CN111508611A - Intelligent selection method and device for multiple solutions and related equipment - Google Patents
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Abstract
The invention discloses an intelligent selection method and device of multiple solutions, computer equipment and a storage medium, which are applied to the technical field of computers and are used for solving the technical problem that an optimal solution is difficult to select when the current problem is solved according to a historical solution. The method provided by the invention comprises the following steps: querying a history solution of a plurality of history processing objects corresponding to the object to be processed; when the history solutions corresponding to the history processing objects comprise at least two different types, acquiring the characteristics included in each history solution; sequencing the characteristics included by each historical solution according to a pre-trained tree integration model; obtaining a score coefficient of each feature; calculating a score for each historical solution based on the ranked results and the score coefficients; and taking the historical solution with the highest score as the solution of the object to be processed.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an intelligent selection method and device of multiple solutions, computer equipment and a storage medium.
Background
With the development of the era and the accumulation of data, the current problems often need to be processed according to historical data, and when the same problems as the existing historical problems are faced, the same problems can be directly processed according to historical solutions of the corresponding problems in order to improve the solving efficiency of solving the corresponding problems.
However, when solving the existing problems according to the historical problems and the historical solutions, we find that for the same technical problem, the historical solutions are often determined by more than one kind, which kind of specific selection is generally determined manually, for the user with experience in solving the related problems, the solutions determined by the user himself may be better, but not necessarily the optimal solutions determined according to various indexes, and for the general user, it is more difficult to determine which of the optimal solutions is.
Disclosure of Invention
The embodiment of the invention provides a multi-solution intelligent selection method, a multi-solution intelligent selection device, computer equipment and a storage medium, and aims to solve the technical problem that an optimal solution is difficult to select when the current problem is solved according to a historical solution.
According to the intelligent selection method of multiple solutions provided by the invention, the method comprises the following steps:
querying a history solution of a plurality of history processing objects corresponding to the object to be processed;
when the history solutions corresponding to the history processing objects comprise at least two different types, acquiring the characteristics included in each history solution;
sequencing the characteristics included by each historical solution according to a pre-trained tree integration model;
obtaining a score coefficient of each feature;
calculating a score for each historical solution based on the ranked results and the score coefficients;
and taking the historical solution with the highest score as the solution of the object to be processed.
According to the invention, a multi-solution intelligent selection device is provided, which comprises:
the query module is used for querying historical solutions of a plurality of historical processing objects corresponding to the object to be processed;
a feature acquisition module for acquiring, when the history solutions corresponding to the history processing objects include at least two different kinds, a feature included in each of the history solutions;
the sorting module is used for sorting the characteristics included by each historical solution according to a pre-trained tree integration model;
a coefficient acquisition module for acquiring a score coefficient of each feature;
a calculating module for calculating a score of each historical solution according to the sorted result and the score coefficient;
and the scheme determining module is used for taking the historical solution with the highest score as the solution of the object to be processed.
According to the invention, a computer device is proposed, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the intelligent selection method of multiple solutions described above when executing the computer program.
According to the invention, a computer-readable storage medium is proposed, which stores a computer program that, when being executed by a processor, carries out the steps of the above-mentioned multi-solution intelligent selection method.
The multi-solution intelligent selection method, the device, the computer equipment and the storage medium provided by the invention query the historical solutions of a plurality of historical processing objects corresponding to the objects to be processed, when the historical solutions corresponding to the historical processing objects comprise at least two different solutions, the characteristics included in each historical solution are obtained, then the characteristics included in each historical solution are sorted according to a pre-trained tree integration model, the score of each historical solution is calculated according to the obtained score coefficient and the sorting result of each characteristic, then the historical solution with the highest score is taken as the solution of the objects to be processed, so that the historical solution selected by the multi-solution intelligent selection method provided by the invention is optimal no matter how many historical solutions for solving the current problem, the method and the device can improve the solution efficiency of a certain problem and ensure that the effect of the selected historical solution can best meet the expectation of a user.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a diagram of an application environment for a multi-solution intelligent selection method in an embodiment of the present invention;
FIG. 2 is a flow chart of a method for intelligent selection of multiple solutions in one embodiment of the present invention;
FIG. 3 is a flow chart of a method for intelligent selection of multiple solutions in another embodiment of the present invention;
FIG. 4 is a schematic diagram of an intelligent selection device with multiple solutions according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a computer device according to an embodiment of the 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 some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The multi-solution intelligent selection method provided by the application can be applied to the application environment as shown in fig. 1, wherein the computer device can communicate with the external device through the network. Computer devices include, but are not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices, among others. The server may be implemented as a stand-alone server or as a server cluster consisting of a plurality of servers.
