A method and device for allocating parking spaces for online car-hailing

Abstract

The present invention provides a method and device for allocating parking spaces for online ride-hailing vehicles, which relates to the field of intelligent traffic management technology. The method comprises: calculating a multi-dimensional score by obtaining comprehensive information about parking spaces, including a path complexity score and a parking space free time window score; obtaining a comprehensive parking space score based on a weighted sum of the multi-dimensional score and a preset weight coefficient; and allocating a parking space for pickup to an order based on the comprehensive parking space score. The present invention solves the problem that the prior art has difficulty in comprehensively evaluating parking space matching by considering multi-dimensional information such as path complexity and parking space free time window, resulting in the allocation and management of pickup parking spaces being difficult to meet the needs of high-density passenger flow and complex scenarios. The multi-dimensional score also takes into account the passenger arrival time score and the parking space load rate score, thereby achieving optimal allocation of parking space loads and reducing passenger waiting time. The present invention also adjusts the weight coefficient according to preset rules, thereby improving the flexibility and accuracy of parking space allocation.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent traffic management, and in particular to a method and device for allocating parking spaces for passengers on an online ride-hailing service. Background Art

[0002] At transportation hubs like airports and train stations, as well as large commercial complexes, the scientific and rational allocation and management of ride-hailing spaces is key to improving operational efficiency and optimizing the user experience. However, existing technologies are undeniably limited in practical application, making them difficult to meet the demands of high-density passenger flow and complex, ever-changing scenarios.

[0003] At the allocation level, traditional ride-hailing dispatching often relies on static allocation, fixed parking spaces, and a first-come, first-served queuing system. This lack of consideration for factors like the complexity of passengers' dynamic movement paths and the time windows during which parking spaces become available leads to low parking space utilization and significantly increased passenger wait times. Existing systems operate on a single dimension, relying solely on the time the order was received or simple distance calculations to allocate parking spaces. These deficiencies completely ignore critical information such as passengers' real-time location, predicted walking time, and parking space load. These shortcomings directly lead to resource waste. Parking spaces are unevenly distributed, with some spaces remaining unused for extended periods while others are overcrowded. This requires repeated communication and coordination between drivers and passengers, significantly increasing operating costs and management complexity. Furthermore, existing technologies lack dynamic adjustment capabilities and rely excessively on manual management. This makes it impossible to dynamically adjust parking space allocation strategies based on real-time passenger flow fluctuations, such as flight delays and sudden congestion, resulting in poor adaptability across the entire system. Summary of the Invention

[0004] An embodiment of the present invention provides a method for allocating parking spaces for online ride-hailing vehicles, aiming to solve the problem that the existing technology has difficulty in comprehensively evaluating parking space matching by considering multi-dimensional information such as path complexity and parking space idle time windows, resulting in the allocation and management of passenger parking spaces being unable to meet the needs of high-density passenger flow and complex scenarios.

[0005] In order to achieve the above-mentioned purpose, a method for allocating parking spaces for online ride-hailing passengers comprises the following steps:

[0006] After receiving a passenger's ride request, obtain comprehensive information about all parking spaces, including: passenger check-in time, passenger check-in location, passenger historical walking speed, parking space location, path complexity score, and parking space availability window;

[0007] Calculate the multi-dimensional scores of all parking spaces based on the comprehensive information, the multi-dimensional scores including: path complexity score and parking space idle time window score; where:

[0008] The path complexity score is calculated using the following formula:

[0009] ,

[0010] Where, For the The path complexity score of each parking space; The first The path complexity score of each parking space is set, where, when the path feature is a direct path, the predetermined first path complexity score is set; when the path feature requires an elevator transfer, the predetermined second path complexity score is set; when the path feature requires a long detour, the predetermined third path complexity score is set; when the path feature requires waiting for luggage, the predetermined fourth path complexity score is set; the first path complexity score is less than the second path complexity score, the second path complexity score is less than the third path complexity score, and the third path complexity score is less than the fourth path complexity score;

[0011] The parking space idle time window score is calculated using the following formula:

[0012] ,

[0013] ,

[0014] ,

[0015] Where, For the Parking space vacancy window score for each parking space; t a Estimated arrival time for passengers; t ws The starting time of the parking space idle time window is configured as the current system time or the time when the last passenger completed boarding; t sig Check-in time for passengers; Check-in location and The path distance between the parking spaces; v The passenger's historical walking speed; is the correction time introduced according to the path complexity; Z The overlap between the passenger's estimated arrival time and the parking space's free time window; t end The end time of the parking space free time window is configured as the expected start time of the next order; For the The overlap of parking spaces; Z min is the minimum value of the overlap of all boarding spaces; Zmax is the maximum value of the overlap of all boarding spaces;

[0016] Obtain a comprehensive parking space score based on the weighted sum of the multi-dimensional scores and the preset weight coefficients;

[0017] A pickup parking space is allocated for the order based on the comprehensive parking space score.

