Parking space lock management method and system based on Internet of Things

Abstract

The invention is suitable for the technical field of parking space lock management, and provides a parking space lock management method and system based on the Internet of Things. Acquiring an equipment operation log of wearable equipment, a historical parking log of a specified parking area and an initial parking lock priority matching value generated for a reservation user in real time; by combining the real-time emotional state and the historical behavior preference of the user, the priority matching value of the parking space lock is dynamically optimized, and the personalized and intelligent level of parking space distribution is improved. The system obtains the emotion score and behavior characteristics of the user in real time by using the operation log of the wearable device, identifies the state of the user through the emotion correction factor, and preferentially allocates the parking space with higher convenience in a high-pressure situation, thereby improving the user experience.

Description

Technical Field

[0001] The present invention belongs to the technical field of parking lock management, and in particular relates to a parking lock management method and system based on the Internet of Things. Background Art

[0002] With the rapid development of Internet of Things technology, the management mode of smart parking lots has gradually changed from traditional manual management to automation and intelligence. In the existing technology, the interconnection between parking locks and parking lot management platforms has realized the basic parking reservation function, and the parking space allocation and unlocking are completed by remotely controlling the parking locks, providing users with a more convenient parking experience. Some systems can also recommend parking spaces to users based on the real-time occupancy status and historical data of the parking lot. However, these systems still face many limitations in practical applications.

[0003] Most current smart parking systems rely on static rules to allocate parking spaces, such as the geographic location of the parking space, the congestion status of the parking lot, or the user's membership level. Although these methods can improve parking efficiency to a certain extent, they ignore the real-time needs and personalized behaviors of users, making it difficult to achieve accurate matching in highly dynamic scenarios. In addition, although existing systems can optimize parking space allocation strategies through historical data, they lack in-depth mining of users' long-term behavioral preferences and are difficult to dynamically adjust allocation rules based on the habits of different users, thus affecting user satisfaction and resource utilization efficiency.

[0004] On the other hand, with the popularity of wearable devices, these devices can collect rich user physiological and behavioral data, such as heart rate, activity status, and emotional scores. However, the current parking lock management system has not effectively integrated the real-time data provided by wearable devices, and has failed to make full use of these data to optimize parking space allocation. The psychological state of users when making parking demands, such as nervousness, haste, or relaxation, may directly affect their priority of parking demand, but the existing technology has a weak ability to respond to such dynamic demands, which often leads to poor user experience, especially in complex scenarios such as peak hours or special events. Summary of the invention

[0005] The purpose of the present invention is to provide a parking lock management method and system based on the Internet of Things, aiming to solve the problems raised in the background technology.

[0006] The present invention is implemented in this way: a parking lock management method based on the Internet of Things, the method comprising:

[0007] When a reservation user wearing a wearable device makes a parking space reservation request to a designated parking area, the device operation log of the wearable device, the historical parking log of the designated parking area, and the initial parking space lock priority matching value generated in real time for the reservation user are obtained;

[0008] Based on the device operation log, the current emotion score of the reservation user in the preset time period before the request is made is obtained, and the average emotion score corresponding to the current request time point and the preset time period extracted from the first preset number of non-parking space reservation scenarios. If the current emotion score is higher than the average, the difference between the two is calculated as the emotion correction factor;

[0009] Parse the historical parking logs and equipment operation logs of the reservation user, screen out the non-reservation parking logs located in the designated parking area and consistent with the current sentiment score, extract the second preset number of parking space selection data, calculate the corresponding value range of the historical preference priority of the reservation user, and compare it with the corresponding value range of the preset standard priority to obtain the degree of deviation as the deviation correction factor;

[0010] The initial parking lock priority matching value is corrected by combining the emotion correction factor and the deviation correction factor to generate an optimized priority matching value.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, based on the device operation log, the current emotion score of the reservation user in the preset period before the request is made, and the average emotion score corresponding to the current request time point and the preset period extracted from the first preset number of non-parking space reservation scenarios, if the current emotion score is higher than the average, the difference between the two is calculated as the emotion correction factor, the step includes:

[0012] Parse the device operation log and extract the current emotion score of the reservation user in the preset period before making the request;

[0013] Extracting a first preset number of historical emotion scores of non-parking space reservation scenarios from the device operation log, where the historical emotion scores correspond to the current demand proposal time point and a preset time period before the current demand proposal time point, and calculating an average value of the historical emotion scores as the average emotion score value;

[0014] Determine whether the current emotion score is higher than the average emotion score. If so, quantify the difference between the two, obtain the difference value, and use it as the emotion correction factor.

[0015] As a further limitation of the technical solution of the embodiment of the present invention, the steps of parsing the historical parking logs and equipment operation logs of the reservation user, screening out the non-reservation parking logs located in the designated parking area and consistent with the current emotion score, extracting a second preset number of parking space selection data, calculating the corresponding value range of the historical preference priority of the reservation user, and comparing it with the corresponding value range of the preset standard priority to obtain the degree of deviation as the deviation correction factor include:

[0016] Synchronously parse historical parking logs and equipment operation logs to filter out non-reserved parking logs that fall within the current sentiment score threshold range and are located in the designated parking area;

[0017] Extracting a second preset number of parking space selection data from the filtered non-reservation parking logs, analyzing the priority corresponding value range of the parking space involved in each parking space selection data, and calculating the midpoint average value of these priority corresponding value ranges;

[0018] Retrieve the preset standard priority corresponding value range, calculate the midpoint value of the preset standard priority corresponding value range as the benchmark midpoint value, compare the midpoint average value with the benchmark midpoint value, and calculate the degree of deviation between the two as the deviation correction factor.

[0019] As a further limitation of the technical solution of the embodiment of the present invention, the steps of correcting the initial parking lock priority matching value by combining the emotion correction factor and the deviation correction factor to generate the optimized priority matching value include:

[0020] Retrieve the priority matching value correction formula, and correct the initial parking lock priority matching value in combination with the emotion correction factor and the deviation correction factor to obtain the optimized priority matching value;

[0021] According to the optimized priority matching value, the priority corresponding value range it falls into is determined, and suitable parking spaces and locks are screened within the priority corresponding value range, and the results are allocated to the reservation users.

