Smart city Internet of Things equipment data acquisition and management platform

Abstract

The invention belongs to the technical field of smart city management, and relates to a smart city Internet of Things equipment data acquisition and management platform, which provides an appropriate lessee screening mechanism for an online lessee by combining two factors of a parking space occupation position and a parking space idle time period of the online lessee, so that the online lessee can select the lessee from the parking space occupation position and the parking space idle time period of the online lessee. The recommendation coefficient of each suitable lesser is calculated by using the parking space renting reputation score and the parking space renting price, and the parking space lesser recommendation sequence of the online lesser is generated, so that the defects of the existing method in the aspects of reputation evaluation and parking space price consideration are made up. Secondly, based on historical renting behaviors of each registered renter and historical renting behaviors of each registered renter in the parking space sharing software, a renting time period is published for a parking space of each registered renter, and a parking space reservation renting time period of each registered renter is optimized; the experience degree and satisfaction degree of registered users in parking space sharing software are comprehensively improved, and convenient development of smart city parking is promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart city construction management, and specifically relates to a data collection and management platform for smart city Internet of Things devices. Background Art

[0002] Smart city construction is a comprehensive project, covering multiple fields such as intelligent transportation, intelligent environmental protection, intelligent medical care, and intelligent communities, aiming to improve urban management efficiency and service levels by using information technology. Under this framework, urban parking management, as an important part of the intelligent transportation system, is facing the challenges brought about by the accelerated urbanization process, namely the difficulty of parking caused by the surge in vehicle ownership, which not only profoundly affects the convenience of daily travel and quality of life of citizens, but also becomes a key bottleneck restricting the further expansion and optimization of cities. Therefore, strengthening urban parking management and improving the utilization rate of parking resources and the convenience of the parking process through intelligent means have become topics that must be highly valued and explored in depth in the construction of smart cities.

[0003] There are also some solutions related to smart city parking management in the prior art, such as the dedicated automobile parking space sharing network management platform with Chinese patent publication number 112489482A, which establishes a connection through the parking lot login platform, and the parking space sharing owner can send the idle time period, address, and parking space number to the platform. Users choose to share vacant parking spaces through the application, connect with the parking space owner with the license plate number and contact information, and pay a deposit to establish a rental relationship. The management system confirms the entry and lifts the barrier to release, navigates and guides the vehicle to the rented parking space, and the system automatically deducts the fee and returns the balance when leaving the parking space, building a bridge of communication between the parking space sharing owner and the user.

[0004] Although the above scheme proposes a solution to smart city parking management, the existing smart city parking management technology still has the following limitations, specifically: 1. When recommending parking spaces to lessees, the existing parking space sharing software mainly relies on the lessor's free time period and the shortest distance to the lessee's parking space as the basis for recommendation. Not only does it lack an effective credit evaluation mechanism to distinguish between high-credit and low-credit parking space lessors, which may lead to poor experience or safety risks for the lessee, it also ignores the lack of consideration of parking space prices and is unable to provide a variety of parking space options to meet the different budget needs of lessees.

[0005] 2. The existing technology lacks optimization considerations for the registered lessors to publish rental time periods and the registered lessors to reserve rental time periods in the parking space sharing software. From the perspective of the lessor, due to the lack of accurate time period management optimization, the lessor may return early or late when publishing the rental time period, which may not only disrupt the lessor's personal plan and delay his important matters, but also may cause a gap period, further reducing his potential income from renting parking spaces. From the perspective of the lessor, the lessee may arrive early or late during the reserved rental period, which may reduce the lessee's user experience and increase his time cost. Summary of the invention

[0006] In order to overcome the shortcomings of the background technology, the embodiments of the present invention provide a smart city Internet of Things device data collection and management platform, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] The purpose of the present invention can be achieved through the following technical solutions: a smart city Internet of Things device data collection and management platform, including: a parking space rental request receiving module, a parking space recommendation service module, a rental period optimization analysis module, a rental period optimization feedback module, a rental period optimization analysis module, a rental period optimization feedback module and a cloud database.

[0008] The parking space rental request receiving module is connected to the parking space recommendation service module, the parking space recommendation service module is connected to the rental period optimization analysis module, the rental period optimization analysis module is connected to the rental period optimization feedback module, the rental period optimization feedback module is connected to the rental period optimization analysis module, the rental period optimization analysis module is connected to the rental period optimization feedback module, and the cloud database is respectively connected to the parking space recommendation service module, the rental period optimization analysis module, and the rental period optimization analysis module.