In an embodiment, as shown in fig. 2, a multi-solution intelligent selection method is provided, which is illustrated by taking the computer device in fig. 2 as an example, and includes the following steps S101 to S106.
S101, querying a history solution of a plurality of history processing objects corresponding to the object to be processed.
In one embodiment, the object to be treated includes, but is not limited to, a contaminated area, a sick patient, a machine to be repaired, and the like, and the object requiring correction or repair or diagnosis may be used as the object to be treated in the present embodiment.
In this embodiment, the historical solution represents a solution to the same problem as the current problem that has previously occurred.
In one embodiment, as shown in fig. 2, the step S101 further includes the following steps S201 to S203.
S201, receiving attribute information and defect indexes of the object to be processed.
In one embodiment, the attribute information represents information of the object to be treated, when the object to be treated is a patient, the attribute information of the object to be treated may be name, age, sex, weight, etc. of the patient, and the defect index includes, but is not limited to, past medical history, current body temperature, current medical condition, etc. of the patient.
When the object to be processed is a machine to be repaired, the attribute information of the object to be processed may be a model of the machine, and the defect indicator of the object to be processed includes, but is not limited to, a specific fault condition such as a fault type of the machine.
S202, inquiring a plurality of corresponding history processing objects from a database according to the attribute information and the defect index.
In one embodiment, the query arrival history processing object represents attribute information which is specifically the same as or similar to the current object to be processed and has the same defect index.
Further, the similar attribute information is, for example, that the age of the current patient is within 5 years of the age of a certain patient in the history, and it is determined that the history processing object and the current object to be processed have similar ages.
S203, inquiring the history solution of each history processing object from the database.
In one embodiment, the history solutions may be one or more, and further, different history processing objects queried in step S202 may have the same history solution or different history solutions.
S102, when the historical solutions corresponding to the historical processing objects comprise at least two different types, acquiring the characteristics included by each type of the historical solutions.
In one embodiment, when the object to be treated is a patient, the characteristics in the corresponding historical solution include, but are not limited to, the cost of treatment, the long-term treatment strategy included in the course of treatment, and the short-term treatment strategy.
S103, sequencing the features included in each historical solution according to a pre-trained tree integration model.
In one embodiment, the tree integration model is an xgboost (extreme Gradient boosting) model, and the training method of the xgboost model is the prior art and can be obtained by adopting conventional steps of feature input, parameter adjustment and result output.
One scenario according to the present embodiment is for example: the historical solutions include features such as cost of treatment, long-term treatment strategy, and short-term treatment strategy. The priority of the sequencing result obtained by the tree integration model is as follows from high to low in sequence: cost, long-term treatment strategy, short-term treatment strategy.
And S104, acquiring a score coefficient of each feature.
In one embodiment, the step S104 further includes:
querying the number of successful history processing objects in the dimensionality of each feature when the corresponding history processing object selects the history solution from the database;
calculating the historical success rate of the corresponding characteristics according to the number of the inquired successes and the total number of the historical processing objects;
and taking the historical success rate of the query as a score coefficient of the corresponding characteristic.
One usage scenario according to the present embodiment is for example: when the historical solution is selected by querying the corresponding historical processing object from the database, the number of the patients without complications after the long-term treatment strategy is used is 80, and the patient selecting the long-term treatment strategy in all the historical solutions of the corresponding historical processing object is 100, so that the calculated historical success rate of the long-term treatment strategy is 80%, and the score coefficient of the characteristic of the long-term treatment strategy is 0.8.
