Information generation method and device

Abstract

The invention discloses an information generation method and device, and relates to the technical field of deep learning. A specific embodiment of the method comprises the following steps: acquiring feature information of a plurality of to-be-introduced carrier objects; inputting the feature information of the plurality of to-be-introduced carrier objects into a preset scoring model to obtain scores of the to-be-introduced carrier objects; and based on the score of each to-be-introduced carriage object, determining a target carriage object in the plurality of to-be-introduced carriage objects. According to the embodiment, the introduction efficiency of the carrier is effectively improved, and the time cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of computer technology, specifically to the field of deep learning technology, and in particular to an information generation method and device. Background Art

[0002] Under current circumstances, the main method of attracting carriers is for business personnel to visit through online social platforms or offline logistics parks. The above two methods have the following main defects: the offline logistics park visit method requires manual visits to logistics companies and manual review, which is inefficient and may fail to find logistics companies; the manual screening method mainly relies on business connections to introduce carriers, which has high business requirements. Summary of the invention

[0003] Embodiments of the present application provide an information generation method, apparatus, device, and storage medium.

[0004] According to the first aspect, an embodiment of the present application provides an information generation method, the method comprising: obtaining characteristic information of multiple carrier objects to be introduced; inputting the characteristic information of the multiple carrier objects to be introduced into a preset scoring model to obtain a score for each carrier object to be introduced; and determining a target carrier object from the multiple carrier objects to be introduced based on the score for each carrier object to be introduced.

[0005] According to the second aspect, an embodiment of the present application provides an information generating device, which includes: an acquisition module, configured to acquire characteristic information of multiple carrier objects to be introduced; a scoring module, configured to input the characteristic information of multiple carrier objects to be introduced into a preset scoring model to obtain a score for each carrier object to be introduced; and a determination module, configured to determine a target carrier object from multiple carrier objects to be introduced based on the score of each carrier object to be introduced.

[0006] According to the third aspect, an embodiment of the present application provides an electronic device, which includes one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement an information generation method as in any embodiment of the first aspect.

[0007] According to a fourth aspect, an embodiment of the present application provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements an information generation method as in any embodiment of the first aspect.

[0008] The present application obtains characteristic information of multiple carriers to be introduced; inputs the characteristic information of multiple carriers to be introduced into a preset scoring model to obtain a score for each carrier to be introduced; and based on the score for each carrier to be introduced, determines a target carrier from the multiple carriers to be introduced, thereby effectively improving the carrier's introduction efficiency and reducing time costs.

[0009] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0011] Figure 2 is a flow chart of an embodiment of the information generation method according to the present application;

[0012] Figure 3 is a schematic diagram of an application scenario of the information generation method according to the present application;

[0013] Figure 4 is a flow chart of another embodiment of the information generation method according to the present application;

[0014] Figure 5 is a schematic diagram of an embodiment of an information generating device according to the present application;

[0015] Figure 6 It is a structural diagram of a computer system suitable for implementing a server of an embodiment of the present application. DETAILED DESCRIPTION

[0016] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0017] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0018] Figure 1 An exemplary system architecture 100 is shown to which embodiments of the information generation method of the present application may be applied.

[0019] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0020] The terminal devices 101, 102, 103 interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications, such as information generation applications, communication applications, etc., may be installed on the terminal devices 101, 102, 103.

[0021] Terminal devices 101, 102, 103 can be hardware or software. When terminal devices 101, 102, 103 are hardware, they can be various electronic devices with display screens, including but not limited to mobile phones and laptops. When terminal devices 101, 102, 103 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software or software modules (for example, to provide information generation services), or they can be implemented as a single software or software module. No specific limitation is made here.

[0022] Server 105 can be a server that provides various services, for example, obtaining characteristic information of multiple carriers to be introduced; inputting the characteristic information of multiple carriers to be introduced into a preset scoring model to obtain the score of each carrier to be introduced; and determining a target carrier from multiple carriers to be introduced based on the score of each carrier to be introduced.

