CN114048907A - Service mode determination method and device, electronic equipment and storage medium - Google Patents
Service mode determination method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a service mode determining method, a service mode determining device, electronic equipment and a storage medium, which can acquire object information of an object to be served; the object information includes: at least one item of personal basic information, body health information, living environment information and social activity information of the object to be served; mapping the object information of the object to be served to obtain a characteristic vector of the object to be served as a first characteristic vector; inputting the first feature vector into a pre-trained grade prediction model to obtain a service grade of an object to be served, which is output by the grade prediction model; and determining a service mode corresponding to the service level of the object to be served in the corresponding relation between the preset service level and the service mode. Based on the processing, the influence of personal subjective factors can be eliminated, the accuracy of the determined service mode is improved, the service quality is improved, the time cost and the labor cost can be reduced, and the efficiency of determining the service mode is improved.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for determining a service mode, an electronic device, and a storage medium.
Background
With the increasing and rapid increase of the aging population, the old-aged people have a great social concern for the service problem of the old-aged people. In order to better provide old people with a care service, in the related art, a questionnaire survey is manually performed on the old people, and the questionnaire survey result is manually evaluated to determine the service mode of the old people. For example, the health condition of the elderly is acquired through questionnaire, and the health condition of the elderly is evaluated to determine the period of health examination for the elderly.
However, in the above process, the questionnaire survey results are manually evaluated to determine the service mode of the old people, and due to the influence of personal subjective factors, the determined service mode may not meet the service requirements of the old people, which may reduce the service quality, and the manual evaluation of the questionnaire survey results requires a large time cost and a large labor cost, resulting in a low efficiency of determining the service mode.
Disclosure of Invention
The embodiment of the invention aims to provide a service mode determining method, a service mode determining device, electronic equipment and a storage medium, so as to eliminate the influence of personal subjective factors, improve the accuracy of the determined service mode, improve the service quality, reduce the time cost and the labor cost and improve the efficiency of determining the service mode. The specific technical scheme is as follows:
in a first aspect of the present invention, a method for determining a service mode is provided, where the method includes:
acquiring object information of an object to be served; wherein the object information includes: at least one item of personal basic information, body health information, living environment information and social activity information of the object to be served;
mapping the object information of the object to be served to obtain a characteristic vector of the object to be served as a first characteristic vector;
inputting the first feature vector to a pre-trained grade prediction model to obtain the service grade of the object to be served, which is output by the grade prediction model; the grade prediction model is obtained by training based on sample object information and sample service grade of a sample object;
and determining a service mode corresponding to the service level of the object to be served in a preset corresponding relation between the service level and the service mode.
Optionally, the level prediction model includes: the sensor comprises a linear module, a nonlinear module, a first multilayer sensor, a second multilayer sensor and an output module;
the inputting the first feature vector into a pre-trained grade prediction model to obtain the service grade of the object to be served, which is output by the grade prediction model, includes:
performing linear transformation on the first feature vector through the linear module to obtain a second feature vector;
carrying out nonlinear transformation on the first feature vector through the nonlinear module to obtain a third feature vector;
performing linear transformation on the third feature vector through the first multilayer perceptron to obtain a fourth feature vector;
performing feature fusion on the second feature vector and the fourth feature vector to obtain a fifth feature vector;
performing linear transformation on the fifth feature vector through the second multilayer perceptron to obtain a first numerical value;
and normalizing the first numerical value through the output module to obtain a second numerical value for representing the service grade of the object to be served.
Optionally, the personal basic information of the object to be served includes at least one of the following items: the age, sex, marital status and number of children of the object to be served;
the physical health information of the subject to be served comprises at least one of the following: the height, the weight and the current disease information of the object to be served;
the living environment information of the object to be served comprises at least one of the following items: the number of dining places and the number of activity centers in a preset geographic range of the residence of the object to be served;
the social activity information of the object to be served comprises at least one of the following items: the type and the frequency of community activities participated in by the object to be served in the historical time period.
Optionally, mapping the object information of the object to be served to obtain a feature vector of the object to be served as a first feature vector, where the mapping process includes:
determining numerical information and non-numerical information in the object information of the object to be served; wherein the numerical information is information represented by numerical values; the non-numerical information is other information except the numerical information;
coding the non-numerical information to obtain a sixth feature vector;
carrying out normalization processing on the numerical information, and carrying out linear transformation on the normalized numerical information to obtain a seventh eigenvector;
and splicing the sixth feature vector and the seventh feature vector to obtain the feature vector of the object to be served as the first feature vector.
