Method and system for dynamically adjusting beverage making time sequence in remote ordering scene
Through intelligent scheduling of equipment resources and dynamic optimization of production queues, the problem of difficult coordinated optimization of beverage delivery timeliness and taste quality in remote ordering scenarios has been solved, and beverages have been delivered in the best sensory state, reducing user waiting time and order conflict rate.
Patent Information
- Application Number
- CN202510751078.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-06
- Publication Date
- 2025-09-16
AI Technical Summary
In the remote ordering scenario, it is difficult to coordinately optimize the timeliness of beverage delivery and taste quality, resulting in rapid attenuation of sensory indicators such as coffee oxidation and milk foam collapse, and a high error rate in predicting the time required to pick up the meal, causing users to have severe waiting anxiety.
Through intelligent scheduling of equipment resources and dynamic optimization of production queues, we receive real-time order information, track route time, match the beverage characteristics database, calculate the sensory shelf life and beverage production time, and dynamically adjust the beverage production sequence to ensure that beverages are delivered in the best sensory state.
It significantly improves the timeliness and taste quality of beverage delivery, reduces order conflict rate and user waiting time, and ensures the best sensory state of beverages when picking up meals.
Smart Images
Figure CN120655022A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of remote ordering, and in particular to a method and system for dynamically adjusting the timing of beverage production in a remote ordering scenario. Background Art
[0002] In the current remote ordering scenario, the beverage delivery process of automatic coffee robots faces a technical bottleneck in which timeliness and taste quality are difficult to coordinately optimize.
[0003] Traditional systems usually adopt static scheduling strategies, planning beverage production based on the order of order receipt or a fixed production schedule, resulting in a serious mismatch between the production completion time and the user's actual arrival time: production too early will cause sensory indicators such as coffee oxidation and milk foam collapse to rapidly decay (for example, the taste of latte decreases by 30% 5 minutes after production), while production too late will cause users to feel anxious while waiting.
[0004] At the same time, existing path tracking technology relies on users to manually input the estimated arrival time or simple speed estimation based on straight-line distance. It does not integrate real-time traffic data and differences in user movement methods (walking, driving, etc.), resulting in an error rate of up to 25%-40% in the prediction of meal pickup time, making it impossible to provide reliable input for time scheduling.
[0005] In addition, the sensory shelf life modeling of beverages is rough and does not quantify the dynamic impact of different formulas (such as sugar content and additives) on the quality decay rate, resulting in the solidification of shelf life parameters (such as a uniform setting of 10 minutes), which cannot adapt to complex customization needs.
[0006] In summary, the existing technology has a technical problem in which it is difficult to balance the delivery timeliness and taste quality of beverages in remote ordering scenarios, resulting in a mismatch between the production sequence of automatic coffee robots and the user's meal pickup time, thereby reducing the user's ordering experience. Summary of the Invention
[0007] To solve the above problems, the purpose of the embodiments of the present invention is to provide a method and system for dynamically adjusting the timing of beverage production in a remote ordering scenario. Through intelligent scheduling of equipment resources and dynamic optimization of the production queue, the technical effect of significantly improving the timeliness and taste quality of beverage delivery, reducing order conflict rate and user waiting time, and ensuring that the beverage is in the best sensory state when picking up the meal is achieved.
[0008] To achieve the above-mentioned objectives, the present invention provides a method for dynamically adjusting the beverage production sequence in a remote ordering scenario, comprising: receiving real-time order information of a newly added ordering user, wherein the real-time order information includes the user's real-time location and order item attributes; tracking the path time based on the user's real-time location, and outputting a benchmark meal pickup time; after decomposing the order item attributes, outputting the sensory shelf life and beverage production time by matching the decomposition results to a beverage characteristic database; calculating and outputting a sensory quality maintenance window based on the benchmark meal pickup time, sensory shelf life and beverage production time; locally calling the beverage production timing information, using the sensory quality maintenance window to traverse the beverage production timing information to correct order conflicts, and locate the order insertion node; packaging the order item attributes and inserting them into the beverage production timing information based on the order insertion node, and dynamically adjusting the beverage production timing.