[0022] As a further limitation of the technical solution of the embodiment of the present invention, the priority matching value correction formula is: , where M opt Refers to the optimized priority matching value, M init Refers to the initial parking lock priority matching value, F emo refers to the emotion correction factor, K1 refers to the adjustment coefficient of the emotion correction factor, and F dev refers to the deviation correction factor, and K2 refers to the adjustment coefficient of the deviation correction factor;

[0023] In the priority matching value correction formula: , where S cur refers to the current sentiment score, S avg refers to the average sentiment rating;

[0024] , where R hist Refers to the midpoint average, R std Refers to the benchmark midpoint value.

[0025] A parking lock management system based on the Internet of Things, the system comprises: a data acquisition module, an emotion correction factor determination module, a deviation correction factor determination module and a priority matching value correction module, wherein:

[0026] The data acquisition module is used to obtain the device operation log of the wearable device, the historical parking log of the designated parking area, and the initial parking lock priority matching value generated in real time for the reservation user when the reservation user wearing the wearable device makes a parking reservation request to the designated parking area;

[0027] The emotion correction factor determination module is used to obtain, based on the device operation log, the current emotion score of the reservation user in a preset period before the request is made, and the average emotion score corresponding to the current request time point and the preset period extracted from the first preset number of non-parking space reservation scenarios. If the current emotion score is higher than the average, the difference between the two is calculated as the emotion correction factor;

[0028] The deviation correction factor determination module is used to analyze the historical parking logs and equipment operation logs of the reservation user, screen out the non-reservation parking logs located in the designated parking area and consistent with the current sentiment score, extract the second preset number of parking space selection data, calculate the corresponding value range of the historical preference priority of the reservation user, and compare it with the corresponding value range of the preset standard priority to obtain the degree of deviation as the deviation correction factor;

[0029] The priority matching value correction module is used to correct the initial parking lock priority matching value by combining the emotion correction factor and the deviation correction factor to generate an optimized priority matching value.

[0030] As a further limitation of the technical solution of the embodiment of the present invention, the emotion correction factor determination module specifically includes:

[0031] A current situation score acquisition unit is used to parse the device operation log and extract the current emotion score of the reservation user in the preset period before the reservation request is made;

[0032] The emotion score average calculation unit is used to extract the historical emotion scores of the first preset number of non-parking space reservation scenarios from the device operation log, where the historical emotion scores correspond to the current demand time point and the preset time period before it, and calculate the average value of the historical emotion scores as the emotion score average value;

[0033] The emotion correction factor determination unit is used to determine whether the current emotion score is higher than the average emotion score. If so, the difference between the two is quantified to obtain the difference value and use it as the emotion correction factor.

[0034] As a further limitation of the technical solution of the embodiment of the present invention, the deviation correction factor determination module specifically includes:

[0035] A log screening unit, used for synchronously parsing historical parking logs and equipment operation logs, and screening out non-reserved parking logs that contain the current sentiment score threshold range and are located in the designated parking area;

[0036] a midpoint average value calculation unit, configured to extract a second preset number of parking space selection data from the filtered non-reservation parking logs, analyze a priority corresponding value range of the parking space involved in each parking space selection data, and calculate a midpoint average value of these priority corresponding value ranges;

[0037] The deviation correction factor determination unit is used to call the preset standard priority corresponding value range, and calculate the midpoint value of the preset standard priority corresponding value range as the benchmark midpoint value, compare the midpoint average value with the benchmark midpoint value, and calculate the degree of deviation between the two as the deviation correction factor.

[0038] As a further limitation of the technical solution of the embodiment of the present invention, the priority matching value correction module specifically includes:

[0039] A formula application unit is used to retrieve a priority matching value correction formula, and to correct the initial parking lock priority matching value in combination with a mood correction factor and a deviation correction factor to obtain an optimized priority matching value;

[0040] The parking lock and parking space matching unit is used to determine the priority corresponding value range it falls into according to the optimized priority matching value, and to screen the suitable parking lock and parking space in the priority corresponding value range, and distribute the result to the reservation user.

[0041] As a further limitation of the technical solution of the embodiment of the present invention, the priority matching value correction formula is: , where M opt Refers to the optimized priority matching value, M init Refers to the initial parking lock priority matching value, F emo refers to the emotion correction factor, K1 refers to the adjustment coefficient of the emotion correction factor, and F dev refers to the deviation correction factor, and K2 refers to the adjustment coefficient of the deviation correction factor;

[0042] In the priority matching value correction formula: , where S cur refers to the current sentiment score, S avg Refers to the average sentiment rating;

[0043] , where R hist Refers to the midpoint average, Rstd Refers to the benchmark midpoint value.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention dynamically optimizes the parking lock priority matching value by combining the user's real-time emotional state and historical behavior preferences, thereby improving the personalization and intelligence level of parking space allocation. Using the operation log of the wearable device, the system obtains the user's emotional score and behavioral characteristics in real time, identifies the user's state through the emotional correction factor, and prioritizes the allocation of more convenient parking spaces in high-stress situations, thereby improving the user experience.

[0046] The introduction of the deviation correction factor enables the system to dynamically adjust the allocation strategy according to the user's long-term parking preferences, and significantly improves the accuracy of parking space allocation and resource utilization by combining wearable device data. This invention overcomes the limitations of traditional static allocation rules and achieves efficient matching of user needs and parking resources by dynamically adjusting the priority matching value, thus avoiding resource waste.

[0047] This solution is suitable for complex scenarios such as peak parking and special events. It can quickly respond to user needs and provide priority services. It can also be expanded to traffic management in smart cities and has broad application prospects and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flowchart of a method provided by an embodiment of the present invention;

[0049] Figure 2 A flow chart of determining an emotion correction factor according to a device operation log in a method provided by an embodiment of the present invention;

[0050] Figure 3 A flow chart of determining a deviation correction factor according to historical parking logs and equipment operation logs in a method provided in an embodiment of the present invention;

[0051] Figure 4 A flow chart of correcting the initial parking lock priority matching value in the method provided in an embodiment of the present invention;

[0052] Figure 5 An application architecture diagram of a system provided by an embodiment of the present invention;

[0053] Figure 6 A structural block diagram of an emotion correction factor determination module in a system provided by an embodiment of the present invention;

[0054] Figure 7 A structural block diagram of a deviation correction factor determination module in a system provided by an embodiment of the present invention;

[0055] Figure 8This is a structural block diagram of a priority matching value correction module in a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0058] Specifically, a parking lock management method based on the Internet of Things includes the following steps:

[0059] Step S100, when a reservation user wearing a wearable device makes a parking space reservation request to a designated parking area, the device operation log of the wearable device, the historical parking log of the designated parking area, and the initial parking space lock priority matching value generated in real time for the reservation user are obtained.

