CN115438946B - Resource matching method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a resource matching method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: when N resource demands are received, determining a target group comprising M target personnel, wherein N is greater than or equal to 2, and M is greater than or equal to 1; inputting requirement characteristic information corresponding to N resource requirements and personnel characteristic information corresponding to M target personnel respectively into a target model, and acquiring a matching parameter set which comprises K matching parameters and corresponds to each target personnel respectively, wherein K is smaller than or equal to N; and sequencing the K resource requirements according to the corresponding matching parameter set for each target person, and sending the K resource requirements to the terminal of the target person so as to display the sequenced K resource requirements. The application can realize personalized and intelligent sequencing of the resource demands, and display the resource demands more suitable for the target personnel at important positions, so that the target personnel can find the suitable resource demands more easily, and the reasonable matching of the target personnel and employer demands is realized.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and apparatus for resource matching, an electronic device, and a storage medium.
Background
In recent years, with the rapid development of the internet, applications supporting business services are increasingly favored by users. The user can issue requirements (such as household requirements, maintenance requirements and the like) through the related application, and service personnel can receive orders through the related application. However, because the number of daily user demands is large, when the demands are pushed to service personnel, if the demands are directly displayed without sorting, the time and effort consumed by the service personnel for browsing are wasted, the efficiency is low, and the users cannot be matched with the proper service personnel easily.
At present, when ordering user demands, the first mode corresponds to two modes, namely, according to the ordering mode of a rule strategy, the user demands meeting the working categories of service personnel are displayed in front and the user demands not meeting the working categories of the service personnel are displayed in back through the working categories selected by the service personnel and the working categories of the user demands. For example, if the attendant is a caretaker, then the caretaker type of household needs are ordered first and the non-caretaker type of household needs are ordered later. The second way is to order the user demands close to the service personnel before and the user demands far from the service personnel after according to the ordering way of the distance strategy.
The current sorting mode cannot perform personalized and intelligent sorting of user demands for service personnel, for example, a certain household personnel selects a working class of a nurse and the household personnel is good at child care, and the household demands with child care demands cannot be displayed before according to the two sorting modes. Because the personalized and intelligent ordering of the user demands cannot be performed, proper service personnel cannot be matched for the user and proper business service cannot be matched for the service personnel easily, and loss of high-quality users and high-quality service personnel is easily caused.
Disclosure of Invention
The embodiment of the application provides a resource matching method, a device, electronic equipment and a storage medium, which are used for solving the problems that in the prior art, individuation and intelligent ordering of user demands cannot be performed, and poor matching between the user demands and service personnel is easy to occur.
In a first aspect, an embodiment of the present application provides a resource matching method, which is applied to a target platform, and includes:
under the condition that N resource demands are received, determining a target group comprising M target persons, wherein N is an integer greater than or equal to 2, M is an integer greater than or equal to 1, and the target persons have the capability of meeting at least part of the N resource demands;
Inputting requirement characteristic information corresponding to the N resource requirements and personnel characteristic information corresponding to the M target personnel respectively into a target model, and obtaining a matching parameter set corresponding to each target personnel output by the target model, wherein the matching parameter set comprises K matching parameters corresponding to the target personnel and K resource requirements, and K is an integer smaller than or equal to N;
aiming at each target person, sequencing the K resource demands according to a matching parameter set corresponding to the target person, and sending the K resource demands to a terminal of the target person so as to display the sequenced K resource demands at the terminal of the target person;
and the sorting order corresponding to the resource requirement with high matching parameters is the front.
In a second aspect, an embodiment of the present application provides a resource matching device, which is applied to a target platform, including:
the system comprises a determining module, a judging module and a judging module, wherein the determining module is used for determining a target group comprising M target personnel under the condition of receiving N resource demands, N is an integer greater than or equal to 2, M is an integer greater than or equal to 1, and the target personnel have the capability of meeting at least part of the N resource demands;
The input acquisition module is used for inputting the requirement characteristic information corresponding to the N resource requirements and the personnel characteristic information corresponding to the M target personnel respectively into a target model, and acquiring a matching parameter set corresponding to each target personnel output by the target model, wherein the matching parameter set comprises K matching parameters corresponding to the target personnel and K resource requirements, and K is an integer smaller than or equal to N;
the ordering and transmitting module is used for ordering the K resource demands according to the matching parameter set corresponding to each target person and transmitting the K resource demands to the terminal of the target person so as to display the ordered K resource demands on the terminal of the target person;
and the sorting order corresponding to the resource requirement with high matching parameters is the front.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a memory, and a computer program stored on the memory and executable on the processor, where the computer program is executed by the processor to implement the resource matching method according to the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements a resource matching method as described in the first aspect above.
According to the technical scheme, under the condition that N resource demands are received, a target group comprising M target persons is determined, the demand characteristic information corresponding to the N resource demands and the personnel characteristic information corresponding to the M target persons are acquired, the acquired information is input into the target model, the matching parameter set comprising K matching parameters corresponding to each target person output by the target model is acquired, the matching condition of the target persons and the resource demands can be acquired based on the target model, the K resource demands corresponding to the target persons are ordered and sent to the terminals of the target persons, personalized and intelligent ordering of the resource demands can be achieved, the resource demands more suitable for the target persons are displayed at important display positions, the target persons can find the resource demands more easily, and reasonable matching of the target persons and employer demands is achieved.
