Urban popularity prediction method and system based on life traffic energy big data analysis

Abstract

The invention discloses an urban popularity prediction method and system based on life traffic energy big data analysis, and relates to the technical field of big data analysis and energy management, and the method comprises the steps: collecting first data through a first device; processing and analyzing the first data; and the city popularity score is calibrated and predicted through image recognition. According to the urban popularity prediction method based on life traffic energy big data analysis provided by the invention, the possible overload condition is identified in advance by analyzing the regional power consumption and current load condition, the risk of urban power grid load management is reduced, and the urban popularity prediction accuracy is improved by predicting popularity distribution. Corresponding measures are taken in advance in a congestion or high-load area to optimize the energy use efficiency, the reliability of a prediction result is enhanced through image recognition calibration, a data basis is provided for urban sustainable development and resource allocation through long-term storage and popularity trend analysis of data, and the prediction efficiency is improved. The method has better effects in the aspects of reliability, management risk and energy use efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis and energy management, and in particular to a method for predicting urban heat based on big data analysis of living, transportation and energy. Background Art

[0002] Energy big data refers to the method of achieving efficient use and conservation of energy by analyzing and processing massive amounts of data in the energy field. Its main backgrounds are as follows: energy demand. With the development of the global economy and the growth of population, energy demand continues to increase, and traditional energy production and use methods can no longer meet current needs, so it is necessary to find new ways of energy utilization; the transformation of energy structure. The global energy structure is changing from traditional energy to renewable energy. In this process, big data technology can help us better predict and adjust energy production and consumption to adapt to this transformation; the development of big data technology. With the continuous development of big data technology, the potential of big data in the energy field has gradually emerged. Through the analysis of energy big data Collection, processing and analysis can better understand the energy production and consumption, and thus achieve efficient use of energy; the challenges of environmental and climate change, environment and climate have become the focus of global attention, through energy big data can better understand and evaluate the impact of energy production and consumption on the environment, and then adopt more environmentally friendly energy policies; the rise of energy Internet, energy Internet is a new energy model, which combines Internet technology and energy systems, and uses big data technology to achieve intelligent production and consumption of energy, improve energy utilization, and reduce energy waste. Energy big data has become an important development direction in the world's energy field today. It can not only improve energy utilization efficiency, but also help us better solve and respond to the challenges of mixed testing and climate change.

[0003] In the past few decades, the popularization of the Internet and the rapid development of computer technology have led to an explosive growth in the generation and storage of data. These data include personal information, social media data, transaction records, sensor data, etc. The scale of these data is huge, and traditional data processing methods are no longer competent. Therefore, new technical means are needed to process these massive data. The development of cloud computing technology has made the storage and calculation of massive data more convenient and efficient. The popularity of the Internet of Things has enabled more and more devices and sensors to be connected to the Internet, generating a large amount of real-time data. The collection and processing of these data requires the help of big data technology to realize real-time analysis and application of data. In addition, artificial intelligence and machine learning algorithms require a large amount of data for training and learning. Only when the data scale is large enough can better results be achieved. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing big data analysis methods lack mature big data collection, processing and analysis methods, lack of gateway devices that can be used for data fusion processing, and optimization problems on how to ensure data security.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a city heat prediction method based on life, transportation and energy big data analysis, comprising collecting first data through a first device; processing and analyzing the first data; and calibrating and predicting the city heat score through image recognition.

[0007] As a preferred solution of the city heat prediction method based on life, transportation and energy big data analysis described in the present invention, wherein: the collection of first data through the first device includes arranging energy sensors and gateway equipment in the area to be analyzed.

[0008] As a preferred solution of the city heat prediction method based on life, transportation and energy big data analysis described in the present invention, wherein: the collection of the first data through the first device also includes the edge gateway acquiring the first data in real time.

[0009] As a preferred solution of the city heat prediction method based on life, transportation and energy big data analysis described in the present invention, wherein: the processing and analysis of the first data includes obtaining a first total score based on the predicted city and cable current carrying capacity monitoring.

[0010] As a preferred solution of the city heat prediction method based on life, transportation and energy big data analysis described in the present invention, the processing and analysis of the first data also includes comparing the first data before and after to obtain the change of the first data per unit time and the second total score.

[0011] As a preferred solution of the city heat prediction method based on life, transportation and energy big data analysis described in the present invention, wherein: the calibration and prediction of the city heat score through image recognition includes obtaining a third total score based on the first and second total scores, and obtaining a fourth total score through image recognition.

