Modular sensing hardware and algorithm integration system for sensing and adjusting community living microclimate

Abstract

The invention relates to the technical field of environment monitoring, in particular to a modular sensing hardware and algorithm integration system for sensing and adjusting community human living microclimate, which comprises an environment parameter sensing module, a data coding module, a comfort degree voting module, a data analysis module, a demand analysis module and an environment regulation and control module. According to the invention, through the use of the modular sensor with a unified interface, microclimate data of different dimensions can be collected more accurately and flexibly according to actual demands, a more suitable environment adjustment scheme is made based on actual environment data, and through the wireless communication technology, data transmission is more rapid and reliable, and the data transmission efficiency is improved. According to the method and the system, delay and errors in the data transmission process are reduced, high efficiency of data processing is ensured, equipment parameters can be adjusted more accurately through comprehensive analysis of user feedback and environment data, so that the optimal environment adjusting effect is achieved, the overall energy use efficiency is optimized, and the method and the system have positive influences on improvement of comfort and maintenance of environment sustainability.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, in particular to a modular sensing hardware and algorithm integration system for community human settlement microclimate perception and regulation. Background Art

[0002] The technical field of environmental monitoring mainly focuses on the real-time monitoring and regulation of environmental quality, including the comprehensive assessment and management of climate change, air quality, water quality, and soil conditions. By using sensors, data transmission, and analysis technologies, it provides scientific and technological support for environmental management to ensure that the impact of human activities on the environment is minimized. The core content includes the whole process from data collection to processing and then to feedback, focusing on achieving precise control and optimization of environmental factors through technical means to improve the quality of life and ensure ecological balance.

[0003] Among them, the modular sensing hardware and algorithm integration system for community human settlement microclimate perception and regulation refers to a real-time monitoring and management system for the microclimate characteristics of residential areas, which uses modular sensing hardware and integrated algorithms to achieve precise collection and processing of environmental data. The system collects microclimate data such as temperature, humidity, light, and wind speed through sensors installed at different positions in the community, and uses artificial intelligence algorithms to analyze and predict the data to adjust environmental equipment to achieve the purpose of regulating the community environment. The design of the system allows adding or replacing modules according to specific needs to adapt to different community environments and user requirements.

[0004] Although the existing technologies cover a wide range in the field of environmental monitoring, they have deficiencies in dealing with real-time performance and personalized regulation. Especially in community environmental management, conventional environmental monitoring systems mostly focus on collecting macro environmental data and neglect the attention to the individual comfort of residents and real-time response. For example, traditional systems use fixed preset values for air quality or temperature regulation and cannot be adjusted according to the immediate feedback of residents, resulting in unsatisfactory environmental regulation in specific situations, such as during seasonal transitions or special weather conditions. There are also delays in data transmission and processing, affecting the timeliness and accuracy of environmental regulation, leading to energy waste or failure to meet the actual needs of residents, affecting the quality of life of residents and their trust in the environmental management system. Summary of the Invention

[0005] To address the deficiencies in the real-time processing and personalized regulation of existing technologies, especially in community environmental management, conventional environmental monitoring systems mainly focus on collecting macro environmental data and neglect the attention to and real-time response to the comfort of individual residents. For example, traditional systems adopt fixed preset values for air quality or temperature regulation and cannot be adjusted according to the immediate feedback of residents, resulting in unsatisfactory environmental regulation in specific situations, such as during seasonal transitions or under special weather conditions. There are also delays in data transmission and processing, affecting the timeliness and accuracy of environmental regulation, leading to energy waste or failure to meet the actual needs of residents, and affecting the quality of life of residents and their trust in the environmental management system. Embodiments of the present invention provide a modular sensing hardware and algorithm integration system for community human settlement microclimate perception and regulation. The technical solutions are as follows:

[0006] On the one hand, a modular sensing hardware and algorithm integration system for community human settlement microclimate perception and regulation is provided, and the system includes:

[0007] The environmental parameter sensing module collects multiple microclimate data of community human settlements through modular sensors, monitors the environmental conditions in the measured area in real time, and uses modular sensing hardware with unified interface sizes and definitions to replace different sensors according to real-time needs to obtain an environmental data set;

[0008] The data encoding module performs real-time transmission of data through wireless communication according to the environmental data set, avoids delays and error rates during data transmission, verifies the stability of data transmission, and obtains a data transmission record;

[0009] The comfort voting module builds an interactive interface using NFC technology based on the data transmission record. Users wirelessly interact with distributed voting devices through their mobile phones to collect the scores and perceptions of users on the real-time environmental comfort of the community, and obtain user comfort feedback information;

[0010] The data analysis module analyzes the user feedback and environmental data according to the user comfort feedback information, adjusts the working parameters of community devices, and obtains an optimized regulation plan;

[0011] The demand analysis module ranks the regional demands according to the optimized regulation plan with reference to the overall energy consumption data and regional demands of community human settlements to obtain a full-region regulation decision;

[0012] The environmental regulation module integrates the PPO algorithm based on the full-region regulation decision to adjust the parameters of air conditioners, fresh air, and lighting devices in the community to obtain the microclimate perception and regulation results.

[0013] As a further solution of the present invention, the environmental data set includes temperature readings, humidity levels, wind speed measurements, light intensity, air quality index, the data transmission record includes transmission success rate, transmission delay, error rate, the user comfort feedback information includes user ratings, perception descriptions, voting timestamps, the optimized regulation plan includes set temperature ranges, lighting brightness levels, frequencies of fresh air equipment, the full-region regulation decision includes equipment operation strategies for differentiated regions, energy consumption limits, regulation schedules, and the microclimate perception and regulation results include environmental parameter change information, satisfaction improvement ratio, energy consumption optimization data.

