CN110245791B - Order processing method and system - Google Patents
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Abstract
The invention provides an order processing method and an order processing system, which solve the problems of low efficiency and high cost of the conventional manual order dispatching, shorten the dispatching path of an Anwei engineer and improve the dispatching efficiency. The order processing method comprises the following steps: sorting the order; matching an ann and maintenance engineer according to the sorted order, and generating a plan scheduling list; and generating a dispatching route of the order through a service simulation algorithm model according to the plan scheduling list.
Description
Technical Field
The invention belongs to the field of logistics, and particularly relates to an order processing method and system.
Background
With diversification of after-sale business development, the time-efficient requirement of users on home service of an ann and maintenance engineer is increasing. The traditional mode that a website informant manually assigns a service order to an ann and maintenance engineer is long in time consumption, large in workload and high in error rate, after the ann and maintenance engineer receives the order, a home-going route is arranged according to experience, the time consumed on the road is long, and the service timeliness of a user cannot be guaranteed. The traditional mode of dispatching orders to enter the home is relatively lagged behind, and the business requirements of high-speed development and the urgent requirements of users on the punctuality of the entrance are far from being met. At present, most of the orders sent to the home in the same industry are manually sent to the security engineer, the security engineer does not plan the orders sent to the home in the same day, the time for the order sending is random, a large amount of time is consumed on the way, the phenomenon of ordering is serious for a user, and the timeliness of the order sending cannot be guaranteed.
Disclosure of Invention
The invention provides an order processing method and an order processing system, which solve the problems of low efficiency and high cost of the conventional manual order dispatching, shorten the dispatching path of an Anwei engineer and improve the dispatching efficiency.
In order to solve the technical problem, the embodiment of the invention adopts the following technical scheme:
in a first aspect, an embodiment of the present invention provides an order processing method, where the method includes:
sorting the order;
matching an ann and maintenance engineer according to the sorted order, and generating a plan scheduling list;
and generating a dispatching route of the order through a service simulation algorithm model according to the plan scheduling list.
With reference to the first aspect, as a first implementable technical solution, the sorting the order includes:
according to the procedure and the operation plan of the order, carrying out primary sorting according to the operation date and the operation area of the order, and classifying the order according to the order service resources;
carrying out geographic informatization on the order by using a POI and reverse address analysis method;
sorting the order after the geographic information according to the operation time;
and carrying out block pretreatment on the orders sorted according to the operation time, and numbering the orders.
With reference to the first aspect, as a second implementable technical solution, the matching an ann and maintenance engineer according to the sorted order includes:
according to a user historical service order, carrying out user portrait on an order user, and carrying out level division on the user according to the user portrait to obtain user weight;
calculating attribute weight of the order by adopting a service simulation algorithm model;
calculating the comprehensive weight of the order according to the user weight and the attribute weight of the order, and sequencing the order;
adopting a big data algorithm and the grade of an ann's maintenance engineer to image the ann's maintenance engineer;
and matching the sorted orders with the imaged safety and maintenance engineers through a service simulation algorithm model to generate the safety and maintenance engineers matched with the orders.
With reference to the second implementable technical solution of the first aspect, as a third implementable technical solution, the matching, according to the sorted order and the imaged ann-level engineer, by using the service simulation algorithm model to generate the ann-level engineer matched with the order includes:
inquiring a service network point matched with the service order;
sorting service orders of reserved jobs for m days in the future;
sorting the sorted service orders;
calculating the time consumption of the sequenced service orders;
and dividing the service orders into route maps according to the image of an engineer, the service area, the order time consumption and the thermodynamic coefficient, and distributing the service orders in each route map to reserve the home operation time.
With reference to the first aspect, as a fourth implementable technical solution, the generating a route for dispatching an order through a service simulation algorithm model according to the plan schedule includes:
calculating the distance and time for going to the order delivery address according to the order delivery address, and analyzing historical average time consumption of all order types;
calculating the distance and time from the previous order delivery address to the next order delivery address;
calculating the time-consuming sequence of all orders of the current day security engineer according to the LBS distance sequence of the security engineer mobile terminal and the historical average time-consuming sequence of each order type;
and combining the planning data of all routes with the comprehensive weight grade of the order, and arranging and combining all distances through an optimal algorithm to obtain an optimal delivery path.
With reference to the first aspect, as a fifth implementable technical solution, the method further includes:
receiving an order to be dispatched, and dynamically modifying priority information of the order to be dispatched;
and adding the order to be dispatched into a planning and scheduling order through a service simulation algorithm model according to the information of the order to be dispatched.
