normal measuring point data to obtain the measuring point data of the device to be detected at the time granularity; A calculation unit is used to calculate the energy consumption cost of the indicator to be detected using the measurement point data.

8. The device according to claim 7, characterized in that The computing unit is specifically used for: The producer process outputs the measurement point data to the kafka queue; The consumer process receives the measurement point data from the kafka queue, and uses the measurement point data to calculate the energy consumption cost of the indicator to be detected.

9. The device according to claim 7 or 8, characterized in that The computing unit is specifically used for: Get indicator configuration information; If the indicator configuration information indicates that the billing type is time-sharing billing, then determining the energy consumption of each time period in the measurement point data; Determine the product of the energy consumption in each time period and the corresponding billing price, and determine the energy consumption fee according to the sum of the products; If the indicator configuration information indicates that the billing type is standard billing, determining the total amount of energy consumption in the measurement point data; The energy consumption fee is determined based on the product of the total energy consumption and the corresponding billing price.

10. The device according to claim 7, characterized in that The obtaining unit is specifically used for: Adding the delayed measurement point data and the normal measurement point data of the indicator to be detected under the same window to obtain the measurement point data of the indicator to be detected under the window; The measurement point data corresponding to each window at the time granularity are added together to obtain the measurement point data of the device to be detected at the time granularity.

Images