Drawings
FIG. 1 is a schematic diagram of a resource matching method according to an embodiment of the present application;
FIG. 2 is a flowchart of a resource matching method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a resource matching device according to an embodiment of the present application;
Fig. 4 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
In various embodiments of the present application, it should be understood that the sequence numbers of the following processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Aiming at the problems that in the prior art, individuation and intelligent ordering of user demands cannot be performed, poor matching of the user demands and service personnel is easy to occur, and loss of high-quality users and high-quality service personnel is easy to occur, the embodiment of the application provides a resource matching method, which can reasonably and effectively order the resource demands, display the more suitable resource demands at important display positions, enable related personnel to find the resource demands more easily, and realize reasonable matching of resources.
The following describes a resource matching method provided by the embodiment of the present application, where the method provided by the present application is applied to a target platform, and as shown in fig. 1, the method includes the following steps:
step 101, under the condition that N resource demands are received, determining a target group comprising M target personnel, wherein N is an integer greater than or equal to 2, M is an integer greater than or equal to 1, and the target personnel have the capability of meeting at least part of the N resource demands.
The resource matching method provided by the application is applied to the target platform, and the target platform can determine the target group aiming at N resource requirements under the condition that the target platform receives the N resource requirements. The target platform in this embodiment may be a comprehensive platform for providing services for different types of resource requirements, or may be a dedicated platform for providing services for the same type of resource requirements, and the target platform may be understood as a provider for providing services for the resource requirements. The target platform can establish a communication path between personnel belonging to the target platform and a user (employer) to provide the user with appropriate personnel to achieve reasonable matching of resources.
The target platform determines a target group including at least one target person based on the received N resource requirements, and for any target person, has the ability to satisfy at least some of the N resource requirements. The resources in this embodiment may be understood as virtual service resources such as home administration, moving, dispatch, maintenance, etc., where the target person is a corresponding service person providing service; resources may also be understood as other resources such as devices, traffic, etc., in which case the target person may be a service person, such as a merchant, administrator, etc., who provides the resources such as devices, traffic, etc.
The value of M may be greater than N, or may be less than or equal to N, for the case where M is greater than N, the number of target persons is greater than the number of resource demands, for the case where M is equal to N, the number of target persons is equal to the number of resource demands, and for the case where M is less than N, the number of target persons is less than the number of resource demands. Under normal conditions, when the personnel corresponding to the target platform are sufficient, in order to ensure that the resource requirements can be timely distributed to the proper personnel, the value of M is required to be greater than or equal to N, and the situation that the resource requirements cannot be timely distributed due to the fact that the value of M is smaller than N, N is avoided.
Step 102, inputting requirement characteristic information corresponding to the N resource requirements and personnel characteristic information corresponding to the M target personnel respectively into a target model, and obtaining a matching parameter set corresponding to each target personnel output by the target model, wherein the matching parameter set comprises K matching parameters corresponding to the target personnel and K resource requirements, and K is an integer smaller than or equal to N.
After the target group is determined, the requirement characteristic information corresponding to N resource requirements and the personnel characteristic information corresponding to M target personnel can be acquired, the requirement characteristic information corresponding to N resource requirements and the personnel characteristic information corresponding to M target personnel are taken as input parameters and input into the target model, so that the matching parameters of the target personnel and the resource requirements are evaluated based on the target model, and the matching parameter set output by the target model for each target personnel respectively is acquired.
The matching parameter set corresponding to the target person may include K matching parameters corresponding to matching between the target person and K resource requirements, where K is an integer less than or equal to N, that is, the K resource requirements are at least some of the N resource requirements, and the values of K corresponding to different target persons may be different. When the value of K is smaller than N, the matching parameter set corresponding to the target personnel comprises matching parameters corresponding to the matching of the target personnel and part of the N resource demands, and when the value of K is equal to N, the matching parameter set corresponding to the target personnel comprises matching parameters corresponding to the matching of the target personnel and the N resource demands.
Step 103, aiming at each target person, sorting the K resource demands according to a matching parameter set corresponding to the target person, and sending the K resource demands to a terminal of the target person so as to display the sorted K resource demands on the terminal of the target person; and the sorting order corresponding to the resource requirement with high matching parameters is the front.
After the matching parameter sets corresponding to each target person are obtained through the target model, aiming at each target person, sorting the K resource requirements according to the K matching parameters in the matching parameter sets corresponding to the target person, and after sorting is completed, sending the sorted K resource requirements to the terminal of the target person, so that the terminal of the target person displays the sorted K resource requirements.
When the K resource demands are ordered based on the K matching parameters, the higher the matching parameters are, the more forward the ordering order corresponding to the resource demands is, namely the K resource demands are ordered based on the order of the matching parameters from high to low. And when the terminal of the target person displays the sequenced K resource demands, displaying the sequenced K resource demands on an application interface of a target application program of the terminal, wherein the target application program is a client application program corresponding to the target platform. Because the K resource demands are arranged in the order from high to low according to the matching parameters, when the K resource demands are displayed, the resource demands with the front ranking are displayed at important positions, and the resource demands with the rear ranking are displayed at other positions, so that a target person can know the resource demands with the front ranking preferentially. For example, the first-order resource requirement may be displayed before and the second-order resource requirement may be displayed after, the first-order resource requirement may be displayed at a central position, and the second-order resource requirement may be displayed at a boundary position, which may include other implementations.
