CN113205391B - Historical order matching degree based order dispatching method, electronic equipment and computer readable medium - Google Patents

Historical order matching degree based order dispatching method, electronic equipment and computer readable medium Download PDF

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CN113205391B
CN113205391B CN202110563928.0A CN202110563928A CN113205391B CN 113205391 B CN113205391 B CN 113205391B CN 202110563928 A CN202110563928 A CN 202110563928A CN 113205391 B CN113205391 B CN 113205391B
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黄武
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Changsha Daojia Youxiang Home Economics Service Co ltd
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Abstract

The invention belongs to the technical field of Internet order dispatching and provides an order dispatching method based on historical order matching degree, electronic equipment and a computer readable medium. The method comprises the following steps: setting the weight of the matching rule; acquiring historical order data; matching the historical orders with the matching rules to obtain the matching value of each historical order to each matching rule; calculating the weight coefficient of each matching rule according to the matching value of the historical order and the weight of the matching rule; calculating the matching degree of each historical order according to the matching value of the historical order, the weight of the matching rule and the weight coefficient; and calculating the average matching degree of the historical orders of the service personnel according to the matching degree of the historical orders, and allocating a new service order to the service personnel according to the average matching degree of the service personnel. The method improves the accuracy of the order matching degree prediction, improves the dispatching unit experience of the user, eliminates the influence of artificial subjective factors on the weight of the matching rule, and can more objectively reflect the influence factors of order matching.

Description

Historical order matching degree based order dispatching method, electronic equipment and computer readable medium
Technical Field
The invention belongs to the technical field of Internet, is particularly suitable for the technical field of Internet dispatching orders, and more particularly relates to an order dispatching method based on historical order matching degree, electronic equipment and a computer readable medium.
The order in the invention refers to an order issued by a user through an internet service level in a mobile terminal APP mode and other modes, and related service personnel need to receive the order and process the order. The order is generally referred to herein as a service class order.
Background
With the development of internet technology, people have become accustomed to shopping online or subscribing to services. For services reserved on the internet, such as transportation services, delivery services, home services, maintenance services, month-to-law services, etc., orders initiated on the internet need to be assigned to contracted service providers.
In a conventional order allocation method, a matching degree of each service provider with an order is predicted based on order information and information of the service provider, and the order is allocated according to a certain allocation rule based on a value of the matching degree.
However, the existing order dispatching method can only utilize the static information of the service provider, so that the real order processing quality of the service provider when the order is dispatched is not considered. When some service providers have good rigid conditions but the user satisfaction is not high, the existing order dispatching method cannot effectively discriminate and still dispatch the order with high strength, which is not beneficial to the improvement of the user experience of the internet service platform.
In addition, for a specific service type, the evaluation rule and the weight thereof for the service provider to determine whether the order is matched are often preset by experts, but the evaluation rule and the weight thereof cannot be updated in time along with the change of environment, personnel, service type and the like, so that the existing order matching method cannot be effectively updated along with the actual situation, thereby reducing the overall matching degree of the order dispatch.
Disclosure of Invention
Technical problem to be solved
The invention aims to solve the problem that the prior art has low dispatching quality due to inaccurate calculation of the order matching degree.
(II) technical scheme
In order to solve the above technical problem, an aspect of the present invention provides a method for dispatching a service order based on historical order matching degree, for dispatching the service order to a service person, according to which the method comprises the following steps: setting the weight of a matching rule, wherein the matching rule is an index for evaluating whether the order is matched with the user expectation, and the weight represents the importance degree of the matching rule; obtaining historical order data, wherein the historical order data is historical orders in a specific time period; matching the historical orders with the matching rules to obtain the matching value of each historical order to each matching rule; calculating a weight coefficient of each matching rule according to the matching value of the historical order and the weight of the matching rule, wherein the weight coefficient is positively correlated with the matching rate of the matching rule in the historical order; calculating the matching degree of each historical order according to the matching value of the historical order, the weight of the matching rule and the weight coefficient; and calculating the average matching degree of the historical orders of the service personnel according to the matching degree of the historical orders, and allocating a new service order for the service personnel according to the average matching degree of the service personnel.
