Baseroom reservation method and electronic equipment

Abstract

The invention discloses a compartment reservation method and electronic equipment, and the method comprises the steps: receiving compartment reservation information which comprises a to-be-reserved time period; obtaining a predetermined time period of each predeterminable compartment, and calculating fragment time periods according to the to-be-predetermined time period and each predetermined time period; calculating a recommendation weight of each fragment time period, and determining a target predeterminable compartment corresponding to a target fragment time period according to the recommendation weight; and binding the booking information of the compartment with the target booking compartment, and updating a target predetermined time period of the target booking compartment according to the to-be-booked time period. According to the method, the recommendation weight of the fragment time period generated after the to-be-predetermined time period is added is calculated for all the predeterminable compartments capable of meeting the predetermined requirements, the predeterminable compartments are determined according to the recommendation weight, and generation of fragment time periods which cannot be digested is avoided as far as possible.

Description

Technical Field

[0001] This application relates to the field of resource reservation technology, and in particular to a method and electronic device for reserving private rooms. Background Technology

[0002] For private rooms, there are idle and reserved times. As the number of customer reservations increases, the remaining idle time becomes more fragmented, resulting in a waste of the available time for private rooms and an inability to maximize the sales time of inventory. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and electronic device for booking private rooms, thereby reducing fragmented time in inventory.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for booking a private room includes the following steps: Receive private room reservation information, wherein the private room reservation information includes the time period to be reserved; Obtain the reserved time slots for each bookable private room, and calculate the fragmented time range based on the unbooked time slots and each reserved time slot; Calculate the recommendation weight for each of the fragmented time periods, and determine the target bookable private room corresponding to the target fragmented time period based on the recommendation weight; The room reservation information is bound to the target bookable room, and the booked time period is updated according to the unbooked time period.

[0005] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: An electronic device includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for reserving a private room.

[0006] The beneficial effects of this invention are as follows: after receiving the private room reservation information, instead of directly matching an available private room based on the waiting time slot, for all available private rooms that can meet the reservation requirements, the recommended weight of the corresponding fragmented time slots after adding the waiting time slot is calculated, and the target available private room is determined based on the recommended weight. In this way, while meeting the personalized time needs of customers, the generation of fragmented time slots that cannot be utilized is avoided as much as possible, thereby reducing the number of fragments in the inventory data and improving the utilization rate of private rooms. Attached Figure Description

[0007] Figure 1 A flowchart illustrating the steps of a private room reservation method and electronic device provided in this embodiment of the invention; Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0008] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0009] For private room resources, as long as users are allowed to customize their booking time, there will inevitably be unbooked fragmented time. These unbooked fragmented times prevent the utilization rate of private rooms from being maximized. For example, if a user books a private room and leaves early, subsequent customers can only avoid the previous customer's booking time and choose other booking times. Furthermore, there may also be fragmented time between customer A's booked departure time and customer B's booked arrival time. Fragmented time also makes it difficult to meet long-term booking demands. For example, customer C needs to book a private room for an entire afternoon, but private rooms a1 and a2 are already booked for a period of time, resulting in no single private room being available for a continuous afternoon of free time.

[0010] To address the aforementioned technical problems, this application provides a method and electronic device for booking private rooms, applicable to various booking scenarios, such as KTV rooms and shared tea room rooms, where time and space are the commodities. Please refer to... Figure 1 One method for booking a private room according to this application includes steps 110 to 140.

[0011] Step 110: Receive private room reservation information, which includes the time period to be reserved. For example, the time period to be reserved is 19:00-21:00, which can be the time period that the user wants to reserve the treasure chest through the user terminal.

[0012] Step 120: Obtain the reserved time slots for each bookable private room, and calculate fragmented time slots based on the unbooked time slots and each reserved time slot.

[0013] In one optional implementation, if the time period to be booked is not consecutive with the most recent booked time period before or after it, then the resulting fragmented time period is calculated. For example, if there are three bookable private rooms, namely room A, room B, and room C, then the booked time period for room A is 16:00-18:00, the booked time period for room B is 15:00-17:00, and the booked time period for room C is empty. The current time period to be booked is 19:00-21:00. Therefore, the time period to be booked is added to room A, resulting in the fragmented time period 18:00-19:00. The time period to be booked is added to room B, resulting in the fragmented time period 17:00-19:00. The time period to be booked is added to room C, but since there is no booked time period for room C, no new fragmented time period is generated.