In a second aspect, an embodiment of the present invention provides an order processing system, where the system includes:
a sorting module: used for sorting the order;
a matching module: the system is used for matching an ann and maintenance engineer according to the sorted order and generating a plan scheduling list;
a generation module: and the dispatching route is used for generating an order through a service simulation algorithm model according to the plan scheduling list.
With reference to the second aspect, as a first implementable technical solution, the sorting module includes:
a first sorting unit: the system is used for carrying out primary sorting according to the working procedures and the working plans of the orders and the working dates and the working areas of the orders and classifying the orders according to the order service resources;
an informatization unit: the system is used for carrying out geographic informatization on the order by utilizing a POI and reverse address analysis method;
a second sorting unit: the order sorting module is used for sorting the order subjected to geographic information according to the operation time;
a pretreatment unit: and the system is used for carrying out block pretreatment on the orders sorted according to the operation time and numbering the orders.
With reference to the second aspect, as a second implementable technical solution, the matching module includes:
an acquisition unit: the system comprises a user history service order, a user image generation unit, a user weight acquisition unit and a user classification unit, wherein the user history service order is used for carrying out user image on an order user according to a user history service order, and carrying out level division on the user according to the user image to obtain the user weight;
the first calculation unit: the attribute weight is used for calculating the attribute weight of the order by adopting a service simulation algorithm model;
a second calculation unit: the comprehensive weight of the order is calculated according to the user weight and the attribute weight of the order, and the order is sequenced;
an image unit: the system is used for adopting a big data algorithm and the grade of an Anwei engineer to image the Anwei engineer;
a generation unit: and matching the sorted orders with the imaged safety and maintenance engineers through a service simulation algorithm model to generate the safety and maintenance engineers matched with the orders.
With reference to the second implementable aspect of the second aspect, as a third implementable aspect, the generation unit includes:
the inquiry subunit: the service network is used for inquiring the service network points matched with the service orders;
a sorting subunit: a service order for sorting reserved jobs for m days in the future;
a sorting subunit: for ordering said sorted service orders;
a calculation subunit: calculating the time consumption of the sequenced service orders;
dividing the subunits: the system is used for dividing the service orders into route maps according to the image of an engineer, the service area, the order time consumption and the thermodynamic coefficient, and distributing the service orders in each route map to reserve the time for home operation.
With reference to the second aspect, as a fourth implementable technical solution, the generating module includes:
the third calculating unit is used for calculating the distance and time for going to the order delivery address according to the order delivery address and analyzing historical average consumed time of all order types;
the fourth calculating unit is used for calculating the distance and time from the previous order delivery address to the next order delivery address;
the fifth calculation unit is used for calculating all orders of the current day safety engineer according to LBS route sequencing of the safety engineer mobile terminal and historical average time-consuming sequencing of each order type;
and the sixth calculating unit is used for arranging and combining all the distances through an optimal algorithm by combining all the route planning data with the comprehensive weight grade of the order to obtain an optimal delivery path.
Compared with the prior art, the order processing method and the order processing system solve the problems of low efficiency and high cost of the conventional manual order dispatching, shorten the dispatching path of an Ann maintenance engineer and improve the dispatching efficiency. The order processing method of the embodiment includes: sorting the order; matching an security engineer according to the sorted order; and generating a dispatching path of the order through a service simulation algorithm model according to the order and the matched ann and maintenance engineer. The method of the embodiment automatically realizes order sorting, the matching of the safety maintenance engineer and the generation of the dispatching path, solves the problem of manual order distribution, saves time, labor and economic cost, and simultaneously recommends the optimal dispatching path for the safety maintenance engineer.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is an architectural diagram of an application of an embodiment of the present invention;
FIG. 2 is a flow chart of a method of an embodiment of the present invention;
FIG. 3 is a flowchart of step S10 of the method of an embodiment of the present invention;
fig. 4 is a block diagram of a system architecture of an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are exemplary only for explaining the present invention and are not construed as limiting the present invention. As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. It will be understood by those skilled in the art that, unless otherwise defined, all terms (including 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 belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.
The order processing method of the present embodiment can be applied to the architecture shown in fig. 1. The architecture comprises an order server, an intelligent server and a dispatching terminal. The order server is used for generating orders. The user places an order on the webpage according to the self requirement, and the order is automatically stored in the order server. The order server transmits the order to the intelligent server. And the intelligent server processes the received order, matches with a proper security engineer and selects an optimal delivery path. Meanwhile, the intelligent server sends the order information to the dispatching terminal.