According to a preferred embodiment of the invention, the method further comprises: and resetting the weight according to the ratio of the weight of the matching rule and the weight coefficient.
According to a preferred embodiment of the present invention, the matching value is binary data for indicating whether the order satisfies the matching rule.
According to a preferred embodiment of the present invention, the weight coefficient of each matching rule is calculated according to the following formula:
Figure BDA0003079851140000021
wherein, C i Is the weight coefficient of the ith matching rule, m is the historical order quantity, W i Is the weight, x, of the ith matching rule ii And the matching value of the jth order to the ith matching rule is, wherein i and j are natural numbers.
According to the preferred embodiment of the present invention, the matching degree of each historical order is calculated according to the following formula:
Figure BDA0003079851140000031
wherein S is j Is the matching degree of the jth historical order, and n is the number of matching rules.
According to the preferred embodiment of the present invention, the matching degree of each historical order is calculated according to the following formula:
Figure BDA0003079851140000032
wherein S is j Is the matching degree of the jth historical order, and n is the number of matching rules.
According to a preferred embodiment of the present invention, assigning a new service order to the service person according to the average degree of matching of the service person comprises:
setting a target order dispatching rate for service personnel according to the average matching degree;
and distributing service orders for the service personnel according to the target order dispatching rate of the service personnel.
According to a preferred embodiment of the present invention, assigning a new service order to the service person according to the average degree of matching of the service person comprises:
and calculating the prediction matching degree of the new order and each service person based on a dispatching model, wherein the dispatching model is obtained by training historical order data, and the historical order data comprises historical average matching degree of the service persons.
A second aspect of the invention proposes an electronic device comprising a processor and a memory for storing a computer-executable program, which, when executed by the processor, performs the method as described above.
A third aspect of the present invention provides a computer-readable medium storing a computer-executable program, wherein the computer-executable program, when executed, implements the method as described above.
(III) advantageous effects
In one aspect, the present invention proposes to apply the historical order matching degree as an attribute information of the service provider to provide prediction accuracy. On the other hand, the matching degree algorithm of the historical orders is improved, so that the influence of the artificial (expert) subjective factors on the weight of the matching rule is eliminated, and the influence factors of order matching are reflected more truly and objectively.
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FIG. 1 is a schematic view of an application scenario of a historical order matching degree-based order dispatching method according to the present invention;
FIG. 2 is a flow diagram of a method of dispatching orders based on historical order matching, according to an embodiment of the invention;
FIG. 3 is a table of weights and matching values of matching rules of a historical order matching degree-based order dispatching method according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an electronic device of one embodiment of the invention;
fig. 5 is a schematic diagram of a computer-readable recording medium of an embodiment of the present invention.
Detailed Description
The present invention is described below with reference to examples. It should be noted that the flow chart in the drawings is only an exemplary flow demonstration, and does not represent that all the contents, operations and steps in the flow chart are necessarily included in the scheme of the invention, and the sequence shown in the drawing is not necessarily executed. For example, some operations/steps in the flowcharts may be divided, some operations/steps may be combined or partially combined, and the like, and the execution order shown in the flowcharts may be changed according to actual situations without departing from the gist of the present invention.
The block diagrams in the figures generally represent functional entities and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and/or processing unit devices and/or microcontroller devices.
The same reference numerals denote the same or similar elements, components, or parts throughout the drawings, and thus, a repetitive description thereof may be omitted hereinafter. It will be further understood that, although the terms first, second, third, etc. may be used herein to describe various elements, components, or sections, these elements, components, or sections should not be limited by these terms. That is, these phrases are used only to distinguish one from another. For example, a first device may also be referred to as a second device without departing from the spirit of the present invention. Furthermore, the term "and/or", "and/or" is intended to include all combinations of any one or more of the listed items.
