A Multi-Source Management Method and System for Smart Communities

Abstract

This invention discloses a multi-source management method and system for smart communities, relating to the field of multi-source management in smart communities, for realizing differentiated resource scheduling based on individual sensitivity. The method includes: upon sensing external environmental fluctuation events, acquiring changes in the operating status of intelligent environmental control equipment within a resident unit; the external environmental fluctuation events are determined based on the fluctuation amplitude of external environmental parameters; assessing the response intensity of the resident unit to the external environmental fluctuation events based on the changes in operating status; determining the environmental sensitivity corresponding to the response intensity from a preset mapping relationship as the environmental sensitivity of the resident unit based on the response intensity; the preset mapping relationship includes mapping relationships between different response intensities and different environmental sensitivities; and scheduling resources for the resident unit based on the environmental sensitivity.

Description

Technical Field

[0001] This invention relates to the field of multi-source management in smart communities, and in particular to a method and system for multi-source management in smart communities. Background Technology

[0002] As the community management team strives to improve the living experience and deepen energy conservation and emission reduction, they have decided to further expand the coverage of the central management platform to individual residential units. This expansion involves integrating smart home devices voluntarily connected by residents, such as smart thermostats, smart lighting controllers, and indoor air quality sensors, into the central management system. The core objective is to collect energy consumption data and indoor environmental parameters at the resident level. By aggregating and analyzing this massive amount of information, the platform aims to identify the overall energy consumption patterns of the community and provide residents with personalized energy-saving suggestions. Furthermore, with resident authorization, it can even automatically adjust their smart home devices to achieve overall energy optimization at the community level while ensuring individual comfort.

[0003] However, residents' daily routines, work patterns, and personal preferences vary significantly. For example, some residents may be away from home for extended periods, while others stay home all day; some residents may prefer natural ventilation by opening windows, even when the air conditioning is running, while others rely entirely on the HVAC system. The platform's initial energy-saving algorithm was designed based on general assumptions about typical resident living patterns and comfort levels, failing to adequately consider these individualized needs and behavioral patterns. This directly resulted in a low adoption rate of energy-saving recommendations.

[0004] The platform's core objective is to optimize overall community energy consumption and maintain the comfort of the public environment. However, when a sudden cold snap hits, if the platform attempts to reduce the overall heating load by suggesting higher indoor temperatures or delaying the start of heating in public areas, it may inadvertently harm the comfort and even health of vulnerable residents who have stricter and more stable requirements for indoor temperature ranges. Similarly, during localized air pollution events, if the platform reduces ventilation rates in public areas to save energy, residents using these facilities may be exposed to suboptimal air quality. Finding a balance between the macro-level goal of optimizing overall community resources and the micro-level necessity of ensuring the comfort and well-being of individual residents is a pressing technical challenge. Summary of the Invention

[0005] This invention provides a multi-source management method for smart communities, which enables differentiated resource scheduling based on individual sensitivity.

[0006] Firstly, to address the aforementioned technical problems, this invention provides a multi-source management method for smart communities, comprising: upon sensing external environmental fluctuation events, acquiring changes in the operating status of intelligent environmental control equipment within a resident unit; the external environmental fluctuation events are determined based on the fluctuation amplitude of external environmental parameters; assessing the response intensity of the resident unit to the external environmental fluctuation events based on the changes in operating status; determining the environmental sensitivity corresponding to the response intensity from a preset mapping relationship as the environmental sensitivity of the resident unit based on the response intensity; the preset mapping relationship includes mapping relationships between different response intensities and different environmental sensitivities; and scheduling resources for the resident unit based on the environmental sensitivity.

[0007] Optionally, the changes in operating status include real-time operating power and set temperature. Based on the changes in operating status, the response strength of the resident unit to external environmental fluctuation events is assessed, including: determining the power increase magnitude and continuous operating time of the intelligent environmental control equipment; the power increase magnitude is the increase in real-time operating power of the intelligent environmental control equipment after the occurrence of an external environmental fluctuation event compared to the real-time operating power before the occurrence of the external environmental fluctuation event; the continuous operating time is the duration of operation of the intelligent environmental control equipment above the preset power after the occurrence of the external environmental fluctuation event; the power increase magnitude and continuous operating time are weighted and summed to obtain the response strength of the resident unit to external environmental fluctuation events.

[0008] Optionally, resource allocation for residential units can be carried out based on environmental sensitivity, including: determining the dominant factors of environmental sensitivity; the dominant factors include: building physical defects, specific behaviors of residents, and actual physiological sensitivity of residents; and allocating resources for residential units based on the dominant factors of environmental sensitivity.

[0009] Optionally, changes in operating status, including real-time operating power and set temperature, determine the dominant factors of environmental sensitivity, including: when external environmental fluctuations are detected, extracting the power data sequence of the intelligent environmental control device from the real-time operating power and set temperature; analyzing the power data sequence to extract the real-time operating power ramp-up rate, peak power maintenance stability, power fluctuation frequency and amplitude, and time taken to reach the set temperature; and judging the dominant factors of environmental sensitivity based on the real-time operating power ramp-up rate, peak power maintenance stability, power fluctuation frequency and amplitude, and time taken to reach the set temperature.

[0010] Optionally, based on the real-time operating power ramp-up rate, peak power maintenance stability, power fluctuation frequency and amplitude, and time taken to reach the set temperature, the dominant factors of environmental sensitivity are determined, including: when the real-time operating power ramp-up rate, peak power maintenance stability, power fluctuation frequency and amplitude, and time taken to reach the set temperature all partially match multiple preset discrimination rules, the matching degree between the morphological features of the power data sequence and each preset discrimination rule is calculated; based on the matching degree, the morphological features of the power data sequence are normalized to obtain a normalized matching degree; based on the normalized matching degree, the preset discrimination rule with the highest matching degree with the morphological features of the power data sequence is identified; and the dominant factor of environmental sensitivity is determined as the factor corresponding to the preset discrimination rule with the highest matching degree.

[0011] Optionally, resource scheduling for residential units can be implemented based on the dominant factor of environmental sensitivity, including: when the dominant factor of environmental sensitivity is building physical defects, pushing building energy-saving renovation suggestions to the smart terminals of the residential units to achieve differentiated resource scheduling and control; when the dominant factor of environmental sensitivity is specific resident behavior, pushing behavioral energy-saving prompts to the smart terminals of the residential units, and slightly reducing the operating power of the smart environmental control equipment of the residential units according to the duration and impact of the specific behavior to achieve differentiated resource scheduling and control; when the dominant factor of environmental sensitivity is the actual physiological sensitivity of the residents, setting the operating parameters of the smart environmental control equipment of the residential units to an uninterruptible state to achieve differentiated resource scheduling and control.

[0012] Optionally, after slightly reducing the operating power of the smart environmental control equipment in the resident unit, the method further includes: continuously monitoring the changes in the operating power of the smart environmental control equipment in the resident unit; when the change in operating power does not achieve the preset energy-saving effect, and the resident unit does not manually adjust the operating parameters of the smart environmental control equipment, determining whether there is an ineffective intervention trend based on the fluctuation range and duration of the operating power; if there is an ineffective intervention trend, pausing the further push of behavioral energy-saving prompts and slightly reducing the operating power of the smart environmental control equipment again; pushing interactive query information to the smart terminal of the resident unit; and adjusting subsequent energy-saving strategies based on the feedback of the interactive query information.

[0013] Optionally, based on the feedback from the interactive inquiry information, the subsequent energy-saving strategy can be adjusted, including: when there is a difference between the resident's interactive inquiry information feedback and the operating power data analysis results of the resident's unit's smart environmental control equipment, calculating the degree of difference between the feedback and the operating power data analysis results; dynamically adjusting the weights of the feedback and operating power data analysis results in the energy-saving strategy formulation based on the degree of difference; merging the feedback and operating power data analysis results based on the adjusted weights to generate a merged energy-saving strategy; and adjusting the interval between pushing out energy-saving reminder information again or slightly reducing the operating power of the smart environmental control equipment again based on the merged energy-saving strategy.

