Information pushing method, information pushing device, electronic equipment and readable medium

Abstract

The embodiment of the invention discloses an information pushing method, an information pushing device, electronic equipment and a readable medium. A specific embodiment of the information pushing method comprises the following steps: in response to received request information of a preset transportation task, obtaining cargo data indicated by the request information and object data of candidate objects; screening and extracting data according to the preset transportation task, the cargo data and the object data to obtain feature data of each feature factor influencing execution of the preset transportation task; analyzing and processing the feature data of each feature factor, and determining candidate objects matched with the preset transportation task; and generating push information based on the information of the matched candidate object, and pushing the push information to the target object terminal. The implementation mode is related to the logistics technology, and the matched driver can be determined by analyzing the characteristic factors influencing the transaction rate of the transportation task. Therefore, the completion rate of transportation tasks can be improved, and the transportation cost can be reduced.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of logistics technology, and in particular to an information push method, an information push device, an electronic device, and a readable medium. Background Art

[0002] There are generally two types of logistics transportation methods used on e-commerce platforms. One is self-operated transportation on fixed routes; the other is using some third-party or individual driver capacity to carry out transportation. Figure 1 As shown, platform merchants can post temporary transport needs to a designated app. Individual drivers can submit quotes for these temporary freight needs (inquiries). Finally, operations staff will select individual drivers with suitable prices to carry these trips.

[0003] However, the inventors found that since the above-mentioned transport tasks are temporary and time-sensitive, there are often very few drivers (1-2) who quote. This not only affects the transaction rate and transport timeliness of the transport demand, but also leads to excessively high transport costs.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the Invention

[0005] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0006] Some embodiments of the present disclosure propose an information push method, an information push device, an electronic device, a computer-readable medium, and a computer program product to solve one or more of the technical problems mentioned in the above background technology section.

[0007] In a first aspect, some embodiments of the present disclosure provide an information push method, comprising: in response to receiving request information for a preset transport task, obtaining cargo data indicated by the request information, and object data of a candidate object; screening and extracting data based on the preset transport task, cargo data, and object data to obtain characteristic data of various characteristic factors that affect the execution of the preset transport task; analyzing and processing the characteristic data of each characteristic factor to determine a candidate object that matches the preset transport task; generating push information based on the information of the matched candidate object, and pushing the push information to the target object terminal to determine the object that executes the preset transport task.

[0008] In some embodiments, the characteristic data of each characteristic factor is analyzed and processed to determine the candidate object that matches the preset transportation task, including: inputting the characteristic data of each characteristic factor into a recommendation model, and determining the candidate object that matches the preset transportation task based on the output result of the recommendation model, wherein the recommendation model is used to perform matching analysis on the input characteristic data.

[0009] In some embodiments, the recommendation model uses a funnel analysis model to determine candidate objects that match the preset transportation task step by step, and outputs information of at least a specified number of candidate objects.

[0010] In some embodiments, the characteristic factors include at least one of the following: object characteristics, cargo source characteristics, operation characteristics, ongoing task characteristics and historical task characteristics; wherein, the cargo source characteristics represent the relevant information of the cargo transported by the preset transportation task; the operation characteristics represent the demand information of the preset transportation task; the ongoing task characteristics represent the relevant information of the transportation task being performed by the candidate object; and the historical task characteristics represent the relevant information of the transportation tasks historically performed by the candidate object.

[0011] In some embodiments, the recommendation model adopts the analysis method of the funnel analysis model to determine the candidate objects that match the preset transportation task step by step, including: matching analysis of each candidate object based on the operation characteristics to obtain a first matching result; matching analysis of the candidate objects in the first matching result based on the characteristics of the task in execution to obtain a second matching result; matching analysis of the candidate objects in the second matching result based on the characteristics of the task in execution and the source of goods to obtain a third matching result; matching analysis of the candidate objects in the third matching result based on the historical task characteristics to obtain a fourth matching result; based on the fourth matching result, determining the candidate object that matches the preset transportation task, wherein each characteristic factor includes at least one influencing factor.

[0012] In some embodiments, push information is generated based on the information of the matching candidate objects, and the push information is pushed to the target object terminal, including: generating push information containing the contact information of the matching candidate objects, and pushing the push information to the terminal that sent the request information to select the object to perform the preset transportation task.