[0014] As a further solution of the present invention, the environmental parameter perception module includes:

[0015] The sensor configuration sub-module collects a plurality of microclimate data of community human settlements through modular sensors, including temperature, humidity, wind speed, TVOC, noise, and light intensity sensors, collects microclimate data of temperature, humidity, and wind speed, adjusts parameters according to the sensitivity of the modular sensors, optimizes the data collection frequency, and obtains the microclimate raw data set;

[0016] The data processing sub-module uses the microclimate raw data set, performs data processing, removes outliers, adjusts the data format according to preset standards, and adjusts the sensor configuration to match data changes, obtaining the environmental state analysis result;

[0017] The data output sub-module sets the data transmission frequency and output format according to the environmental state analysis result, synchronously updates the data, verifies the consistency of the output result, and obtains the environmental data set.

[0018] As a further solution of the present invention, the data encoding module includes:

[0019] The data transmission sub-module encodes and encrypts the environmental data set, uses wireless communication technology, sets a data packet retransmission mechanism to handle estimated signal losses, and adjusts the data transmission frequency to match differentiated network conditions, obtaining a real-time transmission data stream;

[0020] The error detection sub-module uses the real-time transmission data stream, implements error detection and correction to avoid the transmission error rate, monitors the arrival rate and integrity of data packets, adjusts transmission parameters, and obtains the data error correction result;

[0021] The timestamp recording sub-module analyzes the delays and error patterns during the data transmission process based on the data error correction result, records the timestamp and status of each data transmission, calculates the data transmission stability value, and evaluates the overall transmission stability, obtaining the data transmission record.

[0022] As a further solution of the present invention, the formula for calculating the data transmission stability value is as follows:

[0023]

[0024] Among them, E is the data transmission stability value, T represents the time stamp of real-time data transmission, T0 represents the reference time stamp, D represents the number of errors in real-time data transmission, D0 represents the expected number of errors, α represents the weight coefficient, β represents the influence weight, and γ represents the adjustment coefficient.

[0025] As a further solution of the present invention, the comfort voting module includes:

[0026] The interactive interface setting sub-module activates the interactive interface of the distributed voting device based on the data transmission record through NFC technology, customizes the interface layout and user interaction method, optimizes the interface response speed, and generates a user interaction interface configuration.

[0027] The wireless interaction sub-module enables users to use the user interaction interface configuration to wirelessly connect a mobile phone to the distributed voting device, set up connection verification and data synchronization mechanisms, check the security and real-time nature of data transmission, and obtain real-time interaction records.

[0028] The comfort perception sub-module collects users' ratings and perceptions of the comfort of the community environment based on the real-time interaction records, organizes and analyzes user data, and obtains user comfort feedback information.

[0029] As a further solution of the present invention, the data analysis module includes:

[0030] The data comparison sub-module analyzes the user comfort feedback information, compares it with the real-time community environment data, identifies the correlation between environmental factors and user comfort, evaluates the differences in user feedback under different environmental conditions, and obtains the environmental and comfort analysis results.

[0031] The device parameter optimization sub-module adjusts the operating parameters of community devices based on the environmental and comfort analysis results, including temperature controllers and humidity regulators, sets up automated adjustment logic to respond to environmental changes, evaluates the adaptability and efficiency of device operations, and obtains the adaptability evaluation results.

[0032] The solution adjustment sub-module adopts the adaptability evaluation results to formulate and dynamically adjust the operation plan of community devices, optimize the overall environmental comfort, integrate user comfort feedback information and device automated adjustment logic, and obtain the optimized control plan.

[0033] As a further solution of the present invention, the demand analysis module includes:

[0034] The performance evaluation sub-module evaluates the energy efficiency ratio and implementation feasibility of the optimized control scheme by combining the overall energy consumption data of the community's human settlements with the optimized control scheme, scores the effectiveness of the differential measures in the optimized scheme, and generates the evaluation result of the control scheme;

[0035] The demand ranking sub-module ranks the demands of the differential regions based on the evaluation result of the control scheme, referring to the regional demands and environmental impacts, and identifies the key demand points to obtain the demand priority list;

[0036] The resource allocation optimization sub-module uses the demand priority list, referring to the environmental comfort, energy consumption, and residents' satisfaction, to control the entire region, optimize the resource allocation, and obtain the control decision for the entire region.

[0037] As a further solution of the present invention, the environmental control module includes:

[0038] The decision integration sub-module integrates the PPO algorithm into the control logic of the community equipment based on the control decision for the entire region, configures the algorithm parameters, matches the requirements of the real-time community environment, calculates the quantization value of the control logic, and obtains the algorithm integration configuration;

[0039] The equipment output sub-module automatically adjusts the working parameters of the air conditioner, fresh air, and lighting equipment according to the algorithm integration configuration, adjusts the equipment output according to the change of real-time data, and verifies that the equipment adjustment is synchronized with the environmental change to obtain the equipment adjustment record;

[0040] The adjustment effect evaluation sub-module monitors the performance of the adjusted equipment and the environmental change situation according to the equipment adjustment record, analyzes the impact of the equipment adjustment on the community microclimate, evaluates the adjustment effect, and obtains the microclimate perception and adjustment result.

[0041] As a further solution of the present invention, the formula for calculating the quantization value of the control logic is as follows:

[0042]

[0043] Among them, F(x) is the quantization value of the control logic, P represents the environmental fitness score, E s represents the real-time community environmental state, E t represents the target environmental state, R represents the feedback regulation coefficient, D m represents the average value of the equipment control deviation, and C represents the normalization constant.

