Property management method and system based on cloud service

Abstract

The invention provides a property management method and system based on cloud service. Belongs to the technical field of property management. The method comprises the steps that an electric vehicle intelligent parking management infrastructure based on cloud service is constructed, and electric vehicle users and property management personnel are connected with a cloud service platform through mobile terminals; the state information of the parking space is transmitted to an edge device through a sensor, the state information is preliminarily processed through the edge device, and the preliminarily processed state information is transmitted to a cloud service platform; and the cloud service platform stores and processes the received state information, analyzes and judges the parking behavior of the electric vehicle according to a preset rule, and generates a corresponding management suggestion. The state information of the parking space is captured in real time through various sensors installed on the parking space, the state information comprises the occupation condition, the charging state and the like of the parking space, and the real-time performance and the accuracy of data are ensured.

Description

Technical Field

[0001] The present invention proposes a property management method and system based on cloud services, belonging to the technical field of property management. Background Art

[0002] With the rapid advancement of urbanization and the change in people's travel methods, electric vehicles, as an environmentally friendly and convenient means of transportation, have been favored by the general public. However, with the rapid increase in the number of electric vehicles, their parking problems have become increasingly prominent. At present, the phenomenon of electric vehicles being parked randomly is common, which not only destroys the city's appearance and environment, affects the quality of life of citizens, but also brings huge challenges to urban management and safety.

[0003] Traditional property management methods often seem powerless when faced with the problem of electric vehicle parking. On the one hand, traditional property management methods mostly rely on manual inspections and on-site supervision, which are inefficient and difficult to fully cover; on the other hand, due to the lack of effective data collection and analysis methods, it is difficult for property management personnel to accurately judge and promptly intervene in the parking behavior of electric vehicles. Therefore, developing a new property management method to achieve comprehensive and efficient supervision of electric vehicle parking behavior has become an urgent problem to be solved. Summary of the invention

[0004] The present invention provides a cloud service-based property management method and system to solve the problems mentioned in the above background technology:

[0005] The present invention proposes a cloud service-based property management method, the method comprising:

[0006] Build a cloud-based electric vehicle intelligent parking management infrastructure, where electric vehicle users and property management personnel connect to the cloud service platform through mobile terminals;

[0007] The status information of the parking space is transmitted to the edge device through the sensor, the status information is preliminarily processed by the edge device and the preliminarily processed status information is transmitted to the cloud service platform;

[0008] The cloud service platform stores and processes the received status information, analyzes and judges the parking behavior of electric vehicles according to preset rules, and generates corresponding management suggestions;

[0009] The management suggestions are pushed to property management personnel or electric vehicle users through the cloud service platform. After receiving the management suggestions, the management personnel or electric vehicle users will take corresponding measures according to the management suggestions.

[0010] The present invention proposes a cloud service-based property management system, the system comprising:

[0011] Facility construction module: Construct an intelligent parking management infrastructure for electric vehicles based on cloud services. Electric vehicle users and property management personnel are connected to the cloud service platform through mobile terminals;

[0012] Data processing module: Transmit the status information of the parking space to the edge device through sensors. The edge device preliminarily processes the status information and transmits the preliminarily processed status information to the cloud service platform;

[0013] Analysis and judgment module: The cloud service platform stores and processes the received status information, analyzes and judges the parking behavior of electric vehicles according to preset rules, and generates corresponding management suggestions;

[0014] Corresponding processing module: Push the management suggestions to property management personnel or electric vehicle users through the cloud service platform. After receiving the management suggestions, the management personnel or electric vehicle users make corresponding treatments according to the management suggestions.

[0015] Advantages of the present invention: Various sensors installed on the parking space capture the status information of the parking space in real time, including the occupancy situation and charging status of the parking space, ensuring the real-time nature and accuracy of the data. The edge device preliminarily processes the received data, effectively reducing the processing pressure on the cloud platform, improving the response speed and processing efficiency of the entire system; the edge device compresses and encrypts the preliminarily processed data to ensure the security and privacy of the data during the transmission process. The cloud service platform decrypts and decompresses the received data to further ensure the security of the data during storage and processing; the cloud service platform analyzes and judges the parking behavior of electric vehicles according to the received data and generates corresponding management suggestions. Through the machine learning model, the proposed management strategies are simulated and tested and the effects are predicted, making the decision-making more scientific and reasonable; through the real-time monitoring and analysis of the parking space status, the usage of the parking space can be grasped more accurately, the allocation of parking space resources can be optimized, and the waste of resources can be avoided. At the same time, the standardization and management of electric vehicle parking behaviors also contribute to improving the order and environment of the entire property area; by pushing management suggestions to electric vehicle users and property management personnel, both parties can timely understand the usage of the parking space and existing problems, facilitating the adoption of corresponding measures for treatment. This can not only improve the satisfaction and experience of users, but also contribute to improving the efficiency and quality of property management. Description of the Drawings

[0016] Figure 1 It is a flowchart of the method steps of the present invention;

[0017] Figure 2 It is a system module diagram of the present invention. Detailed Embodiments

[0018] In order to more clearly understand the above objects, features, and advantages of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.

[0019] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention. The described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0021] An embodiment of the present invention, as Figure 1 shown, is a property management method based on cloud services, and the method includes:

[0022] S1. Construct an intelligent parking management infrastructure for electric vehicles based on cloud services, and connect electric vehicle users and property management personnel to the cloud service platform through mobile terminals;

[0023] S2. Transmit the status information of the parking spaces to the edge device through sensors, and perform preliminary processing on the status information through the edge device and transmit the preliminarily processed status information to the cloud service platform; multiple sensors are installed in one parking space, one parking point contains multiple parking spaces, and one edge device is deployed at one parking point;

[0024] S3. The cloud service platform stores and processes the received status information, analyzes and judges the parking behavior of electric vehicles according to preset rules, and generates corresponding management suggestions;

[0025] S4. Push the management suggestions to property management personnel or electric vehicle users through the cloud service platform, and the management personnel or electric vehicle users perform corresponding processing according to the management suggestions after receiving the management suggestions.

[0026] The working principle of the above technical solution is as follows: clarify the functional requirements of the electric vehicle intelligent parking management system, including but not limited to functions such as parking space status monitoring, user reservation, vehicle positioning, and charging management; select a suitable cloud service platform provider to ensure meeting the requirements in aspects such as big data storage, high-speed computing, stable communication, and security; deploy multiple sensors at each parking space to detect information such as the occupancy of the parking space and the charging status of electric vehicles in real time; set up an edge device in a parking area, which has the functions of data collection, preliminary processing, and transmission, and can summarize the data of the parking space sensors and upload them to the cloud in a secure manner; develop and configure the cloud service platform, establish a database storage structure, and implement the functions of receiving, storing, querying, and analyzing the parking space status data; develop corresponding mobile applications for property management personnel and electric vehicle users respectively, enabling them to view the parking space status, receive notifications, submit requests, etc. through mobile terminals such as mobile phones in real time; the sensors on the parking space capture the parking space status data in real time and transmit it to the edge device deployed at the corresponding parking point. The edge device has certain data processing and filtering capabilities, and can quickly conduct preliminary processing on the original data, such as eliminating invalid information and performing simple data analysis, and then upload the processed valuable data to the cloud to reduce bandwidth consumption and response latency. After receiving the parking space status information from the edge device, the cloud service platform stores this information in the database and conducts big data analysis through a preset algorithm model. According to the predefined parking management rules, the platform will conduct in-depth mining and intelligent analysis on the parking behaviors of electric vehicle users, such as judging whether there are phenomena such as illegal parking, long-term occupation, and distribution of idle parking spaces. Based on the analysis results, the cloud service platform can automatically generate targeted management suggestions, such as optimizing parking space allocation, prompting users to park reasonably, warning potential safety hazards or resource waste problems. The cloud service platform timely pushes the generated management suggestions to the corresponding property management personnel and / or electric vehicle users, notifying them to take corresponding measures to improve the parking situation or comply with relevant regulations. Property management personnel can adjust on-site management measures according to the suggestions, such as dispatching security personnel for patrol, optimizing the parking space layout, and implementing temporary control. After receiving the suggestions, electric vehicle users can immediately adjust their parking behaviors, such as parking in the recommended idle parking space or following the parking regulations to avoid illegal parking.