[0014] If the time period to be booked is consecutive to the most recent booked time period before or after it, then the time period to be booked is considered a fragmented time period. For example, if there are three bookable private rooms, namely room D, room E, and room F, then the booked time periods for room D are 16:00-18:00 and 19:00-21:00, the booked time periods for room E are 15:00-17:00 and 19:00-21:00, and the booked time periods for room F are empty. The time period to be booked is 18:00-19:00. This time period is added to room D. Since the start time of the time period to be booked corresponds to the end time of the booked time period, that is, the time period to be booked is consecutive to the most recent booked time period before it, then 18:00-19:00 is considered a fragmented time period. Similarly, room E has a time period to be booked that is consecutive to the most recent booked time period after it, and the corresponding fragmented time period is also 18:00-19:00. To improve calculation accuracy, the system can be configured to obtain the first pre-booked time period before the expected time period and the second pre-booked time period after the expected time period. The expected time period will be directly used as a fragmented time period only if the difference between the end time of the first pre-booked time period and the start time of the second pre-booked time period is less than a preset duration, such as 2 hours.

[0015] In one alternative implementation, since there may be cases where the reserved time slots for some private rooms are consecutive with the unreserved time slots for others, and not consecutive for others, in order to make it easier to uniformly judge the recommended weights calculated for different private rooms, the condition can be ignored and the new fragments generated by calculation can be applied to all private rooms.

[0016] Step 130: Calculate the recommendation weight for each of the fragmented time slots, and determine the target bookable private rooms corresponding to the target fragmented time slots based on the recommendation weights. That is, calculate the recommendation weight for the obtained fragmented time slots 18:00-19:00 and 17:00-19:00 respectively. If there are no fragmented time slots, they are directly excluded. That is, it is preferred to make reservations in bookable private rooms with existing orders. Determine the target bookable private rooms based on the calculation results.

[0017] Step 140: Bind the private room reservation information to the target available private room, and update the target booked time period of the target available private room according to the unbooked time period. For example, if private room A is determined to be the target available private room, then bind the private room reservation information to private room A, such as binding the identity information in the private room reservation information to private room A, and update the unbooked time period corresponding to the private room reservation information to the booked time period of private room A.

[0018] As described above, upon receiving room reservation information, the system does not directly match an available room based on the pending reservation time slot. Instead, it calculates the recommendation weight of fragmented time slots after adding the pending reservation time slot for all available rooms that can meet the reservation demand. If a new fragmented time slot is generated, it prioritizes recommending available rooms with lower recommendation weights for that time slot to prevent high-weight time slots from becoming fragmented time slots. If an existing fragmented time slot can be eliminated, it prioritizes scheduling orders for the eliminateable fragmented time slots with higher weights. This process avoids high-weight time slots as much as possible when new fragmented time slots are generated and schedules high-weight time slots when fragmented time slots can be eliminated, thereby reducing fragmented time slots in the inventory and improving the overall utilization rate of the rooms. If, during the judgment process, some private rooms generate new fragmented time slots while others eliminate existing fragmented time slots, a normalization method can be used for comparison. For example, for generating new fragmented time slots, set a maximum recommendation weight Amax and a minimum recommendation weight Amin. When calculating the recommendation weight A1 of the fragmented time slot, normalize it to A2 = (A1 - Amin) / (Amax - Amin). For eliminating fragmented time slots, set a maximum recommendation weight Bmax and a minimum recommendation weight Bmin. When calculating the recommendation weight B1 of the fragmented time slot, normalize it to B2 = (B1 - Bmin) / (Bmax - Bmin).

[0019] In one embodiment of this application, step 120, which calculates the fragmented time period based on the time period to be predetermined and each predetermined time period, includes steps 121 to 123.

[0020] Step 121: Obtain the first start time and the first end time of the time period to be reserved, and obtain the second start time and the second end time of the time period already reserved; taking the above-mentioned private room A as an example, the first start time of the time period to be reserved is 19:00, the first end time of the time period to be reserved is 21:00, the second start time of the time period already reserved is 16:00, and the second end time of the time period already reserved is 18:00.