As shown in fig. 2, an order processing method according to an embodiment of the present invention includes:
s10, sorting the order;
s20, matching an security engineer according to the sorted order;
and S30, generating a delivery path of the order through a service simulation algorithm model according to the order and the matched ann and maintenance engineer.
In the above embodiment, the system automatically performs order processing. After the orders are sorted, an ann and maintenance engineer is automatically matched. A common way in the prior art is manual dispatch. Compared with the prior art, the method of the embodiment automatically performs order dispatching. This improves the work efficiency of dispatching. Meanwhile, in the method of the embodiment, the delivery path of the order is automatically measured and calculated through the service simulation algorithm model. The ann engineer may not dispatch only one piece at a time. The optimal dispatching path is selected facing a plurality of orders, so that the dispatching time can be shortened, and the dispatching efficiency is improved.
According to the method, an order is assigned to an insurance engineer through intelligent sorting algorithm service and path scheduling algorithm service, and the optimal entry route of the insurance engineer is recommended.
The intelligent sorting algorithm service is used for intelligently sorting and recommending orders, and a sorting algorithm service engine is constructed by utilizing a big data algorithm and based on a map POI technology. The intelligent sorting algorithm service may include four modules: firstly, the order is finely and intelligently sorted according to the factors such as the process, time, area, required capacity and qualification and the like of the order. And then, constructing a set of accurate intelligent recommendation engine for the security engineer by utilizing the portrait of the security engineer and the portrait of the user, recommending the order to the security engineer, and generating a plan schedule. In the process, the system automatically and dynamically adjusts the scheduling list in real time for emergency scenes such as emergency insertion and the like.
And distributing the service orders to an ann and maintenance engineer through a fine sorting algorithm, and planning paths for all orders of the ann and maintenance engineer who is on the door of the day through path scheduling algorithm service. And combining the route planning data with the operation list weight grade, arranging and combining all distances through an optimal algorithm, and combining the routes which have the least time consumption, are the closest in route and meet the user requirements to the greatest extent to obtain the optimal route planning.
In the embodiment, a traditional manual dispatching mode of a website informant to an ann's maintenance engineer, who orders to be homed on the same day according to experience is changed into an automatic dispatching mode of a system, and a dispatching path is automatically planned. Meanwhile, the embodiment breaks the binding relationship between the original network point and the security engineer, and generates the optimal delivery path by comprehensively considering the address of the user, the expected door-to-door time of the user, the portrait of the security engineer and the order priority. The method and the system not only liberate the manpower of network information operators and reduce the labor cost, but also distribute the orders to more reasonable security engineers, shorten the service duration of the orders and improve the shopping experience of users.
As a preferred example, as shown in fig. 3, in step S10, the sorting the order specifically includes:
s101, according to the order process and the operation plan, the primary sorting is carried out according to the operation date and the operation area of the order, and the order is classified according to the order service resources.
The job plan is the projected delivery period for the service order. The job date is the home service date of the service order. The service qualification refers to the installation qualification of the air conditioner, the maintenance qualification of the air conditioner and the installation qualification of the gas water heater. For example, a service order-to-after-market system knows the time of service desired by the customer and the qualification of the order required, and can sort orders at the same time and with the same qualification in a sorted manner.
S102, geographic information of the order is achieved by using a POI and reverse address analysis method. For example, the province, city, district, street and detailed address of the service order are known, and the specific latitude and longitude is obtained by calling an interface provided by a high-grade map to analyze geographic information according to the service address.
S103, sorting the order after geographic information according to the operation time. For example, the order is sorted by job time and sorted by morning and afternoon by user desired service time period.
S104, preprocessing the orders sorted according to the operation time in a block mode and numbering the orders. For example: the daily period is divided into two periods of morning and afternoon, wherein the morning (9:00-13:00) and the afternoon (13:00-18: 00). In the morning (9:00-13:00), if 300 sheets exist in the block, the 300 sheets are numbered from 1 to 300 in sequence.