[0027] The effects of the above technical solution are as follows: Multiple sensors are arranged on a single parking space to obtain accurate data on the vehicle parking status in all directions, and to monitor the occupancy of the parking space in real time, which can facilitate property management personnel to promptly grasp the overall usage situation of the parking lot. At the same time, by using edge devices to process data locally, the network transmission burden can be reduced, the data processing speed can be increased, and the real-time nature of data reception and processing can be ensured. The cloud service platform can effectively identify idle parking spaces, frequently used parking spaces, and long-term occupied parking spaces, etc., by storing and analyzing a large amount of parking space status information, thereby helping the property to formulate more reasonable parking space allocation strategies and improve the utilization rate of parking resources. Through preset rules and algorithms, the system can automatically analyze the parking behaviors of electric vehicle users, detect irregular behaviors and generate management suggestions, such as warnings for illegal parking and optimization of parking guidance, etc., which improves the refined level of property management. The management suggestions are directly pushed to relevant personnel, enabling property management personnel to take prompt actions, and electric vehicle users can also receive personalized parking guidance and service reminders, enhancing the user experience. Property management personnel and electric vehicle users can interact with the cloud service platform at any time through mobile terminals, without geographical restrictions, which greatly facilitates daily management and user inquiries, and also facilitates quick responses in case of emergencies. By adopting the technical solution based on cloud services, the cost of manual inspections can be significantly reduced, the burden on property staff can be alleviated, and at the same time, since parking-related problems can be foreseen and solved in advance, it also helps to improve the service quality and image of overall property management. The intelligent analysis of electric vehicle parking behaviors helps to prevent phenomena such as illegal occupation and random parking, which is conducive to maintaining the order and safety of the parking lot and promoting the compliance of electric vehicle charging safety management.

[0028] In one embodiment of the present invention, the S2; includes:

[0029] S21. Through various sensors installed on the parking space; such as a parking space occupancy sensor, a charging status sensor, and an environmental monitoring sensor; to capture the status information data of the parking space in real time; such as whether the parking space is occupied, whether the electric vehicle is charging, the charging power, the environmental temperature and humidity, etc.

[0030] S22. The various sensors transmit the collected status information data to the edge device through a communication link;

[0031] S23. The edge device performs preliminary processing on the received status information data and transmits the preliminarily processed status information data to the cloud service platform.

[0032] The working principle of the above technical solution is as follows: Various sensors on the parking space are responsible for real-time monitoring of status information in different dimensions. For example, the parking space occupancy sensor is used to detect whether the parking space is currently occupied by an electric vehicle. When an electric vehicle parks in or drives away from the parking space, the sensor will immediately sense and record the change in the parking space occupancy status. The charging status sensor monitors the charging status of the electric vehicle on the parking space, including whether charging is in progress, charging current, voltage, and charging power and other relevant information, so as to understand the usage status of the charging pile and the charging process of the electric vehicle. The environmental monitoring sensor collects environmental data around the parking space, such as temperature, humidity, air quality and other indicators. The status information data collected by various sensors is transmitted to the edge device deployed at each parking point through a wireless or wired communication link (such as Internet of Things technologies like LoRa, Wi-Fi, Bluetooth, Zigbee, etc.). After receiving the raw data sent from each sensor, the edge device (such as an edge gateway or an embedded server) performs preliminary data cleaning, screening, integration and other processing tasks, eliminates invalid or incorrect data, compresses or converts the format of the valid data to make it more suitable for transmission over the network. The status information data that has been preliminarily processed is sent by the edge device to the cloud service platform, and the cloud platform stores this data.

[0033] The effects of the above technical solution are as follows: By using various sensors on the parking space to capture the parking space status information in real time, the system can grasp the occupancy situation of the parking space, the charging status of the electric vehicle and the environmental conditions in real time, ensuring that the parking lot management personnel can make a quick response and improving the resource scheduling efficiency. Using the edge device to perform preliminary processing on the data collected by the sensors can reduce the transmission of useless data, relieve the receiving and processing burden of the cloud service platform. At the same time, due to the characteristics of edge computing for in-situ processing, the latency is greatly reduced, and the real-time performance and response speed of the entire system are improved.

[0034] In one embodiment of the present invention, S23; includes:

[0035] S231. The edge device receives the status information data from various sensors on the parking space through a pre-defined communication protocol and performs format conversion on the received status information data;

[0036] S232. Store the format-converted status information data into different computing units of the edge device according to the parking space label, and the different computing units partition the received status information data according to different sensor types; the parking space label is also the parking space number;

[0037] S233. Sort the status information data within the partition according to the timestamp of data collection; perform data integrity check and validity detection on the status information data in different partitions; check whether the received data packet is complete and whether there are packet loss or packet error phenomena; verify the rationality of each item of status information, such as checking whether the parking space occupancy status is within the expected range and whether the charging power exceeds the safety limit, etc.;

[0038] S234. Preprocess the status information data in different partitions to obtain the preprocessed data, and integrate the data collected by different sensors of the same parking space after preprocessing;

[0039] S235. Use the built-in algorithm to perform a preliminary analysis on the integrated data to determine whether there are problems with the parking space, where the problems include long-term occupancy and abnormal charging;

[0040] S236. Compress the status information data of different parking spaces after preliminary processing respectively, and sort the data according to the importance level; that is, if there are problems with the parking space, it is important, and if there are no problems, it is less important;

[0041] S237. Use the encryption algorithm to encrypt the compressed status information data, and transmit the encrypted status information data to the cloud service platform according to the priority through the multi-channel transmission protocol.

[0042] The working principle of the above technical solution is as follows: The edge device communicates with various sensors on the parking space through a predefined communication protocol and receives the status information data sent by them. The received data is usually in the original format, and the edge device will convert its format to make it conform to the unified data processing standard; according to the parking space marker (parking space number), the converted data is stored in different computing units of the edge device, and each computing unit is responsible for processing the data of a certain type of sensor. The stored data is sorted according to the timestamp of data collection to ensure that the data is arranged in chronological order for later analysis; the integrity of the status information data stored in each computing unit is checked to verify whether the data packet is complete without omission, and phenomena such as packet loss and packet error are excluded. At the same time, the rationality of each status information is verified, for example, it is confirmed that the parking space occupancy status conforms to the actual situation, the charging power is within the normal and safe range, etc.; the data collected by different sensors for the same parking space is integrated to form the comprehensive status information of the parking space. Then, the integrated data is preprocessed, including operations such as noise removal, filtering, and data cleaning, to provide a high-quality data basis for subsequent analysis; the built-in algorithm is used to perform preliminary analysis on the integrated and preprocessed data to identify whether there are potential problems in the parking space, such as long-term occupancy, abnormal charging, etc. This step helps to detect problems in a timely manner and take countermeasures in advance; according to whether there are the above problems in the parking space, the status information data of different parking spaces is compressed and corresponding priorities are assigned, and the data of the problem parking spaces is uploaded first. This can not only save network transmission resources but also ensure the priority transmission of important information; on the basis of data compression, the encryption algorithm is used to encrypt the status information data to ensure the security and privacy protection of the data during the transmission process. Finally, through the multi-channel transmission protocol, the encrypted status information data is sent to the cloud service platform according to the priority for further analysis and processing by the cloud and the generation of management suggestions.