[0014] Optionally, the weights of feedback and operating power data analysis results in energy-saving strategy formulation can be dynamically adjusted according to the degree of difference. This includes: when the degree of difference between feedback and operating power data analysis results is within a preset medium range, switching the weight adjustment strategy of feedback and operating power data analysis results to a step-by-step adjustment mode; setting a weight stabilization observation period longer than a preset duration; and during the weight stabilization observation period, adopting feedback weights related to resident comfort assurance and adjusting the weights of operating power data analysis results.

[0015] Secondly, the present invention provides a smart community multi-source management system, the system comprising:

[0016] The environmental sensing module is used to acquire external environmental fluctuation events and changes in the operating status of intelligent environmental control devices within the residential unit; external environmental fluctuation events are determined based on the fluctuation amplitude of external environmental parameters;

[0017] The operation monitoring module is used to assess the response strength of residential units to external environmental fluctuations based on changes in operational status.

[0018] The sensitivity assessment module is used to determine the environmental sensitivity of a resident unit based on the response intensity from a preset mapping relationship; the preset mapping relationship includes the mapping relationship between different response intensities and different environmental sensitivities.

[0019] The resource scheduling module is used to schedule resources for residential units based on environmental sensitivity.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] This application discloses a multi-source management method for smart communities. It acquires information on external environmental fluctuations and changes in the operational status of intelligent environmental control devices within residential units, and assesses the response intensity of each unit to these fluctuations based on these changes. On this basis, it determines the environmental sensitivity corresponding to the response intensity from a preset mapping relationship, and ultimately allocates resources to each unit based on this sensitivity. This method effectively identifies individual differences in the responses of residential units to external environmental fluctuations and assesses their environmental sensitivity accordingly, thereby achieving differentiated resource allocation based on individual sensitivity. This solves the problem of existing smart community management systems struggling to balance the macro-level goal of overall community resource optimization with the micro-level necessity of ensuring individual resident comfort and well-being when faced with diverse living habits and unpredictable external environmental fluctuations. Through this technical solution, this application avoids the discomfort and low adoption rates that may result from traditional "one-size-fits-all" energy-saving recommendations and automated adjustments, effectively improving energy efficiency and user satisfaction, and achieving a harmonious unity between overall community energy optimization and individual comfort assurance. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of a multi-source management method for smart communities provided in an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram of another smart community multi-source management method provided in an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of a smart community multi-source management system provided in an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0026] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] The following specific embodiments will provide a detailed introduction and explanation of a smart community multi-source management method provided in this application.

[0028] Reference Figure 1 This invention provides a multi-source management method for smart communities, comprising the following steps:

[0029] S1, upon sensing external environmental fluctuations, acquires information on changes in the operating status of intelligent environmental control devices within the resident unit.

[0030] Among them, external environmental fluctuation events are determined based on the fluctuation range of external environmental parameters.

[0031] The changes in the operating status of intelligent environmental control equipment within a residential unit refer to the changes in the operating modes, power consumption, and set parameters of intelligent devices in the home, such as intelligent air conditioners, intelligent fresh air systems, and intelligent underfloor heating, before and after the occurrence of external environmental fluctuations. These changes reflect the actual actions taken by residents to cope with external environmental fluctuations or the results of automatic adjustments by the equipment.

[0032] As one possible implementation, the system can monitor external environmental parameters through environmental sensors deployed throughout the community. When the fluctuation of one or more environmental parameters exceeds a preset threshold, the system can identify and record it as an external environmental fluctuation event. Upon sensing such an event, changes in the operating status of the smart environmental control devices within the resident unit can be obtained through a smart home gateway or by direct device connection to a cloud platform.

[0033] For example, when the outdoor temperature drops sharply in a short period of time, the system will mark it as a cold wave event. At this time, data such as the real-time operating power, set temperature, and operating mode of the smart air conditioner in the resident's unit will be continuously collected and uploaded.

[0034] For example, environmental sensors can be temperature sensors, humidity sensors, air quality sensors, etc.

[0035] S2. Assess the response strength of residential units to external environmental fluctuations based on changes in operating status.

[0036] Response intensity is an indicator that measures the degree to which a resident unit responds to fluctuations in the external environment. It comprehensively considers changes in the operating status of smart environmental control equipment, such as the increase in equipment power and continuous operating time, to quantify the "effort" residents put into maintaining indoor comfort. Higher response intensity generally means that residents are more sensitive to fluctuations in the external environment or have higher requirements for indoor environmental comfort.

[0037] As one possible implementation, the system can quantify the response intensity by analyzing indicators such as the increase in device operating power and the duration of operation. For example, the response intensity can be obtained by multiplying the power increase by the duration of operation.

[0038] As another possible implementation, the system can determine the power increase and continuous operating time of the intelligent environmental control equipment, and perform a weighted summation of the power increase and continuous operating time to obtain the response strength of the resident unit to external environmental fluctuation events.

[0039] Among them, the power increase rate is the increase in the real-time operating power of the intelligent environmental control device after the occurrence of an external environmental fluctuation event compared to the real-time operating power before the occurrence of the external environmental fluctuation event; the continuous operating time is the operating time of the intelligent environmental control device above the preset power after the occurrence of an external environmental fluctuation event.

[0040] It should be noted that the weights of power increase and continuous operating time can be set as needed and are not restricted.

[0041] Basis for determining weights:

[0042] 1. System Design Goals: If the primary goal of a smart community management system is to maximize energy conservation and emission reduction, then factors that directly lead to high energy consumption, such as prolonged high-power operation, may be given higher weight. If the system focuses more on ensuring residents' personalized comfort, then factors that reflect residents' immediate comfort needs or sensitivity to environmental changes, such as rapid and significant increases in power consumption, may be given higher weight.

[0043] 2. Resident Type and Preferences: Different types of resident units may have varying sensitivities to environmental changes and different comfort preferences. For example, families with elderly members or children may be more sensitive to temperature changes, and their "power increase magnitude" may require a higher weighting to ensure a rapid response to their comfort needs. The system can dynamically adjust the weighting for different resident units based on resident profiles or historical behavioral data to achieve a more personalized assessment of response intensity.

[0044] 3. Nature of External Environmental Fluctuations: Different external environmental fluctuations, such as sudden cold waves, sustained high temperatures, and localized air pollution, may affect residents in different ways. For example, during a sudden cold wave, the "power increase" may be more important because it reflects the effort made by the equipment to quickly resist the cold; while during sustained high temperatures, the "continuous operating time" may be more indicative because it reflects the continuous energy consumption of the equipment to maintain a cool environment for an extended period.

[0045] In one example, suppose a smart community management system aims to balance energy conservation and comfort. When a sudden drop in external temperature occurs, the system needs to assess the response strength of resident units.

[0046] Scenario 1: After the temperature suddenly dropped, the smart air conditioner in resident unit A rapidly increased its real-time operating power from 500W to 2000W (a power increase of 1500W), but only maintained this for 1 hour before stabilizing at a lower power.

[0047] Scenario 2: After the temperature in resident unit B dropped sharply, the real-time operating power of the smart air conditioner increased from 500W to 1000W (the power increase was 500W), and it continued to run for 5 hours before stabilizing.

[0048] If, based on expert experience and historical data analysis, the system deems "power increase magnitude" more critical in reflecting residents' immediate discomfort, while "continuous running time" reflects residents' need for long-term stable comfort, the system can set an initial weighting: for example, setting the weight of "power increase magnitude" to 0.7 and the weight of "continuous running time" to 0.3.

[0049] Therefore, for unit A: Response intensity = (1500W × 0.7) + (1 hour × 0.3) = 1050 + 0.3 = 1050.3 (assuming uniform units or normalization).

[0050] For resident unit B: Response intensity = (500W × 0.7) + (5 hours × 0.3) = 350 + 1.5 = 351.5 (assuming uniform units or normalization).

[0051] By using this weighted summation, the system can quantify the response strength of different resident units when faced with the same external environmental fluctuations. In this example, resident unit A has a higher response strength, which may indicate that it is more sensitive to temperature changes, and the system will prioritize ensuring its comfort in subsequent scheduling.

[0052] It is important to note that in practical applications, the power increase and continuous operating time may need to be standardized or normalized to ensure the reasonableness of the weighted summation. For example, the power increase and continuous operating time can be normalized to the range of 0-1 before weighted summation. The specific values ​​of the weights will be continuously adjusted and optimized based on the actual system operation and optimization objectives.

