Service order dynamic allocation method and system based on multi-dimensional data analysis
The service order dynamic allocation method, which utilizes multi-dimensional data analysis, solves the problem of chain delays caused by emergency order insertion, optimizes order allocation, and improves resource utilization efficiency and service quality.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 匠达(苏州)科技有限公司
- Filing Date
- 2026-04-14
- Publication Date
- 2026-05-12
AI Technical Summary
Existing scheduling technologies lack multi-dimensional data analysis, which makes it easy for emergency order insertions to disrupt the original order plan, causing a chain of delays and unreasonable resource allocation.
The service order dynamic allocation method based on multi-dimensional data analysis optimizes order allocation to the most suitable group by comprehensively evaluating user information, order urgency, and the delay and interference impact of order insertion on its own and other group orders.
It achieves global scheduling optimization, avoids cascading delays, improves resource utilization efficiency and service quality, and achieves a balance between user satisfaction, operating costs and resource load.
Smart Images
Figure CN122022407A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing methods, specifically to a method and system for dynamically allocating service orders based on multi-dimensional data analysis. Background Technology
[0002] With the deep integration of the digital economy and service-oriented manufacturing, offline engineering service industries such as equipment maintenance, home appliance after-sales service, and on-site technical services have entered a stage of large-scale development, with the total number of market orders continuing to climb and customers placing higher demands on services. At the same time, service orders are showing significant fragmentation, with frequent occurrences of emergency orders and changes in service requirements, posing unprecedented challenges to the global optimization capabilities, dynamic response capabilities, and risk control capabilities of the order scheduling and allocation system.
[0003] Existing scheduling technologies are mostly designed for "passive response," only capable of making localized, temporary adjustments for situations such as order changes or urgent order insertions. They lack the ability to predict risks and analyze chain reactions based on multi-dimensional historical and real-time operational data. When unexpected situations such as urgent order insertions occur, localized manual adjustments can easily trigger cascading delays across the entire order chain, completely disrupting the smooth execution rhythm of the original task sequence. Traditional scheduling methods typically allocate resources based solely on user priority and the current busy level of each order service group when handling urgent order insertions. This single-dimensional decision-making model, lacking a global simulation and impact assessment of the entire task chain after an order is inserted, easily leads to unexpected disruptions to the original order plans within a group, causing cascading delays and ultimately resulting in unreasonable resource allocation and task sequence arrangement. Summary of the Invention
[0004] To address the technical problem that current scheduling schemes for temporary and urgent orders easily disrupt existing order plans and cause cascading delays, thereby reducing the level of order task management, the present invention aims to provide a service order dynamic allocation method and system based on multi-dimensional data analysis. The specific technical solution adopted is as follows: This invention provides a method for dynamically allocating service orders based on multi-dimensional data analysis, the method comprising: The urgency of the current temporary order is determined based on the user information of the target user and the temporary order information of the current temporary order. The group into which the order can be inserted is determined based on the arranged order information of each arranged order in the service group. By combining temporary order information and scheduled order information, determine the potential delay of the current temporary order to itself and the potential interference to other orders in the insertable group; Based on the arranged order information, the total order completion ease of the insertable group is determined, and the target insertion group for the current temporary order is determined by combining the delay probability, the interference probability, and the total order completion ease.
[0005] Furthermore, determining the urgency of the current temporary order based on the target user's user information and the current temporary order's temporary order information includes: Determine the complaint information and priority in the user information, and determine the interval between the required arrival time in the temporary order information and the current time. The urgency level of the current temporary order is determined by combining the complaint information, the priority, and the interval duration.
[0006] Further, the step of determining the insertable group based on the arrangement order information of each arranged order in the service group includes: Based on the arrangement information of each arranged order in the service group, determine the idle time period between any two adjacent arranged orders; If the required arrival time of the current temporary order falls within the idle period and the duration of the idle period is longer than the estimated service duration of the current temporary order, then the service group is designated as an insertable group.
[0007] Furthermore, by combining temporary order information and scheduled order information, the potential delay caused by inserting the current temporary order into the insertable group is determined, including: Determine the estimated completion time of the current temporary order before it is inserted into the insertable group of previously scheduled orders; Determine the duration of the interval between the required arrival time and the previous estimated completion time in the provisional order information; The degree of delay that inserting the current temporary order into the insertable group may cause is determined based on the interval duration.
[0008] Furthermore, determining the potential delay caused by inserting the current temporary order into the insertable group based on the interval duration includes: Determine the previous scheduled position of the current temporary order in the insertable group where it is inserted into the previously scheduled order, and determine the location distance between the required arrival location and the previous scheduled position in the temporary order information; By combining the interval duration and the location distance, the potential delay caused by inserting the current temporary order into the insertable group is determined.
