Intelligent energy consumption data acquisition method and system

Abstract

The invention provides an intelligent energy consumption data acquisition method and system. The method comprises the following steps: acquiring energy consumption data, personnel activity data and park greening data; fusing the energy consumption data, the personnel activity data and the park greening data to obtain a fused data set; performing comprehensive analysis processing on the fused data to obtain an analysis result; performing edge processing on the analysis result to obtain data after edge processing; and transmitting the data subjected to edge processing to a remote server or terminal equipment. According to the invention, the energy consumption value error of each part and the whole of the building is reduced by collecting the multi-dimensional data, remote monitoring and management are realized, the labor cost is reduced, comprehensive and accurate energy consumption data are provided for a user, and the user is helped to realize the purposes of energy conservation and emission reduction.

Description

Technical Field

[0001] The present application relates to the field of energy management technology, and specifically to an intelligent energy consumption data collection method and system. Background Art

[0002] With the growing global energy crisis and rising awareness of environmental protection, energy management and energy conservation and emission reduction have become core issues in today's society. Accurately monitoring and analyzing energy consumption across a wide range of energy consumption areas is the cornerstone of achieving efficient energy management.

[0003] However, current traditional data acquisition equipment has obvious limitations in the field of energy consumption monitoring: traditional equipment cannot accurately capture the energy consumption changes of complex equipment, resulting in large data errors; most of the current traditional data equipment can only collect data and cannot deeply analyze energy consumption trends and efficiency, which limits the formulation of energy management strategies; traditional equipment requires manual operation and cannot automatically identify and adjust data collection strategies, which increases costs and reduces accuracy; traditional equipment has limited communication methods, which is not conducive to remote monitoring and affects the speed and stability of data transmission.

[0004] Given the above challenges, how to achieve high-precision measurement, in-depth data analysis capabilities, and intelligent management features to provide users with comprehensive and accurate energy consumption data, help achieve energy conservation and emission reduction goals, and meet the diversified needs of modern energy management has become a key issue that needs to be addressed. Summary of the Invention

[0005] In order to overcome the above problems existing in the prior art, the present application provides an intelligent energy consumption data collection method and system, which adopts the following technical solutions:

[0006] In a first aspect, the present application provides an intelligent energy consumption data collection method, comprising:

[0007] Obtain energy consumption data, personnel activity data and park greening data;

[0008] Fusing the energy consumption data, personnel activity data, and park greening data to obtain a fused data set;

[0009] Performing comprehensive analysis and processing on the fused data to obtain analysis results;

[0010] Performing edge processing on the analysis results to obtain edge-processed data;

[0011] The edge-processed data is transmitted to a remote server or terminal device.

[0012] Furthermore, the acquisition of energy consumption data, personnel activity data and park greening data includes:

[0013] Collect water, electricity, gas and energy consumption data of buildings or parks based on smart electricity meters, smart water meters and smart gas meters;

[0014] Based on the sensor, the number of people entering and leaving the building and their activities are collected to obtain personnel activity data;

[0015] Obtain park greening data based on image recognition technology and geographic information system.

[0016] Furthermore, the energy consumption data, personnel activity data and park greening data are fused to obtain a fused data set, including:

[0017] Acquiring a fusion data set includes temporal correlation and spatial correlation of the energy consumption data, personnel activity data, and park greening data.

[0018] Furthermore, the acquisition of the fused data set includes temporal correlation and spatial correlation of the energy consumption data, personnel activity data, and park greening data, including:

[0019] Preprocessing the energy consumption data, personnel activity data, and park greening data, including cleaning the data and removing missing values ​​and outliers;

[0020] Standardize the preprocessed data and perform temporal and spatial correlation on the standardized data;

[0021] The time association includes: using time as the key index, associating and matching energy consumption data with personnel activity data and park greening data at the same time point, establishing a time series data structure, arranging different types of data in chronological order, and obtaining time-related data;

[0022] Spatial association includes: matching personnel activity data and park greening data with corresponding building area energy consumption data based on building layout and park planning; determining the personnel activity monitoring range and park greening impact range corresponding to each building area; and spatially integrating data from different sources by establishing a spatial mapping relationship to obtain spatially associated data.

[0023] Furthermore, the performing comprehensive analysis on the fused data set to obtain analysis results includes:

[0024] Based on the spatial correlation data in the fused dataset, the population density of different building areas in different time periods is obtained. The actual area of ​​each building area is determined using the spatial mapping relationship. The population density is calculated based on the number of people in the personnel activity data. Based on the personnel activity records in the time correlation data, the personnel activity intensity index is obtained.