[0009] In one implementation, the route duration is tracked based on the user's real-time location, a benchmark meal pickup duration is output, and the following processing is performed:
[0010] After receiving the real-time ordering information, activate the sliding tracking window to intermittently collect the location information of the newly added ordering user to obtain Q updated GPS coordinates; construct a spatial dimensionality reduction trajectory by arranging the Q updated GPS coordinates in time sequence; extract the displacement speed characteristics from the spatial dimensionality reduction trajectory, and determine the real-time movement mode according to the displacement speed characteristics; take the user's real-time location as the starting point and the drink and meal pickup point as the end point, perform displacement path fitting based on the spatial dimensionality reduction trajectory and the real-time movement mode, and output the real-time displacement path; according to the real-time displacement path and displacement speed characteristics, fit and output the benchmark meal pickup time.
[0011] In one implementation, after decomposing the individual attributes of the order, the decomposition results are matched against a beverage characteristics database to output the sensory shelf life and beverage preparation time, and the following processing is performed:
[0012] Using predefined attribute decomposition rules, the order item attributes are decomposed into basic beverage attributes and an additive list; the basic beverage attributes are used to traverse the beverage production information table to obtain the basic production time and the sensory shelf life; if the additive list is a non-empty set, the additive list is used to traverse the additional item adjustment information library to obtain a time-related impact table; the time-related impact table is used to compensate for the basic production time to obtain the beverage production time.
[0013] In one implementation, the following processing is performed:
[0014] A shelf life association information library is pre-built, and the additive list is used to traverse the shelf life association information library to output a sensory association impact table; the sensory association impact table is used to compensate the sensory shelf life to obtain a compensated shelf life; the sensory shelf life and the beverage preparation time are packaged and output.
[0015] In one implementation, the following processing is performed:
[0016] If the additive list is an empty set, the basic preparation time is output as the beverage preparation time.
[0017] In one implementation, the sensory quality maintenance window is calculated and output based on the benchmark meal pickup time, sensory shelf life, and beverage preparation time, and the following processing is performed:
[0018] Take 1 / M of the sensory shelf life as the target meal pickup time limit window; take the receiving node of the real-time order information as the starting point, delay the benchmark meal pickup time, and output a simulated meal pickup node; take the simulated meal pickup node as the starting point, reversely calculate the drink production time to obtain a simulated production node; take the simulated production node as the median point, embed it into the target meal pickup time limit window, and locate the sensory quality maintenance window.
[0019] In one implementation, the sensory quality maintenance window is used to traverse the beverage production time sequence information to correct order conflicts, locate the order insertion node, and perform the following processing:
[0020] K beverage production sequences of K functional support devices are obtained by screening according to the basic beverage attributes, wherein the K beverage production sequences constitute the beverage production sequence information; the sensory quality maintenance window is used to traverse the K beverage production sequences to perform order conflict detection to obtain multiple alternative insertion nodes; multiple load association information of multiple functional support devices are called according to the multiple alternative insertion node mappings; the multiple functional support devices are quantitatively sorted according to the multiple load association information to locate the order insertion node, wherein the order insertion node has an order insertion device identifier.
[0021] In one implementation, based on the order insertion node, the order item attributes are packaged and inserted into the beverage preparation sequence information to dynamically adjust the beverage preparation sequence, and the following processing is performed:
[0022] The order item attributes and the beverage preparation time are encapsulated into a standardized order object; and the standardized order is synchronized to the beverage preparation timing information with the order insertion device identifier and the order insertion node as constraints.
[0023] To achieve the above-mentioned objectives, the present invention also provides a device for dynamically adjusting the beverage production sequence in a remote ordering scenario, comprising: an order receiving unit for receiving real-time order information of a newly added ordering user, wherein the real-time order information includes the user's real-time location and order item attributes; a time tracking unit for tracking the path time based on the user's real-time location, and outputting a benchmark meal pickup time; an attribute matching unit for decomposing the order item attributes, and then matching the decomposition results with a beverage characteristic database to output a sensory shelf life and beverage production time; a sensory maintenance calculation unit for calculating and outputting a sensory quality maintenance window based on the benchmark meal pickup time, sensory shelf life and beverage production time; an insertion node positioning unit for locally calling the beverage production timing information, using the sensory quality maintenance window to traverse the beverage production timing information to correct order conflicts, and locate the order insertion node; a dynamic adjustment unit for packaging the order item attributes and inserting them into the beverage production timing information according to the order insertion node, so as to dynamically adjust the beverage production timing.