Optionally, the training process of the level prediction model includes the following steps:
acquiring sample object information of the sample object and a sample numerical value used for representing a sample service level of the sample object;
mapping the sample object information to obtain a characteristic vector of the sample object as a sample characteristic vector;
and taking the sample characteristic vector as input data of a grade prediction model of an initial structure, taking a sample numerical value used for expressing the sample service grade of the sample object as output data of the grade prediction model of the initial structure, and adjusting model parameters of the grade prediction model of the initial structure until the grade prediction model of the initial structure reaches a preset convergence condition to obtain a trained grade prediction model.
In a second aspect of the present invention, there is provided a service method determination apparatus, including:
the acquisition module is used for acquiring the object information of the object to be served; wherein the object information includes: at least one item of personal basic information, body health information, living environment information and social activity information of the object to be served;
the mapping module is used for mapping the object information of the object to be served to obtain a characteristic vector of the object to be served as a first characteristic vector;
the prediction module is used for inputting the first feature vector to a pre-trained grade prediction model to obtain the service grade of the object to be served, which is output by the grade prediction model; the grade prediction model is obtained by training based on sample object information and sample service grade of a sample object;
and the determining module is used for determining the service mode corresponding to the service level of the object to be served in the preset corresponding relation between the service level and the service mode.
Optionally, the level prediction model includes: the sensor comprises a linear module, a nonlinear module, a first multilayer sensor, a second multilayer sensor and an output module;
the prediction module is specifically configured to perform linear transformation on the first feature vector through the linear module to obtain a second feature vector;
carrying out nonlinear transformation on the first feature vector through the nonlinear module to obtain a third feature vector;
performing linear transformation on the third feature vector through the first multilayer perceptron to obtain a fourth feature vector;
performing feature fusion on the second feature vector and the fourth feature vector to obtain a fifth feature vector;
performing linear transformation on the fifth feature vector through the second multilayer perceptron to obtain a first numerical value;
and normalizing the first numerical value through the output module to obtain a second numerical value for representing the service grade of the object to be served.
Optionally, the personal basic information of the object to be served includes at least one of the following items: the age, sex, marital status and number of children of the object to be served;
the physical health information of the subject to be served comprises at least one of the following: the height, the weight and the current disease information of the object to be served;
the living environment information of the object to be served comprises at least one of the following items: the number of dining places and the number of activity centers in a preset geographic range of the residence of the object to be served;
the social activity information of the object to be served comprises at least one of the following items: the type and the frequency of community activities participated in by the object to be served in the historical time period.
Optionally, the mapping module is specifically configured to determine numerical information and non-numerical information in the object information of the object to be served; wherein the numerical information is information represented by numerical values; the non-numerical information is other information except the numerical information;
coding the non-numerical information to obtain a sixth feature vector;
carrying out normalization processing on the numerical information, and carrying out linear transformation on the normalized numerical information to obtain a seventh eigenvector;
and splicing the sixth feature vector and the seventh feature vector to obtain the feature vector of the object to be served as the first feature vector.
Optionally, the apparatus further includes a training module, configured to obtain sample object information of the sample object, and a sample numerical value used for representing a sample service level of the sample object;
mapping the sample object information to obtain a characteristic vector of the sample object as a sample characteristic vector;
and taking the sample characteristic vector as input data of a grade prediction model of an initial structure, taking a sample numerical value used for expressing the sample service grade of the sample object as output data of the grade prediction model of the initial structure, and adjusting model parameters of the grade prediction model of the initial structure until the grade prediction model of the initial structure reaches a preset convergence condition to obtain a trained grade prediction model.
The embodiment of the invention also provides electronic equipment which comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;
a memory for storing a computer program;
and a processor for implementing any of the above-described service mode determination method steps when executing the program stored in the memory.
An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the method for determining a service mode is implemented.
An embodiment of the present invention further provides a computer program product including instructions, which when run on a computer, causes the computer to execute any one of the service mode determination methods described above.
The service mode determining method provided by the embodiment of the invention can acquire the object information of the object to be served; the object information includes: at least one item of personal basic information, body health information, living environment information and social activity information of the object to be served; mapping the object information of the object to be served to obtain a characteristic vector of the object to be served as a first characteristic vector; inputting the first feature vector into a pre-trained grade prediction model to obtain a service grade of an object to be served, which is output by the grade prediction model; the grade prediction model is obtained by training based on sample object information and sample service grade of a sample object; and determining a service mode corresponding to the service level of the object to be served in the corresponding relation between the preset service level and the service mode.