[0018] Furthermore, the comprehensive information also includes the number of parking queues and parking capacity. The multi-dimensional score also includes: passenger arrival time score and / or parking space load rate score; wherein the passenger arrival time score is calculated using the following formula:

[0019] ,

[0020] Where, For the Passenger arrival time score for each parking space;

[0021] The parking space load rate score is calculated using the following formula:

[0022] ,

[0023] ,

[0024] ,

[0025] Where, For the Parking space load factor score for each parking space; L a is the load rate of the current parking space; For the Historical order completion rate for parking spaces; n a The number of queues for parking spaces; N a is the parking space capacity; For the The number of historical completed orders for parking spaces; O sum For the The total number of historical orders for parking spaces.

[0026] Furthermore, the comprehensive parking space score is calculated using the following formula:

[0027] ,

[0028] ,

[0029] Where, For the Comprehensive parking space rating for each parking space; k 1 is the preset passenger arrival time scoring weight; k 2 is the preset parking space load rate scoring weight; k 3 is the preset path complexity scoring weight; k 4 is the preset parking space idle time window scoring weight.

[0030] Furthermore, the weight coefficient is adjusted according to a preset rule, wherein the rule includes:

[0031] When the trigger condition is that the passenger flow exceeds the preset passenger flow threshold, the parking space load rate score weight is increased and the path complexity score weight is reduced;

[0032] When the trigger condition is that there is a parking space with a queue number greater than 80% of the parking space capacity, the parking space load rate score weight of the parking space is reduced;

[0033] When the trigger condition is that the average waiting time of passengers is greater than the preset waiting time threshold, the parking space idle time window score weight is increased and the path complexity score weight is reduced;

[0034] When the trigger condition is that there is a parking space closed due to a fault, all weight coefficients of the parking space with the fault are adjusted to 0;

[0035] When the trigger condition is an obstacle on the path, the path complexity score weight of the boarding space affected by the obstacle is increased;

[0036] When the trigger condition is a concentrated arrival of passengers, the parking space free time window score weight is increased. Concentrated arrival of passengers is determined by pre-set passenger arrival conditions, such as when more passengers arrive than the scheduled number within a predetermined time period, or when a concentrated arrival of passengers is caused by flight delays.

[0037] Furthermore, a predetermined priority is pre-configured for each rule; when the triggering conditions of multiple rules are met at the same time, the weights are adjusted according to the order of the predetermined priorities.

[0038] Furthermore, the system also includes verifying whether the weight adjustment meets the standards using evaluation indicators, including: average passenger waiting time, failure rate of driver and passenger rendezvous within a designated parking space idle time window, and / or parking space load balance, where the parking space load balance is the standard deviation of all parking space load rate scores. If the weight adjustment does not meet the standards or there is an anomaly, one or more of the following methods are used to handle the situation:

[0039] Automatic rollback: if any of the evaluation indicators fails to meet the standard after the weight adjustment, the weight will be automatically restored to the last time it met the standard, and an exception log will be recorded;

[0040] Manual intervention: After three consecutive automatic rollbacks, an alarm is pushed to the administrator to manually adjust the rule base parameter threshold or weight parameter;

[0041] Fault isolation: For parking equipment failure scenarios, the system automatically isolates the faulty parking space and resets the weight through interface signals after repair.

[0042] Furthermore, the passenger check-in time and the passenger check-in location are obtained when the passenger checks in on the APP or mini program.

[0043] Furthermore, allocating a boarding space for an order includes one or more of the following:

[0044] For each order, the parking space with the highest comprehensive score is assigned as a candidate pickup space;

[0045] When the comprehensive parking scores of multiple parking spaces are the same, the parking space with the highest parking load rate score will be prioritized as a candidate for passenger pickup;

[0046] When all parking spaces are overloaded, a dynamic adjustment mechanism is triggered, including extending the parking space idle time window and / or activating spare parking spaces.