[0024] Compared with the existing technology, the method and system for dynamically adjusting the beverage production sequence in the remote ordering scenario according to the present invention, through intelligent scheduling of equipment resources and dynamic optimization of the production queue, achieves the technical effect of significantly improving the timeliness of beverage delivery and taste quality, reducing order conflict rate and user waiting time, and ensuring that the beverage is in the best sensory state when picking up the meal. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0026] Figure 1 A flow chart of a method for dynamically adjusting the timing of beverage production in a remote ordering scenario provided by an embodiment of the present invention is shown.
[0027] Figure 2 A schematic diagram of the structure of a device for dynamically adjusting the timing of beverage production in a remote ordering scenario provided by an embodiment of the present invention is shown.
[0028] Explanation of the accompanying symbols: order receiving unit 1, time-consuming tracking unit 2, attribute matching unit 3, sensory maintenance calculation unit 4, insertion node positioning unit 5, dynamic adjustment unit 6. DETAILED DESCRIPTION
[0029] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.
[0030] The method for dynamically adjusting the beverage production sequence in a remote ordering scenario provided by an embodiment of the present invention achieves the technical effect of significantly improving the timeliness of beverage delivery and taste quality, reducing order conflict rate and user waiting time, and ensuring that the beverage is in the best sensory state when picking up the meal through intelligent scheduling of equipment resources and dynamic optimization of the production queue.
[0031] Example 1, a flowchart of a method for dynamically adjusting the timing of beverage production in a remote ordering scenario provided by an embodiment of the present invention, see Figure 1 , the method comprising:
[0032] Step S100: Receive real-time order information of a newly added ordering user, wherein the real-time order information includes the user's real-time location and order item attributes.
[0033] Specifically, in this embodiment, the coffee order center is an integrated device that receives remote orders and makes coffee drinks, and is integrated with multiple coffee-making robots. The coffee order center receives real-time order information from newly added ordering users in real time, wherein the real-time order information is order request data, which specifically includes the real-time location of the user when the user places the order and the order item attributes including the type of drink (such as American coffee, matcha latte), customized parameters (such as sugar content, ice amount) and additive configuration (such as pearls, milk cap).
[0034] Step S200: Track the path time based on the user's real-time location and output the benchmark meal pickup time.
[0035] In one implementation, the path duration is tracked based on the user's real-time location, and a benchmark meal pickup duration is output. Step S200 includes:
[0036] Step S210: After receiving the real-time order information, activate the sliding tracking window to intermittently collect the location information of the newly added ordering user to obtain Q updated GPS coordinates.
[0037] Step S220: constructing a spatial dimension reduction trajectory by arranging the Q updated GPS coordinates in time sequence.
[0038] Step S230: After extracting the displacement velocity feature from the spatial dimension reduction trajectory, the real-time movement mode is determined according to the displacement velocity feature.
[0039] Step S240: Taking the user's real-time location as the starting point and the drink and meal pickup point as the end point, a displacement path is fitted according to the spatial dimension reduction trajectory and the real-time movement mode, and the real-time displacement path is output.
[0040] Step S250: According to the real-time displacement path and displacement speed characteristics, the benchmark meal pickup time is fitted and output.
[0041] Specifically, in this embodiment, user GPS data collection is legally permitted from the moment real-time order information is received until the newly-ordered user receives their coffee. Based on this, upon receiving the real-time order information, a dynamic sliding tracking window mechanism is activated to continuously collect GPS positioning data for newly-ordered users at preset intervals (e.g., every 30 seconds). After eliminating signal drift noise through multi-source sensor fusion technology, a high-precision geographic coordinate sequence with a timestamp is generated. This sequence consists of Q updated GPS coordinates. The size of this sliding window is dynamically adjusted based on the user's movement speed. A short window (e.g., 5 sampling points) is used at high speeds to improve real-time performance, while a long window (e.g., 15 sampling points) is used at low speeds to enhance trajectory stability.
[0042] The Q collected GPS coordinate points are arranged in strict time sequence according to the timestamps. The trajectory breakpoints caused by signal loss are supplemented by the cubic spline interpolation algorithm. The geographic coordinate system (WGS84) is converted to a local plane coordinate system (such as UTM) to generate a spatial dimension reduction trajectory. The spatial dimension reduction trajectory is a two-dimensional spatial dimension reduction trajectory curve.