[0012] As a preferred solution of the city heat prediction method based on life, transportation and energy big data analysis described in the present invention, the method of calibrating and predicting the city heat score through image recognition also includes calibrating the third total score to obtain the final city heat prediction total score and evaluate it.

[0013] Another object of the present invention is to provide a city heat prediction system based on big data analysis of life, transportation and energy, which can calibrate the predicted city heat results through a calibration prediction module and adjust the heat score according to the calibrated data, thereby solving the problem of lack of flexibility and immediacy of current prediction results.

[0014] As a preferred solution of the city heat prediction system based on life, transportation and energy big data analysis described in the present invention, it includes: a data acquisition module, an analysis and processing module, and a calibration prediction module; the data acquisition module is used to collect first data through a first device; the analysis and processing module is used to process and analyze the first data; the calibration prediction module is used to calibrate and predict the city heat score through image recognition.

[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program, and wherein the processor executes the computer program to implement a step of a city heat prediction method based on life, transportation and energy big data analysis.

[0016] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a city heat prediction method based on life, transportation and energy big data analysis are implemented.

[0017] Beneficial effects of the present invention: The urban heat prediction method based on life and transportation energy big data analysis provided by the present invention accurately reflects the real-time heat distribution of the city through energy data collection and preliminary transmission links, identifies possible overload conditions in advance through analysis of regional electricity consumption and current load conditions, and reduces the risk of urban power grid load management. By predicting the heat distribution, corresponding measures are taken in advance in congested or high-load areas to optimize energy utilization efficiency. Through image recognition and calibration, the reliability of the prediction results is enhanced. Through long-term storage of data and heat trend analysis, a data basis is provided for urban sustainable development and resource allocation. The present invention achieves better results in reliability, risk management and energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0019] Figure 1 This is an overall flow chart of the city heat prediction method based on life, transportation and energy big data analysis provided in the first and second embodiments of the present invention.

[0020] Figure 2 A diagram of an edge gateway device for predicting urban heat according to the first and second embodiments of the present invention, providing an urban heat prediction method based on analysis of life, transportation and energy big data.

[0021] Figure 3 This is an overall module diagram of a city heat prediction system based on life, transportation and energy big data analysis provided in the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0023] Example 1

[0024] Reference Figure 1-Figure 2 , which is an embodiment of the present invention, provides a city heat prediction method based on life, transportation and energy big data analysis, including:

[0025] S1: Collecting first data through a first device.

[0026] Furthermore, collecting the first data by the first device includes arranging energy sensors and gateway devices in the area to be analyzed.

[0027] In the embodiment of the present application, the first device is an edge gateway device for predicting urban heat based on big data analysis of life, transportation and energy;

[0028] The device includes a storage unit, a communication unit, a processing unit, a drive unit, etc., and supports simultaneous access to sensors of multiple different protocol types. The interface types of this gateway device include HDMI, ETH, USB, RS485Ⅰ, RS485Ⅱ, RS232, CAN, NB / 4G SIM, etc., and the supported communication protocols include DLT645 protocol, Modbus protocol, HTTP protocol, UART, MQTT and other protocols;

[0029] The device can access multiple types of sensors at the same time, and maintain real-time connection and communication between the gateway device and multiple sensors, and obtain data storage from different sensors in real time, so as to serve as data support for the subsequent operation of corresponding application functions and support the relevant results of application functions.

[0030] In an optional embodiment, the first device may also be implemented in other ways, such as arranging integrated sensor units with multi-mode communication functions at multiple key points in the area to be measured, the sensor units have wireless data transmission functions, and support multiple communication protocols such as Wi-Fi, NB-IoT, LoRa, etc., so that the collected energy data can be directly transmitted to the cloud server for processing and analysis without an edge gateway;

[0031] The integrated sensor unit realizes the combination of different sensors through modular design. It can integrate current sensors, electric meters and water meter sensors in one device. It has low power consumption characteristics and is suitable for long-term operation in an environment with a lack of power supply. In addition, the integrated sensor unit can select the appropriate communication protocol according to the actual situation of the use area to achieve efficient and low-latency data transmission;

[0032] With this solution, data collection is no longer limited to fixed edge gateway layouts, and has greater flexibility and adaptability, especially suitable for scenarios with regional expansion or uneven monitoring density.

[0033] Furthermore, collecting the first data through the first device also includes an edge gateway acquiring the first data in real time.

[0034] In the embodiment of the present application, the first data is the current, electricity consumption and water consumption. By deploying current sensors, electricity meters, water meters and cameras in the area to be analyzed, the energy data information such as the current, electricity consumption, water consumption, etc. in the area are recorded in real time, and they are connected to the edge gateway to ensure that the sensor-gateway link is unobstructed and the corresponding data is collected in real time.