[0009] The parking space rental request receiving module is used to receive the parking space rental request content of each online renter in the current parking space sharing software, including the parking space reference rental position and the parking space rental period.

[0010] The parking space recommendation service module is used to extract the parking space rental content published by each online lessor in the current parking space sharing software, including the parking space’s location, idle time period and rental price, respond to the parking space rental requests from each online lessor in the current parking space sharing software, and provide parking space recommendation services for each current online lessor.

[0011] The rental period optimization analysis module is used to extract the historical rental behavior information of each registered lessor in the parking space sharing software, and analyze the optimization needs of each registered lessor for the release of parking space rental periods and their corresponding optimization parameters.

[0012] The rental period optimization feedback module is used to wait for each registered lessor to log in to the parking space sharing software, and to feedback the rental period optimization parameters of the parking spaces corresponding to each registered lessor in the form of pop-up windows.

[0013] The rental period optimization analysis module is used to extract the historical rental behavior information of each registered renter in the parking space sharing software, and analyze the parking space reservation rental period optimization needs and corresponding optimization parameters of each registered renter.

[0014] The rental period optimization feedback module is used to wait for each registered renter to log in to the parking space sharing software, and to provide feedback on the parking space reservation rental period optimization parameters corresponding to each registered renter in the form of a pop-up window.

[0015] The cloud database is used to store the parking space sharing software's recommended preset straight-line distance for parking spaces, the reasonable deviation time interval for registered lessors to rent out and return parking spaces, and the reasonable deviation time interval for registered lessees to rent out and enter parking spaces.

[0016] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention provides an online lessor with a screening mechanism for suitable lessors by combining two factors: the location of the parking space occupied by the online lessor and the idle time period of the parking space. The present invention further calculates the recommendation coefficient of each suitable lessor by using the parking space rental credit score and the parking space rental price, and generates a parking space lessor recommendation sequence for the online lessor, thereby making up for the shortcomings of the existing methods in terms of credit evaluation mechanism and parking space price considerations, and providing the online lessor with a more comprehensive and objective basis for selecting lessors.

[0017] (2) The present invention analyzes the parking space rental credit score of each suitable lessor from multiple dimensions based on the historical cumulative number of rentals, historical cumulative rental duration, historical cumulative delayed departure duration, and historical cumulative early return duration, so as to scientifically and intuitively reflect the actual rental behavior and service quality of the lessor, and enhance the reliability and accuracy of the parking space rental credit evaluation of the suitable lessor.

[0018] (3) The present invention analyzes the optimization requirements of each registered lessor for the release of parking spaces and their corresponding optimization parameters based on the historical rental behavior information of each registered lessor in the parking space sharing software, thereby providing each registered lessor with a more personalized parking space release mechanism, helping to promote the convenience of rental arrangements and the growth of rental income of the registered lessors.

[0019] (4) The present invention analyzes the optimization requirements of each registered lessee for parking space reservation and its corresponding optimization parameters based on the historical rental behavior information of each registered lessee in the parking space sharing software, and helps to realize the intelligent adjustment and optimization of the reservation and rental time period of each registered lessee. This can not only reduce the time waste and inconvenience caused by improper reservation for the lessee, but also improve the utilization efficiency of urban parking spaces and reduce the vacancy rate of the lessors. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0021] Figure 1 It is a schematic diagram of module connection of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Reference Figure 1 As shown, the present invention provides a smart city Internet of Things device data collection and management platform, including: a parking space rental request receiving module, a parking space recommendation service module, a rental period optimization analysis module, a rental period optimization feedback module, a rental period optimization analysis module, a rental period optimization feedback module and a cloud database.

[0024] The parking space rental request receiving module is connected to the parking space recommendation service module, the parking space recommendation service module is connected to the rental period optimization analysis module, the rental period optimization analysis module is connected to the rental period optimization feedback module, the rental period optimization feedback module is connected to the rental period optimization analysis module, the rental period optimization analysis module is connected to the rental period optimization feedback module, and the cloud database is respectively connected to the parking space recommendation service module, the rental period optimization analysis module, and the rental period optimization analysis module.