In order to solve the above technical problem, the present invention provides a dispatching method based on historical order matching degree, which is used for dispatching a service order to a service provider. Fig. 1 is a schematic view of an application scenario of a historical order matching degree-based order dispatching method according to the present invention. As shown in fig. 1, a user orders a relevant service on an internet service platform 2 through a client 1, generates an order, and the internet service platform 2 distributes the order to a corresponding service provider and issues the order to a client 3 of the service provider. It should be noted that fig. 1 is only a schematic representation, and in a practical situation, the internet service platform 2 and the client 1, 3, etc. may need to interact with information in thousands of clients within the same time side, so that the internet service platform 2 needs to timely and accurately dispatch the corresponding order to the corresponding service provider. This requires the internet service platform 2 to provide powerful information processing capabilities and have order distribution policies and/or algorithms that are intelligent and efficient enough to make reasonable order assignments.
It should be noted that the present invention is not limited to specific order types and service types provided by the service platform, and the method of the present invention can be applied as long as the order needs to be assigned to different processors. The order referred to herein is typically a service order, but does not exclude a goods order. Service orders may be assigned to different service providers or service personnel, and the services may be delivery, home, urban taxi, maintenance, etc. The commodity order may be distributed to the store or distributor, and the commodity may be any commodity capable of being traded over network.
The following description will take a service order as an example. In order to solve the problems in the prior art, the invention proposes to use the historical order matching degree of the service provider to predict the matching degree of the new order. It should be noted that, the present invention uses the information of the historical order matching degree of the service provider to predict the matching degree of the service provider to the new order, and the historical order matching degree generally refers to the average matching degree of the historical order, and can be used as a parameter or converted into a label data for processing. However, the degree of matching of the new order predicted by the present invention is not determined entirely by the average degree of matching of the historical orders, but is generally determined by comprehensive calculation based on the attribute information of the order itself, the attribute information of the service provider, and the like. The average match of the historical orders of the service provider is used as part of the attribute information of the service provider.
In the prior art, the matching degree of the historical orders can be calculated by judging whether the completed historical orders are matched with a specific matching rule or not. The historical order matching degree is often used for evaluating the completion quality of the order of the supplier, which is served by the service, so as to grade, assess or level the service provider. In one aspect, the present invention proposes to apply the historical order matching degree as an attribute information of the service provider to provide prediction accuracy. On the other hand, the matching degree algorithm of the historical orders is improved, so that the influence of the artificial (expert) subjective factors on the weight of the matching rule is eliminated, and the influence factors of order matching are reflected more truly and objectively.
Here, the matching rule is an index for evaluating whether or not the order matches with the user's desire. Obviously, the evaluation of whether the user expectation is met includes subjective factors, and the selection of the matching rule also includes subjective factors. After the matching rule is determined, the weight of the matching rule needs to be preset, and usually, the weight is set by an expert. The invention can provide an interface for setting the weight by the expert at the background of the platform and store the weight set by the expert.
Taking the housekeeping service as an example, the matching rules set by the experts include: rule1 whether female; rule2 whether the age is between 25-35 years; rule3, whether the working life exceeds 3 years; rule4 whether the number of incoming orders is greater than 5 in the last month, etc. The expert may be a product manager, customer service personnel, after-sales personnel, etc. Meanwhile, the invention requires experts to set the weight of the matching rule when setting the matching rule. The weight represents the importance of the matching rule.
Due to the difference between human cognition and subjective feeling, the expert is difficult to avoid the situation of inaccurate understanding when setting the weight of the matching rule. For example, some experts consider that "rule 2: whether the age is 25-35 years" is a very important index, and "rule 4: whether the number of recent bills is more than 5 times in a month" is a less important index. Thus, the weight of rule2 can be set to 5, and the weight of rule4 can be set to 1. In order to eliminate the problem caused by such subjective difference, the present invention improves the algorithm of matching degree, which will be described later.
In order to obtain the matching degree of the historical orders, the invention needs to obtain historical order data, and the historical order data is historical orders in a specific time period. After the historical order data is obtained, the historical orders are matched with the matching rules one by one, and the matching value of each historical order to each matching rule is obtained. Preferably, for the convenience of calculation, the matching value is a binary data, i.e., "yes" or "no", or "true" or "false", which may be represented by 1, 0.