[0043] The instantaneous velocity vector set between consecutive coordinate points is extracted from the spatial dimension reduction trajectory, and its mean, variance and acceleration statistics are calculated as the displacement velocity features.
[0044] Traffic mode classification rules are preset. For example, the traffic mode classification rules are as follows:
[0045]
[0046] The displacement speed feature is used to match and locate the real-time movement mode in the traffic mode classification rule.
[0047] Taking the user's real-time location as the starting point and the beverage and meal pick-up point as the end point, the route calculation engine of the traffic mode corresponding to the real-time movement mode in the map service API (such as Gaode route planning) is called to calculate the time-optimal path and output the real-time displacement path.
[0048] According to the congestion condition of the real-time displacement path and the displacement speed characteristics of the real-time movement mode, the benchmark meal pickup time representing the estimated value of the meal pickup time is calculated and output.
[0049] This embodiment calculates the time taken to pick up a meal by tracking the user's trajectory, thereby achieving the technical effect of improving the accuracy of the calculation of the time taken to pick up a meal and providing reference information for subsequent order sorting optimization.
[0050] Step S300: After decomposing the single item attributes of the order, the decomposition results are matched with the beverage characteristic database to output the sensory shelf life and the time required to prepare the beverage.
[0051] In one implementation, after decomposing the individual attributes of the order, the decomposition results are matched against a beverage characteristics database to output the sensory shelf life and beverage preparation time. Step S300 includes:
[0052] Step S310: Using predefined attribute decomposition rules, decompose the attributes of the single item into basic beverage attributes and a list of additives.
[0053] Step S320: Use the basic beverage attributes to traverse the beverage production information table to obtain the basic production time and the sensory shelf life.
[0054] Step S330: If the additive list is a non-empty set, the additive list is used to traverse the additional item adjustment information library to obtain a time-consuming association impact table.
[0055] Step S340: Use the time-related impact table to compensate for the basic production time to obtain the beverage production time.
[0056] In one implementation, the method further includes:
[0057] Step S350: pre-constructing a quality-related information database, and using the additive list to traverse the quality-related information database, and outputting a sensory association impact table.
[0058] Step S360: Apply the sensory association impact table to compensate the sensory shelf life to obtain a compensated shelf life.
[0059] Step S370: Pack and output the sensory shelf life and beverage preparation time.
[0060] In one implementation, if the additive list is an empty set, the basic preparation time is output as the beverage preparation time.
[0061] Specifically, in this embodiment, through regular expression matching or JSON key-value extraction technology, the core beverage types in the order information (such as "Iced American Coffee") are identified as basic beverage attributes, and the customized options (such as "half sugar", "double milk foam") and additives (such as "pearls", "coconut") are parsed into a list of additives to form a standardized data object.
[0062] In this embodiment, the beverage preparation information table uses beverage type as the primary key and stores the basic preparation time (e.g., a latte requires 180 seconds for grinding, extraction, and frothing) and the sensory shelf life (e.g., the critical time for foam collapse is 300 seconds) for each beverage type. The basic beverage attributes are used to traverse the beverage preparation information table to obtain the basic preparation time and sensory shelf life.
[0063] If the additive list is an empty set, the basic preparation time is output as the beverage preparation time.
[0064] If the additive list is a non-empty set, it indicates that additives are present and that the additives will extend the coffee making time. Based on this, when the additive list is a non-empty set, the additional item adjustment information library is traversed using the additive list, and the impact value of each additive on the coffee making time is queried item by item to obtain a time-related impact table.
[0065] It should be understood that the additional item adjustment information library uses the name of the additive as an index to record the additional operation time required for each additive (e.g., adding "pearls" requires an additional 30 seconds of cooking time, and adding "milk cap" requires 20 seconds of whipping time).
[0066] The basic production time is dynamically corrected using a linear superposition model. Specifically, the basic production time is summed with the additive adjustment value. For example, if the basic production time of a latte is 180 seconds, and the time-related impact table includes 30 seconds for pearls and 20 seconds for milk cap, then after adding 30 seconds for pearls and 20 seconds for milk cap to the basic production time of the latte (180 seconds), the total production time is updated to 230 seconds.