According to the implementation process, under the condition that N resource demands are received, a target group comprising M target persons is determined, the demand characteristic information corresponding to the N resource demands and the personnel characteristic information corresponding to the M target persons are acquired, the acquired information is input into the target model, the matching parameter set comprising K matching parameters corresponding to each target person output by the target model is acquired, the matching condition of the target persons and the resource demands can be acquired based on the target model, the K resource demands corresponding to the target persons are ordered and sent to the terminals of the target persons, personalized and intelligent ordering of the resource demands can be achieved, the resource demands more suitable for the target persons are displayed at important display positions, the target persons can find the resource demands more easily, and reasonable matching of the target persons and employer demands is achieved.
In the following description of the process of determining the target group, step 101, in the case that N resource requirements are received, determines a target group including M target persons, including:
according to the N resource requirements, screening out the M target persons from the persons belonging to the target platform; and generating the target group according to the M target personnel selected.
Under the condition that N resource demands of a user are received by the target platform, M target persons can be screened out from persons belonging to the target platform and having the capability of meeting the resource demands according to the N resource demands. For each target person screened, the target person has the capability of meeting at least part of the N resource requirements, and it can be understood that the target person can provide corresponding service for at least part of the N resource requirements.
Under the condition that the value of M is 1, the target personnel can have the capability of meeting N resource requirements, and under the condition that the value of M is greater than or equal to 2, each target personnel can have the capability of meeting at least part of the N resource requirements.
After M target persons are screened out, a target group is generated according to the screened M target persons, and aggregation of the target persons can be achieved, so that matching parameters of the target persons and resource requirements are evaluated based on a target model aiming at the target persons in the target group.
According to the implementation process, the target personnel are screened out, the target group is generated according to the screened target personnel, aggregation of the target personnel can be achieved, and matching parameters of the target personnel and resource requirements can be evaluated for the target personnel in the target group based on the target model.
The following describes a process of inputting the demand characteristic information and the personnel characteristic information into the target model, when the demand characteristic information corresponding to the N resource demands and the personnel characteristic information corresponding to the M target personnel respectively are input into the target model, the method includes: coding the demand characteristic information corresponding to the N resource demands and the non-numerical value type information in the personnel characteristic information corresponding to the M target personnel respectively to obtain target coding data; and inputting the target coding data, the requirement characteristic information corresponding to the N resource requirements and the numerical value type information in the personnel characteristic information corresponding to the M target personnel respectively into the target model.
When the demand characteristic information corresponding to the N resource demands and the personnel characteristic information corresponding to the M target personnel are input into the target model, the demand characteristic information corresponding to the N resource demands is required to be subjected to information division to obtain non-numerical value type information and numerical value type information, and accordingly, the personnel characteristic information corresponding to the M target personnel is required to be subjected to information division to obtain the non-numerical value type information and the numerical value type information.
After distinguishing the numerical value type information and the non-numerical value type information, coding processing can be performed on the non-numerical value type information in the requirement characteristic information corresponding to the N resource requirements respectively, and coding processing can be performed on the non-numerical value type information in the personnel characteristic information corresponding to the M target personnel respectively, so that target coding data can be obtained through coding processing. And then inputting the target coding data, numerical value type information in the requirement characteristic information corresponding to N resource requirements respectively and numerical value type information in the personnel characteristic information corresponding to M target personnel respectively into a target model so as to evaluate the resource requirements and the matching parameters of the target personnel based on the target model.
It should be noted that, in the embodiment of the present application, the target model may be an Xgboost model, and of course, may also be other models, for example, may be a neural network model, a bayesian network model, a support vector machine (Support Vector Machine, SVM) model, or a logistic regression model, which is not limited in particular.
The process of sorting the K resource demands of the target personnel according to the matching parameter set and feeding back the sorting result is described below. When the K resource requirements are sequenced according to the matching parameter set corresponding to the target person and sent to the terminal of the target person, the method comprises the following steps:
Determining priority orders corresponding to the K resource demands according to the K matching parameters corresponding to the target personnel; the K resource demands are ordered according to the order of the priority from high to low, and the ordered K resource demands are sent to the terminal of the target person so that the K resource demands are displayed at the terminal of the target person according to the order of the priority from high to low; wherein the matching parameter is matching degree or matching score, and the matching parameter is positively correlated with the priority order.
The matching parameters in this embodiment are matching degree or matching score of the resource requirement and the target person, wherein the higher the matching degree is, the higher the priority of the resource requirement is, and the higher the matching score is, the higher the priority of the resource requirement is, and the target model is used for evaluating the matching degree or matching score of the resource requirement and the target person.
For any target person, when the K resource demands corresponding to the target person are ordered according to the matching parameter set corresponding to the target person, the priority order corresponding to the K resource demands can be determined according to the K matching degrees or matching scores corresponding to the target person, the priority order is positively correlated with the matching degrees or matching scores, then the K resource demands are ordered according to the priority order from high to low, namely the K resource demands are ordered according to the matching degrees or matching scores from high to low, the ordered K resource demands are sent to the terminal of the target person, the K resource demands are displayed at important positions according to the priority order at the terminal of the target person, for example, the resource demands with high priority are displayed before the resource demands with high priority, the resource demands with low priority are displayed after the resource demands with low priority, and the target person can know the resource demands with high matching degrees or matching scores in priority.