Based on the processing, the grade prediction model is obtained by training based on the object information of the sample object and the sample service grade, and the grade prediction model can learn to obtain the mapping relation between the object information and the service grade. Correspondingly, the service level of the object to be served can be determined through the level prediction model and the object information of the object to be served, and then the service mode corresponding to the service level of the object to be served is determined, the questionnaire survey result of the old people does not need to be evaluated manually, so that the service mode of the old people is determined, the influence of personal subjective factors can be eliminated, the accuracy of the determined service mode is improved, the service quality is improved, the time cost and the labor cost can be reduced, and the efficiency of determining the service mode is improved.
Of course, not all of the advantages described above need to be achieved at the same time in the practice of any one product or method of the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other embodiments can be obtained by referring to these drawings.
Fig. 1 is a flowchart of a service mode determination method according to an embodiment of the present invention;
fig. 2 is a flowchart of another service mode determining method according to an embodiment of the present invention;
fig. 3 is a flowchart of another service mode determining method according to an embodiment of the present invention;
fig. 4 is a flowchart of another service mode determining method according to an embodiment of the present invention;
fig. 5 is a structural diagram of a service mode determining apparatus according to an embodiment of the present invention;
fig. 6 is a structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived from the embodiments given herein by one of ordinary skill in the art, are within the scope of the invention.
Referring to fig. 1, fig. 1 is a flowchart of a service mode determining method according to an embodiment of the present invention, where the method may be applied to an electronic device, and the electronic device may be a terminal or may also be a server. The method may comprise the steps of:
s101: and acquiring object information of the object to be served.
Wherein the object information includes: at least one item of personal basic information, physical health information, living environment information and social activity information of the object to be served.
S102: and mapping the object information of the object to be served to obtain a characteristic vector of the object to be served as a first characteristic vector.
S103: and inputting the first feature vector into a pre-trained grade prediction model to obtain the service grade of the object to be served, which is output by the grade prediction model.
The grade prediction model is obtained by training based on sample object information and sample service grade of a sample object.
S104: and determining a service mode corresponding to the service level of the object to be served in the corresponding relation between the preset service level and the service mode.
Based on the service mode determination method provided by the embodiment of the invention, the grade prediction model is obtained by training the object information based on the sample object and the sample service grade, and the grade prediction model can learn to obtain the mapping relation between the object information and the service grade. Correspondingly, the service level of the object to be served can be determined through the level prediction model and the object information of the object to be served, and then the service mode corresponding to the service level of the object to be served is determined, the questionnaire survey result of the old people does not need to be evaluated manually, so that the service mode of the old people is determined, the influence of personal subjective factors can be eliminated, the accuracy of the determined service mode is improved, the service quality is improved, the time cost and the labor cost can be reduced, and the efficiency of determining the service mode is improved.
For step S101, the object to be served may be an old person, a handicapped person, or the like. When the object to be served is the old, the service mode of the object to be served is a mode for providing old care service for the old. The endowment service may include: physical health examination service, psychological consultation service, rehabilitation and nursing service, household service and the like.
The object information of the object to be serviced may include: at least one item of personal basic information, physical health information, living environment information and social activity information of the object to be served.
The personal basic information of the object to be served includes at least one of the following items: age, sex, marital status, and number of children of the object to be served.
The physical health information of the subject to be served comprises at least one of: height, weight and current disease information of the object to be served. The current disease information may include: whether the patient has the disease, the type of the disease and the disease duration, etc.
The living environment information of the object to be served comprises at least one of the following items: the number of dining places and the number of activity centers within a preset geographic range of the residence of the object to be served. The preset geographic range may be a range of a community to which the residence of the object to be served belongs.
The social activity information of the object to be served includes at least one of: the type and the times of the community activities participated in by the object to be served in the historical time period. The historical time period may be set by a technician based on experience, for example, the historical time period may be the last week from the current time, or the historical time period may be the last month from the current time, but is not limited thereto. The types of community activities attended may include: literary and artistic performance activities, physical exercise activities, safety education activities and the like.
For step S102, after the object information of the object to be served is acquired, the electronic device may perform mapping processing on the object information of the object to be served, so as to obtain a feature vector (i.e., a first feature vector) of the object to be served.