[0013] In some embodiments, generating push information based on the information of the matching candidate object and pushing the push information to the target object terminal also includes: generating push information representing a preset transportation task, and pushing the push information to the mobile terminal of the matching candidate object, wherein the push method includes at least one of the following: voice call, text message, and application message.

[0014] In the second aspect, some embodiments of the present disclosure provide an information push device, including: a data acquisition unit, configured to obtain cargo data indicated by the request information and object data of a candidate object in response to receiving request information for a preset transportation task; a data extraction unit, configured to screen and extract data based on the preset transportation task, cargo data and object data, and obtain feature data of various feature factors that affect the execution of the preset transportation task; an object matching unit, configured to analyze and process the feature data of each feature factor, and determine a candidate object that matches the preset transportation task; an information push unit, configured to generate push information based on the information of the matched candidate object, and push the push information to the target object terminal to determine the object that executes the preset transportation task.

[0015] In some embodiments, the object matching unit is further configured to input the feature data of each feature factor into a recommendation model, and determine the candidate object that matches the preset transportation task based on the output result of the recommendation model, wherein the recommendation model is used to perform matching analysis on the input feature data.

[0016] In some embodiments, the object matching unit is further configured to recommend that the model adopts a funnel analysis model to determine candidate objects matching the preset transportation task step by step, and output information of at least a specified number of candidate objects.

[0017] In some embodiments, the characteristic factors include at least one of the following: object characteristics, cargo source characteristics, operation characteristics, ongoing task characteristics and historical task characteristics; wherein, the cargo source characteristics represent the relevant information of the cargo transported by the preset transportation task; the operation characteristics represent the demand information of the preset transportation task; the ongoing task characteristics represent the relevant information of the transportation task being performed by the candidate object; and the historical task characteristics represent the relevant information of the transportation tasks historically performed by the candidate object.

[0018] In some embodiments, the object matching unit is further configured to perform matching analysis on each candidate object based on operational characteristics to obtain a first matching result; perform matching analysis on the candidate objects in the first matching result based on the characteristics of the task being executed to obtain a second matching result; perform matching analysis on the candidate objects in the second matching result based on the characteristics of the task being executed and the source of goods to obtain a third matching result; perform matching analysis on the candidate objects in the third matching result based on the historical task characteristics to obtain a fourth matching result; based on the fourth matching result, determine the candidate objects that match the preset transportation task, wherein each characteristic factor includes at least one influencing factor.

[0019] In some embodiments, the information push unit is further configured to generate push information containing contact information of matching candidate objects, and push the push information to the terminal that sent the request information to select an object to perform the preset transportation task.

[0020] In some embodiments, the information push unit is further configured to generate push information representing a preset transportation task, and push the push information to a mobile terminal of a matching candidate object, wherein the push method includes at least one of the following: voice call, text message, and application message.

[0021] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: 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 one or more processors, the one or more processors implement the information push method described in any implementation method in the above-mentioned first aspect.

[0022] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the information push method described in any implementation manner in the above-mentioned first aspect is implemented.

[0023] In a fifth aspect, some embodiments of the present disclosure provide a computer program product, including a computer program, which, when executed by a processor, implements the information push method described in any implementation manner in the above-mentioned first aspect.

[0024] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: the information push method of some embodiments of the present disclosure can improve the transaction rate of transportation tasks. Specifically, in the related art, for temporary transportation needs, usually individual drivers quote temporary freight needs, and the operation staff selects individual drivers with suitable prices in the background to undertake this transportation demand. Since the sources of goods are temporary transportation needs and have time requirements, the driver may fail to quote or fail to quote for various reasons. This results in very few drivers quoting. In this way, the background operation staff have too few drivers to choose from, which can easily lead to the failure of transportation needs, or have to choose drivers with high quotes. This not only affects the transaction rate and transportation timeliness of transportation needs, but also leads to excessively high transportation costs.

