A configuration method for a smart shared community

Abstract

This invention relates to the field of resource allocation and discloses a configuration method for a smart shared community. This method addresses the problem that resource maintenance intervals cannot be dynamically adjusted based on real-time conditions during shared community resource configuration. The method includes: assigning a unique resource code to each shared resource within the community and storing the resource code in the Internet of Things (IoT); constructing a digital map of shared resources; classifying users and configuring permissions for each user category and different shared resources; collecting usage records in real-time based on the resource code and storing each usage record in the IoT; statistically analyzing the status information of each shared resource in real-time; evaluating a maintenance index based on the status information; determining maintenance based on the maintenance index; and if maintenance is required, performing maintenance on the shared resource and adjusting the maintenance interval based on the maintenance index. This effectively improves the accuracy of shared community configuration.

Description

Technical Field

[0001] This invention relates to the field of resource allocation, and more specifically to a configuration method for a smart shared community. Background Technology

[0002] A smart shared community is an efficient, intelligent, and sustainable living service platform built in urban communities, industrial parks, and industrial clusters by integrating advanced technologies such as the Internet of Things, cloud computing, artificial intelligence, and big data analytics. Its core objective is to improve community management efficiency and residents' quality of life by achieving shared use, intelligent control, and dynamic operation and maintenance of community resources through IoT-based resource scheduling and digital user management.

[0003] Smart shared communities are widely used in various scenarios such as access control, shared parking, express delivery lockers, public lighting, shared electric vehicles, and smart fitness equipment, involving multiple technical fields such as identity recognition, device networking, data collection, permission configuration, anomaly detection, and energy consumption control.

[0004] In the process of smart community resource management, IoT devices are often used to uniformly access and remotely manage shared resources, and resource scheduling and service publishing are realized through the community operation platform. Current mainstream technical solutions usually rely on unique device codes for resource identification, and by setting fixed time periods, unified inspection, maintenance, and data cleaning operations are performed on various shared resources to ensure the basic stability of equipment operation.

[0005] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0006] In real-world applications, the usage of shared resources within communities varies significantly. For example, some electric vehicles, access control systems, or parcel lockers are frequently used during peak hours, while others remain idle for extended periods. If maintenance is still performed at a uniform interval, it could pose safety hazards, leading to resource waste and increased maintenance costs. Summary of the Invention

[0007] In order to overcome the above-mentioned defects of the prior art, the present invention provides a configuration method for a smart sharing community to solve the problems existing in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A configuration method for a smart shared community includes the following steps: Step 1: Assign a unique resource code to each shared resource within the community and store the resource code in the Internet of Things (IoT); Step 2: Construct a digital map of shared resources based on the resource codes; Step 3: Classify users into categories, and configure permissions for each user category with different shared resources based on the digital map; Step 4: During user usage of shared resources, collect usage records in real time based on the resource codes. These records include the shared resource's operating status and the user category, and store each usage record in the IoT; Step 5: Statistically analyze the status information of each shared resource in real time, assess the status information to obtain a maintenance index, and determine maintenance based on the maintenance index; Step 6: If maintenance is determined to be necessary, perform maintenance on the shared resource and determine the appropriate maintenance method based on the assessment results. The maintenance interval is adjusted based on the maintenance index. The steps for obtaining the maintenance index are as follows: A testing period is set; the usage frequency and total runtime of shared resources within the testing period are obtained; a usage impact coefficient is calculated based on the usage frequency and total runtime; fault information of shared resources within the testing period is obtained, including the number of fault records and the time interval between each fault record; a fault impact coefficient is calculated based on the fault information of shared resources; operating information of shared resources within the testing period is obtained, including the operating temperature and energy consumption of shared resources; an anomaly impact coefficient is calculated based on the operating information of shared resources; the usage impact coefficient, fault impact coefficient, and anomaly impact coefficient are normalized; and the maintenance index is evaluated based on the normalized usage impact coefficient, fault impact coefficient, and anomaly impact coefficient. The specific steps for obtaining the maintenance index are as follows: In the formula, This is expressed as a maintenance index. This is expressed as the normalized usage impact coefficient. This is expressed as the normalized fault impact coefficient. This is expressed as the normalized anomaly impact coefficient. , , This is represented by the weighting coefficients of the impact coefficient, the fault impact coefficient, and the anomaly impact coefficient. The steps for obtaining the anomaly impact coefficient are as follows: First, set a normal operating temperature range, obtain the highest operating temperature value, and count the number of times the resource operating temperature is greater than or equal to the highest operating temperature value during the detection period, recording this as the temperature anomaly count. Then, obtain the duration of each temperature anomaly and use the dynamic deviation scoring method to evaluate the temperature anomaly count and its duration to obtain the degree of temperature anomaly. Second, set a normal operating energy consumption range, obtain the highest operating energy consumption value, and count the number of times the resource operating energy consumption value is greater than or equal to the highest operating energy consumption value during the detection period, recording this as the energy consumption anomaly count. Then, obtain the duration of each energy consumption anomaly. Calculate the mean of each energy consumption anomaly duration to obtain the average energy consumption anomaly duration. Calculate the standard deviation of the energy consumption anomaly duration using the standard deviation formula based on the average energy consumption anomaly duration and the duration of each energy consumption anomaly. Finally, calculate the degree of energy consumption anomaly based on the energy consumption anomaly count, the average energy consumption anomaly duration, and the standard deviation of the energy consumption anomaly duration. The specific steps for obtaining this degree of energy consumption anomaly are as follows: In the formula, This indicates the degree of energy consumption anomaly. This is represented by the number of times energy consumption is abnormal. This is expressed as the average duration of energy consumption anomalies. It is expressed as the standard deviation of the duration of energy consumption anomalies; the anomaly influence coefficient is calculated based on the degree of temperature anomaly and the degree of energy consumption anomaly.

