Smart public service infrastructure management method based on QR code and RFID

Abstract

The present invention discloses a smart public service infrastructure management method based on QR code and RFID, which specifically relates to the technical field of public service infrastructure management, and includes the following steps: generating a unique coding group of a facility and binding it to a QR code and an RFID, collecting, integrating, and analyzing data, and then applying fuzzy logic to determine the maintenance type based on the analysis results; constructing a public service infrastructure management model and obtaining a comprehensive loss index of the target public service infrastructure; dividing all public service infrastructure into high-loss infrastructure and low-loss infrastructure based on the comprehensive loss index; maintaining or replacing and upgrading the high-loss infrastructure according to a preset strategy; the present invention can evaluate the comprehensive loss of a facility, and can optimize resource allocation based on the loss index and maintenance requirements of the facility, concentrate energy and resources on key maintenance of high-loss facilities, and improve the overall operating efficiency of public service facilities.

Description

Technical Field

[0001] The present invention relates to the technical field of public service infrastructure management, and more specifically, to a smart public service infrastructure management method based on QR code and RFID. Background Art

[0002] With the acceleration of urbanization, the number and scale of public service infrastructure (such as bus stops, nursing homes, community service stations, etc.) are constantly expanding. The management and maintenance of these infrastructures have become increasingly complex and important. At present, facility maintenance decisions mainly rely on experience and regular inspections, which cannot detect and handle facility failures in a timely manner, resulting in untimely facility maintenance and updates. In addition, due to the lack of data support, it is difficult to formulate scientific maintenance strategies, which easily leads to waste of resources and shortened facility life. Therefore, a smart public service infrastructure management method based on QR code and RFID is proposed. Summary of the invention

[0003] To achieve the above object, the present invention provides the following technical solutions:

[0004] The smart public service infrastructure management method based on QR code and RFID includes the following steps:

[0005] A unique coding group is generated based on the facility type and geographical location of the public service infrastructure, and an information collection sensor is embedded in the public service infrastructure. The unique coding group of the target public service infrastructure and all the information collection sensors are bound to the QR code and RFID of the public service infrastructure;

[0006] Collect, integrate and analyze the data from QR codes and RFID, then apply fuzzy logic to determine the maintenance type based on the analysis results;

[0007] Construct a public service infrastructure management model and transmit the maintenance type judgment results to the public service infrastructure management model to obtain the comprehensive loss index of the target public service infrastructure;

[0008] All public service infrastructure is divided into high-loss infrastructure and low-loss infrastructure according to the comprehensive loss index;

[0009] Maintain or replace and upgrade high-loss infrastructure according to preset strategies.

[0010] In a preferred embodiment, generating a unique code group based on the facility type and geographical location of the public service infrastructure refers to:

[0011] Determine the types of public service facilities, assign a type code to each type, use a geographic information system to determine the geographical location of each facility, assign a unique area code to the geographical location of each facility, and then combine the facility type code and the geographical location code to generate a unique facility code, i.e., a unique code group.

[0012] In a preferred embodiment, collecting, integrating and analyzing the data of the QR code and RFID refers to:

[0013] Regularly obtain QR code and RFID data, store the collected QR code and RFID data in a unified database and establish data association to ensure that the QR code and RFID data of each facility can correctly correspond, then fuse them to obtain fused data, perform volatility analysis on the target data types in the fused data, obtain the volatility index corresponding to all data types, and obtain the volatility index set after summarizing.

[0014] In a preferred embodiment, the fusion logic of the fused data is as follows: fused data = joint data set based on facility coding ∪ timestamp sorting.

[0015] In a preferred embodiment, the volatility index is obtained by the following logic:

[0016] Determine the moving average window size k and obtain the time series data {x 1 , x 2 , ..., x n}, n represents the total length of the time series data, and the average value of each window is calculated to obtain the calculated sequence data {MA 1 , M.A. 2 , ..., MA n-k+1}, n-k+1 represents the number of valid data points, starting from the kth data point and ending at the nth data point, a total of n-k+1 data points, calculate the absolute deviation |x at each time point t -MA t |, x t is the original data value, MA t It represents the moving average at time point t, taking the average of all absolute deviations, and the formula is: MADD is the volatility index of the target data type.