In the preferred embodiment, the orders are sorted and sorted for subsequent processing. In the preferred embodiment, based on a big data technology, a map POI technology is utilized to perform area portrayal on an order, an order operation area is determined, and a refined sorting algorithm service engine is constructed by combining LBS and a big data algorithm, so that the accuracy of operation intelligent scheduling is improved, the labor cost is reduced, the randomness of manual order division is reduced, and the operation efficiency and the user experience are improved. The refined sorting algorithm service engine is realized by an algorithm for accurately dispatching the whole after-sales intelligent scheduling to people.
As a preferred example, the step S20, matching the ann and maintenance engineer, specifically includes:
s201, according to the historical user order and the user behavior, user portrayal is conducted on the order user, grading is conducted on the user according to the user portrayal, and user weight is obtained.
As an example, user historical orders and user behavior include: the type, evaluation, complaint and activeness of the historical reservation service of the user. As an example, the user profile includes high service satisfaction, detail focus, no complaint for 3 months. The user profile level can be divided into: common users, good comment users, easy complaint users, VIP users and cattle users. And the user weight is comprehensively calculated according to the user portrait grade, the use behavior, the favorable rating and the complaint rate.
S202, calculating the attribute weight of the order by adopting a service simulation algorithm model. And the service simulation algorithm model calculates the attribute weight of the order according to the dimensions of the order class, the brand, the operation project, the quality assurance identification and the like.
And the service simulation algorithm model carries out attribute weight division on the order according to the service address, the commodity category and the service qualification dimension of the service order. For example: the service order air conditioner installation service address is in 'XX unit of X unit of suburban street of Kangmen of Kangning district of south Jing city of Jiangsu province', the system calculates that the average service time (the time consumed for signing in service from the upper door) is 2.5 hours according to the historical order occurring in the district, the average service time of the district exceeds the normal air conditioner installation service time and belongs to an abnormal district, and according to the district condition fed back by the historical service order engineer, the narrow air conditioner well designed in the district is known, the installation difficulty is large, the attribute weight of the order is high, and the engineer with rich experience needs to be assigned.
S203, calculating the comprehensive weight of the order according to the user weight and the attribute weight of the order. And comprehensively calculating the comprehensive weight of the order according to the user weight, the average service duration of the service order and the difficulty degree of the cell where the service address is located. The orders are sorted. And ordering the orders according to the comprehensive weight of the orders.
S204, a big data algorithm and the grade of the safety and maintenance engineer are adopted to image the safety and maintenance engineer. The grade of the Anwei engineer can be comprehensively graded according to an Anwei engineer service grading system, and specifically, the grade of the engineer (such as gold medal engineers, senior engineers, middle engineers, common engineers and interviewer engineers) can be graded according to the working age, daily operation behaviors, exclusive areas, goodness rate, complaint rate, service skill grade and awarding times of the Anwei engineer.
S205, matching is carried out according to the ordered orders and the imaged safety and maintenance engineers through a service simulation algorithm model, and an safety and maintenance engineer matched with the orders is generated.
The step S205 specifically includes:
s2051 queries the service network points matched with the service orders. For example, the service network point matched with the service order is matched with the service scope of the network point and the constraint of contract signing through the service address of the service order.
S2052 sorts the service orders for the reserved jobs m days in the future. For example, according to the current system time, service orders of the target reserved operation dates in the next two days are taken out, and the service orders are sorted and classified according to the service resources.
S2053 orders the sorted service orders. For example, for service orders classified according to service qualifications, the longitude and latitude of the service address and the longitude and latitude of the starting point of the network point area are calculated, and the orders are sorted according to the sequence of the distance from near to far.
S2054 calculates the time consumed by the ordered service orders. For example, each service order takes time as predicted time of route of high navigation + historical average service duration.
S2055, dividing the service orders into route maps according to the images of engineers, service areas, order time consumption and thermodynamic coefficients, and distributing the service orders in each route map to reserve the time for homework. If the service order is changed from the early stage to the priority of the current day, the service order is entitled to the advanced operation. The thermodynamic coefficient refers to the operation area covered by an engineer, operation qualification grade and engineer comprehensive service grade information which are analyzed according to a historical service work order of the engineer.
In the preferred embodiment, based on a service simulation algorithm model, a user is imaged around user behavior data through massive service order image information of an after-sale system, an accurate safety and maintenance engineer intelligent recommendation engine is constructed, a proper safety and maintenance engineer is automatically matched for the user, and the after-sale service quality is improved.
For example: service scoring system composite scoring: the RY00000001 worker has the qualifications of air conditioner installation 0.98, black electricity installation 0.66 and cleaning and maintenance 0.55. RY00000002 has the qualification of air conditioner installation 0.97, black electricity installation 0.6 and cleaning and maintenance 0.44. RY00000003 has the qualification of black electricity installation 0.7 and cleaning and maintenance 0.61.