In order to more accurately calculate the matching degree of the historical orders, a weight coefficient is introduced into the weight of the matching rule. And calculating a weight coefficient of each matching rule according to the matching value of the historical order and the weight of the matching rule, wherein the weight coefficient is positively correlated with the matching rate of the matching rule in the historical order. After the weight coefficient is obtained, the matching degree of each historical order can be calculated according to the matching value of the historical order, the weight of the matching rule and the weight coefficient.
Preferably, the weight coefficient may be a weighted value of a product of a matching value of the matching rule in all historical orders and a weight. In one embodiment, the weight coefficient of each matching rule is calculated according to the following formula:
Figure BDA0003079851140000071
wherein, C i Is the weight coefficient of the ith matching rule, m is the historical order quantity, W i Is the weight, x, of the ith matching rule ij And for the matching value of the jth order to the ith matching rule, i and j are both natural numbers.
Therefore, the matching degree of each historical order is calculated according to the following formula:
Figure BDA0003079851140000072
in another embodiment, the matching degree is calculated as follows.
Figure BDA0003079851140000073
Finally, the invention calculates the average matching degree of the historical orders of the service personnel according to the matching degree of the historical orders, and then allocates a new service order for the service personnel according to the average matching degree of the service personnel. One way to dispatch a new service order is to set a target dispatch rate for the service personnel based on the average match and then distribute the service order for each service personnel based on the target dispatch rate for each service personnel. That is to say, in principle, the orders are dispatched as many as possible with high matching degree, and the orders are dispatched as few as possible with low matching degree, so as to avoid that the user experience is further influenced by too high orders with low matching degree. Of course, the present invention may employ a predetermined strategy to dispatch, for example, when the average historical match is below a threshold, the dispatching is suspended, or other measures are taken.
In another preferred embodiment, the predicted matching degree of the new order and each service person is calculated based on a dispatching model, wherein the dispatching model is obtained by training historical order data, and the historical order data comprises historical average matching degree of the service persons. The order model may be a machine self-learning based model that is trained from historical order data. According to the invention, the historical average matching rate of each service provider is obtained, the historical average matching rate can be subjected to labeling processing and used as an attribute parameter input model of the service provider for training, and the influence degree of each parameter on the matching degree can be obtained through learning of a machine self-learning model. The target variable of the model can be user evaluation of historical orders and the like, and the invention is not limited to the selection of the type and the target variable of the machine self-learning model.
According to a preferred embodiment of the invention, the method further comprises resetting the weight according to a ratio of the weight of the matching rule and the weight coefficient. As described above, since the expert has subjectivity in setting the weight, the present invention proposes to correct the degree of matching using the weight coefficient. Further, a mechanism for adjusting the weight itself in accordance with the weight coefficient may be cited to make the weight itself more biased toward reality. The weight itself may be corrected periodically or aperiodically, and at the time of correction, it may be corrected in accordance with a predetermined weight correction step or a predetermined rule. For example, when the ratio of the weight to the weight coefficient is too high, it indicates that the matching rule corresponding to the weight only affects a small number of orders, so the weight of the matching rule can be adjusted downward. And vice versa.
FIG. 2 is a flow chart of a method of dispatching based on historical order matching in one embodiment of the invention. As shown in fig. 2, the method comprises the steps of:
step S1 is to set a weight of a matching rule, which is an index for evaluating whether or not the order matches the user' S desire, the weight indicating the degree of importance of the matching rule.
FIG. 3 is a table of the weights and matching values of a matching rule according to the embodiment. As shown in fig. 3, for convenience of description, 10 matching rules (rule) are selected from the table: rulel, rule2, rule3. The weights of the matching rules are uniformly set by experts.
And step S2, obtaining historical order data, wherein the historical order data is historical orders in a specific time period. As shown in fig. 3, the table also lists data for 10 orders (orders), but in practice the number of rules may be tens or hundreds, and the number of orders may be tens of thousands, or even hundreds of thousands.
And step S3, matching the historical orders with the matching rules to obtain the matching value of each historical order to each matching rule.
In this embodiment, each matching rule corresponds to binary data, which is represented by 1 and 0, where 1 represents a matching rule and 0 represents a non-matching rule.