[0053] S3. Based on the response intensity, determine the environmental sensitivity corresponding to the response intensity from the preset mapping relationship as the environmental sensitivity of the resident unit.

[0054] The preset mapping relationships include mapping relationships between different response intensities and different environmental sensitivities.

[0055] Environmental sensitivity is a comprehensive assessment of the environmental needs of a residential unit. It reflects the ease with which a unit can maintain comfort in the face of external environmental fluctuations, or the residents' tolerance for environmental changes. Environmental sensitivity can be categorized into different levels, such as "low sensitivity," "medium sensitivity," and "high sensitivity," or more detailed numerical ranges.

[0056] In one example, the pre-defined mapping could be a lookup table or a function model. For instance, if a response intensity value falls within a certain range, its environmental sensitivity is classified as "high sensitivity"; if it falls within another range, it is classified as "medium sensitivity." This mapping is pre-established and can be adjusted and optimized based on historical data and expert experience. For example, by analyzing historical data from a large number of residents, it can be found that when the response intensity reaches a certain value, residents typically exhibit higher sensitivity to environmental changes, thus mapping that response intensity value to the "high sensitivity" category.

[0057] The specific form of the "preset mapping relationship":

[0058] The "preset mapping relationship" is essentially a knowledge base used to transform quantified response intensity into an assessment of the sensitivity of a resident's unit environment. Its specific forms can include, but are not limited to, the following two main types:

[0059] Lookup Table format:

[0060] This is a discrete mapping method that divides the response intensity into several intervals or levels, each interval or level corresponding to a preset environmental sensitivity category. For example, the response intensity value can be divided into three intervals: "low," "medium," and "high," which are mapped to the environmental sensitivity categories of "low sensitivity," "medium sensitivity," and "high sensitivity," respectively. Once the system assesses the response intensity of a resident unit, it only needs to find the interval to which the response intensity value belongs in the lookup table to determine the corresponding environmental sensitivity.

[0061] Function Model Form:

[0062] This is a continuous mapping form that describes the relationship between response intensity and environmental sensitivity through a mathematical function or algorithmic model. This function model can take response intensity as input and output a continuous environmental sensitivity value, or the probability of belonging to different sensitivity categories. For example, a linear function, a polynomial function, or a more complex nonlinear model (such as a neural network-based model) can be constructed to dynamically calculate environmental sensitivity.

[0063] Method for constructing "preset mapping relationships":

[0064] The system can collect a large amount of data on the changes in the operating status of smart environmental control devices in residential units under different external environmental fluctuations, as well as the corresponding response intensities. Simultaneously, it combines resident comfort feedback (obtained through questionnaires, interactive inquiries via smart terminals), energy consumption data, and even complaint records to analyze the correlation between different response intensities and actual environmental sensitivity. Through statistical analysis, cluster analysis, or association rule mining of this historical data, potential patterns between response intensity and environmental sensitivity can be discovered, thereby establishing a mapping relationship.

[0065] The parameters for the "preset mapping relationship" are based on: resident behavior patterns and comfort preferences, building physical characteristics, and the balance between energy-saving goals and comfort.

[0066] Resident Behavioral Patterns and Comfort Preferences: By analyzing the daily routines, work patterns, and preferences for environmental parameters such as temperature and humidity of a large number of residents, typical behavioral patterns of different resident groups can be identified. For example, if data shows that a certain type of resident experiences a significant increase in the power of their smart environmental control devices and runs them for extended periods when the external temperature changes slightly, accompanied by higher comfort requirements, then their response intensity can be mapped to a higher level of environmental sensitivity.

[0067] Building physical characteristics: The building physical characteristics of a residential unit, such as its age, structure, insulation performance, and door and window sealing, affect its resistance to changes in the external environment. For example, a residential unit with poor insulation may require smart environmental control equipment to exert more effort to maintain indoor comfort under the same external environmental fluctuations, thus exhibiting a higher response intensity. Therefore, the impact of these building characteristics on response intensity needs to be considered when setting up mapping relationships.

[0068] Balancing Energy Saving Goals and Comfort: The parameter settings for the mapping relationship also need to consider the balance between the overall energy saving goals of the community and ensuring the comfort of individual residents. For example, when setting the threshold between response intensity and environmental sensitivity, it's not advisable to be too aggressive and classify all high response intensities as "low sensitivity" to force energy saving, as this could lead to a decrease in resident comfort. Conversely, it shouldn't be too conservative, as this would prevent the full realization of energy-saving potential. The parameter settings should aim to find an optimal balance point that maximizes energy savings while ensuring the comfort of the vast majority of residents.

[0069] In one example, suppose we use a lookup table to construct a "preset mapping relationship" and take "response intensity" as input to output the "environmental sensitivity" level.

[0070] The response strength can be obtained by weighted summing the "power boost" and "duration" of the intelligent environmental control device. For example, define the response strength value as: (Power Boost) / (Duration) Weight 1) + (Continuous running time) Weight 2). Assume weight 1 = 0.6 and weight 2 = 0.4.

[0071] Scenario 1: When the outside temperature suddenly dropped, the air conditioner in a certain residential unit increased its power by 500W and ran continuously for 2 hours. Response intensity value = (500 0.6) + (2 0.4) = 300.8.

[0072] Scenario 2: In another resident's unit, when the outside temperature suddenly dropped, the air conditioner's power increased by 200W and ran continuously for 0.5 hours. Response intensity value = (200 0.6) + (0.5 0.4) = 120.2.

[0073] By analyzing air conditioning operation data and corresponding comfort questionnaire feedback from all residents over the past year, it was found that when the response intensity value was below 150, over 90% of residents reported comfort and had no energy-saving complaints; when the response intensity value was between 150 and 350, approximately 30% of residents occasionally reported mild discomfort or manually adjusted the equipment; when the response intensity value exceeded 350, over 60% of residents frequently manually adjusted the equipment or complained of poor comfort. To balance energy saving and comfort, 150 and 350 were used as key thresholds for distinguishing sensitivity, because a value below 150 generally indicates good building performance or that residents are not sensitive to environmental changes, while a value above 350 may indicate building defects or that residents have higher physiological needs.

[0074] Therefore, a preset mapping relationship can be set as follows: the environmental sensitivity corresponding to the response intensity value [0, 150) is low sensitivity;

[0075] The environmental sensitivity corresponding to the response intensity value [150, 350) is: medium sensitivity.

[0076] The environmental sensitivity corresponding to the response intensity value [350, ∞) is: high sensitivity;

[0077] Based on the above mapping relationship, the response intensity value of the resident unit in Scenario 1 is 300.8, and its environmental sensitivity will be determined as "medium sensitivity". The response intensity value of the resident unit in Scenario 2 is 120.2, and its environmental sensitivity will be determined as "low sensitivity". The system will perform differentiated resource scheduling for the two resident units based on these sensitivity assessment results.

[0078] S4. Based on environmental sensitivity, resource allocation is carried out for residential units.

[0079] Resource scheduling refers to the system's differentiated and personalized management and optimization of smart environmental control equipment or related resources based on the environmental sensitivity of each resident unit. This may include, but is not limited to, adjusting equipment operating parameters, pushing personalized energy-saving suggestions, and optimizing energy allocation strategies to maximize the overall energy efficiency of the community while ensuring resident comfort.

[0080] As one possible implementation, for residential units assessed as "highly sensitive," the system, when optimizing the overall energy consumption of the community, would prioritize ensuring the comfort of their indoor environment, minimizing intervention in their smart environmental control devices, or providing more personalized energy-saving suggestions to avoid impacting their comfort experience. For residential units deemed "lowly sensitive," the system might implement more proactive energy optimization scheduling without affecting their comfort, such as fine-tuning the operating power of their equipment during peak electricity consumption periods.

[0081] Understandably, by transforming abstract resident behaviors into quantifiable sensitivity indicators, the system can understand and differentiate the individualized needs of different residents. Ultimately, the resource scheduling module allocates resources to resident units based on the determined environmental sensitivity. In this way, this application achieves a complete process from environmental perception to behavioral assessment, then to sensitivity identification, and finally to personalized resource scheduling. This effectively resolves the contradiction between general strategies and individual differences in traditional management methods, thereby optimizing the overall energy efficiency of the community while ensuring resident comfort.