[0023] It should be noted that the server 105 can be hardware or software. When the server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules (for example, for providing information generation services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0024] It should be noted that the information generation method provided in the embodiments of the present disclosure can be executed by the server 105, or by the terminal devices 101, 102, 103, or by the server 105 and the terminal devices 101, 102, 103 in cooperation with each other. Accordingly, the various parts (such as various units, sub-units, modules, sub-modules) included in the information generation device can be all set in the server 105, or all set in the terminal devices 101, 102, 103, or respectively set in the server 105 and the terminal devices 101, 102, 103.

[0025] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0026] Figure 2 A flow chart 200 of an embodiment of an information generation method applicable to the present application is shown. In this embodiment, the information generation method includes the following steps:

[0027] Step 201, obtaining characteristic information of multiple carrier objects to be introduced.

[0028] In this embodiment, the execution subject (such as Figure 1 The server 105 or terminal devices 101, 102, 103) shown in the figure can obtain multiple, for example, 10, 100, etc., characteristic information of the carrier objects to be introduced input by the user in a wired or wireless manner.

[0029] Here, the characteristic information of the carrier to be introduced can be determined based on the carrier qualification information of the carrier to be introduced.

[0030] Among them, the carrier qualification information may include at least one of the following: the name of the carrier object, the creation time of the carrier object, the legal representative, the status of the carrier object, the registered capital, the address of the carrier object, the business scope, the paid-in capital, the type of carrier object, telephone information, etc.

[0031] The wireless connection methods mentioned above may include but are not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0032] Step 202, input the characteristic information of multiple carriers to be introduced into a preset scoring model to obtain the score of each carrier to be introduced.

[0033] In this embodiment, the execution entity may input the characteristic information of multiple carrier objects to be introduced into a preset scoring model to obtain the score corresponding to each carrier object to be introduced.

[0034] Among them, the preset scoring model can be a neural network model with scoring function in existing technology or future development technology, such as multi-layer perceptron MLP, GBDT, deepFm, wide&deep, etc., which is not limited in this application.

[0035] Here, the MLP model is a 3-layer network, including an input layer, a hidden layer, and an output layer, and the 3-layer network performs nonlinear activation and feature crossover. Among them, the number of neuron nodes in the input layer is the number of features, which is determined by the number of feature dimensions after the final summary in the model feature engineering. The hidden layer can be a neuron network containing 10 nodes, and the output layer is the specific number to be classified, that is, 2 categories.

[0036] Here, the preset scoring model can be trained based on feature information samples annotated with the introduction results.

[0037] The introduction result is used to indicate whether the carrier object has been introduced.

[0038] Here, the score of each carrier to be introduced is used to indicate the tendency of the corresponding introduction result of the carrier to be introduced to be that the introduction is required, and the higher the score, the higher the tendency.

[0039] In some optional methods, the scoring model is trained in the following ways: obtaining big data information of the carrier object; cleaning and integrating the big data information to obtain the cleaned and integrated big data information; recalling data according to one of the following: name of the carrier object, category of the carrier object, business scope and registered capital, to obtain the recalled carrier object; obtaining a feature information sample based on the operating qualification information of the recalled carrier object; dividing the feature information samples into positive samples and negative samples according to the introduction results of the corresponding historical preset time period to train the initial scoring model, to obtain a trained scoring model.

[0040] In this implementation, the preset scoring model can be trained specifically through the following steps: acquiring data - cleaning and integration - recalling - modeling - dividing positive and negative samples - building a model - model training - model tuning - model evaluation.

[0041] Among them, obtaining data specifically refers to obtaining big data information of the carrier object, and the big data information includes the carrier qualification information of each carrier object.

[0042] Cleaning and integration refers to the cleaning and integration of big data information, such as filling in missing values, filtering invalid data, and timely updating of data based on the drivers' willingness to participate in quoting, so as to obtain cleaned and integrated big data information.

