Data processing method, system, device, equipment, medium and product

Abstract

The invention relates to a data processing method, system and device, equipment, a medium and a product. The method comprises the following steps: acquiring a reference data interval of each reference sensor in the energy management system and acquisition data of a target sensor under the condition that the energy management system is monitored to be on line with the target sensor; matching the acquired data with each reference data interval, and determining attribute information of the target sensor; and inputting the target sensor into the energy management system based on the attribute information of the target sensor. By adopting the method, the equipment installation registration efficiency can be improved.

Description

Technical Field

[0001] This application relates to the field of sensor management technology, and in particular to a data processing method, system, apparatus, device, medium, and product. Background Technology

[0002] With the rapid development of IoT technology, more and more companies are adopting energy management systems to monitor their equipment and work environments in order to improve management efficiency.

[0003] In related technologies, when using an energy management system for monitoring, it is usually necessary to install sensor devices in the working environment, register each sensor in each working scenario to the energy management system, and then the energy management system communicates with each sensor device to obtain energy emission results based on the data monitored by the sensor devices.

[0004] However, the relevant technologies suffer from low accuracy in energy emission results. Summary of the Invention

[0005] Therefore, it is necessary to provide a data processing method, system, device, equipment, medium, and product that can improve the accuracy of energy emission results in response to the above-mentioned technical problems.

[0006] This application provides a data processing method, the method comprising:

[0007] The reference data of the reference sensors in each region are corrected based on the environmental variable factors in each region of the energy management system; the reference sensors in the energy management system are distributed in different regions.

[0008] Using the corrected reference data, energy consumption data for each region was determined.

[0009] Based on the energy consumption data of each region, the energy emission results of each region are determined.

[0010] In one embodiment, the environmental variable factors include a time factor; the method for obtaining the environmental variable factors for each region includes:

[0011] For each region, obtain the standard data corresponding to each time period within that region;

[0012] The fluctuation range between the standard data corresponding to each time period and the standard data corresponding to the reference time period is calculated and used as the time factor of the region; the reference time period is any time period in each time period.

[0013] In one embodiment, acquiring standard data corresponding to each time period in the region includes:

[0014] For any given time period, obtain the sequence of collected data for that time period;

[0015] From the collected data sequence, select the target data that matches the preset quantity;

[0016] Based on the data collected from each target, the standard data corresponding to the time period is determined.

[0017] In one embodiment, the preset quantity includes a first preset quantity and a second preset quantity; filtering target collected data that matches the preset quantity from the collected data sequence includes:

[0018] From the collected data sequence, select the first target collected data that matches the first preset quantity and the second target collected data that matches the second preset quantity;

[0019] The data collected from the first target and the data collected from the second target are combined to obtain the target data.

[0020] In one embodiment, the target acquisition data includes first target acquisition data and second target acquisition data; based on each target acquisition data, standard data corresponding to a time period is determined, including:

[0021] The weighted sum of the data collected from each first target and each second target is calculated to obtain the standard data corresponding to the time period.

[0022] In one embodiment, the energy emission results for each region are determined based on energy consumption data for each region, including:

[0023] For each region, obtain the regional process scenario;

[0024] Energy consumption data is applied to the energy accounting formula for the process scenarios in the region to obtain the energy emission results for the region.

[0025] This application also provides an energy management system for performing the steps of the method in any of the embodiments.

[0026] This application also provides a data processing apparatus, including:

[0027] The data correction module is used to correct the reference data of the reference sensors in each region of the energy management system based on the environmental variable factors of each region; the reference sensors in the energy management system are distributed in different regions.

[0028] The energy consumption determination module is used to determine the energy consumption data for each area using corrected reference data.

[0029] The results determination module is used to determine the energy emission results for each region based on the energy consumption data of each region.

[0030] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method in any of the above embodiments.

[0031] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method in any of the above embodiments.

[0032] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method in any of the above embodiments.

[0033] The aforementioned data processing methods, systems, devices, equipment, media, and products correct reference data from reference sensors in each region of the energy management system based on environmental variable factors. The reference sensors in the energy management system are distributed across different regions. Using the corrected reference data, regional energy consumption data is determined, and based on this regional energy consumption data, regional energy emissions are determined. This method considers the impact of the environment in each region on sensor acquisition performance, corrects sensor data from different regions based on environmental variable factors, provides an accurate data source for calculating energy consumption data in each region, improves the reliability of energy consumption data in each region, and thus enhances the authenticity and accuracy of the energy emissions results determined based on the energy consumption data. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a diagram illustrating the application environment of a data processing method in one embodiment.