And step S4, calculating the weight coefficient of each matching rule according to the matching value of the historical order and the weight of the matching rule. The weight coefficient of the matching rule in this embodiment is calculated as follows:
Figure BDA0003079851140000081
wherein, C i Is the weight coefficient of the ith matching rule, W i Is the weight of the ith matching rule, X ij And for the matching value of the jth order to the ith matching rule, i and j are natural data from 1 to 10.
And step S5, calculating the matching degree of each historical order according to the matching value of the historical order, the weight of the matching rule and the weight coefficient.
In this embodiment, the matching degree of each historical order is calculated according to the following formula:
Figure BDA0003079851140000091
alternatively, the matching degree is calculated as follows.
Figure BDA0003079851140000092
And step S6, calculating the average matching degree of the historical orders of the service personnel according to the matching degree of the historical orders, and allocating a new service order for the service personnel according to the average matching degree of the service personnel.
In this embodiment, a neural network based machine self-learning model is employed for the dispatching. The model is trained from historical order data. The machine learning model can be a decision model, and the decision model can comprise a rational decision model, a limited rational decision model, a progressive decision model and the like. The machine learning model can process the plurality of order matching pairs based on a preset rule and a preset algorithm during construction, and then at least one matching order is determined. The matching order may be an order for delivery to a service provider's client (e.g., a housekeeping's cell phone).
Historical order data can be used as a training set based on the domestic historical order data, attribute characteristics of order information and driver information are extracted from the historical order data and used as model input, so that whether a label of an order matching pair is formed by a user and a service provider in the historical order data is reflected as a correct standard, and a preset matching rule of a decision model is continuously optimized. The attribute characteristics of the order information may include user attribute characteristics (e.g., a user's housekeeping needs), time characteristics (e.g., a desired time to visit), etc., and the housekeeping service provider attribute information may include personnel attribute characteristics (e.g., a willingness to pick up an order, a current location, a distance), etc.
Further, in this embodiment, the steps S1 to S6 are performed sequentially in the manner described above. Further, after the weight coefficient is obtained at step S4, step S7 is performed to reset the weight according to the ratio of the weight of the matching rule and the weight coefficient.
In the embodiment, the historical order matching degree is applied as the attribute information of the service provider, so that the accuracy of matching degree prediction is improved. Meanwhile, the matching degree algorithm of the historical orders is improved, so that the influence of artificial (expert) subjective factors on the weight of the matching rule is eliminated, and the influence factors of order matching are reflected more truly and objectively.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, which includes a processor and a memory, where the memory stores a computer-executable program, and when the computer program is executed by the processor, the processor executes a vehicle intelligent assistance pushing method based on rotation angle monitoring.
As shown in fig. 4, the electronic device is in the form of a general purpose computing device. The number of the processors can be one or more, and the processors can work together. The invention also does not exclude that distributed processing is performed, i.e. the processors may be distributed over different physical devices. The electronic device of the present invention is not limited to a single entity, and may be a sum of a plurality of entity devices.
The memory stores a computer executable program, typically machine readable code. The computer readable program may be executed by the processor to enable an electronic device to perform the method of the invention, or at least some of the steps of the method.
The memory may include volatile memory, such as Random Access Memory (RAM) and/or cache memory, and may also be non-volatile memory, such as read-only memory (ROM).
Optionally, in this embodiment, the electronic device further includes an I/O interface, which is used for data exchange between the electronic device and an external device. The I/O interface may be a local bus representing one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, and/or a memory storage device using any of a variety of bus architectures.
It should be understood that the electronic device shown in fig. 4 is only one example of the present invention, and elements or components not shown in the above example may be further included in the electronic device of the present invention. For example, some electronic devices further include a display unit such as a display screen, and some electronic devices further include a human-computer interaction element such as a button, a keyboard, and the like. Electronic devices are considered to be covered by the present invention as long as the electronic devices are capable of executing a computer-readable program in a memory to implement the method of the present invention or at least a part of the steps of the method.