[0043] Recall will be carried out in the following ways: recall based on the name of the carrier, recalling the carrier whose name is the logistics carrier; recalling based on the category of the carrier, recalling the carrier whose category is the road transport industry; recalling based on the business scope, recalling the carrier whose business scope is related to cargo transportation; recalling based on the registered capital, recalling the carrier whose registered capital is greater than the source requirement.

[0044] Modeling, specifically, modeling the carrier qualification information of the recalled carrier, and obtaining the characteristic information of the recalled carrier, whose characteristic dimensions may include: carrier name, carrier category, business scope, registered capital, carrier type, and carrier status. Among them, for the characteristics of the carrier name, carrier category, business scope, etc., Chinese word segmentation can be performed and divided into finer-grained range attributes to form new features. For information such as carrier category, registered capital, carrier type, and carrier status, one-hot encoding features are used. The pipeline mechanism is used to serialize the processing mechanisms of all feature engineering and finally summarize them into a total feature information.

[0045] The positive and negative samples are divided into two types: specifically, the carriers introduced in the adjacent preset historical time period, such as the past year, are determined as positive samples, and the carriers that have not been introduced in the past year are determined as negative samples.

[0046] The model is constructed, specifically, the features after serial processing of the pipeline mechanism are input into the multi-layer perceptron (MLP) model. The MLP model is a 3-layer network, including input layer, hidden layer, output layer, and the 3-layer network performs nonlinear activation and feature crossover. Among them, the number of neuron nodes in the input layer is the number of features, which is determined by the number of feature dimensions after the final summary in the model feature engineering. The hidden layer is a neuron network containing 10 nodes, and the output layer is the specific number to be classified, that is, 2 categories.

[0047] Model training involves randomly dividing input data into a training set and a test set, such as an 8:2 ratio. The training set is used to train the model data, and the test set is used to evaluate the model effect.

[0048] Model tuning, specifically tuning the parameters of the scoring model, mainly includes the maximum number of iterations, learning rate adjustment, optimization method adjustment, activation function selection, dropout setting, and deep layer depth setting.

[0049] Model evaluation is to use 20% of the input data for evaluation. The evaluation indicators mainly include the AUC indicator of the classification model and the precision, recall rate and F1 value of positive and negative samples. The evaluation method is explained using the positive sample as an example:

[0050] Precision = data predicted as positive samples and actually positive samples / all data predicted as positive samples.

[0051] Recall rate = data predicted to be positive samples and actually positive samples / all positive sample data.

[0052] F1 value = 2*precision*recall / (recall+precision).

[0053] This implementation method obtains big data information of the carrier object; cleans and integrates the big data information to obtain the cleaned and integrated big data information; recalls data according to one of the following: the name of the carrier object, the type of carrier object, the business scope and the registered capital, to obtain the recalled carrier object; obtains a feature information sample based on the operating qualification information of the recalled carrier object; divides the feature information samples into positive samples and negative samples according to the introduction results of the corresponding historical preset time period to train the initial scoring model, to obtain a trained scoring model, and improves the accuracy and reliability of the trained scoring model.

[0054] Step 203: Based on the scores of the carriers to be introduced, a target carrier is determined from the multiple carriers to be introduced.

[0055] In this embodiment, after obtaining the scores corresponding to the carriers to be introduced, the execution entity may first sort the carriers to be introduced according to the scores, and determine the target carrier based on the sorting results.

[0056] Specifically, the executing entity may determine a preset number of carriers to be introduced that are ranked at the top as target carriers.

[0057] Among them, the preset number can be set according to experience and actual needs, for example, 9, 29, etc., and this application does not limit this.

[0058] Continue to see Figure 3 , Figure 3 It is a schematic diagram of an application scenario of the information generating method according to this embodiment.