[0025] Based on this, the information push method of some embodiments of the present disclosure can obtain relevant data, such as cargo data of the transport task and object data of candidate objects, when receiving request information for a transport task. Then, feature data of characteristic factors that affect whether the transport task can be executed can be extracted. Thus, by analyzing these feature data, the object that matches the transport task can be determined, and then push information is generated. In this way, the number of objects (drivers) that users can select can be increased. And the recommended objects can be actively contacted based on the push information, which can effectively increase the probability of completing the transport task and ensure the timeliness of transportation. This can improve the overall completion rate of the transport task. In addition, quoting from multiple recommended objects can also help select objects with lower quotations, thereby reducing transportation costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0027] Figure 1 It is a schematic diagram of the processing flow of some embodiments of existing temporary transportation requirements;

[0028] Figure 2 is a flowchart of some embodiments of the information push method disclosed herein;

[0029] Figure 3A is a flowchart of other embodiments of the information push method disclosed herein;

[0030] Figure 3B is a schematic diagram of some embodiments of the recommendation model of the present disclosure;

[0031] Figure 3C is a schematic diagram of an application scenario of the information push method of some embodiments of the present disclosure;

[0032] Figure 4 It is a schematic structural diagram of some embodiments of the information push device disclosed in the present invention;

[0033] Figure 5 It is a structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0034] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0035] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0036] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0037] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0038] With regard to the collection, storage, and use of user personal information (such as driver contact information, vehicle information, and historical transportation tasks) involved in this disclosure, before performing corresponding operations, relevant organizations or individuals must fulfill obligations such as conducting personal information security impact assessments, fulfilling the obligation to inform the personal information subject, and obtaining the prior authorization and consent of the personal information subject.

[0039] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0040] Figure 1 A process 100 of some embodiments of the information push method according to the present disclosure is shown. The method includes the following steps:

[0041] Step 101 : in response to receiving request information of a preset transport task, obtaining cargo data indicated by the request information and object data of a candidate object.

[0042] In some embodiments, the execution subject of the information push method (eg Figure 3CThe server that runs the algorithm to recommend the driver model can communicate with other electronic devices through wired or wireless connections. For example, an application for publishing transportation tasks can generate a request for a preset transportation task after receiving a preset transportation task request from an application user (such as a merchant or individual) and send it to the execution entity. Alternatively, the application user can also use a user terminal to directly send the request for the preset transportation task to the execution entity.

[0043] It should be noted that the execution entity can be the support Figure 1 The server side of the application can be the same execution entity or different execution entities. In addition, the above-mentioned preset transportation task can be any transportation task that represents the demand for cargo transportation, such as a transportation task that requires third-party transportation capacity or individual driver transportation capacity (such as a temporary transportation task, etc.).

[0044] In some embodiments, upon receiving a request for a pre-set transport task, the execution entity may obtain the cargo data indicated by the request and the object data of the candidate object. The cargo data may be data representing the attributes of the cargo, such as the shipping and receiving addresses, type, weight, and so on. The candidate object may be any object capable of executing (carrying) the transport task, such as a driver or courier. The object data may be personal data related to the transport task, such as object attribute information, information about the vehicle driven by the object, historical transport task information, and so on.

[0045] For example, a request message might include data about the cargo to be transported in a pre-set transport task. The execution entity can then extract the cargo data from the request message and retrieve information about all relevant application users (e.g., users registered as drivers) from the application as candidate object data.

[0046] In some embodiments, during the daily operations of a transportation management system (such as a TMS), data tables are often used for data statistics and management. Therefore, the executing entity can obtain these data tables from the system or database to obtain the required cargo and object data. It will be appreciated that transportation management systems typically utilize a unified scheduling management platform, enabling integrated network-based services.

[0047] In some embodiments, to reduce the amount of data to be processed later, the execution entity may also perform a preliminary screening of these objects before obtaining the object data to obtain candidate objects that meet the preset transportation tasks. For example, for some special transportation tasks, there are usually some requirements on the vehicle size and functions. For example, fresh produce requires a cold chain vehicle. In this case, the execution entity can screen the vehicles driven by each object based on the preset transportation tasks and / or cargo data. In this way, the object data of the candidate objects that the vehicle meets is obtained.

[0048] Step 102 , based on the preset transport task, cargo data and object data, data is screened and extracted to obtain characteristic data of various characteristic factors that affect the execution of the preset transport task.

[0049] In some embodiments, the execution entity may filter and extract data based on the preset transportation task, cargo data, and object data obtained in step 101, thereby obtaining characteristic data of various characteristic factors that affect the execution of the preset transportation task.