[0035] In an optional embodiment, the first data can also collect other supplementary data by adding multiple types of sensors while collecting environmental data, such as environmental data such as temperature and humidity, air pressure and light intensity. The additional data is collected in real time by environmental sensors and uploaded to the edge gateway or cloud processing platform synchronously with the energy data, further enriching the basic data for urban heat prediction;

[0036] In addition to electricity and water consumption, temperature and humidity data can reflect the impact of the external environment on energy demand. The fluctuation of air pressure data in different areas of the city can be used to analyze the contribution of air flow to urban heat. Light intensity can be used to evaluate the heating effect of solar radiation on different areas, thereby calibrating energy consumption in more detail.

[0037] Example 2

[0038] Reference Figure 1-Figure 2 , which is an embodiment of the present invention, provides a city heat prediction method based on life, transportation and energy big data analysis, including:

[0039] S2: Process and analyze the first data.

[0040] Furthermore, the first data is processed and analyzed including obtaining a first total score based on predicted city and cable current carrying capacity monitoring.

[0041] It should be noted that the edge gateway uses the real-time current data collected by the current sensor, runs the cable current carrying capacity monitoring application function based on the predicted city, and obtains the first total score expressed as:

[0042]

[0043] Among them, S1 is the first total score, S1 value is 0-100, when a1>(s1*5), S1=100, a1 is the real-time current data, s1 is the fixed parameter cable cross-sectional area.

[0044] Furthermore, processing and analyzing the first data also includes comparing the first data before and after to obtain a change in the first data per unit time and a second total score.

[0045] It should be noted that the edge gateway uses the electricity consumption data collected by the meter to compare the electricity consumption data before and after the forecast city, and the change of electricity consumption data per unit time is expressed as:

[0046]

[0047] Among them, b1 is the last power consumption data, b2 is the current power consumption data, t1 is the interval between two data, and the edge gateway uses the water consumption data collected by the water meter to compare the water consumption data before and after based on the predicted city, and the change of water consumption data per unit time is expressed as:

[0048]

[0049] Among them, c1 is the last water consumption data, c2 is the current water consumption data, and t2 is the interval between the two data. Based on the predicted city's living energy data usage, the electricity consumption and water consumption data are calculated to obtain the second total score expressed as:

[0050] S2=Q1*x1+Q2*x2

[0051] x1+x2=1

[0052] Among them, S2 is the second total score, and the value of S2 is 0-100.

[0053] S3: Calibrate and predict city heat scores through image recognition.

[0054] Furthermore, calibrating and predicting the city heat score through image recognition includes obtaining a third total score based on the first and second total scores, and obtaining a fourth total score through image recognition.

[0055] It should be noted that, based on the first total score and the second total score, the total heat score of the predicted city is expressed as:

[0056] S3=S1*y1+S2*y2

[0057] y1+y2=1

[0058] Among them, S3 is the third total score, and the value of S3 is 0-100. The edge gateway performs pedestrian and vehicle flow image recognition based on the predicted city through camera image recognition to obtain the fourth total score S4;

[0059] When the total score of the city heat prediction is 0-20, the city heat is extremely low; when the total score of the city heat prediction is 21-40, the city heat is low; when the total score of the city heat prediction is 41-60, the city heat is medium; when the total score of the city heat prediction is 61-80, the city heat is high; when the total score of the city heat prediction is 81-100, the city heat is extremely high.

[0060] Furthermore, calibrating and predicting the city heat score through image recognition also includes calibrating the third total score to obtain the final city heat prediction total score and conduct an evaluation.

[0061] It should be noted that the predicted total score of city heat is calibrated through image recognition to obtain the final total score of city heat prediction. If the city heat level corresponding to the S4 score value of image recognition is higher than the city heat level corresponding to S3, the calibrated total score of city heat prediction is expressed as:

[0062] S=S3*n1+S4*n2

[0063] n1+n2=1

[0064] Among them, if the city heat level corresponding to the S4 score value of image recognition is lower than the city heat level corresponding to S3, the calibrated city heat prediction total score is expressed as:

[0065] S=S3*n1-S4*n2

[0066] n1+n2=1

[0067] The popularity of the predicted city is evaluated based on the final total score. When the total score of the city heat prediction is 0-20, the city heat is extremely low; when the total score of the city heat prediction is 21-40, the city heat is low; when the total score of the city heat prediction is 41-60, the city heat is medium; when the total score of the city heat prediction is 61-80, the city heat is high; when the total score of the city heat prediction is 81-100, the city heat is extremely high.