In other embodiments, the step S104 further comprises:
when the characteristics of the historical solution comprise resources required to be consumed by selecting the historical solution, acquiring historical values of the resources required to be consumed from a database;
displaying the historical value of the resource to be consumed;
and receiving a score coefficient input by a user aiming at the historical value of the resource.
In one embodiment, obtaining historical values of the resource to be consumed from the database may be the money spent using the historical solution.
And S105, calculating the score of each historical solution according to the sorted result and the score coefficient.
In one embodiment, the step S105 further comprises:
obtaining the sequencing result of the characteristics included by each historical solution according to the tree integration model;
and taking the total number of the features included in the historical solution as the scores of the features with the highest priority in the sorting result, and gradually decreasing the total number of the features according to the sorting result to be taken as the scores of the corresponding features.
In one embodiment, the step of calculating a score for each historical solution based on the ranked results and the score coefficients further comprises:
taking the product of the score coefficient and the fraction of the corresponding feature as the score of the feature in the historical solution;
and summing the scores of all the characteristics included in the historical solution to obtain the score of the historical solution.
And S106, taking the historical solution with the highest score as the solution of the object to be processed.
One usage scenario according to the present embodiment is for example: when a doctor analyzes a new medical record, firstly, similar patients in a evidence-based database are found according to basic information, inspection and examination indexes and the like of the patients, then, the curative effects of different treatment schemes in the similar patients are compared, and if the multidimensional evidence-based database points to the same scheme, the scheme is the final medical record analysis result; if the multidimensional evidence-based approach points to different schemes, such as scheme 1 with the best long-term curative effect, but scheme 2 with the most proper cost, the features included in each historical solution need to be sorted through a pre-trained tree integration model, the important sequence of each feature is obtained, and the scores of various historical solutions are calculated.
In the use scenario, the priority is assumed to be cost, long-term efficacy and short-term efficacy, and the calculation method is as follows:
1) scoring the evidence-based results according to the priority sequence and the gradually decreasing sequence, namely, the cost is 3 points, the long-term curative effect is 2 points and the short-term curative effect is 1 point;
2) taking the percentage of the scheme in evidence-based results as a score coefficient, if 70% of the results are considered to be completely acceptable, 80% of the results have no complications, and 90% of the results reach the standard in short-term saccharification;
3) calculating the scheme final score, namely 3 × 0.7+2 × 0.8+1 × 0.9 ═ 4.6;
4) and finding the final score of each treatment scheme according to the calculation mode, and selecting the scheme with the highest score as the optimal analysis result.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The intelligent selection method for multiple solutions provided in this embodiment queries the historical solutions of multiple historical processing objects corresponding to the object to be processed, obtains the features included in each historical solution when the historical solutions corresponding to the historical processing objects include at least two different solutions, ranks the features included in each historical solution according to a pre-trained tree integration model, calculates the score of each historical solution according to the score coefficient and ranking result of each obtained feature, and then uses the historical solution with the highest score as the solution of the object to be processed, so that the historical solution selected by the intelligent selection method for multiple solutions provided by the present invention is optimal no matter how many historical solutions solve the current problem, and while improving the solution efficiency of a certain problem, and ensuring that the effect of the selected historical solution can best meet the expectation of the user.
In an embodiment, a multi-solution intelligent selection device is provided, and the multi-solution intelligent selection device corresponds to the multi-solution intelligent selection method in the above embodiment one to one. As shown in fig. 4, the multi-solution intelligent selection apparatus 100 includes a query module 11, a feature acquisition module 12, a ranking module 13, a coefficient acquisition module 14, a calculation module 15, and a solution determination module 16. The functional modules are explained in detail as follows:
and the query module 11 is used for querying the historical solutions of a plurality of historical processing objects corresponding to the object to be processed.