[0082] In one possible design, such as Figure 2 As shown, in order to allocate resources to residential units based on environmental sensitivity, this application may further include the following steps:

[0083] S101. Determine the dominant factors of environmental sensitivity.

[0084] The dominant factors include: building physical defects, specific resident behaviors, and residents' actual physiological sensitivities.

[0085] Among these, building physics defects can be understood as problems with the physical structure or equipment of the residential unit, such as poor thermal insulation, inadequate sealing of doors and windows, and outdated smart environmental control equipment. These defects lead to additional energy consumption to maintain indoor comfort. Resident-specific behaviors refer to habits exhibited by residents in using smart environmental control equipment or in daily life that may affect energy efficiency or environmental comfort, such as frequently opening and closing doors and windows, running high-power equipment for extended periods, and setting extreme temperatures. Residents' actual physiological sensitivity refers to an individual resident's actual physiological perception and adaptability to parameters such as environmental temperature and humidity. This may vary due to factors such as age, health status, and personal preferences, and should generally not be excessively interfered with.

[0086] As one possible implementation, the system pre-stores the operating status change data of different dominant factors and corresponding intelligent environmental control devices. The system can match the current operating status change of the intelligent environmental control devices with the operating status change data of different dominant factors and corresponding intelligent environmental control devices, and determine the dominant factor with the highest matching degree as the dominant factor of environmental sensitivity.

[0087] S102. Based on the dominant factors of environmental sensitivity, resource allocation is carried out for residential units.

[0088] This means that the system continuously compares different measurements of the same parameter, such as readings from different sensors or readings from the same sensor at different times. If the difference between these measurements exceeds a pre-set acceptable range and this deviation persists, it can be determined that the corresponding sensor may be faulty, drifting, or being interfered with, thus identifying its abnormal state.

[0089] As one possible approach, when the dominant factor in environmental sensitivity is building physical defects, the system can push building energy-saving renovation suggestions to the smart terminals of residential units to enable differentiated resource scheduling and control for residential units.

[0090] For example, physical defects in a building could include poor insulation or inadequate sealing of doors and windows. The system will then push specific energy-saving renovation recommendations to the smart terminals of each resident's unit. These recommendations may include, but are not limited to, replacing doors and windows with high-efficiency energy-saving ones, adding wall insulation layers, and repairing cracks. The aim is to fundamentally improve the building's energy consumption performance, thereby achieving differentiated resource scheduling and control based on physical defects.

[0091] As another possible implementation, when the dominant factor of environmental sensitivity is the specific behavior of the resident, the system can push energy-saving reminder information to the smart terminal of the resident unit, and slightly reduce the operating power of the smart environmental control equipment of the resident unit according to the duration and impact of the specific behavior, so as to carry out differentiated resource scheduling and control of the resident unit.

[0092] For example, specific resident behaviors, such as leaving doors and windows open when turning on the air conditioner or heater, or adjusting the set temperature of the smart environmental control device to an extreme value, will trigger a system that pushes an energy-saving reminder to the resident's smart terminal. Simultaneously, based on the duration of this specific behavior and its impact on energy consumption, the system will slightly reduce the operating power of the smart environmental control device in the resident's unit. This slight reduction is usually imperceptible to the user, but it can achieve significant energy savings over the long term, thus enabling differentiated resource scheduling and control based on resident behavior.

[0093] As another possible implementation, when the dominant factor of environmental sensitivity is the actual physiological sensitivity of the residents, the system can set the operating parameters of the intelligent environmental control equipment of the resident unit to an uninterruptible state in order to carry out differentiated resource scheduling and control of the resident unit.

[0094] For example, residents who are elderly, infants, or have special health needs may have lower tolerance for ambient temperature. In such cases, to prioritize the comfort and health of residents, the operating parameters of the smart environmental control devices in the unit will be set to a non-intervention state. This means the system will not automatically adjust or intervene in the operating parameters of these devices, ensuring that residents can fully and autonomously control their living environment, thereby achieving differentiated resource scheduling and control based on physiological sensitivities.

[0095] In some preferred embodiments, a specific example is given below. Suppose that when the external ambient temperature drops sharply, the operating power of a resident's smart environmental control device (such as an air conditioner) increases significantly and remains at high power for an extended period, thus being assessed as having high environmental sensitivity. In this case, the system will further analyze the specific dominant factors leading to its high sensitivity.

[0096] Specifically, if a comprehensive analysis of a resident's unit's historical energy consumption data, building structure information, and resident feedback reveals issues such as poor wall insulation and inadequate window sealing, the system will determine that the primary factor contributing to the unit's environmental sensitivity is a building physics defect. In this case, the resource allocation strategy will focus on pushing building energy-saving renovation recommendations to the resident's smart terminal, such as recommending the replacement of high-efficiency insulation materials or upgrading doors and windows, to fundamentally address the energy loss problem.

[0097] For example, if analysis reveals that residents in a particular unit frequently adjust their air conditioner settings to extreme values ​​(e.g., 18°C ​​in summer, 28°C in winter) when the external temperature changes, or keep windows open for extended periods while the air conditioner is on, the system will determine that the dominant factor in their environmental sensitivity is this specific resident behavior. In this case, resource allocation strategies will include pushing energy-saving reminders to the resident's smart device, such as advising them to set appropriate temperature ranges or close doors and windows. The system may also slightly reduce the operating power of the smart environmental control equipment based on the duration and impact of this specific behavior, guiding residents to develop more energy-efficient habits.

[0098] For example, if the system learns from a resident's health records, age information, or their proactive feedback that there is an elderly person or infant in the unit who is particularly sensitive to temperature changes, and the operating parameters of their smart environmental control equipment are consistently maintained within a specific comfort range, then the system will determine that the dominant factor in their environmental sensitivity is the resident's actual physiological sensitivity. In this case, the resource allocation strategy will prioritize ensuring the resident's comfort, setting the operating parameters of the unit's smart environmental control equipment to a non-intervention state to avoid any automatic adjustments or energy-saving interventions that might affect their comfort.

[0099] For example, a slight reduction could be within a range of 1% of the current power or 0.5°C for the temperature.

[0100] The initial minor adjustment is based on "the duration and impact of the specific behavior." This means the system analyzes how long a resident's specific behavior (e.g., setting the air conditioner to an excessively low temperature for an extended period, or opening windows while the air conditioner is on) lasts, and how much that behavior impacts energy consumption. The longer the behavior lasts and the greater its impact, the more sufficient the system's basis for making a minor adjustment.

[0101] Subsequent adjustments based on and magnitude of reduction: After a minor reduction, the system will continuously monitor changes in operating power. If the change in operating power does not achieve the preset energy-saving effect, and the resident does not manually adjust the equipment parameters, the system will determine if there is an "ineffective intervention trend." If an ineffective intervention trend exists, the system will pause further reductions and send an interactive inquiry message to the resident.

[0102] At this point, subsequent energy-saving strategies (including any further minor adjustments) will be adjusted based on feedback from interactive inquiries. Specifically, when there is a discrepancy between the resident's feedback and the operational power data analysis results of the smart environmental control equipment, the system will calculate the degree of this discrepancy. Then, based on this degree of discrepancy, the system will dynamically adjust the weights of the feedback information and the operational power data analysis results in the energy-saving strategy formulation. Finally, based on the adjusted weights, the two information are merged to generate a "merged energy-saving strategy." This merged energy-saving strategy is the primary basis for any subsequent minor adjustments.

[0103] Is the range of adjustment adjustable? Yes, the range of adjustment is adjustable. For example, based on the "integrated energy-saving strategy," the system can "adjust the magnitude of the further slight reduction in the operating power of the intelligent environmental control equipment." This means that the system does not use a fixed adjustment range, but can dynamically adjust the subsequent adjustment range based on the actual feedback from residents and energy consumption data.

[0104] In one example, suppose a smart air conditioner in a residential unit is set to 18°C ​​for an extended period during the summer, and the system detects that its operating power is consistently high. The dominant factor in determining its environmental sensitivity is "specific behavior of the resident".