[0050] It should be noted that, as mentioned in the background section, the low closing rate for temporary transport requests is often due to a very small number of drivers providing quotes, typically only one or two. This leaves backend operators with too few drivers to choose from, often leading to failed bids or forced selection of drivers with high quotes. This not only impacts closing rates and delivery timelines, but also leads to excessively high transport costs. Since temporary transport requests often have time constraints, quotes are typically valid for 15-45 minutes. Consequently, drivers may not receive a quote in a timely manner due to ongoing tasks, delayed delivery (e.g., not logging into the app), be interrupted by other tasks, or have a specific transport request (e.g., a long distance or a tight timeframe). These factors can all lead to drivers failing to quote or being unable to quote. A quote typically refers to a merchant posting a transport request from point A to point B, seeking a driver to carry the transport. Drivers must first submit quotes, and the winning bidder must be selected with the appropriate quote. The primary document corresponding to a driver's quote is the quote. The quote is typically the driver's own document, known as the quote.

[0051] In some embodiments, the implementing entity may consider the above factors as characteristic factors. These are superficial factors that fail to truly identify business pain points and fail to objectively analyze the actual influencing factors using data. Such an implementation would lead to over-engineering and ultimately fail to achieve a substantial improvement in the RFQ closing rate.

[0052] Optionally, based on the inventor's years of experience in developing quotation systems, the characteristic factors identified through analysis and screening may include at least one of the following: object characteristics, supply characteristics, operation characteristics, ongoing task characteristics, and historical task characteristics. Supply characteristics may represent information related to the goods transported by a preset transport task. Operation characteristics may represent demand information for the preset transport task. Active task characteristics may represent information related to the transport task currently being executed by the candidate. Historical task characteristics may represent information related to transport tasks previously executed by the candidate.

[0053] In addition, these characteristic factors can also include the following influencing factors:

[0054] Factors affecting supply characteristics

[0055]

[0056]

[0057] Operational characteristics influencing factors

[0058]

[0059] Influencing factors of task characteristics during execution

[0060] Impact Factor illustrate Executing task status Start, start, end missions, touch fences, etc. Execution position The driver's current mission location The city where the mission is being executed The city where the mission is being executed Abnormal information of tasks in execution Vehicle problems, epidemics, road closures due to heavy fog, etc.

[0061] Influencing factors of historical task characteristics (completed inquiry information)

[0062] Impact Factor illustrate Weekly transaction volume Weekly number of winning bids for RFQs Weekly transaction history price Weekly transaction price history Weekly average transaction price Weekly average transaction price Average first quote time per order The time between the first quotation and the bid opening for each quotation

[0063] In some embodiments, the execution entity may extract the characteristic data of the above-mentioned characteristic factors, ie, the actual value of each influencing factor, from the following data table.

[0064]

[0065] Step 103 : Analyze and process the characteristic data of each characteristic factor to determine a candidate object that matches the preset transportation task.

[0066] In some embodiments, the execution entity may analyze and process the characteristic data of various characteristic factors to identify candidate objects that match the preset transportation task. For example, the execution entity may first screen candidate objects that meet the preset transportation task and have no active tasks. Next, the execution entity may select the top candidate objects as matching objects for the preset transportation task, sorted by weekly average transaction price (low to high), or by rating (high to low).

[0067] Optionally, to improve the accuracy of the analysis results, the executing entity may input the characteristic data of each characteristic factor into a recommendation model. In other words, the recommendation model may be used to perform data analysis. The recommendation model can then be used to perform a matching analysis on the input characteristic data. Based on the output of the recommendation model, candidate objects that match the preset transportation task are determined. For example, the candidate objects output by the model may be directly determined as matching objects. In another example, the executing entity may select at least a specified number of candidate objects as matching objects based on the predicted matching degree of each candidate object output by the model. The specified number can be set based on actual circumstances, such as 3 to 5.

[0068] Here, the recommendation model can use various common machine learning tools, such as the distributed gradient boosting library (XGBoost), the attention model (DIN, Deep Interest Network), and the low-order and high-order feature interaction fusion model (such as DeepFM) to improve the machine learning algorithm. For example, the distributed gradient boosting machine learning algorithm optimized under the gradient boosting framework can be used. Figure 3A As shown in the figure, in the sorting stage, if the data is prepared and the characteristic factors are obtained, the characteristic model learning can be carried out. That is, the recommendation model can be used to summarize and learn the five characteristic factors mentioned above. Then, the characteristic model interaction can be carried out. Figure 3A As shown in the figure, the combination method can be used to summarize the combined learning results of the five characteristic factors. In fact, it is a combined interactive learning of the above five characteristic factors.