[0021] Step 122: Obtain the later start time from the first start time and the second start time, and obtain the earlier end time from the first end time and the second end time. For example, if the later start time is 19:00 and the earlier end time is 18:00, since only rooms with available time slots are considered available when determining the availability of private rooms, there will be no overlap between the waiting-to-book time slot and the already booked time slot. If there are multiple already booked time slots, calculate the fragmented time slots by obtaining the most recent already booked time slot before the waiting-to-book time slot and the most recent already booked time slot after the waiting-to-book time slot.

[0022] Step 123: Obtain the fragmented time period from the later start time to the earlier end time. For example, if the later start time is 19:00 and the earlier end time is 18:00, then the fragmented time period is 18:00-19:00.

[0023] In this way, regardless of whether the time period to be booked is before or after the already booked time period, the fragmented time periods between the two periods can be accurately identified. The weights of these fragmented time periods are then further calculated to minimize the generation of fragmented time periods within commonly used time periods.

[0024] In one embodiment of this application, steps 101 to 103 are also included.

[0025] Step 101: Obtain the private rooms with available time slots during the pending reservation period to obtain the bookable private rooms. Step 101 can be executed between steps 110 and 120. That is, only private rooms that have not been booked within the time slot corresponding to the pending reservation period will be selected as bookable private rooms. Continuing the above example, if the pending reservation period is 19:00-21:00, and the available time slot for private rooms is 20:00-24:00, then those rooms will not be selected as bookable private rooms. This reduces the difficulty of calculating fragmented time slots in the subsequent calculations and improves the system's processing efficiency in scenarios with multiple concurrent orders.

[0026] Step 102: If the number of bookable private rooms is 1, then the bookable private room is selected as the target bookable private room and step 140 is executed.

[0027] Step 103: If the number of available private rooms is greater than 1, then proceed to step 120.

[0028] As described above, after obtaining the time slot to be booked, the system first filters out private rooms that are available during the corresponding time slot. This means that private rooms with no pre-booked time slots within the desired time slot are considered available for booking. If only one available room exists, no further judgment is needed; it can be directly provided as the target available room. If multiple available rooms exist, then it is necessary to further calculate the weight of the fragmented time slots after adding them to the desired time slot to determine which available room to select as the target available room. This process saves time by eliminating subsequent calculations when only one available room exists, thus accelerating processing efficiency.

[0029] In one embodiment of this application, calculating the recommendation weight for each fragment time period in step 130 includes step 131.

[0030] Step 131: Calculate the recommended weight of the fragmented time slot based on its length, price factor, and utilization rate factor. The price factor and utilization rate factor are obtained from the historical time slot prices and historical time slot booking rates of all private rooms.

[0031] In this way, the weight of fragmented time slots is obtained based on the length, price factor, and utilization factor. The total weight of fragmented time slots can be sorted by comprehensively considering the attributes of each dimension, making the final fragmented time slots easier to select.

[0032] In one embodiment of this application, the calculation of the recommendation weight for each fragmented time period in step 130 includes: N = t × p × u; In the formula, N represents the recommendation weight, t represents the length, p represents the price factor, and u represents the utilization rate factor; the higher the price during the historical time period, the larger the price factor; the higher the booking rate during the historical time period, the smaller the utilization rate factor.

[0033] Taking the three private rooms A, B, and C as examples, the recommendation weight for the fragmented time slot of 18:00-19:00 is N=60×1.8×1.3=140.4, and the historical reservation rate for this time slot is low, with a utilization factor of 1.3; the recommendation weight for the fragmented time slot of 17:00-19:00 is N=120×1.0×0.9=108, with a utilization factor of 0.9; private room C does not generate fragmented time and is not recommended by default.

[0034] Taking the three private rooms D, E, and F as examples, the recommended weight of the fragmented time period 18:00-19:00 eliminated after adding private room D is 140.4, and it is completely merged into the waiting time period 18:00-19:00; the recommended weight of the fragmented time period 18:00-19:00 eliminated after adding treasure chest E is 54. Since the recommended weight of its corresponding fragmented time period 17:00-19:00 is 108, and it has not been completely eliminated, the value is taken as half. Therefore, private room A will be recommended first. After insertion, fragments with high recommended weights are merged to minimize the weight of existing fragments, so as to maximize the utilization of peak period resources, avoid empty private rooms, and reduce fragment data in the inventory.