[0009] Furthermore, by combining temporary order information and scheduled order information, the potential interference of inserting a current temporary order into an insertable group with other orders in the insertable group is determined, including: Determine the next scheduled position of the current temporary order in the insertable group of already scheduled orders, and determine the location distance between the required arrival location and the next scheduled position in the temporary order information; Determine the difference between the location distance and the maximum location distance in historical orders, and determine the proportion of the estimated service time in the temporary order information compared to the maximum service time in historical orders; By combining the distance difference and the service duration ratio, the degree of interference that inserting the current temporary order into the insertable group may cause to other subsequently arranged orders in the insertable group is determined.
[0010] Furthermore, combining the distance difference and the service duration weight, the degree of interference that inserting a current temporary order into an insertable group might cause to other subsequently scheduled orders in the insertable group is determined, including: Determine the potential delay of inserting the current temporary order into the insertable group, and the average urgency of other subsequently scheduled orders; By combining the probability of delay, the average urgency level, the distance difference, and the proportion of service duration, the degree of interference that inserting the current temporary order into the insertable group may cause to other subsequently arranged orders in the insertable group is determined.
[0011] Further, based on the arranged order information, the ease of completing the total number of orders that can be inserted into the group is determined, including: Based on the arranged order information, determine the total estimated service time and total estimated travel distance of the arranged orders that can be inserted into the group; By combining the total estimated service time and the total estimated travel distance, the ease of completing the total order that can be inserted into the group is determined.
[0012] Further, the target insertion group for the current temporary order is determined by combining the probability of delay, the probability of interference, and the ease of completing the total order, including: Based on the likelihood of delays, the likelihood of interference, and the ease of completing the total orders, determine the appropriateness of adding the current temporary order to the insertable group; The most suitable insertable group is selected as the target insertion group for the current temporary order, and the current temporary order is inserted between the corresponding adjacent arranged orders in the target insertion group.
[0013] This invention also provides a service order dynamic allocation system based on multi-dimensional data analysis, the system being used to implement the service order dynamic allocation method based on multi-dimensional data analysis as described in any of the preceding claims; the system includes: The order association analysis module is used to determine the urgency of the current temporary order based on the target user's user information and the temporary order information of the current temporary order, and to determine the groups that can be inserted based on the arranged order information of each arranged order in the service group; By combining temporary order information and scheduled order information, determine the potential delay of the current temporary order to itself and the potential interference to other orders in the insertable group; The global scheduling optimization module is used to determine the ease of completing the total number of orders that can be inserted into the group based on the arranged order information, and to determine the target insertion group for the current temporary order by combining the probability of delay, the probability of interference, and the ease of completing the total number of orders.
[0014] The present invention has the following beneficial effects: This invention, by comprehensively evaluating user information of users who are currently placing orders and temporary order information, can more accurately identify truly urgent orders. During allocation, it pre-simulates the delay impact of order placement on itself and other tasks in the same group, effectively avoiding the cascading delays that are easily caused by traditional methods. Simultaneously, by considering the real-time load of the groups, it ensures the balance of order task allocation and prevents some resources from becoming overloaded. Ultimately, while prioritizing key orders, it allocates orders to the groups with the lowest overall cost and most suitable load, achieving global scheduling optimization, maintaining the stability and efficiency of the overall task flow, and realizing a dual improvement in service quality and operational efficiency.
[0015] Furthermore, the dynamic order allocation based on multi-dimensional data analysis in this invention can significantly improve the overall efficiency of the scheduling system. It not only achieves high-precision matching of resources and demand, maximizing service efficiency and capacity utilization, but also effectively predicts and avoids the risk of chain delays caused by emergency order insertions, ensuring the smooth execution of task sequences. Ultimately, the system achieves an optimal balance between service quality, operating costs, and resource load while meeting users' personalized needs and timeliness commitments. Attached Figure Description
[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 A flowchart illustrating the steps of a service order dynamic allocation method based on multi-dimensional data analysis, provided in one embodiment of the present invention; Figure 2This is a detailed flowchart of step S1 in a service order dynamic allocation method based on multi-dimensional data analysis provided in an embodiment of the present invention. Figure 3 This is a detailed flowchart of step S2 in a service order dynamic allocation method based on multi-dimensional data analysis provided in an embodiment of the present invention. Figure 4 A detailed flowchart of step S2 in a service order dynamic allocation method based on multi-dimensional data analysis provided in another embodiment of the present invention; Figure 5 This is a detailed flowchart of step S3 in a service order dynamic allocation method based on multi-dimensional data analysis provided in an embodiment of the present invention. Figure 6 This is a schematic diagram of the hardware operating environment of the service order dynamic allocation device based on multi-dimensional data analysis involved in the embodiments of the present invention; Figure 7 This is a schematic diagram of the framework structure of the service order dynamic allocation system based on multi-dimensional data analysis involved in the embodiments of the present invention. Detailed Implementation
[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a service order dynamic allocation method based on multi-dimensional data analysis proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0020] To facilitate understanding of the various embodiments of the present invention, the main objectives and specific scenarios targeted by the present invention will be described herein.