[0059] exist Figure 3 In the application scenario, the execution subject 301 can obtain the characteristic information 302 of multiple carrier objects to be introduced, for example, 10, 100, etc., input by the user in a wired or wireless manner. Here, the characteristic information of the carrier object to be introduced can be determined based on the carrier qualification information of the carrier object to be introduced. The characteristic information 302 of multiple carrier objects to be introduced is input into the preset scoring model 303 to obtain the score 304 of each carrier object to be introduced. Here, the preset scoring model 303 can be trained based on the characteristic information sample of the carrier object marked with the introduction result. Based on the score 304 of each carrier object to be introduced, the target carrier object is determined from the multiple carrier objects to be introduced.

[0060] The information generation method disclosed in the present invention obtains characteristic information of multiple carriers to be introduced; inputs the characteristic information of the multiple carriers to be introduced into a preset scoring model to obtain a score for each carrier to be introduced; and based on the score for each carrier to be introduced, determines a target carrier from the multiple carriers to be introduced, thereby effectively improving the efficiency of introducing carriers and saving time costs.

[0061] Further references Figure 4 , which shows Figure 2 The process 400 of another embodiment of the information generation method is shown. In this embodiment, the process 400 of the information generation method may include the following steps:

[0062] Step 401, obtaining characteristic information of multiple carrier objects to be introduced.

[0063] In this embodiment, the implementation details and technical effects of step 401 can refer to the description of step 201 and will not be repeated here.

[0064] Step 402, input the characteristic information of multiple carriers to be introduced into a preset scoring model to obtain the score corresponding to each carrier to be introduced.

[0065] In this embodiment, the implementation details and technical effects of step 402 can refer to the description of step 202 and will not be repeated here.

[0066] Step 403: Based on the scores of the carriers to be introduced, an initial carrier is determined from the multiple carriers to be introduced.

[0067] In this embodiment, after obtaining the scores of each carrier object to be introduced, the execution entity may first sort the carrier objects to be introduced according to the scores of each carrier object to be introduced, and determine a preset number of carrier objects to be introduced with the highest scores as initial carrier objects.

[0068] In some optional methods, an initial carrier object is determined from multiple carrier objects to be introduced based on the scores of each carrier object to be introduced, including: based on the scores of each carrier object to be introduced and the attribute information of whether each carrier object to be introduced has been introduced, an initial carrier object is determined from multiple carrier objects to be introduced.

[0069] In this implementation, the executing entity can sort multiple carrier objects to be introduced according to the scores of each carrier object to be introduced, and determine a preset number of carrier objects to be introduced with the highest rankings as primary carrier objects; further, obtain the attribute information of whether each carrier object in the primary carrier objects has been introduced, filter out the carrier objects corresponding to the attribute information that have been introduced in the primary carrier objects, and obtain the initial carrier objects.

[0070] This implementation method helps to improve the accuracy of the determined initial carrier object by determining the initial carrier object from multiple carrier objects to be introduced based on the score of each carrier object to be introduced and the attribute information of whether each carrier object to be introduced has been introduced, which helps to improve the accuracy of the determined initial carrier object.

[0071] Step 404: determine a target carrier from among the initial carriers based on the indication information of whether each carrier from among the initial carriers agrees to the introduction.

[0072] In this embodiment, after determining the initial carrier object, the execution entity may further obtain indication information of whether each carrier object in the initial carrier object agrees to introduce the transport, and determine the carrier object in the initial carrier object that corresponds to the indication information of agreeing to introduce the transport as the target carrier object.

[0073] In some optional embodiments, the method further includes: determining, among the initial carrier objects, the carrier objects that correspond to the indication information of not agreeing to introduce, as non-target introduction objects; determining the non-target introduction objects as new training samples, and training and updating the scoring model.

[0074] In this implementation, after determining the initial carrier objects, the executing entity may further obtain the indication information of whether each carrier object in the initial carrier objects agrees to the introduction, and determine the carrier objects in the initial carrier objects that correspond to the indication information of not agreeing to the introduction as non-target introduction objects; determine the non-target introduction objects as new training samples, and train and update the scoring model.