[0035] In this way, the longer the fragmented time slot, the higher its price, and the lower the historical booking rate of the time slot, the greater the corresponding recommendation weight. This can generate as many consecutive fragmented time slots as possible, avoid the problem of fragmented time slots being too short to be booked, and prioritize booking time slots with low historical booking rates.

[0036] In one embodiment of this application, steps 210 to 230 are further included. Steps 210 to 230 obtain the price factor and utilization factor prior to step 130.

[0037] Step 210: Obtain historical data for all private rooms. This historical data includes unit time periods sliced ​​into preset durations, along with price data, number of slices, total slice duration, and average slice duration for each unit time period. If there are multiple types of private rooms, such as large, medium, and small rooms, historical data can be collected separately for each type. Since the room reservation information received in step 110 typically includes room types, the recommendation weight calculated in subsequent step 130 is also based on the specific room type.

[0038] For example, the original historical data includes room types, a list of booked time slots for each room, and a list of fragmented time slots. The fragmented time slot list in the historical data is the list of unbooked available time slots. Both the booked time slot list and the fragmented time slot list include the start time, end time, and length of each time slot, such as 18:00-19:00, 60 minutes. By slicing the original historical data into units of time slots, we can obtain the cancellation rate, average order value, number of fragments, total fragment duration, and average fragment duration for each time slot. The fragment distribution pattern can be visualized with the X-axis representing the unit of time slot and the Y-axis representing the number of fragments, total fragment duration, or average fragment duration. For example, if the time period is half an hour, the X-axis coordinates would be 12:00-12:30, 12:30-13:00, etc.; the number of fragments is the total number of fragment time periods within a unit time period, counted separately for each room type; the total fragment duration is the cumulative length of all fragment time periods within that unit time period; the average fragment duration is the average length of a single fragment within that unit time period. For example, when obtaining the fragment distribution of a "small room from 19:00-19:30 (peak time period)," the possible statistical results are: there are 3 fragments within that unit time period, with a total duration of 45 minutes, and an average fragment duration of 15 minutes. Another example is that the probability of a fragment being booked for a "small room from 14:00-14:30 (off-peak time period)" is 70% (easy to book), so the utilization factor will be set to 0.9 (low value) to prioritize booking other time slots. The fragment booking probability = the total actual booking duration of fragments within that unit time period / the total duration of fragments within that unit time period.

[0039] For example: 18:00-22:00 (unit time period), the total time of fragments in the same type of private room is 2 hours (that is, all fragments in this time period add up to 2 hours); in historical data, the total time of these fragments actually booked by customers is 0.8 hours; then the probability of fragments being booked = 0.8 / 2 = 40%, which means that there is a 40% chance that the fragments in this time period will be used.

[0040] Step 220: Obtain the price factor for each unit time period based on the price data. For example, it may also include time period attributes, such as peak or off-peak periods, which can be set by the merchant. For example, 19:00-1:00 the next day is a peak period. The price factor corresponding to the peak period is higher, and the price factor corresponding to the off-peak period is lower. For example, the price factor for the peak period is 1.8, the price factor for the transition from off-peak to peak period is 0.9, and the price factor for the off-peak period is 0.8.

[0041] Step 230: Obtain the utilization factor for each unit time period based on the number of fragments, the total duration of fragments, and the average duration of fragments.

[0042] In this way, price factors and utilization factors are obtained based on the historical data of all private rooms. Common factors are summarized from the historical data of private rooms to summarize the characteristics of different time periods. Thus, the recommendation weight of fragmented time periods can be calculated based on the unit time period in which the fragmented time periods are located, thereby reducing the number of fragmented time periods.