[0017] In a preferred embodiment, applying fuzzy logic to determine the maintenance type based on the analysis results means:

[0018] The volatility index of data types is converted into a format suitable for fuzzy logic analysis. All converted data are defined as input variables and divided into different fuzzy sets. The maintenance type is defined as the output variable and divided into fuzzy sets. Fuzzy rules are formulated to describe the maintenance requirements under different combinations of data types. According to fuzzy logic, reasoning is performed to determine the maintenance type corresponding to the maintenance requirements of each facility.

[0019] In a preferred embodiment, building a public service infrastructure management model refers to:

[0020] Multiple characteristic indicators that affect facility losses of the target type of public service infrastructure are obtained and used as input data for the public service infrastructure management model. The comprehensive loss index of this type of public service infrastructure is used as the output data of the public service infrastructure management model. When the preset performance evaluation indicators meet the expected requirements, the construction of the public service infrastructure management model is completed.

[0021] In a preferred embodiment, dividing all public service infrastructure into high-loss infrastructure and low-loss infrastructure according to the comprehensive loss index means:

[0022] Obtain the comprehensive loss index of the target public service infrastructure, and then compare the comprehensive loss index with the preset loss threshold. If the comprehensive loss index is greater than the preset loss threshold, it is classified as high-loss infrastructure. If the comprehensive loss index is not greater than the preset loss threshold, it is classified as low-loss infrastructure.

[0023] In a preferred embodiment, maintenance or replacement and upgrading of high-loss infrastructure according to a preset strategy refers to: obtaining m consecutive output results of the target public service infrastructure in the public service infrastructure management model, namely the comprehensive loss index, and then calculating the coefficient of variation. If the coefficient of variation is greater than the preset variation threshold, replacement and upgrading are performed; if the coefficient of variation is not greater than the preset variation threshold, maintenance is performed. The coefficient of variation is calculated in the following way: first, the average value μ of the m comprehensive loss indices and the standard deviation σ of the m comprehensive loss indices are obtained, and then the ratio of the standard deviation σ to the average value μ is calculated to obtain the coefficient of variation.

[0024] Technical effects and advantages of the present invention:

[0025] The present invention can realize the information and refined management of public service infrastructure through the combination of QR code and RFID technology. Automated data collection and processing reduces manual operations and improves management efficiency. RFID technology can realize real-time monitoring of facilities, detect and repair faults in advance, and reduce the inconvenience and losses caused by equipment failures. Combined with QR code technology, detailed information of facilities can be obtained at any time, improving the accuracy and timeliness of maintenance.

[0026] In the present invention, QR codes and RFID provide double protection to ensure that each facility has a unique identification, and all maintenance and update records can be traced, which improves the transparency and traceability of facility management. Through data analysis and fuzzy logic, the comprehensive loss of the facility can be evaluated, and the maintenance type can be intelligently determined to formulate more scientific maintenance and update strategies, extend the service life of the facility, and reduce operating costs. The present invention can optimize resource allocation according to the loss index and maintenance needs of the facility, concentrate energy and resources on key maintenance of high-loss facilities, and improve the overall operating efficiency of public service facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0028] Figure 1 This is a schematic diagram of the smart public service infrastructure management method based on QR code and RFID in the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Example 1

[0031] The smart public service infrastructure management method based on QR code and RFID includes the following steps:

[0032] A unique coding group is generated based on the facility type and geographical location of the public service infrastructure, and information collection sensors are embedded in the public service infrastructure. The unique coding group of the target public service infrastructure and all information collection sensors are bound to the QR code and RFID of the public service infrastructure; the data of the QR code and RFID are collected, integrated and analyzed, and then fuzzy logic is applied to determine the maintenance type based on the analysis results; a public service infrastructure management model is constructed and the maintenance type determination results are transmitted to the public service infrastructure management model to obtain the comprehensive loss index of the target public service infrastructure; all public service infrastructure are divided into high-loss infrastructure and low-loss infrastructure based on the comprehensive loss index; high-loss infrastructure is maintained or replaced and upgraded according to preset strategies.