[0025] The parking space rental request receiving module is used to receive the parking space rental request content of each online renter in the current parking space sharing software, including the parking space reference rental position and the parking space rental period.

[0026] It should be noted that the above parking space rental request content is uploaded by the online lessor terminal, wherein the parking space reference rental position is the current driving position or the expected parking position of the online lessor, which can be selected by the online lessor.

[0027] The parking space recommendation service module is used to extract the parking space rental release content of each online lessor in the current parking space sharing software, including the parking space's location, idle time period and rental price, respond to the parking space rental requests of each online lessor in the current parking space sharing software, and provide parking space recommendation services for each current online lessor.

[0028] It should be noted that the above-mentioned parking space rental release content is uploaded by the online lessor terminal itself.

[0029] Specifically, the historical rental behavior information includes the rental time periods of each historical parking space rental, and the departure time points and return time points of the registered lessor vehicles corresponding to each historical parking space rental.

[0030] It should be noted that the published rental time periods of each historical parking space rental in the above-mentioned historical rental behavior information are obtained by extracting the parking space supply-side record log in the parking space sharing software, and the departure time and return time of the registered lessor's vehicle corresponding to each historical parking space rental are obtained through the online barrier monitoring and identification of the parking lot corresponding to the registered lessor's parking space.

[0031] Specifically, the parking space recommendation service for each current online lessor includes: recording a certain online lessor in the current parking space sharing software as a target object, and extracting the parking space reference rental position and parking space rental period of the target object.

[0032] The parking space reference rental position of the target object is taken as the circle point, and the parking space recommendation preset limited straight-line distance of the parking space sharing software stored in the cloud database is taken as the radius to construct the parking space recommendation area circle of the target object.

[0033] According to the parking space occupied positions and parking space idle periods of each online lessor in the current parking space sharing software, each online lessor whose parking space occupied positions are within the parking space recommended area circle of the target object and whose parking space idle period includes the parking space rental period of the target object is recorded as each suitable lessor.

[0034] Extract the historical rental behavior information of each suitable lessor in the parking space sharing software, and analyze the parking space rental reputation score of each suitable lessor i , i is the number of each suitable lessor, i = 1, 2, ..., a, combined with the parking space rental price q of each suitable lessor in the current parking space sharing software i , calculate the recommendation coefficient β of each suitable lessori , in They are the preset parking space rental credit score and the corresponding weight ratio of the parking space rental price. and f0 and q0 are respectively the parking space rental credit score and parking space rental price recommended by the preset parking space lessor for reference.

[0035] It should be noted that the basis for setting the above-mentioned parking space rental credit score and the corresponding weight ratio of the parking space rental price is that the parking space rental credit score reflects the service quality and credibility of the lessor. When selecting a suitable lessor, giving priority to the credit score can better protect the rights and interests of the lessee and avoid service quality problems caused by the lessor's poor credibility. Price is not the only decision-making factor, and a low price does not always mean high cost performance. Considering that the parking space rental market may have inflated or inflated prices, over-reliance on price factors may increase the risk of the lessee. Therefore, setting the credit score weight ratio to a larger value can help reduce the risk to a certain extent and ensure that the lessee obtains high-quality services and parking experience.

[0036] For example, the above The specific value may be 0.6 or 0.4.

[0037] It should also be noted that the parking space rental credit score and parking space rental price recommended by the above-mentioned preset parking space lessors can be specifically referred to the calculated average of the parking space rental credit score and parking space rental price of each suitable lessor.

[0038] The suitable lessors are arranged in descending order according to the recommendation coefficient, and a recommended sequence of parking space lessors for the target object is generated, which is displayed in a pop-up window to the display terminal of the target object, thereby providing parking space recommendation services for the current online lessors.

[0039] The embodiment of the present invention provides an online lessor with a suitable lessor screening mechanism by combining two factors, namely, the parking space location and the parking space vacancy period of the online lessor. The recommendation coefficient of each suitable lessor is further calculated by using the parking space rental credit score and the parking space rental price, and a parking space lessor recommendation sequence for the online lessor is generated, thereby making up for the deficiencies of the existing methods in terms of the credit evaluation mechanism and the parking space price considerations, and providing the online lessor with a more comprehensive and objective basis for selecting the lessor parking space.