[0010] Preferably, the step of constructing a shared resource digital map based on resource codes is as follows: extracting resource code information of all registered shared resources from the Internet of Things, and obtaining the installation location information of each resource, including GPS coordinates, building number, floor location, and room number; based on the resource codes, further extracting the corresponding function type, functional module, and real-time usage status of the resource; constructing a community spatial coordinate system in a three-dimensional manner in the digital platform according to the extracted location information, and mapping the resource instance corresponding to each resource code to a specific location in the coordinate system; grouping resources according to their function category and location information to generate a shared resource digital map.

[0011] Preferably, the step of obtaining the fault impact coefficient is as follows: within the detection time period, obtain the time point of each fault, interpolate the time points of adjacent faults to obtain the interval time between each fault; calculate the difference between every two consecutive time intervals to obtain the interval change value, calculate the mean of each interval change value to obtain the fault growth rate; calculate the fault impact coefficient based on the fault growth rate and the number of fault records. The specific steps are as follows: In the formula, This is expressed as the failure impact coefficient. This is represented by the number of fault records. This indicates the degree of fault growth. It is represented as a constant term.

[0012] Preferably, the step of using the dynamic deviation scoring method to evaluate the number and duration of temperature anomalies to obtain the degree of temperature anomaly is as follows: For each anomaly segment, calculate the average absolute deviation of the temperature from the normal operating temperature range and record it as the amplitude term; for each anomaly segment, calculate the maximum rate of increase or decrease of the anomaly segment and record it as the suddenness term; obtain the duration of the anomaly segment and record it as the duration term; calculate the deviation score of the anomaly segment based on the amplitude term, suddenness term, and duration term, and the specific steps for obtaining the score are as follows: In the formula, This indicates the deviation score for the abnormal segment. Represented as the amplitude term, Represented as a continuous term, This is represented as a sudden event; the weight of each anomaly is calculated using the anomaly time series step-by-step method. The specific steps are as follows: In the formula Let represent the weight of the i-th anomaly segment, where i represents the i-th anomaly segment. This is represented by the number of temperature anomalies; the degree of temperature anomaly is calculated based on the number of anomalies, deviation score, and weight. The specific steps for obtaining this information are as follows: In the formula, This represents the degree of temperature anomaly in the i-th segment. This is expressed as the number of temperature anomalies. Let the weight of the i-th anomaly be denoted as . This represents the deviation score for the i-th abnormal segment.

[0013] Preferably, the maintenance judgment step based on the maintenance index is as follows: compare the maintenance index with the maintenance threshold; if the maintenance index is greater than or equal to the maintenance threshold, it is determined that maintenance is required; if the maintenance index is less than the maintenance threshold, it is determined that maintenance is not required.