[0043] The effects of the above technical solution are as follows: The edge device receives and processes the data of the parking space sensors in real time, avoiding the delay caused by all data being transmitted to the cloud for processing, and improving the response speed and processing efficiency of the entire system. At the same time, data format conversion and partition storage enable the edge device to efficiently process different types of sensor data in a targeted manner. Data is partitioned and stored according to the parking space number (parking space marker), ensuring the orderliness and pertinence of the data, which is conducive to subsequent data retrieval and analysis. At the same time, integrating the data collected by different sensors of the same parking space helps to build a comprehensive view of the parking space status, providing a solid foundation for subsequent analysis and decision-making. Sorting by timestamp ensures the chronological order of the data. Data integrity checks and validity detections can timely detect and correct errors during data transmission, ensuring the accuracy and integrity of the data. The rationality verification mechanism can prevent the inflow of abnormal status information at the source, such as real-time monitoring of the parking space occupancy status and charging power to timely detect abnormal occupancy and charging risks. The built-in algorithm performs a preliminary analysis on the integrated data, which can quickly identify problems such as long-term occupancy and abnormal charging of the parking space, realizing fault warning and resource scheduling optimization. This process helps property management personnel to timely discover and solve problems, improving the utilization rate of parking spaces and charging safety. Compressing the state information data after preliminary processing reduces the bandwidth resources required for transmission. At the same time, priority sorting is performed according to the importance of the data, and the data of parking spaces with anomalies is preferentially transmitted, ensuring the timely transmission of key information. Encrypting the compressed data and securely transmitting the data to the cloud service platform through a multi-channel transmission protocol effectively protects data privacy and security and increases the data transmission efficiency.

[0044] In an embodiment of the present invention, the S237; includes:

[0045] After the data compression is completed, the edge device first selects an encryption algorithm to encrypt the compressed state information data;

[0046] The edge device configures a multi-channel transmission protocol, and the multi-channel transmission protocol adopts a load balancing and failover mechanism, dynamically selecting a transmission channel according to the network condition and data priority;

[0047] After the encryption is completed, the edge device packs the encrypted data according to a preset data packet size and generates a unique identifier (such as a serial number or timestamp) for each data packet;

[0048] According to the priority of the data, the edge device distributes the packed data to the corresponding transmission channels; the data with high priority (such as the parking space status information with long-term occupancy or abnormal charging) will be distributed to the high-bandwidth and low-latency channels. At the same time, the edge device monitors the load conditions of each channel and dynamically adjusts the data distribution strategy;

[0049] During the data transmission process, the edge device continuously monitors the transmission status of each channel; once a fault (such as network interruption, data transmission error, etc.) is detected in a certain channel, it immediately triggers the fault detection mechanism and attempts to re-transmit the data through other available channels; at the same time, the edge device records the fault information;

[0050] After receiving the data packet, the cloud service platform sends a confirmation message to the edge device to confirm the successful reception of the data;

[0051] If the edge device does not receive the confirmation message within the preset time, it determines that the data packet is lost and triggers the re-transmission mechanism; at the same time, the edge device records the number of re-transmissions and the data packet loss rate;

[0052] Both the edge device and the cloud service platform record the log information during the data transmission process.

[0053] The working principle of the above technical solution is as follows: After data compression is completed, the edge device first selects a suitable encryption algorithm to encrypt the compressed status information data. Considering data security and transmission efficiency, strong encryption algorithms such as the Advanced Encryption Standard (AES) are used to ensure the privacy and integrity of data during transmission. The application process of the encryption algorithm includes generating an encryption key, performing an encryption operation on the compressed data, and generating an encrypted data block. The edge device configures a multi-channel transmission protocol to ensure that data can be transmitted to the cloud service platform efficiently and reliably. The multi-channel transmission protocol adopts a load balancing and failover mechanism, and dynamically selects a transmission channel according to the network condition and data priority. The protocol configuration includes setting channel priorities, configuring load balancing strategies, and setting up fault detection and recovery mechanisms. After encryption is completed, the edge device packs the encrypted data according to a preset packet size and generates a unique identifier (such as a sequence number or timestamp) for each packet. Packet marking helps the cloud service platform identify the order and integrity of packets during reception, facilitating subsequent data decryption and decompression. According to the data priority, the edge device distributes the packed data to the corresponding transmission channels. High-priority data (such as the status information of parking spaces with long-term occupancy or abnormal charging) will be assigned to high-bandwidth and low-latency channels to ensure the timely transmission and processing of data. At the same time, the edge device monitors the load conditions of each channel and dynamically adjusts the data distribution strategy to optimize resource utilization and transmission efficiency. During data transmission, the edge device continuously monitors the transmission status of each channel. Once a fault (such as network interruption, data transmission error, etc.) is detected in a certain channel, the fault detection mechanism is immediately triggered, and an attempt is made to re-transmit the data through other available channels. At the same time, the edge device records the fault information for subsequent fault analysis and network optimization. After receiving the packet, the cloud service platform sends a confirmation message to the edge device to confirm the successful reception of the data. If the edge device does not receive the confirmation message within the preset time, it is determined that the packet is lost, and the retransmission mechanism is triggered. The retransmission mechanism adopts an exponential backoff strategy to reduce network congestion and data transmission latency. At the same time, the edge device records the number of retransmissions and the packet loss rate for subsequent network performance evaluation and optimization. Both the edge device and the cloud service platform record the log information during data transmission, including data transmission time, channel selection, packet size, priority, confirmation / retransmission status, etc. By analyzing the transmission logs, the efficiency, reliability, and resource utilization of data transmission can be evaluated, providing data support for subsequent network optimization and fault troubleshooting.

[0054] The effects of the above technical solutions are as follows: By encrypting the compressed status information data on the edge device, the security and privacy protection of the data during transmission are ensured. This can prevent sensitive information from being intercepted and interpreted by unauthorized third parties; configuring a multi-channel transmission protocol and adopting a load balancing and failover mechanism can dynamically select the best transmission channel according to the network conditions and data priorities. This can not only improve the efficiency of data transmission but also ensure the reliable transmission of data when the network is unstable or some channels fail; packing the encrypted data according to the preset packet size and generating a unique identifier (such as a serial number or timestamp) for each packet helps to reorganize and verify the data at the receiving end, ensuring the integrity and sequentiality of the data; according to the priority of the data (for example, the status information of parking spaces with long-term occupancy or abnormal charging), the packed data is allocated to the corresponding high-bandwidth and low-latency transmission channels. This ensures that critical data can be transmitted to the cloud service platform quickly and stably; once a fault is detected in a certain channel, the fault detection mechanism is immediately triggered, and an attempt is made to retransmit the data through other available channels. At the same time, the fault information is recorded for subsequent analysis and optimization. If the edge device does not receive an acknowledgment message within the preset time, it is determined that the packet is lost, and the retransmission mechanism is triggered to further ensure the reliability of data transmission; both the edge device and the cloud service platform record the log information during the data transmission process. These logs are of great value for monitoring the operation status of the entire system, troubleshooting problems, and performance tuning.