[0060] In an embodiment of the present invention, the technical solution of the present invention is carried out for reservation users wearing wearable devices. In the implementation process, the system first needs to confirm whether the reservation user is wearing a wearable device by interacting with the user terminal device. This is usually completed through a binding operation authorized by the user. For example, when registering a parking system account, the reservation user binds his wearable device to the account so that the system can access the operation log of his device in real time. Through this mechanism, the system can automatically identify whether the reservation user is wearing a wearable device, thereby accurately providing dynamic parking lock allocation services for target users.

[0061] The designated parking area has certain particularities. The parking spaces in the area are equipped with parking locks, and the parking spaces and parking locks support advance reservation functions. When the reservation user successfully reserves a parking space, when the vehicle arrives at the designated parking lock, the parking lock can automatically recognize the user and complete the unlocking action. This interactive mechanism improves the convenience and intelligence level of parking. In addition, the parking spaces in the designated parking area have been divided into different priority corresponding value ranges based on the existing technical framework and objective factors such as location, convenience and safety. These priority corresponding value ranges are directly mapped to the parking lock priority matching values, which are used to identify the priority of each parking lock in the allocation.

[0062] The generation of parking lock priority matching values ​​usually relies on the user analysis method in the prior art. When the system analyzes the reservation user, it will comprehensively consider the user's membership level, parking behavior history, preference data, and current environmental factors, such as parking lot congestion or vehicle driving path. Based on these data, the system generates the initial parking lock priority matching value in real time through a preset algorithm. It should be noted that this technical solution has been disclosed and applied in the prior art, and its actual effect has been verified.

[0063] The device operation logs of wearable devices are obtained through the communication interface with the wearable devices. These logs include the user's real-time physiological data (such as heart rate, emotional score, etc.), activity status, and timestamps recorded by the device, which are used to dynamically analyze the user's psychological state and behavior patterns. The historical parking logs of designated parking areas are generated through the built-in monitoring and data recording module of the parking management system, mainly including parking space usage history, vehicle flow, user parking records, and reservation behavior data. These data, combined with the device operation logs, provide an accurate analysis basis for optimizing parking space lock allocation.

[0064] Furthermore, the parking lock management method based on the Internet of Things also includes the following steps:

[0065] Step S200, based on the device operation log, obtain the current emotion score of the reservation user within the preset time period before making the request, and the average emotion score corresponding to the current request time point and the preset time period extracted from the first preset number of non-parking space reservation scenarios. If the current emotion score is higher than the average, calculate the difference between the two as the emotion correction factor.

[0066] Specifically, Figure 2 A flow chart for determining a mood correction factor based on a device operation log is shown.

[0067] The method includes obtaining the current emotion score of the reservation user in a preset period before making a request based on the device operation log, and the average emotion score corresponding to the current time point of making a request and the preset period extracted from the first preset number of non-parking space reservation scenarios. If the current emotion score is higher than the average, the difference between the two is calculated as the emotion correction factor. Specifically, the method includes the following steps:

[0068] Step S201, parsing the device operation log and extracting the current emotion score of the reservation user in the preset period before making a request;

[0069] Step S202, extracting a first preset number of historical emotion scores of non-parking space reservation scenarios from the device operation log, wherein the historical emotion scores correspond to the current demand time point and a preset period before the current demand time point, and calculating the average value of the historical emotion scores as the average emotion score;

[0070] Step S203, determining whether the current emotion score is higher than the average emotion score, and if so, quantifying the difference between the two to obtain a difference value, and using the difference value as an emotion correction factor.

[0071] In an embodiment of the present invention, the first preset number is set based on ensuring that the calculation of the average value of the emotion score has sufficient statistical significance. If the number of samples is too small, the average value of the emotion score may not accurately reflect the overall trend of the historical emotional state. Therefore, the first preset number needs to comprehensively consider factors such as the amount of historical data in the device operation log, the coverage of non-parking reservation scenarios, and the system computing performance, and is usually set to an appropriate value that balances computing efficiency and accuracy. The setting of the preset time period is based on the behavioral patterns of the reserved users and the update cycle of the device operation data. By selecting a time period that matches the duration of the user's daily activities, short-term changes in the user's emotional state can be more accurately captured, providing a reasonable comparison benchmark for the current emotion score.

[0072] The purpose of selecting historical emotion scores for the current demand time point and the preset time period before it is to provide a reference benchmark with time series relevance. The user's emotional state has a certain dynamic volatility and is closely related to the time point. Therefore, by extracting historical emotion scores in a period close to the current demand time point, the average emotion level of the user in a similar time context can be more accurately reflected, thereby ensuring the reference and validity of the average emotion score. The acquisition of historical emotion scores is carried out in reverse time series.

[0073] Judging whether the current emotion score is higher than the average emotion score and quantifying the difference between the two is to explain the significant change between the user's current emotion state and his historical average emotion state. The significance of the emotion correction factor is to capture the degree of deviation of the user's current emotion state in a quantitative way, so as to dynamically adjust the parking lock priority matching value. If the current emotion score is significantly higher than the historical average level, it may indicate that the user is in a state of tension or anxiety. At this time, the system can appropriately increase the user's priority matching value and give priority to allocating more convenient parking locks, thereby improving the user experience and the accuracy of system response. The introduction of the emotion correction factor enables the system to make more personalized and intelligent decisions based on the user's real-time status.

[0074] Furthermore, the parking lock management method based on the Internet of Things also includes the following steps:

[0075] Step S300, parse the historical parking logs and equipment operation logs of the reserved user, filter out the non-reserved parking logs located in the designated parking area and consistent with the current sentiment score, extract a second preset number of parking space selection data, calculate the corresponding value range of the historical preference priority of the reserved user, and compare it with the corresponding value range of the preset standard priority to obtain the degree of deviation as a deviation correction factor.