[0014] Preferably, the step of adjusting the maintenance interval based on the maintenance index is as follows: obtaining the initial maintenance interval, calculating the ratio of the maintenance threshold to the maintenance index to obtain the adjustment factor, and multiplying the adjustment factor with the initial maintenance interval to obtain the actual maintenance interval.

[0015] The technical effects and advantages of this invention are as follows:

[0016] Each shared resource within the community is assigned a unique resource code, which is then stored in the Internet of Things (IoT) to construct a digital map of shared resources. Users are categorized, and permissions are configured for each user category and different shared resources. Usage records are collected in real time based on the resource codes, and each usage record is stored in the IoT. The status information of each shared resource is statistically analyzed in real time, and a maintenance index is obtained based on the status information. Maintenance decisions are made based on the maintenance index, and if maintenance is required, the shared resource is maintained. The maintenance interval is adjusted according to the maintenance index, effectively improving the accuracy of the shared community configuration. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a configuration method for a smart shared community, as provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The configuration method of a smart shared community involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This invention provides a configuration method for a smart shared community, such as... Figure 1 As shown, it includes the following steps:

[0020] Step 1: Assign a unique resource code to each shared resource in the community and store the resource code in the Internet of Things (IoT). The resource code includes information such as resource type, resource function, installation area, and module to which it belongs. This provides a unique identification basis for subsequent device registration, resource management, and permission configuration, ensuring that all IoT resources are identifiable and traceable.

[0021] Assigning a unique resource code to each shared resource within the community and storing it in the Internet of Things (IoT) system facilitates full lifecycle management and accurate identification of resources. Each resource establishes a correspondence between the physical world and the digital system through its unique code, enabling the system to accurately track the resource's installation location, functional attributes, operational status, and historical records. Simultaneously, the resource code serves as the fundamental identifier for data interaction and access control, supporting multi-module collaborative operation, user permission binding, and abnormal behavior location, significantly improving the intelligence, standardization, and scalability of community resource management.

[0022] Step 2: Construct a digital map of shared resources based on resource codes. The digital map, based on spatial coordinates, integrates functional information, usage status, and geographical location of various shared resources to form a multi-level community resource view. This view supports user queries, platform scheduling, and subsequent permission management, improving the overall visualization of resources.

[0023] Building a digital map of shared resources based on resource codes enables spatial and structured management of various shared resources within a community. By combining resource codes with geographic location, functional information, and status data, the system can intuitively display the distribution pattern, usage status, and operational activity of resources on the platform, facilitating quick user queries, efficient scheduling by administrators, and intelligent system recommendations. Simultaneously, the digital map, as a unified visual entry point, also supports access control, anomaly location, and maintenance path planning, improving the transparency, real-time nature, and operational efficiency of resource management.

[0024] In this embodiment, it should be specifically explained that the steps for constructing a shared resource digital map based on resource coding are as follows:

[0025] The system extracts resource code information from all registered shared resources within the Internet of Things (IoT) and obtains the installation location information for each resource. This location information may include GPS coordinates, building number, floor location, or room number. The resource code serves as a unique identifier, linking the physical and spatial attributes of the resource and providing a fundamental data source for constructing a digital map.

[0026] Based on resource coding, the function type (e.g., access control, lighting, electric vehicles, lockers), the functional module (e.g., access management, transportation, public services), and real-time usage status (e.g., idle, running, awaiting maintenance) corresponding to the resource are further extracted. This step enriches the resource node information on the map, so that each resource not only has spatial positioning but also carries structured function and status labels;

[0027] Based on the extracted location information, a three-dimensional community spatial coordinate system is constructed in the digital platform, and the resource instance corresponding to each resource code is mapped to a specific location in this coordinate system. This mapping operation ensures that the resource nodes in the digital map are spatially consistent with the actual deployment, achieving virtual-real synchronization.

[0028] After completing the location of basic resource nodes, the resources are grouped according to their functional categories, service areas, or building units to generate a shared resource digital map.

[0029] Step 3: Categorize users, including residents, property management, and visitors. Based on the shared resource digital map, configure permissions for each user category and different shared resources to achieve hierarchical sharing and precise authorization control of resources, ensuring the security and compliance of device use.