According to the implementation process, the K resource demands corresponding to the target personnel can be sequenced according to the matching degree or matching score output by the target model, and fed back to the target personnel, so that personalized and intelligent sequencing of the resource demands is realized, the resource demands more suitable for the target personnel are displayed at important display positions, and the target personnel can find the resource demands more easily.
Before the target model is used for evaluation, the target model needs to be obtained through model training, and a process of training the target model is described below. The method provided by the embodiment of the application further comprises the following steps:
acquiring a personnel characteristic information set corresponding to a plurality of first personnel in a sample set, a behavior characteristic information set corresponding to the plurality of first personnel in a preset period and a demand characteristic information set, wherein the preset period is a historical period, and the first personnel has the capability of meeting resource demands and belongs to the target platform;
generating a target data set according to the personnel characteristic information set, the behavior characteristic information set and the demand characteristic information set;
model training is carried out based on the target data set, the target model is determined, and the target model is used for evaluating matching parameters between personnel with the capability of meeting resource requirements and the resource requirements;
The personnel characteristic information set comprises personnel characteristic information corresponding to the first personnel respectively, the demand characteristic information set comprises demand characteristic information associated with the first personnel in the preset time period, the behavior characteristic information set comprises behavior characteristic information of the first personnel aiming at resource demands on a target application program in the preset time period, and the target application program is a client application program corresponding to the target platform.
Before model training, a sample set corresponding to a plurality of first persons is required to be determined, wherein the plurality of first persons are at least part of all persons corresponding to the target platform and having the capability of meeting the resource requirement. After the sample set is determined, the set of person feature information, the set of behavioral feature information, and the set of demand feature information are obtained in a database through a structured query language (Structured Query Language, SQL) script. The personnel characteristic information set comprises personnel characteristic information corresponding to a plurality of first personnel respectively; the demand characteristic information set comprises demand characteristic information associated with a plurality of first personnel in a preset period, wherein the preset period is a selected historical period; the behavior feature information set comprises behavior feature information of a plurality of first personnel aiming at historical resource demands on a target application program in a preset period, and the behavior feature information can comprise order receiving behaviors of the resource demands and browsing behaviors of the resource demands. That is, the information included in the demand characteristic information set and the behavior characteristic information set is history information. It should be noted that, the demand characteristic information associated with the first person may include demand characteristic information corresponding to a historical resource demand browsed by the first person and demand characteristic information corresponding to other historical resource demands commonly recommended by the historical resource demand, and demand characteristic information corresponding to a historical resource demand of the first person and demand characteristic information corresponding to other historical resource demands commonly recommended by the historical resource demand.
After the person feature information set, the behavior feature information set, and the demand feature information set are acquired, a target data set may be generated based on the person feature information set, the behavior feature information set, and the demand feature information set, and then model training may be performed based on the target data set to determine a target model.
The following description is related to taking a target model as an XGBoost model as an example, the structure of the XGBoost model is similar to the bifurcation of a tree, and the structure can be changed continuously along with continuous training of the model. Model parameters of the XGBoost model may be set to: the number of the sub learners is 100, the learning rate is 0.1, the loss function is a cross entropy loss function, and the regular term size is 0.1. The number of sub learners can be colloquially understood as the number of branches, the size of the learning rate can be colloquially understood as the change size of the tree shape by each training iteration, and the size of the regular term can be colloquially understood as the number limit of useless leaf nodes.
When training the XGBoost model, a fit () method in the XGBClassification is called by using preset model parameters, and training is performed based on data in the target data set. The XGBoost model outputs a matching score, that is, the matching parameter at this time is a matching score, and the matching score may be a model score between 0 and 1.
Optionally, when generating the target data set according to the personnel characteristic information set, the behavior characteristic information set and the requirement characteristic information set, the method includes:
encoding the non-numerical type information in the personnel characteristic information set and the demand characteristic information set to obtain first encoded data; coding the behavior characteristic information in the behavior characteristic information set to obtain second coded data; and generating the target data set according to the first coded data, the second coded data, and numerical value type information in the personnel characteristic information set and the demand characteristic information set.
When the target data set is generated, the information in the personnel characteristic information set can be divided to obtain numerical value type information and non-numerical value type information, and accordingly, the information in the demand characteristic information set needs to be divided to obtain the numerical value type information and the non-numerical value type information. And then coding the non-numerical type information in the personnel characteristic information set and the demand characteristic information set to obtain first coded data, wherein a one-hot encoding mode can be adopted for processing when the first coded data are coded, and the first coded data are not limited to the one-hot encoding mode.
When the target data set is generated, the behavior feature information in the behavior feature information set may be encoded to obtain second encoded data, and at this time, a label-encoding mode may be used for processing, which is not limited to this encoding mode.
After the first encoded data and the second encoded data are acquired, a target data set may be generated according to the first encoded data, the second encoded data, the personnel characteristic information set, and the numerical value type information in the demand characteristic information set.
By encoding non-numeric type information in the personnel feature information and the demand feature information, text or enumeration type data can be encoded to ensure unification of data formats for model training through data preprocessing. After the target data set is acquired, for the case where the target model is an XGBoost model, the target data set may be saved as csv format, and the data is imported into the device memory using a file import function in the pandas tool to provide sample data for model training.