[0033] It should be noted that:

[0034] QR code is a two-dimensional barcode technology, which contains a pattern composed of black and white squares, and can be quickly read and parsed by a camera or scanner. Low cost: the cost of making a QR code is very low, and only needs to be printed or generated as an electronic picture; Ease of use: users can read the QR code information by using a mobile phone camera or scanning device; Flexibility: It can store various types of information, such as URLs, text, contact information, etc. RFID is a wireless radio frequency identification technology that uses electromagnetic fields or radio waves to identify and track tags attached to objects. No contact: It can be read at a long distance without physical contact. Fast speed: The reading speed is fast and multiple tags can be read at the same time. Durability: Tags can be embedded or attached to various materials and are suitable for various environments. In the present invention, QR codes and RFID are used complementary in the management of smart public service infrastructure, and RFID inspection robots or drones can be used for automatic inspection, read facility RFID tags, and check facility status and other information. For example, the sensor module monitors the current, voltage, and light parameters of street lights in real time, and then the sensor transmits the monitoring data to the RFID tag. Similarly, the information collected each time can be transmitted to the information contained in the QR code tag, and the corresponding information is displayed when the QR code is scanned, read and parsed.

[0035] The unique code groups generated based on the type and geographic location of public service infrastructure refer to:

[0036] Determine the type of public service facilities, assign a type code to each type, use a geographic information system to determine the geographical location of each facility, assign a unique area code to the geographical location of each facility, and then combine the facility type code and the geographical location code to generate a unique facility code, that is, a unique code group, such as facility type coding: code different types of public service facilities, such as: street lights (L), trash cans (G), public toilets (T), etc.; geographical location coding: use a geographic information system (GIS) to encode the geographical location of each facility, for example: use longitude and latitude, area numbers, etc.; unique code generation: combine the facility type code and the geographical location code to generate a unique facility code, for example: L-001-20230611 (street light, the first facility located in a certain place, coding date).

[0037] Collecting, integrating and analyzing QR code and RFID data refers to:

[0038] Regularly obtain QR code and RFID data, store the collected QR code and RFID data in a unified database and establish data association to ensure that the QR code and RFID data of each facility can correspond correctly, then fuse them to obtain fused data, perform volatility analysis on the target data types in the fused data, obtain the volatility index corresponding to all data types, and obtain the volatility index set after summarizing;Embed sensors in facilities to monitor the status of equipment (such as current, voltage, temperature, etc.) in real time. The sensor data is transmitted to the central management system through wireless communication modules (such as Wi-Fi, LoRa, Zigbee, etc.). The QR code is used for manual data acquisition and verification during maintenance and management. The sensor monitors the status of the equipment in real time and sends the data to the background system through the wireless communication module. The background system analyzes the data, generates a real-time monitoring report and collects the information in the QR code. In addition, maintenance personnel or inspection robots can be regularly arranged to scan the QR code on the facility to obtain the latest equipment status. The real-time data update obtained by QR code scanning is illustrated as follows: in public toilets and street lights QR codes and sensors are installed on facilities such as garbage cans. The sensors monitor the status of the equipment in real time and upload the data to the background system. The QR codes are used for on-site scanning by maintenance personnel to quickly obtain and verify equipment information. Each urban facility (such as traffic lights, surveillance cameras, and charging piles) is installed with QR codes and IoT devices. IoT devices monitor the status of facilities in real time and transmit the data to the urban management platform. Managers can quickly access detailed information and monitoring data of the facilities on the management platform by scanning QR codes. Without changing the passive characteristics of the QR codes, real-time monitoring and management of facilities can be achieved, thereby improving the management efficiency and service quality of public service facilities. Similarly, without changing the passive characteristics of RFID, Based on the characteristics of the facilities, real-time monitoring and management of facilities can be achieved, and the management efficiency and service quality of public service facilities can be improved. For example, an RFID tag is embedded in each facility for automatic, remote reading and real-time monitoring. RFID readers are deployed near the facilities to automatically read the information of RFID tags. RFID readers read RFID tags on facilities regularly or on demand to automatically collect real-time status data. By embedding information collection sensors in each public service facility, data (such as usage status, current, voltage, temperature, humidity, etc.) is collected in real time, and the data read by the QR code and RFID is fused according to the facility code to form complete facility status data. The fusion of fused data The logic is as follows: Fusion data = joint data set based on facility code ∪ timestamp sorting, establish a database, store QR code and RFID data in the database, ensure that the database can efficiently store and retrieve data, store the collected QR code and RFID data in a unified database, establish data association, ensure that the QR code and RFID data of each facility can correspond correctly, collect data from various data sources, such as QR code scanning, RFID reading, sensor data, collect data from various data sources (such as QR code scanning, RFID reading, sensor data), form an initial data set, group data by facility code, sort the joint data set of each facility by timestamp, and ensure that all data sources use a unified facility coding specification. ;