[0067] It should be understood that different additives will have different influences and interferences on the attenuation changes of the sensory quality of beverages. Based on this, this embodiment de-subjects data based on the experience of experts in the food field and constructs the shelf life association information database. The shelf life association information database stores the influence coefficients of different additives on the attenuation rate of the sensory quality of beverages (such as "pearls" causing the shelf life to be shortened to 80%, and "milk cap" shortening it to 70%).
[0068] The additive list is used to traverse the shelf life association information library to output a sensory association impact table, which provides nonlinear attenuation model parameters for shelf life compensation.
[0069] The compensated shelf life is calculated using a multiplicative attenuation model. For example, if the basic shelf life is 300 seconds, the pearl influence coefficient is 0.8, and the milk cap coefficient is 0.7, then the compensated shelf life is 300×0.8×0.7=168 seconds. Based on this, the sensory association influence table is applied to compensate the sensory shelf life to obtain the compensated shelf life. The sensory shelf life and the beverage preparation time are packaged and output.
[0070] This embodiment calculates the sensory shelf life and beverage preparation time by combining the interference effects of additives, thereby achieving the technical effect of improving the accuracy of the obtained sensory shelf life and beverage preparation time, and providing reference benchmark data for subsequent timing planning and quality maintenance window calculation.
[0071] Step S400: Calculate and output the sensory quality maintenance window based on the benchmark meal pickup time, sensory shelf life, and beverage preparation time.
[0072] In one implementation, the sensory quality maintenance window is calculated and output based on the benchmark meal pickup time, sensory shelf life, and beverage preparation time. Step S400 includes:
[0073] Step S410: Use the sensory shelf life of 1 / M as the target meal pickup time window.
[0074] Step S420: Taking the receiving node of the real-time order information as the starting point, delay the benchmark meal pickup time and output a simulated meal pickup node.
[0075] Step S430: Taking the simulated meal-picking node as the starting point, reversely calculate the time taken to make the drink to obtain a simulated production node.
[0076] Step S440: The simulated production node is used as the median point, embedded into the target meal pickup time window, and the sensory quality maintenance window is located.
[0077] Specifically, in this embodiment, the sensory shelf life (i.e., the total length of time the beverage maintains the best taste after it is prepared) is divided into M equal parts, and the first section 1 / M duration is taken as the target meal pickup time limit window, wherein the M value is dynamically set according to the beverage type. For example, espresso is set to M=5 (the first 20%) due to its fast oxidation rate, while smoothies are set to M=10 (the first 10%) due to their stable physical state.
[0078] Taking the receiving node (timestamp) of the real-time order information as the starting point, the benchmark meal pickup time calculated in step S200 is superimposed with the time delay to generate a theoretical prediction value of the user's arrival time, namely the simulated meal pickup node.
[0079] The production start time is determined by using a reverse engineering method. Specifically, starting from the simulated meal-picking node, the time consumed in making the drink is reversed to obtain a simulated production node (production start time).
[0080] Taking the simulated production node as the median point, the target meal pickup time window is extended forward and backward by 1 / 2 to locate the sensory quality maintenance window, which ensures that the drink is in the gentle stage of the quality attenuation curve when the meal is picked up.
[0081] Step S500: Locally call the beverage production timing information, use the sensory quality maintenance window to traverse the beverage production timing information to correct order conflicts, and locate the order insertion node.
[0082] In one implementation, the sensory quality maintenance window is used to traverse the beverage production timing information to correct order conflicts and locate the order insertion node. Step S500 includes:
[0083] Step S510: Filter and obtain K beverage preparation timings of K functional support devices according to the basic beverage attributes, wherein the K beverage preparation timings constitute the beverage preparation timing information.
[0084] Step S520: Use the sensory quality maintenance window to traverse the K beverage production sequences to perform order conflict detection and obtain multiple candidate insertion nodes.
[0085] Step S530: Call multiple load association information of multiple function support devices according to the multiple candidate insertion node mappings.
[0086] Step S540: Quantitatively sort the multiple functional support devices according to the multiple load association information to locate the order insertion node, wherein the order insertion node has an order insertion device identifier.
[0087] Specifically, in this embodiment, by querying the device capability matrix database, devices that cannot meet the current beverage production requirements are excluded (for example, devices without milk frothing function do not participate in latte order allocation), and K functional support devices are screened out from multiple coffee making robots.