The process of model training is described below, in which model training is performed based on the target data set, the target model for evaluating matching parameters between personnel and resource requirements is determined, comprising:
Dividing the target data set into a training data set and a test data set;
model training is carried out according to the training data set, and when the evaluation accuracy of the matching parameters corresponding to the test data set is greater than a target threshold value, model training is completed, and the target model is determined;
wherein the training data set and the test data set each include positive sample data and negative sample data, the positive sample data being associated with an acquisition behavior and the negative sample data being associated with a browsing behavior.
When model training is performed based on the target data set, data in the target data set can be divided into training data and test data to form the training data set and the test data set, wherein the ratio of the number of the training data to the number of the test data is a preset value, for example, the ratio of the number of the training data to the number of the test data is 8:2, then model training is performed based on the training data set, matching parameter evaluation accuracy is verified based on the test data set, and when the matching parameter evaluation accuracy corresponding to the test data set is greater than a target threshold (for example, 0.7), model training and target model determination are completed.
It should be noted that, the training data set and the test data set both include positive sample data and negative sample data, the positive sample data may include a historical resource requirement of the first person to receive the order and data corresponding to a resource requirement pushed together with the historical resource requirement, and the negative sample data may include a historical resource requirement browsed by the first person and data corresponding to a resource requirement pushed together with the historical resource requirement.
According to the implementation process, model training is performed based on the training data set, verification is performed based on the test data set, and training can be stopped when the matching parameter evaluation accuracy corresponding to the test data set meets the condition, so that a target model can be determined.
The resources in the embodiment of the application can be virtual service resources such as home administration, moving, dispatch, maintenance and the like, and can also be other resources such as equipment, flow and the like; corresponding requirement characteristic information and personnel characteristic information are different according to different resource requirements. For example, the resource requirement is a maintenance requirement, and the requirement characteristic information may include: demand work content, demand service address, demand budget, demand service time, etc., the personnel characteristic information may include: service life, maintenance skills, maintenance rewards, etc.; or, if the resource requirement is a moving requirement, the requirement characteristic information may include: demand work content, demand service address, demand budget, demand service time, etc., the personnel characteristic information may include: working range, moving rewards, practise experience, etc.
In an optional embodiment of the present application, the resource requirement is a household requirement, and the target person is a target household person; the demand characteristic information at least comprises: the type of demand work, the content of demand work, the demand service address, the demand budget, and the demand service time; the personnel characteristic information at least comprises: the type of work of the housekeeping staff, the location of the housekeeping staff, the household skills of the housekeeping staff, the expected salary of the housekeeping staff, the age of the housekeeping staff and the service life of the housekeeping staff.
In this embodiment, the resource requirement is a household requirement, and the target person is a target household person selected from a plurality of household persons. Wherein, the housekeeping staff refer to the service staff engaged in the housekeeping industry and generally comprise a nurse, a child-care, a clock, a month-care, etc.; the home requirement refers to the work requirement of an employer (user) for finding a career, for finding a child's sister, for finding a clock, for finding a month's sister, etc. issued by a home type application program.
For the household demand, the corresponding demand characteristic information at least comprises: the type of demand work, the content of the demand work, the demand service address, the demand budget, and the demand service time. The required work type refers to the type of housekeeping corresponding to the household requirement, such as nurse, sister-in-law, sanitation and the like; the required work content is matched with the required work type, and specific work content is indicated, such as cooking, cleaning, child care and the like; the demand service address refers to an address of a user-specified home service; the demand budget is the payment budget of the user for the household demand, for example, the household demand is a caretaker, the payment budget is 4000 yuan per month, or the household demand is a cleaning agent, and the payment budget is 200 yuan in a single time; the required service time refers to the time corresponding to the household service designated by the user, for example, the working time of the nurse is from Monday to Friday, and the service time of cleaning is from 8 months, 7 days, 15 points to 17 points.
For household personnel, the personnel characteristic information at least comprises: the type of work of the housekeeping staff, the location of the housekeeping staff, the household skills of the housekeeping staff, the expected salary of the housekeeping staff, the age of the housekeeping staff and the service life of the housekeeping staff. The working types of the housekeeping staff are working types corresponding to the housekeeping services which can be provided by the housekeeping staff, such as nurse, child-care law, sanitation and the like; the household skills of the household staff refer to the good working contents of the household staff, such as cooking, cleaning, nursing, etc.; for the housekeeping staff in the sample set, the position of the housekeeping staff refers to the corresponding position when the housekeeping staff receives a bill or browses the housekeeping requirements, and for the target housekeeping staff, the position of the housekeeping staff refers to the latest reported position information (such as longitude and latitude information) of the housekeeping staff; the desired salary of the home staff may be the desired monthly salary of the home staff or the desired salary of the single home service of the home staff; the age of the household personnel and the age of the household personnel from the life of the household personnel are determined based on the personal profile of the household personnel.
It should be noted that, the demand feature information corresponding to the administrative demands and the personnel feature information corresponding to the administrative personnel may be obtained in the database, and specifically may be that the demand feature information corresponding to the administrative demands and the personnel feature information corresponding to the administrative personnel are obtained from the database through the SQL script.