[0025] Based on the dynamic changes in population density and personnel activity intensity indicators in different building areas, energy consumption adjustments for different types of equipment are obtained;

[0026] Based on the park greening data in the fused dataset, the plant species and greening areas of different building areas were obtained. Based on the quantitative relationship model between greening area, plant species and air conditioning energy savings, the regulation information of park greening on building energy consumption in different seasons and time periods was obtained, and the influencing factors of greening factors were determined.

[0027] Based on the energy consumption data in the fused data, the energy consumption adjustments of different types of equipment, and the influencing factors of greening factors, the energy consumption values ​​of each part of the building and the entire building are obtained;

[0028] Based on the time series, the energy consumption trend of each part of the building and the energy consumption of the whole building is analyzed, and the energy consumption composition ratio in different time periods is obtained.

[0029] Furthermore, performing edge processing on the analysis result to obtain edge-processed data includes:

[0030] Set up local storage space based on actual needs and the storage capacity of edge processing devices to temporarily store analysis results. When the communication network is unstable or interrupted, the edge processing device caches the analysis results and transmits them after the network is restored.

[0031] Based on a preset compression algorithm, the analysis results stored or cached locally on the edge processing device are compressed to obtain compressed data.

[0032] Before transmitting the compressed data, it is encrypted based on a preset encryption algorithm.

[0033] In a second aspect, the present application also provides an intelligent energy consumption data collection system, comprising:

[0034] Data acquisition module, used to obtain energy consumption data, personnel activity data and park greening data;

[0035] A data set fusion module is used to fuse the energy consumption data, personnel activity data and park greening data to obtain a fused data set;

[0036] An analysis result acquisition module is used to perform comprehensive analysis and processing on the fusion data to obtain analysis results;

[0037] An edge processing module, configured to perform edge processing on the analysis results to obtain edge-processed data;

[0038] The data transmission module is used to transmit the edge-processed data to a remote server or terminal device.

[0039] In a third aspect, the present application provides an electronic device, comprising:

[0040] One or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, cause the device to perform the method as described in the first aspect.

[0041] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer-readable storage medium is run on a computer, the computer executes the method described in the first aspect.

[0042] In a fifth aspect, the present application provides a computer program, which, when executed by a computer, is used to execute the method described in the first aspect.

[0043] In one possible design, the program in the fifth aspect may be stored in whole or in part on a storage medium packaged with the processor, or may be stored in whole or in part on a memory not packaged with the processor.

[0044] This application has the following beneficial effects:

[0045] This application obtains energy consumption data, personnel activity data and park greening data; fuses the energy consumption data, personnel activity data and park greening data to obtain a fused data set; comprehensively analyzes and processes the fused data to obtain analysis results; performs edge processing on the analysis results to obtain edge-processed data; and transmits the edge-processed data to a remote server or terminal device. By collecting multi-dimensional data, this application reduces the error in energy consumption values ​​of various parts and the entire building, realizes remote monitoring and management, reduces labor costs, provides users with comprehensive and accurate energy consumption data, and helps users achieve the goal of energy conservation and emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is an exemplary system architecture diagram to which the embodiments of the present application can be applied;

[0047] Figure 2 This is a flow chart of the intelligent energy consumption data collection method according to an embodiment of the present application;

[0048] Figure 3 This is a flow chart of obtaining analysis results according to an embodiment of the present application;

[0049] Figure 4 This is a flow chart of edge processing in an embodiment of the present application;

[0050] Figure 5A schematic diagram of obtaining energy consumption values ​​of various parts and the entire building provided in an embodiment of the present application;

[0051] Figure 6 This is a system flow chart of an embodiment of the present application;

[0052] Figure 7 It is a schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0054] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0055] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0056] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0057] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0058] Terminal devices 101, 102, and 103 can be various electronic devices with display screens and support web browsing, including but not limited to smartphones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Group Audio Layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Group Audio Layer 4) players, laptop computers, desktop computers, etc.

[0059] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0060] It should be noted that the intelligent energy consumption data collection method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the intelligent energy consumption data collection system is generally set in the server / terminal device.

[0061] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0062] Continue to refer Figure 2 , the figure shows a flow chart of an intelligent energy consumption data collection method of the present application, the method comprising the following steps:

[0063] Step 201: Acquire energy consumption data, personnel activity data, and park greening data.