[0036] Figure 2 This is a flowchart illustrating a data processing method in one embodiment;

[0037] Figure 3 This is a flowchart illustrating the steps for obtaining environmental factors in one embodiment;

[0038] Figure 4 This is a flowchart illustrating the standard data acquisition steps in one embodiment;

[0039] Figure 5This is a schematic diagram of the hardware environment for a data processing method in one embodiment;

[0040] Figure 6 This is a schematic diagram of temperature sensor data acquisition in one embodiment;

[0041] Figure 7 This is a structural block diagram of a data processing device in one embodiment;

[0042] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] The data processing method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, the energy management system 102 communicates with sensors 104 in various areas via a network connection. A data storage system stores the data that the energy management system 102 needs to process. The data storage system can be integrated into the energy management system 102, or it can be located in the cloud or on other network servers. The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services. The sensors 104 can be, but are not limited to, pressure sensors, temperature sensors, humidity sensors, etc.

[0045] With the rapid development of IoT technology, more and more companies are adopting energy management systems to monitor their equipment and work environments in order to improve management efficiency.

[0046] In related technologies, when using an energy management system for monitoring, it is usually necessary to install sensor devices in the working environment, register each sensor in each working scenario to the energy management system, and then the energy management system communicates with each sensor device to achieve monitoring of each working scenario.

[0047] However, related technologies suffer from low accuracy in energy emission results. Therefore, this application provides a data processing method, system, apparatus, device, medium, and product that improves the accuracy of energy emission results by correcting data from sensor devices.

[0048] The technical solution of this application and how it solves the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0049] In one exemplary embodiment, such as Figure 2 As shown, a data processing method is provided, including the following steps:

[0050] S201, Based on the environmental variable factors of each region in the energy management system, the reference data of the reference sensors in each region are corrected; the reference sensors in the energy management system are distributed in different regions.

[0051] Reference sensors in the energy management system are distributed in different areas. The environmental changes in each area are different, which also affects the data acquisition performance of the reference sensors to varying degrees, resulting in errors between the reference data of the reference sensors and the actual data of the corresponding devices.

[0052] Each region corresponds to multiple sets of environmental variable factors, characterizing the impact of environmental changes in that region on the data acquisition performance of the reference sensor. Obtaining the environmental variable factors for each region is equivalent to clarifying the degree of deviation in the data acquired by the reference sensor in each region, thus clarifying the direction for correcting the reference data of the reference sensor.

[0053] For any given region, based on the corresponding environmental variable factors, a correction method is determined for the reference data of the reference sensor in that region, and the reference data is corrected once using this correction method. Furthermore, the number of corrections for that region is consistent with the number of sets of environmental variable factors for that region.

[0054] For any region, taking the region as an example containing N sets of environmental variable factors, the reference data of the reference sensor is corrected N times to eliminate the influence of environmental variables of different dimensions on the reference data, and the corrected collection data of the reference sensor in each region is obtained, where N is an integer greater than 1.

[0055] S202, using the corrected reference data, determines the energy consumption data for each region.

[0056] The reference data characterizes the state parameters of the device corresponding to the sensor under operating conditions, reflecting to some extent the energy consumption of the device in that area. The corrected reference data is used as the standard state data for that area. Based on the correspondence between the standard state data and the standard energy consumption data in each area, the standard energy consumption data that matches the standard state data for that area is determined.

[0057] S203, based on the energy consumption data of each region, determines the energy emission results of each region.

[0058] Based on the standard mapping relationship between energy consumption and carbon emissions in each region, the carbon emission data corresponding to the energy consumption data in each region is determined as the energy emission result for each region.

[0059] In this embodiment, reference data from reference sensors in each region of the energy management system are corrected based on environmental variable factors. The reference sensors in the energy management system are distributed across different regions. The corrected reference data is used to determine the energy consumption data for each region, and based on this energy consumption data, the energy emission results for that region are determined. This method considers the impact of the environment in each region on sensor acquisition performance, corrects the sensor data for different regions based on environmental variable factors, provides an accurate data source for calculating energy consumption data for each region, improves the reliability of energy consumption data for each region, and thus enhances the authenticity and accuracy of the energy emission results determined based on the energy consumption data.