[0021] Main objective: This invention determines the urgency of services based on user priority and historical behavior. It considers the delays caused by inserting orders into different task groups. It analyzes the interference of inserting orders into task groups on existing tasks. Simultaneously, it analyzes the daily task load of the current group to rationally allocate current orders.
[0022] Specific Scenarios Targeted: Compared to traditional scheduling, multi-dimensional data-driven dynamic allocation achieves a leap from "passive response" to "proactive optimization." It breaks the limitations of relying on only a single or a few rules, providing a globally optimal solution for each scheduling task by comprehensively analyzing multiple factors such as user value, time pressure, service capabilities, and real-time environment. This not only greatly improves resource utilization efficiency and task completion smoothness but also proactively avoids delay risks, ensuring service commitments. Its core value lies in its ability to simultaneously achieve three core business goals: improved user satisfaction, optimized operating costs, and balanced resource load. This invention comprehensively determines the urgency of an order based on user priority and historical behavior data. Subsequently, it assesses the potential delays and interference to other tasks within the group if the order is inserted into an existing group. Simultaneously, it comprehensively analyzes the total daily load of the target group and, based on the multi-dimensional evaluation results, selects the optimal group for the order with a reasonable load and minimal impact.
[0023] The following description, in conjunction with the accompanying drawings, details a specific scheme for a service order dynamic allocation method based on multi-dimensional data analysis provided by the present invention.
[0024] Example 1: For a method for dynamically allocating service orders based on multi-dimensional data analysis provided by this invention, please refer to [link / reference]. Figure 1 The diagram illustrates a flowchart of a service order dynamic allocation method based on multi-dimensional data analysis provided in an embodiment of the present invention.
[0025] The service order dynamic allocation method based on multi-dimensional data analysis includes: Step S1: Determine the urgency of the current temporary order based on the target user's user information and the temporary order information of the current temporary order; determine the insertion group based on the arrangement order information of each arranged order in the service group. Through the API (Application Programming Interface) of the business system (such as the home appliance repair platform), you can obtain the order details of various orders (including order information such as the main tasks and service duration).
[0026] By connecting to the standard API interface of the business system, we can obtain details of arranged service orders (hereinafter referred to as "arranged orders"), their completion status, and the tasks to be completed for different service groups.
[0027] To demonstrate the company's professionalism in the product installation phase and provide users with a better consumer experience, different orders should be scheduled correctly according to the service arrival time requested by the user. At the same time, user priority (e.g., determining priority through VIP levels) and historical complaint rates should also be considered to more accurately address user needs and complaints.
[0028] Specifically, please refer to Figure 2 Step S1, determining the urgency of the current temporary order based on the target user's user information and the current temporary order's temporary order information, includes: Step S11: Determine the complaint information and priority in the user information, and determine the interval between the required arrival time in the temporary order information and the current time. Step S12: Determine the urgency of the current temporary order by combining the complaint information, the priority, and the interval duration.
[0029] In this embodiment, the complaint rate is obtained based on the order records of user a (as the target user, referring to any user) in the system's user information. .
[0030] By querying the basic information of users in the system, the priority level of user A can be obtained. For example, priority can be determined by user A's VIP membership level, with higher VIP levels resulting in higher priority. Priority can also be determined by user spending levels or usage frequency; there are no restrictions here.
[0031] Calculate the interval between the required service arrival time (or the earliest arrival time if the required arrival time is a range) and the current time in the temporary order information of user a's order k (as the current temporary order). (Consider the scenario where orders in different groups have already been assigned for the day, and order k is suddenly inserted).
[0032] When user A's complaint rate The larger the value, the greater the priority of user a. With maximum priority The difference The smaller the value, the longer the interval between the service arrival time and the current time required by order k. The smaller the time, the more urgent the service for order k, and the more important it is to avoid delays in delivery.