In an embodiment of the present invention, on the basis of fig. 1, referring to fig. 2, step S102 may include the following steps:
s1021: and determining numerical information and non-numerical information in the object information of the object to be served.
Wherein, the numerical information is information expressed by numerical values; non-numeric information is information other than numeric information.
S1022: and coding the non-numerical information to obtain a sixth feature vector.
S1023: and carrying out normalization processing on the numerical information, and carrying out linear transformation on the normalized numerical information to obtain a seventh eigenvector.
S1024: and splicing the sixth feature vector and the seventh feature vector to obtain the feature vector of the object to be served as the first feature vector.
The numerical information is information expressed by numerical values, and for example, the age, height, weight, and the like of the object to be served are numerical information. The non-numerical information is information other than numerical information, and for example, marital status, sex, disease information, and the like of the object to be served are non-numerical information.
The electronic equipment can determine numerical information and non-numerical information in the object information of the object to be served. Then, according to a preset coding mode, coding is carried out on the non-numerical information so as to convert the non-numerical information into a numerical value which can be processed by a grade prediction model, and a sixth feature vector is obtained. The preset encoding mode may be hash encoding, or the preset encoding mode may also be one-hot encoding, or the preset encoding mode may also be mean encoding, and the like, which is not specifically limited in this embodiment. The electronic device may further perform linear transformation on the numerical information based on a preset function to obtain a seventh feature vector. The preset function may be a sigmoid function.
Furthermore, the electronic device may splice the sixth feature vector and the seventh feature vector to obtain a feature vector of the object to be served (i.e., the first feature vector).
For step S103, the grade prediction model may be any one of an ANN (Artificial Neural Network) model, a Deep fm (Deep Factorization Machine) model, an NCF (Neural Collaborative Filtering) model, and a Wide & Deep (width and depth networks for classification and regression) model.
After obtaining the first feature vector, the electronic device may input the first feature vector to a pre-trained class prediction model, and obtain a service class of the object to be served, which is output by the class prediction model.
In one embodiment of the invention, the class prediction model comprises: the sensor comprises a linear module, a nonlinear module, a first multilayer sensor, a second multilayer sensor and an output module. Accordingly, on the basis of fig. 1, referring to fig. 3, step S103 may include the following steps:
s1031: and performing linear transformation on the first feature vector through a linear module to obtain a second feature vector.
S1032: and carrying out nonlinear transformation on the first feature vector through a nonlinear module to obtain a third feature vector.
S1033: and performing linear transformation on the third feature vector through the first multilayer perceptron to obtain a fourth feature vector.
S1034: and performing feature fusion on the second feature vector and the fourth feature vector to obtain a fifth feature vector.
S1035: and performing linear transformation on the fifth feature vector through the second multilayer perceptron to obtain a first numerical value.
S1036: and normalizing the first numerical value through an output module to obtain a second numerical value for representing the service grade of the object to be served.
The grade prediction model comprises: the sensor comprises a linear module, a nonlinear module, a first multilayer sensor, a second multilayer sensor and an output module. The linear module may be MLP (multi layer Perceptron). The nonlinear module may be DNN (Deep Neural Networks). The DNN includes a plurality of fully-connected layers, and each fully-connected layer includes a preset activation function for performing a nonlinear transformation on a feature vector input to the fully-connected layer. The preset activation function may be a ReLU (Rectified Linear Unit). The output module includes a preset normalization function, for example, the output module may include a sigmoid function, or the output module may also include a Softmax function, but is not limited thereto.
The electronic device can perform linear transformation on the first feature vector through the linear module to obtain a second feature vector, and perform nonlinear transformation on the first feature vector through the nonlinear module to obtain a third feature vector. The electronic device may further perform linear transformation on the third feature vector through a first multi-layer perceptron (MLP) to obtain a fourth feature vector.
Then, the electronic device may perform feature fusion on the second feature vector and the fourth feature vector to obtain a fifth feature vector. For example, the electronic device may stitch the second feature vector and the fourth feature vector to obtain a fifth feature vector. Or the electronic device may also add elements at positions corresponding to the second feature vector and the fourth feature vector to obtain a fifth feature vector.
Furthermore, the electronic device can perform linear transformation on the fifth feature vector through the second multilayer perceptron to obtain a first numerical value, and perform normalization processing on the first numerical value through the output module to obtain a second numerical value used for representing the service level of the object to be served.