[0047] Furthermore, after a pick-up space is allocated for an order, the parking space number and free time window information of the pick-up space will be sent to the passenger and driver through the APP or mini program, and the number of parking spaces in the queue and the free time window information of the pick-up space will be updated at the same time.

[0048] In a second aspect, the present invention provides a device for allocating parking spaces for passengers on an online ride-hailing vehicle, comprising a memory and a processor, wherein the memory stores at least one program, and the at least one program is executed by the processor to implement the method for allocating parking spaces for passengers on an online ride-hailing vehicle as described above.

[0049] The above technical solution has the following technical effects:

[0050] By obtaining comprehensive parking space information, a multi-dimensional score is calculated, including a path complexity score and a parking space idle time window score. A comprehensive parking space score is obtained by weighting the multi-dimensional scores with preset weight coefficients. This comprehensive parking space score is then assigned to the order. This solves the problem that existing technologies struggle to comprehensively assess parking space matching by considering multi-dimensional information such as path complexity and parking space idle time windows, resulting in difficulties in allocating and managing parking spaces to meet the needs of high-density passenger flow and complex scenarios.

[0051] In a further embodiment, the multi-dimensional scoring also takes into account the passenger arrival time score and the parking space load rate score to achieve optimal allocation of parking space load and reduce passenger waiting time.

[0052] In a further embodiment, the weight coefficient is adjusted according to preset rules, so that the multi-dimensional comprehensive score can be adjusted according to the real-time operation situation, thereby improving the flexibility and accuracy of parking space allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of a method for allocating parking spaces for online ride-hailing passengers according to one embodiment of the present invention;

[0054] Figure 2 A schematic diagram of a rule structure for adjusting multi-dimensional scoring weight coefficients in one embodiment of the present invention;

[0055] Figure 3 This is a structural diagram of a device for allocating passenger parking spaces for online ride-hailing vehicles according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] To further illustrate various embodiments, the present invention is provided with accompanying drawings. These drawings form part of the present disclosure and are primarily used to illustrate the embodiments and, in conjunction with the relevant description in the specification, to explain the operating principles of the embodiments. By referring to these drawings, one of ordinary skill in the art will understand other possible embodiments and the advantages of the present invention. The components in the figures are not drawn to scale, and similar reference numerals are generally used to represent similar components.

[0057] The present invention will now be further described with reference to the accompanying drawings and specific embodiments.

[0058] Example 1:

[0059] Figure 1 This is a flow chart of a method for allocating parking spaces for online ride-hailing passengers according to an embodiment of the present invention. The method of this embodiment includes the following steps:

[0060] After receiving a passenger's ride-hailing order, comprehensive information about all parking spaces is obtained, including the passenger's check-in time, passenger check-in location, passenger's historical walking speed, parking space location, path complexity score, number of parking space queues, parking space capacity, and parking space idle time window. The parking space idle time window is defined as the time period from the current system time or the time when the last passenger completed boarding to the expected start of the next order. In one specific implementation, the passenger's check-in time and the passenger's check-in location are obtained by the passenger scanning a dedicated QR code in the hub exit corridor and launching the hub-specific APP or mini-program to check in.

[0061] Calculate a multi-dimensional score for all parking spaces based on the comprehensive information, including: path complexity score, parking space idle time window score, passenger arrival time score and / or parking space load rate score; where:

[0062] The path complexity score is calculated using the following formula:

[0063] ,

[0064] Where, For the The path complexity score of each parking space; The first The path complexity score of each parking space is set, where, when the path feature is a direct path, the predetermined first path complexity score is set; when the path feature requires an elevator transfer, the predetermined second path complexity score is set; when the path feature requires a long detour, the predetermined third path complexity score is set; when the path feature requires waiting for luggage, the predetermined fourth path complexity score is set; the first path complexity score is less than the second path complexity score, the second path complexity score is less than the third path complexity score, and the third path complexity score is less than the fourth path complexity score; in a specific implementation, the first path complexity score is 1; the second path complexity score is 2; the third path complexity score is 3; and the fourth path complexity score is 4. The higher the path complexity score, the lower the path complexity score.