[0105] Initial minor adjustment: Based on the duration of this specific behavior and the impact of high energy consumption, the system slightly reduces the air conditioner's operating power, for example, slightly adjusting the set temperature from 18℃ to 18.5℃. This 0.5℃ adjustment is "minor" and residents may not easily notice it.

[0106] Monitoring and Feedback: The system continuously monitors the air conditioner's operating power. If it detects that despite a 0.5°C adjustment, the power consumption does not decrease significantly, and the resident has not manually adjusted the temperature, the system determines that there may be an ineffective intervention trend. In this case, the system will push an interactive query to the resident's smart terminal, such as: "Hello, we have noticed that the air conditioner's energy consumption is high. Are you satisfied with the current indoor temperature?"

[0107] Adjustment Basis and Adjustability: Scenario 1: Residents report feeling "a bit hot." In this case, there is a discrepancy between the resident's feedback and the high energy consumption data analysis results (the system believes the temperature is low enough to cause high energy consumption). The system calculates the degree of difference and dynamically adjusts the weights, potentially giving higher weight to the resident's feedback. Based on the integrated strategy, the system determines that further reducing power may cause discomfort to residents, therefore deciding to adjust the "further slight reduction" to 0 (i.e., no further reduction), or even slightly increase it by 0.2℃ to meet the residents' comfort needs.

[0108] Scenario 2: Resident feedback: "Feels just right." In this case, there is a moderate difference between the resident's feedback and the high energy consumption data analysis results (the system believes that the high energy consumption is due to excessively low temperature). The system marks this resident's unit as a "strategy-sensitive unit" and switches to "step-by-step adjustment mode." The system sets a relatively long observation period (e.g., 24 hours). During this period, the system will prioritize the resident's "feels just right" comfort feedback and adjust the weight of the operating power data analysis results accordingly. During the observation period, the system may attempt to slightly reduce the power again, but the magnitude will be smaller (e.g., from 0.5℃ to 0.2℃), and continue to observe the resident's reaction to ensure that energy savings are gradually achieved without affecting comfort.

[0109] The aforementioned technical solutions significantly enhance the accuracy and effectiveness of multi-source management methods in smart communities. By identifying the dominant factors affecting environmental sensitivity, a "one-size-fits-all" approach to resource allocation can be avoided, enabling more refined energy management and more personalized services. This not only helps improve overall energy efficiency and reduce unnecessary energy waste but also maximizes resident comfort and satisfaction, preventing user resentment caused by inappropriate intervention. Furthermore, this differentiated allocation strategy provides a more solid foundation for the sustainable development of smart communities.

[0110] In one possible design, the operating state changes include real-time operating power and set temperature. To determine the dominant factors of environmental sensitivity, this application also includes the following steps:

[0111] S201. When an external environmental fluctuation event is detected, the power data sequence of the intelligent environmental control device is extracted from the real-time operating power and the set temperature.

[0112] Among them, real-time operating power refers to the actual power consumption of the intelligent environmental control equipment at a specific point in time, while set temperature refers to the target temperature set by the resident on the intelligent environmental control equipment.

[0113] Specifically, when the system senses an external environmental fluctuation event, such as a sudden drop in temperature or a sudden change in solar radiation intensity, it will immediately extract the power data sequence associated with the period before and after the occurrence of the fluctuation event from the historical operating data of the intelligent environmental control equipment.

[0114] S202. Analyze the power data sequence to extract the real-time operating power ramp-up rate, peak power maintenance stability, power fluctuation frequency, amplitude, and time taken to reach the set temperature.

[0115] Among them, the real-time operating power ramp-up rate reflects the speed at which the equipment rapidly increases power to reach the set temperature or maintain comfort; peak power maintenance stability indicates whether the equipment can stably maintain this state after reaching a high power operating state to cope with continuous environmental changes; power fluctuation frequency and amplitude reveal the degree and range of frequent adjustments in the equipment's power output, which may be related to the resident's refined operation or the equipment's own adjustment characteristics; and the time taken to reach the set temperature measures the time required for the equipment to reach the resident's set comfort temperature from startup or low load state.

[0116] S203. Based on the real-time operating power ramp-up rate, peak power maintenance stability, power fluctuation frequency and amplitude, and time taken to reach the set temperature, determine the dominant factors of environmental sensitivity.

[0117] As one possible implementation, the system can determine the dominant factors of environmental sensitivity based on the following steps:

[0118] S2031. When the real-time operating power ramp-up rate, peak power maintenance stability, power fluctuation frequency and amplitude, and time taken to reach the set temperature partially match multiple preset discrimination rules, the system can calculate the matching degree between the morphological characteristics of the power data sequence and each preset discrimination rule.

[0119] For example, a power curve may exhibit both rapid increases (potentially related to residents' actual physiological sensitivity) and subtle, continuous fluctuations (potentially related to building physics defects). In such cases, to more accurately identify the dominant factor, it is necessary to calculate the matching degree between the morphological characteristics of the power data sequence and each preset discrimination rule.

[0120] Matching degree can be understood as the degree of similarity or conformity between the actual morphological features of the power data sequence and the typical morphological features defined by each preset discrimination rule. It can be calculated by various quantification methods, such as distance calculation based on feature vectors, correlation analysis, or probability values ​​output by machine learning models.

[0121] The specific content of the preset discrimination rules: Each preset discrimination rule contains a set of descriptions of the power data sequence morphological characteristics of intelligent environmental control devices. These characteristics include the real-time operating power ramp-up rate, peak power maintenance stability, power fluctuation frequency, amplitude, and time taken to reach the set temperature.

[0122] 1. Judgment rules corresponding to "building physical defects":

[0123] Rise rate of real-time operating power: The initial rise may be fast, but due to continuous heat loss caused by building structure (such as poor insulation, air leakage), the equipment may need to run for a long time to reach the set temperature, or it may be difficult to maintain a stable temperature.

[0124] Peak power stability: The equipment may frequently operate at high power, but the indoor temperature is still prone to fluctuations, causing the equipment to run at a high load continuously to compensate for heat loss, making it difficult to maintain a stable peak power.

[0125] Power fluctuation frequency and amplitude: Power may fluctuate frequently in small increments at a relatively high average level to continuously compensate for the loss of heat or cold due to physical defects, but the overall trend is to maintain high energy consumption.

[0126] The time required to reach the set temperature is longer than normal, and may even be insufficient to reach or maintain the set temperature in extreme external environments.

[0127] 2. Judgment rules corresponding to "specific behaviors of residents":

[0128] The real-time operating power has a large fluctuation in its rate of increase, and may experience multiple rapid increases or sudden decreases due to residents frequently opening and closing windows, adjusting the set temperature, or using other equipment.

[0129] Peak power stability is extremely poor. The equipment's operation is frequently interrupted by residents' activities, making it difficult to maintain a stable power level for extended periods, resulting in irregular and drastic fluctuations in the power curve.

[0130] Power fluctuation frequency and amplitude: high frequency and large amplitude. The power curve shows obvious and drastic fluctuations that are directly related to the resident's behavior. For example, opening a window causes a sudden drop in power, and closing the window causes a sudden increase in power to quickly restore comfort.

[0131] Time taken to reach the set temperature: This is highly unstable and may take a long time, as residents' behavior constantly interferes with the system's ability to reach a stable state.

[0132] 3. Judgment rules corresponding to "residents' true physiological sensitivity":

[0133] Real-time operating power ramp-up rate: The initial ramp-up may be faster to quickly reach the occupant's desired comfort zone.

[0134] Peak power maintenance stability: High. Once the set comfortable temperature is reached, the device strives to maintain it within a very narrow temperature range, resulting in relatively stable power output with minimal fluctuations to ensure the occupant's continued comfort.

[0135] Power fluctuation frequency and amplitude: low frequency and small amplitude. The equipment will make fine adjustments to maintain stability, with a smooth power curve to avoid drastic fluctuations and provide a stable and comfortable environment.

[0136] The time required to reach the set temperature may be moderate, but once reached, the system will prioritize maintaining its stability rather than pursuing rapid changes.