[0044] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0045] By real-time monitoring of the community environment and real-time adjustment of environmental equipment parameters, the response speed and accuracy of environmental management are effectively improved. Through the use of modular sensors, different microclimate data can be collected more accurately, and a more suitable environmental adjustment plan can be formulated based on the actual environmental data. Through wireless communication technology, data transmission is faster and more reliable, reducing delays and errors during data transmission and ensuring the efficiency of data processing. The NFC technology is used to collect real-time feedback from users, enhancing the interactivity and user experience of the system. Through the comprehensive analysis of user feedback and environmental data, the equipment parameters can be adjusted more precisely to achieve the optimal environmental adjustment effect. By comprehensively considering user comfort and environmental factors, the overall energy use efficiency is optimized, which has a positive impact on improving the quality of life and maintaining ecological balance. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on the drawings without creative efforts.

[0047] Figure 1 is a schematic diagram of the modular sensing hardware and algorithm integration system for community human settlement microclimate perception and adjustment provided by the embodiments of the present invention;

[0048] Figure 2 is a schematic diagram of the system framework of the present invention;

[0049] Figure 3 is a flowchart of the environmental parameter sensing module in the present invention;

[0050] Figure 4 is a flowchart of the data encoding module in the present invention;

[0051] Figure 5 is a flowchart of the comfort voting module in the present invention;

[0052] Figure 6 is a flowchart of the data analysis module in the present invention;

[0053] Figure 7 is a flowchart of the demand analysis module in the present invention;

[0054] Figure 8 is a flowchart of the environmental regulation module in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The following will describe the technical solutions in the present invention in conjunction with the drawings.

[0056] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0057] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0058] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0059] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0060] See also Figure 1 , 2 The embodiment of the present invention provides a modular sensing hardware and algorithm integration system for sensing and regulating community human settlement microclimate, the system comprising:

[0061] The environmental parameter perception module collects multiple microclimate data of the community through modular sensors, including temperature, humidity, wind speed, TVOC, noise and light intensity sensors, and monitors the environmental conditions in the measured area in real time. It also uses modular perception hardware, unified interface size and definition, and replaces differentiated sensors according to real-time needs to obtain environmental data sets;

[0062] The data encoding module transmits data in real time through wireless communication according to the environmental data set. Through data encoding, it avoids the delay and error rate in the data transmission process, verifies the stability of the data transmission process, and obtains the data transmission record.

[0063] The comfort voting module records data transmission and uses NFC technology to build an interactive interface. Users can wirelessly interact with distributed voting devices through their mobile phones to collect users' ratings and perceptions of the real-time comfort of the community environment and obtain user comfort feedback information.

[0064] The data analysis module applies deep learning algorithms on local edge devices based on the user comfort feedback information. By analyzing the user feedback and environmental data, it adjusts the working parameters of community devices, optimizes environmental control, and obtains an optimized control plan.

[0065] The demand analysis module, according to the optimized control plan, refers to the overall energy consumption data of the community's human settlements and regional demands, prioritizes the regional demands, and obtains a full-region control decision.

[0066] The environmental control module, based on the full-region control decision, integrates the PPO algorithm, adjusts the parameters of the community's air conditioners, fresh air, and lighting devices, matches the overall environmental needs of the community, and obtains the microclimate perception and adjustment results.

[0067] The environmental data set includes temperature readings, humidity levels, wind speed measurements, light intensity, air quality index. The data transmission record includes transmission success rate, transmission delay, error rate. The user comfort feedback information includes user ratings, perception descriptions, voting timestamps. The optimized control plan includes the set temperature range, lighting brightness level, frequency of fresh air devices. The full-region control decision includes the device operation strategies, energy consumption limits, control schedules for different regions. The microclimate perception and adjustment results include environmental parameter change information, satisfaction improvement ratio, energy consumption optimization data.

[0068] Please refer to Figure 2 、 3 For the environmental parameter perception module, it includes:

[0069] The sensor configuration sub-module collects a variety of microclimate data of the community's human settlements through modular sensors, including temperature, humidity, wind speed, TVOC, noise, and light intensity sensors. It collects the microclimate data of temperature, humidity, and wind speed, adjusts the parameters according to the sensitivity of the modular sensors, optimizes the data collection frequency, and the execution process of obtaining the original microclimate data set is as follows;

[0070] Through the configuration and parameter adjustment of modular sensors, the effective collection of microclimate data such as temperature, humidity, and wind speed is achieved. Modular sensors refer to sensors with different uses but unified interfaces that can be connected to a main control module. Different sensors are installed according to different situations (for example, a photosensitive sensor may not be required in a scenario with non-adjustable light sources), which form the nodes of this system. Modular sensors are set according to specific sensitivity and data collection frequency requirements. The coordination among sensors ensures the comprehensiveness and accuracy of data. By adjusting the collection parameters in real time, not only can the system adapt to environmental changes, but also the overall data collection efficiency can be optimized. Analyze the time series of data to determine that certain time periods are peak periods for data collection, and the frequency can be reduced during these time periods to save resources. The response ability of sensors to environmental changes depends on the configured sensitivity and stability. The system analyzes the response data of each sensor, compares its performance under different environmental conditions, and adjusts the sensitivity and parameter settings. By analyzing the collected microclimate data, the system can predict the environmental trends in the next period of time, which is extremely important for adjusting data collection strategies and sensor configurations. For example, if it is predicted that the temperature will rise significantly in the next few days, the system will increase the sampling rate of the temperature sensor to more accurately monitor temperature changes, providing valuable environmental information for community residents and managers, and obtaining the original microclimate data set.