Fig. 5 is a schematic diagram of a computer-readable recording medium of an embodiment of the present invention. As shown in fig. 5, the computer-readable recording medium stores a computer-executable program, and when the computer-executable program is executed, the method for vehicle intelligent assistance push based on rotation angle monitoring according to the present invention is implemented. The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
From the above description of the embodiments, those skilled in the art will readily appreciate that the present invention can be implemented by hardware capable of executing a specific computer program, such as the system of the present invention, and electronic processing units, servers, clients, mobile phones, control units, processors, etc. included in the system, and that the present invention can also be implemented by a vehicle including at least a part of the above system or component. The invention can also be implemented by computer software for performing the method of the invention, for example, by control software executed by a microprocessor, an electronic control unit, a client, a server, etc. of the locomotive side. It should be noted that the computer software for executing the method of the present invention is not limited to be executed by one or a specific hardware entity, but may also be implemented in a distributed manner by hardware entities without specific details, for example, some method steps executed by the computer program may be executed at the locomotive end, and another part may be executed in the mobile terminal or the smart helmet, etc. For computer software, the software product may be stored in a computer readable storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or may be distributed over a network, as long as it enables the electronic device to perform the method according to the present invention.
In describing particular embodiments, specific details of structures, properties, effects, or other features are set forth in order to provide a thorough understanding of the embodiments by one skilled in the art. However, it is not excluded that a person skilled in the art may implement the invention in a specific case without the above-described structures, performances, effects or other features. Through the above description of the embodiments, those skilled in the art can easily understand the present invention for a scenario of large order assignment, such as message distribution, distribution of advertisements on channels, etc.
While the foregoing detailed description has described in detail certain embodiments of the invention with reference to certain specific aspects, embodiments and advantages thereof, it should be understood that the invention is not limited to any particular computer, virtual machine, or electronic device, as various general purpose machines may implement the invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.

Claims (7)

1. A method for dispatching a service order to a service provider based on historical order matching, the method comprising the steps of:
setting the weight of a matching rule, wherein the matching rule is an index for evaluating whether the order is matched with the user expectation, and the weight represents the importance degree of the matching rule;
acquiring historical order data, wherein the historical order data is a historical order in a specific time period;
matching the historical orders with the matching rules to obtain the matching value of each historical order to each matching rule;
calculating a weight coefficient of each matching rule according to the matching value of the historical order and the weight of the matching rule, wherein the weight coefficient is positively correlated with the matching rate of the matching rule in the historical order, and the weight coefficient of each matching rule is calculated according to the following formula:
Figure FDA0003657516550000011
wherein, C i Is the weight coefficient of the ith matching rule, m is the historical order quantity, W i Is the weight, x, of the ith matching rule ij Matching values of the jth order to the ith matching rule, wherein i and j are natural numbers;
calculating the matching degree of each historical order according to the matching value of the historical order, the weight of the matching rule and the weight coefficient, wherein the matching degree of each historical order is calculated according to the following formula:
Figure FDA0003657516550000012
wherein S is j The matching degree of the jth historical order, and n is the number of matching rules;
and calculating the average matching degree of the historical orders of the service personnel according to the matching degree of the historical orders, and allocating a new service order for the service personnel according to the average matching degree of the service personnel.
2. The method for dispatching orders based on historical order matching degree as claimed in claim 1, further comprising: and resetting the weight according to the ratio of the weight of the matching rule to the weight coefficient.
3. The order dispatching method based on historical order matching degree as claimed in claim 1, wherein the matching value is binary data for indicating whether the order satisfies the matching rule.
4. The historical order matching based order dispatching method of claim 1, wherein dispatching a new service order for a service person according to the average matching of the service person comprises:
setting a target order dispatching rate for service personnel according to the average matching degree;
and distributing service orders for the service personnel according to the target order dispatching rate of the service personnel.
5. The historical order matching based order dispatching method of claim 1, wherein dispatching a new service order for the service person based on the average matching of the service person comprises:
and calculating the prediction matching degree of the new order and each service person based on a dispatching model, wherein the dispatching model is obtained by training historical order data, and the historical order data comprises historical average matching degree of the service persons.
6. An electronic device comprising a processor and a memory, the memory for storing a computer-executable program, characterized in that:
the computer executable program, when executed by the processor, performs the method of any of claims 1-5.
7. A computer-readable medium storing a computer-executable program, wherein the computer-executable program, when executed, implements the method of any of claims 1-5.
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