[0069] Furthermore, the execution subject can predict the matching degree through the recommendation model to obtain high-quality recommended objects (drivers). Here, the recommendation model can use the analysis method of the funnel analysis model to determine the candidate objects that match the preset transportation task step by step. And it can output information of at least a specified number of candidate objects. Figure 3B As can be seen from the figure, the funnel analysis model can be used internally in the recommendation model to filter downward step by step to match the appropriate driver.

[0070] Specifically, first, a matching analysis can be performed on each candidate object (driver in the driver resource pool) based on operational characteristics (such as vehicle model requirements and vehicle requirements) to obtain a first matching result. Next, a matching analysis can be performed on the candidate objects in the first matching result based on the characteristics of the task being executed (such as task status and abnormal conditions) to obtain a second matching result. Thereafter, a matching analysis can be performed on the candidate objects in the second matching result based on the characteristics of the task being executed and the source of goods to obtain a third matching result. Next, a matching analysis can be performed on the candidate objects in the third matching result based on the historical task characteristics to obtain a fourth matching result. Finally, based on the fourth matching result, the candidate objects that match the preset transportation task can be determined. For example, if the number of drivers in the fourth matching result is not less than a specified number, all of these driver information can be output, or some can be selected for output. For another example, if the number of drivers in the fourth matching result is less than a specified number, some additional drivers can be selected from the third matching result, thereby outputting the fourth matching result and the additionally selected driver information.

[0071] Step 104: Generate push information based on the information of the matched candidate object, and push the push information to the target object terminal.

[0072] In some embodiments, based on the information of the matching candidate objects obtained in step 103, the execution entity may generate push information, and may push the push information to the target object terminal to determine the object that will ultimately execute the preset transportation task.

[0073] For example, the execution entity can generate a push message containing the contact information of matching candidates. This push message can then be sent to the terminal that sent the request, such as the merchant or the application's backend operator, who can then select the candidate to perform the preset transport task. The terminal can then display an interface for the push message. The user can then see a list of recommended, high-quality drivers with the highest matching scores. This interface also displays information such as the specific contact number, driver name, distance, and number of transactions. This allows the user to proactively contact the recommended high-quality drivers for quotes, thereby increasing the success rate of transport tasks.

[0074] Optionally, the execution entity may also generate a push message indicating a pre-set transport task and may push the push message to the mobile terminal of a matching candidate. Specifically, the push message may indicate a new transport task and include key information about the task to facilitate understanding by the candidate (driver). The push method may include at least one of the following: voice call, text message, app message, etc.

[0075] It can be seen from the above description that the information push method of some embodiments of the present disclosure can improve the transaction rate of transportation tasks. Specifically, when receiving the request information of the transportation task, relevant data can be obtained, such as the cargo data of the transportation task and the object data of the candidate object. Then, the characteristic data of the characteristic factors that affect whether the transportation task can be executed can be extracted. Therefore, by analyzing these characteristic data, the object that matches the transportation task is determined, and then the push information is generated. In this way, the number of objects (drivers) that users can select can be increased. And the recommended objects can be actively contacted based on the push information, which can effectively increase the probability of the transportation task being completed and ensure the transportation timeliness. Thereby, the overall completion rate of the transportation task can be improved. In addition, quoting by multiple recommended objects can also help to select objects with lower quotations, thereby reducing transportation costs.

[0076] As an example, in Figure 3C In the application scenario shown, merchants can post inquiries for supply (i.e., transportation requirements) on the app. Supply generally refers to transportation requirements from point A to point B. This generates a new supply information entry on the app. At this point, the app can input information such as origin and destination, vehicle type, vehicle, mileage, duration, cargo, location coordinates (e.g., GPS, Global Positioning System), and quote information into the algorithmic driver recommendation model. The algorithmic driver recommendation model analyzes five characteristic factors to output recommended high-quality drivers. The app can then display relevant driver information based on the model's output.

[0077] The information push method of the present disclosure can achieve the following objectives:

[0078] 1. Activate drivers who submit quotations on inquiry forms and increase their initiative and enthusiasm in submitting quotations;

[0079] 2. Increase the number of drivers available for back-end operations staff to choose and quote, with the target expected number of drivers increasing by 3-5 times;

[0080] 3. By increasing the number of drivers submitting quotes on inquiries, we can increase price competition, lower transaction prices on inquiries, and reduce transportation costs;

[0081] Fourth, by establishing a driver recommendation model, the backend automatically recommends suitable and high-quality drivers with quotes, and operators who are available to choose quotes can proactively contact high-quality drivers;

[0082] 5. For drivers who are currently on a mission, we will determine whether the driver is available to accept the order based on the distance from the delivery address of the inquiry mission, and proactively contact the driver by phone to obtain a quote;

[0083] 6. The driver recommendation model can increase the number of quotations, improve the transaction rate of inquiries, and reduce the risk of inquiries failing to bid.