[0076] Specifically, Figure 3 A flow chart for determining a deviation correction factor based on historical parking logs and equipment operation logs is shown.

[0077] The method includes parsing the historical parking logs and equipment operation logs of the reservation user, screening out the non-reservation parking logs located in the designated parking area and matching the current sentiment score, extracting the second preset number of parking space selection data, calculating the corresponding value range of the historical preference priority of the reservation user, and comparing it with the corresponding value range of the preset standard priority to obtain the degree of deviation as the deviation correction factor, and specifically including the following steps:

[0078] Step S301, synchronously analyzing historical parking logs and equipment operation logs, and filtering out non-reserved parking logs that fall within the current emotion score threshold range and are located in a designated parking area;

[0079] Step S302, extracting a second preset number of parking space selection data from the filtered non-reservation parking logs, analyzing the priority corresponding value range of the parking space involved in each parking space selection data, and calculating the midpoint average value of these priority corresponding value ranges;

[0080] Step S303, retrieve the preset standard priority corresponding value range, calculate the midpoint value of the preset standard priority corresponding value range as the benchmark midpoint value, compare the midpoint average value with the benchmark midpoint value, and calculate the degree of deviation between the two as the deviation correction factor.

[0081] In an embodiment of the present invention, in order to filter out non-reserved parking logs that contain the current emotion score threshold range and are located in the designated parking area based on two logs, two conditions need to be met at the same time. First, the system extracts records containing emotion scores from the device operation logs, and filters out logs that match the current emotion scores to ensure that the emotion scores fall within the set threshold range. The threshold range is usually set based on the user's real-time emotion score and the fluctuation range of historical data to capture behavioral data related to the current emotional state. Secondly, combined with the parking records related to the designated parking area in the historical parking logs, the extracted log data is matched, and the non-reserved parking logs located in the designated parking area are filtered out. By meeting these two conditions at the same time, the log records related to the current emotional state and the target parking area can be accurately located, thereby providing reliable data support for subsequent analysis.

[0082] The second preset number is set based on ensuring the statistical significance of the sample data while taking into account the real-time and efficiency of the calculation. Too few samples may lead to instability in the calculation of the value range corresponding to the historical preference priority, while too many samples may increase the complexity of the calculation and affect the system response speed. Generally speaking, the second preset number is set based on the parking space distribution density, log recording frequency, and representativeness of user behavior in the parking area, which ensures that the sample coverage is sufficiently representative and meets the performance requirements of the system's real-time response.

[0083] The setting of the value range corresponding to the preset standard priority is based on the actual distribution characteristics and management strategies of the parking spaces in the parking area. This range is usually based on the classification of parking space attributes in parking lot planning, such as factors such as location convenience, safety, and user flow, and is preset by the system administrator or parking management system based on existing technology and experience. Its significance is to provide a static reference standard for parking space allocation to evaluate the degree of deviation from the user's historical preference priority. By comparing with user behavior, the system can more intuitively judge the difference between personalized needs and parking lot planning standards, thereby optimizing the parking space allocation strategy.

[0084] The midpoint average is compared with the benchmark midpoint value and the degree of deviation between the two is calculated. The significance of the deviation correction factor is to quantify the difference between the actual selection preference of the user and the standard priority of the parking lot even when the user is in a situation similar to the current mood. This calculation can dynamically reflect whether the user's behavior pattern tends to choose a high-priority or low-priority parking space, and convert this difference into a specific numerical indicator for optimizing the parking space lock priority matching value. The introduction of the deviation correction factor enables the system to match user needs more accurately, especially when the user's emotional state is similar, further improving the personalization and intelligence of parking space allocation, while effectively improving the utilization rate of parking lot resources. Through this mechanism, the system achieves the global balance and efficiency of parking management while meeting user needs.

[0085] Furthermore, the parking lock management method based on the Internet of Things also includes the following steps:

[0086] Step S400: The initial parking lock priority matching value is corrected by combining the emotion correction factor and the deviation correction factor to generate an optimized priority matching value.

[0087] Specifically, Figure 4 A flow chart for modifying the initial parking lock priority matching value is shown.

[0088] The initial parking lock priority matching value is corrected by combining the emotion correction factor and the deviation correction factor, and the optimized priority matching value is generated, which specifically includes the following steps:

[0089] Step S401, calling the priority matching value correction formula, and correcting the initial parking lock priority matching value in combination with the emotion correction factor and the deviation correction factor to obtain an optimized priority matching value;

[0090] Step S402, according to the optimized priority matching value, determine the priority corresponding value range it falls into, and select suitable parking spaces and locks in the priority corresponding value range, and distribute the results to the reservation user.

[0091] The priority matching value correction formula is: , where M opt Refers to the optimized priority matching value, M init Refers to the initial parking lock priority matching value, F emo refers to the emotion correction factor, K1 refers to the adjustment coefficient of the emotion correction factor, and F dev refers to the deviation correction factor, and K2 refers to the adjustment coefficient of the deviation correction factor;

[0092] In the priority matching value correction formula: , where S cur refers to the current sentiment score, S avg Refers to the average sentiment rating;

[0093] , where R hist Refers to the midpoint average, R std Refers to the benchmark midpoint value.

[0094] In the embodiment of the present invention, the combined benefit of combining the emotion correction factor and the deviation correction factor is that they dynamically adjust the initial parking lock priority matching value from the perspectives of the user's immediate state and historical behavior preference. The emotion correction factor can capture the real-time needs of the user in a specific emotional state, ensuring that the system can respond flexibly according to the user's current urgency or emotional state. The deviation correction factor provides a reference dimension for long-term behavior patterns by analyzing the difference between the user's historical selection preferences and the parking lot standard priority. The combined use of the two not only improves the system's adaptability to user needs, but also improves the level of personalization of parking space allocation.

[0095] This method solves the problem of insufficient dynamic adjustment in the existing technology. In traditional systems, parking space allocation is usually based on static rules or simple real-time status, and lacks comprehensive consideration of users' emotional states and historical behaviors. This results in the allocation results not fully matching the actual needs of users, and it is difficult to effectively utilize parking resources. By introducing emotion correction factors and deviation correction factors, the system can more comprehensively consider the diversity and real-time nature of user needs, optimize resource allocation efficiency, and improve user experience. In terms of application prospects, this solution can be widely used in smart parking lots and traffic management systems in smart cities, and has strong scalability and applicability.