[0043] In one embodiment of this application, step 132 is included after step 130: if the length of the target fragmented time period is less than a length threshold, then the total price of the target fragmented time period is calculated according to a preset discount. For example, if a customer who places an order later has a fragmented time period between the start time of the unbooked time period in the room reservation information and the end time of the nearest booked time period in the target bookable room that is before the unbooked time period, and the length of the fragmented time period is less than a set value, such as 30 minutes, then a preset discount is applied to recommend the sale to the user who is sending room reservation information, in order to improve the utilization rate of the fragmented time period. For example, if the discounted price is 60 yuan per hour and the fragmented time is 13 minutes, then the selling price is 13 yuan.

[0044] After step 140, step 141 is also included: returning the total price and the target fragmented time period to the user terminal, determining whether a confirmation message has been received from the user terminal, and if so, updating the target booked time period of the target bookable room according to the unbooked time period and the target fragmented time period.

[0045] In one embodiment of this application, after step 140, step 142 is further included: obtaining the room reservation information corresponding to the start time of the target fragmented time period, returning the total price and the target fragmented time period to the user terminal corresponding to the bound room reservation information, determining whether a confirmation message has been received from the user terminal, and if so, storing the fragmented time period as associated with the bound room reservation information, and updating the target reserved time period of the target bookable room according to the fragmented time period. For example, if the customer who sent the room reservation information has already placed an order, and the room reservation information has a fragmented time period with the pre- or post-reserved time period, a message is sent to remind the customer of the previous or subsequent order that they can purchase the discounted time period at a preset discounted price.

[0046] Steps 141 and 142 can be performed individually or sequentially, with the other being executed only after a confirmation message or rejection message is received. This avoids the problem of fragmented time slots being confirmed and booked by different parties, while increasing the probability of fragmented time slots being sold to reduce the number of fragmented time slots in inventory. Specifically, if multiple target fragmented time slots are generated after the addition of the waiting-to-book time slots, step 142 or step 141 is executed sequentially for each target fragmented time slot to avoid duplicate bookings. Priority can be given to querying the user terminal corresponding to the room booking information with shorter target fragmented time slots. Furthermore, if the duration of the fragmented time slot is too short, it can be directly given to the room booking information that came before or after it, reducing the number of fragmented time slots in inventory, alleviating data storage pressure, and increasing customer loyalty.

[0047] In this way, when the length of the target fragment time is less than the length threshold, the probability of it being booked as a complete time period is extremely low, or it is already less than the reservation unit time set by the platform. For example, some platforms can only make reservations in lengths of one hour or half an hour. Therefore, for target fragment time periods of less than one hour or half an hour, it is impossible to book them as a complete time period. So, if the length of the target fragment time period is less than the length threshold, the target fragment time period is directly recommended to the user who is currently sending room reservation information with a total price calculated by a preset discount. That is, the target fragment time period that is too short is directly recommended to the user who is currently booking a room with a discount, thereby improving the utilization rate of fragment time periods.

[0048] In one embodiment of this application, steps 310 to 350 are also included. Step 310: Obtain the current time and the end time of all the booking information of the target bookable private rooms.

[0049] Step 320: Obtain the target private room reservation information where the end time is after the current time and less than the reminder threshold.

[0050] Step 330: If the first bookable private room corresponding to the target private room reservation information has a target fragment time period with the end time as the start time, then calculate the total price of the target fragment time period according to the preset discount.

[0051] Step 340: Send the total price and room expiration reminder to the user's terminal.

[0052] Step 350: If the confirmation information from the user terminal is received, the reserved time period of the first bookable room is updated according to the target fragmented time period, and the target fragmented time period is bound to the target room reservation information.

[0053] It is evident that, in addition to recommending fragmented time slots when processing room reservation information, the system also recommends fragmented time slots during the process of executing room reservation information. For example, if a user has already consumed in a room and there is a fragmented time slot between the current order and the next order, then when a renewal reminder is triggered, the song selection screen will display a renewal reminder and a preset discount price (e.g., 60 yuan per hour, with a fragmented time slot of 13 minutes, the price is 13 yuan), recommending that the user is currently using a room to further reduce the fragmented time slots in the inventory. The renewal reminder can be configured, for example, 30 minutes, 15 minutes, or 5 minutes before the end time. It can be set to a trapezoidal pattern at each time point, or a single reminder can be selected.