[0033] This allows us to determine the urgency of user a's order k. ;in To represent a minimum constant, avoid using a denominator of 0, such as 0.0001 (unless otherwise specified). In each embodiment, it can be the minimum value); norm represents the normalization function, such as maximum and minimum value normalization. Unless otherwise specified, norm can also be the maximum and minimum value normalization. In addition, to make the calculation results more rigorous, each parameter of the formula in this embodiment and subsequent embodiments (such as...) can also be... , The data (e.g., values) are normalized before being used in calculations. This embodiment mainly describes the implementation process; the specific mathematical calculation process can be further adjusted according to the needs of actual application scenarios. The maximum and minimum values of the normalization process can be set according to specific scenarios and data numerical characteristics. Adjusting, calibrating, or optimizing the maximum and minimum values does not constitute a limitation of this invention.
[0034] The above process can also be used to calculate the urgency of all orders that need to be completed on the same day, and use this as a reference for order insertion scheduling.
[0035] Specifically, step S1, which determines the insertable group based on the arrangement order information of each arranged order in the service group, includes: Based on the arrangement information of each arranged order in the service group, determine the idle time period between any two adjacent arranged orders; If the required arrival time of the current temporary order falls within the idle period and the duration of the idle period is longer than the estimated service duration of the current temporary order, then the service group is designated as an insertable group.
[0036] Based on the above embodiments, in this embodiment, the currently scheduled service orders for each service group are iterated through. Service groups whose required service arrival time is idle (i.e., the required delivery time falls within the idle period between any two adjacent scheduled orders), and whose idle period duration (i.e., the interval between the estimated completion time of the preceding scheduled order and the scheduled arrival time of the following scheduled order) is greater than 1.5 times the estimated service time of the current temporary order (this multiple is determined based on historical service delay statistics and is adjustable), are designated as insertable groups for order k. If no insertable group exists, the user can be contacted to modify the service date or time.
[0037] Step S2: Combine the temporary order information and the scheduled order information to determine the potential delay of the current temporary order to itself and the potential interference to other orders in the insertable group; When a temporary order needs to be inserted into a single group, the degree of delay of the current temporary order needs to be judged based on the completion time and distance performance of the previous orders in the group. Furthermore, the forced lag performance of the original orders in the group after the temporary order is inserted needs to be assessed to determine the interference caused by the addition of the temporary order.
[0038] It should be noted that temporary orders need to be inserted between appropriate adjacent scheduled orders in the service group. That is, the required arrival time of the current temporary order must be within the idle time period between the corresponding adjacent scheduled orders. Assuming that the current temporary order is inserted, we need to consider the impact of this insertion behavior on the scheduled orders before it and the scheduled orders after it.
[0039] Because the ad hoc order was inserted suddenly on the same day, and the grouping had already been completed, it needs to be inserted into the originally scheduled group to ensure timely delivery. However, the actual completion time of each order may not perfectly match the estimated completion time in the schedule. Therefore, even if the ad hoc order is inserted into the grouping schedule at a seemingly appropriate time, it may still cause service delays. The likelihood of delays for ad hoc orders can be assessed by considering the interval between the estimated completion time of adjacent orders and the required arrival time of the inserted order, as well as the distance between the orders.
[0040] Specifically, in one embodiment, please refer to Figure 3 Step S2, which combines temporary order information and scheduled order information to determine the potential delay caused by inserting the current temporary order into the insertable group, includes: Step S21: Determine the estimated completion time of the current temporary order before the scheduled orders in the insertable group; Step S22: Determine the interval between the required arrival time and the previous estimated completion time in the temporary order information; Step S23: Determine the potential delay caused by inserting the current temporary order into the insertable group based on the interval duration.
[0041] More specifically, step S23 includes: Determine the previous scheduled position of the current temporary order in the insertable group where it is inserted into the previously scheduled order, and determine the location distance between the required arrival location and the previous scheduled position in the temporary order information; By combining the interval duration and the location distance, the potential delay caused by inserting the current temporary order into the insertable group is determined.
[0042] In this embodiment, within any single insertable group b, the interval between the estimated completion time of the scheduled order immediately preceding the insertable position and the service arrival time required by order k is calculated. .
[0043] Calculate the distance between the location of the adjacent scheduled order and the required delivery location of order k. .
[0044] When the interval between the estimated completion time of the order arranged before the insertable group b and the required service arrival time of order k is... The smaller the value, the greater the distance between the current location of the order and the service location required by order k. Maximum distance between all historical orders ratio When the value is larger, the further away the preceding orders are and the shorter the time interval, the more likely it is that the service arrival of order k will be delayed due to the completion time of the preceding orders, and the greater the risk of delay.