[0055] In one embodiment of the present invention, the S3; includes:

[0056] S31. Decrypt and decompress the received status information data in the order of receipt by the cloud service platform, and divide the cloud service platform into multiple storage spaces according to the parking space markers;

[0057] S32. The storage space receives and updates the real-time status of each parking space, performs persistent storage on the real-time status data, and performs intelligent analysis on the real-time status of each parking space; the intelligent analysis includes: Behavior pattern analysis: Based on time series analysis, statistics on the parking habits, occupancy duration, charging frequency, etc. of electric vehicle users are carried out to discover regular behavior patterns. Anomaly detection: According to the preset rules, monitor whether there are abnormal behaviors such as illegal parking, overtime occupancy, and illegal charging. Correlation analysis: Combine the mutual influence between different parking spaces to analyze the overall utilization efficiency of the parking lot and potential optimization space.

[0058] S33. Generate management strategies and optimization suggestions for electric vehicle parking behaviors according to the preset business rules and analysis results;

[0059] S34. Simulate and test the proposed management strategies and optimization suggestions and predict the effects through a machine learning model.

[0060] The working principle of the above technical solution is as follows: The cloud service platform first decrypts and decompresses the status information data encrypted and compressed by the edge device for transmission to restore the original data content. The cloud service platform is divided into multiple logical storage spaces according to the parking space markings. The storage space receives and stores the latest status information of each parking space in real time to ensure the persistent storage of data and form a continuous historical record library. Based on time series data, statistical methods are used to analyze the behavior characteristics of electric vehicle users, such as parking frequency, average occupancy duration, charging habits, etc., to identify and establish user behavior patterns for precise management and services. Based on preset business rules, the system automatically monitors the usage of parking spaces and identifies abnormal behaviors that do not comply with regulations, such as illegal parking, overtime occupancy, and illegal charging. Through the comprehensive analysis of the status of all parking spaces in the entire parking lot, the possible usage correlations between parking spaces are explored, and the overall space utilization rate and fluidity of the parking lot are evaluated and optimized. According to the results obtained from the above analysis, the cloud service platform generates specific management strategies and optimization suggestions for electric vehicle parking behaviors according to preset business rules. These suggestions may involve adjusting parking fees, optimizing the parking space layout, formulating personalized service plans, etc. Further, by applying machine learning models, the proposed management strategies and optimization suggestions are simulated and tested to predict the possible actual effects after implementation, thereby helping managers select the optimal solution to improve the operation efficiency and service quality of the parking lot.

[0061] The effects of the above technical solutions are as follows: By decrypting and decompressing the status information in the receiving order, the security and integrity of the data are ensured, and at the same time, the data processing efficiency is improved, enabling the real-time status information to quickly and accurately enter the subsequent processing stage; dividing the cloud service platform into multiple storage spaces according to the parking space markers helps to achieve structured data management, improve the data retrieval speed, and is also conducive to resource isolation, enhancing the stability and scalability of the system; the real-time status monitoring and persistent storage mechanism ensure the complete recording of the parking space status data, providing comprehensive basic data for subsequent intelligent analysis. The intelligent analysis module can deeply understand the behavior habits of electric vehicle users, such as parking time distribution, occupancy duration, charging requirements, etc., which is beneficial to the reasonable planning and allocation of resources, improving the user experience, and can also be used as the basis for customized services and preferential policies. The anomaly detection function can promptly detect bad behaviors such as illegal parking, overtime occupancy, and illegal charging, effectively reducing potential safety hazards, maintaining good parking order, and protecting the rights and interests of other users. Through correlation analysis, it helps to understand the operation status of the entire parking lot, identify bottleneck areas in parking space utilization rate, optimize parking space configuration and traffic guidance, thereby improving the overall utilization efficiency and revenue. Combining the preset business rules and the results of intelligent analysis, the system can automatically generate targeted management strategies and optimization suggestions to help parking lot managers make scientific decisions. By using machine learning models to simulate and test the proposed management strategies, it is possible to evaluate their feasibility and expected effects in the actual environment in advance, reduce implementation risks, and ensure more accurate and effective resource allocation and policy adjustment.

[0062] In one embodiment of the present invention, the S31 includes:

[0063] The cloud service platform first receives data packets from edge devices, and each data packet contains encrypted status information data and a unique identifier;

[0064] After receiving, the cloud service platform performs a preliminary verification on the data packets; after the verification passes, the cloud service platform decrypts the encrypted status information data according to the preset encryption algorithm and key;

[0065] The decrypted data is still in a compressed state, and the cloud service platform uses the corresponding decompression algorithm to perform a decompression operation on the compressed data;

[0066] According to the parking space markers, the cloud service platform divides the storage space into multiple independent areas, and each area corresponds to a specific parking space; the decompressed status information data is classified into the corresponding storage space according to the parking space markers;

[0067] The cloud service platform uses a distributed storage system to perform persistent storage on the classified status information data; at the same time, the cloud service platform regularly performs data backup operations to copy the status information data to a remote storage medium or a backup server;

[0068] The cloud service platform creates an index for the stored status information data; the index is based on keyword fields such as parking space markers and timestamps, supporting fast positioning and retrieval of status information for a specific time period or specific parking space.

[0069] During the storage process, the cloud service platform continuously monitors the quality and integrity of the data.

[0070] For the detected data quality problems, the cloud service platform will trigger an early warning mechanism, notify relevant personnel for handling, and take corresponding corrective measures.

[0071] The working principle of the above technical solution is as follows: The cloud service platform first receives data packets from edge devices. Each data packet contains encrypted status information data and a unique identifier (such as a serial number or timestamp). After receiving, the cloud service platform conducts a preliminary verification of the data packets, including verifying the integrity of the data packets (such as checksum checking), confirming the order of the data packets (through the identifier), and checking whether the data packets conform to the expected format and size. After passing the verification, the cloud service platform decrypts the encrypted status information data according to the preset encryption algorithm and key. The decryption process ensures the privacy and security of the data while restoring the original form of the data. The decrypted data is still in a compressed state, and the cloud service platform uses corresponding decompression algorithms (such as gzip, bzip2, etc.) to perform decompression operations on the compressed data to restore the integrity and readability of the data. According to the parking space markings (such as parking space numbers, location information, etc.), the cloud service platform divides the storage space into multiple independent areas, each corresponding to a specific parking space. The decompressed status information data is classified into the corresponding storage space according to the parking space markings for subsequent data management and analysis. The cloud service platform uses a distributed storage system (such as Hadoop HDFS, Ceph, etc.) to perform persistent storage on the classified status information data. The storage system has high availability and fault tolerance to ensure that the data remains complete and accessible in the face of hardware failures or network interruptions. At the same time, the cloud service platform regularly performs data backup operations, copying the status information data to remote storage media or backup servers to prevent data loss or damage. To improve the data retrieval efficiency, the cloud service platform creates indexes for the stored status information data. The indexes are based on keyword fields such as parking space markings and timestamps, supporting fast location and retrieval of the status information for a specific time period or a specific parking space. In addition, the cloud service platform adopts data compression and index optimization technologies (such as columnar storage, data sharding, etc.) to further reduce the storage cost and improve the data retrieval speed. During the storage process, the cloud service platform continuously monitors the quality and integrity of the data. By regularly performing data verification and consistency checks, it ensures that the stored status information data is accurate and timely discovers and repairs potential data errors or inconsistencies. For the discovered data quality problems, the cloud service platform will trigger an early warning mechanism to notify relevant personnel for processing and take corresponding corrective measures.