The second value output by the class prediction model belongs to [0, 1], and different second values represent different classes of service. A smaller second value indicates a higher corresponding service level. For example, when the second value output by the level prediction model belongs to [0.1, 0.2), it indicates that the service level of the object to be served is one level, when the second value output by the level prediction model belongs to [0.2, 0.3), it indicates that the service level of the object to be served is two levels, when the second value output by the level prediction model belongs to [0.3, 0.4), it indicates that the service level of the object to be served is three levels, and the first level service level is higher than the second level service level, and the second level service level is higher than the third level service level, and so on, the service level represented by the different second values can be determined.
In an embodiment of the present invention, before determining the service level of the object to be served based on the level prediction model, the electronic device may further train the level prediction model of the initial structure based on a preset training sample to obtain a trained level prediction model.
Accordingly, the training process of the grade prediction model may include the following steps:
step 1, obtaining sample object information of a sample object and a sample numerical value used for representing a sample service level of the sample object.
And 2, mapping the sample object information to obtain a characteristic vector of the sample object as a sample characteristic vector.
And 3, taking the sample characteristic vector as input data of the grade prediction model of the initial structure, taking a sample numerical value for expressing the sample service grade of the sample object as output data of the grade prediction model of the initial structure, and adjusting model parameters of the grade prediction model of the initial structure until the grade prediction model of the initial structure reaches a preset convergence condition to obtain the trained grade prediction model.
The electronic device may obtain sample object information of the sample object and a sample numerical value used for representing a sample service level of the sample object, and perform mapping processing on the sample object information to obtain a feature vector (i.e., a sample feature vector) of the sample object. The manner in which the electronic device performs the mapping process on the object information of the sample object can refer to the related description of the foregoing embodiments.
In order to improve the accuracy of the trained grade prediction model, the selected sample objects can comprise sample objects in different areas, different age groups, different physical conditions and the like, so that the diversity and randomness of the samples are ensured.
The electronic device may input the sample feature vector to the level prediction model of the initial structure, and obtain a value (which may be referred to as a prediction value) output by the level prediction model of the initial structure and used for representing the service level of the sample object. Then, the electronic device may calculate a loss function value representing a difference between the sample value and the prediction value, and adjust a model parameter of the level prediction model of the initial structure based on the calculated loss function value until the level prediction model of the initial structure reaches a preset convergence condition, so as to obtain a trained level prediction model.
The preset convergence condition may be that the number of times of training of the hierarchical prediction model of the initial structure reaches a preset number of times, for example, the number of times of training of the hierarchical prediction model of the initial structure reaches 200 times. Alternatively, the preset condition may be that the loss function values obtained by calculating for a preset number of times in succession are all smaller than a preset value, for example, the loss function values obtained by calculating for 5 times in succession are all smaller than 0.01.
For step S104, the object to be served may be an old person, and the service manner may be a period of providing an endowment service (e.g., a physical health examination service, a psychological consultation service, a home service, etc.) to the object to be served. The higher the service level, the shorter the interval between adjacent cycles of the service mode.
Furthermore, after the service level of the object to be served is determined, the service mode corresponding to the service level of the object to be served can be determined in the preset corresponding relationship between the service level and the service mode.
For example, the service classes may include one, two, three, four classes. The service mode is a period of performing psychological consultation service on the object to be served. The preset corresponding relation between the service level and the service object comprises the following steps: the primary performs psychological consultation service once a week; the secondary level correspondingly performs psychological consultation service every two weeks; the third level correspondingly performs psychological consultation service every three weeks; the four levels correspond to psychological counseling service every four weeks.
If the service level of the object to be served is two levels, psychological consultation can be determined to be performed on the object to be served every two weeks.
In an embodiment of the present invention, since the object information of the object to be served changes with time, in order to improve the accuracy of determining the service level and to ensure the timeliness of the determined service manner, the object information of the object to be served may be periodically updated. For example, the object information of the object to be served is reacquired every other week.
In addition, for the object to be served for which the service level has been determined by the level prediction model, the service level of the object to be served may be determined manually in the form of a questionnaire. Furthermore, the level prediction model can be trained again based on the object information of the object to be served and the manually determined service level of the object to be served, so that the level prediction model is optimized, and the accuracy of the service level determined by the level prediction model is improved.
Referring to fig. 4, fig. 4 is a flowchart of another service mode determination method provided in the embodiment of the present invention.