[0064] In one possible implementation, obtaining energy consumption data, personnel activity data, and park greening data includes:

[0065] Collect water, electricity, gas and energy consumption data of buildings or parks based on smart electricity meters, smart water meters and smart gas meters.

[0066] For example, each electricity user in a building installs a smart meter, which transmits electricity usage data to the data acquisition module via wired or wireless communication methods (such as WIFI, Zigbee, LoRa, etc.). The meter collection frequency can be set to once per minute to ensure the timeliness of the data. Connect smart water meters to the building's main water pipe and each branch water pipe to collect total water flow data in real time. Use the built-in communication interface of the data acquisition module, such as the RS485 interface, to regularly read the water meter data, with the collection interval set to once every 5 minutes, to accurately record water consumption. Install a smart gas meter on the gas pipeline. The gas meter has a data transmission function and can send the collected gas flow value to the data acquisition module wirelessly (such as Bluetooth, Wi-Fi, etc.). The collection cycle is once every 10 minutes for calculating energy consumption.

[0067] Based on the sensors, the number of people entering and leaving the building and their activities are collected to obtain personnel activity data.

[0068] For example, visual sensors, such as high-definition cameras or infrared sensors, can be deployed at building entrances and exits and in key areas. Using image processing technology and target recognition algorithms, these sensors count the number of people entering and exiting the building, as well as their movement paths. When people enter or leave the building, the sensors automatically record the time and number of people, and transmit this data in real time to the data acquisition module.

[0069] Obtain park greening data based on image recognition technology and geographic information system.

[0070] For example, image recognition technology is used to capture images of the park's green areas using high-definition cameras installed within the park. These images are then analyzed and processed to identify information such as plant species and coverage area. Furthermore, geographic information systems (GIS) are used to obtain the geographic location and extent of the green areas, providing a foundation for subsequent analysis.

[0071] Step 202: Fusing the energy consumption data, personnel activity data, and park greening data to obtain a fused data set.

[0072] In an embodiment of the present application, the energy consumption data, personnel activity data and park greening data are fused to obtain a fused data set, including: obtaining a fused data set, including temporal association and spatial association of the energy consumption data, personnel activity data and park greening data.

[0073] The fusion data set is obtained by temporally and spatially correlating the energy consumption data, personnel activity data, and park greening data. Specifically, the following contents are included:

[0074] The energy consumption data, personnel activity data and park greening data are preprocessed, including cleaning the data and removing missing values ​​and outliers.

[0075] The preprocessed data is standardized, and the standardized data is temporally and spatially correlated.

[0076] The time association includes: using time as the key index, associating and matching energy consumption data with personnel activity data and park greening data at the same time point, establishing a time series data structure, arranging different types of data in chronological order, and obtaining time-related data;

[0077] Spatial association includes: matching personnel activity data and park greening data with corresponding building area energy consumption data based on building layout and park planning; determining the personnel activity monitoring range and park greening impact range corresponding to each building area; and spatially integrating data from different sources by establishing a spatial mapping relationship to obtain spatially associated data.

[0078] Standardize the preprocessed data. For example, unify different types of data into a specific data structure (i.e., standardize it). For example, add timestamps, data source identifiers, and other information to each type of data to ensure that the data can be accurately identified and processed in subsequent analysis.

[0079] Temporal and spatial correlations are performed on standardized data. For example, relationships between data can be established to integrate different data collected at the same time. For example, building electricity consumption data at a certain moment can be correlated with personnel activity data (such as the number of people and activity areas) and park greening data (such as green area and plant species) at that time to comprehensively analyze the relationship between energy consumption and other factors.

[0080] Step 203: Perform comprehensive analysis on the fused data to obtain analysis results.

[0081] In a possible implementation, the fusion data is comprehensively analyzed and processed to obtain analysis results. Please refer to Figure 3 , specifically including:

[0082] Step 31: Based on the spatial correlation data in the fused dataset, obtain the population density of different building areas in different time periods. Using the spatial mapping relationship, determine the actual area of ​​each building area. Calculate the population density based on the number of people in the personnel activity data using the formula: population density = number of people in the area / area of ​​the area. Based on the personnel activity records in the time correlation data, obtain the personnel activity intensity index. For example, calculate the frequency of interaction between personnel and equipment (such as the number of times they use office equipment or take the elevator). A high interaction frequency indicates a high activity intensity.