When 15 orders of air conditioner installation, 20 orders of blackout installation and 5 orders of cleaning and maintenance occur in an area, service order distribution is carried out according to the principle that the worker service comprehensive grading is sorted from high to low according to the principle that the service period overall plan of the current service order appointment operation date is based on, for example, the appointment operation date is 4 months and 16 days, the operation time is 9:00-13:00 and 14: 00-18:00 obtained according to the service period overall plan. The following results are obtained by calculation:
the RY00000001 worker is divided into 10 units of air conditioner installation; RY00000002 workers are divided into 5 units for air conditioner installation and 5 units for black electricity installation; RY00000003 workers are classified into 12 lists for black electricity installation and 8 lists for cleaning and maintenance.
As a preferred example, in step S30, according to the order and the matched ann engineer, calculating, through a service simulation algorithm model, a route through which the ann engineer finishes dispatching the order, specifically including:
s301, according to the order delivery address, calculating the distance and time for the order delivery address, and analyzing the historical average time consumption of all order types. The route and time for the order delivery address are calculated, and the route and time can be calculated by adopting the longitude and latitude of the order delivery address, the longitude and latitude of the starting point of an engineer and a transportation tool used by the engineer and calling a Goods map SDK module. The order type refers to a service order type in the after-sales operation system, such as a new machine installation order, a door-to-door maintenance order, a door-to-door cleaning order, a delivery repair order, a consignment repair order and the like.
S302 calculates the distance and time from the previous order dispatch address to the next order dispatch address. This step calculates the distance and time required by the engineer to go from the previous order to the next order after the completion of one order per day, and is used to confirm the predicted time to go to the next order.
S303, calculating the time-consuming sequence of all orders of the current day safety engineer according to LBS distance sequence of the safety engineer mobile terminal and historical average time-consuming sequence of each order type.
S304, all the distances are arranged and combined through an optimal algorithm by combining all the route planning data with the comprehensive weight grade of the order, and an optimal delivery path is obtained.
The route planning data includes the navigation distance and estimated travel time for the worker to travel from the departure point to the first order, the average job duration of the service order, the navigation distance and estimated travel time to travel to the next order. Such as: the starting point of daily work of an engineer is a Zifeng building, the longitude and latitude of the Zifeng building are analyzed according to a high-level map to be (118.782781,32.061262), the service address analysis longitude and latitude of a service order comprises (A, B, C, D, E, F, G) 7 longitude and latitude points, the distance to the G and the time to the G are shortest from the 7 points before the Zifeng building are calculated through the high-level map, the order of the G point is a first order, the shortest distance to the next point and the time to the next point are calculated by taking the longitude and latitude of the G point as the starting point to obtain a point A, and the optimal dispatch path is G, A, D, E, F, C, B by analogy.
In the preferred embodiment, the service simulation algorithm model is a service order weight algorithm model. After receiving the order, the security engineer contacts the user by using the VOIP, confirms the expected delivery time with the user and schedules all job orders needing to be visited on the same day. By means of algorithm optimization provided by BI, accuracy and dispatching efficiency are improved, the automation degree of overall operation is improved, and the manual burden is reduced.
When the user meets special conditions or emergency conditions and needs to change the dispatching time, the order is taken as a to-be-dispatched order. As a preferred example, the method further comprises:
receiving an order to be dispatched, and dynamically modifying priority information of the order to be dispatched;
and adding the order to be dispatched into a plan scheduling order through a service simulation algorithm model according to the order information to be dispatched.
In the preferred embodiment, the order to be scheduled is added to the planning schedule. When the user needs to adjust the dispatching time, the system receives the user requirement information and adjusts the position of the order in the scheduling list. Therefore, the adjusted order dispatching time can meet the user requirements, and the service experience is improved.
According to the method, the most appropriate security engineer is analyzed and recommended through a big data algorithm (security engineer qualification, historical operation records, security engineer scheduling, security engineer level and the like) according to the weighted order qualification, interest points and client expected time. And then, confirming the service time interval through the VOIP power connection user, and carrying out dispatching arrangement by matching with the recommended security engineer. And the big data algorithm is weighted and sorted according to the work orders of the dispatched safety and maintenance engineers and numbered. Then, order time-consuming sequencing is carried out. And then, dispatching path recommendation is carried out, all the route planning data are combined with the job order weight levels, all the distances are arranged and combined through an optimal algorithm, and the routes which are least in time consumption, closest in route and capable of meeting the user appeal to the greatest extent are combined to obtain the optimal path planning. Meanwhile, the big data can provide a plurality of sets of optimal door-to-door paths for the reference selection of the safety and maintenance engineer, and the big data recommends the path plan most conforming to the habit of the safety and maintenance engineer according to the historical service track of the safety and maintenance engineer.