[0072] The effects of the above technical solution are as follows: The cloud service platform receives data packets from edge devices. Each data packet contains encrypted status information data and a unique identifier, ensuring the identifiability and integrity of the data during transmission; performs preliminary verification on the received data packets to ensure the validity and correctness of the data packets, avoiding the impact of invalid or incorrect data on subsequent processing; the cloud service platform decrypts the encrypted status information data according to the preset encryption algorithm and key, ensuring the security and privacy protection of the data; the decrypted data is still in a compressed state, and the cloud service platform performs decompression operations using corresponding decompression algorithms, improving the efficiency and speed of data processing; according to the parking space markings, the cloud service platform divides the storage space into multiple independent areas, each area corresponding to a specific parking space, facilitating data management and retrieval; the decompressed status information data is classified into the corresponding storage space according to the parking space markings, improving the organization and structure of the data; the cloud service platform adopts a distributed storage system to perform persistent storage on the classified status information data, ensuring the reliability and availability of the data; at the same time, the cloud service platform regularly performs data backup operations, copying the status information data to a remote storage medium or backup server, further enhancing the security and disaster tolerance of the data; the cloud service platform creates an index for the stored status information data, based on keywords such as parking space markings and timestamps, supporting fast positioning and retrieval of the status information of a specific time period or a specific parking space, improving the efficiency and accuracy of data query; during the storage process, the cloud service platform continuously monitors the quality and integrity of the data, promptly discovers and processes data quality problems, ensuring the accuracy and reliability of the data; for the discovered data quality problems, the cloud service platform will trigger an early warning mechanism, notify relevant personnel for processing, and take corresponding corrective measures, further improving the level of data quality management.

[0073] In one embodiment of the present invention, the S34 includes:

[0074] S341. Convert the management strategy to be adopted into a form that can be input into the model as an input variable of the model, and use the machine learning model to conduct simulation experiments on different management strategies to predict the response and changes of the system under the new strategy;

[0075] S342. Analyze the expected effects under different strategies based on the results output by the model, such as the improvement degree of parking space utilization rate, the change in user satisfaction, the amount of operating cost savings, etc.

[0076] S343. Based on the simulation test results and effect evaluation, optimize and adjust the original strategy, and re-enter the optimized strategy into the model for simulation and effect prediction; until a satisfactory optimization goal is achieved.

[0077] The working principle of the above technical solution is as follows: Convert the management strategy to be verified or optimized into a quantitative or qualitative parameter form, making it an input variable acceptable to the machine learning model. Use the trained machine learning model to simulate various possible management strategy scenarios and predict the future state changes of the system under different strategies. The model may utilize historical data to simulate real-world scenarios and infer the change trends and values of multiple key indicators such as the parking space utilization rate, user satisfaction, and operating costs in the parking lot after the implementation of new strategies. Conduct a detailed analysis of the prediction results output by the model, compare the advantages and disadvantages of the effects corresponding to different strategies, and find the optimal or near-optimal solution. The evaluation indicators may include but are not limited to the increase in the parking space utilization rate, the change in the number of user complaints or satisfaction evaluations, and the degree of savings in operating costs. According to the results of the simulation test and effect prediction, revise and optimize the existing strategy. Continuously input the optimized strategy into the machine learning model again for a new round of simulation and prediction, forming a closed-loop feedback process until the optimal management strategy that meets the preset optimization goals (such as maximizing the parking space utilization rate, achieving the highest user satisfaction, and minimizing the operating costs, etc.) is found.

[0078] The effects of the above technical solution are as follows: By converting the management strategy into an input variable of the machine learning model, the system can utilize the powerful learning and prediction capabilities of the model to conduct simulation experiments on different strategies and predict the actual responses and changes of the system after the implementation of new strategies. This method is more scientific and rigorous compared to traditional empiricism-based decision-making and can help managers make more accurate and effective decisions; According to the model prediction results, analyzing the changes in factors such as the parking space utilization rate, user satisfaction, and operating costs under different strategies can help managers understand and select those management strategies that can maximize the parking space utilization rate, improve user satisfaction, and reduce costs, thereby optimizing the allocation of parking lot resources and enhancing the overall operating efficiency; By conducting simulation tests first, the possible consequences of different management strategies can be foreseen before formal implementation, thereby reducing the trial-and-error costs in actual operations, avoiding the adverse effects of blind decision-making, and simultaneously taking preventive and response measures against possible risks in advance; This technical solution allows managers to iteratively optimize the original strategy based on the model prediction, forming a continuous improvement cycle process until the ideal optimization goal is achieved. This can ensure that the management strategy always keeps up with the times and adapts to the ever-changing market demands and user behaviors; By introducing machine learning into property management, it promotes the transformation from the traditional passive management mode to an active, intelligent, and data-driven direction, enhancing the technological content and modernization level of property management.

[0079] An embodiment of the present invention, as Figure 2 shown, is a property management system based on cloud services, and the system includes:

[0080] Facility construction module: Construct the intelligent parking management infrastructure for electric vehicles based on cloud services. Electric vehicle users and property management personnel connect to the cloud service platform through mobile terminals;

[0081] Data processing module: Transmit the status information of the parking spaces to the edge device through sensors, and perform preliminary processing on the status information through the edge device and transmit the preliminarily processed status information to the cloud service platform; Multiple sensors are installed in one parking space, one parking point contains multiple parking spaces, and one edge device is deployed at one parking point

[0082] Analysis and judgment module: The cloud service platform stores and processes the received status information, analyzes and judges the parking behavior of electric vehicles according to preset rules, and generates corresponding management suggestions;

[0083] Corresponding processing module: Push the management suggestions to the property management personnel or electric vehicle users through the cloud service platform. After receiving the management suggestions, the management personnel or electric vehicle users perform corresponding processing according to the management suggestions.

[0084] The working principle of the above technical solution is as follows: clarify the functional requirements of the intelligent parking management system for electric vehicles, including but not limited to functions such as parking space status monitoring, user reservation, vehicle positioning, and charging management; select a suitable cloud service platform provider to ensure meeting the requirements in aspects such as big data storage, high-speed computing, stable communication, and security; deploy multiple sensors at each parking space to detect information such as the occupancy of the parking space and the charging status of electric vehicles in real time; set up an edge device in a parking area, which has the functions of data collection, preliminary processing, and transmission, and can aggregate the data of the parking space sensors and upload them to the cloud in a secure manner; develop and configure the cloud service platform, establish a database storage structure, and implement the functions of receiving, storing, querying, and analyzing the parking space status data; develop corresponding mobile applications for property management personnel and electric vehicle users respectively, enabling them to view the parking space status, receive notifications, submit requests, etc. through mobile terminals such as mobile phones in real time; the sensors on the parking space capture the parking space status data in real time and transmit it to the edge device deployed at the corresponding parking point. The edge device has a certain data processing and filtering ability, and can quickly perform preliminary processing on the original data, such as eliminating invalid information, performing simple data analysis, and then uploading the processed valuable data to the cloud to reduce bandwidth consumption and response latency. After receiving the parking space status information from the edge device, the cloud service platform stores this information in the database and performs big data analysis through a preset algorithm model. According to the predefined parking management rules, the platform will conduct in-depth mining and intelligent analysis of the parking behaviors of electric vehicle users, such as judging whether there are phenomena such as illegal parking, long-term occupancy, and distribution of idle parking spaces. Based on the analysis results, the cloud service platform can automatically generate targeted management suggestions, such as optimizing parking space allocation, prompting users to park reasonably, warning of potential safety hazards or resource waste problems. The cloud service platform timely pushes the generated management suggestions to the corresponding property management personnel and / or electric vehicle users, notifying them to take corresponding measures to improve the parking situation or comply with relevant regulations. Property management personnel can adjust on-site management measures according to the suggestions, such as dispatching security personnel for patrol, optimizing the parking space layout, and implementing temporary control. After receiving the suggestions, electric vehicle users can immediately adjust their parking behaviors, such as parking in the recommended idle parking space or following the parking regulations to avoid illegal parking.