[0040] Specifically, the analysis of the parking space rental reputation score of each suitable lessor includes: extracting the rental time period of each historical parking space rental from the historical rental behavior information of each suitable lessor, and counting the historical cumulative rental times m of each suitable lessor. i and historical accumulated rental time ti .

[0041] Extract the departure time and return time of each parking space rental corresponding to the lessor's vehicle from the historical rental behavior information of each lessor, and calculate the historical cumulative delayed departure time t of each lessor i ′ and the historical cumulative early return time t i ″.

[0042] Then by the formula The parking space rental credit score of each suitable lessor is obtained, where m0 and t0 are the cumulative rental times and cumulative rental duration of the preset parking space rental credit evaluation reference, and π is 180°.

[0043] It should be noted that the cumulative number of rentals and cumulative rental duration referred to in the above-mentioned preset parking space rental credit evaluation can be obtained by referring to the credit evaluation rule guide or credit standard agreement of the registered lessor of the parking space sharing software.

[0044] The embodiment of the present invention analyzes the parking space rental credit score of each suitable lessor from multiple dimensions based on the historical cumulative number of rentals, historical cumulative rental duration, historical cumulative delayed departure duration, and historical cumulative early return duration, so as to scientifically and intuitively reflect the actual rental behavior and service quality of the lessor, and enhance the reliability and accuracy of the parking space rental credit evaluation of the suitable lessor.

[0045] The rental period optimization analysis module is used to extract the historical rental behavior information of each registered lessor in the parking space sharing software, and analyze the parking space rental period optimization requirements and corresponding optimization parameters of each registered lessor.

[0046] Specifically, the analysis of the optimization needs of each registered lessor for the release of parking space rental time periods includes: extracting the end time points T of the release of rental time periods of each historical parking space rental from the historical rental behavior information of each registered lessor in the parking space sharing software; jw And the return time of the registered lessor’s vehicle for each parking space rental in history Where j is the number of each registered lessor, j = 1, 2, ..., b, w is the number of each historical parking space rental, w = 1, 2, ..., c, calculate the return deviation time ΔT of each registered lessor's historical parking space rental j , Where c is the number of historical parking space rentals.

[0047] Extract the reasonable deviation time range of parking space rental return of registered lessors of parking space sharing software stored in the cloud database, and determine whether there is a need to optimize the parking space rental period for each registered lessor whose historical parking space rental return deviation time is outside the reasonable deviation time range of parking space rental return of registered lessors of parking space sharing software.

[0048] Specifically, the analysis of the optimization parameters corresponding to the parking space rental period of each registered lessor includes: recording each registered lessor with a parking space rental period optimization demand as each lessor to be optimized, extracting the return deviation time ΔT of the historical parking space rental of each lessor to be optimized j′ , j′ is the serial number of each lessor to be optimized, j′=1, 2, ..., b′.

[0049] Extract the lower and upper limits of the reasonable deviation time interval of the parking space rental return of the parking space registered lessor of the parking space sharing software, and record them as ΔT respectively 上 , ΔT 下 .

[0050] If ΔT j′ <ΔT 下 , it means that the optimization direction of the end time point of the rental period of the parking space to be optimized is forward adjustment, and the absolute difference between the lower limit value and the return deviation time is used as the forward adjustment time.

[0051] If ΔT j′ >ΔT 上 , it means that the optimization direction of the end time point of the rental period of the parking space released by the lessor to be optimized is post-adjustment, and the absolute difference between the upper limit value and the return deviation time is used as the post-adjustment time, and then the corresponding optimization parameters of the rental period of the parking space released by each lessor to be optimized are obtained.

[0052] The optimization parameters corresponding to the parking space rental time periods of each registered lessor who does not have the need to optimize the parking space rental time periods will be automatically set to 0, and no pop-up prompt will be displayed.

[0053] The rental period optimization feedback module is used to wait for each registered lessor to log in to the parking space sharing software, and to feedback the rental period optimization parameters of the parking space corresponding to each registered lessor in the form of a pop-up window.

[0054] The embodiment of the present invention analyzes the optimization requirements of each registered lessor for releasing parking space and its corresponding optimization parameters based on the historical rental behavior information of each registered lessor in the parking space sharing software, thereby providing each registered lessor with a more personalized parking space release mechanism, helping to promote the convenience of rental arrangements and the growth of rental income of the registered lessor.