[0039] The volatility index is obtained through the following logic:

[0040] First determine the target data type, select the specific data type that needs to be analyzed for volatility, then perform data extraction, extract the time series data of the target data type from the joint data set, determine the window size k of the moving average, and obtain the time series data {x 1 , x 2 , ..., x n}, n represents the total length of the time series data, and the average value of each window is calculated to obtain the calculated sequence data {MA 1 , M.A. 2 , ..., MA n-k+1}, n-k+1 represents the number of valid data points, starting from the kth data point and ending at the nth data point, a total of n-k+1 data points, calculate the absolute deviation |x at each time point t -MA t |, x t is the original data value, MA t It represents the moving average at time point t, taking the average of all absolute deviations, and the formula is: MADD is the volatility index of the target data type. When calculating the moving average, the first k data points cannot be calculated because there are not enough data points before them. For example, if k=3, then when calculating the moving average: the first moving average MA3 uses the 1st, 2nd, and 3rd data points; the second moving average MA4 uses the 2nd, 3rd, and 4th data points, and so on, until the last moving average MAn. Therefore, the number of time points that can calculate the moving average is from k to n, that is, n - k+1 time points represent the number of valid data points, that is, the number of time points in the time series that can calculate the moving average absolute deviation. The volatility index reflects the stability of the data. A high volatility index indicates that the data fluctuates greatly, the operation status of the facility is unstable, and there may be many problems or potential risks. A low volatility index indicates that the data fluctuates less, the operation status of the facility is relatively stable, and the performance and usage are relatively reliable. A high volatility index may indicate that the facility has a potential risk of failure or has already encountered problems, which requires special attention and the adoption of corresponding preventive or corrective maintenance measures. The volatility index can serve as an important basis for formulating maintenance strategies. Facilities with high volatility indexes may require more frequent inspections and maintenance, while facilities with low volatility indexes may only require routine preventive maintenance.

[0041] Applying fuzzy logic to determine the maintenance type based on the analysis results means:

[0042] The volatility index of the data type is converted into a format suitable for fuzzy logic analysis, all the converted data are defined as input variables and divided into different fuzzy sets, the maintenance type is defined as the output variable and divided into fuzzy sets, fuzzy rules are formulated to describe the maintenance requirements under different data type combinations, and the maintenance type corresponding to the maintenance requirements of each facility is determined based on fuzzy logic. For example: for each type of data (such as failure rate, frequency of use, environmental conditions, etc.), its volatility index is calculated. Assuming that the volatility index of each type of data has been obtained, the volatility index is normalized so that its value is between [0,1] to facilitate subsequent fuzzy logic analysis. The normalized volatility index is defined as the input variable of the fuzzy logic system. Assuming there are three input variables: failure rate volatility index F, frequency of use volatility index U, and environmental condition volatility index E, each input variable is divided into different fuzzy sets, for example, divided into three fuzzy sets of "Low", "Medium", and "High". Fuzzy set of failure rate F: {Low, Medium, High}; fuzzy set of usage frequency U: {Low, Medium, High}; fuzzy set of environmental condition E: {Low, Medium, High}; define maintenance type as the output variable of the fuzzy logic system, and divide the maintenance type into different fuzzy sets, such as "preventive maintenance (PM)", "corrective maintenance (CM)", and "replacement maintenance (RU)". The fuzzy set of maintenance type is: {PM, CM, RU}. Fuzzy rules are formulated according to the combination of different data types. For example: Rule 1: If F is Low and U is Low and E is Low, then the maintenance type is PM; Rule 2: If F is High or U is High, then the maintenance type is CM; Rule 3: If F is High and U is High and E is High, then the maintenance type is RU. The actual value of each input variable is converted into the membership of the fuzzy set. For example, for a certain value of the failure rate F, its membership of "Low", "Medium" and "High" is calculated by the fuzzy membership function. Fuzzy rules are applied for reasoning, and the membership of the output variable is calculated according to the membership of the input variable. The membership of the fuzzy output variable is converted into a specific maintenance type. For example, the weighted average method is used to obtain the final maintenance type. For example, the final maintenance type membership is calculated by the weighted average method: PM membership = 0.2, CM membership = 0.5, RU membership = 0.1. Since CM has the highest membership, it is finally determined that the facility needs corrective maintenance (CM). Through the above steps, fuzzy logic can be used to systematically analyze the volatility data of the facility and accurately determine the maintenance needs and types of each facility. This method combines the analysis of multiple types of data, which helps to formulate more accurate maintenance strategies and improve the efficiency and effectiveness of facility management.