[0085] The effects of the above technical solution are as follows: Multiple sensors are arranged on one parking space to obtain accurate data on the vehicle parking status in all directions, and the occupancy of the parking space can be monitored in real time, which is convenient for property management personnel to promptly grasp the overall usage of the parking lot. At the same time, by using edge devices to process data nearby, the network transmission burden can be reduced, the data processing speed can be increased, and the real-time nature of data reception and processing can be ensured. The cloud service platform can effectively identify idle parking spaces, frequently used parking spaces, and long-term occupied parking spaces, etc., by storing and analyzing a large amount of parking space status information, so as to help the property develop a more reasonable parking space allocation strategy and improve the utilization rate of parking resources. Through preset rules and algorithms, the system can automatically analyze the parking behaviors of electric vehicle users, discover irregular behaviors and generate management suggestions, such as warnings for illegal parking, optimizing parking guidance, etc., which improves the refined level of property management. The management suggestions are directly pushed to relevant personnel, enabling property management personnel to take actions quickly, and electric vehicle users can also receive personalized parking guidance and service reminders, enhancing the user experience. Property management personnel and electric vehicle users can interact with the cloud service platform at any time through mobile terminals, without geographical restrictions, which greatly facilitates daily management and user queries, and is also convenient for quick response in case of emergencies. By adopting the technical solution based on cloud services, the cost of manual inspections can be significantly reduced, the burden on property staff can be alleviated, and at the same time, since parking-related problems can be foreseen and solved in advance, it also helps to improve the service quality and image of overall property management. The intelligent analysis of electric vehicle parking behaviors helps to prevent phenomena such as illegal occupation and random parking, which is conducive to maintaining the order and safety of the parking lot and promoting the compliance of electric vehicle charging safety management.

[0086] In one embodiment of the present invention, the data processing module includes:

[0087] Data acquisition module: Through various sensors installed on the parking space, such as parking space occupancy sensors, charging status sensors, and environmental monitoring sensors, it captures the status information data of the parking space in real time, such as whether the parking space is occupied, whether the electric vehicle is charging, charging power, environmental temperature and humidity, etc.

[0088] Data transmission module: The various sensors transmit the collected status information data to the edge device through a communication link.

[0089] Initial processing module: The edge device performs initial processing on the received status information data and transmits the initially processed status information data to the cloud service platform.

[0090] The working principle of the above technical solution is as follows: Various sensors on the parking space are responsible for real-time monitoring of status information in different dimensions. For example, the parking space occupancy sensor is used to detect whether the parking space is currently occupied by an electric vehicle. When an electric vehicle parks in or drives away from the parking space, the sensor will immediately sense and record the change in the parking space occupancy status. The charging status sensor monitors the charging status of the electric vehicle on the parking space, including whether charging is in progress, charging current, voltage, and charging power and other relevant information, so as to understand the usage status of the charging pile and the charging process of the electric vehicle. The environmental monitoring sensor collects environmental data around the parking space, such as temperature, humidity, air quality and other indicators. The status information data collected by various sensors is transmitted to the edge device deployed at each parking point through a wireless or wired communication link (such as Internet of Things technologies like LoRa, Wi-Fi, Bluetooth, Zigbee, etc.). After receiving the raw data sent from each sensor, the edge device (such as an edge gateway or an embedded server) performs preliminary data cleaning, screening, integration and other processing tasks, eliminates invalid or incorrect data, compresses or converts the format of the valid data to make it more suitable for transmission over the network. The status information data that has been preliminarily processed is sent by the edge device to the cloud service platform, and the cloud platform stores this data.

[0091] The effects of the above technical solution are as follows: By using various sensors on the parking space to capture the parking space status information in real time, the system can grasp the occupancy situation of the parking space, the charging status of the electric vehicle and the environmental conditions in real time, ensuring that the parking lot management personnel can make a quick response and improving the resource scheduling efficiency. Using the edge device to perform preliminary processing on the data collected by the sensors can reduce the transmission of useless data, relieve the receiving and processing burden of the cloud service platform. At the same time, due to the characteristics of edge computing for in-situ processing, the latency is greatly reduced, and the real-time performance and response speed of the entire system are improved.

[0092] In one embodiment of the present invention, the preliminary processing module includes:

[0093] Format conversion module: The edge device receives the status information data from various sensors on the parking space through a pre-defined communication protocol and performs format conversion on the received status information data.

[0094] Partitioned storage module: According to the parking space label, the status information data after format conversion is stored in different computing units of the edge device. The different computing units partition the received status information data according to different sensor types. The parking space label is also the parking space number.

[0095] Data verification module: Sort the status information data in the partition according to the time stamp of data collection; Check the data integrity and validity of the status information data in different partitions; Check whether the received data packet is complete and whether there are packet loss or packet error phenomena; Verify the rationality of each item of status information, such as checking whether the parking space occupancy status is within the expected range and whether the charging power exceeds the safety limit, etc.;

[0096] Data preprocessing module: Preprocess the status information data in different partitions to obtain the preprocessed data, and integrate the data collected by different sensors of the same parking space after preprocessing;

[0097] Analysis and judgment module: Use the built-in algorithm to preliminarily analyze the integrated data to judge whether there are problems with the parking space, and the problems include long-term occupancy and abnormal charging;

[0098] Data compression module; Compress the status information data of different parking spaces after preliminary processing respectively, and sort the data according to the importance level; That is, if there are problems with the parking space, it is important, and if there are no problems, it is less important;

[0099] Data encryption module: Use the encryption algorithm to encrypt the compressed status information data, and transmit the encrypted status information data to the cloud service platform according to the priority through the multi-channel transmission protocol.

[0100] The working principle of the above technical solution is as follows: The edge device communicates with various sensors on the parking space through a predefined communication protocol and receives the status information data sent by them. The received data is usually in the original format, and the edge device will convert its format to make it adapt to the unified data processing standard; according to the parking space mark (parking space number), the data after format conversion is stored in different computing units of the edge device, and each computing unit is responsible for processing the data of a certain type of sensor. The stored data is sorted according to the time stamp of data collection to ensure that the data is arranged in chronological order for later analysis; the integrity of the status information data stored in each computing unit is checked to verify whether the data packet is complete and free of loss or error, and phenomena such as packet loss and wrong packets are excluded. At the same time, the rationality of each status information is verified, for example, it is confirmed that the parking space occupancy status conforms to the actual situation, and the charging power is within the normal and safe range, etc.; the data collected by different sensors for the same parking space is integrated to form the comprehensive status information of the parking space. Then, the integrated data is preprocessed, including operations such as noise removal, filtering, and data cleaning, to provide a high-quality data basis for subsequent analysis; the built-in algorithm is used to perform preliminary analysis on the integrated and preprocessed data to identify whether there are potential problems in the parking space, such as long-term occupancy, abnormal charging, etc. This step helps to detect problems in a timely manner and take countermeasures in advance; according to whether there are the above problems in the parking space, the status information data of different parking spaces is compressed and corresponding priorities are assigned, and the data of the problem parking spaces is uploaded first. This can not only save network transmission resources but also ensure the priority transmission of important information; on the basis of data compression, the encryption algorithm is used to encrypt the status information data to ensure the security and privacy protection of the data during transmission. Finally, through the multi-channel transmission protocol, the encrypted status information data is sent to the cloud service platform according to the priority for further analysis and generation of management suggestions by the cloud.