[0071] The data processing sub-module uses the original microclimate data set to perform data processing, remove outliers, adjust the data format according to preset standards, and adjust the sensor configuration to match the data changes. The execution process for obtaining the environmental status analysis result is as follows;

[0072] Use the original microclimate data set for data processing, according to the formula:

[0073]

[0074] Calculate the variance BR of the data set, identify and remove outliers;

[0075] In the formula, x i represents the value of a single data point, represents the average value of data point i, and n represents the total number of data points;

[0076] Consider the temperature data collected by a temperature sensor within a specific hour: x = [22.5, 23.0, 21.5, 50.0, 22.0], where 50.0 is obviously an outlier;

[0077]

[0078] Apply the formula to calculate the degree of data dispersion:

[0079]

[0080] A high variance value indicates the presence of outliers in the dataset. Detecting and removing abnormal data is crucial for ensuring the accuracy and reliability of the analysis results. This step is essential for maintaining the quality of the entire dataset because accurate environmental status analysis depends on the quality and integrity of the data.

[0081] The data output sub-module sets the data transmission frequency and output format according to the environmental status analysis results, synchronously updates the data, and verifies the consistency of the output results. The execution process of obtaining the environmental dataset is as follows;

[0082] Adjust the data transmission frequency and output format to optimize the efficiency and accuracy of data transmission. This adjustment ensures the synchronous update and consistency verification of the data output with the environmental status analysis results. The analysis of the environmental status depends on the integration of multiple sensor data, such as temperature, humidity, wind speed, etc., to ensure that the information in the dataset can accurately reflect the environmental conditions. By adjusting the data format and transmission frequency, data can be processed and transmitted more effectively, reducing delays and errors during data transmission. Provide the analysis results to users in an appropriate format, including charts, which not only provide a visual representation of the data but also optimize the accessibility and understandability of the data. Push the temperature trend chart and alerts for abnormal climate events to the public display screen in the community in real-time, or directly send them to the user's smartphone through an application programming interface (API) to provide real-time environmental monitoring data for community residents and obtain the environmental dataset.

[0083] Please refer to Figure 2 、 4 , the data encoding module includes:

[0084] The data transmission sub-module encodes and encrypts the environmental dataset, uses wireless communication technology, sets a packet retransmission mechanism to cope with the estimated signal loss, and adjusts the data transmission frequency to match different network conditions. The execution process of obtaining the real-time transmission data stream is as follows;

[0085] By encoding and encrypting the environmental dataset, ensure the security of the data during wireless communication. Adopt advanced encryption technologies such as AES or RSA to prevent the data from being stolen or tampered with during transmission. Set a packet retransmission mechanism to cope with the estimated signal loss situation. This mechanism is based on a prediction algorithm that evaluates the estimated packet loss rate and automatically triggers retransmission to ensure the integrity and reliability of the data. Dynamically adjust the data transmission frequency according to different network conditions, such as signal strength and bandwidth limitations, optimize the data stream and reduce the probability of network congestion. These measures ensure that the data can be stably transmitted in various network environments, provide instant environmental monitoring information, and support further data analysis and decision-making to obtain the real-time transmission data stream.

[0086] The error detection sub-module uses real-time transmission data streams to implement error detection and correction to avoid transmission error rates, monitor the arrival rate and integrity of data packets, adjust transmission parameters, and the execution process of obtaining the data error correction result is as follows;

[0087] Utilize real-time transmission data streams to implement error detection and correction to reduce the transmission error rate and ensure data quality. Adopt checksum technologies such as CRC or checksum to verify each data packet to ensure that it has not been damaged or tampered with during transmission. By monitoring the arrival rate and integrity of data packets, errors can be detected and corrected in a timely manner, maintaining the cleanliness and integrity of the data stream. According to the real-time monitoring results, dynamically adjust transmission parameters such as window size or timeout time. The adjustment helps optimize the overall transmission efficiency and reduce the probability of error occurrence, enhancing the reliability of data transmission, ensuring the accuracy and integrity of data, supporting high-quality data analysis and applications, and obtaining the data error correction result.

[0088] The timestamp recording sub-module, based on the data error correction result, analyzes the delays and error patterns in the data transmission process, records the timestamp and status of each data transmission, calculates the data transmission stability value, and evaluates the overall transmission stability. The execution process of obtaining the data transmission record is as follows;

[0089] The formula for calculating the data transmission stability value is as follows:

[0090]

[0091] Among them, E is the data transmission stability value, T represents the timestamp of real-time data transmission, T0 represents the reference timestamp, D represents the number of errors in real-time data transmission, D0 represents the expected number of errors, α represents the weight coefficient, β represents the influence weight, and γ represents the adjustment coefficient;

[0092] Parameter meanings and setting values:

[0093] T is the timestamp of real-time data transmission, from the system's time marking device, reflecting the specific time when the data packet is received, in milliseconds;

[0094] T0 is the reference timestamp, set as the timestamp at the start of transmission, used to calculate the time difference from the start of transmission to the current time;

[0095] D is the number of errors in real-time data transmission, statistically obtained through network monitoring tools, reflecting the number of errors in the current data packet;

[0096] D0 is the expected number of errors, which can be the average number of errors in the previous period or a predetermined error threshold, used to measure the deviation from the normal level;

[0097] The values of α, β, and γ are obtained through historical data analysis and experimental tests to ensure they are within a reasonable range. Taking the typical environment of a data center as an example, α can be set to 1.5 to emphasize the impact of time delay on stability, β can be set to 0.5 to slightly weaken the impact of changes in the number of errors, and γ can be set to 0.1 to ensure the stability of calculations.