[0060] Environmental variable factors may be the same or different for different regions, and environmental variable factors for the same region may be one set or multiple sets. The following explanation uses the example of environmental variable factors for a region including time factors to illustrate the steps for obtaining environmental variable factors.

[0061] In one exemplary embodiment, such as Figure 3 As shown, environmental variable factors include time factors; the methods for obtaining environmental variable factors for each region include:

[0062] S301, for each region, obtain the standard data corresponding to each time period in the region.

[0063] For each region, a time period corresponds to a standard data point, which characterizes the standard status data of the standard industry within that region during that data segment. Within each time period, sensors in the standard industry collect multiple data points. The standard data for each time period can be the maximum, minimum, or average value among the collected data. In this embodiment, the standard data for each region within each time period can be pre-stored in a database. This allows for direct querying and retrieval of the standard data from the database based on the region identifier and time period identifier.

[0064] S302, calculate the fluctuation range between the standard data corresponding to each time period and the standard data corresponding to the reference time period, and use it as the time factor of the region; the reference time period is any time period in each time period.

[0065] Choose any one of multiple time periods as a reference time period, and calculate the ratio between the standard data of each time period and the standard data of the reference time period. This ratio serves as the time factor for the region in different time periods. In this case, the time factor is a fraction between 0 and 1. When correcting the reference data collected in different time periods based on the time factor, the corrected reference data can be obtained by dividing the reference data of each time period by the corresponding time factor.

[0066] Optionally, the difference between the standard data for each time period and the standard data for the reference time period is calculated as the time factor for that region in different time periods. In this case, the time factor is a real number. Then, when correcting the reference data collected in different time periods based on the time factor, the reference data for each time period can be added to the corresponding time factor to obtain the corrected reference data.

[0067] In this embodiment of the application, for each region, standard data corresponding to each time period in the region is obtained, and any time period is used as a reference time period. The fluctuation range between the standard data corresponding to each time period and the standard data corresponding to the reference time period is calculated as the time factor of the region, so as to objectively quantify the degree of change of the same data in the region in different time periods and improve the authenticity of the time factor.

[0068] The implementation of the standard data in the foregoing embodiments will be further explained below. In an exemplary embodiment, such as... Figure 4 As shown, the standard data corresponding to each time period in the region is obtained, including:

[0069] S401: For any given time period, acquire the sequence of collected data for that time period.

[0070] For any given time period, multiple data points collected from the standard industry within that time period are preprocessed, and then sorted to obtain the data collection sequence for that time period.

[0071] S402, Filter out the target data that matches the preset quantity from the collected data sequence.

[0072] For example, the maximum value matching the preset quantity is selected from the collected data sequence in descending order and used as the target collected data; or the minimum value matching the preset quantity is selected from the collected data sequence in ascending order and used as the target collected data; or, the median of the collected data sequence is used as a benchmark to obtain the collected data adjacent to the median and satisfying the preset quantity, and used as the target collected data.

[0073] In an exemplary embodiment, the preset quantity includes a first preset quantity and a second preset quantity; filtering target collected data that matches the preset quantity from the collected data sequence includes:

[0074] From the collected data sequence, select the first target collected data that matches the first preset quantity and the second target collected data that matches the second preset quantity; summarize the first target collected data and the second target collected data to obtain the target collected data.

[0075] The first preset quantity and the second preset quantity are two values ​​set based on experience. The value of the first preset quantity and the second preset quantity are both less than the number of data collected in the data collection sequence. The sum of the values ​​of the first preset quantity and the second preset quantity is less than or equal to the number of data collected in the data collection sequence.

[0076] Taking a data collection sequence containing 30 data points, with a first preset quantity and a second preset quantity of 3 as an example, 3 data points are determined from the data collection sequence in descending order, and 3 data points are determined in ascending order. The 6 determined data points are then used as the target data points.

[0077] In this embodiment of the application, a first target data set matching a first preset quantity and a second target data set matching a second preset quantity are selected from the data set. In this way, the target data set obtained by summarizing the first target data set and the second target data set can cover the extreme values ​​in the data set to a large extent, thus ensuring the balance and rationality of the target data set.

[0078] S403, based on the data collected from each target, determine the standard data corresponding to the time period.