[0088] Further call K beverage production time sequences that represent the current beverage production task status of K functional support devices. For each device, scan its beverage production time sequence forward from the current time, find the position that satisfies the sensory quality maintenance window and is completely embedded in the idle period and retains the equipment cleaning interval before and after, and mark it as an alternative insertion node. If there is an ongoing order in the device queue, calculate the difference between its completion time and the start time of the new order to ensure that the minimum interval required by the hygiene operation specification (such as 30 seconds). The final output result is 0 or more legal insertion time slots corresponding to each device, and output the multiple alternative insertion nodes.
[0089] The device load monitoring module obtains load-related information corresponding to each candidate insertion node. Load-related information includes but is not limited to: current queue length, estimated total queue time, device health score (based on recent failure rate), and energy consumption efficiency.
[0090] Furthermore, multiple load association information of the multiple candidate insertion nodes are weightedly fused to obtain multiple load quantization values. It should be understood that the larger the load quantization value, the less suitable the candidate insertion node is for inserting a new coffee making task.
[0091] The plurality of functional support devices are quantitatively sorted according to the plurality of load association information to locate the point insertion node and the target support device.
[0092] This embodiment is based on the functions and task loads of multiple coffee-making robots in the sensory quality maintenance window and the coffee ordering center, and locates support equipment and order insertion nodes, thereby achieving scientific order task allocation and sorting, and reducing the probability of daily use loss of coffee robots.
[0093] Step S600: Based on the order insertion node, the order item attributes are packaged and inserted into the beverage production sequence information to dynamically adjust the beverage production sequence.
[0094] In one implementation, based on the order insertion node, the order item attributes are packaged and inserted into the beverage preparation sequence information to dynamically adjust the beverage preparation sequence. Step S600 includes:
[0095] Step S610: Encapsulate the order item attributes and beverage preparation time into a standardized order object.
[0096] Step S620: Synchronize the standardized order to the beverage production timing information using the order insertion device identifier and the order insertion node as constraints.
[0097] Specifically, in this embodiment, based on the beverage preparation time and sensory shelf life parameters generated in step S300, combined with the original point single attribute received in step S100 (such as beverage type, sugar content, additive configuration), the scattered data is encapsulated into a structured standardized order object to ensure that the downstream device can execute it without secondary conversion when parsing.
[0098] Based on the order insertion device identifier and insertion node timestamp determined in step S540, the standardized order object is inserted into the target device's production time sequence queue. This process triggers dynamic queue rescheduling, automatically shifting the time window for subsequent orders and recalculating their sensory quality maintenance window. Queue change events are simultaneously broadcast to associated devices via a messaging middleware (e.g., Kafka), ensuring strong consistency of time sequence status across multiple devices.
[0099] This embodiment achieves the technical effect of significantly improving the timeliness and taste quality of beverage delivery, reducing order conflict rate and user waiting time, and ensuring that beverages are in the best sensory state when picking up meals through intelligent scheduling of equipment resources and dynamic optimization of production queues.
[0100] Example 2, a structural diagram of a system for dynamically adjusting the timing of beverage production in a remote ordering scenario provided by an embodiment of the present invention, see Figure 2 , the system comprising:
[0101] The order receiving unit 1 is used to receive the real-time order information of the newly added ordering user, wherein the real-time order information includes the user's real-time location and order item attributes.
[0102] The time-consuming tracking unit 2 is used to track the path time-consuming based on the real-time location of the user and output the benchmark meal-picking time-consuming.
[0103] The attribute matching unit 3 is used to decompose the single attribute of the order, match the decomposition result with the beverage characteristic database, and output the sensory shelf life and the time required to prepare the beverage.
[0104] The sensory maintenance calculation unit 4 is used to calculate and output the sensory quality maintenance window based on the benchmark meal collection time, sensory shelf life and beverage preparation time.
[0105] The insertion node positioning unit 5 is used to locally call the beverage production timing information, use the sensory quality maintenance window to traverse the beverage production timing information to correct order conflicts, and locate the order insertion node.
[0106] The dynamic adjustment unit 6 is used to package the order item attributes and insert them into the beverage production sequence information according to the order insertion node, so as to dynamically adjust the beverage production sequence.