[0043] Building a public service infrastructure management model means:

[0044] A plurality of characteristic indicators that affect the loss of facilities of the target type of public service infrastructure are obtained and used as the input data of the public service infrastructure management model. The comprehensive loss index of the public service infrastructure of this type is used as the output data of the public service infrastructure management model. When the preset performance evaluation indicators meet the expected requirements, the construction of the public service infrastructure management model is completed. It should be noted that the plurality of characteristic indicators include the ratio of the total number of each maintenance type to the total number of all maintenance types under the preset time window. The types of public service infrastructure that need to be managed and monitored (such as street lights, bus stops, water supply systems, etc.) are clearly defined. The plurality of characteristic indicator data that affect the loss of facilities are collected. These indicators include but are not limited to the frequency of use of facilities, the failure rate, and the ratio of the total number of each maintenance type to the total number of all maintenance types under the preset time window. The time window for data analysis is set such as one month, one quarter, and one year. The time window can be adjusted accordingly for different types of public service infrastructure. All characteristic indicators are vectorized, an input data set X is constructed, and the comprehensive loss index Y is set as the output data of the model. A suitable machine learning algorithm is selected to construct a management model such as a neural network. The input data set X and the output data Y are input into the machine learning algorithm for training. The public service infrastructure management model is constructed. The model performance is evaluated by preset performance evaluation indicators such as mean square error to ensure that the model prediction results meet the expected requirements.

[0045] All public service infrastructure is divided into high-loss infrastructure and low-loss infrastructure according to the comprehensive loss index:

[0046] Obtain the comprehensive loss index of the target public service infrastructure, and then compare the comprehensive loss index with the preset loss threshold. If the comprehensive loss index is greater than the preset loss threshold, it is classified as high-loss infrastructure. If the comprehensive loss index is not greater than the preset loss threshold, it is classified as low-loss infrastructure. The loss thresholds of different public service infrastructures can be adjusted accordingly to adapt to the type of public service infrastructure.

[0047] Maintaining or replacing and upgrading high-loss infrastructure according to preset strategies means: obtaining m consecutive output results of the target public service infrastructure in the public service infrastructure management model, namely the comprehensive loss index, and then calculating the coefficient of variation. If the coefficient of variation is greater than the preset variation threshold, replacement and upgrading are performed; if the coefficient of variation is not greater than the preset variation threshold, maintenance is performed. The coefficient of variation is calculated in the following way: first, the average value μ of the m comprehensive loss indexes and the standard deviation σ of the m comprehensive loss indexes are obtained, and then the ratio of the standard deviation σ to the average value μ is calculated to obtain the coefficient of variation; obtaining m consecutive output results of the target facility from the public service infrastructure management model, namely the comprehensive loss index CI i , CI 1 , CI 2 , ..., CI m , calculate the average value μ of m comprehensive loss indices: Calculate the standard deviation σ of m comprehensive loss indices: Calculate the ratio of the standard deviation σ to the mean μ to get the coefficient of variation CV: For facilities with high coefficients of variation, replacement or upgrade is carried out according to preset strategies to ensure the stability and reliability of the facilities. For facilities with low coefficients of variation, routine maintenance is carried out to extend the service life of the facilities and maintain their performance. The comprehensive loss index of high-loss infrastructure can be effectively analyzed to determine whether replacement, upgrade or maintenance is needed, thereby ensuring the reliability and stability of public service infrastructure.