[0137] Judgment Criteria: After the morphological characteristics of the power data sequence of the intelligent environmental control device are extracted, the system will determine the dominant factors of environmental sensitivity based on the following judgment criteria:

[0138] 1. Calculating the matching degree: When features such as the real-time operating power ramp-up rate, peak power stability, power fluctuation frequency and amplitude, and time taken to reach the set temperature partially match multiple preset discrimination rules (i.e., the actual data features have a certain degree of similarity to the descriptions of multiple rules, but do not completely match any one of them), the system will calculate the matching degree between the morphological features of the power data sequence and each preset discrimination rule. The matching degree is a quantitative indicator that measures the similarity between the actual data features and the rule descriptions. It can be implemented through various algorithms, such as distance calculation based on feature vectors (e.g., Euclidean distance, cosine similarity), correlation analysis, or probability values ​​output by a pre-trained classification model.

[0139] 2. Normalization Processing: To ensure the comparability of matching degrees between different discrimination rules, the system normalizes the morphological features of the power data sequence based on the calculated matching degree, obtaining a normalized matching degree. Normalization maps all matching degree values ​​to a uniform scale range (e.g., between 0 and 1), eliminating dimensional differences and making subsequent comparisons more fair and accurate.

[0140] 3. Identify the highest matching rule: The system will identify the preset discrimination rule with the highest matching degree with the morphological features of the power data sequence based on the normalized matching degree. This means that among all rules with partial matching, the system will select the rule that best matches the actual operating data pattern of the current resident unit.

[0141] 4. Determine the dominant factor: Ultimately, the system determines the dominant factor of environmental sensitivity as the factor corresponding to the preset discrimination rule with the highest matching degree. For example, if the matching degree with the "resident-specific behavior" rule is the highest, then the dominant factor of environmental sensitivity for that resident unit is determined to be the resident-specific behavior.

[0142] Through this set of criteria, the system can effectively solve the problem of fuzzy matching between actual data characteristics and multiple rules. By quantitative comparison and selection, it ensures that the identification of the dominant factors of environmental sensitivity is more accurate and objective, thereby providing a reliable basis for subsequent differentiated resource scheduling.

[0143] S2032. Based on the matching degree, the morphological characteristics of the power data sequence are normalized to obtain the normalized matching degree.

[0144] The purpose of normalization is to eliminate the dimensional differences that may exist between different matching degree calculation methods or different discrimination rules, so that all matching degree values ​​are within a uniform scale range (e.g., between 0 and 1), thereby ensuring that subsequent comparisons and selections are fair and effective.

[0145] S2033. Based on the normalized matching degree, identify the preset discrimination rule that has the highest matching degree with the morphological features of the power data sequence.

[0146] S2034. The dominant factor of environmental sensitivity is determined as the factor corresponding to the preset discrimination rule with the highest matching degree.

[0147] In some preferred embodiments, a specific example is given below. Assume that after an external environmental fluctuation event occurs, the real-time operating power data sequence of the intelligent environmental control device in a residential unit exhibits the following characteristics: a relatively fast power ramp-up rate, moderate peak power stability, low but large power fluctuation frequency, and a moderate time to reach the set temperature. The system has preset discrimination rules targeting three main factors: "building physical defects," "resident-specific behaviors," and "resident's actual physiological sensitivity."

[0148] For example, the discrimination rule for "building physical defects" may correspond to the characteristic of continuous small fluctuations in power and difficulty in stabilizing at the set power; the discrimination rule for "specific behaviors of residents" may correspond to the characteristic of frequent and large adjustments in power and nonlinear changes; while the discrimination rule for "actual physiological sensitivity of residents" may correspond to the characteristic of rapid power response and stable maintenance within the comfort range.

[0149] When the power data sequence of the aforementioned residential unit is analyzed, the power data sequence may partially match the rule of "resident-specific behavior" (e.g., a certain degree of power adjustment) and also partially match the rule of "building physical defects" (e.g., some inexplicable fluctuations). In this case, the solution of this application calculates the matching degree between the morphological characteristics of the power data sequence and each preset discrimination rule. Assuming the calculation results are: a matching degree of 0.75 with the "resident-specific behavior" rule, a matching degree of 0.60 with the "building physical defects" rule, and a matching degree of 0.40 with the "resident's true physiological sensitivity" rule.

[0150] Subsequently, these matching scores were normalized. For example, if the maximum matching score was 0.75, the normalized scores might be 1.00, 0.80, and 0.53. Based on the normalized matching scores, the system identified the highest matching score with the "resident-specific behavior" rule. Therefore, the dominant factor for the environmental sensitivity of this resident unit was determined to be "resident-specific behavior".

[0151] Through the aforementioned technical solution, this application enables refined identification of the dominant factors affecting the environmental sensitivity of residential units. Compared to relying solely on a rough assessment of response intensity, this solution, through in-depth analysis of the operating power data sequence of intelligent environmental control equipment, extracts multiple dimensions of characteristics, including ramp-up rate, peak power maintenance stability, power fluctuation frequency and amplitude, and time taken to reach the set temperature. This allows for a more accurate and comprehensive determination of whether environmental sensitivity stems from building physics defects, specific resident behaviors, or the resident's actual physiological sensitivity. This refined judgment capability enables more targeted subsequent resource scheduling and energy-saving strategies, avoiding resource waste or decreased comfort that may result from a "one-size-fits-all" management approach, and significantly improving the intelligence level and energy-saving effect of multi-source management in smart communities.

[0152] In one possible design, when the dominant factor in environmental sensitivity is a resident's specific behavior, after slightly reducing the operating power of the smart environmental control equipment in the resident unit, this application further includes the following steps:

[0153] S301. Continuously monitor the changes in the operating power of the intelligent environmental control equipment in the residential unit.

[0154] S302. When the change in operating power does not achieve the preset energy-saving effect, and the resident unit does not manually adjust the operating parameters of the intelligent environmental control equipment, determine whether there is an ineffective intervention trend based on the fluctuation range and duration of the operating power.

[0155] The preset energy-saving effect can be a specific percentage reduction in energy consumption or a target power value. If the system detects that despite sending energy-saving prompts or making power adjustments, the actual operating power of the equipment has not decreased significantly or has returned to a high level in a short period of time, and the resident has not actively adjusted the parameters, then the current intervention measures may not have been effective.

[0156] For example, if the power rebounds quickly after a fine-tuning, or remains high for an extended period after a warning, there may be ineffective intervention.

[0157] S303. If there is a trend of ineffective intervention, then suspend the push of energy-saving reminder messages and slightly reduce the operating power of the intelligent environmental control equipment again.

[0158] In practical applications, if the system determines that there is a trend of ineffective intervention, it will immediately pause the push of energy-saving reminders and slightly reduce the operating power of the smart environmental control devices. This is to avoid excessive disturbance to residents or causing negative emotions, while preventing the continued ineffective system intervention.

[0159] S304. Push interactive inquiry information to the smart terminal of the resident unit.

[0160] In one example, the system pushes interactive query information to the smart terminals of the resident unit, such as a mobile app, smart speaker, or smart display.

[0161] This inquiry is used to ask residents about their actual needs, comfort preferences, or opinions on current energy-saving strategies. For example, it might ask, "Are you feeling uncomfortable?", "Are you satisfied with the current indoor temperature?", or "Which energy-saving suggestions do you think are more in line with your habits?"

[0162] S305. Adjust subsequent energy-saving strategies based on feedback from interactive queries.

[0163] As one possible implementation, when there is a discrepancy between the resident's interactive inquiry feedback and the operating power data analysis results of the resident's unit's smart environmental control equipment, the system can calculate the degree of difference between the feedback and the operating power data analysis results; based on the degree of difference, dynamically adjust the weights of the feedback and operating power data analysis results in the energy-saving strategy formulation; based on the adjusted weights, merge the feedback and operating power data analysis results to generate a merged energy-saving strategy; and based on the merged energy-saving strategy, adjust the interval for pushing energy-saving reminders again or slightly reduce the operating power of the smart environmental control equipment again.

[0164] Specifically, the degree of difference between the calculated feedback and the analysis results of the operating power data can be achieved using various methods. For example, the difference can be quantified by comparing the deviation between the comfort preferences reported by residents (such as "feeling cold" or "feeling hot") and the energy consumption trends reflected by the actual operating power data of the smart environmental control equipment (such as continuously increasing, decreasing, or stabilizing power). This degree of difference can be a numerical value representing the extent of the deviation between the two.