[0107] In one implementation, the time consumption tracking unit 2 is further configured to:
[0108] After receiving the real-time ordering information, activate the sliding tracking window to intermittently collect the location information of the newly added ordering user to obtain Q updated GPS coordinates; construct a spatial dimensionality reduction trajectory by arranging the Q updated GPS coordinates in time sequence; extract the displacement speed characteristics from the spatial dimensionality reduction trajectory, and determine the real-time movement mode according to the displacement speed characteristics; take the user's real-time location as the starting point and the drink and meal pickup point as the end point, perform displacement path fitting based on the spatial dimensionality reduction trajectory and the real-time movement mode, and output the real-time displacement path; according to the real-time displacement path and displacement speed characteristics, fit and output the benchmark meal pickup time.
[0109] In one implementation, the attribute matching unit 3 is further configured to:
[0110] Using predefined attribute decomposition rules, the order item attributes are decomposed into basic beverage attributes and an additive list; the basic beverage attributes are used to traverse the beverage production information table to obtain the basic production time and the sensory shelf life; if the additive list is a non-empty set, the additive list is used to traverse the additional item adjustment information library to obtain a time-related impact table; the time-related impact table is used to compensate for the basic production time to obtain the beverage production time.
[0111] In one implementation, the attribute matching unit 3 is further configured to:
[0112] Pre-building a quality-related information database, and using the additive list to traverse the quality-related information database to output a sensory association impact table;
[0113] Applying the sensory association impact table to compensate the sensory shelf life to obtain a compensated shelf life;
[0114] The sensory shelf life and beverage preparation time-consuming packaging output.
[0115] In one implementation, the attribute matching unit 3 is further configured to, if the additive list is an empty set, output the basic preparation time as the beverage preparation time.
[0116] In one implementation, the sensory maintenance calculation unit 4 is further configured to:
[0117] Take 1 / M of the sensory shelf life as the target meal pickup time limit window; take the receiving node of the real-time order information as the starting point, delay the benchmark meal pickup time, and output a simulated meal pickup node; take the simulated meal pickup node as the starting point, reversely calculate the drink production time to obtain a simulated production node; take the simulated production node as the median point, embed it into the target meal pickup time limit window, and locate the sensory quality maintenance window.
[0118] In one implementation, the insertion node positioning unit 5 is further configured to:
[0119] K beverage production sequences of K functional support devices are obtained by screening according to the basic beverage attributes, wherein the K beverage production sequences constitute the beverage production sequence information; the sensory quality maintenance window is used to traverse the K beverage production sequences to perform order conflict detection to obtain multiple alternative insertion nodes; multiple load association information of multiple functional support devices are called according to the multiple alternative insertion node mappings; the multiple functional support devices are quantitatively sorted according to the multiple load association information to locate the order insertion node, wherein the order insertion node has an order insertion device identifier.
[0120] In one implementation, the dynamic adjustment unit 6 is further configured to:
[0121] The order item attributes and the beverage preparation time are encapsulated into a standardized order object; and the standardized order is synchronized to the beverage preparation timing information with the order insertion device identifier and the order insertion node as constraints.
[0122] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.
Claims
1. A method for dynamically adjusting the timing of beverage production in a remote ordering scenario, characterized in that: include: Receive real-time order information from a newly added ordering user, wherein the real-time order information includes the user's real-time location and order item attributes; Tracking the route time based on the user's real-time location and outputting a benchmark meal pickup time; After decomposing the individual attributes of the order, the sensory shelf life and the beverage preparation time are output by matching the decomposition results with the beverage characteristic database; Calculate and output the sensory quality maintenance window based on the benchmark meal pickup time, sensory shelf life, and beverage preparation time; Locally calling beverage production timing information, using the sensory quality maintenance window to traverse the beverage production timing information to correct order conflicts, and locating the order insertion node; According to the order insertion node, the order item attributes are packaged and inserted into the beverage production sequence information to dynamically adjust the beverage production sequence.
2. The method for dynamically adjusting the timing of beverage production in a remote ordering scenario according to claim 1, characterized in that: Tracking the route duration based on the user's real-time location and outputting a benchmark meal pickup duration includes: After receiving the real-time order information, activate the sliding tracking window to intermittently collect the location information of the newly added ordering user to obtain Q updated GPS coordinates; Constructing a spatial dimension reduction trajectory by arranging the Q updated GPS coordinates in time sequence; After extracting displacement velocity features from the spatial dimension reduction trajectory, determining the real-time movement mode according to the displacement velocity features; Taking the user's real-time location as the starting point and the drink and meal pickup point as the end point, a displacement path is fitted based on the spatial dimension reduction trajectory and the real-time movement mode, and a real-time displacement path is output; The benchmark meal-picking time is output by fitting based on the real-time displacement path and displacement speed characteristics.