[0101] The effects of the above technical solutions are as follows: The edge device receives and processes the data of the parking space sensors in real time, avoiding the delay caused by transmitting all data to the cloud for processing, and improving the response speed and processing efficiency of the entire system. At the same time, data format conversion and partition storage enable the edge device to efficiently process different types of sensor data in a targeted manner. Data partition storage based on the parking space number (parking space marker) ensures the orderliness and pertinence of the data, facilitating subsequent data retrieval and analysis. At the same time, integrating the data collected by different sensors of the same parking space helps to construct a comprehensive view of the parking space status, providing a solid foundation for subsequent analysis and decision-making. Sorting by timestamp ensures the chronological order of the data. Data integrity checking and validity detection can timely detect and correct errors in the data transmission process, ensuring the accuracy and integrity of the data. The rationality verification mechanism can prevent the inflow of abnormal status information at the source, such as real-time monitoring of the parking space occupancy status and charging power, and timely detecting abnormal occupancy and charging risks. The built-in algorithm conducts preliminary analysis on the integrated data, which can quickly identify problems such as long-term occupancy and abnormal charging of the parking space, realizing fault warning and optimization of resource scheduling. This process helps property management personnel to timely discover and solve problems, improving the utilization rate of parking spaces and charging safety. Compressing the preliminarily processed status information data reduces the bandwidth resources required for transmission. At the same time, priority sorting is performed according to the importance of the data, and the data of parking spaces with anomalies is preferentially transmitted, ensuring the timely transmission of key information. Encrypting the compressed data and securely transmitting the data to the cloud service platform through a multi-channel transmission protocol effectively protects data privacy and security and increases the data transmission efficiency.

[0102] In one embodiment of the present invention, the analysis and judgment module; includes:

[0103] Decryption and decompression module: Decrypt and decompress the received status information data in the order of reception by the cloud service platform, and divide the cloud service platform into multiple storage spaces according to the parking space marker;

[0104] Data storage module: The storage space receives and updates the real-time status of each parking space, persistently stores the real-time status data, and conducts intelligent analysis on the real-time status of each parking space; The intelligent analysis includes: Behavioral pattern analysis: Based on time series analysis, statistically analyze the parking habits, occupancy duration, charging frequency, etc. of electric vehicle users to discover regular behavioral patterns. Anomaly detection: Monitor whether there are abnormal behaviors such as illegal parking, overtime occupancy, and illegal charging according to preset rules. Correlation analysis: Combine the mutual influence between different parking spaces to analyze the utilization efficiency of the overall parking lot and potential optimization space.

[0105] Suggestion Generation Module: Generate management strategies and optimization suggestions for electric vehicle parking behaviors according to preset business rules and analysis results;

[0106] Effect Evaluation Module: Conduct simulation tests and effect predictions on the proposed management strategies and optimization suggestions through machine learning models.

[0107] The working principle of the above technical solution is as follows: The cloud service platform first decrypts and decompresses the status information data received from the edge device after encryption and compression transmission to restore the original data content. The cloud service platform is divided into multiple logical storage spaces according to the parking space markings, and the storage spaces receive and store the latest status information of each parking space in real time to ensure the persistent storage of data and form a continuous historical record library; Based on time series data, statistical methods are used to analyze the behavior characteristics of electric vehicle users, such as parking frequency, average occupancy duration, charging habits, etc., to identify and establish user behavior patterns for precise management and services; Based on preset business rules, the system automatically monitors the usage of parking spaces and identifies abnormal behaviors that do not comply with regulations, such as illegal parking, overtime occupancy, and illegal charging; Through the comprehensive analysis of the status of all parking spaces in the entire parking lot, explore the possible usage correlations between parking spaces, evaluate and optimize the overall space utilization rate and liquidity of the parking lot; According to the results obtained from the above analysis, the cloud service platform generates specific management strategies and optimization suggestions for electric vehicle parking behaviors according to preset business rules, and these suggestions may involve adjusting parking fees, optimizing the parking space layout, formulating personalized service plans, etc. Further, through the application of machine learning models, simulation tests are conducted on the proposed management strategies and optimization suggestions to predict the possible actual effects after implementation, so as to help managers select the optimal solution to improve the operation efficiency and service quality of the parking lot.

[0108] The effects of the above technical solution are as follows: By decrypting and decompressing the status information in the receiving order, the security and integrity of the data are ensured, and at the same time, the data processing efficiency is improved, enabling the real-time status information to quickly and accurately enter the subsequent processing stage; dividing the cloud service platform into multiple storage spaces according to the parking space markings helps to achieve structured data management, improve the data retrieval speed, and is also conducive to resource isolation, enhancing the stability and scalability of the system; the real-time status monitoring and persistent storage mechanism ensure the complete recording of the parking space status data, providing comprehensive basic data for subsequent intelligent analysis. The intelligent analysis module can deeply understand the behavior habits of electric vehicle users, such as parking time distribution, occupancy duration, charging requirements, etc., which is beneficial to the reasonable planning and allocation of resources, improving the user experience, and can also be used as the basis for customized services and preferential policies. The abnormal detection function can promptly detect bad behaviors such as illegal parking, overtime occupancy, and illegal charging, effectively reducing potential safety hazards, maintaining good parking order, and protecting the rights and interests of other users. Through correlation analysis, it helps to understand the operation status of the entire parking lot, identify bottleneck areas in parking space utilization rate, optimize parking space configuration and traffic guidance, thereby improving the overall utilization efficiency and revenue. Combining the preset business rules and the results of intelligent analysis, the system can automatically generate targeted management strategies and optimization suggestions to help parking lot managers make scientific decisions. By using machine learning models to simulate and test the proposed management strategies, the feasibility and expected effects in the actual environment can be evaluated in advance, reducing the implementation risk and ensuring that the resource allocation and policy adjustment are more accurate and effective.

[0109] In one embodiment of the present invention, the effect evaluation module includes:

[0110] The strategy prediction module: converts the management strategy to be adopted into a form that can be input into the model as the input variable of the model, and uses the machine learning model to conduct simulation experiments on different management strategies to predict the response and changes of the system under the new strategy;

[0111] The effect analysis module: analyzes the expected effects under different strategies based on the results output by the model, such as the improvement degree of parking space utilization rate, the change in user satisfaction, and the amount of operating cost savings.

[0112] The optimization and adjustment module: based on the simulation test results and effect evaluation, optimizes and adjusts the original strategy, and re-enters the optimized strategy into the model for simulation and effect prediction until a satisfactory optimization goal is achieved.

[0113] The working principle of the above technical solution is as follows: The management strategies to be verified or optimized are transformed into quantitative or qualitative parameter forms to make them input variables acceptable to machine learning models. The trained machine learning model is used to simulate various possible management strategy scenarios and predict the future state changes of the system under different strategies. The model may use historical data to simulate real-world scenarios and infer the change trends and values of multiple key indicators such as the parking space utilization rate, user satisfaction, and operating costs in the parking lot after the implementation of new strategies. The predicted results output by the model are analyzed in detail, the advantages and disadvantages of different strategies are compared, and the optimal or near-optimal solutions are sought. The evaluation indicators may include, but are not limited to, the improvement range of the parking space utilization rate, the change in the number of user complaints or satisfaction evaluations, and the degree of operating cost savings. According to the results of simulation tests and effect predictions, the existing strategies are revised and optimized. Continuously input the optimized strategies into the machine learning model again for a new round of simulation and prediction, forming a closed-loop feedback process until the optimal management strategy that meets the preset optimization goals (such as maximizing the parking space utilization rate, the highest user satisfaction, the lowest operating cost, etc.) is found.