[0065] The parking space idle time window score is calculated using the following formula:

[0066] ,

[0067] ,

[0068] Where, For the Parking space vacancy window score for each parking space; Z The overlap between the passenger's estimated arrival time and the parking space's free time window; t end The end time of the parking space free time window is configured as the estimated start time of the next order. In a specific implementation, if there are no subsequent orders, it is set to a larger value, such as 24 hours; For the The overlap of parking spaces; Z min is the minimum value of the overlap of all boarding spaces; Z max is the maximum value of the overlap of all boarding spaces;

[0069] The passenger arrival time score is calculated using the following formula:

[0070] ,

[0071] ,

[0072] Where, For the Passenger arrival time score for each parking space; t a Estimated arrival time for passengers; t ws The starting time of the parking space idle time window is configured as the current system time or the time when the last passenger completed boarding. In one specific implementation, the larger value between the two is taken. t sig Check-in time for passengers; Check-in location and The path distance between the parking spaces; v The passenger's historical walking speed; This is a predetermined correction time introduced based on the complexity of the path. In one specific implementation, when the path characteristic requires an elevator change, the correction time is 20 seconds. This specific time is only exemplary and the specific correction time is pre-set based on the specific scenario and situation.

[0073] The parking space load factor score is calculated using the following formula:

[0074] ,

[0075] ,

[0076] ,

[0077] Where, For the Parking space load factor score for each parking space; L a is the load rate of the current parking space; For the Historical order completion rate for parking spaces; n a The number of queues for parking spaces; N a is the parking space capacity; For the The number of historical completed orders for parking spaces; O sum For the The total number of historical orders for parking spaces;

[0078] The comprehensive parking space score is obtained by weighted summation of the multi-dimensional scores and the preset weight coefficients, and is calculated using the following formula:

[0079] ,

[0080] ,

[0081] Where, For the Comprehensive parking space rating for each parking space; k 1 is the preset passenger arrival time scoring weight; k 2 is the preset parking space load rate scoring weight; k 3 is the preset path complexity scoring weight; k 4 is the preset parking space idle time window scoring weight. In a specific implementation, the initial weights are k1=0.4, k2=0.3, k3=0.2, and k4=0.1;

[0082] Assign a pickup space to the order based on the overall parking space score, including one or more of the following:

[0083] For each order, the parking space with the highest comprehensive score is assigned as a candidate pickup space;

[0084] When the comprehensive parking scores of multiple parking spaces are the same, the parking space with the highest parking load rate score will be prioritized as a candidate for passenger pickup;

[0085] When all parking spaces are overloaded, a dynamic adjustment mechanism is triggered, including extending the parking space idle time window and / or activating spare parking spaces.

[0086] In a specific implementation, after a pick-up space is allocated for an order, the parking space number and free time window information of the pick-up space are sent to the passenger and driver through an APP or mini-program, and the number of parking spaces in the queue and the free time window information of the parking space are updated at the same time.

[0087] Example 2:

[0088] This embodiment of the present invention further includes adjusting the weight coefficients of each score in the comprehensive score according to preset rules. For example, a corresponding weight coefficient adjustment strategy is set according to a predetermined rule. Figure 2 This is a schematic diagram of the rule structure for adjusting the multi-dimensional scoring weight coefficients in one embodiment of the present invention. This embodiment combines typical scenarios of transportation hubs to design weight adjustment rules covering typical scenarios such as peak hours, parking congestion, long waiting times, equipment failures, and external events, and clarifies the weight adjustment strategy and priority in each scenario, such as Figure 2 As shown, the preset rules include one or more of the following:

[0089] When the trigger condition is that the passenger flow exceeds the preset passenger flow threshold, the parking space load rate score weight is increased and the path complexity score weight is reduced. The priority of this rule is configured as the first priority;

[0090] When the trigger condition is that there is a parking space with a queue number greater than 80% of the parking space capacity, the parking load rate score weight of the parking space is reduced, and the priority of this rule is configured as the second priority;

[0091] When the trigger condition is that the average waiting time of passengers is greater than the preset waiting time threshold, the parking space idle time window score weight is increased and the path complexity score weight is reduced. The priority of this rule is configured as the second priority;

[0092] When the trigger condition is that there is a parking space closed due to a fault, all weight coefficients of the parking space with the fault are adjusted to 0, and the priority of this rule is configured as the third priority;

[0093] When the trigger condition is an obstacle on the path, the path complexity score weight of the passenger boarding space affected by the obstacle is increased, and the priority of this rule is configured as the third priority;

[0094] When the trigger condition is a concentrated arrival of passengers due to flight delays, the scoring weight of the parking space free time window is increased, and the priority of this rule is configured as the third priority;

[0095] In a specific implementation, a predetermined priority is pre-configured for each rule; when the trigger conditions of multiple rules are met at the same time, the weights are adjusted according to the order of the predetermined priorities, where the first priority is lower than the second priority, and the second priority is lower than the third priority.