During processing, an exception condition may occur. Time zone locking is carried out on order change, manual allocation is not allowed, the system limit time period is kept consistent with the scheduling algorithm, and the condition that an operation time is changed at will by an security engineer to influence the execution effect of intelligent scheduling is prevented. The system initiates order cancellation and reassignment, and carries out reminding and routing update recommendation of security engineers, the handheld client side pushes update messages in real time, and all schedule changes can be checked by the history record of the message center. And carrying out rerouting calculation on the temporary incoming order, evaluating and sequencing by a big data algorithm, actively refreshing the current operation scheduling plan of the handheld client, and limiting temporary scheduling change for order types with special requirements on time efficiency.
The embodiment combines the portrait of an Anwei engineer, the portrait of a user, the service operation duration, the path distance, the order attribute and the like, and ensures high accuracy of the algorithm.
As shown in fig. 4, an embodiment of the present invention further provides an order processing system, including:
a sorting module: used for sorting the order;
a matching module: the system is used for matching an ann and maintenance engineer according to the sorted order and generating a plan scheduling list;
a generation module: and the dispatching route is used for generating an order delivery route through a service simulation algorithm model according to the plan scheduling list.
In the above embodiment, the system automatically performs order processing. After the order is sorted by the sorting module, the security engineer is automatically matched by the matching module. A common way in the prior art is manual dispatch. Compared with the prior art, the system of the embodiment automatically performs order dispatching. This improves the work efficiency of dispatching. Meanwhile, in the system of the embodiment, the generation module automatically measures and calculates the delivery path of the order. The ann engineer may not dispatch one piece at a time. The optimal dispatching path is selected facing a plurality of orders, so that the dispatching time can be shortened, and the dispatching efficiency is improved.
Preferably, the sorting module includes:
a first sorting unit: the system is used for carrying out primary sorting according to the working procedures and the working plans of the orders and the working dates and the working areas of the orders and classifying the orders according to the order service resources;
an informatization unit: the system is used for carrying out geographic informatization on the order by utilizing a POI and reverse address analysis method;
a second sorting unit: the order sorting module is used for sorting the order subjected to geographic information according to the operation time;
a pretreatment unit: and the system is used for carrying out block pretreatment on the orders sorted according to the operation time and numbering the orders.
Preferably, the matching module includes:
an acquisition unit: the system comprises a user history service order acquisition unit, a user image acquisition unit, a user weight acquisition unit and a user image generation unit, wherein the user history service order acquisition unit is used for carrying out user image on an order user and carrying out level division on the user according to the user image to acquire the user weight;
the first calculation unit: the attribute weight is used for calculating the attribute weight of the order by adopting a service simulation algorithm model;
a second calculation unit: the comprehensive weight of the order is calculated according to the user weight and the attribute weight of the order, and the order is sequenced;
an image unit: the system is used for adopting a big data algorithm and the grade of an ann's maintenance engineer to image the ann's maintenance engineer;
a generation unit: and matching the sorted orders with the imaged safety and maintenance engineers through a service simulation algorithm model to generate the safety and maintenance engineers matched with the orders.
Preferably, the generating unit includes:
the inquiry subunit: the service network is used for inquiring the service network points matched with the service orders;
a sorting subunit: a service order for sorting reserved jobs for m days in the future;
a sorting subunit: for ordering said sorted service orders;
a calculation subunit: calculating the time consumption of the sequenced service orders;
dividing the subunits: the system is used for dividing the service orders into route maps according to the image of an engineer, the service area, the order time consumption and the thermodynamic coefficient, and distributing the service orders in each route map to reserve the time for home operation.
Preferably, the generating module includes:
the third calculating unit is used for calculating the distance and time for going to the order delivery address according to the order delivery address and analyzing historical average consumed time of all order types;
the fourth calculating unit is used for calculating the distance and the time from the previous order delivery address to the next order delivery address;
the fifth calculation unit is used for calculating all orders of the current day safety engineer according to LBS route sequencing of the safety engineer mobile terminal and historical average time-consuming sequencing of each order type;
and the sixth calculating unit is used for arranging and combining all the distances by the optimal algorithm according to the comprehensive weight grades of the orders by using all the route planning data to obtain the optimal delivery path.