[0030] Step 4: During the process of users using shared resources, usage records are collected in real time according to the resource code. The usage records include the operating status of the shared resources and the user category. Each usage record is stored in the Internet of Things. The operating status includes the resource code, usage time and operation result.

[0031] Step 5: Real-time statistics of the status information of each shared resource, evaluation of the status information to obtain the maintenance index, and maintenance judgment based on the maintenance index.

[0032] In this embodiment, it should be specifically explained that the maintenance index acquisition step is as follows:

[0033] Set a detection period, obtain the usage frequency and total runtime of shared resources within the detection period, and calculate the usage impact coefficient based on the usage frequency and total runtime. The specific steps are as follows:

[0034] ;

[0035] In the formula, This is expressed as an influence coefficient; Indicated as usage frequency, This represents the total runtime. The higher the frequency of resource usage or the longer the duration of a single runtime, the greater the overall load and the more significant the impact on equipment aging and performance degradation. By taking the square root of the product of these two data points, we can balance the amplification effect of extreme values ​​and avoid the distortion of calculation results caused by excessively high frequency or duration. On the other hand, it also reflects the non-linear superposition relationship between the two, which is more in line with the gradual law of resource wear and tear in actual use.

[0036] Obtain fault information of shared resources within the detection period. The fault information includes the number of fault records and the time interval between each fault record. Calculate the fault impact coefficient based on the fault information of the shared resources.

[0037] Obtain the operating information of shared resources during the detection period, including the operating temperature and energy consumption of the shared resources, and calculate the anomaly impact coefficient based on the operating information of the shared resources;

[0038] The impact coefficient, failure impact coefficient, and anomaly impact coefficient will be normalized. The maintenance index will then be calculated based on these normalized impact coefficients. The specific steps for obtaining the index are as follows:

[0039] ;

[0040] In the formula, This is expressed as a maintenance index. This is represented as the normalized usage impact coefficient. As the usage of shared resources accumulates, the corresponding usage impact coefficient increases, thereby driving up the overall maintenance index. The more frequently equipment is used and the higher the load, the more prone it is to wear, aging, or functional degradation, thus increasing its potential failure risk and requiring earlier maintenance. Using the usage impact coefficient as a positive driver of the maintenance index helps achieve dynamic maintenance management based on actual usage conditions, preventing frequently used resources from being overlooked before their maintenance cycle, thereby improving the overall system security and operational accuracy. This is represented as the normalized fault impact coefficient. As the number of faults occurring in shared resources accumulates during operation, the maintenance index increases accordingly. This direct correlation reflects the direct impact of fault behavior on resource health; that is, the more frequent and concentrated the faults, the more likely the resource is to be in an abnormal or critical state, requiring timely maintenance intervention. By incorporating the fault impact coefficient as a key component of the maintenance index, the system's sensitivity to potential hazards can be enhanced, enabling earlier and more accurate maintenance responses, and improving the reliability and continuity of shared resource operation. The anomaly impact coefficient, after normalization, indicates that an increase in the maintenance index occurs when abnormal behavior of shared resources increases or its frequency rises during operation. This direct correlation reflects the close relationship between potential abnormal states of resources and maintenance needs. Even without explicit failures, persistent anomalies may indicate equipment performance degradation or impending failure. By using the anomaly impact coefficient as a positive driver of the maintenance index, the system can identify minor but persistent anomaly trends in advance, enabling predictive maintenance and improving the foresight of maintenance and system stability. , , This is expressed as the weighted coefficients of the impact coefficient, the failure impact coefficient, and the anomaly impact coefficient, and... , , , The weighting parameters for the maintenance index are obtained through the Analytic Hierarchy Process (AHP), a multi-criteria decision analysis method suitable for weighting and prioritizing multiple influencing factors in complex systems. This method first decomposes the target problem into a hierarchical structure, including a target layer, a criterion layer, and an indicator layer. Then, by constructing a pairwise comparison judgment matrix, the importance of each influencing factor is subjectively assessed pairwise, using a scale of 1 to 9 to represent its relative importance. Next, the judgment matrix is ​​normalized using the eigenvector method to calculate the weight coefficients of each factor. The AHP organically combines expert knowledge with quantitative calculation, possessing characteristics such as clear structure, controllable operation, and interpretable results, and is widely used in areas such as indicator system construction, risk assessment, and resource allocation. In this invention, this method is used to model the importance of the usage influence coefficient, fault influence coefficient, and anomaly influence coefficient, thereby obtaining the weight parameters required for calculating the maintenance index.