[0083] Step 32: Based on the dynamic changes in occupancy density and activity intensity indicators in different building areas, energy consumption adjustments for different types of equipment are obtained. For example, for air conditioning systems, the impact of factors such as occupant heat dissipation, changes in indoor air circulation caused by occupant activity (such as frequent opening and closing of doors and windows affecting ventilation), and changes in occupant comfort requirements for temperature and humidity on the air conditioning load are considered. When occupancy density or activity intensity increases, indoor heat and humidity loads may change, requiring the air conditioner to consume more energy to maintain a comfortable environment. For example, in a conference room with a large number of people and frequent activities, the required cooling power of the air conditioner during the meeting is calculated based on the number of people, activity duration, and activity intensity (such as movement, equipment use, etc.), combined with model coefficients, to determine the increase in energy consumption.

[0084] Step 33: Based on the park greening data in the fused dataset, the plant species and greening areas of different building areas are obtained. Based on the quantitative relationship model between greening area, plant species, and air conditioning energy savings, information on the regulation of park greening on building energy consumption in different seasons and time periods is obtained, and the influencing factors of greening factors are determined.

[0085] Step 34: Based on the energy consumption data in the fused data, the energy consumption adjustments of different types of equipment, and the influencing factors of greening factors, the energy consumption values ​​of each part of the building and the entire building are obtained;

[0086] Step 35: Perform energy consumption trend analysis on the energy consumption values ​​of each part of the building and the entire building based on the time series, and obtain the energy consumption composition ratio in different time periods.

[0087] Step 204: perform edge processing on the analysis result to obtain edge-processed data.

[0088] In a possible implementation, edge processing is performed on the analysis results to obtain edge-processed data. Please refer to Figure 4 , specifically including:

[0089] Step 41: Based on actual needs and the storage capacity of the edge processing device, local storage space is set up to temporarily store analysis results. Furthermore, if the communication network is unstable or interrupted, the edge processing device caches the analysis results and transmits them again after the network is restored. For example, the edge processing device sets up local cache space to temporarily store accurate energy consumption analysis results. When the communication network is unstable or interrupted, data is cached and transmitted again after the network is restored to ensure data is not lost. The cache size can be set based on actual needs and device storage capacity, for example, the energy consumption analysis data for the last hour can be cached.

[0090] Step 42 compresses the analysis results stored or cached locally on the edge processing device based on a preset compression algorithm to obtain compressed data. For example, a suitable data compression algorithm, such as a lossless compression algorithm (e.g., LZ77, LZ78, etc.), is used to compress cached or to-be-transmitted data, thereby reducing the amount of data transmitted and improving transmission efficiency. The compression ratio can be adjusted based on data characteristics and network conditions to minimize transmission bandwidth requirements while ensuring data accuracy.

[0091] Step 43: Before transmitting the compressed data, the data is encrypted using a pre-set encryption algorithm. For example, to ensure data security, energy consumption values ​​and carbon values ​​are encrypted using an encryption algorithm (such as AES or RSA) before data transmission. The encryption key can be updated regularly to ensure the confidentiality and integrity of the data during transmission and prevent data theft or tampering.

[0092] Step 205: Transmit the edge-processed data to a remote server or terminal device.

[0093] Before data transmission, the communication mode is selected. The communication module automatically selects the appropriate communication mode for data transmission based on the actual application scenario and environmental conditions. In locations with stable Ethernet access, Ethernet connection is preferred to ensure high-speed and stable data transmission. In areas with high mobility or where Ethernet cabling is inconvenient, such as temporary monitoring points within the park, wireless communication methods (such as 4G and 5G) are switched to ensure that data can be uploaded to remote servers or terminal devices in a timely manner.

[0094] The data transmission process involves transmitting energy consumption data processed by the edge processing module via the selected communication method. The communication module adheres to the corresponding communication protocol (such as TCP / IP, MQTT, UDP, etc.) to ensure accurate data transmission to the designated destination. Furthermore, a data retransmission mechanism automatically resends data in the event of a data transmission failure, improving data transmission reliability.

[0095] Please refer to Figure 5 , which is a schematic diagram of obtaining energy consumption values ​​of various parts and the entire building provided in an embodiment of the present application. Its specific working principle is as follows:

[0096] Obtain electricity consumption data, water consumption data, gas consumption data, park greening data, personnel activity data and clean energy data.

[0097] The electricity consumption data, water consumption data, gas consumption data, park greening data, personnel activity data and clean energy data collected at the same time point are integrated to obtain integrated data.