[0096] In other implementations, more or different weight coefficient adjustment rules are set according to specific circumstances, such as the specific circumstances of an airport; more or fewer priorities are set according to the setting of the rules.

[0097] In one specific implementation, the weight adjustment is verified to be up to standard using evaluation indicators, including: average passenger waiting time, failure rate of driver-passenger rendezvous within a designated parking space idle time window, and / or parking space load balance. In one specific implementation, the parking space load balance is the standard deviation of all parking space load rate scores. When the weight adjustment does not meet the standard or there is an anomaly, one or more of the following methods are used to handle the situation:

[0098] Automatic rollback: if any of the evaluation indicators fails to meet the standard after the weight adjustment, the weight will be automatically restored to the last time it met the standard, and an exception log will be recorded;

[0099] Manual intervention: After three consecutive automatic rollbacks, an alarm is pushed to the administrator to manually adjust the rule base parameter threshold or weight parameter;

[0100] Fault isolation: For parking equipment failure scenarios, the system automatically isolates the faulty parking space and resets the weight through interface signals after repair.

[0101] Example 3:

[0102] Figure 3 FIG. 1 is a structural diagram of a parking space allocation device for online car-hailing passengers in one embodiment of the present invention. Figure 3 As shown, the device includes a processor 301, a memory 302, a bus 303, and a computer program stored in the memory 302 and executable on the processor 301. The processor 301 includes one or more processing cores. The memory 302 is connected to the processor 301 via the bus 303. The memory 302 is used to store program instructions. When the processor executes the computer program, the steps in the above-mentioned method embodiment of the first embodiment of the present invention are implemented.

[0103] Furthermore, as an executable solution, the online car-hailing passenger parking space allocation device can be a computer unit, which can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The computer unit may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that the composition structure of the above-mentioned computer unit is only an example of a computer unit and does not constitute a limitation of the computer unit. It can include more or fewer components than the above-mentioned, or a combination of certain components, or different components. For example, the computer unit may also include input and output devices, network access devices, buses, etc., which are not limited in the embodiments of the present invention.

[0104] Furthermore, as an executable solution, the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the computer unit and connects various parts of the entire computer unit using various interfaces and lines.

[0105] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the computer unit by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on the use of the mobile phone. Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0106] Although the present invention has been particularly shown and described in conjunction with preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made to the present invention without departing from the spirit and scope of the invention as defined in the appended claims, and all such changes are within the scope of protection of the present invention.

Claims

1. A method for allocating parking spaces for online car-hailing, characterized in that: The following steps are involved: After receiving a passenger's ride request, obtain comprehensive information about all parking spaces, including: passenger check-in time, passenger check-in location, passenger historical walking speed, parking space location, path complexity score, and parking space availability window; Calculate the multi-dimensional scores of all parking spaces based on the comprehensive information, the multi-dimensional scores including: path complexity score and parking space idle time window score; where: The path complexity score is calculated using the following formula: ， Where, For the The path complexity score of each parking space; The first The path complexity score of each parking space is set, where, when the path feature is a direct path, the predetermined first path complexity score is set; when the path feature requires an elevator transfer, the predetermined second path complexity score is set; when the path feature requires a long detour, the predetermined third path complexity score is set; when the path feature requires waiting for luggage, the predetermined fourth path complexity score is set; the first path complexity score is less than the second path complexity score, the second path complexity score is less than the third path complexity score, and the third path complexity score is less than the fourth path complexity score; The parking space idle time window score is calculated using the following formula: ， ， ， Where, For the Parking space vacancy window score for each parking space; t a Estimated arrival time for passengers; t ws The starting time of the parking space idle time window is configured as the current system time or the time when the last passenger completed boarding; t sig Check-in time for passengers; Check-in location and The path distance between the parking spaces; v The passenger's historical walking speed; is the scheduled correction time introduced according to the path complexity; Z The overlap between the passenger's estimated arrival time and the parking space's free time window; t end The end time of the parking space free time window is configured as the expected start time of the next order; For the The overlap of parking spaces; Z min is the minimum value of the overlap of all boarding spaces; Z max is the maximum value of the overlap of all boarding spaces; Obtain a comprehensive parking space score based on the weighted sum of the multi-dimensional scores and the preset weight coefficients; A pickup parking space is allocated for the order based on the comprehensive parking space score.