[0098] Substitute the parameters into the formula for calculation: Let T = 1597254000 milliseconds (the current timestamp), T0 = 1597253990 milliseconds (the reference timestamp), D = 10 errors (the current number of errors), and D0 = 5 errors (the expected number of errors). Substituting the parameters into the formula gives:

[0099]

[0100] The results show that the stability value of the data transmission process is 9.34. The magnitude of this value can be used to evaluate the quality of data transmission. A higher value indicates more stable data transmission, while a lower value indicates problems in the data transmission process, such as increased latency or errors. This value can be used as a basis for subsequent optimization and problem-solving.

[0101] Please refer to Figure 2 、 5 , and the comfort voting module includes:

[0102] Based on the data transmission records, the interactive interface setting sub-module activates the interactive interface of the distributed voting device through NFC technology, customizes the interface layout and user interaction methods, optimizes the interface response speed, and generates the execution process of the user interactive interface configuration as follows;

[0103] For different types of voting devices, interface customization is carried out. Through NFC technology, not only is the device activated, but also the interface layout and interaction methods are customized according to the device performance and user needs, optimizing the response speed and user experience of the interface. This includes optimizing the layout and dynamic effects of interface elements such as buttons, sliders, and feedback information to ensure that the interaction process is both intuitive and efficient. Optimization is carried out based on previous data transmission records to achieve fast loading and response, accelerating the speed of interface switching and operation feedback. The interface configuration of each device has been rigorously tested to ensure performance stability and operation simplicity in various usage scenarios, aiming to improve the user's operation convenience and overall experience, enhance the interaction between the user and the device, and enable the user to make selections and vote more freely, generating the user interactive interface configuration.

[0104] For the wireless interaction sub-module, the user uses the user interactive interface configuration to wirelessly connect a mobile phone to the distributed voting device, sets up connection verification and data synchronization mechanisms, and checks the security and real-time nature of data transmission. The execution process of obtaining real-time interaction records is as follows;

[0105] Enable users to establish a wireless connection with distributed voting devices via mobile phones and operate using a user interaction interface configuration, including setting up connection verification and data synchronization mechanisms to ensure the security and real-time nature of data during the connection process. Adopt the Advanced Encryption Standard and real-time data synchronization technology to ensure encrypted protection of data during transmission and no delay in updates. Also implement a continuity verification program to prevent the connection from being illegally truncated or tampered with during the interaction process. Users can securely and conveniently send voting data from mobile devices. At the same time, monitor the connection status and data transmission speed, and adjust the transmission frequency and synchronization interval in real time to optimize the interaction efficiency of the entire system. Not only reflect the continuity and security of the interaction, but also provide detailed information about the interaction process, including the time, sequence, and duration of user operations, providing a basis for further data analysis and user experience optimization to obtain real-time interaction records.

[0106] The comfort perception sub-module, based on real-time interaction records, collects users' ratings and perceptions of the comfort of the community environment, and the execution process of sorting and analyzing user data to obtain user comfort feedback information is as follows;

[0107] Sort and analyze users' ratings and perceptions of the comfort of the community environment. This process includes classifying, summarizing, and trend analysis of user feedback to determine the factors that most affect users' comfort. Adopt advanced data analysis techniques, such as clustering analysis and sentiment analysis, to analyze users' ratings and comments, identify positive or negative perception factors, and also perform time series analysis on the collected data to monitor the changing trends of comfort perception. The analysis results help community managers understand the specific needs and dissatisfaction factors of residents, take corresponding improvement measures, provide valuable data support for service providers in the community, and be able to adjust the community environment according to the actual feelings of residents, improving residents' satisfaction and quality of life, and obtaining user comfort feedback information.

[0108] Please refer to Figure 2 、 6 The data analysis module includes:

[0109] The data comparison sub-module analyzes user comfort feedback information, compares it with real-time community environment data, identifies the correlation between environmental factors and user comfort, and evaluates the differences in user feedback under different environmental conditions. The execution process of obtaining the analysis results of the environment and comfort is as follows;

[0110] Compare user comfort feedback with community environment data according to the formula:

[0111]

[0112] where AC represents the correlation coefficient, x b represents a certain item of data in user comfort feedback, and y bRepresents the corresponding environmental data, and A represents the number of data pairs b;

[0113] Set the user's feedback on the temperature comfort of a certain community as [20, 22, 19, 21], and the environmental temperature data for the same period as [18, 20, 18, 19];

[0114] Make a comparison and calculate the normalized sum of the squares of the relative differences for each pair of data:

[0115]

[0116] The calculation results show the correlation between user comfort and actual environmental data. A lower value indicates a high degree of consistency, indicating that the environmental conditions match the user's feelings highly, verifying the effectiveness of the environmental adjustment measures.

[0117] Based on the environmental and comfort analysis results, the equipment parameter optimization sub-module adjusts the operating parameters of community equipment, including temperature controllers and humidity regulators, sets up automated adjustment logic to respond to environmental changes, evaluates the adaptability and efficiency of equipment operations, and the execution process for obtaining the adaptability evaluation results is as follows;

[0118] Adjust the operating parameters of community equipment, such as temperature controllers and humidity regulators, through intelligent algorithms. The automated logic responds to environmental changes, optimizes the response time and energy efficiency of the equipment. By analyzing the collected data, automatically identify the changing trends of environmental parameters and adjust the equipment settings to optimize environmental conditions. If the analysis shows that the current humidity exceeds the user's comfort range, the humidity regulator will automatically reduce the indoor humidity. Also evaluate the adaptability and efficiency of equipment operations to ensure that each adjustment is based on the latest environmental data and user feedback, helping managers understand the actual effects of equipment adjustments, further optimize equipment configurations and operating parameters, and obtain the adaptability evaluation results.