[0055] The rental period optimization analysis module is used to extract the historical rental behavior information of each registered renter in the parking space sharing software, and analyze the parking space reservation rental period optimization requirements and corresponding optimization parameters of each registered renter.

[0056] Specifically, the historical rental behavior information includes the reserved rental time period of each historical parking space rental and the entry time point of the vehicle of the registered renter corresponding to each historical parking space rental.

[0057] It should be noted that the reserved rental time periods for each historical parking space rental in the above-mentioned historical rental behavior information are obtained by extracting the parking space demand-side record log in the parking space sharing software, and the entry time points of the registered renter's vehicles corresponding to each historical parking space rental are obtained through the online barrier monitoring and identification of the parking lot corresponding to the registered renter's reserved parking space.

[0058] Specifically, the analysis of the optimization requirements of the parking space reservation rental time period of each registered renter includes: extracting the starting time point T of each historical parking space reservation rental time period from the historical rental behavior information of each registered renter in the parking space sharing software; g ' h And the entry time of the registered renter’s vehicle corresponding to each parking space rental in history Where g is the number of each registered renter, g = 1, 2, ..., d, h is the number of each historical parking space rental, h = 1, 2, ..., n, calculate the entry deviation time ΔT of each registered renter's historical parking space rental g ′.

[0059] The reasonable deviation time range for parking space rental entry of registered renters of parking space sharing software stored in the cloud database is extracted to determine whether there is a need to optimize the parking space reservation rental time period for each registered renter whose historical parking space rental entry deviation time is outside the reasonable deviation time range for parking space rental entry of registered renters of parking space sharing software.

[0060] Specifically, the ΔT g The calculation formula of ′ is: Where n is the number of historical parking space rentals.

[0061] Specifically, the analysis of the optimization parameters corresponding to the parking space reservation rental period of each registered renter includes: recording each registered renter who has a parking space reservation rental period optimization demand as each renter to be optimized, extracting the entry deviation time ΔT of the historical parking space rental of each renter to be optimized g ″, g′ is the serial number of each tenant to be optimized, g′=1, 2, ..., d′.

[0062] Extract the lower and upper limits of the reasonable deviation time interval of the parking space rental entry of the parking space sharing software registered renter, and record them as ΔT respectively. 上 ′, ΔT 下 ' 下 .

[0063] If ΔT g ″<ΔT 下 ' 下 , it means that the optimization direction of the starting time point of the parking space reservation rental period to be optimized is forward adjustment, and the absolute difference between the lower limit value and the entry deviation time is used as the forward adjustment time.

[0064] If ΔT g ″>ΔT 上 ' 上 , it means that the optimization direction of the starting time point of the parking space reservation rental period to be optimized is post-adjustment, and the absolute difference between the upper limit value and the entry deviation time is used as the post-adjustment time, and then the corresponding optimization parameters of the parking space reservation rental period of each lessee to be optimized are obtained.

[0065] The optimization parameters corresponding to the parking space reservation rental time period of each registered renter who does not have the need to optimize the parking space reservation rental time period are automatically set to 0, and no pop-up prompt is set.

[0066] The rental period optimization feedback module is used to wait for each registered renter to log in to the parking space sharing software and to provide feedback on the parking space reservation rental period optimization parameters corresponding to each registered renter in the form of a pop-up window.

[0067] The embodiment of the present invention analyzes the optimization requirements of the parking space reservation rental period of each registered lessee and its corresponding optimization parameters based on the historical rental behavior information of each registered lessee in the parking space sharing software, and helps to realize the intelligent adjustment and optimization of the reservation rental period of each registered lessee, which can not only reduce the time waste and inconvenience caused by improper reservation of the lessee, but also improve the utilization efficiency of urban parking spaces and reduce the vacancy rate of the lessors.

[0068] The cloud database is used to store the preset limited straight-line distance of the parking space recommendation of the parking space sharing software, the reasonable deviation time interval for the registered lessor to rent out the parking space, and the reasonable deviation time interval for the registered lessee to rent out the parking space.

[0069] The data sources in the cloud database of this embodiment are shown in Table 1 below.