[0045] This allows us to determine the potential delay in inserting order k into group b. This allows us to assess the potential delays of inserting order k into different groups, serving as a reference for the service arrangement of order k and making the arrangement more reasonable.
[0046] Specifically, in another embodiment, please refer to Figure 4 Step S2, which combines temporary order information and scheduled order information to determine the potential interference of inserting the current temporary order into the insertable group with other orders in the insertable group, includes: Step S201: Determine the subsequent arrangement position of the current temporary order in the insertable group of the already arranged orders, and determine the location distance between the required arrival position and the subsequent arrangement position in the temporary order information; Step S202: Determine the distance difference between the location and the maximum location in historical orders, and determine the service duration ratio of the estimated service duration in the temporary order information to the maximum service duration in historical orders; Step S203: Combining the distance difference and the service duration ratio, determine the degree of interference that inserting the current temporary order into the insertable group may cause to other subsequently arranged orders in the insertable group.
[0047] More specifically, step S203 includes: Determine the potential delay of inserting the current temporary order into the insertable group, and the average urgency of other subsequently scheduled orders; By combining the probability of delay, the average urgency level, the distance difference, and the proportion of service duration, the degree of interference that inserting the current temporary order into the insertable group may cause to other subsequently arranged orders in the insertable group is determined.
[0048] Based on the above embodiments, the appropriateness of inserting temporary orders in this embodiment should not only consider their own timely delivery performance after insertion, but also whether the original orders in the group can be completed in a timely manner after insertion, and how likely they are to be interfered with.
[0049] View the estimated service time for order k retrieved by the system. The estimated service duration here can be the interval between the requested arrival time and the estimated completion time.
[0050] The system obtains the required arrival location of order k from the service location of the adjacent scheduled order after the insertion of group b (the subsequent scheduled location), and calculates the location distance between the two locations. .
[0051] When the expected service duration Maximum service duration of historical orders The ratio (service duration proportion) The larger the distance between the two positions, the greater the distance. Maximum distance from historical orders The distance compared to The smaller the value, the less likely there is a delay in inserting order k into group b. The larger the value, the greater the mean urgency of the remaining orders (other subsequently scheduled orders) after group b is inserted. The larger the value of k, the greater the interference that inserting order k into group b will have on the existing orders in group b, and the more likely it is to cause delays in the actual service of subsequent orders. This is the mean urgency level of all scheduled orders in this group that are after the insertion point.
[0052] This allows us to determine the potential interference of order k's insertion with the existing orders in group b. By using the above implementation methods, the potential interference level when order k is inserted into all insertable groups is obtained. This is used as one of the considerations for arranging order k, making the resulting arrangement more reasonable and ensuring the successful completion of all orders.
[0053] Step S3: Determine the total order completion ease of the insertable group based on the arranged order information, and determine the target insertion group for the current temporary order by combining the delay probability, the interference probability, and the total order completion ease.
[0054] The arrangement of ad-hoc orders should not only consider the user's perspective but also the balance of order workload among service groups. The workload of different groups should be quantified by the total estimated service time and estimated travel distance of the orders scheduled for the day. Furthermore, the most suitable group for insertion should be determined by considering the urgency, delay performance, and level of disruption of the orders to be inserted.
[0055] Specifically, please refer to Figure 5 Step S3, determining the ease of completing the total number of orders that can be inserted into the group based on the arranged order information, includes: Step S31: Determine the total estimated service time and total estimated travel distance of the arranged orders in the insertable group based on the arranged order information; Step S32: Combine the total estimated service time and the total estimated travel distance to determine the ease of completing the total orders that can be inserted into the group.
[0056] Based on the above embodiments, in this embodiment, the total estimated service time of group b before order k is inserted is calculated based on the order arrangement information. .
[0057] Based on the existing order service locations and sequences for group b on that day, the estimated total travel distance is calculated. Obtain it.
[0058] When inserting the total estimated service time of group b before insertion The shorter the distance, the more likely the total distance required will be. The shorter the timeframe, the less tasks group b has on that day, and the more willing it is to accept adding more orders k. This indicates the degree to which group b has a relatively low workload (also known as the ease of completing the total orders). .
[0059] Specifically, step S3, which combines the probability of delay, the probability of interference, and the ease of completing the total order to determine the target insertion group for the current temporary order, includes: Based on the likelihood of delays, the likelihood of interference, and the ease of completing the total orders, determine the appropriateness of adding the current temporary order to the insertable group; The most suitable insertable group is selected as the target insertion group for the current temporary order, and the current temporary order is inserted between the corresponding adjacent arranged orders in the target insertion group.