[0048] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0049] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0050] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0051] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A smart public service infrastructure management method based on QR code and RFID, characterized in that: The following steps are involved: A unique coding group is generated based on the facility type and geographical location of the public service infrastructure, and an information collection sensor is embedded in the public service infrastructure. The unique coding group of the target public service infrastructure and all the information collection sensors are bound to the QR code and RFID of the public service infrastructure; Collect, integrate and analyze the data from QR codes and RFID, then apply fuzzy logic to determine the maintenance type based on the analysis results; Construct a public service infrastructure management model and transmit the maintenance type judgment results to the public service infrastructure management model to obtain the comprehensive loss index of the target public service infrastructure; All public service infrastructure is divided into high-loss infrastructure and low-loss infrastructure according to the comprehensive loss index; Maintain or replace and upgrade high-loss infrastructure according to preset strategies; The volatility index is obtained through the following logic: Determine the window size k of the moving average and obtain the time series data {x1, x2, ..., x n }, n represents the total length of the time series data, and the average value of each window is calculated to obtain the calculated sequence data {MA1, MA2, ..., MA n-k+1 }, n-k+1 represents the number of valid data points, starting from the kth data point and ending at the nth data point, a total of n-k+1 data points, calculate the absolute deviation |x at each time point t -MA t |, x t is the original data value, MA t It represents the moving average at time point t, taking the average of all absolute deviations, and the formula is: MADD is the volatility index of the target data type; Building a public service infrastructure management model means: A number of characteristic indicators that affect the loss of facilities of the target type of public service infrastructure are obtained and used as input data of the public service infrastructure management model. The comprehensive loss index of the public service infrastructure of this type is used as the output data of the public service infrastructure management model. When the preset performance evaluation indicators meet the expected requirements, the construction of the public service infrastructure management model is completed; All public service infrastructure is divided into high-loss infrastructure and low-loss infrastructure according to the comprehensive loss index: Obtaining the comprehensive loss index of the target public service infrastructure, and then comparing the comprehensive loss index with a preset loss threshold. If the comprehensive loss index is greater than the preset loss threshold, it is classified as a high-loss infrastructure; if the comprehensive loss index is not greater than the preset loss threshold, it is classified as a low-loss infrastructure; Maintaining or replacing and upgrading high-loss infrastructure according to preset strategies means: obtaining m consecutive output results of the target public service infrastructure in the public service infrastructure management model, namely the comprehensive loss index, and then calculating the coefficient of variation. If the coefficient of variation is greater than the preset variation threshold, replacement and upgrading are carried out. If the coefficient of variation is not greater than the preset variation threshold, maintenance is carried out. The coefficient of variation is calculated in the following way: first, the average value μ of the m comprehensive loss indices and the standard deviation σ of the m comprehensive loss indices are obtained, and then the ratio of the standard deviation σ to the average value μ is calculated to obtain the coefficient of variation.

2. The smart public service infrastructure management method based on QR code and RFID according to claim 1 is characterized in that: The unique code groups generated based on the type and geographic location of public service infrastructure refer to: Determine the types of public service facilities, assign a type code to each type, use a geographic information system to determine the geographical location of each facility, assign a unique area code to the geographical location of each facility, and then combine the facility type code and the geographical location code to generate a unique facility code, i.e., a unique code group.

3. The smart public service infrastructure management method based on QR code and RFID according to claim 2 is characterized in that: Collecting, integrating and analyzing QR code and RFID data refers to: Regularly obtain QR code and RFID data, store the collected QR code and RFID data in a unified database and establish data association to ensure that the QR code and RFID data of each facility can correctly correspond, then fuse them to obtain fused data, perform volatility analysis on the target data types in the fused data, obtain the volatility index corresponding to all data types, and obtain the volatility index set after summarizing.

4. The smart public service infrastructure management method based on QR code and RFID according to claim 3 is characterized in that: The fusion logic of fused data is as follows: fused data = joint data set based on facility coding ∪ timestamp sorting.

5. The smart public service infrastructure management method based on QR code and RFID according to claim 4 is characterized in that: Applying fuzzy logic to determine the maintenance type based on the analysis results means: The volatility index of data types is converted into a format suitable for fuzzy logic analysis. All converted data are defined as input variables and divided into different fuzzy sets. The maintenance type is defined as the output variable and divided into fuzzy sets. Fuzzy rules are formulated to describe the maintenance requirements under different combinations of data types. According to fuzzy logic, reasoning is performed to determine the maintenance type corresponding to the maintenance requirements of each facility.

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