When the object to be served is an old person, acquiring demographic data, personal information data, health related data and surrounding environment data of the old person, namely acquiring object information of the object to be served. The demographic data is the social activity information in the foregoing embodiment, the personal information data is the personal basic information in the foregoing embodiment, the health related data is the physical health information in the foregoing embodiment, and the ambient environment data is the living environment information in the foregoing embodiment.
Then, mapping processing is carried out on the object information of the old people to obtain a first feature vector of the old people, the first feature vector is input into a pre-trained grade prediction model, and the service grade of the old people output by the grade prediction model is obtained. And then, determining a service mode corresponding to the service level of the old in the preset corresponding relation between the service level and the service mode.
Based on the processing, the grade prediction model is obtained by training based on the object information of the sample object and the sample service grade, and the grade prediction model can learn to obtain the mapping relation between the object information and the service grade. Correspondingly, the service level of the object to be served can be determined through the level prediction model and the object information of the object to be served, then the service mode corresponding to the service level of the object to be served is determined, the questionnaire survey result of the old people does not need to be evaluated manually, the service mode of the old people is determined, the time cost and the labor cost can be reduced, the efficiency of determining the service mode is improved, the influence of personal subjective factors can be eliminated, the accuracy of the determined service mode is improved, the service quality is improved, targeted old care service can be provided for the old people, accurate old care service is realized, and the sustainability of old care results is improved.
Corresponding to the embodiment of the method in fig. 1, referring to fig. 5, fig. 5 is a structural diagram of a service mode determining apparatus provided in an embodiment of the present invention, where the apparatus includes:
an obtaining module 501, configured to obtain object information of an object to be served; wherein the object information includes: at least one item of personal basic information, body health information, living environment information and social activity information of the object to be served;
a mapping module 502, configured to perform mapping processing on the object information of the object to be served, to obtain a feature vector of the object to be served, where the feature vector is used as a first feature vector;
the prediction module 503 is configured to input the first feature vector to a pre-trained class prediction model, so as to obtain a service class of the object to be served, which is output by the class prediction model; the grade prediction model is obtained by training based on sample object information and sample service grade of a sample object;
the determining module 504 is configured to determine, in a preset correspondence between service levels and service manners, a service manner corresponding to the service level of the object to be serviced.
Optionally, the level prediction model includes: the sensor comprises a linear module, a nonlinear module, a first multilayer sensor, a second multilayer sensor and an output module;
the prediction module 503 is specifically configured to perform linear transformation on the first feature vector through the linear module to obtain a second feature vector;
carrying out nonlinear transformation on the first feature vector through the nonlinear module to obtain a third feature vector;
performing linear transformation on the third feature vector through the first multilayer perceptron to obtain a fourth feature vector;
performing feature fusion on the second feature vector and the fourth feature vector to obtain a fifth feature vector;
performing linear transformation on the fifth feature vector through the second multilayer perceptron to obtain a first numerical value;
and normalizing the first numerical value through the output module to obtain a second numerical value for representing the service grade of the object to be served.
Optionally, the personal basic information of the object to be served includes at least one of the following items: the age, sex, marital status and number of children of the object to be served;
the physical health information of the subject to be served comprises at least one of the following: the height, the weight and the current disease information of the object to be served;
the living environment information of the object to be served comprises at least one of the following items: the number of dining places and the number of activity centers in a preset geographic range of the residence of the object to be served;
the social activity information of the object to be served comprises at least one of the following items: the type and the frequency of community activities participated in by the object to be served in the historical time period.
Optionally, the mapping module 502 is specifically configured to determine numerical information and non-numerical information in the object information of the object to be served; wherein the numerical information is information represented by numerical values; the non-numerical information is other information except the numerical information;
coding the non-numerical information to obtain a sixth feature vector;
carrying out normalization processing on the numerical information, and carrying out linear transformation on the normalized numerical information to obtain a seventh eigenvector;
and splicing the sixth feature vector and the seventh feature vector to obtain the feature vector of the object to be served as the first feature vector.
Optionally, the apparatus further includes a training module, configured to obtain sample object information of the sample object, and a sample numerical value used for representing a sample service level of the sample object;
mapping the sample object information to obtain a characteristic vector of the sample object as a sample characteristic vector;
and taking the sample characteristic vector as input data of a grade prediction model of an initial structure, taking a sample numerical value used for expressing the sample service grade of the sample object as output data of the grade prediction model of the initial structure, and adjusting model parameters of the grade prediction model of the initial structure until the grade prediction model of the initial structure reaches a preset convergence condition to obtain a trained grade prediction model.