[0075] This implementation method determines the carrier objects corresponding to the indication information of not agreeing to introduce in the initial carrier objects as non-target introduction objects; determines the non-target introduction objects as new training samples, trains and updates the scoring model, thereby realizing timely updating of the scoring model and ensuring the effectiveness of the scoring model.

[0076] from Figure 4 It can be seen that Figure 2 Compared with the corresponding embodiments, the process 400 of the information generation method in this embodiment reflects that based on the scores of each carrier to be introduced, an initial carrier is determined from multiple carriers to be introduced; based on the indication information of whether each carrier in the initial carrier agrees to the introduction, a target carrier is determined from the initial carriers, which helps to further improve the accuracy of the determined target carrier, and thus improve the effectiveness of the carrier introduction.

[0077] Further references Figure 5 As an implementation of the methods shown in the above figures, the present application provides an embodiment of an information generating device, which is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0078] like Figure 5 As shown, the information generating device 500 of this embodiment includes: an acquisition module 501 , a scoring module 502 and a determination module 503 .

[0079] Among them, the acquisition module 501 can be configured to obtain characteristic information of multiple carrier objects to be introduced.

[0080] The scoring module 502 may be configured to input characteristic information of a plurality of carrier objects to be introduced into a preset scoring model to obtain a score for each carrier object to be introduced.

[0081] The determination module 503 may be configured to determine a target carrier object from a plurality of carrier objects to be introduced based on the scores of each carrier object to be introduced.

[0082] In some optional modes of this embodiment, the determination module is further configured to determine an initial carrier from multiple carriers to be introduced based on the scores of each carrier to be introduced; and determine a target carrier from the initial carriers based on indication information of whether each carrier among the initial carriers agrees to the introduction.

[0083] In some optional methods of this embodiment, an initial carrier object is determined from the multiple carrier objects to be introduced based on the scores of each carrier object to be introduced, including: determining the initial carrier object from the multiple carrier objects to be introduced based on the scores of each carrier object to be introduced and the attribute information of whether each carrier object to be introduced has been introduced.

[0084] In some optional aspects of this embodiment, the method further includes: an update module, configured to determine, among the initial carrier objects, the carrier objects that correspond to the indication information of not agreeing to introduce, as non-target introduction objects; determine the non-target introduction objects as new training samples, and perform training and update on the scoring model.

[0085] In some optional methods of the present embodiment, the preset scoring model is trained in the following ways: obtaining big data information of the carrier object; cleaning and integrating the big data information to obtain the cleaned and integrated big data information; recalling data according to one of the following items: name of the carrier object, category of the carrier object, business scope and registered capital, to obtain the recalled carrier object; obtaining a feature information sample based on the operating qualification information of the recalled carrier object; dividing the feature information samples into positive samples and negative samples according to the introduction results of the corresponding historical preset time period to train the initial scoring model, to obtain a trained scoring model.

[0086] It should be noted that the collection, collection, updating, analysis, processing, use, transmission, storage and other aspects of user personal information involved in the technical solution of this disclosure are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security and national security.

[0087] According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.

[0088] like Figure 6 , is a block diagram of an electronic device according to an information generation method according to an embodiment of the present application.

[0089] 600 is a block diagram of an electronic device according to an information generation method of an embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0090] like Figure 6 As shown, the electronic device includes: one or more processors 601, a memory 602, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 601 is taken as an example.

[0091] The memory 602 is a non-transient computer-readable storage medium provided in the present application. The memory stores instructions executable by at least one processor to enable the at least one processor to perform the information generation method provided in the present application. The non-transient computer-readable storage medium of the present application stores computer instructions, which are used to enable a computer to perform the information generation method provided in the present application.

[0092] The memory 602 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to the information generation method in the embodiment of the present application (for example, the attached Figure 5The processor 601 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 602, that is, the information generation method in the above method embodiment is implemented.