[0165] The dynamic adjustment of the weights of feedback and operational power data analysis results in energy-saving strategy formulation, based on the degree of difference, refers to the system intelligently allocating the importance of resident feedback and operational power data analysis results in the decision-making process according to the calculated degree of difference. For example, when the degree of difference is small, both can be given similar weights; when the degree of difference is large, the system can appropriately reduce the weight of one or increase the weight of the other according to preset rules or machine learning models to ensure the rationality of the final strategy.

[0166] In practical applications, based on the adjusted weights, the results of feedback and operational power data analysis are integrated to generate a fused energy-saving strategy. This can be understood as comprehensively considering the resident feedback and operational power data analysis results after weight adjustment to form a more comprehensive and balanced energy-saving strategy. For example, weighted average, decision tree, or fuzzy logic methods can be used for fusion to ensure that the strategy can both take into account the individual needs of residents and meet the actual energy consumption optimization goals.

[0167] Therefore, based on the integrated energy-saving strategy, the interval between sending energy-saving reminders again or the magnitude of slightly reducing the operating power of smart environmental control devices can be adjusted. This aims to refine subsequent energy-saving intervention measures according to the integrated strategy. For example, if the integrated strategy indicates that residents have a low acceptance of energy-saving reminders, the interval between reminders can be extended; if the integrated strategy shows that residents are highly sensitive to slight power reductions, the magnitude of the reduction can be reduced, thereby improving the acceptance and effectiveness of energy-saving measures.

[0168] In some preferred embodiments, a specific example is given below. Suppose that when the external ambient temperature rises, the power consumption data analysis of a resident's smart environmental control device (such as an air conditioner) shows that its power consumption remains at a high level, indicating significant energy consumption. At this time, the system pushes a behavioral energy-saving prompt to the resident's smart terminal according to the above scheme and slightly reduces the air conditioner's operating power. However, the resident provides feedback through interactive inquiry, stating, "I feel a bit hot, I hope the temperature can be lowered." The system then calculates the degree of difference between the resident's "feeling hot" feedback and the high-power operation data of the air conditioner. If the system determines that there is a moderate difference between the resident's feedback and the actual energy consumption data (e.g., the power is high but still within a comfortable range, while the resident subjectively feels hot), the system dynamically adjusts the weights, potentially giving the resident's feedback a higher weight to ensure comfort, while still considering the power data to avoid excessive energy consumption. Based on the adjusted weights, the system generates a fused energy-saving strategy, for example, no longer continuing to slightly reduce the power, but instead fine-tuning the air conditioner's set temperature to a slightly lower but still energy-efficient level, or extending the interval between the next behavioral energy-saving prompt to avoid frequent interruptions. In this way, the system can respond more intelligently to the real needs of residents while taking into account energy-saving goals, avoiding user discomfort or resistance caused by blind intervention.

[0169] In one possible design, in order to dynamically adjust the weight of feedback and operating power data analysis results in energy-saving strategy formulation according to the degree of difference, this application also includes:

[0170] S401. When the difference between the feedback and the operating power data analysis results is within a preset medium range, the weight adjustment strategy for the feedback and operating power data analysis results is switched to a step-by-step adjustment mode.

[0171] Specifically, "the difference between the feedback and operational power data analysis results is within a preset moderate range" means that the calculated difference between the feedback and operational power data analysis results falls within a pre-defined numerical range that is neither small nor large. For example, this moderate range can be determined through historical data analysis, expert experience, or machine learning models, aiming to identify resident units that require more granular and cautious intervention from the system. When the difference is within this range, it indicates a certain degree of inconsistency between the resident's actual experience and the state inferred by the system based on data, requiring the system to adopt more flexible and adaptive strategies.

[0172] S402. Set a weighted stability observation period longer than the preset duration.

[0173] Setting a weight stabilization observation period longer than the preset time means giving the system enough time to collect new operational data and resident feedback after each weight adjustment in order to evaluate the effectiveness of the current strategy and the residents' adaptation.

[0174] For example, the observation period can be extended from the usual few hours to several days, thereby ensuring that the system has more sufficient and stable data support before making further adjustments, and avoiding incorrect judgments due to short-term fluctuations.

[0175] S403. During the weight stabilization observation period, adopt the feedback weights related to resident comfort assurance and adjust the weights of the operational power data analysis results.

[0176] For example, if a resident reports discomfort, even if the power consumption data analysis shows the current state is energy-efficient, the system will tend to increase the weight of the feedback to ensure the resident's comfort. Simultaneously, the system will adjust the weight of the power consumption data analysis results based on resident feedback to better reflect the resident's actual needs, thereby gradually guiding residents to achieve energy conservation while ensuring comfort.

[0177] This application's solution effectively addresses the issues of strategy instability and poor user experience that can arise from traditional dynamic adjustments by introducing a refined judgment of the degree of difference between feedback and operational power data analysis results, and by adopting a specific weight adjustment strategy for moderate differences. When the system identifies a resident unit as a strategy-sensitive unit—that is, when there is a moderate difference between the resident's actual experience and the system's data analysis—the system no longer adopts a one-size-fits-all adjustment approach. Instead, by switching to a tiered adjustment mode, the system can gradually adjust the energy-saving strategy in small, rapid steps, avoiding resident discomfort or resentment caused by radical adjustments. Simultaneously, setting a longer weight stabilization observation period allows the system sufficient time to verify the effect of each adjustment and collect more stable data, thus avoiding the risk of making erroneous decisions based on short-term fluctuations. More importantly, during the observation period, feedback weights related to resident comfort are prioritized, and the weights of operational power data analysis results are adjusted accordingly. This makes the formulation of energy-saving strategies more human-centered, truly placing resident comfort at the core, thereby increasing resident acceptance and compliance with energy-saving strategies.

[0178] In some preferred embodiments, a specific example is given below. Suppose that when the external ambient temperature drops, the intelligent environmental control device in a residential unit shows through its power data analysis that the room temperature has reached the set value and the energy consumption is within a reasonable range, but the resident reports through interactive inquiry that they still feel slightly cold. At this time, the system calculates and finds that the difference between the resident's feedback and the power data analysis results is within a preset moderate range.

[0179] Subsequently, the system switches its weighting adjustment strategy for feedback and operating power data analysis results to a tiered adjustment mode. For example, instead of immediately and drastically increasing the operating power of the smart environmental control equipment, the system increases the power in small increments of 5%, with a weighted stabilization observation period of, for example, 24 hours. During the observation period, the system prioritizes resident feedback regarding feeling cold and adjusts the weighting of the operating power data analysis results accordingly, making it more inclined to meet resident comfort needs in subsequent strategy formulation. For example, the system may fine-tune the set temperature or fan speed during the observation period without significantly increasing energy consumption, while continuously monitoring resident feedback and changes in equipment operating power. If residents no longer report discomfort during the observation period, the current tiered adjustment is considered effective, and the strategy is maintained; if residents still report discomfort, the system will proceed to the next tier of fine-tuning until resident comfort is guaranteed. In this way, the system can gradually optimize energy-saving strategies while ensuring resident comfort, avoiding negative impacts caused by excessive intervention or neglect of user feelings.

[0180] like Figure 3 As shown in the figure, this embodiment of the invention also provides a smart community multi-source management system. The system includes:

[0181] The environmental sensing module is used to acquire external environmental fluctuation events and changes in the operating status of intelligent environmental control devices within the residential unit; external environmental fluctuation events are determined based on the fluctuation amplitude of external environmental parameters;

[0182] The operation monitoring module is used to assess the response strength of residential units to external environmental fluctuations based on changes in operational status.

[0183] The sensitivity assessment module is used to determine the environmental sensitivity of a resident unit based on the response intensity from a preset mapping relationship; the preset mapping relationship includes the mapping relationship between different response intensities and different environmental sensitivities.

[0184] The resource scheduling module is used to allocate resources to residential units based on environmental sensitivity. The strategy adjustment module is used to adjust the heating strategy of the oven based on the identified physicochemical stage.