3. The method for dynamically adjusting the timing of beverage production in a remote ordering scenario according to claim 2, characterized in that: After decomposing the individual attributes of the order, the decomposition results are matched with the beverage characteristics database to output the sensory shelf life and beverage preparation time, including: Using predefined attribute decomposition rules, decomposing the single item attributes of the order into basic beverage attributes and a list of additives; Using the basic beverage attributes to traverse the beverage production information table, the basic production time and the sensory shelf life are obtained; If the additive list is a non-empty set, traversing the additional item adjustment information base using the additive list to obtain a time-consuming association impact table; The basic preparation time is compensated by using the time-related impact table to obtain the beverage preparation time.
4. The method for dynamically adjusting the timing of beverage production in a remote ordering scenario according to claim 3, characterized in that: Also includes: Pre-building a quality-related information database, and using the additive list to traverse the quality-related information database to output a sensory association impact table; Applying the sensory association impact table to compensate the sensory shelf life to obtain a compensated shelf life; The sensory shelf life and beverage preparation time-consuming packaging output.
5. The method for dynamically adjusting the timing of beverage production in a remote ordering scenario according to claim 3, characterized in that: If the additive list is an empty set, the basic preparation time is output as the beverage preparation time.
6. The method for dynamically adjusting the timing of beverage production in a remote ordering scenario according to claim 3, characterized in that: The sensory quality maintenance window is calculated and output based on the benchmark meal pickup time, sensory shelf life, and beverage preparation time, including: The sensory shelf life of 1 / M is used as the target meal pickup window; Taking the receiving node of the real-time order information as the starting point, delaying the benchmark meal pickup time, and outputting a simulated meal pickup node; Taking the simulated meal-picking node as the starting point, reversely inferring the drink-making time to obtain a simulated production node; The simulated production node is used as the median point, embedded in the target meal pickup time window, and the sensory quality maintenance window is located.
7. The method for dynamically adjusting the timing of beverage production in a remote ordering scenario according to claim 6, characterized in that: The sensory quality maintenance window is used to traverse the beverage production time sequence information to correct order conflicts and locate the order insertion node, including: According to the basic beverage attributes, K beverage production time sequences of K functional support devices are obtained by screening, wherein the K beverage production time sequences constitute the beverage production time sequence information; Using the sensory quality maintenance window to traverse the K beverage production time sequences to perform order conflict detection, and obtain multiple candidate insertion nodes; Invoke multiple load association information of multiple function support devices according to the multiple candidate insertion node mappings; The plurality of function support devices are quantitatively sorted according to the plurality of load association information to locate the order insertion node, wherein the order insertion node has an order insertion device identifier.
8. The method for dynamically adjusting the timing of beverage production in a remote ordering scenario according to claim 7, characterized in that: According to the order insertion node, the order item attributes are packaged and inserted into the beverage production sequence information to dynamically adjust the beverage production sequence, including: Encapsulate the order item attributes and drink preparation time into a standardized order object; The standardized order is synchronized to the beverage production timing information with the order insertion device identifier and the order insertion node as constraints.
9. A dynamic adjustment system for beverage production timing in a remote ordering scenario, characterized in that: For implementing the steps of the method according to any one of claims 1 to 8, the system comprises: An order receiving unit, configured to receive real-time order information from a newly added ordering user, wherein the real-time order information includes the user's real-time location and order item attributes; A time-consuming tracking unit is used to track the route time based on the real-time location of the user and output a benchmark meal pickup time; An attribute matching unit, configured to decompose the individual attributes of the order, match the decomposition results with a beverage characteristics database, and output a sensory shelf life and a beverage preparation time; a sensory maintenance calculation unit, configured to calculate and output a sensory quality maintenance window based on the benchmark meal pickup time, sensory shelf life, and beverage preparation time; An insertion node positioning unit is used to locally call beverage production timing information, use the sensory quality maintenance window to traverse the beverage production timing information to correct order conflicts, and locate the order insertion node; The dynamic adjustment unit is used to package the order item attributes and insert them into the beverage production timing information according to the order insertion node, so as to dynamically adjust the beverage production timing.