[0098] The building's electricity energy consumption value is obtained based on the collected electricity consumption of each unit in the building; the water energy consumption value is obtained based on the water temperature difference based on the collected total water flow of the building; and the gas energy consumption value is obtained based on the gas flow value collected by the smart gas meter.

[0099] Based on the collected park greening data, the plant morphological characteristics are extracted, the physiological characteristics of the plants are analyzed, and the carbon recycling capacity of the park greening is obtained based on the calculation model.

[0100] Count the people entering and leaving the building, and based on preset parameters, obtain the additional energy consumption of equipment caused by people's activities in the building.

[0101] Obtain data on wind, solar, and storage in the park, as well as clean energy conversion data.

[0102] The electricity energy consumption value, water energy consumption value, gas energy consumption value, carbon recovery capacity, additional energy consumption value of equipment caused by human activities, and clean energy conversion data are converted into carbon values.

[0103] System overall operation and verification

[0104] 1. Installation and Deployment: Install the various modules of the intelligent energy consumption data collection device in the appropriate locations of the building or campus, ensuring that the sensors are correctly installed and the communication connection is stable. Initialize the system settings, such as configuring collection parameters, communication parameters, and algorithm parameters, to ensure that the system enters normal operation.

[0105] 2. Testing and Calibration: Before official operation, perform system testing. Use standard metering equipment to compare and calibrate the collected data to ensure data accuracy. For example, compare the energy data collected by the smart meter with the measurements of a high-precision energy meter, and adjust the meter's calibration coefficient to keep the error within the allowable range.

[0106] 3. Actual Operation and Optimization: After the system is put into actual use, its operation is continuously monitored. Actual energy consumption data is collected and compared with traditional energy consumption measurement methods or historical energy consumption data to evaluate system performance and energy conservation. Based on actual operational feedback, precise energy consumption algorithms, data collection frequency, communication parameters, and other factors are optimized and adjusted to continuously improve the accuracy and reliability of the system, providing strong support for energy management and energy conservation and emission reduction.

[0107] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0108] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified 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 of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0109] Continue to refer Figure 6 The intelligent energy consumption data acquisition system described in this embodiment includes:

[0110] Data acquisition module 601, used to acquire energy consumption data, personnel activity data and park greening data;

[0111] The data set fusion module 602 is used to fuse the energy consumption data, personnel activity data and park greening data to obtain a fused data set;

[0112] The analysis result acquisition module 603 is used to perform comprehensive analysis on the fusion data to obtain analysis results;

[0113] An edge processing module 604 is configured to perform edge processing on the analysis results to obtain edge-processed data;

[0114] The data transmission module 605 is used to transmit the edge-processed data to a remote server or terminal device.

[0115] To solve the above technical problems, the present application also provides a computer device. Figure 7 , Figure 7 This is a basic structural block diagram of the computer device in this embodiment.

[0116] The computer device 7 includes a memory 7a, a processor 7b, and a network interface 7c that are interconnected through a system bus. It should be noted that the figure only shows a computer device 7 having components 7a-7c, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art will understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0117] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0118] The memory 7a includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, a magnetic disk, an optical disk, etc. In some embodiments, the memory 7a may be an internal storage unit of the computer device 7, such as the hard disk or memory of the computer device 7. In other embodiments, the memory 7a may also be an external storage device of the computer device 7, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the computer device 7. Of course, the memory 7a may also include both the internal storage unit of the computer device 7 and its external storage device. In this embodiment, the memory 7a is generally used to store the operating system and various application software installed on the computer device 7, such as the program code of the intelligent energy consumption data collection method. In addition, the memory 7a can also be used to temporarily store various types of data that have been output or are to be output.

[0119] In some embodiments, the processor 7b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 7b is generally used to control the overall operation of the computer device 7. In this embodiment, the processor 7b is used to execute program code stored in the memory 7a or process data, such as executing the program code of the intelligent energy consumption data collection method.

[0120] The network interface 7c may include a wireless network interface or a wired network interface. The network interface 7c is generally used to establish a communication connection between the computer device 7 and other electronic devices.

[0121] The present application also provides another embodiment, namely, providing a non-volatile computer-readable storage medium, which stores a program of an intelligent energy consumption data collection method. The intelligent energy consumption data collection can be executed by at least one processor so that the at least one processor performs the steps of the intelligent energy consumption data collection method as described above.

[0122] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0123] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present application specification and the accompanying drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present application.