Because the demand characteristic information and the personnel characteristic information need to be subjected to information division, non-numerical value type information and numerical value type information are obtained, and the information division situation is explained below under the condition that the resource demand is a household demand. In the demand characteristic information, the numerical value type information may include a demand budget and a demand service time, the non-numerical value type information may include a demand work type and a demand work content, the numerical value type information may include a demand service address when the demand service address is in a longitude and latitude form, and the non-numerical value type information may include a demand service address when the demand service address is in a text address form.
For the personnel characteristic information, the numerical value type information may include expected salary of the housekeeping personnel, age of the housekeeping personnel and service life of the housekeeping personnel, the non-numerical value type information may include work type of the housekeeping personnel and household skill of the housekeeping personnel, when the position of the housekeeping personnel is in longitude and latitude form, the numerical value type information may include the position of the housekeeping personnel, and when the position of the housekeeping personnel is in text form, the non-numerical value type information may include the position of the housekeeping personnel.
Taking resource requirements as household requirements as an example, the resource matching method provided by the application is introduced through an integral implementation flow, and referring to fig. 2, the method comprises the following steps:
step 201, acquiring a personnel characteristic information set corresponding to a plurality of housekeeping personnel in a sample set, a behavior characteristic information set corresponding to the plurality of housekeeping personnel in a preset period and a demand characteristic information set.
Step 202, generating a target data set according to the personnel characteristic information set, the behavior characteristic information set and the demand characteristic information set.
And 203, performing model training based on the target data set, and determining a target model for evaluating matching parameters between the household personnel and the household demands.
Step 204, in the case that N hometown demands are received, determining a target hometown group including M target hometown personnel.
Step 205, inputting requirement characteristic information corresponding to N household demands and personnel characteristic information corresponding to M target household personnel respectively into a target model, and obtaining a matching parameter set corresponding to each target household personnel output by the target model, wherein the matching parameter set comprises K matching parameters corresponding to the matching of the target household personnel and K household demands, and K is an integer smaller than or equal to N.
Step 206, determining the priority order corresponding to the K household demands according to the K matching parameters corresponding to the target household personnel for each target household personnel.
Step 207, sorting the K household demands according to the order of the priority from high to low, and sending the sorted K household demands to the terminal of the target household personnel, so as to display the K household demands at the terminal of the target household personnel according to the order of the priority from high to low; the matching parameters are matching degree or matching score, and the matching parameters are positively correlated with the priority order.
According to the implementation process, effective data for model training can be obtained through data acquisition and arrangement firstly, model training is conducted based on set model parameters and the effective data to determine a target model, a matching parameter set of target housekeeping staff is obtained based on the target model, K household demands corresponding to the target housekeeping staff are ordered according to the matching parameter set, reasonable ordering of the household demands can be achieved, the housekeeping staff can find suitable household demands more easily, and reasonable matching of the housekeeping staff and employer demands is achieved.
The overall implementation flow of the resource matching method provided by the embodiment of the application determines the target group comprising M target persons under the condition that N resource demands are received, acquires the demand characteristic information corresponding to N resource demands and the personnel characteristic information corresponding to M target persons, inputs the acquired information into the target model, acquires the matching parameter set comprising K matching parameters corresponding to each target person output by the target model, can acquire the matching condition of the target persons and the resource demands based on the target model, and can realize personalized and intelligent ordering of the resource demands by ordering the K resource demands corresponding to the target persons and sending the K resource demands to the terminals of the target persons, so that the resource demands more suitable for the target persons are displayed in important display positions, the target persons can find the resource demands more easily and reasonably match the target persons and employer demands.
Further, aggregation of target personnel can be achieved by screening out target personnel and generating a target group according to the screened target personnel, so that corresponding matching parameters are evaluated for the target personnel in the target group based on a target model; the K resource demands corresponding to the target personnel are ordered according to the matching degree or the matching score output by the target model and fed back to the target personnel, and personalized and intelligent ordering of the resource demands can be realized based on the matching score or the matching degree.
The application provides a resource matching device, which is applied to a target platform, and is shown in FIG. 3, and comprises:
a determining module 301, configured to determine, when N resource requirements are received, a target group including M target people, where N is an integer greater than or equal to 2, and M is an integer greater than or equal to 1, and the target people have an ability to satisfy at least some of the N resource requirements;
the input obtaining module 302 is configured to input requirement characteristic information corresponding to the N resource requirements and personnel characteristic information corresponding to the M target personnel respectively into a target model, obtain a matching parameter set corresponding to each target personnel output by the target model, where the matching parameter set includes K matching parameters corresponding to the matching of the target personnel with K resource requirements, and K is an integer less than or equal to N;
the ordering and sending module 303 is configured to order the K resource requirements according to a matching parameter set corresponding to each target person, and send the K resource requirements to a terminal of the target person, so as to display the K ordered resource requirements at the terminal of the target person;
And the sorting order corresponding to the resource requirement with high matching parameters is the front.
Optionally, the determining module includes:
the screening sub-module is used for screening the M target persons from the persons belonging to the target platform according to the N resource requirements;
the first generation sub-module is used for generating the target group according to the screened M target persons.
Optionally, the input acquisition module includes:
the first acquisition sub-module is used for encoding the demand characteristic information corresponding to the N resource demands and the non-numerical value type information in the personnel characteristic information corresponding to the M target personnel respectively to acquire target encoded data;
and the input sub-module is used for inputting the target coding data, the requirement characteristic information corresponding to the N resource requirements respectively and the numerical value type information in the personnel characteristic information corresponding to the M target personnel respectively into the target model.