Based on the service mode determining device provided by the embodiment of the invention, the grade prediction model is obtained based on object information of the sample object and sample service grade training, and the grade prediction model can learn to obtain the mapping relation between the object information and the service grade. Correspondingly, the service level of the object to be served can be determined through the level prediction model and the object information of the object to be served, and then the service mode corresponding to the service level of the object to be served is determined, the questionnaire survey result of the old people does not need to be evaluated manually, so that the service mode of the old people is determined, the influence of personal subjective factors can be eliminated, the accuracy of the determined service mode is improved, the service quality is improved, the time cost and the labor cost can be reduced, and the efficiency of determining the service mode is improved.
An embodiment of the present invention further provides an electronic device, as shown in fig. 6, including a processor 601, a communication interface 602, a memory 603, and a communication bus 604, where the processor 601, the communication interface 602, and the memory 603 complete mutual communication through the communication bus 604,
a memory 603 for storing a computer program;
the processor 601 is configured to implement the following steps when executing the program stored in the memory 603:
acquiring object information of an object to be served; wherein the object information includes: at least one item of personal basic information, body health information, living environment information and social activity information of the object to be served;
mapping the object information of the object to be served to obtain a characteristic vector of the object to be served as a first characteristic vector;
inputting the first feature vector to a pre-trained grade prediction model to obtain the service grade of the object to be served, which is output by the grade prediction model; the grade prediction model is obtained by training based on sample object information and sample service grade of a sample object;
and determining a service mode corresponding to the service level of the object to be served in a preset corresponding relation between the service level and the service mode.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
Based on the electronic equipment provided by the embodiment of the invention, the grade prediction model is obtained by training the object information based on the sample object and the sample service grade, and the grade prediction model can learn to obtain the mapping relation between the object information and the service grade. Correspondingly, the service level of the object to be served can be determined through the level prediction model and the object information of the object to be served, and then the service mode corresponding to the service level of the object to be served is determined, the questionnaire survey result of the old people does not need to be evaluated manually, so that the service mode of the old people is determined, the influence of personal subjective factors can be eliminated, the accuracy of the determined service mode is improved, the service quality is improved, the time cost and the labor cost can be reduced, and the efficiency of determining the service mode is improved.
In another embodiment of the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any of the service mode determination methods described above.
In another embodiment of the present invention, a computer program product containing instructions is further provided, which when run on a computer causes the computer to execute any one of the service mode determination methods in the above embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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 an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus, the electronic device, the computer-readable storage medium, and the computer program product embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and in relation to them, reference may be made to the partial description of the method embodiments.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (12)
1. A service mode determination method, characterized in that the method comprises:
acquiring object information of an object to be served; wherein the object information includes: at least one item of personal basic information, body health information, living environment information and social activity information of the object to be served;
mapping the object information of the object to be served to obtain a characteristic vector of the object to be served as a first characteristic vector;
inputting the first feature vector to a pre-trained grade prediction model to obtain the service grade of the object to be served, which is output by the grade prediction model; the grade prediction model is obtained by training based on sample object information and sample service grade of a sample object;
and determining a service mode corresponding to the service level of the object to be served in a preset corresponding relation between the service level and the service mode.
2. The method of claim 1, wherein the class prediction model comprises: the sensor comprises a linear module, a nonlinear module, a first multilayer sensor, a second multilayer sensor and an output module;
the inputting the first feature vector into a pre-trained grade prediction model to obtain the service grade of the object to be served, which is output by the grade prediction model, includes:
performing linear transformation on the first feature vector through the linear module to obtain a second feature vector;
carrying out nonlinear transformation on the first feature vector through the nonlinear module to obtain a third feature vector;
performing linear transformation on the third feature vector through the first multilayer perceptron to obtain a fourth feature vector;
performing feature fusion on the second feature vector and the fourth feature vector to obtain a fifth feature vector;
performing linear transformation on the fifth feature vector through the second multilayer perceptron to obtain a first numerical value;
and normalizing the first numerical value through the output module to obtain a second numerical value for representing the service grade of the object to be served.
3. The method of claim 1, wherein the personal basic information of the object to be served comprises at least one of: the age, sex, marital status and number of children of the object to be served;
the physical health information of the subject to be served comprises at least one of the following: the height, the weight and the current disease information of the object to be served;
the living environment information of the object to be served comprises at least one of the following items: the number of dining places and the number of activity centers in a preset geographic range of the residence of the object to be served;
the social activity information of the object to be served comprises at least one of the following items: the type and the frequency of community activities participated in by the object to be served in the historical time period.