[0079] In one exemplary embodiment, the mean of the collected data for each target can be calculated to obtain the standard data corresponding to that time period.

[0080] In an exemplary embodiment, the target acquisition data includes first target acquisition data and second target acquisition data; based on each target acquisition data, standard data corresponding to a time period is determined, including:

[0081] The weighted sum of the data collected from each first target and each second target is calculated to obtain the standard data corresponding to the time period.

[0082] For any given time period, the sum of the weights of the first target data and the second target data for each time period is 1. Calculate the product of each first target data and its corresponding weight, and the product of the second target data and its corresponding weight. Then, sum the above product results to obtain the standard data corresponding to that time period.

[0083] In this embodiment of the application, for any time period, corresponding weight values ​​are assigned to each first target data collection data and each second target data collection data, and the weighted sum of each first target data collection data and each second target data collection data is used as the standard data corresponding to that time period. This can more evenly integrate the data characteristics of each target data collection data and improve the effectiveness and reference value of the standard data.

[0084] In this embodiment, target data matching a preset quantity is selected from the data sequence collected in any time period, and standard data corresponding to the time period is determined based on each target data. This is equivalent to using local data from the data sequence collected in the time period to calculate the standard data corresponding to the time period. While ensuring the validity of the standard data, the amount of calculation is reduced and the speed of obtaining the standard data is improved.

[0085] The foregoing embodiments described the method for obtaining the reference data (standard data) for correction. After correcting the reference data, it is necessary to recalculate the energy consumption data and then the carbon emission data to achieve accurate monitoring and optimization of energy emissions in various regions. The following embodiment further illustrates the method for obtaining carbon emission data.

[0086] In one exemplary embodiment, the energy emission results for each region are determined based on the energy consumption data of each region, including:

[0087] For each region, the process scenario of the region is obtained; the energy consumption data is applied to the energy accounting formula of the process scenario of the region to obtain the energy emission results of the region.

[0088] Each region corresponds to one or more process scenarios, and different process scenarios correspond to different energy accounting formulas. Based on this, the process scenarios for each region can be determined in advance from the energy management system's database, thereby obtaining the energy accounting formulas corresponding to each process scenario in each region. After determining the process scenarios corresponding to a region, energy consumption data is input into the energy accounting formulas corresponding to the process scenarios to determine the energy emission results corresponding to the process scenarios, which is also the energy emission results for that region.

[0089] When a region corresponds to a specific process scenario, the energy consumption data is input into the energy accounting formula of the process scenario to obtain the energy emission results for the region.

[0090] In the case of multiple process scenarios in a region, the energy consumption data of the region is divided to obtain the process energy consumption data corresponding to each process scenario. Then, the process energy consumption data corresponding to each process scenario is applied to the corresponding energy accounting formula to obtain the energy emission results corresponding to each process scenario. Finally, the energy emission results corresponding to each process scenario are accumulated to obtain the energy emission result of the region.

[0091] In this embodiment of the application, considering that carbon emissions will vary under different process scenarios, energy consumption data is applied to the energy accounting formula of the process scenario in the region to obtain energy emission results that are more consistent with the process scenario in the region, thereby improving the accuracy of the emission results.

[0092] In one exemplary embodiment, a data processing method is provided, comprising the following steps:

[0093] (1) For each region, obtain the process scenario in the region and the collection data sequence of the standard process in the region for each time period.

[0094] (2) For any time period, select the first target data that matches the first preset quantity and the second target data that matches the second preset quantity from the data collection sequence; calculate the weighted sum of each first target data and each second target data to obtain the standard data corresponding to the time period.

[0095] (3) Calculate the fluctuation range between the standard data corresponding to each time period and the standard data corresponding to the reference time period, and use it as the time factor of the region; the reference time period is any time period in each time period.

[0096] (4) Correct the reference data of the reference sensors in each region according to the time factor of each region in the energy management system; the reference sensors in the energy management system are distributed in different regions.

[0097] (5) Use the corrected reference data to determine the energy consumption data for each region.

[0098] (6) Apply the energy consumption data to the energy accounting formula of the process scenario in the region to obtain the energy emission results of the region.

[0099] In this embodiment of the application, considering the impact of the environment of each region on the sensor acquisition performance, the sensor data of different regions are corrected according to the environmental variable factors of each region, providing an accurate data source for the calculation of energy consumption data in each region, improving the reliability of energy consumption data in each region, and thus improving the authenticity and accuracy of the energy emission results determined based on the energy consumption data.