Optionally, the ordering sending module includes:
the determining submodule is used for determining priority orders corresponding to the K resource demands according to the K matching parameters corresponding to the target personnel;
the sequencing and sending sub-module is used for sequencing the K resource demands according to the sequence from high priority to low priority, and sending the sequenced K resource demands to the terminal of the target person so as to display the K resource demands at the terminal of the target person according to the sequence from high priority to low priority;
Wherein the matching parameter is matching degree or matching score, and the matching parameter is positively correlated with the priority order.
Optionally, the apparatus further comprises:
the system comprises an acquisition module, a target platform and a storage module, wherein the acquisition module is used for acquiring a personnel characteristic information set corresponding to a plurality of first personnel in a sample set, a behavior characteristic information set corresponding to the plurality of first personnel in a preset period and a demand characteristic information set, the preset period is a history period, and the first personnel has the capability of meeting the resource demand and belongs to the target platform;
the generation module is used for generating a target data set according to the personnel characteristic information set, the behavior characteristic information set and the demand characteristic information set;
the training determining module is used for carrying out model training based on the target data set, determining the target model, and evaluating matching parameters between personnel with the capability of meeting the resource requirement and the resource requirement;
the personnel characteristic information set comprises personnel characteristic information corresponding to the first personnel respectively, the demand characteristic information set comprises demand characteristic information associated with the first personnel in the preset time period, the behavior characteristic information set comprises behavior characteristic information of the first personnel aiming at resource demands on a target application program in the preset time period, and the target application program is a client application program corresponding to the target platform.
Optionally, the generating module includes:
the second acquisition sub-module is used for encoding the non-numerical value type information in the personnel characteristic information set and the demand characteristic information set to acquire first encoded data;
the third acquisition sub-module is used for encoding the behavior characteristic information in the behavior characteristic information set to acquire second encoded data;
and the second generation sub-module is used for generating the target data set according to the first coded data, the second coded data, the personnel characteristic information set and the numerical value type information in the demand characteristic information set.
Optionally, the training determination module includes:
dividing the target data set into a training data set and a test data set by a sub-module;
and the training determination sub-module is used for performing model training according to the training data set, and completing model training and determining the target model when the evaluation accuracy of the matching parameters corresponding to the test data set is greater than a target threshold value.
Optionally, the resource requirement is a household requirement, and the target person is a target household person;
the demand characteristic information at least comprises: the type of demand work, the content of demand work, the demand service address, the demand budget, and the demand service time;
The personnel characteristic information at least comprises: the type of work of the housekeeping staff, the location of the housekeeping staff, the household skills of the housekeeping staff, the expected salary of the housekeeping staff, the age of the housekeeping staff and the service life of the housekeeping staff.
For the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points.
The embodiment of the application also provides electronic equipment, which comprises: the processor, the memory, store the computer program on the memory and can run on the processor, this computer program realizes each process of the above-mentioned resource matching method embodiment when being carried out by the processor, and can reach the same technical result, in order to avoid repetition, will not be repeated here.
For example, fig. 4 shows a schematic physical structure of an electronic device. As shown in fig. 4, the electronic device may include: processor 410, communication interface (Communications Interface) 420, memory 430 and communication bus 440, wherein processor 410, communication interface 420 and memory 430 communicate with each other via communication bus 440. The processor 410 may call logic instructions in the memory 430, the processor 410 being configured to perform the steps of: under the condition that N resource demands are received, determining a target group comprising M target persons, wherein N is an integer greater than or equal to 2, M is an integer greater than or equal to 1, and the target persons have the capability of meeting at least part of the N resource demands; inputting requirement characteristic information corresponding to the N resource requirements and personnel characteristic information corresponding to the M target personnel respectively into a target model, and obtaining a matching parameter set corresponding to each target personnel output by the target model, wherein the matching parameter set comprises K matching parameters corresponding to the target personnel and K resource requirements, and K is an integer smaller than or equal to N; aiming at each target person, sequencing the K resource demands according to a matching parameter set corresponding to the target person, and sending the K resource demands to a terminal of the target person so as to display the sequenced K resource demands at the terminal of the target person; and the sorting order corresponding to the resource requirement with high matching parameters is the front. Processor 410 may also perform other aspects of embodiments of the present application, which are not further described herein.
Further, the logic instructions in the memory 430 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application.
The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, realizes the processes of the above-mentioned resource matching method embodiment, and can achieve the same technical effects, and in order to avoid repetition, the description is omitted here. Wherein the computer readable storage medium is selected from Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present application and the scope of the claims, which are to be protected by the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (10)
1. A resource matching method applied to a target platform, comprising:
under the condition that N resource demands are received, determining a target group comprising M target persons, wherein N is an integer greater than or equal to 2, M is an integer greater than or equal to 1, and the target persons have the capability of meeting at least part of the N resource demands;
inputting requirement characteristic information corresponding to the N resource requirements and personnel characteristic information corresponding to the M target personnel respectively into a target model, and obtaining a matching parameter set corresponding to each target personnel output by the target model, wherein the matching parameter set comprises K matching parameters corresponding to the matching of the target personnel and K resource requirements, K is an integer less than or equal to N, and the target model is used for evaluating the matching degree or matching score of the resource requirements and the target personnel;
for each target person, sorting the K resource requirements according to the matching parameter set corresponding to the target person, and sending the sorted K resource requirements to the terminal of the target person so as to display the sorted K resource requirements on the terminal of the target person, wherein the sorting comprises the following steps: determining a priority order corresponding to the K resource demands according to K matching parameters corresponding to the target personnel, sequencing the K resource demands according to a priority order from high to low, and sending the sequenced K resource demands to a terminal of the target personnel so as to display the K resource demands at the terminal of the target personnel according to the priority order from high to low, wherein the matching parameters are matching degrees or matching scores, and the matching parameters are positively related to the priority order;
And the sorting order corresponding to the resource requirement with high matching parameters is the front.