[0068] Example 3

[0069] One embodiment of the present invention provides a city heat prediction method based on life, transportation and energy big data analysis. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0070] The central area of ​​a city (with an area of ​​about 10 square kilometers) was selected as the experimental area. The experiment used the system equipment of the present invention, including several types of sensors (electricity meters, water meters, current sensors, cameras, etc.) and edge gateway devices. The sensor installation locations in the test area were accurately planned to cover the main streets and intersections with dense traffic in the area to ensure real-time and comprehensive data collection.

[0071] 15 current sensors, 20 electricity meters and 10 water meters are arranged in the experimental area to monitor the electricity consumption, water consumption and real-time current in the area. The data collection frequency is set to once a minute. The system transmits the data to the database in real time through the edge gateway. Cameras are installed at the intersection of major streets to monitor pedestrian and vehicle flows. The image data is updated every 10 seconds. The pedestrian and vehicle density parameters are extracted through intelligent image recognition technology and converted into numerical data.

[0072] As shown in Table 1, by combining energy consumption (electricity consumption, water consumption) with dynamic traffic (people and vehicle flow density), the present invention breaks the traditional single energy load evaluation mode and constructs a more comprehensive heat prediction model. The changes in different data dimensions directly affect the adjustment of heat scores, making heat analysis more diversified and accurate. Through the dynamic calibration mechanism of image recognition technology, the system can adjust the score in real time during the time period with high aggregation density, avoiding the problem of delayed or inaccurate scoring in the traditional mode. For example, the calibrated scores in monitoring periods 3 and 6 are significantly higher than the preliminary scores, indicating that the system timely improves the prediction value under high people and vehicle flow density conditions, showing the accuracy and real-time performance of the present invention. On the basis of real-time prediction, the method of the present invention integrates the historical data model to optimize the scoring calculation process, so that the scoring is not affected by individual outliers. For example, in the score changes from period 1 to period 6, the fluctuations in electricity and water consumption did not cause drastic fluctuations in the score, indicating that it has better data anti-interference ability and can generate stable and trending data.

[0073] Table 1 Experimental data table

[0074]

[0075] Example 4

[0076] Reference Figure 3 , which is an embodiment of the present invention, provides a city heat prediction system based on life, transportation and energy big data analysis, including: a data acquisition module, an analysis and processing module, and a calibration prediction module.

[0077] The data acquisition module is used to collect the first data through the first device; the analysis and processing module is used to process and analyze the first data; and the calibration and prediction module is used to calibrate and predict the city heat score through image recognition.

[0078] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0079] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0080] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0081] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0082] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A city heat prediction method based on big data analysis of life, transportation and energy, characterized in that: include: Collecting first data through a first device; Processing and analyzing the first data; Calibrate and predict city heat scores through image recognition.

2. The city heat prediction method based on life, transportation and energy big data analysis as claimed in claim 1 is characterized by: The collecting of the first data by the first device includes arranging energy sensors and gateway equipment in the area to be analyzed.

3. The city heat prediction method based on life, transportation and energy big data analysis as claimed in claim 1 is characterized by: The collecting of the first data by the first device also includes the edge gateway acquiring the first data in real time.

4. The city heat prediction method based on life, transportation and energy big data analysis as claimed in claim 3 is characterized by: The processing and analyzing of the first data includes obtaining a first total score based on predicted cities and cable current carrying capacity monitoring.

5. The city heat prediction method based on life, transportation and energy big data analysis as claimed in claim 4 is characterized by: The processing and analyzing of the first data also includes comparing the first data before and after to obtain the change of the first data per unit time and the second total score.

6. The city heat prediction method based on life, transportation and energy big data analysis as claimed in claim 5 is characterized by: The method of calibrating and predicting the city heat score through image recognition includes obtaining a third total score based on the first and second total scores, and obtaining a fourth total score through image recognition.

7. The city heat prediction method based on life, transportation and energy big data analysis according to claim 6 is characterized by: The method of calibrating and predicting the city heat score by image recognition also includes calibrating the third total score to obtain the final city heat prediction total score and perform evaluation.

8. A system using the city heat prediction method based on life, transportation and energy big data analysis as claimed in any one of claims 1 to 7, characterized in that: It includes data acquisition module, analysis and processing module, and calibration prediction module; The data acquisition module is used to acquire first data through a first device; The analysis and processing module is used to process and analyze the first data; The calibration prediction module is used to calibrate and predict the city heat score through image recognition.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the city heat prediction method based on life, transportation and energy big data analysis described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the city heat prediction method based on life, transportation and energy big data analysis described in any one of claims 1 to 7 are implemented.

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