Preferably, the system further comprises:
and a modification module: the system comprises a receiving module, a scheduling module and a display module, wherein the receiving module is used for receiving an order to be scheduled and dynamically modifying priority information of the order to be scheduled;
adding a module: and the order form generation module is used for adding the order form to be dispatched into a plan scheduling order through a service simulation algorithm model according to the order form information to be dispatched.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, it is relatively simple to describe, and reference may be made to some descriptions of the method embodiment for relevant points.
Those skilled in the art will appreciate that the methods or systems for implementing the embodiments described above can be implemented via computer program instructions. The computer program instructions are loaded onto a programmable data processing apparatus, such as a computer, to cause corresponding instructions to be executed on the programmable data processing apparatus to implement the functions of the method or system of the above-described embodiments.
Those skilled in the art can make non-inventive technical improvements to the present application based on the above-described embodiments without departing from the spirit of the present invention. Such modifications are to be considered within the scope of the claims of the present application.
Claims (9)
1. An order processing method, characterized in that the method comprises:
sorting the order;
matching an ann and maintenance engineer according to the sorted order, and generating a plan scheduling list;
generating a dispatching route of the order through a service simulation algorithm model according to the plan scheduling list;
generating a route for dispatching orders through a service simulation algorithm model according to the plan scheduling list, wherein the route comprises the following steps:
calculating the distance and time for going to the order delivery address according to the order delivery address, and analyzing historical average time consumption of all order types;
calculating the distance and time from the previous order delivery address to the next order delivery address;
calculating all orders of the current day security engineer according to LBS distance sequencing of the security engineer mobile terminal and historical average time-consuming sequencing of each order type;
and (4) arranging and combining all distances through an optimal algorithm by combining all route planning data with the comprehensive weight grade of the order to obtain an optimal delivery path.
2. The method of claim 1, wherein said sorting the order comprises:
according to the procedure and the operation plan of the order, carrying out primary sorting according to the operation date and the operation area of the order, and classifying the order according to the order service resources;
carrying out geographic informatization on the order by using a POI and reverse address analysis method;
sorting the order after the geographic information according to the operation time;
and carrying out block pretreatment on the orders sorted according to the operation time, and numbering the orders.
3. The method of claim 1, wherein said matching an ann engineer based on said sorted orders comprises:
according to a user historical service order, carrying out user portrait on an order user, and carrying out level division on the user according to the user portrait to obtain user weight;
calculating attribute weight of the order by adopting a service simulation algorithm model;
calculating the comprehensive weight of the order according to the user weight and the attribute weight of the order, and sequencing the order;
adopting a big data algorithm and the grade of an ann's maintenance engineer to image the ann's maintenance engineer;
and matching the sorted orders with the imaged safety and maintenance engineers through a service simulation algorithm model to generate the safety and maintenance engineers matched with the orders.
4. The method of claim 3, wherein said generating an ann engineer matching said ordered orders by a service simulation algorithm model based on said mapped ann engineer and said ordered orders comprises:
inquiring a service network point matched with the service order;
sorting service orders of reserved jobs for m days in the future;
sorting the sorted service orders;
calculating the time consumption of the sequenced service orders;
and dividing the service orders into route maps according to the image of an engineer, the service area, the order time consumption and the thermodynamic coefficient, and distributing the service orders in each route map to reserve the home operation time.
5. The method of claim 1, wherein the method further comprises:
receiving an order to be dispatched, and dynamically modifying priority information of the order to be dispatched;
and adding the order to be dispatched into a plan scheduling order through a service simulation algorithm model according to the order information to be dispatched.
6. An order processing system, the system comprising:
a sorting module: used for sorting the order;
a matching module: the system is used for matching an ann and maintenance engineer according to the sorted order and generating a plan scheduling list;
a generation module: the dispatching route is used for generating an order through a service simulation algorithm model according to the plan scheduling list;
the generation module comprises:
the third calculating unit is used for calculating the distance and time for going to the order delivery address according to the order delivery address and analyzing historical average consumed time of all order types;
the fourth calculating unit is used for calculating the distance and the time from the previous order delivery address to the next order delivery address;
the fifth calculation unit is used for calculating the time-consuming sequence of all orders of the current day security engineer according to the LBS distance sequence of the security engineer mobile terminal and the historical average time-consuming sequence of each order type;
and the sixth calculating unit is used for arranging and combining all the distances by the optimal algorithm according to the comprehensive weight grades of all the route planning data and the orders to obtain the optimal delivery path.