2. The method of claim 1, wherein the determining a target group comprising M target persons in the event that N resource demands are received comprises:
according to the N resource requirements, screening out the M target persons from the persons belonging to the target platform;
and generating the target group according to the M target personnel selected.
3. The method according to claim 1, wherein inputting the requirement feature information corresponding to the N resource requirements and the personnel feature information corresponding to the M target personnel respectively into a target model includes:
coding the demand characteristic information corresponding to the N resource demands and the non-numerical value type information in the personnel characteristic information corresponding to the M target personnel respectively to obtain target coding data;
and inputting the target coding data, the requirement characteristic information corresponding to the N resource requirements and the numerical value type information in the personnel characteristic information corresponding to the M target personnel respectively into the target model.
4. The method according to claim 1, wherein the method further comprises:
Acquiring a personnel characteristic information set corresponding to a plurality of first personnel in a sample set, a behavior characteristic information set corresponding to the plurality of first personnel in a preset period and a demand characteristic information set, wherein the preset period is a historical period, and the first personnel has the capability of meeting resource demands and belongs to the target platform;
generating a target data set according to the personnel characteristic information set, the behavior characteristic information set and the demand characteristic information set;
model training is carried out based on the target data set, the target model is determined, and the target model is used for evaluating matching parameters between personnel with the capability of meeting resource requirements and the resource requirements;
the personnel characteristic information set comprises personnel characteristic information corresponding to the first personnel respectively, the demand characteristic information set comprises demand characteristic information associated with the first personnel in the preset time period, the behavior characteristic information set comprises behavior characteristic information of the first personnel aiming at resource demands on a target application program in the preset time period, and the target application program is a client application program corresponding to the target platform.
5. The method of claim 4, wherein generating the target data set from the set of person feature information, the set of behavioral feature information, and the set of demand feature information comprises:
encoding the non-numerical type information in the personnel characteristic information set and the demand characteristic information set to obtain first encoded data;
coding the behavior characteristic information in the behavior characteristic information set to obtain second coded data;
and generating the target data set according to the first coded data, the second coded data, and numerical value type information in the personnel characteristic information set and the demand characteristic information set.
6. The method of claim 4 or 5, wherein the model training based on the target dataset, determining the target model, comprises:
dividing the target data set into a training data set and a test data set;
model training is carried out according to the training data set, and when the evaluation accuracy of the matching parameters corresponding to the test data set is greater than a target threshold value, model training is completed, and the target model is determined.
7. The method of any one of claims 1 to 5, wherein the resource requirement is a household requirement and the target person is a target household person;
the demand characteristic information at least comprises: the type of demand work, the content of demand work, the demand service address, the demand budget, and the demand service time;
the personnel characteristic information at least comprises: the type of work of the housekeeping staff, the location of the housekeeping staff, the household skills of the housekeeping staff, the expected salary of the housekeeping staff, the age of the housekeeping staff and the service life of the housekeeping staff.
8. A resource matching apparatus for use with a target platform, comprising:
the system comprises a determining module, a judging module and a judging module, wherein the determining module is used for determining a target group comprising M target personnel under the condition of receiving N resource demands, N is an integer greater than or equal to 2, M is an integer greater than or equal to 1, and the target personnel have the capability of meeting at least part of the N resource demands;
the input acquisition module is used for inputting the requirement characteristic information corresponding to the N resource requirements and the personnel characteristic information corresponding to the M target personnel respectively into a target model, acquiring a matching parameter set corresponding to each target personnel output by the target model, wherein the matching parameter set comprises K matching parameters corresponding to the matching of the target personnel and K resource requirements, K is an integer smaller than or equal to N, and the target model is used for evaluating the matching degree or matching score of the resource requirements and the target personnel;
The sorting and sending module is configured to sort the K resource requirements according to a matching parameter set corresponding to each target person, and send the K resource requirements to a terminal of the target person, so as to display the K sorted resource requirements on the terminal of the target person, where the sorting and sending module includes: determining a priority order corresponding to the K resource demands according to K matching parameters corresponding to the target personnel, sequencing the K resource demands according to a priority order from high to low, and sending the sequenced K resource demands to a terminal of the target personnel so as to display the K resource demands at the terminal of the target personnel according to the priority order from high to low, wherein the matching parameters are matching degrees or matching scores, and the matching parameters are positively related to the priority order;
and the sorting order corresponding to the resource requirement with high matching parameters is the front.
9. An electronic device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, which when executed by the processor implements the steps of the resource matching method of any of claims 1 to 7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps in the resource matching method of any of claims 1 to 7.
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