4. The method according to claim 1, wherein mapping the object information of the object to be served to obtain the feature vector of the object to be served as a first feature vector comprises:
determining numerical information and non-numerical information in the object information of the object to be served; wherein the numerical information is information represented by numerical values; the non-numerical information is other information except the numerical information;
coding the non-numerical information to obtain a sixth feature vector;
carrying out normalization processing on the numerical information, and carrying out linear transformation on the normalized numerical information to obtain a seventh eigenvector;
and splicing the sixth feature vector and the seventh feature vector to obtain the feature vector of the object to be served as the first feature vector.
5. The method of claim 1, wherein the training process of the class prediction model comprises the steps of:
acquiring sample object information of the sample object and a sample numerical value used for representing a sample service level of the sample object;
mapping the sample object information to obtain a characteristic vector of the sample object as a sample characteristic vector;
and taking the sample characteristic vector as input data of a grade prediction model of an initial structure, taking a sample numerical value used for expressing the sample service grade of the sample object as output data of the grade prediction model of the initial structure, and adjusting model parameters of the grade prediction model of the initial structure until the grade prediction model of the initial structure reaches a preset convergence condition to obtain a trained grade prediction model.
6. A service mode determination apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring the object information of the object to be served; wherein the object information includes: at least one item of personal basic information, body health information, living environment information and social activity information of the object to be served;
the mapping module is used for mapping the object information of the object to be served to obtain a characteristic vector of the object to be served as a first characteristic vector;
the prediction module is used for inputting the first feature vector to a pre-trained grade prediction model to obtain the service grade of the object to be served, which is output by the grade prediction model; the grade prediction model is obtained by training based on sample object information and sample service grade of a sample object;
and the determining module is used for determining the service mode corresponding to the service level of the object to be served in the preset corresponding relation between the service level and the service mode.
7. The apparatus of claim 6, wherein the class prediction model comprises: the sensor comprises a linear module, a nonlinear module, a first multilayer sensor, a second multilayer sensor and an output module;
the prediction module is specifically configured to perform linear transformation on the first feature vector through the linear module to obtain a second feature vector;
carrying out nonlinear transformation on the first feature vector through the nonlinear module to obtain a third feature vector;
performing linear transformation on the third feature vector through the first multilayer perceptron to obtain a fourth feature vector;
performing feature fusion on the second feature vector and the fourth feature vector to obtain a fifth feature vector;
performing linear transformation on the fifth feature vector through the second multilayer perceptron to obtain a first numerical value;
and normalizing the first numerical value through the output module to obtain a second numerical value for representing the service grade of the object to be served.
8. The apparatus of claim 6, wherein the personal basic information of the object to be served comprises at least one of: the age, sex, marital status and number of children of the object to be served;
the physical health information of the subject to be served comprises at least one of the following: the height, the weight and the current disease information of the object to be served;
the living environment information of the object to be served comprises at least one of the following items: the number of dining places and the number of activity centers in a preset geographic range of the residence of the object to be served;
the social activity information of the object to be served comprises at least one of the following items: the type and the frequency of community activities participated in by the object to be served in the historical time period.
9. The apparatus according to claim 6, wherein the mapping module is specifically configured to determine numeric information and non-numeric information in the object information of the object to be served; wherein the numerical information is information represented by numerical values; the non-numerical information is other information except the numerical information;
coding the non-numerical information to obtain a sixth feature vector;
carrying out normalization processing on the numerical information, and carrying out linear transformation on the normalized numerical information to obtain a seventh eigenvector;
and splicing the sixth feature vector and the seventh feature vector to obtain the feature vector of the object to be served as the first feature vector.
10. The apparatus of claim 6, further comprising a training module for obtaining sample object information of the sample object and a sample value representing a sample service level of the sample object;
mapping the sample object information to obtain a characteristic vector of the sample object as a sample characteristic vector;
and taking the sample characteristic vector as input data of a grade prediction model of an initial structure, taking a sample numerical value used for expressing the sample service grade of the sample object as output data of the grade prediction model of the initial structure, and adjusting model parameters of the grade prediction model of the initial structure until the grade prediction model of the initial structure reaches a preset convergence condition to obtain a trained grade prediction model.
11. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any one of claims 1 to 5 when executing a program stored in the memory.
12. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of the claims 1-5.
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