[0096] The implementation process of the technical solution of the present invention is described in detail below through a specific example to demonstrate the practical application effect of dynamically optimizing parking lock allocation.

[0097] Assume that a parking lot contains parking locks with priority values ​​ranging from 80 to 100, and is divided into four priority areas: high priority area (95 to 100), medium-high priority area (90 to 94), medium priority area (85 to 89), and low priority area (80 to 84). A user makes a parking reservation request, and the initial parking lock priority matching value is generated by the system based on his membership level and historical parking records, with an initial value of 88. At this time, the system extracts the user's current sentiment score from the user's wearable device operation log as 85, while the average historical sentiment score is 80. By calculation, the sentiment correction factor is the current sentiment score minus the average historical sentiment score, divided by the average historical sentiment score, that is, (85-80) / 80=0.0625.

[0098] Next, the system parses the user's historical parking logs and selects the non-reserved parking logs that match the current sentiment score. Assume that 10 parking space selection data are extracted from these logs, and the midpoint average of the historical preference priority corresponding value range is calculated to be 91, while the midpoint value of the preset standard priority corresponding value range is 87. Through calculation, the deviation correction factor is the historical preference priority midpoint minus the standard midpoint, divided by the standard midpoint, that is, (91-87) / 87=0.0459.

[0099] The system retrieves the priority matching value correction formula, multiplying the initial priority matching value by 1 plus the emotion correction factor multiplied by the weight 0.5 plus the deviation correction factor multiplied by the weight 0.4 to obtain the optimized priority matching value. The calculation process is 88*[1+(0.0625*0.5)+(0.0459*0.4)], and the optimization result is 88*1.0466≈92.10.

[0100] According to the optimized priority matching value, the system determines that the priority corresponding value range it falls into is the medium and high priority area (90 to 94), and selects a specific parking lock within this range. Assuming that the parking lock is numbered C15, the system assigns it to the user and automatically unlocks it after the vehicle arrives, completing the allocation process. Through this example, it can be seen that under the combined effect of the emotion correction factor and the deviation correction factor, the optimized priority matching value is more in line with the actual needs of users. The system improves the accuracy and efficiency of parking space allocation while meeting personalized needs.

[0101] Furthermore, Figure 5 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0102] Among them, in another preferred embodiment provided by the present invention, a parking lock management system based on the Internet of Things includes:

[0103] The data acquisition module 100 is used to obtain the device operation log of the wearable device, the historical parking log of the designated parking area, and the initial parking space lock priority matching value generated in real time for the reservation user when the reservation user wearing the wearable device makes a parking space reservation request to the designated parking area.

[0104] In an embodiment of the present invention, the technical solution of the present invention is carried out for reservation users wearing wearable devices. In the implementation process, the system first needs to confirm whether the reservation user is wearing a wearable device by interacting with the user terminal device. This is usually completed through a binding operation authorized by the user. For example, when registering a parking system account, the reservation user binds his wearable device to the account so that the system can access the operation log of his device in real time. Through this mechanism, the system can automatically identify whether the reservation user is wearing a wearable device, thereby accurately providing dynamic parking lock allocation services for target users.

[0105] The designated parking area has certain particularities. The parking spaces in the area are equipped with parking locks, and the parking spaces and parking locks support advance reservation functions. When the reservation user successfully reserves a parking space, when the vehicle arrives at the designated parking lock, the parking lock can automatically recognize the user and complete the unlocking action. This interactive mechanism improves the convenience and intelligence level of parking. In addition, the parking spaces in the designated parking area have been divided into different priority corresponding value ranges based on the existing technical framework and objective factors such as location, convenience and safety. These priority corresponding value ranges are directly mapped to the parking lock priority matching values, which are used to identify the priority of each parking lock in the allocation.

[0106] The generation of parking lock priority matching values ​​usually relies on the user analysis method in the prior art. When the system analyzes the reservation user, it will comprehensively consider the user's membership level, parking behavior history, preference data, and current environmental factors, such as parking lot congestion or vehicle driving path. Based on these data, the system generates the initial parking lock priority matching value in real time through a preset algorithm. It should be noted that this technical solution has been disclosed and applied in the prior art, and its actual effect has been verified.

[0107] The device operation logs of wearable devices are obtained through the communication interface with the wearable devices. These logs include the user's real-time physiological data (such as heart rate, emotional score, etc.), activity status, and timestamps recorded by the device, which are used to dynamically analyze the user's psychological state and behavior patterns. The historical parking logs of designated parking areas are generated through the built-in monitoring and data recording module of the parking management system, mainly including parking space usage history, vehicle flow, user parking records, and reservation behavior data. These data, combined with the device operation logs, provide an accurate analysis basis for optimizing parking space lock allocation.

[0108] Furthermore, the parking lock management system based on the Internet of Things also includes:

[0109] The emotion correction factor determination module 200 is used to obtain the current emotion score of the reservation user within a preset period before making a request based on the device operation log, and the average emotion score corresponding to the current request time point and the preset period extracted from a first preset number of non-parking space reservation scenarios. If the current emotion score is higher than the average, the difference between the two is calculated as the emotion correction factor.

[0110] Specifically, Figure 6 It shows a structural block diagram of the emotion correction factor determination module 200 in the system provided by an embodiment of the present invention.

[0111] Among them, in the preferred embodiment provided by the present invention, the emotion correction factor determination module 200 specifically includes:

[0112] The current situation score acquisition unit 201 is used to parse the device operation log and extract the current emotion score of the scheduled user in the preset period before the request is made;

[0113] The emotion score average calculation unit 202 is used to extract a first preset number of historical emotion scores of non-parking space reservation scenarios from the device operation log, where the historical emotion scores correspond to the current demand time point and the preset time period before it, and calculate the average value of the historical emotion scores as the emotion score average value;

[0114] The emotion correction factor determination unit 203 is used to determine whether the current emotion score is higher than the average emotion score. If so, the difference between the two is quantified to obtain a difference value, which is used as the emotion correction factor.