7. The system of claim 6, wherein the sorting module comprises:
a first sorting unit: the system is used for carrying out primary sorting according to the working procedures and the working plans of the orders and the working dates and the working areas of the orders and classifying the orders according to the order service resources;
an informatization unit: the system is used for carrying out geographic informatization on the order by utilizing a POI and reverse address analysis method;
a second sorting unit: the order sorting module is used for sorting the order subjected to geographic information according to the operation time;
a pretreatment unit: and the system is used for carrying out block pretreatment on the orders sorted according to the operation time and numbering the orders.
8. The system of claim 6, wherein the matching module comprises:
an acquisition unit: the system comprises a user history service order, a user image generation unit, a user weight acquisition unit and a user classification unit, wherein the user history service order is used for carrying out user image on an order user according to a user history service order, and carrying out level division on the user according to the user image to obtain the user weight;
the first calculation unit: the attribute weight is used for calculating the order form by adopting a service simulation algorithm model;
a second calculation unit: the comprehensive weight of the order is calculated according to the user weight and the attribute weight of the order, and the order is sequenced;
an image unit: the system is used for adopting a big data algorithm and the grade of an ann's maintenance engineer to image the ann's maintenance engineer;
a generation unit: and matching the sorted orders with the imaged safety and maintenance engineers through a service simulation algorithm model to generate the safety and maintenance engineers matched with the orders.
9. The system of claim 8, wherein the generating unit comprises:
the inquiry subunit: the service network is used for inquiring the service network points matched with the service orders;
a sorting subunit: a service order for sorting reserved jobs for m days in the future;
a sorting subunit: for ordering said sorted service orders;
a calculation subunit: calculating the time consumption of the sequenced service orders;
dividing the subunits: the system is used for dividing the service orders into route maps according to the image of an engineer, the service area, the order time consumption and the thermodynamic coefficient, and distributing the service orders in each route map to reserve the time for home operation.
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| CN111027867A (en) * | 2019-12-12 | 2020-04-17 | 蚁安居(天津)网络技术有限公司 | Automatic ordering system and order tracking system for home service |
| CN112990807B (en) * | 2019-12-18 | 2023-04-07 | 顺丰科技有限公司 | Item delivery time consumption analysis method and device, computer equipment and storage medium |
| CN112307379B (en) * | 2020-07-15 | 2023-07-28 | 长沙市到家悠享家政服务有限公司 | Page generation method, device and equipment |
| CN112184365B (en) * | 2020-09-08 | 2023-12-08 | 深圳市道旅旅游科技股份有限公司 | Abnormal order state work order processing method and device |
| CN112184388B (en) * | 2020-10-12 | 2024-02-13 | 上海燕汐软件信息科技有限公司 | Home decoration service list distribution method, device and storage medium |
| CN112509164A (en) * | 2020-10-30 | 2021-03-16 | 长沙市到家悠享网络科技有限公司 | Attendance card-punching method, attendance card-punching device, attendance card-punching equipment and storage medium |
| CN113222689A (en) * | 2021-04-26 | 2021-08-06 | 北京京东拓先科技有限公司 | Service order processing method, device, equipment, medium and program product |
| CN113191664B (en) * | 2021-05-18 | 2023-05-26 | 绍兴鹿鸣网络科技有限公司 | Clothing supply chain management method and system |
| CN113191509A (en) * | 2021-05-27 | 2021-07-30 | 广州广电运通智能科技有限公司 | Intelligent order dispatching method, equipment, medium and product based on maintenance personnel portrait |
| CN113516262A (en) * | 2021-07-30 | 2021-10-19 | 杭州智果科技有限公司 | Maintenance work order charging system |
| CN113505904A (en) * | 2021-07-30 | 2021-10-15 | 杭州智果科技有限公司 | Maintenance work order distribution system |
| CN114118868A (en) * | 2021-12-08 | 2022-03-01 | 福寿康(上海)医疗养老服务有限公司 | Scheduling system and method |
| CN114418471A (en) * | 2022-03-31 | 2022-04-29 | 广州平云小匠科技有限公司 | Intelligent planning and visual management method and system for work orders |
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