[0060] Based on the above embodiments, in this embodiment, the ease of completing the total orders for group b is... The larger the value, the greater the urgency of order k. The larger the value, the greater the potential delay in inserting order k into group b. The smaller the value, the less likely the insertion of order k will interfere with the existing orders in group b. The smaller the order size, the less likely the insertion of order k will cause delays to other orders, and order k can be processed in a timely manner. Order k is more suitable to be added to the order tasks of this group b.
[0061] Therefore, we can determine the suitability of adding order k to group b. The suitability of order k for adding to all groups b is calculated, and the group b with the highest suitability is selected as the target insertion group for adding order k, which is then added between the corresponding adjacent arranged orders (this part has been described in the previous embodiments).
[0062] The above process allows for the appropriate arrangement of multiple urgent orders on the same day.
[0063] The service orders and service times for each group are compressed and stored in the database, and then displayed visually on the staff's screen in a table format. This serves as a reference for staff to make modifications based on actual needs. See Table 1: Table 1 This invention, by comprehensively evaluating user information of users who are currently placing orders and temporary order information, can more accurately identify truly urgent orders. During allocation, it pre-simulates the delay impact of order placement on itself and other tasks in the same group, effectively avoiding the cascading delays that are easily caused by traditional methods. Simultaneously, by considering the real-time load of the groups, it ensures the balance of order task allocation and prevents some resources from becoming overloaded. Ultimately, while prioritizing key orders, it allocates orders to the groups with the lowest overall cost and most suitable load, achieving global scheduling optimization, maintaining the stability and efficiency of the overall task flow, and realizing a dual improvement in service quality and operational efficiency.
[0064] Furthermore, the dynamic order allocation based on multi-dimensional data analysis in this invention can significantly improve the overall efficiency of the scheduling system. It not only achieves high-precision matching of resources and demand, maximizing service efficiency and capacity utilization, but also effectively predicts and avoids the risk of chain delays caused by emergency order insertions, ensuring the smooth execution of task sequences. Ultimately, the system achieves an optimal balance between service quality, operating costs, and resource load while meeting users' personalized needs and timeliness commitments.
[0065] Example 2: This invention also proposes a service order dynamic allocation device based on multi-dimensional data analysis. The device can be a computer, a server, or other data analysis and computing equipment, or a combination of multiple devices.
[0066] like Figure 6 As shown, Figure 6 This is a schematic diagram of the hardware operating environment of the service order dynamic allocation device based on multi-dimensional data analysis involved in the embodiments of the present invention.
[0067] like Figure 6As shown, the service order dynamic allocation device based on multi-dimensional data analysis may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display or an input unit such as a control panel; the user interface 1003 may also include standard wired or wireless interfaces. The network interface 1004 may optionally include standard wired or wireless interfaces (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or stable non-volatile memory, such as a disk drive. The memory 1005 may also optionally be a storage system independent of the aforementioned processor 1001. The memory 1005, as a computer storage medium, may include a service order dynamic allocation program based on multi-dimensional data analysis (hereinafter referred to as the "service order dynamic allocation program").
[0068] Those skilled in the art will understand that Figure 6 The hardware structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0069] Continue to refer to Figure 6 , Figure 6 The memory 1005, which is a computer-readable storage medium, may include an operating system, a user interface module, a network communication module, and a service order dynamic allocation program based on multi-dimensional data analysis.
[0070] exist Figure 6 In this embodiment, the network communication module is mainly used to connect to the server and can communicate with the server for data. The processor 1001 can call the service order dynamic allocation program based on multi-dimensional data analysis stored in the memory 1005 and execute the steps in the above embodiments.
[0071] The hardware structure of the service order dynamic allocation device based on the above-mentioned multi-dimensional data analysis is used to implement various embodiments of the service order dynamic allocation method based on multi-dimensional data analysis of the present invention.
[0072] In addition, this invention also provides a service order dynamic allocation system based on multi-dimensional data analysis (hereinafter referred to as the "service order dynamic allocation system"), please refer to... Figure 7 The service order dynamic allocation system based on multi-dimensional data analysis includes: The order association analysis module A10 is used to determine the urgency of the current temporary order based on the target user's user information and the temporary order information of the current temporary order; to determine the insertable group based on the arranged order information of each arranged order in the service group; and to determine the degree of delay that inserting the current temporary order into the insertable group may cause to itself and the degree of interference to other orders in the insertable group by combining the temporary order information and the arranged order information. The global scheduling optimization module A20 is used to determine the ease of completing the total order of insertable groups based on the arranged order information, and to determine the target insertion group of the current temporary order by combining the probability of delay, the probability of interference and the ease of completing the total order.