[0115] In an embodiment of the present invention, the first preset number is set based on ensuring that the calculation of the average value of the emotion score has sufficient statistical significance. If the number of samples is too small, the average value of the emotion score may not accurately reflect the overall trend of the historical emotional state. Therefore, the first preset number needs to comprehensively consider factors such as the amount of historical data in the device operation log, the coverage of non-parking reservation scenarios, and the system computing performance, and is usually set to an appropriate value that balances computing efficiency and accuracy. The setting of the preset time period is based on the behavioral patterns of the reserved users and the update cycle of the device operation data. By selecting a time period that matches the duration of the user's daily activities, short-term changes in the user's emotional state can be more accurately captured, providing a reasonable comparison benchmark for the current emotion score.

[0116] The purpose of selecting historical emotion scores for the current demand time point and the preset time period before it is to provide a reference benchmark with time series relevance. The user's emotional state has a certain dynamic volatility and is closely related to the time point. Therefore, by extracting historical emotion scores in a period close to the current demand time point, the average emotion level of the user in a similar time context can be more accurately reflected, thereby ensuring the reference and validity of the average emotion score. The acquisition of historical emotion scores is carried out in reverse time series.

[0117] Judging whether the current emotion score is higher than the average emotion score and quantifying the difference between the two is to explain the significant change between the user's current emotion state and his historical average emotion state. The significance of the emotion correction factor is to capture the degree of deviation of the user's current emotion state in a quantitative way, so as to dynamically adjust the parking lock priority matching value. If the current emotion score is significantly higher than the historical average level, it may indicate that the user is in a state of tension or anxiety. At this time, the system can appropriately increase the user's priority matching value and give priority to allocating more convenient parking locks, thereby improving the user experience and the accuracy of system response. The introduction of the emotion correction factor enables the system to make more personalized and intelligent decisions based on the user's real-time status.

[0118] Furthermore, the parking lock management system based on the Internet of Things also includes:

[0119] The deviation correction factor determination module 300 is used to parse the historical parking logs and equipment operation logs of the reserved user, screen out the non-reserved parking logs located in the designated parking area and consistent with the current emotion score, extract a second preset number of parking space selection data, calculate the corresponding value range of the historical preference priority of the reserved user, and compare it with the corresponding value range of the preset standard priority to obtain the degree of deviation as the deviation correction factor.

[0120] Specifically, Figure 7 The structure block diagram of the deviation correction factor determination module 300 in the system provided by the embodiment of the present invention is shown.

[0121] In a preferred embodiment of the present invention, the deviation correction factor determination module 300 specifically includes:

[0122] The log screening unit 301 is used to synchronously parse the historical parking logs and the equipment operation logs, and screen out the non-reserved parking logs that contain the current emotion score threshold range and are located in the designated parking area;

[0123] The midpoint average value calculation unit 302 is used to extract a second preset number of parking space selection data from the filtered non-reservation parking logs, analyze the priority corresponding value range of the parking space involved in each parking space selection data, and calculate the midpoint average value of these priority corresponding value ranges;

[0124] The deviation correction factor determination unit 303 is used to retrieve the preset standard priority corresponding value range, and calculate the midpoint value of the preset standard priority corresponding value range as the benchmark midpoint value, compare the midpoint average value with the benchmark midpoint value, and calculate the degree of deviation between the two as the deviation correction factor.

[0125] In an embodiment of the present invention, in order to filter out non-reserved parking logs that contain the current emotion score threshold range and are located in the designated parking area based on two logs, two conditions need to be met at the same time. First, the system extracts records containing emotion scores from the device operation logs, and filters out logs that match the current emotion scores to ensure that the emotion scores fall within the set threshold range. The threshold range is usually set based on the user's real-time emotion score and the fluctuation range of historical data to capture behavioral data related to the current emotional state. Secondly, combined with the parking records related to the designated parking area in the historical parking logs, the extracted log data is matched, and the non-reserved parking logs located in the designated parking area are filtered out. By meeting these two conditions at the same time, the log records related to the current emotional state and the target parking area can be accurately located, thereby providing reliable data support for subsequent analysis.

[0126] The second preset number is set based on ensuring the statistical significance of the sample data while taking into account the real-time and efficiency of the calculation. Too few samples may lead to instability in the calculation of the value range corresponding to the historical preference priority, while too many samples may increase the complexity of the calculation and affect the system response speed. Generally speaking, the second preset number is set based on the parking space distribution density, log recording frequency, and representativeness of user behavior in the parking area, which ensures that the sample coverage is sufficiently representative and meets the performance requirements of the system's real-time response.

[0127] The setting of the value range corresponding to the preset standard priority is based on the actual distribution characteristics and management strategies of the parking spaces in the parking area. This range is usually based on the classification of parking space attributes in parking lot planning, such as factors such as location convenience, safety, and user flow, and is preset by the system administrator or parking management system based on existing technology and experience. Its significance is to provide a static reference standard for parking space allocation to evaluate the degree of deviation from the user's historical preference priority. By comparing with user behavior, the system can more intuitively judge the difference between personalized needs and parking lot planning standards, thereby optimizing the parking space allocation strategy.

[0128] The midpoint average is compared with the benchmark midpoint value and the degree of deviation between the two is calculated. The significance of the deviation correction factor is to quantify the difference between the actual selection preference of the user and the standard priority of the parking lot even when the user is in a situation similar to the current mood. This calculation can dynamically reflect whether the user's behavior pattern tends to choose a high-priority or low-priority parking space, and convert this difference into a specific numerical indicator for optimizing the parking space lock priority matching value. The introduction of the deviation correction factor enables the system to match user needs more accurately, especially when the user's emotional state is similar, further improving the personalization and intelligence of parking space allocation, while effectively improving the utilization rate of parking lot resources. Through this mechanism, the system achieves the global balance and efficiency of parking management while meeting user needs.

[0129] Furthermore, the parking lock management system based on the Internet of Things also includes:

[0130] The priority matching value correction module 400 is used to correct the initial parking lock priority matching value by combining the emotion correction factor and the deviation correction factor to generate an optimized priority matching value.

[0131] Specifically, Figure 8 It shows a structural block diagram of the priority matching value correction module 400 in the system provided by an embodiment of the present invention.