[0070] Table 1

[0071]

[0072]

[0073] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. Smart city IoT device data collection and management platform, characterized by: include: The parking space rental request receiving module is used to receive the parking space rental request content of each online renter in the current parking space sharing software, including the parking space reference rental location and the parking space rental period; The parking space recommendation service module is used to extract the parking space rental content published by each online lessor in the current parking space sharing software, including the parking space's location, free time period and rental price, respond to the parking space rental requests of each online lessor in the current parking space sharing software, and provide parking space recommendation services for each current online lessor; The rental period optimization analysis module is used to extract the historical rental behavior information of each registered lessor in the parking space sharing software, and analyze the optimization needs of each registered lessor for the release of parking space rental periods and their corresponding optimization parameters; The rental period optimization feedback module is used to wait for each registered lessor to log in to the parking space sharing software, and to feedback the rental period optimization parameters of the parking space corresponding to each registered lessor in the form of a pop-up window; The rental period optimization analysis module is used to extract the historical rental behavior information of each registered renter in the parking space sharing software, and analyze the parking space reservation rental period optimization needs and corresponding optimization parameters of each registered renter; The rental period optimization feedback module is used to wait for each registered renter to log in to the parking space sharing software, and to provide feedback on the parking space reservation rental period optimization parameters corresponding to each registered renter in the form of a pop-up window; The cloud database is used to store the parking space sharing software's recommended preset straight-line distance for parking spaces, the reasonable deviation time interval for registered lessors to rent out and return parking spaces, and the reasonable deviation time interval for registered lessees to rent out and enter parking spaces.

2. The smart city IoT device data collection and management platform according to claim 1 is characterized by: The historical rental behavior information includes the published rental time periods of each historical parking space rental, and the departure time points and return time points of the registered lessor vehicles corresponding to each historical parking space rental.

3. The smart city IoT device data collection and management platform according to claim 2 is characterized by: The parking space recommendation service for each current online renter includes: recording a certain online renter in the current parking space sharing software as a target object, extracting the parking space reference rental position and parking space rental period of the target object; The target object's parking space reference rental position is taken as the circle point, and the parking space recommendation preset limited straight-line distance of the parking space sharing software stored in the cloud database is taken as the radius to construct the target object's parking space recommendation area circle; According to the parking space occupied positions and parking space idle periods of each online lessor in the current parking space sharing software, each online lessor whose parking space occupied positions are within the parking space recommended area circle of the target object and whose parking space idle period includes the parking space rental period of the target object is recorded as each suitable lessor; Extract the historical rental behavior information of each suitable lessor in the parking space sharing software, and analyze the parking space rental reputation score of each suitable lessor i , i is the number of each suitable lessor, i = 1, 2, ..., a, combined with the parking space rental price q of each suitable lessor in the current parking space sharing software i , calculate the recommendation coefficient β of each suitable lessor i , in They are the preset parking space rental credit score and the corresponding weight ratio of the parking space rental price. and f0 and q0 are the parking space rental credit score and parking space rental price recommended by the preset parking space lessor; The suitable lessors are arranged in descending order according to the recommendation coefficient, and a recommended sequence of parking space lessors for the target object is generated, which is displayed in a pop-up window to the display terminal of the target object, thereby providing parking space recommendation services for the current online lessors.

4. The smart city IoT device data collection and management platform according to claim 3 is characterized by: The analysis of the parking space rental credit score of each suitable lessor includes: extracting the rental time period of each parking space rental in the historical rental behavior information of each suitable lessor, and counting the historical cumulative rental times m of each suitable lessor. i and historical accumulated rental time t i ; Extract the departure time and return time of each parking space rental corresponding to the lessor's vehicle from the historical rental behavior information of each lessor, and calculate the historical cumulative delayed departure time t of each lessor i ′ and the historical cumulative early return time t i ″; Then by the formula The parking space rental credit score of each suitable lessor is obtained, where m0 and t0 are the cumulative rental times and cumulative rental duration of the preset parking space rental credit evaluation reference, and π is 180°.

5. The smart city Internet of Things device data collection and management platform according to claim 2 is characterized by: The analysis of the optimization requirements of each registered lessor for the release of parking space rental time periods includes: extracting the end time points T of the release of rental time periods of each historical parking space rental from the historical rental behavior information of each registered lessor in the parking space sharing software. jw And the return time of the registered lessor’s vehicle for each parking space rental in history Where j is the number of each registered lessor, j = 1, 2, ..., b, w is the number of each historical parking space rental, w = 1, 2, ..., c, calculate the return deviation time ΔT of each registered lessor's historical parking space rental j , Where c is the number of historical parking space rentals; Extract the reasonable deviation time range of parking space rental return of registered lessors of parking space sharing software stored in the cloud database, and determine whether there is a need to optimize the parking space rental period for each registered lessor whose historical parking space rental return deviation time is outside the reasonable deviation time range of parking space rental return of registered lessors of parking space sharing software.