[0054] As described above, for ongoing private room reservations, a reminder will be sent when the current time approaches the end time of the reserved time slot. If there are fragmented time slots starting from the end time, these fragmented time slots will be recommended to the user while the reminder is being sent. If a confirmation message is received from the user, the fragmented time slot will also be directly incorporated into the private room reservation information. In this way, fragmented time slots that cannot be recorded as complete segments will be sold at a discount to the user corresponding to the previous private room reservation information, making them more likely to be purchased and thus improving the utilization rate of fragmented time slots.

[0055] In one embodiment of this application, steps 410 to 420 are also included.

[0056] Step 410: Obtain the target private room bound to the private room reservation information being executed.

[0057] Step 420: Collect the room data of the target private room, and determine whether there is an early departure based on the room data. If so, obtain the current time and take the time from the current time to the end time of the room reservation information as the idle time period of the target private room.

[0058] For example, after confirming that a private room is empty, on-site staff can adjust the room data on the management platform, changing the room status to "Available," and the available time slot will then be relisted for booking. Alternatively, room data, including the list of requested songs, can be retrieved in real time to determine if the list has remained unchanged within a preset time period. For instance, if the list remains unchanged for 30 minutes, the room status can be automatically changed to "Available." A confirmation process can be added here; for example, if the list remains unchanged for 30 minutes, a confirmation message can be sent to the user's device. Only after receiving confirmation from the user that they have left should the room status be changed to "Available," further reducing the possibility of accidental operation.

[0059] As described above, the system retrieves the data of the target private room corresponding to the room reservation information during the execution process. If there is an early departure, the current time to the end time of the room reservation information is re-established as the idle time period. The idle time period can be reserved, thereby realizing the automatic circulation of the remaining time after early departure. In addition to reducing fragmented time at the inventory level, it also reduces fragmented time from a practical perspective.

[0060] In one embodiment of this application, the method further includes: scanning fragmented time periods during off-peak hours before the daily business day, predicting future demand based on historical data, and "compactly arranging" the already bound room reservation information for similar types of private rooms. That is, only the rooms bound to the room reservation information are modified, without adjusting the start or end time of the room reservation information. For example, orders for room B from 16:00 to 17:00 and room C from 14:00 to 15:00 are moved to room A. The original reserved time period for room A was 13:00 to 14:00. After merging, the time interval becomes 13:00 to 14:00, 14:00 to 15:00, and 16:00 to 17:00. Although the middle 15:00 to 16:00 is a new fragmented time period, the original room B and room C become completely vacant.

[0061] Price factors can be dynamically updated based on time period weights. For example, peak, peak-to-off-peak, and off-peak coefficients can be adjusted based on recent booking data to adapt to actual business trends.

[0062] The following is an example of a private room reservation method from this application being applied in a real-world scenario.

[0063] Scenario: There are 2 private rooms available, with reserved time slots of 1:00-2:30 and 2:40-3:00 respectively. The interval between these slots is 10 minutes (which is within the merchant's preset time slot).

[0064] Algorithm operation: Perform one of the following operations according to the preset configuration: Moving the start time forward changes the start time of the next private room from 2:40 to 2:30 and the end time to 2:50. (The end time can also remain unchanged at 3:00, with the 10 minutes considered complimentary to the customer.) Moving the start time backward changes the start time of the previous private room from 1:00 to 1:10 and the scheduled end time to 2:40. (The start time can also remain unchanged at 1:00, with the 10 minutes considered complimentary to the customer.)

[0065] Please refer to Figure 2 This application also provides an electronic device 500, including a memory 502, a processor 501, and a computer program stored in the memory 502 and running on the processor. When the processor 501 executes the computer program, it implements the various steps in the above-described method for reserving a private room.

[0066] The friendliness effect of the electronic device in this application is the same as that of the aforementioned private room reservation method, and will not be repeated here.