[0100] In one exemplary embodiment, see [link to example]. Figure 5 , Figure 5 This is a schematic diagram of the hardware environment of the energy management system. Figure 5 This includes a device cluster, a gateway, and a cloud-deployed energy management system. The device cluster comprises multiple sensors. The gateway collects data from the device cluster and transmits it to the energy management system, which then registers the device attribute information within the cluster. Figure 5 In the aforementioned hardware environment, a carbon emission monitoring method is provided, applied to an energy management system, comprising the following steps:

[0101] (1) Define the daily time period, geographical location, season, and other special time period range for the data collected by each reference sensor device in the energy management system, and collect data within different collection segments, classifying and summarizing the collected data in a segmented form. Then, use the heap sort algorithm to arrange the collected data sequence within the time and space attribute segments to obtain the collected data interval with time period and spatial attributes, which serves as the reference data interval.

[0102] (2) Compare the data collected by the target sensor with the reference data range of each reference sensor to determine the function, data attributes and other information of the target sensor, and enter the target sensor into the energy management system.

[0103] (3) Divide the data into four quarters based on the quarters, classify and calculate the data collected by each sensor in the four quarters, and output the data set of the four time periods.

[0104] For example, using a temperature sensor as an example, please refer to [link / reference]. Figure 6 , Figure 6 This is a schematic diagram of temperature data collected by a temperature sensor from January to December.

[0105] (4) Obtain temperature data of the standard process industry in the region where the sensor is located for four quarters, summarize and calculate the above data to obtain quarterly data with quarterly attribute deviation, and determine the quarterly factor of the region.

[0106] The temperature data for each quarter is continuously sorted using heap sort to obtain the smallest and largest three-digit values ​​for each quarter. The weighted sum of these six-digit values ​​is then calculated to obtain the standard data for that quarter. Next, the ratio of the standard data for each quarter to the standard data for spring is calculated to obtain the quarterly factor for each quarter.

[0107] (5) Based on the quarterly factor for each quarter, the collected data for the sensor's region is corrected to obtain the corrected data. The expression for the correction calculation is as follows:

[0108]

[0109] in, This refers to the corrected data. This is uncorrected collected data. This represents the quarterly factor for four quarters.

[0110] (6) Calculate the actual energy consumption and carbon emissions of each region based on the corrected data.

[0111] Using the above method, precise data collection and reference values ​​can be obtained for various enterprise zones, such as water tank usage and temperature changes in process equipment. Furthermore, the scope can be narrowed down to a single street to correct for monitoring deviations in actual operations across different areas.

[0112] In this embodiment, the sensor is installed easily and powered on. Once operational, the data uploaded by the sensor is processed by the cloud. After a period of data collection, suitable reference values ​​are matched. Following multiple rounds of calibration, the system can automatically perform a series of tasks, including intelligently assigning names and data units to the sensor, setting virtual measurement points on the platform, and logging into the device. This effectively saves manpower, reduces implementation risks, and improves device registration efficiency. Furthermore, because the reference data generated by the cloud algorithm comprehensively considers the influence of seasonality, regionality, and other attributes, it can correct the enterprise's monitoring backend, thereby improving production efficiency and security to a certain extent.

[0113] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0114] Based on the same inventive concept, this application also provides an energy management system for implementing the data processing method described above. The solution provided by this system is similar to the implementation described in the above method; therefore, the specific limitations in one or more energy management system embodiments provided below can be found in the limitations of the data processing method described above, and will not be repeated here.

[0115] Based on the same inventive concept, this application also provides a data processing apparatus for implementing the data processing method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more data processing apparatus embodiments provided below can be found in the limitations of the data processing method described above, and will not be repeated here.

[0116] In one exemplary embodiment, such as Figure 7 As shown, a data processing device is provided, including: a data correction module 701, an energy consumption determination module 702, and a result determination module 703, wherein:

[0117] The data correction module 701 is used to correct the reference data of the reference sensors in each region based on the environmental variable factors of each region in the energy management system; the reference sensors in the energy management system are distributed in different regions.

[0118] Energy consumption determination module 702 is used to determine the energy consumption data for each area using corrected reference data.