[0093] The memory 602 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created by the use of the electronic device generating information, etc. In addition, the memory 602 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 602 may optionally include a memory remotely arranged relative to the processor 601, and these remote memories may be connected to the electronic device generating information via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0094] The electronic device of the information generation method may further include: an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603 and the output device 604 may be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0095] The input device 603 can receive input digital or character information, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a track ball, a joystick, and other input devices. The output device 604 may include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0096] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0097] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for programmable processors and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or means (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0098] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0099] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0100] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0101] According to the technical solution of the embodiment of the present application, the carrier's introduction efficiency is effectively improved and the time cost is reduced.

[0102] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0103] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for generating information, the method comprising: include: Acquire characteristic information of a plurality of carriers to be introduced, wherein the characteristic information is determined based on the carrier qualification information of the carriers to be introduced; Inputting the characteristic information of the plurality of carrier objects to be introduced into a preset scoring model to obtain a score of each carrier object to be introduced, wherein the preset scoring model is trained based on characteristic information samples marked with introduction results; Based on the scores of the carriers to be introduced, a target carrier is determined from among the multiple carriers to be introduced.

2. The method according to claim 1, in, The step of determining a target carrier from among the plurality of carriers to be introduced based on the scores of the carriers to be introduced comprises: Determining an initial carrier from among the multiple carriers to be introduced based on the scores of the carriers to be introduced; Based on the indication information of whether each carrier object in the initial carrier objects agrees to the introduction, a target carrier object is determined in the initial carrier objects.

3. The method according to claim 2, in, The step of determining an initial carrier from among the plurality of carriers to be introduced based on the scores of the carriers to be introduced comprises: Based on the scores of the carrier objects to be introduced and the attribute information of whether the carrier objects to be introduced have been introduced, an initial carrier object is determined from the multiple carrier objects to be introduced.

4. The method according to claim 3, further comprising: include: Determine, among the initial carrier objects, the carrier objects corresponding to the indication information of not agreeing to the introduction as non-target introduction objects; The non-target introduced objects are identified as new training samples, and the scoring model is trained and updated.

5. The method according to claim 1, in, The preset scoring model is trained in the following way: Obtain big data information of the transport object; Clean and integrate the big data information to obtain cleaned and integrated big data information; Data is recalled according to one of the following: name of carrier, category of carrier, business scope and registered capital, and the recalled carrier is obtained; Based on the operating qualification information of the recalled carrier, a characteristic information sample is obtained; According to the introduction results of the corresponding historical preset time period, the feature information samples are divided into positive samples and negative samples to train the initial scoring model to obtain a trained scoring model.

6. An information generating device, the device include: An acquisition module is configured to acquire characteristic information of a plurality of carrier objects to be introduced, wherein the characteristic information is determined based on the carrier qualification information of the carrier objects to be introduced; A scoring module is configured to input the characteristic information of the plurality of carrier objects to be introduced into a preset scoring model to obtain a score of each carrier object to be introduced, wherein the preset scoring model is trained based on characteristic information samples marked with introduction results; The determination module is configured to determine a target carrier object from the plurality of carrier objects to be introduced based on the scores of the carrier objects to be introduced.

7. The device according to claim 6, in, The determination module is further configured to: Determining an initial carrier from among the multiple carriers to be introduced based on the scores of the carriers to be introduced; Based on the indication information of whether each carrier object in the initial carrier objects agrees to the introduction, a target carrier object is determined in the initial carrier objects.

8. The device according to claim 7, in, The step of determining an initial carrier from among the plurality of carriers to be introduced based on the scores of the carriers to be introduced comprises: Based on the scores of the carrier objects to be introduced and the attribute information of whether the carrier objects to be introduced have been introduced, an initial carrier object is determined from the multiple carrier objects to be introduced.

9. An electronic device, It is characterized in that include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor so that the at least one processor can perform the method according to any one of claims 1 to 5.

10. A non-transitory computer-readable storage medium storing computer instructions, It is characterized in that The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 5.

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