2. The method for allocating parking spaces for online car-hailing passengers according to claim 1, characterized in that: The comprehensive information also includes the number of parking queues and parking capacity. The multi-dimensional score also includes: passenger arrival time score and / or parking space load rate score; wherein the passenger arrival time score is calculated using the following formula: ， Where, For the Passenger arrival time score for each parking space; The parking space load rate score is calculated using the following formula: ， ， ， Where, For the Parking space load factor score for each parking space; L a is the load rate of the current parking space; For the Historical order completion rate for parking spaces; n a The number of queues for parking spaces; N a is the parking space capacity; For the The number of historical completed orders for parking spaces; O sum For the The total number of historical orders for parking spaces.

3. The method for allocating parking spaces for online car-hailing passengers according to claim 2, characterized in that: The comprehensive parking space score is calculated using the following formula: ， ， Where, For the Comprehensive parking space rating for each parking space; k 1 is the preset passenger arrival time scoring weight; k 2 is the preset parking space load rate scoring weight; k 3 is the preset path complexity scoring weight; k 4 is the preset parking space idle time window scoring weight.

4. The method for allocating parking spaces for online car-hailing passengers according to claim 3, characterized in that: The method further includes adjusting the weight coefficient according to a preset rule, wherein the rule includes one or more of the following: When the trigger condition is that the passenger flow exceeds the preset passenger flow threshold, the parking space load rate score weight is increased and the path complexity score weight is reduced; When the trigger condition is that there is a parking space with a queue number greater than 80% of the parking space capacity, the parking space load rate score weight of the parking space is reduced; When the trigger condition is that the average waiting time of passengers is greater than the preset waiting time threshold, the parking space idle time window score weight is increased and the path complexity score weight is reduced; When the trigger condition is that there is a parking space closed due to a fault, all weight coefficients of the parking space with the fault are adjusted to 0; When the trigger condition is an obstacle on the path, the path complexity score weight of the boarding space affected by the obstacle is increased; When the trigger condition is the concentrated arrival of passengers, the scoring weight of the parking space idle time window is increased.

5. The method for allocating parking spaces for online car-hailing passengers according to claim 4, characterized in that: A predetermined priority is configured for each rule in advance; when the triggering conditions of multiple rules are met at the same time, the weights are adjusted according to the order of the predetermined priorities.

6. The method for allocating parking spaces for online car-hailing passengers according to claim 5, characterized in that: The method also includes verifying whether the weight adjustment meets the standards using evaluation indicators, including: average passenger waiting time, failure rate of driver and passenger rendezvous within a designated parking space idle time window, and / or parking space load balance, where the parking space load balance is the standard deviation of all parking space load rate scores. If the weight adjustment does not meet the standards or there is an anomaly, one or more of the following methods are used to handle the situation: Automatic rollback: if any of the evaluation indicators fails to meet the standard after the weight adjustment, the weight will be automatically restored to the last time it met the standard, and an exception log will be recorded; Manual intervention: After three consecutive automatic rollbacks, an alarm is pushed to the administrator to manually adjust the rule base parameter threshold or weight parameter; Fault isolation: For parking equipment failure scenarios, the system automatically isolates the faulty parking space and resets the weight through interface signals after repair.

7. The method for allocating parking spaces for online ride-hailing passengers according to claim 1, characterized in that: The passenger check-in time and the passenger check-in location are obtained when the passenger checks in on the APP or mini program.

8. The method for allocating parking spaces for online ride-hailing passengers according to claim 1, characterized in that: Allocating a pickup space for an order may include one or more of the following: For each order, the parking space with the highest comprehensive score is assigned as a candidate pickup space; When the comprehensive parking scores of multiple parking spaces are the same, the parking space with the highest parking load rate score will be prioritized as a candidate for passenger pickup; When all parking spaces are overloaded, a dynamic adjustment mechanism is triggered, including extending the parking space idle time window and / or activating spare parking spaces.

9. The method for allocating parking spaces for online ride-hailing passengers according to claim 1, characterized in that: After allocating a pick-up space for an order, the parking space number and free time window information of the pick-up space will be sent to the passenger and driver through the APP or mini program, and the number of parking spaces in the queue and the free time window information of the pick-up space will be updated at the same time.

10. A device for allocating parking spaces for online car-hailing, characterized in that: It includes a memory and a processor, the memory stores at least one program, and the at least one program is executed by the processor to implement the method for allocating passenger parking spaces for online car-hailing as described in any one of claims 1 to 9.

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