[0132] Among them, in the preferred implementation manner provided by the present invention, the priority matching value correction module 400 specifically includes:

[0133] The formula application unit 401 is used to retrieve the priority matching value correction formula, and correct the initial parking lock priority matching value in combination with the emotion correction factor and the deviation correction factor to obtain an optimized priority matching value;

[0134] The parking lock and parking space matching unit 402 is used to determine the priority corresponding value range it falls into according to the optimized priority matching value, and select the suitable parking lock and parking space in the priority corresponding value range, and distribute the result to the reservation user.

[0135] The priority matching value correction formula is: , where M opt Refers to the optimized priority matching value, M init Refers to the initial parking lock priority matching value, F emo refers to the emotion correction factor, K1 refers to the adjustment coefficient of the emotion correction factor, and F dev refers to the deviation correction factor, and K2 refers to the adjustment coefficient of the deviation correction factor;

[0136] In the priority matching value correction formula: , where S cur refers to the current sentiment score, S avg Refers to the average sentiment rating;

[0137] , where R hist Refers to the midpoint average, R std Refers to the benchmark midpoint value.

[0138] In the embodiment of the present invention, the combined benefit of combining the emotion correction factor and the deviation correction factor is that they dynamically adjust the initial parking lock priority matching value from the perspectives of the user's immediate state and historical behavior preference. The emotion correction factor can capture the real-time needs of the user in a specific emotional state, ensuring that the system can respond flexibly according to the user's current urgency or emotional state. The deviation correction factor provides a reference dimension for long-term behavior patterns by analyzing the difference between the user's historical selection preferences and the parking lot standard priority. The combined use of the two not only improves the system's adaptability to user needs, but also improves the level of personalization of parking space allocation.

[0139] This method solves the problem of insufficient dynamic adjustment in the existing technology. In traditional systems, parking space allocation is usually based on static rules or simple real-time status, and lacks comprehensive consideration of users' emotional states and historical behaviors. This results in the allocation results not fully matching the actual needs of users, and it is difficult to effectively utilize parking resources. By introducing emotion correction factors and deviation correction factors, the system can more comprehensively consider the diversity and real-time nature of user needs, optimize resource allocation efficiency, and improve user experience. In terms of application prospects, this solution can be widely used in smart parking lots and traffic management systems in smart cities, and has strong scalability and applicability.

[0140] The implementation process of the technical solution of the present invention is described in detail below through a specific example to demonstrate the practical application effect of dynamically optimizing parking lock allocation.

[0141] Assume that a parking lot contains parking locks with priority values ​​ranging from 80 to 100, and is divided into four priority areas: high priority area (95 to 100), medium-high priority area (90 to 94), medium priority area (85 to 89), and low priority area (80 to 84). A user makes a parking reservation request, and the initial parking lock priority matching value is generated by the system based on his membership level and historical parking records, with an initial value of 88. At this time, the system extracts the user's current sentiment score from the user's wearable device operation log as 85, while the average historical sentiment score is 80. By calculation, the sentiment correction factor is the current sentiment score minus the average historical sentiment score, divided by the average historical sentiment score, that is, (85-80) / 80=0.0625.

[0142] Next, the system parses the user's historical parking logs and selects the non-reserved parking logs that match the current sentiment score. Assume that 10 parking space selection data are extracted from these logs, and the midpoint average of the historical preference priority corresponding value range is calculated to be 91, while the midpoint value of the preset standard priority corresponding value range is 87. Through calculation, the deviation correction factor is the historical preference priority midpoint minus the standard midpoint, divided by the standard midpoint, that is, (91-87) / 87=0.0459.

[0143] The system retrieves the priority matching value correction formula, multiplying the initial priority matching value by 1 plus the emotion correction factor multiplied by the weight 0.5 plus the deviation correction factor multiplied by the weight 0.4 to obtain the optimized priority matching value. The calculation process is 88*[1+(0.0625*0.5)+(0.0459*0.4)], and the optimization result is 88*1.0466≈92.10.

[0144] According to the optimized priority matching value, the system determines that the priority corresponding value range it falls into is the medium and high priority area (90 to 94), and selects a specific parking lock within this range. Assuming that the parking lock is numbered C15, the system assigns it to the user and automatically unlocks it after the vehicle arrives, completing the allocation process. Through this example, it can be seen that under the combined effect of the emotion correction factor and the deviation correction factor, the optimized priority matching value is more in line with the actual needs of users. The system improves the accuracy and efficiency of parking space allocation while meeting personalized needs.

[0145] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0146] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0147] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0149] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A parking lock management method based on the Internet of Things, characterized in that: The method comprises: When a reservation user wearing a wearable device makes a parking space reservation request to a designated parking area, the device operation log of the wearable device, the historical parking log of the designated parking area, and the initial parking space lock priority matching value generated in real time for the reservation user are obtained; Based on the device operation log, the current emotion score of the reservation user in the preset time period before the request is made is obtained, and the average emotion score corresponding to the current request time point and the preset time period extracted from the first preset number of non-parking space reservation scenarios. If the current emotion score is higher than the average, the difference between the two is calculated as the emotion correction factor; Parse the historical parking logs and equipment operation logs of the reservation user, screen out the non-reservation parking logs located in the designated parking area and consistent with the current sentiment score, extract the second preset number of parking space selection data, calculate the corresponding value range of the historical preference priority of the reservation user, and compare it with the corresponding value range of the preset standard priority to obtain the degree of deviation as the deviation correction factor; The initial parking lock priority matching value is corrected by combining the emotion correction factor and the deviation correction factor to generate an optimized priority matching value.

2. The parking lock management method based on the Internet of Things according to claim 1 is characterized in that: Based on the device operation log, the current emotion score of the reservation user in the preset period before the request is made, and the average emotion score corresponding to the current request time point and the preset period extracted from the first preset number of non-parking space reservation scenarios, if the current emotion score is higher than the average, the difference between the two is calculated as the emotion correction factor, including the following steps: Parse the device operation log and extract the current emotion score of the reservation user in the preset period before making the request; Extracting a first preset number of historical emotion scores of non-parking space reservation scenarios from the device operation log, where the historical emotion scores correspond to the current demand proposal time point and a preset time period before the current demand proposal time point, and calculating an average value of the historical emotion scores as the average emotion score value; Determine whether the current emotion score is higher than the average emotion score. If so, quantify the difference between the two, obtain the difference value, and use it as the emotion correction factor.