6. The smart city IoT device data collection and management platform according to claim 5 is characterized by: The analysis of the optimization parameters corresponding to the parking space rental period of each registered lessor includes: recording each registered lessor with a parking space rental period optimization demand as each lessor to be optimized, extracting the return deviation time ΔT of the historical parking space rental of each lessor to be optimized j′ , j′ is the serial number of each lessor to be optimized, j′=1, 2, ..., b′; Extract the lower and upper limits of the reasonable deviation time interval of the parking space rental return of the parking space registered lessor of the parking space sharing software, and record them as ΔT respectively 上 , ΔT 下 ; If ΔT j′ <ΔT 下 , it means that the optimization direction of the end time point of the rental period of the parking space to be optimized is forward adjustment, and the absolute difference between the lower limit value and the return deviation time is used as the forward adjustment time; If ΔT j′ >ΔT 上 , it means that the optimization direction of the end time point of the rental period of the parking space released by the lessor to be optimized is to adjust later, and the absolute difference between the upper limit value and the return deviation time is used as the adjustment time, so as to obtain the optimization parameters corresponding to the rental period of the parking space released by each lessor to be optimized; The optimization parameters corresponding to the parking space rental time periods of each registered lessor who does not have the need to optimize the parking space rental time periods will be automatically set to 0, and no pop-up prompt will be displayed.

7. The smart city IoT device data collection and management platform according to claim 1, characterized in that: The historical rental behavior information includes the reserved rental time periods of each historical parking space rental and the entry time points of the vehicles of the registered renters corresponding to each historical parking space rental.

8. The smart city IoT device data collection and management platform according to claim 7 is characterized by: The analysis of the optimization requirements of the parking space reservation rental time period of each registered renter includes: extracting the starting time point T of each historical parking space reservation rental time period from the historical rental behavior information of each registered renter in the parking space sharing software. g ' h And the entry time of the registered renter’s vehicle corresponding to each parking space rental in history Where g is the number of each registered renter, g = 1, 2, ..., d, h is the number of each historical parking space rental, h = 1, 2, ..., n, calculate the entry deviation time ΔT of each registered renter's historical parking space rental g ′; The reasonable deviation time range for parking space rental entry of registered renters of parking space sharing software stored in the cloud database is extracted to determine whether there is a need to optimize the parking space reservation rental time period for each registered renter whose historical parking space rental entry deviation time is outside the reasonable deviation time range for parking space rental entry of registered renters of parking space sharing software.

9. The smart city Internet of Things device data collection and management platform according to claim 8, characterized in that: The ΔT g The calculation formula of ′ is: Where n is the number of historical parking space rentals.

10. The smart city Internet of Things device data collection and management platform according to claim 8, characterized in that: The analysis of the optimization parameters corresponding to the parking space reservation rental period of each registered renter includes: recording each registered renter who has a parking space reservation rental period optimization demand as each renter to be optimized, extracting the entry deviation time ΔT of the historical parking space rental of each renter to be optimized g ″, g′ is the serial number of each tenant to be optimized, g′=1, 2, ..., d′; Extract the lower and upper limits of the reasonable deviation time interval of the parking space rental entry of the parking space sharing software registered renter, and record them as ΔT′ 上 , ΔT′ 下 ; If ΔT′ g′ <ΔT′ 下 , it means that the optimization direction of the starting time point of the parking space reservation rental period to be optimized is forward adjustment, and the absolute difference between the lower limit value and the entry deviation time is used as the forward adjustment time; If ΔT′ g′ >ΔT′ 上 , it means that the optimization direction of the starting time point of the parking space reservation rental period to be optimized is post-adjustment, and the absolute difference between the upper limit value and the entry deviation time is used as the post-adjustment time, and then the corresponding optimization parameters of the parking space reservation rental period of each lessee to be optimized are obtained; The optimization parameters corresponding to the parking space reservation rental time period of each registered renter who does not have the need to optimize the parking space reservation rental time period are automatically set to 0, and no pop-up prompt is set.

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