[0041] In this embodiment, it should be specifically explained that the steps for obtaining the fault impact coefficient are as follows:

[0042] During the detection period, the time point of each fault is obtained, and the time interval between each fault is calculated by interpolating the time points of adjacent faults.

[0043] The difference between every two consecutive time intervals is calculated to obtain the interval change value. The mean of each interval change value is calculated to obtain the degree of fault growth.

[0044] By calculating the changing trend of time intervals between adjacent failures to assess the degree of failure growth, this method can dynamically reflect whether equipment has entered a failure-intensive phase, thus identifying the deterioration process of equipment operating status earlier and more accurately than simply counting the number of failures. This method has good timeliness and trend sensitivity, effectively avoiding the interference of occasional anomalies on the judgment results and improving the accuracy and foresight of maintenance decisions. Furthermore, the calculation logic of this indicator is clear, the data is easily obtained, and it is convenient to use in conjunction with other health assessment parameters, making it an important supporting tool for achieving intelligent and predictive operation and maintenance management.

[0045] The fault impact coefficient is calculated based on the degree of fault growth and the number of fault records. The specific steps for obtaining this coefficient are as follows:

[0046] ;

[0047] In the formula, This is expressed as the failure impact coefficient. This is represented by the number of fault records. This indicates the degree of fault growth. This is represented as a constant term to balance the denominator and prevent it from being zero; for example, it can be 10. The formula combines the number of failures with the degree of failure growth to construct a failure impact coefficient that reflects both failure frequency and failure density trends. With The decrease in the frequency of failures (i.e., failures become more frequent) increases the impact factor, thus amplifying the contribution of the number of failures to the impact factor; conversely, when the frequency decreases, the impact factor increases. When the number of faults is relatively large (indicating that faults are gradually becoming less frequent), this term tends to be smaller, weakening the impact of the number of faults. In this way, the formula can effectively distinguish between high-frequency and dense faults and low-frequency or scattered faults, concisely reflecting the deterioration trend of equipment health status, and helping to improve the system's accuracy in identifying maintenance needs.

[0048] In this embodiment, it should be specifically explained that the steps for obtaining the anomaly impact coefficient are as follows:

[0049] Set the normal operating temperature range, obtain the highest operating temperature value, count the number of times the resource operating temperature is greater than or equal to the highest operating temperature value during the detection period, and record it as the number of temperature anomalies. Obtain the duration of each temperature anomaly, and use the dynamic deviation scoring method to evaluate the number of temperature anomalies and the duration of temperature anomalies to obtain the degree of temperature anomaly.

[0050] Dynamic deviation scoring is a statistical analysis method for comprehensively assessing the severity of temperature anomalies. Based on the actual performance of each temperature anomaly, it quantifies and scores each segment of the anomaly from three dimensions: deviation magnitude, duration, and temperature change rate. A time-weighted mechanism is introduced to give higher weight to recent anomalies. By weighted and integrating the scores of all anomaly segments, dynamic deviation scoring can comprehensively reflect the intensity, persistence, and risk trend of temperature anomalies. Compared with traditional methods, it is more timely and discriminative, making it suitable for temperature risk assessment in intelligent equipment operation monitoring and predictive maintenance scenarios.

[0051] Set the normal operating energy consumption range, obtain the highest operating energy consumption value, count the number of times the resource operating energy consumption value is greater than or equal to the highest operating energy consumption value during the detection period, record it as the energy consumption anomaly count, and obtain the duration of each energy consumption anomaly.

[0052] The average duration of each energy consumption anomaly is calculated by taking the mean of the duration of each anomaly. The standard deviation of the energy consumption anomaly duration is then calculated using the standard deviation formula based on the average duration of the anomaly and the duration of each anomaly.