Claims

1. An intelligent energy consumption data collection method, characterized in that: include: Obtain energy consumption data, personnel activity data and park greening data; The energy consumption data, personnel activity data and park greening data are integrated to obtain a fused data set; Performing comprehensive analysis and processing on the fused data to obtain analysis results; Performing edge processing on the analysis result to obtain edge-processed data; The edge processed data is transmitted to a remote server or terminal device.

2. The intelligent energy consumption data collection method according to claim 1 is characterized in that: The acquisition of energy consumption data, personnel activity data and park greening data includes: Collect water, electricity, gas and energy consumption data of buildings or parks based on smart electricity meters, smart water meters and smart gas meters; Based on the sensors, the number of people entering and leaving the building and their activities are collected to obtain personnel activity data; Obtain park greening data based on image recognition technology and geographic information system.

3. The intelligent energy consumption data collection method according to claim 2 is characterized in that: The step of fusing the energy consumption data, personnel activity data and park greening data to obtain a fused data set includes: Acquiring a fused data set includes temporally correlating and spatially correlating the energy consumption data, personnel activity data, and park greening data.

4. The intelligent energy consumption data collection method according to claim 3 is characterized in that: The obtaining of the fused data set includes temporal association and spatial association of the energy consumption data, personnel activity data and park greening data, including: Preprocessing the energy consumption data, personnel activity data and park greening data, including cleaning the data and removing missing values ​​and outliers; Standardize the preprocessed data, and perform temporal and spatial correlation on the standardized data; The time association includes: using time as the key index, associating and matching the energy consumption data at the same time point with the personnel activity data and the park greening data, establishing a time series data structure, arranging different types of data in chronological order, and obtaining time-related data; The spatial association includes: matching personnel activity data and park greening data with corresponding building area energy consumption data based on building layout and park planning; determining the personnel activity monitoring range and park greening impact range corresponding to each building area; and spatially integrating data from different sources by establishing a spatial mapping relationship to obtain spatially associated data.

5. The intelligent energy consumption data collection method according to claim 4 is characterized in that: The step of performing comprehensive analysis on the fused data set to obtain analysis results includes: Based on the spatial correlation data in the fused data set, the density of people in different building areas in different time periods is obtained, the actual area of ​​each building area is determined by using the spatial mapping relationship, the density of people is calculated according to the number of people in the personnel activity data, and the personnel activity intensity index is obtained based on the personnel activity records in the time correlation data; Based on the dynamic changes in population density and personnel activity intensity indicators in different building areas, obtain the energy consumption adjustment of different types of equipment; Based on the park greening data in the fusion data set, the plant species and greening areas of different building areas are obtained; based on the quantitative relationship model between greening area, plant species and air conditioning energy saving, the regulation information of park greening on building energy consumption in different seasons and time periods is obtained, and the influencing factors of greening factors are determined; Based on the energy consumption data in the fusion data, the energy consumption adjustment of different types of equipment, and the influencing factors of greening factors, the energy consumption values ​​of each part of the building and the building as a whole are obtained; Based on the time series, the energy consumption trend of each part of the building and the energy consumption of the building as a whole is analyzed, and the energy consumption composition ratio in different time periods is obtained.

6. The intelligent energy consumption data collection method according to claim 1, characterized in that: The performing edge processing on the analysis result to obtain edge processed data includes: Set up local storage space based on actual needs and the storage capacity of edge processing devices to temporarily store analysis results. At the same time, when the communication network is unstable or interrupted, the edge processing device caches the analysis results and transmits them after the network is restored. Based on a preset compression algorithm, the analysis results stored locally or cached in the edge processing device are compressed to obtain compressed data; Before the compressed data is transmitted, it is encrypted based on a preset encryption algorithm.

7. An intelligent energy consumption data collection system, used to implement the intelligent energy consumption data collection method of claims 1-6, characterized in that: include: Data acquisition module, used to obtain energy consumption data, personnel activity data and park greening data; A data set fusion module, used to fuse the energy consumption data, personnel activity data and park greening data to obtain a fused data set; An analysis result acquisition module is used to perform comprehensive analysis and processing on the fusion data to obtain analysis results; An edge processing module, used to perform edge processing on the analysis result to obtain edge processed data; The data transmission module is used to transmit the edge processed data to a remote server or terminal device.

8. An electronic device, characterized in that: include: one or more processors; Memory; And one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions, and when the instructions are executed by the device, the device performs the steps of the intelligent energy consumption data collection method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed on a computer, enables the computer to execute the steps of the intelligent energy consumption data collection method according to any one of claims 1 to 6.

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