[0119] The solution adjustment sub-module uses the adaptability evaluation results to formulate and dynamically adjust the operating plan of community equipment, optimize the overall environmental comfort, integrate user comfort feedback information and equipment automated adjustment logic, and the execution process for obtaining the optimized control plan is as follows;

[0120] Formulate and dynamically adjust the operating plan of community equipment, integrate user comfort feedback and equipment automated adjustment logic, ensure that the operating plan can reflect the needs of users and environmental changes in real time. If the user feedback indicates that the temperature in a specific area is too low, automatically increase the heating intensity in that area. Considering energy conservation and efficiency, ensure that the equipment operates in the best state through optimization algorithms. The continuously updated operating plan not only improves environmental comfort but also enhances the efficiency of energy use, aiming to achieve the best balance between environmental comfort and energy efficiency through fine management, improving the quality of life and satisfaction of residents, and obtaining the optimized control plan.

[0121] Please refer to Figure 2 and 7 . The requirements analysis module includes:

[0122] The energy efficiency evaluation sub-module evaluates the energy efficiency ratio and implementation feasibility of the optimized control scheme by combining the overall energy consumption data of the community's human settlements through the optimized control scheme, scores the effectiveness of the differential measures in the optimized scheme, and the execution process of generating the evaluation results of the control scheme is as follows;

[0123] Evaluate the energy efficiency ratio and implementation feasibility of the optimized control scheme, according to the formula:

[0124]

[0125] In the formula, Z represents the energy efficiency ratio, Z saved represents the energy saved through the optimization measures, Z total represents the total energy consumed;

[0126] Set the energy consumption of the overall community's human settlements before optimization to 1000 unit energies, and the total energy consumption after optimization is reduced to 850 units, saving 150 unit energies;

[0127] Substitute the values into the formula to calculate the energy efficiency ratio:

[0128]

[0129] The result shows that through the optimization of the control scheme, the community energy efficiency has increased by 15%, and this result shows the effectiveness score of the control scheme and the energy-saving effect in actual operation, providing a basis for community managers to adjust the control scheme and optimize the energy management strategy.

[0130] The requirements ranking sub-module ranks the requirements of the differential regions based on the evaluation results of the control scheme, referring to the regional requirements and environmental impacts, and identifies the key requirement points. The execution process of obtaining the requirements priority list is as follows;

[0131] Integrate the regional requirements and environmental impacts, refine and rank the requirements of the differential regions. The identification of key requirement points depends on the analysis of environmental data and residents' feedback, which includes the quantification and evaluation of the comfort requirements, safety requirements, and specific facility requirements in different regions of the community. Through advanced statistical methods and machine learning models, calculate the weights and urgencies of each requirement. For example, use clustering algorithms to analyze similar requirements, and time series analysis to predict the change trends of requirements. The analysis helps to accurately identify key requirement points, such as the heat requirements in the elderly community or the improvement of safety facilities in the children's area. Continuously adjust with the update of environmental changes and community feedback to ensure that the resource investment maximizes the satisfaction and comfort of community residents, improves the efficiency and response speed of community services, and makes the resource allocation more reasonable and scientific, obtaining the requirements priority list.

[0132] The resource allocation optimization sub-module uses a demand priority list and, with reference to environmental comfort, energy consumption, and resident satisfaction, regulates the entire area, optimizes resource allocation, and obtains the execution process of the full-area regulation decision as follows;

[0133] Comprehensively regulate the environmental comfort, energy consumption, and resident satisfaction of the community. Considering the environmental monitoring data and the results of the resident satisfaction survey comprehensively, adjust the resource allocation to improve the overall efficiency and effect. Adjust the operating parameters of the air-conditioning system according to the real-time environmental monitoring data to meet the temperature requirements of different areas, or optimize the usage time of lighting and heating equipment according to the energy consumption data to reduce energy waste. The optimization of resource allocation is supported by precise data analysis and intelligent algorithms to ensure that each adjustment is based on the latest requirements and environmental conditions, not only improving the quality of life of residents, but also optimizing energy use and reducing operating costs. Through a continuous data feedback and adjustment mechanism, ensure the real-time update and adaptability of the decision-making, achieve high efficiency and high satisfaction in community management, make the community environmental regulation more refined and user-friendly, and obtain the full-area regulation decision.

[0134] Please refer to Figure 2 、 8 , the environmental regulation module includes:

[0135] Based on the full-area regulation decision, the decision integration sub-module integrates the PPO algorithm into the control logic of community devices, configures the algorithm parameters, matches the requirements of the real-time community environment, and calculates the quantization value of the control logic. The execution process of the algorithm integration configuration is as follows;

[0136] The formula for calculating the quantization value of the control logic is as follows:

[0137]

[0138] Among them, F(x) is the quantization value of the control logic, P represents the environmental fitness score, E s represents the real-time community environmental state, E t represents the target environmental state, R represents the feedback adjustment coefficient, D m represents the average value of the device control deviation, and C represents the normalization constant;

[0139] Parameter meaning and calculation process:

[0140] Environmental fitness score P: This is a value obtained through analysis by special software using data collected by the environmental monitoring system. It is between 0 and 1 and reflects the matching degree between the current settings of the device and the environmental requirements. The set value is 0.85, indicating a relatively high fitness;