[0053] The degree of energy consumption anomaly is calculated based on the number of energy consumption anomalies, the average duration of energy consumption anomalies, and the standard deviation of the duration of energy consumption anomalies. The specific steps for obtaining this information are as follows:

[0054] ;

[0055] In the formula, This indicates the degree of energy consumption anomaly. This is represented by the number of energy consumption anomalies. Using a logarithmic function on the number of anomalies can mitigate the exponential amplification effect caused by the increase in anomaly frequency, making the impact of the number of anomalies gradually increase, which is more in line with the actual perception of risk. This is expressed as the average duration of energy consumption anomalies. It is expressed as the standard deviation of the duration of energy consumption anomalies. For the persistence of anomalies, the average duration is combined with its standard deviation to construct a "strength + fluctuation" composite term, which reflects the continuous pressure and instability of anomalies. The overall formula adopts a multiplicative structure to couple the frequency factor and the persistence factor, so that frequent and persistent anomalies have a stronger weight in the exponential value, thereby achieving sensitive capture and reasonable quantification of complex energy consumption anomaly patterns and improving the intelligent perception capability of the system.

[0056] The abnormality impact coefficient is calculated based on the degree of temperature anomaly and the degree of energy consumption anomaly. The specific steps for obtaining the coefficient are as follows:

[0057] ;

[0058] In the formula, This is expressed as the abnormal impact coefficient. This indicates the degree of temperature anomaly. This indicates the degree of energy consumption anomaly.

[0059] In this embodiment, it should be specifically explained that the steps for evaluating the degree of temperature anomaly using the dynamic deviation scoring method to assess the number of temperature anomalies and the duration of temperature anomalies are as follows:

[0060] For each abnormality, calculate the average absolute deviation of the temperature from the normal operating temperature range and record it as the amplitude term;

[0061] For each anomaly, calculate the maximum rate of increase or decrease of the anomaly in that segment, i.e. the first-order difference with the largest change, and denote it as the burst term.

[0062] Obtain the duration of the anomaly segment, denoted as the duration item. Calculate the deviation score of the anomaly segment based on the amplitude item, the burst item, and the duration item. The specific steps are as follows:

[0063] ;

[0064] In the formula, This indicates the deviation score for the abnormal segment. Represented as the amplitude term, Represented as a continuous term, Indicated as an emergency item;

[0065] The weight of each anomaly is calculated using the anomaly time series step-by-step method. The specific steps are as follows:

[0066] ;

[0067] In the formula Let represent the weight of the i-th anomaly segment, where i represents the i-th anomaly segment. This is expressed as the number of temperature anomalies. The formula indicates that anomalies closer to the current time are more important and are given higher weight.

[0068] The degree of temperature anomaly is calculated based on the number of temperature anomalies, deviation scores, and weights. The specific steps for obtaining this information are as follows:

[0069] ;

[0070] In the formula, This represents the degree of temperature anomaly in the i-th segment. This is expressed as the number of temperature anomalies. Let the weight of the i-th anomaly be denoted as . The deviation score represents the i-th segment of the anomaly. The algorithm characterizes the severity of each temperature anomaly by integrating three core dimensions: the magnitude of temperature deviation, duration, and rate of change, and introduces dynamic time weights to improve the responsiveness to recent anomalies. Compared to traditional mean or variance methods, this method is more sensitive and stable in handling continuous, frequent, highly fluctuating, or sudden anomalies, and can effectively support intelligent maintenance decisions.

[0071] The anomaly time-series step-by-step method is an analysis approach based on dynamic weighting according to time sequence, primarily used to assess the impact of anomalous events at different time periods on overall risk. This method numbers all anomalous events within the detection period in chronological order and assigns weights based on their relative position on the timeline, typically adopting the principle of "the closer to the present, the higher the weight" to enhance sensitivity to recent anomalies. Through this method, the system can strengthen its response to recent high-risk behaviors while retaining accumulated information on historical anomalies, achieving a time-stratified expression of the impact of anomalous events. It is suitable for constructing dynamic evolution models or time-sensitive anomaly assessment mechanisms.