In one embodiment, the object to be treated includes, but is not limited to, a contaminated area, a sick patient, a machine to be repaired, and the like, and the object requiring correction or repair or diagnosis may be used as the object to be treated in the present embodiment.
In this embodiment, the historical solution represents a solution to the same problem as the current problem that has previously occurred.
A feature obtaining module 12, configured to, when the historical solutions corresponding to the historical processing objects include at least two different kinds, obtain a feature included in each of the historical solutions.
In one embodiment, when the object to be treated is a patient, the characteristics in the corresponding historical solution include, but are not limited to, the cost of treatment, the long-term treatment strategy included in the course of treatment, and the short-term treatment strategy.
And the sorting module 13 is used for sorting the features included in each historical solution according to the pre-trained tree integration model.
In one embodiment, the tree integration model is an xgboost (extreme Gradient boosting) model, and the training method of the xgboost model is the prior art and can be obtained by adopting conventional steps of feature input, parameter adjustment and result output.
One scenario according to the present embodiment is for example: the historical solutions include features such as cost of treatment, long-term treatment strategy, and short-term treatment strategy. The priority of the sequencing result obtained by the tree integration model is as follows from high to low in sequence: cost, long-term treatment strategy, short-term treatment strategy.
In one embodiment, when the object to be treated is a patient, the characteristics in the corresponding historical solution include, but are not limited to, the cost of treatment, the long-term treatment strategy included in the course of treatment, and the short-term treatment strategy.
And a coefficient obtaining module 14, configured to obtain a score coefficient of each feature.
And a calculating module 15 for calculating a score of each historical solution according to the sorted result and the score coefficient.
And the scheme determining module 16 is used for taking the historical solution with the highest score as the solution of the object to be processed.
One usage scenario according to the present embodiment is for example: when a doctor analyzes a new medical record, firstly, similar patients in a evidence-based database are found according to basic information, inspection and examination indexes and the like of the patients, then, the curative effects of different treatment schemes in the similar patients are compared, and if the multidimensional evidence-based database points to the same scheme, the scheme is the final medical record analysis result; if the multidimensional evidence-based approach points to different schemes, such as scheme 1 with the best long-term curative effect, but scheme 2 with the most proper cost, the features included in each historical solution need to be sorted through a pre-trained tree integration model, the important sequence of each feature is obtained, and the scores of various historical solutions are calculated.
In the use scenario, the priority is assumed to be cost, long-term efficacy and short-term efficacy, and the calculation method is as follows:
1) scoring the evidence-based results according to the priority sequence and the gradually decreasing sequence, namely, the cost is 3 points, the long-term curative effect is 2 points and the short-term curative effect is 1 point;
2) taking the percentage of the scheme in evidence-based results as a score coefficient, if 70% of the results are considered to be completely acceptable, 80% of the results have no complications, and 90% of the results reach the standard in short-term saccharification;
3) calculating the scheme final score, namely 3 × 0.7+2 × 0.8+1 × 0.9 ═ 4.6;
4) and finding the final score of each treatment scheme according to the calculation mode, and selecting the scheme with the highest score as the optimal analysis result.
In one embodiment, the query module 11 further includes:
the information receiving unit is used for receiving attribute information and defect indexes of the object to be processed;
the object query unit is used for querying a plurality of corresponding historical processing objects from a database according to the attribute information and the defect index;
and the scheme query unit is used for querying the historical solution of each historical processing object from the database.
In one embodiment, the attribute information represents information of the object to be treated, and when the object to be treated is a patient, the attribute information of the object to be treated may be the name, age, sex, weight, etc. of the patient, and the defect index includes, but is not limited to, the past medical history, the current body temperature, the current condition, etc. of the patient.
When the object to be processed is a machine to be repaired, the attribute information of the object to be processed may be a model of the machine, and the defect indicator of the object to be processed includes, but is not limited to, a specific fault condition such as a fault type of the machine.
In one embodiment, the query arrival history processing object represents attribute information which is specifically the same as or similar to the current object to be processed and has the same defect index.