[0141] Environmental state difference |E s -E t |: E s and Et They are the current environmental state measured by sensors on a real - time basis and the preset target environmental state respectively. If the current temperature is 22°C and the target temperature is 25°C, then |E s -E t | = 3;

[0142] Feedback adjustment coefficient R: This coefficient is used to adjust the feedback intensity, which is dynamically adjusted based on the historical response data of the device. The range is set between 0.1 and 0.5, and the set value is 0.3, indicating a medium - strength adjustment;

[0143] Average value D of the device control deviation m : This is calculated from historical data and represents the average deviation between the execution of past control commands and the actual effect. The deviation value can be 0.5°C;

[0144] Normalization constant C: It is used to ensure that the formula output is within a reasonable range. It is set to 5, which is determined based on the past output range of the system;

[0145] Substitute the parameters into the formula for calculation:

[0146]

[0147] The result shows the comprehensive score of the current control strategy after considering environmental fitness, feedback intensity, and control deviation. This score is used to adjust the control logic to ensure that the device better adapts to environmental changes and optimizes the overall performance. This value reflects the comprehensive effect of the control logic adjustment obtained from the formula and is directly applied to the community device control system to optimize device response and adjust control parameters.

[0148] The device output sub - module automatically adjusts the working parameters of air conditioners, fresh air, and lighting equipment according to the algorithm integration configuration, adjusts the device output according to the changes in real - time data, and verifies that the device adjustment is synchronized with the environmental changes. The execution process of obtaining the device adjustment record is as follows;

[0149] Through the integrated algorithm configuration, automatic parameter adjustment of air conditioners, fresh air, and lighting equipment is realized to adapt to the real - time changes in environmental data. Advanced control algorithms are adopted to automatically calculate and adjust the device output parameters according to the real - time data from various sensors, such as temperature, humidity, and light intensity. When the indoor temperature is detected to rise, the cooling intensity of the air conditioner will automatically increase, and at the same time, the lighting intensity is adjusted according to the indoor lighting conditions to maintain the balance between comfort and energy conservation. It reflects the time, intensity, and efficiency of the device's response to environmental changes. By real - time monitoring the synchronous changes in the device output and the environment, the immediacy and accuracy of the adjustment measures can be verified to ensure that all devices can operate in the best state, improve the overall environmental quality and the living experience of residents, and obtain the device adjustment record.

[0150] The adjustment effect evaluation sub-module monitors the performance of the adjusted equipment and the changes in the environment according to the equipment adjustment records, analyzes the impact of equipment adjustment on the community microclimate, evaluates the adjustment effect, and the implementation process of obtaining the microclimate perception and adjustment results is as follows;

[0151] Comprehensively monitor the performance of the adjusted equipment and the environmental changes, analyze the specific impact of the adjustment of air conditioners, fresh air systems, and lighting equipment on the community microclimate, and evaluate the actual effects of various equipment adjustment measures by comparing the environmental data before and after adjustment, such as temperature differences, humidity changes, and lighting conditions. In this process, statistical analysis and model prediction techniques are applied to determine the specific contributions of equipment adjustment to environmental comfort and energy consumption. The evaluation results not only reflect the optimization degree of equipment performance but also reveal the effectiveness of adjustment strategies in improving the community microclimate, providing valuable data support to help decision-makers further optimize the community's environmental management strategies and equipment operation plans to achieve higher energy efficiency and resident satisfaction, and obtain the microclimate perception and adjustment results.

[0152] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A modular sensing hardware and algorithm integration system for community human settlement microclimate perception and regulation, characterized in that, The system includes: The environmental parameter perception module collects a variety of microclimate data of community human settlements through modular sensors, monitors the environmental conditions in the measured area in real time, and uses modular perception hardware with unified interface sizes and definitions. Different sensors can be replaced according to real-time needs to obtain an environmental data set. The data encoding module performs real-time transmission of data through wireless communication according to the environmental data set, avoids delays and error rates during data transmission, verifies the stability of data transmission, and obtains a data transmission record. The comfort voting module builds an interactive interface using NFC technology through the data transmission record. Users wirelessly interact with distributed voting devices through their mobile phones to collect users' ratings and perceptions of the real-time environmental comfort in the community, and obtain user comfort feedback information. The data analysis module analyzes the user comfort feedback information and adjusts the working parameters of community devices by analyzing user feedback and environmental data to obtain an optimized control plan. The demand analysis module prioritizes regional demands according to the optimized control plan, referring to the overall energy consumption data and regional demands of community human settlements, to obtain a full-region control decision. The environmental control module integrates the PPO algorithm based on the full-region control decision to adjust the parameters of air conditioners, fresh air, and lighting devices in the community, and obtains microclimate perception and adjustment results.

2. The modular sensing hardware and algorithm integration system for community human settlement microclimate perception and regulation according to claim 1, wherein The environmental data set includes temperature readings, humidity levels, wind speed measurements, light intensity, and air quality index. The data transmission record includes transmission success rate, transmission delay, and error rate. The user comfort feedback information includes user ratings, perception descriptions, and voting timestamps. The optimized control plan includes set temperature ranges, lighting brightness levels, and frequencies of fresh air devices. The full-region control decision includes device operation strategies, energy consumption limits, and control schedules for different regions. The microclimate perception and adjustment results include environmental parameter change information, satisfaction improvement ratio, and energy consumption optimization data.