[0084] Further references Figure 4 , as a response to the above Figures 2 to 3C The present disclosure provides some embodiments of an information push device. Figures 2 to 3C The information push device can be applied to various electronic devices.

[0085] like Figure 4 As shown, the information push device 400 of some embodiments may include: a data acquisition unit 401, configured to obtain cargo data indicated by the request information and object data of the candidate object in response to receiving request information of a preset transportation task; a data extraction unit 402, configured to screen and extract data according to the preset transportation task, cargo data and object data, and obtain feature data of various feature factors that affect the execution of the preset transportation task; an object matching unit 403, configured to analyze and process the feature data of each feature factor, and determine the candidate object that matches the preset transportation task; an information push unit 404, configured to generate push information based on the information of the matched candidate object, and push the push information to the target object terminal to determine the object that executes the preset transportation task.

[0086] In some embodiments, the object matching unit 403 can be further configured to input the feature data of each feature factor into the recommendation model, and determine the candidate object that matches the preset transportation task based on the output result of the recommendation model, wherein the recommendation model is used to perform matching analysis on the input feature data.

[0087] In some embodiments, the object matching unit 403 may be further configured to recommend a model that adopts a funnel analysis model to determine candidate objects that match the preset transportation task step by step, and output information of at least a specified number of candidate objects.

[0088] In some embodiments, the characteristic factors may include at least one of the following: object characteristics, cargo source characteristics, operation characteristics, ongoing task characteristics and historical task characteristics; wherein the cargo source characteristics represent the relevant information of the cargo transported by the preset transportation task; the operation characteristics represent the demand information of the preset transportation task; the ongoing task characteristics represent the relevant information of the transportation task being performed by the candidate object; and the historical task characteristics represent the relevant information of the transportation tasks historically performed by the candidate object.

[0089] In some embodiments, the object matching unit 403 can be further configured to perform a matching analysis on each candidate object based on the operational characteristics to obtain a first matching result; perform a matching analysis on the candidate objects in the first matching result based on the characteristics of the task being executed to obtain a second matching result; perform a matching analysis on the candidate objects in the second matching result based on the characteristics of the task being executed and the source of goods to obtain a third matching result; perform a matching analysis on the candidate objects in the third matching result based on the historical task characteristics to obtain a fourth matching result; based on the fourth matching result, determine the candidate objects that match the preset transportation task, wherein each characteristic factor includes at least one influencing factor.

[0090] In some embodiments, the information push unit 404 may be further configured to generate push information including contact information of matching candidate objects, and push the push information to the terminal that sent the request information, so as to select an object to perform the preset transportation task.

[0091] In some embodiments, the information push unit 404 can be further configured to generate push information representing the preset transportation task, and push the push information to the mobile terminal of the matching candidate object, wherein the push method includes at least one of the following: voice call, text message, and application message.

[0092] It is understandable that the units described in the information push device 400 are similar to those in the reference Figures 2 to 3C Therefore, the operations, features and beneficial effects described above for the method are also applicable to the information push device 400 and the units included therein, and will not be described in detail here.

[0093] Reference below Figure 5 , which shows a structural diagram of an electronic device 500 suitable for implementing some embodiments of the present disclosure. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0094] like Figure 5 As shown, the electronic device 500 may include a processing device 501 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. Various programs and data required for the operation of the electronic device 500 are also stored in the RAM 503. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0095] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a speaker, a vibrator, etc.; a storage device 508 including, for example, a disk, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Figure 5 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 5 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0096] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from a network via the communication device 509, or installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of some embodiments of the present disclosure are performed.

[0097] It should be noted that the computer-readable medium described in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0098] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (Hypertext Transfer Protocol), and can be interconnected with 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"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0099] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: in response to receiving request information for a preset transport task, obtains cargo data indicated by the request information, as well as object data of candidate objects; screens and extracts data based on the preset transport task, cargo data, and object data to obtain feature data of various feature factors that affect the execution of the preset transport task; analyzes and processes the feature data of various feature factors to determine candidate objects that match the preset transport task; generates push information based on the information of the matched candidate objects, and pushes the push information to the target object terminal to determine the object that executes the preset transport task.