Further, the similar attribute information is, for example, that the age of the current patient is within 5 years of the age of a certain patient in the history, and it is determined that the history processing object and the current object to be processed have similar ages.
In one embodiment, the history solutions may be one or more, and further, different history processing objects queried by the solution querying unit may have the same history solution or different history solutions.
In one embodiment, the coefficient obtaining module 14 further includes:
the number acquisition unit is used for inquiring the number of successful history processing objects in the dimension of each feature when the corresponding history processing objects are selected from the database;
a success rate calculating unit for calculating the history success rate of the corresponding feature according to the successful number of the query and the total number of the history processing objects;
and the coefficient determining unit is used for taking the historical success rate of the query as a score coefficient of the corresponding characteristic.
In other embodiments, the coefficient obtaining module 14 further includes:
a value obtaining unit, configured to obtain a historical value of a resource to be consumed from a database when a feature of the historical solution includes the resource to be consumed by selecting the historical solution;
the display unit is used for displaying the historical value of the resource which needs to be consumed;
and the coefficient determining unit is used for receiving a score coefficient input by a user aiming at the historical value of the resource.
In one embodiment, the calculation module 15 further includes:
a sorting result obtaining unit, configured to obtain a sorting result of the features included in each historical solution according to the tree integration model;
and the feature score calculating unit is used for taking the total number of the features included in the historical solution as the score of the feature with the highest priority in the sorting result, and gradually reducing the total number of the features one by one according to the sorting result to be taken as the score of the corresponding feature.
In one embodiment, the calculation module 15 further includes:
a feature score calculation unit for taking the product of the score coefficient and the score of the corresponding feature as the score of the feature in the history solution;
and the scheme score calculating unit is used for summing the scores of all the characteristics included in the historical solution to obtain the score of the historical solution.
The terms "comprises," "comprising," and "having," and any variations thereof, in this embodiment, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or modules is not necessarily limited to those steps or modules expressly listed, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus, and the division of modules presented in this application is merely a logical division and may be implemented in alternate implementations.
For the specific definition of the multi-solution intelligent selection device, reference may be made to the above definition of the multi-solution intelligent selection method, which is not described herein again. The various modules in the multi-solution intelligent selection device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external server through a network connection. The computer program is executed by a processor to implement a multiple solution intelligent selection method.
In one embodiment, a computer device is provided, which comprises a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the intelligent selection method of the multiple solutions in the above-described embodiments, such as the steps 101 to 106 shown in fig. 2 and other extensions of the method and related steps. Alternatively, the processor, when executing the computer program, implements the functions of the modules/units of the intelligent selection device of the multi-solution in the above-described embodiments, such as the functions of the modules 11 to 16 shown in fig. 4. To avoid repetition, further description is omitted here.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like which is the control center for the computer device and which connects the various parts of the overall computer device using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the computer device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, video data, etc.) created according to the use of the cellular phone, etc.
The memory may be integrated in the processor or may be provided separately from the processor.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the intelligent selection method of the multiple solution scheme of the above-described embodiments, such as the steps 101 to 106 shown in fig. 2 and extensions of other extensions and related steps of the method. Alternatively, the computer program, when executed by the processor, implements the functions of the modules/units of the intelligent selection device of the multi-solution in the above-described embodiments, such as the functions of the modules 11 to 16 shown in fig. 4. To avoid repetition, further description is omitted here.
The method, apparatus, computer device and storage medium for intelligently selecting multiple solutions provided in this embodiment query historical solutions of multiple historical processing objects corresponding to an object to be processed, when the historical solutions corresponding to the historical processing objects include at least two different solutions, obtain features included in each of the historical solutions, sort the features included in each of the historical solutions according to a pre-trained tree integration model, calculate a score of each of the historical solutions according to the obtained score coefficients and sorting results of the features, and then use the historical solution with the highest score as the solution of the object to be processed, so that the historical solution selected according to the method for intelligently selecting multiple solutions provided in the present invention is optimal regardless of how many historical solutions solve the current problem, the method and the device can improve the solution efficiency of a certain problem and ensure that the effect of the selected historical solution can best meet the expectation of a user.