3. The parking lock management method based on the Internet of Things according to claim 2 is characterized in that: The steps of parsing the historical parking logs and equipment operation logs of the reservation user, screening out the non-reservation parking logs located in the designated parking area and matching the current sentiment score, extracting the second preset number of parking space selection data, calculating the corresponding value range of the historical preference priority of the reservation user, and comparing it with the corresponding value range of the preset standard priority to obtain the degree of deviation as the deviation correction factor include: Synchronously parse historical parking logs and equipment operation logs to filter out non-reserved parking logs that fall within the current sentiment score threshold range and are located in the designated parking area; Extracting a second preset number of parking space selection data from the filtered non-reservation parking logs, analyzing the priority corresponding value range of the parking space involved in each parking space selection data, and calculating the midpoint average value of these priority corresponding value ranges; Retrieve the preset standard priority corresponding value range, calculate the midpoint value of the preset standard priority corresponding value range as the benchmark midpoint value, compare the midpoint average value with the benchmark midpoint value, and calculate the degree of deviation between the two as the deviation correction factor.

4. The parking lock management method based on the Internet of Things according to claim 3 is characterized in that: The steps of correcting the initial parking lock priority matching value by combining the emotion correction factor and the deviation correction factor and generating the optimized priority matching value include: Retrieve the priority matching value correction formula, and correct the initial parking lock priority matching value in combination with the emotion correction factor and the deviation correction factor to obtain the optimized priority matching value; According to the optimized priority matching value, the priority corresponding value range it falls into is determined, and suitable parking spaces and locks are screened within the priority corresponding value range, and the results are allocated to the reservation users.

5. The parking lock management method based on the Internet of Things according to claim 4 is characterized in that: The priority matching value correction formula is: , where M opt Refers to the optimized priority matching value, M init Refers to the initial parking lock priority matching value, F emo refers to the emotion correction factor, K1 refers to the adjustment coefficient of the emotion correction factor, and F dev refers to the deviation correction factor, and K2 refers to the adjustment coefficient of the deviation correction factor; In the priority matching value correction formula: , where S cur refers to the current sentiment score, S avg Refers to the average sentiment rating; , where R hist Refers to the midpoint average, R std Refers to the benchmark midpoint value.

6. A parking lock management system based on the Internet of Things, characterized in that: The system comprises: a data acquisition module, an emotion correction factor determination module, a deviation correction factor determination module and a priority matching value correction module, wherein: The data acquisition module is used to obtain the device operation log of the wearable device, the historical parking log of the designated parking area, and the initial parking lock priority matching value generated in real time for the reservation user when the reservation user wearing the wearable device makes a parking reservation request to the designated parking area; The emotion correction factor determination module is used to obtain, based on the device operation log, the current emotion score of the reservation user in a preset period before the request is made, and the average emotion score corresponding to the current request time point and the preset period extracted from the first preset number of non-parking space reservation scenarios. If the current emotion score is higher than the average, the difference between the two is calculated as the emotion correction factor; The deviation correction factor determination module is used to analyze the historical parking logs and equipment operation logs of the reservation user, screen out the non-reservation parking logs located in the designated parking area and consistent with the current sentiment score, extract the second preset number of parking space selection data, calculate the corresponding value range of the historical preference priority of the reservation user, and compare it with the corresponding value range of the preset standard priority to obtain the degree of deviation as the deviation correction factor; The priority matching value correction module is used to correct the initial parking lock priority matching value by combining the emotion correction factor and the deviation correction factor to generate an optimized priority matching value.

7. The parking lock management system based on the Internet of Things according to claim 6 is characterized in that: The emotion correction factor determination module specifically includes: A current situation score acquisition unit is used to parse the device operation log and extract the current emotion score of the reservation user in the preset period before the reservation request is made; The emotion score average value calculation unit is used to extract the historical emotion scores of the first preset number of non-parking space reservation scenarios from the device operation log, where the historical emotion scores correspond to the current demand time point and the preset time period before it, and calculate the average value of the historical emotion scores as the emotion score average value; The emotion correction factor determination unit is used to determine whether the current emotion score is higher than the average emotion score. If so, the difference between the two is quantified to obtain the difference value and use it as the emotion correction factor.

8. The parking lock management system based on the Internet of Things according to claim 7 is characterized in that: The deviation correction factor determination module specifically includes: A log screening unit, used for synchronously parsing historical parking logs and equipment operation logs, and screening out non-reserved parking logs that contain the current sentiment score threshold range and are located in the designated parking area; a midpoint average value calculation unit, configured to extract a second preset number of parking space selection data from the filtered non-reservation parking logs, analyze a priority corresponding value range of the parking space involved in each parking space selection data, and calculate a midpoint average value of these priority corresponding value ranges; The deviation correction factor determination unit is used to call the preset standard priority corresponding value range, and calculate the midpoint value of the preset standard priority corresponding value range as the benchmark midpoint value, compare the midpoint average value with the benchmark midpoint value, and calculate the degree of deviation between the two as the deviation correction factor.

9. The parking lock management system based on the Internet of Things according to claim 8 is characterized in that: The priority matching value correction module specifically includes: A formula application unit is used to retrieve a priority matching value correction formula, and to correct the initial parking lock priority matching value in combination with a mood correction factor and a deviation correction factor to obtain an optimized priority matching value; The parking lock and parking space matching unit is used to determine the priority corresponding value range it falls into according to the optimized priority matching value, and to screen the suitable parking lock and parking space in the priority corresponding value range, and distribute the result to the reservation user.

10. The parking lock management system based on the Internet of Things according to claim 9 is characterized in that: The priority matching value correction formula is: , where M opt Refers to the optimized priority matching value, M init Refers to the initial parking lock priority matching value, F emo refers to the emotion correction factor, K1 refers to the adjustment coefficient of the emotion correction factor, and F dev refers to the deviation correction factor, and K2 refers to the adjustment coefficient of the deviation correction factor; In the priority matching value correction formula: , where S cur refers to the current sentiment score, S avg Refers to the average sentiment rating; , where R hist Refers to the midpoint average, R std Refers to the benchmark midpoint value.

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