[0185] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by a computer program instructing related hardware. This program can be stored in the computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be an internal storage unit of the task execution device (including a data sending end and / or a data receiving end) of any of the foregoing embodiments, such as the hard disk or memory of the task execution device. The computer-readable storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device. Further, the computer-readable storage medium can include both the internal storage unit of the task execution device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the task execution device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0186] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0187] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0188] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. A multi-source management method for smart communities, characterized in that, include: Upon sensing external environmental fluctuations, the system acquires information on changes in the operational status of intelligent environmental control devices within the residential unit. The external environmental fluctuation events are determined based on the fluctuation amplitude of external environmental parameters; Based on the changes in the operating status, assess the strength of the resident unit's response to the external environmental fluctuations. Based on the response intensity, the environmental sensitivity corresponding to the response intensity is determined from a preset mapping relationship as the environmental sensitivity of the resident unit; the preset mapping relationship includes the mapping relationship between different response intensities and different environmental sensitivities; Based on the environmental sensitivity, resource scheduling is performed on the residential units; The changes in operating status include real-time operating power and set temperature. The assessment of the resident unit's response strength to external environmental fluctuations based on these changes includes: Determine the power boost and continuous operating time of the intelligent environmental control device; The power increase is the increase in real-time operating power of the intelligent environmental control device after an external environmental fluctuation event compared to the real-time operating power before the external environmental fluctuation event. The continuous operating time is the duration during which the intelligent environmental control device operates at a power higher than the preset power after an external environmental fluctuation event occurs. The power increase and continuous operating time are weighted and summed to obtain the response strength of the residential unit to the external environmental fluctuation event; The step of allocating resources to the residential unit based on the environmental sensitivity includes: Identify the dominant factors influencing the environmental sensitivity; The dominant factors include: building physical defects, specific resident behaviors, and residents’ actual physiological sensitivity. Based on the dominant factors of the environmental sensitivity, resource allocation is performed for the residential units; The dominant factors for determining the environmental sensitivity include: When the external environmental fluctuation event is detected, the power data sequence of the intelligent environmental control device is extracted from the real-time operating power and the set temperature; The power data sequence is analyzed to extract the real-time operating power ramp-up rate, peak power maintenance stability, power fluctuation frequency, amplitude, and time taken to reach the set temperature. The dominant factors of environmental sensitivity are determined based on the real-time operating power ramp-up rate, peak power maintenance stability, power fluctuation frequency and amplitude, and the time taken to reach the set temperature.

2. The smart community multi-source management method according to claim 1, characterized in that, The dominant factors for determining the environmental sensitivity based on the real-time operating power ramp-up rate, peak power maintenance stability, power fluctuation frequency and amplitude, and time taken to reach the set temperature include: When the real-time operating power ramp-up rate, peak power maintenance stability, power fluctuation frequency and amplitude, and time taken to reach the set temperature all partially match multiple preset discrimination rules, the matching degree between the morphological features of the power data sequence and each preset discrimination rule is calculated. Based on the matching degree, the morphological features of the power data sequence are normalized to obtain the normalized matching degree; Based on the normalized matching degree, a preset discrimination rule with the highest matching degree with the morphological features of the power data sequence is identified; The dominant factor of environmental sensitivity is determined to be the factor corresponding to the preset discrimination rule with the highest matching degree.

3. The smart community multi-source management method according to claim 1, characterized in that, The resource allocation for the residential unit based on the dominant factor of environmental sensitivity includes: When the dominant factor of environmental sensitivity is building physical defects, building energy-saving renovation suggestions are pushed to the smart terminal of the resident unit to carry out differentiated resource scheduling and control for the resident unit; When the dominant factor of the environmental sensitivity is a specific behavior of the resident, energy-saving reminder information is pushed to the smart terminal of the resident unit, and the operating power of the smart environmental control equipment of the resident unit is slightly reduced according to the duration and impact of the specific behavior, so as to carry out differentiated resource scheduling and control of the resident unit. When the dominant factor of environmental sensitivity is the actual physiological sensitivity of the resident, the operating parameters of the intelligent environmental control equipment of the resident unit are set to an uninterruptible state in order to carry out differentiated resource scheduling and control of the resident unit.

4. The multi-source management method for smart communities according to claim 3, characterized in that, After slightly reducing the operating power of the intelligent environmental control equipment in the residential unit, the method further includes: Continuously monitor the changes in the operating power of the intelligent environmental control equipment in the residential unit; When the change in operating power does not achieve the preset energy-saving effect, and the resident unit does not manually adjust the operating parameters of the intelligent environmental control device, it is determined whether there is an ineffective intervention trend based on the fluctuation range and duration of the operating power. If the aforementioned ineffective intervention trend exists, then the push of the energy-saving behavioral prompt information will be paused again and the operating power of the intelligent environmental control device will be slightly reduced again; Push interactive query information to the smart terminal of the residential unit; Based on the feedback from the interactive query information, subsequent energy-saving strategies will be adjusted.

5. The smart community multi-source management method according to claim 4, characterized in that, The step of adjusting subsequent energy-saving strategies based on feedback from the interactive query information includes: When there is a difference between the interactive inquiry feedback from the resident and the operating power data analysis result of the intelligent environmental control device of the resident unit, the degree of difference between the feedback and the operating power data analysis result is calculated. Based on the degree of difference, the weights of the feedback and the operating power data analysis results in the formulation of the energy-saving strategy are dynamically adjusted. Based on the adjusted weights, the feedback and the analysis results of the operating power data are integrated to generate a fused energy-saving strategy; Based on the integrated energy-saving strategy, adjust the interval between pushing the energy-saving reminder information again or the magnitude of slightly reducing the operating power of the intelligent environmental control device again.

6. The multi-source management method for smart communities according to claim 5, characterized in that, The step of dynamically adjusting the weights of the feedback and the operating power data analysis results in the energy-saving strategy formulation based on the degree of difference includes: When the difference between the feedback and the operating power data analysis result is within a preset medium range, the weight adjustment strategy of the feedback and the operating power data analysis result is switched to a step-by-step adjustment mode. Set a weighted stability observation period that is longer than the preset duration; During the weight stabilization observation period, feedback weights related to the resident comfort guarantee are adopted, and the weights of the operating power data analysis results are adjusted.

7. A smart community multi-source management system, characterized in that, The system includes: An environmental sensing module is used to acquire external environmental fluctuation events and changes in the operating status of intelligent environmental control devices within the residential unit; the external environmental fluctuation events are determined based on the fluctuation amplitude of external environmental parameters. The operation monitoring module is used to assess the response strength of the resident unit to external environmental fluctuation events based on the changes in the operation status. The changes in operating status include real-time operating power and set temperature. The assessment of the resident unit's response strength to external environmental fluctuations based on these changes includes: Determine the power boost and continuous operating time of the intelligent environmental control device; The power increase is the increase in real-time operating power of the intelligent environmental control device after an external environmental fluctuation event compared to the real-time operating power before the external environmental fluctuation event. The continuous operating time is the duration during which the intelligent environmental control device operates at a power higher than the preset power after an external environmental fluctuation event occurs. The power increase and continuous operating time are weighted and summed to obtain the response strength of the residential unit to the external environmental fluctuation event; A sensitivity assessment module is used to determine the environmental sensitivity corresponding to the response intensity from a preset mapping relationship as the environmental sensitivity of the resident unit; the preset mapping relationship includes the mapping relationship between different response intensities and different environmental sensitivities; The resource scheduling module is used to schedule resources for the residential unit based on the environmental sensitivity. The step of allocating resources to the residential unit based on the environmental sensitivity includes: Identify the dominant factors influencing the environmental sensitivity; The dominant factors include: building physical defects, specific resident behaviors, and residents’ actual physiological sensitivity. Based on the dominant factors of the environmental sensitivity, resource allocation is performed for the residential units; The dominant factors for determining the environmental sensitivity include: When the external environmental fluctuation event is detected, the power data sequence of the intelligent environmental control device is extracted from the real-time operating power and the set temperature; The power data sequence is analyzed to extract the real-time operating power ramp-up rate, peak power maintenance stability, power fluctuation frequency, amplitude, and time taken to reach the set temperature. The dominant factors of environmental sensitivity are determined based on the real-time operating power ramp-up rate, peak power maintenance stability, power fluctuation frequency and amplitude, and the time taken to reach the set temperature.

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