[0072] In this embodiment, it should be specifically explained that the maintenance judgment step based on the maintenance index is as follows:

[0073] The maintenance index is compared with the maintenance threshold. If the maintenance index is greater than or equal to the maintenance threshold, maintenance is deemed necessary; if the maintenance index is less than the maintenance threshold, maintenance is deemed unnecessary. The maintenance threshold is obtained using an adaptive thresholding method, an algorithm that automatically adjusts the judgment threshold based on dynamic data distribution characteristics. This method continuously collects historical status information of the target resource over different time periods, and combines this with differences in resource type, usage scenario, and operating cycle to automatically generate a maintenance threshold that matches the current status distribution. It can flexibly adjust the threshold standard according to the actual operating conditions of the equipment, avoiding misjudgments caused by fixed thresholds being set too high or too low, improving the accuracy and responsiveness of resource maintenance, and exhibiting good adaptability and scalability.

[0074] Step 6: If maintenance is required, perform maintenance on the shared resources and adjust the maintenance interval according to the maintenance index.

[0075] In this embodiment, it should be specifically explained that the steps for adjusting the maintenance interval based on the maintenance index are as follows:

[0076] Obtain the initial maintenance interval, calculate the ratio of the maintenance threshold to the maintenance index, and obtain the adjustment factor;

[0077] The actual maintenance interval is obtained by multiplying the adjustment factor by the initial maintenance interval.

[0078] By calculating an adjustment factor based on the ratio of a maintenance threshold to a maintenance index, and then adjusting the initial maintenance interval accordingly, dynamic optimization of the maintenance rhythm can be achieved based on the operating status of shared resources. This method allows for shorter maintenance intervals for high-risk equipment due to high maintenance indices, enabling timely identification of potential problems; while extending maintenance cycles for stable equipment with low maintenance indices, avoiding resource waste. Compared to the traditional method of fixed maintenance cycles, this mechanism intelligently adjusts the maintenance frequency based on the actual health status of the resources, achieving a dual improvement in maintenance efficiency and resource utilization, and enhancing the accuracy and adaptability of shared resource management.

[0079] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0080] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A configuration method for a smart shared community, characterized in that, Includes the following steps; Step 1: Assign a unique resource code to each shared resource within the community and store the resource code in the Internet of Things; Step 2: Construct a digital map of shared resources based on resource codes; Step 3: Categorize users to obtain user categories, and configure permissions for each user category with different shared resources based on the shared resource digital map; Step 4: During the process of users using shared resources, usage records are collected in real time according to the resource code. The usage records include the operating status of the shared resources and the user category, and each usage record is stored in the Internet of Things. Step 5: Real-time statistics of the status information of each shared resource, evaluation of the status information to obtain the maintenance index, and maintenance judgment based on the maintenance index; Step 6: If maintenance is determined to be required, perform maintenance on the shared resources and adjust the maintenance interval according to the maintenance index; The steps for obtaining the maintenance index are as follows: Set a detection period, obtain the usage frequency and total runtime of shared resources within the detection period, and calculate the usage impact coefficient based on the usage frequency and total runtime. Obtain fault information of shared resources within the detection period. The fault information includes the number of fault records and the time interval between each fault record. Calculate the fault impact coefficient based on the fault information of the shared resources. Obtain the operating information of shared resources during the detection period, including the operating temperature and energy consumption of the shared resources, and calculate the anomaly impact coefficient based on the operating information of the shared resources; The impact coefficient, failure impact coefficient, and anomaly impact coefficient will be normalized. The maintenance index will then be calculated based on these normalized impact coefficients. The specific steps for obtaining the index are as follows: ； In the formula, This is expressed as a maintenance index. This is expressed as the normalized impact coefficient. This is expressed as the normalized fault impact coefficient. This is expressed as the normalized anomaly impact coefficient. , , This is expressed as the weighting coefficients of the impact coefficient, the fault impact coefficient, and the anomaly impact coefficient. The steps for obtaining the anomaly impact coefficient are as follows: Set the normal operating temperature range, obtain the highest operating temperature value, count the number of times the resource operating temperature is greater than or equal to the highest operating temperature value during the detection period, and record it as the number of temperature anomalies. Obtain the duration of each temperature anomaly, and use the dynamic deviation scoring method to evaluate the number of temperature anomalies and the duration of temperature anomalies to obtain the degree of temperature anomaly. Set the normal operating energy consumption range, obtain the highest operating energy consumption value, count the number of times the resource operating energy consumption value is greater than or equal to the highest operating energy consumption value during the detection period, record it as the energy consumption anomaly count, and obtain the duration of each energy consumption anomaly. The average duration of each energy consumption anomaly is calculated by taking the mean of the duration of each anomaly. The standard deviation of the energy consumption anomaly duration is then calculated using the standard deviation formula based on the average duration of the anomaly and the duration of each anomaly. The degree of energy consumption anomaly is calculated based on the number of energy consumption anomalies, the average duration of energy consumption anomalies, and the standard deviation of the duration of energy consumption anomalies. The specific steps for obtaining this information are as follows: ； In the formula, This indicates the degree of energy consumption anomaly. This is represented by the number of times energy consumption is abnormal. This is expressed as the average duration of energy consumption anomalies. Expressed as the standard deviation of the duration of energy consumption anomalies; The abnormality impact coefficient is calculated based on the degree of temperature anomaly and the degree of energy consumption anomaly.