[0073] Furthermore, the order association analysis module A10 is also used for: Determine the complaint information and priority in the user information, and determine the interval between the required arrival time in the temporary order information and the current time. The urgency level of the current temporary order is determined by combining the complaint information, the priority, and the interval duration.
[0074] Furthermore, the order association analysis module A10 is also used for: Based on the arrangement information of each arranged order in the service group, determine the idle time period between any two adjacent arranged orders; If the required arrival time of the current temporary order falls within the idle period and the duration of the idle period is longer than the estimated service duration of the current temporary order, then the service group is designated as an insertable group.
[0075] Furthermore, the order association analysis module A10 is also used for: Determine the estimated completion time of the current temporary order before it is inserted into the insertable group of previously scheduled orders; Determine the duration of the interval between the required arrival time and the previous estimated completion time in the provisional order information; The degree of delay that inserting the current temporary order into the insertable group may cause is determined based on the interval duration.
[0076] Furthermore, the order association analysis module A10 is also used for: Determine the previous scheduled position of the current temporary order in the insertable group where it is inserted into the previously scheduled order, and determine the location distance between the required arrival location and the previous scheduled position in the temporary order information; By combining the interval duration and the location distance, the potential delay caused by inserting the current temporary order into the insertable group is determined.
[0077] Furthermore, the order association analysis module A10 is also used for: Determine the next scheduled position of the current temporary order in the insertable group of already scheduled orders, and determine the location distance between the required arrival location and the next scheduled position in the temporary order information; Determine the difference between the location distance and the maximum location distance in historical orders, and determine the proportion of the estimated service time in the temporary order information compared to the maximum service time in historical orders; By combining the distance difference and the service duration ratio, the degree of interference that inserting the current temporary order into the insertable group may cause to other subsequently arranged orders in the insertable group is determined.
[0078] Furthermore, the order association analysis module A10 is also used for: Determine the potential delay of inserting the current temporary order into the insertable group, and the average urgency of other subsequently scheduled orders; By combining the probability of delay, the average urgency level, the distance difference, and the proportion of service duration, the degree of interference that inserting the current temporary order into the insertable group may cause to other subsequently arranged orders in the insertable group is determined.
[0079] Furthermore, the global scheduling optimization module A20 is also used for: Based on the arranged order information, determine the total estimated service time and total estimated travel distance of the arranged orders that can be inserted into the group; By combining the total estimated service time and the total estimated travel distance, the ease of completing the total order that can be inserted into the group is determined.
[0080] Furthermore, the global scheduling optimization module A20 is also used for: Based on the likelihood of delays, the likelihood of interference, and the ease of completing the total orders, determine the appropriateness of adding the current temporary order to the insertable group; The most suitable insertable group is selected as the target insertion group for the current temporary order, and the current temporary order is inserted between the corresponding adjacent arranged orders in the target insertion group.
[0081] Furthermore, the present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a service order dynamic allocation program based on multi-dimensional data analysis, wherein, when executed by a processor, the service order dynamic allocation program based on multi-dimensional data analysis implements the steps of the service order dynamic allocation method based on multi-dimensional data analysis as described above.
[0082] The method implemented when the service order dynamic allocation program based on multi-dimensional data analysis is executed can be referred to in various embodiments of the service order dynamic allocation method based on multi-dimensional data analysis of the present invention, and will not be repeated here.
[0083] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0084] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
[0085] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0086] The above description is only a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. All equivalent structural / method transformations made under the inventive concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the scope of protection of the present invention.
Claims
1. A method for dynamically allocating service orders based on multi-dimensional data analysis, characterized in that, The method includes the following steps: The urgency of the current temporary order is determined based on the user information of the target user and the temporary order information of the current temporary order. The group into which the order can be inserted is determined based on the arranged order information of each arranged order in the service group. By combining temporary order information and scheduled order information, determine the potential delay of the current temporary order to itself and the potential interference to other orders in the insertable group; Based on the arranged order information, the total order completion ease of the insertable group is determined, and the target insertion group for the current temporary order is determined by combining the delay probability, the interference probability, and the total order completion ease.
2. The service order dynamic allocation method based on multi-dimensional data analysis according to claim 1, characterized in that, The step of determining the urgency level of the current temporary order based on the target user's user information and the current temporary order information includes: Determine the complaint information and priority in the user information, and determine the interval between the required arrival time in the temporary order information and the current time. The urgency level of the current temporary order is determined by combining the complaint information, the priority, and the interval duration.