[0119] The result determination module 703 is used to determine the energy emission results of each region based on the energy consumption data of each region.

[0120] In an exemplary embodiment, the environmental variable factor includes a time factor; the data processing device includes: a variable factor acquisition module, configured to acquire, for each region, standard data corresponding to each time period in the region; calculate the fluctuation range between the standard data corresponding to each time period and the standard data corresponding to a reference time period, as the time factor of the region; the reference time period is any time period in each time period.

[0121] In an exemplary embodiment, the variable factor acquisition module includes: a sequence acquisition unit, a data filtering unit, and a standard data determination unit, wherein:

[0122] The sequence acquisition unit is used to acquire the collected data sequence for any given time period.

[0123] The data filtering unit is used to filter out target data that matches a preset quantity from the collected data sequence;

[0124] The standard data determination unit is used to determine the standard data corresponding to the time period based on the data collected from each target.

[0125] In an exemplary embodiment, the preset quantity includes a first preset quantity and a second preset quantity; the data filtering unit is further configured to filter out a first target data set matching the first preset quantity and a second target data set matching the second preset quantity from the collected data sequence; and to summarize the first target data set and the second target data set to obtain the target data set.

[0126] In an exemplary embodiment, the target acquisition data includes first target acquisition data and second target acquisition data; the standard data determination unit is further configured to calculate the weighted sum of each first target acquisition data and each second target acquisition data to obtain the standard data corresponding to the time period.

[0127] In an exemplary embodiment, the result determination module 703 includes: a scene determination unit and an energy calculation unit, wherein:

[0128] The scene determination unit is used to obtain the process scene of each region.

[0129] The energy calculation unit is used to apply energy consumption data to the energy accounting formula of the regional process scenario to obtain the regional energy emission results.

[0130] Each module in the aforementioned data processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0131] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a data processing method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0132] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0133] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0134] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0135] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0136] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0137] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0138] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0139] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A data processing method, characterized in that, The method includes: The reference data of the reference sensors in each region of the energy management system are corrected based on the environmental variable factors of each region; the reference sensors in the energy management system are distributed in different regions. Using the corrected reference data, the energy consumption data for each of the aforementioned regions was determined. Based on the energy consumption data of each region, the energy emission results of each region are determined.

2. The method according to claim 1, characterized in that, The environmental variable factors include time factors; the methods for obtaining the environmental variable factors for each region include: For each region, obtain the standard data corresponding to each time period in that region; The fluctuation range between the standard data corresponding to each time period and the standard data corresponding to the reference time period is calculated and used as the time factor of the region; the reference time period is any time period in each time period.

3. The method according to claim 2, characterized in that, The step of obtaining the standard data corresponding to each time period in the region includes: For any given time period, obtain the sequence of collected data for that time period; From the collected data sequence, select target collected data that matches a preset quantity; Based on the data collected from each of the aforementioned targets, the standard data corresponding to the time period is determined.

4. The method according to claim 3, characterized in that, The preset quantity includes a first preset quantity and a second preset quantity; The step of filtering out target data matching a preset quantity from the collected data sequence includes: From the collected data sequence, select the first target collected data that matches the first preset quantity and the second target collected data that matches the second preset quantity; The target acquisition data is obtained by summing the first target acquisition data and the second target acquisition data.

5. The method according to claim 3, characterized in that, The target acquisition data includes first target acquisition data and second target acquisition data; The step of determining the standard data corresponding to the time period based on the collected data from each of the targets includes: The weighted sum of the collected data from each of the first targets and the collected data from each of the second targets is calculated to obtain the standard data corresponding to the time period.

6. The method according to claim 1, characterized in that, The determination of energy emission results for each region based on energy consumption data includes: For each region, obtain the process scenario for that region; The energy consumption data is applied to the energy accounting formula of the process scenario in the region to obtain the energy emission results of the region.

7. An energy management system, characterized in that, The energy management system is used to perform the data processing method described in any of the preceding claims.

8. A data processing apparatus, characterized in that, The device includes: The data correction module is used to correct the reference data of the reference sensors in each region of the energy management system based on the environmental variable factors of each region; the reference sensors in the energy management system are distributed in different regions. The energy consumption determination module is used to determine the energy consumption data for each of the aforementioned regions using corrected reference data. The result determination module is used to determine the energy emission results of each region based on the energy consumption data of each region.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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