[0100] In addition, computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0101] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0102] The units described in some embodiments of the present disclosure may be implemented in software or hardware. The units described may also be provided in a processor. For example, they may be described as follows: a processor includes a data acquisition unit, a data extraction unit, an object matching unit, and an information push unit. The names of these units do not, in some cases, constitute limitations on the units themselves. For example, the data acquisition unit may also be described as a "unit for acquiring cargo data indicated by the request information, as well as object data of candidate objects."

[0103] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0104] Some embodiments of the present disclosure further provide a computer program product, including a computer program, which implements any of the above-mentioned information push methods when executed by a processor.

[0105] The above description is only an illustration of some preferred embodiments of the present disclosure and the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. An information push method, comprising: In response to receiving a request message for a preset transport task, obtaining cargo data indicated by the request message and object data of a candidate object; Screening and extracting data based on the preset transport task, the cargo data, and the object data to obtain characteristic data of various characteristic factors that affect the execution of the preset transport task; Analyzing and processing the characteristic data of each characteristic factor to determine a candidate object that matches the preset transportation task; Push information is generated based on the information of the matched candidate objects, and the push information is pushed to the target object terminal to determine the object to perform the preset transportation task.

2. The information push method according to claim 1, wherein: The analyzing and processing the characteristic data of each characteristic factor to determine a candidate object that matches the preset transportation task includes: The characteristic data of each characteristic factor is input into a recommendation model, and a candidate object matching the preset transportation task is determined based on the output result of the recommendation model, wherein the recommendation model is used to perform matching analysis on the input characteristic data.

3. The information push method according to claim 2, wherein: The recommendation model adopts the analysis method of the funnel analysis model to determine the candidate objects matching the preset transportation task step by step, and outputs information of at least a specified number of candidate objects.

4. The information push method according to claim 3, wherein: The characteristic factors include at least one of the following: object characteristics, supply characteristics, operation characteristics, ongoing task characteristics, and historical task characteristics; The cargo source characteristics represent relevant information of the cargo transported by the preset transport task; The operation characteristics represent the demand information of the preset transportation task; The ongoing task feature represents relevant information of the transportation task being performed by the candidate object; The historical task feature represents relevant information of the transportation tasks historically performed by the candidate object.

5. The information push method according to claim 4, wherein: The recommendation model uses a funnel analysis model to determine candidate objects that match the preset transportation task step by step, including: Performing matching analysis on each candidate object according to the operation characteristics to obtain a first matching result; performing a matching analysis on the candidate objects in the first matching result according to the characteristics of the task being executed, to obtain a second matching result; performing a matching analysis on the candidate objects in the second matching result according to the characteristics of the task being executed and the characteristics of the supply source to obtain a third matching result; performing a matching analysis on the candidate objects in the third matching result according to the historical task characteristics to obtain a fourth matching result; Based on the fourth matching result, candidate objects matching the preset transportation task are determined, wherein each of the characteristic factors includes at least one influencing factor.

6. The information push method according to any one of claims 1 to 5, wherein: The generating of push information based on the information of the matched candidate object and pushing the push information to the target object terminal includes: Generate push information containing contact information of matching candidate objects, and push the push information to the terminal that sent the request information to select an object to perform the preset transportation task.

7. The information push method according to any one of claims 1 to 5, wherein: The generating of push information based on the information of the matched candidate object and pushing the push information to the target object terminal further includes: Generate push information representing the preset transportation task, and push the push information to the mobile terminal of the matching candidate object, wherein the push method includes at least one of the following: voice call, text message, and application message.

8. An information push device, comprising: a data acquisition unit configured to, in response to receiving request information for a preset transport task, acquire cargo data indicated by the request information and object data of a candidate object; a data extraction unit configured to screen and extract data based on the preset transport task, the cargo data, and the object data, to obtain characteristic data of various characteristic factors that affect the execution of the preset transport task; an object matching unit configured to analyze and process the characteristic data of each of the characteristic factors to determine a candidate object that matches the preset transportation task; The information pushing unit is configured to generate push information based on the information of the matched candidate objects, and push the push information to the target object terminal to determine the object to perform the preset transportation task.

9. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the information push method according to any one of claims 1 to 7.

10. A computer-readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the information push method according to any one of claims 1 to 7 is implemented.

11. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the information push method according to any one of claims 1 to 7.

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