[0114] The effects of the above technical solution are as follows: By transforming the management strategies into input variables of the machine learning model, the system can utilize the powerful learning and prediction capabilities of the model to conduct simulation experiments on different strategies and predict the actual responses and changes of the system after the implementation of new strategies. This method is more scientific and rigorous than traditional empiricist decision-making and can help managers make more accurate and effective decisions; According to the model prediction results, analyzing the changes in factors such as the parking space utilization rate, user satisfaction, and operating costs under different strategies can help managers understand and select management strategies that can maximize the parking space utilization rate, improve user satisfaction, and reduce costs, thereby optimizing the parking lot resource allocation and enhancing the overall operation efficiency; By conducting simulation tests in advance, the possible consequences of different management strategies can be foreseen before formal implementation, thus reducing the trial-and-error costs in actual operations, avoiding the adverse effects of blind decision-making, and taking preventive and response measures against possible risks in advance; This technical solution allows managers to iteratively optimize the original strategies based on the model predictions, forming a continuous improvement cycle until the ideal optimization goals are achieved. This can ensure that the management strategies always keep up with the times and adapt to the changing market demands and user behaviors; By introducing machine learning into property management, it promotes the transformation from the traditional passive management mode to an active, intelligent, and data-driven direction, enhancing the technological content and modernization level of property management.

[0115] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A property management method based on cloud services, characterized in that, The method includes: S1. Build an intelligent parking management infrastructure for electric vehicles based on cloud services, and connect electric vehicle users and property management personnel to the cloud service platform through mobile terminals; S2. Transmit the status information of the parking spaces to the edge devices through sensors, and the edge devices perform preliminary processing on the status information and transmit the preliminarily processed status information to the cloud service platform; S3. The cloud service platform stores and processes the received status information, analyzes and judges the parking behavior of electric vehicles according to preset rules, and generates corresponding management suggestions; S4. Push the management suggestions to the property management personnel or electric vehicle users through the cloud service platform, and the management personnel or electric vehicle users perform corresponding processing according to the management suggestions after receiving them.

2. The property management method based on cloud service according to claim 1, characterized in that, The S2 includes: S21. Real-time capture the status information data of the parking spaces through various sensors installed on the parking spaces; S22. The various sensors transmit the collected status information data to the edge devices through the communication link; S23. The edge devices perform preliminary processing on the received status information data, and transmit the preliminarily processed status information data to the cloud service platform.

3. The property management method based on cloud service according to claim 2, wherein The S23 includes: S231. The edge devices receive the status information data from various sensors on the parking spaces through a predefined communication protocol, and perform format conversion on the received status information data; S232. Store the format-converted status information data into different computing units of the edge devices according to the parking space markings, and the different computing units partition the received status information data according to different sensor types; S233. Sort the status information data in the partitions according to the time stamps of data collection; perform data integrity check and validity detection on the status information data in different partitions; S234. Perform preprocessing on the status information data in different partitions to obtain preprocessed data, and integrate the data collected by different sensors of the same parking space after preprocessing; S235. Use built-in algorithms to perform preliminary analysis on the integrated data to judge whether there are problems with the parking spaces, and the existing problems include long-term occupation and abnormal charging; S236. Compress the status information data of different parking spaces after preliminary processing respectively, and sort the data according to the importance level; S237. Encrypt the compressed status information data using an encryption algorithm, and transmit the encrypted status information data to the cloud service platform according to the priority through a multi-channel transmission protocol.

4. The property management method based on cloud service according to claim 1, characterized in that, The S3 includes: S31. Decrypt and decompress the received status information data in the order of reception by the cloud service platform, and divide the cloud service platform into multiple storage spaces according to the parking space markings; S32. The storage spaces receive and update the real-time status of each parking space, perform persistent storage on the real-time status data, and perform intelligent analysis on the real-time status of each parking space; S33. Generate management strategies and optimization suggestions for the parking behavior of electric vehicles according to the preset business rules and analysis results; S34. Conduct simulation tests and effect predictions on the proposed management strategies and optimization suggestions through a machine learning model.

5. The property management method based on cloud service according to claim 4, characterized in that, The S34 includes: S341. Convert the proposed management strategies into a form that can be input into the model as input variables of the model, and use the machine learning model to conduct simulation experiments on different management strategies to predict the responses and changes of the system under the new strategies. S342. Analyze the expected effects under different strategies based on the results output by the model. S343. Based on the simulation test results and effect evaluation, optimize and adjust the original strategies, and re-enter the optimized strategies into the model for simulation and effect prediction.

6. A property management system based on cloud services, characterized in that, The system includes: Facility construction module: Construct an intelligent parking management infrastructure for electric vehicles based on cloud services. Electric vehicle users and property management personnel connect to the cloud service platform through mobile terminals. Data processing module: Transmit the status information of the parking spaces to the edge devices through sensors, and the edge devices perform preliminary processing on the status information and transmit the preliminarily processed status information to the cloud service platform. Analysis and judgment module: The cloud service platform stores and processes the received status information, analyzes and judges the parking behaviors of electric vehicles according to preset rules, and generates corresponding management suggestions. Corresponding processing module: Push the management suggestions to property management personnel or electric vehicle users through the cloud service platform. After receiving the management suggestions, the management personnel or electric vehicle users perform corresponding processing according to the management suggestions.

7. The property management system based on cloud service according to claim 6, wherein The data processing module includes: Data collection module: Real-time capture the status information data of the parking spaces through various sensors installed on the parking spaces. Data transmission module: The various sensors transmit the collected status information data to the edge devices through a communication link. Preliminary processing module: The edge devices perform preliminary processing on the received status information data and transmit the preliminarily processed status information data to the cloud service platform.

8. The property management system based on cloud service according to claim 7, wherein, The preliminary processing module includes: Format conversion module: The edge devices receive the status information data from various sensors on the parking spaces through a pre-defined communication protocol and perform format conversion on the received status information data. Partition storage module: Store the format-converted status information data into different computing units of the edge devices according to the parking space markings. The different computing units partition the received status information data according to different sensor types. Data verification module: Sort the status information data within the partitions according to the time stamps of data collection; perform data integrity checks and validity detections on the status information data in different partitions. Data preprocessing module: Perform preprocessing on the status information data in different partitions to obtain preprocessed data, and integrate the data collected by different sensors of the same parking space after preprocessing. Analysis and judgment module: Use built-in algorithms to perform preliminary analysis on the integrated data to judge whether there are problems with the parking spaces. The existing problems include long-term occupancy and abnormal charging. Data compression module: Compress the status information data of different parking spaces after preliminary processing respectively, and sort the data according to the degree of importance. Data Encryption Module: Encrypt the compressed status information data using an encryption algorithm and transmit the encrypted status information data to the cloud service platform according to the priority through a multi-channel transmission protocol.

9. The property management system based on cloud service according to claim 6, characterized in that, The Analysis and Judgment Module includes: Decryption and Decompression Module: Decrypt and decompress the received status information data according to the order of reception by the cloud service platform, and divide the cloud service platform into multiple storage spaces according to the parking space markers; Data Storage Module: The storage space receives and updates the real-time status of each parking space, performs persistent storage on the real-time status data, and conducts intelligent analysis on the real-time status of each parking space; Suggestion Generation Module: Generate management strategies and optimization suggestions for electric vehicle parking behaviors according to the preset business rules and analysis results; Effect Evaluation Module: Conduct simulation tests and effect predictions on the proposed management strategies and optimization suggestions through a machine learning model.

10. The property management system based on cloud service according to claim 9, wherein The Effect Evaluation Module includes: Strategy Prediction Module: Convert the proposed management strategy into a form that can be input into the model as an input variable of the model, and use the machine learning model to conduct simulation experiments on different management strategies to predict the response and changes of the system under the new strategy; Effect Analysis Module: Analyze the expected effects under different strategies according to the results output by the model; Optimization and Adjustment Module: Optimize and adjust the original strategy based on the simulation test results and effect evaluation, and re-enter the optimized strategy into the model for simulation and effect prediction.

Images