It will be understood by those of ordinary skill in the art that all or a portion of the processes of the methods of the embodiments described above may be implemented by a computer program that may be stored on a non-volatile computer-readable storage medium, which when executed, may include the processes of the embodiments of the methods described above, wherein any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. An intelligent selection method for multiple solutions, comprising:
querying a history solution of a plurality of history processing objects corresponding to the object to be processed;
when the history solutions corresponding to the history processing objects comprise at least two different types, acquiring the characteristics included in each history solution;
sequencing the features included by each historical solution according to a pre-trained tree integration model;
obtaining a score coefficient of each feature;
calculating a score for each historical solution according to the sorted results and the score coefficients;
and taking the historical solution with the highest score as the solution of the object to be processed.
2. The intelligent selection method of multiple solutions according to claim 1, wherein said step of querying historical solutions of a plurality of historical processing objects corresponding to objects to be processed comprises:
receiving attribute information and defect indexes of an object to be processed;
inquiring a plurality of corresponding historical processing objects from a database according to the attribute information and the defect index;
and querying the historical solution of each historical processing object from the database.
3. A multi-solution intelligent selection method according to claim 2, wherein said step of obtaining a score coefficient for each of said features comprises:
querying the corresponding historical processing object from the database, wherein the number of the historical processing objects is successfully found in the dimension of each feature when the historical solution is selected;
calculating the historical success rate of the corresponding characteristics according to the number of the inquired successes and the total number of the historical processing objects;
and taking the historical success rate of the query as a score coefficient of the corresponding characteristic.
4. A multi-solution intelligent selection method according to claim 1, wherein said step of obtaining a score coefficient for each of said features comprises:
when the characteristics of the historical solution comprise resources required to be consumed by selecting the historical solution, acquiring historical values of the resources required to be consumed from a database;
displaying the historical value of the resource to be consumed;
and receiving a score coefficient input by a user aiming at the historical value of the resource.
5. The intelligent selection method of multiple solutions according to any of claims 1 to 4, wherein the step of calculating a score for each historical solution based on the ranked results and the score coefficients comprises:
obtaining a sequencing result of the characteristics included by each historical solution according to the tree integration model;
and taking the total number of the features included in the historical solution as the scores of the features with the highest priority in the sorting results, and gradually decreasing the total number of the features according to the sorting results to be taken as the scores of the corresponding features.
6. The intelligent selection method of multiple solutions of claim 5, wherein the step of calculating a score for each historical solution based on the ranked results and the score coefficients comprises:
taking a product of the score coefficient and a score of a corresponding feature as a score of the feature in the historical solution;
and summing the scores of all the characteristics included in the historical solution to obtain the score of the historical solution.
7. A multi-solution intelligent selection apparatus, the apparatus comprising:
the query module is used for querying historical solutions of a plurality of historical processing objects corresponding to the object to be processed;
a feature acquisition module configured to acquire, when history solutions corresponding to the history processing objects include at least two different kinds, a feature included in each of the history solutions;
the sorting module is used for sorting the characteristics included by each historical solution according to a pre-trained tree integration model;
a coefficient acquisition module for acquiring a score coefficient of each of the features;
a calculating module for calculating a score of each historical solution according to the sorted result and the score coefficient;
and the scheme determining module is used for taking the historical solution with the highest score as the solution of the object to be processed.
8. The multi-solution intelligent selection device of claim 7, wherein the query module comprises:
the information receiving unit is used for receiving attribute information and defect indexes of the object to be processed;
the object query unit is used for querying a plurality of corresponding historical processing objects from a database according to the attribute information and the defect index;
and the scheme query unit is used for querying the historical solution of each historical processing object from the database.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the intelligent selection method of multiple solutions according to any one of claims 1 to 6.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the intelligent selection method of multiple solutions according to any one of claims 1 to 6.
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