[0067] In summary, the private room reservation method and electronic device provided in this application, upon receiving new private room reservation information, determines which target available private room to bind the reservation information to based on the fragmented time slots generated after merging the reservation information with the private room. Specifically, it calculates the recommendation weight corresponding to each fragmented time slot, generating fragmented time slots that are easier to book and have longer durations, while prioritizing the recommendation of shorter, cheaper, and harder-to-book fragmented time slots to users who have already booked rooms for those time slots. This improves the efficiency of recommending fragmented time slots. This allows users to choose their own reservation time slots while reducing the number of fragmented time slots in inventory, lowering the pressure on the database. Furthermore, offering fragmented time slots as gifts or at a discount also improves the user experience. The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method of room reservation, characterized by, The method comprises the following steps: receiving interval reservation information, the interval reservation information comprising a to-be-reserved time period; obtaining a reserved time period of each reservable interval, and calculating a fragment time period according to the to-be-reserved time period and each reserved time period respectively; calculating a recommendation weight of each fragment time period, and determining a target reservable interval corresponding to a target fragment time period according to the recommendation weight; binding the interval reservation information and the target reservable interval, and updating a target reserved time period of the target reservable interval according to the to-be-reserved time period.

2. The method of claim 1, wherein, The step of calculating a fragment time period according to the to-be-reserved time period and each reserved time period respectively comprises the following steps: obtaining a first start time and a first end time of the to-be-reserved time period, and obtaining a second start time and a second end time of the reserved time period; obtaining a later start time from the first start time and the second start time, and obtaining an earlier end time from the first end time and the second end time; obtaining a fragment time period from the later start time to the earlier end time.

3. The method of claim 1, wherein, The method further comprises the following steps: obtaining a reservable interval when the to-be-reserved time period is an idle time period; if the number of the reservable intervals is 1, executing the step of binding the interval reservation information and the target reservable interval by taking the reservable interval as the target reservable interval; if the number of the reservable intervals is greater than 1, executing the step of obtaining a reserved time period of each reservable interval.

4. The method of claim 1, wherein, The step of calculating a recommendation weight of each fragment time period comprises the following steps: calculating the recommendation weight of the fragment time period according to a length, a price factor and a utilization factor of the fragment time period; the price factor and the utilization factor are obtained by historical time period prices and historical time period reservation rates of all intervals.

5. The method of claim 4, wherein, The step of calculating a recommendation weight of each fragment time period comprises the following steps: N = t × p × u; wherein, N represents the recommendation weight, t represents the length, p represents the price factor, and u represents the utilization factor; the higher the historical time period price is, the greater the price factor is; the higher the historical time period reservation rate is, the smaller the utilization factor is.

6. The method of claim 4, wherein, The method further comprises the following steps: obtaining historical data of all intervals, the historical data comprising unit time periods sliced by a preset time length, and price data, fragment quantity, total fragment time length and average fragment time length corresponding to each unit time period; obtaining the price factor of each unit time period according to the price data; obtaining the utilization factor of each unit time period according to the fragment quantity, the total fragment time length and the average fragment time length.

7. The method of claim 2, wherein, After the step of determining a target reservable interval corresponding to a target fragment time period according to the recommendation weight, the method further comprises the following steps: if the length of the target fragment time period is less than a length threshold, calculating a total price of the target fragment time period according to a preset discount; after the step of binding the interval reservation information and the target reservable interval, the method further comprises the following steps: Return the total price and the target fragmented time period to the user terminal, determine whether a confirmation message from the user terminal is received, if yes, update the target reserved time period of the target bookable room according to the to-be-booked time period and the target fragmented time period.

8. The method of claim 1, wherein, Further comprising: Obtain a current time and an end time in all the bound room booking information bound to the target bookable room; Obtain target room booking information whose end time is later than the current time and is less than a preset reminding threshold from the current time; If there is a target fragmented time period with the end time as a start time in the first bookable room corresponding to the target room booking information, calculate a total price of the target fragmented time period according to a preset discount; Send the total price and a room expiration reminding to the user terminal; If a confirmation message from the user terminal is received, update the reserved time period of the first bookable room according to the target fragmented time period, and bind the target fragmented time period to the target room booking information.

9. The method of claim 1, wherein, Further comprising: Obtain a target room bound to the room booking information being executed; Collect room data of the target room, and determine whether there is an early departure according to the room data, if yes, obtain a current time, and take the current time to an end time of the room booking information as an idle time period of the target room.

10. An electronic device, comprising: A computer program product comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor implements each step of the above-mentioned room booking method when executing the computer program.

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