3. The service order dynamic allocation method based on multi-dimensional data analysis according to claim 1, characterized in that, The step of determining the insertable group based on the arrangement order information of each arranged order in the service group includes: Based on the arrangement information of each arranged order in the service group, determine the idle time period between any two adjacent arranged orders; If the required arrival time of the current temporary order falls within the idle period and the duration of the idle period is longer than the estimated service duration of the current temporary order, then the service group is designated as an insertable group.
4. The service order dynamic allocation method based on multi-dimensional data analysis according to claim 1, characterized in that, By combining temporary order information and scheduled order information, determine the potential delay caused by inserting the current temporary order into the insertable group, including: Determine the estimated completion time of the current temporary order before it is inserted into the insertable group of previously scheduled orders; Determine the duration of the interval between the required arrival time and the previous estimated completion time in the provisional order information; The degree of delay that inserting the current temporary order into the insertable group may cause is determined based on the interval duration.
5. The service order dynamic allocation method based on multi-dimensional data analysis according to claim 4, characterized in that, Determining the potential delay caused by inserting the current temporary order into the insertable group based on the stated interval duration includes: Determine the previous scheduled position of the current temporary order in the insertable group where it is inserted into the previously scheduled order, and determine the location distance between the required arrival location and the previous scheduled position in the temporary order information; By combining the interval duration and the location distance, the potential delay caused by inserting the current temporary order into the insertable group is determined.
6. The service order dynamic allocation method based on multi-dimensional data analysis according to claim 1, characterized in that, By combining temporary order information and scheduled order information, determine the potential interference that inserting a current temporary order into an insertable group may cause to other orders in the insertable group, including: Determine the next scheduled position of the current temporary order in the insertable group of already scheduled orders, and determine the location distance between the required arrival location and the next scheduled position in the temporary order information; Determine the difference between the location distance and the maximum location distance in historical orders, and determine the proportion of the estimated service time in the temporary order information compared to the maximum service time in historical orders; By combining the distance difference and the service duration ratio, the degree of interference that inserting the current temporary order into the insertable group may cause to other subsequently arranged orders in the insertable group is determined.
7. The service order dynamic allocation method based on multi-dimensional data analysis according to claim 6, characterized in that, Based on the distance difference and the service duration weighting, determine the potential interference of inserting a current temporary order into the insertable group with other subsequently scheduled orders in the insertable group, including: Determine the potential delay of inserting the current temporary order into the insertable group, and the average urgency of other subsequently scheduled orders; By combining the probability of delay, the average urgency level, the distance difference, and the proportion of service duration, the degree of interference that inserting the current temporary order into the insertable group may cause to other subsequently arranged orders in the insertable group is determined.
8. The service order dynamic allocation method based on multi-dimensional data analysis according to claim 1, characterized in that, Determining the ease of completing the total number of orders that can be inserted into the grouping based on the aforementioned order arrangement information includes: Based on the arranged order information, determine the total estimated service time and total estimated travel distance of the arranged orders that can be inserted into the group; By combining the total estimated service time and the total estimated travel distance, the ease of completing the total order that can be inserted into the group is determined.
9. The service order dynamic allocation method based on multi-dimensional data analysis according to claim 1, characterized in that, The target insertion group for the current temporary order is determined by combining the probability of delay, the probability of interference, and the ease of completing the total order, including: Based on the likelihood of delays, the likelihood of interference, and the ease of completing the total orders, determine the appropriateness of adding the current temporary order to the insertable group; The most suitable insertable group is selected as the target insertion group for the current temporary order, and the current temporary order is inserted between the corresponding adjacent arranged orders in the target insertion group.
10. A service order dynamic allocation system based on multi-dimensional data analysis, characterized in that, The system is used to implement the service order dynamic allocation method based on multi-dimensional data analysis as described in any one of claims 1 to 9; the system includes: The order association analysis module is used to determine the urgency of the current temporary order based on the target user's user information and the temporary order information of the current temporary order; to determine the insertable group based on the arranged order information of each arranged order in the service group; and to determine the degree of delay that inserting the current temporary order into the insertable group may cause to itself and the degree of interference to other orders in the insertable group by combining the temporary order information and the arranged order information. The global scheduling optimization module is used to determine the ease of completing the total number of orders that can be inserted into the group based on the arranged order information, and to determine the target insertion group for the current temporary order by combining the probability of delay, the probability of interference, and the ease of completing the total number of orders.