2. The configuration method for a smart shared community according to claim 1, characterized in that: The steps for constructing a shared resource digital map based on resource codes are as follows: Extract resource code information of all registered shared resources from the Internet of Things, and obtain the installation location information of each resource, including GPS coordinates, building number, floor location and room number; Based on the resource code, the function type, the functional module to which the resource belongs, and the real-time usage status are further extracted; Based on the extracted location information, a three-dimensional community spatial coordinate system is constructed in the digital platform, and the resource instance corresponding to each resource code is mapped to a specific location in the coordinate system. Resources are grouped according to their functional categories and location information to generate a shared digital map of resources.

3. The configuration method for a smart shared community according to claim 1, characterized in that, The steps for obtaining the fault impact coefficient are as follows: During the detection period, the time point of each fault is obtained, and the time interval between each fault is calculated by interpolating the time points of adjacent faults. The difference between every two consecutive time intervals is calculated to obtain the interval change value. The mean of each interval change value is calculated to obtain the degree of fault growth. The fault impact coefficient is calculated based on the degree of fault growth and the number of fault records. The specific steps for obtaining this coefficient are as follows: ； In the formula, This is expressed as the failure impact coefficient. This is represented by the number of fault records. This indicates the degree of fault growth. It is represented as a constant term.

4. The configuration method for a smart shared community according to claim 1, characterized in that: The steps for evaluating the degree of temperature anomalies using the dynamic deviation scoring method to assess the number and duration of temperature anomalies are as follows: For each abnormality, calculate the average absolute deviation of the temperature from the normal operating temperature range and record it as the amplitude term; For each anomaly segment, calculate the maximum rate of increase or decrease of that anomaly segment, and denote it as the burst term; Obtain the duration of the anomaly segment, denoted as the duration item. Calculate the deviation score of the anomaly segment based on the amplitude item, the burst item, and the duration item. The specific steps are as follows: ； In the formula, This indicates the deviation score for the abnormal segment. Represented as the amplitude term, Represented as a continuous term, Indicated as an emergency item; The weight of each anomaly is calculated using the anomaly time series step-by-step method. The specific steps are as follows: ； In the formula Let represent the weight of the i-th anomaly segment, where i represents the i-th anomaly segment. This is expressed as the number of temperature anomalies; The degree of temperature anomaly is calculated based on the number of temperature anomalies, deviation scores, and weights. The specific steps for obtaining this information are as follows: ； In the formula, This represents the degree of temperature anomaly in the i-th segment. This is expressed as the number of temperature anomalies. Let the weight of the i-th anomaly be denoted as . This represents the deviation score for the i-th abnormal segment.

5. The configuration method for a smart shared community according to claim 1, characterized in that: The maintenance judgment steps based on the maintenance index are as follows: The maintenance index is compared with the maintenance threshold. If the maintenance index is greater than or equal to the maintenance threshold, it is determined that maintenance is required; if the maintenance index is less than the maintenance threshold, it is determined that maintenance is not required.

6. The configuration method for a smart shared community according to claim 1, characterized in that: The steps for adjusting maintenance intervals based on the maintenance index are as follows: Obtain the initial maintenance interval, calculate the ratio of the maintenance threshold to the maintenance index, and obtain the adjustment factor; The actual maintenance interval is obtained by multiplying the adjustment factor by the initial maintenance interval.

Images