3. The modular sensing hardware and algorithm integration system for community human settlement microclimate perception and regulation according to claim 1, characterized in that, The environmental parameter perception module includes: The sensor configuration sub-module collects a variety of microclimate data of community human settlements through modular sensors, including temperature, humidity, wind speed, TVOC, noise, and light intensity sensors, collects microclimate data of temperature, humidity, and wind speed, adjusts parameters according to the sensitivity of modular sensors, optimizes the data collection frequency, and obtains a microclimate raw data set. The data processing sub-module uses the microclimate raw data set to perform data processing, removes outliers, adjusts the data format according to preset standards, and adjusts the sensor configuration to match data changes to obtain an environmental status analysis result. The data output sub-module sets the data transmission frequency and output format according to the environmental status analysis result, synchronously updates the data, verifies the consistency of the output result, and obtains an environmental data set.

4. The modular sensing hardware and algorithm integration system for community human settlement microclimate perception and regulation according to claim 1, characterized in that, The data encoding module includes: The data transmission sub-module encodes and encrypts the environmental data set, uses wireless communication technology, sets a data packet retransmission mechanism to cope with estimated signal loss, and adjusts the data transmission frequency to match different network conditions to obtain a real-time transmission data stream. The error detection sub-module uses the real-time transmission data stream to implement error detection and correction to avoid the transmission error rate, monitor the arrival rate and integrity of data packets, adjust transmission parameters, and obtain the data error correction result; The timestamp recording sub-module, based on the data error correction result, analyzes the delay and error patterns in the data transmission process, records the timestamp and status of each data transmission, calculates the data transmission stability value, evaluates the overall transmission stability, and obtains the data transmission record.

5. The modular sensing hardware and algorithm integration system for community human settlement microclimate perception and regulation according to claim 4, characterized in that The formula for calculating the data transmission stability value is as follows: Where E is the data transmission stability value, T represents the timestamp of real-time data transmission, T0 represents the reference timestamp, D represents the number of errors in real-time data transmission, D0 represents the expected number of errors, α represents the weight coefficient, β represents the influence weight, and γ represents the adjustment coefficient.

6. The modular sensing hardware and algorithm integration system for community human settlement microclimate perception and regulation according to claim 1, characterized in that, The comfort voting module includes: The interactive interface setting sub-module, based on the data transmission record, activates the interactive interface of the distributed voting device through NFC technology, customizes the interface layout and user interaction method, optimizes the interface response speed, and generates the user interactive interface configuration; The wireless interaction sub-module enables users to use the user interactive interface configuration to wirelessly connect a mobile phone to the distributed voting device, set up connection verification and data synchronization mechanisms, check the security and real-time nature of data transmission, and obtain the real-time interaction record; The comfort perception sub-module, based on the real-time interaction record, collects users' ratings and perceptions of the community environment comfort, organizes and analyzes user data, and obtains the user comfort feedback information.

7. The modular sensing hardware and algorithm integration system for community human settlement microclimate perception and regulation according to claim 1, characterized in that, The data analysis module includes: The data comparison sub-module analyzes the user comfort feedback information, compares it with the real-time community environment data, identifies the correlation between environmental factors and user comfort, evaluates the user feedback differences under different environmental conditions, and obtains the environmental and comfort analysis result; The device parameter optimization sub-module, based on the environmental and comfort analysis result, adjusts the working parameters of community devices, including temperature controllers and humidity regulators, sets up an automatic adjustment logic to respond to environmental changes, evaluates the adaptability and efficiency of device operations, and obtains the adaptability evaluation result; The solution adjustment sub-module uses the adaptability evaluation result to formulate and dynamically adjust the community device operation plan, optimize the overall environmental comfort, integrate the user comfort feedback information and the device automatic adjustment logic, and obtain the optimized control plan.

8. The modular sensing hardware and algorithm integration system for community human settlement microclimate perception and regulation according to claim 1, characterized in that The demand analysis module includes: The efficiency evaluation sub-module, through the optimized control plan, combines the overall energy consumption data of the community's human settlement, evaluates the energy efficiency ratio and implementation feasibility of the optimized control plan, scores the differential measures in the optimized plan for efficiency, and generates the control plan evaluation result; The demand ranking sub-module, based on the control plan evaluation result, refers to the regional demands and environmental impacts, ranks the demands of different regions, and identifies the key demand points to obtain the demand priority list; The resource allocation optimization sub-module uses the demand priority list, refers to the environmental comfort, energy consumption, and resident satisfaction, conducts control over the entire region, optimizes the resource allocation, and obtains the overall regional control decision.

9. The modular sensing hardware and algorithm integration system for community human settlement microclimate perception and regulation according to claim 1, characterized in that, The environmental control module includes: The decision integration sub-module integrates the PPO algorithm into the control logic of community devices based on the full-region regulation decision, configures algorithm parameters, matches the requirements of the real-time community environment, calculates the quantization value of the control logic, and obtains the algorithm integration configuration; The device output sub-module automatically adjusts the working parameters of air conditioners, fresh air, and lighting devices according to the algorithm integration configuration, adjusts the device output according to the changes in real-time data, verifies that the device adjustment is synchronized with the environmental changes, and obtains the device adjustment record; The adjustment effect evaluation sub-module monitors the performance of the adjusted devices and the environmental changes according to the device adjustment record, analyzes the impact of device adjustment on the community microclimate, evaluates the adjustment effect, and obtains the microclimate perception and adjustment result.

10. The modular sensing hardware and algorithm integration system for community human settlement microclimate perception and regulation according to claim 9, characterized in that, The formula for calculating the quantization value of the control logic is as follows: Among them, F(x) is the control logic quantization value, P represents the environmental fitness score, and E s represents the real-time community environmental state, and E t represents the target environmental state, R represents the feedback adjustment coefficient, and D m represents the average value of the device control deviation, and C represents the normalization constant.

Images