Potential truck customer information determination method based on multi-source information mining and analysis

Abstract

The invention belongs to the technical field of data processing, and particularly discloses a potential truck customer information determination method based on multi-source information mining and analysis, and the method comprises the steps: obtaining the identification information of a target passing truck which does not go out of an island in a target time period; on the basis of the identification information, obtaining sea-crossing historical data of the target sea-crossing truck in an adjacent historical time period, wherein the sea-crossing historical data comprises sea-crossing frequency data, sea-crossing port selection data and frequently-pulled cargo type data; assigning values to the sea-crossing historical data, and determining a sea-crossing frequency quantized value of a target sea-crossing truck, a constant-pulling goods type quantized value and a sea-crossing port selection quantized value for selecting a target port to cross the sea; the quantized value of the frequently-pulled goods type is associated with the time fluctuation factor of the out-of-island goods; carrying out weighting processing on the quantized value, and determining a quantized result of the possibility that the target passing truck selects the target port to pass the sea in the target time period; and ranking the possibility quantification results in a descending order, and obtaining a potential truck customer information ranking table of the target port in the target time period.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and more specifically, relates to a method for determining potential truck customer information based on multi-source information mining and analysis. Background Art

[0002] As an important waterway in China, the Qiongzhou Strait connects Hainan Island and Guangdong Province. Its passenger and vehicle ferry transportation plays a vital role in promoting economic exchanges and personnel exchanges between the two places. At present, the passenger and vehicle ferry transportation in the Qiongzhou Strait has formed three main channels, namely "Xiuying Port-Haian New Port", "Xinhai Port-Xuwen Port" and "Nan Port-Bei Port". Although these three channels are separated from each other geographically, the crossing time and efficiency are basically the same, providing passengers with a variety of crossing options.

[0003] During the off-season of the passenger and vehicle ferry business, due to the relatively abundant supply of tickets, the ports are facing fierce competition, especially for truck fleets and truck drivers. However, due to the uniformity of ticket prices in the Qiongzhou Strait, it is difficult for each port to form differentiated competition in terms of pricing strategy. Therefore, how to mine potential truck customer information and formulate effective marketing strategies to attract fleets and drivers to choose this port area to cross the sea has become the key to increasing the company's revenue and enhancing market competitiveness.

[0004] At present, ports mainly rely on offline manual methods or online announcements to promote and market truck fleets and drivers. This method is not only inefficient, but also difficult to accurately locate target customer groups, resulting in limited marketing effects. With the rapid development of information technology and the deepening of digital transformation, an innovative method is urgently needed to accurately locate target customer groups such as truck fleets and drivers, so as to improve marketing efficiency and reduce marketing costs in response to increasingly fierce market competition. Summary of the invention

[0005] In view of the defects existing in the related technologies, the embodiments of the present application provide a method for determining potential truck customer information based on multi-source information mining and analysis, aiming to solve the problem of difficulty in accurately locating target customer groups in the marketing process of each port.

[0006] In a first aspect, an embodiment of the present application provides a method for acquiring information of potential truck customers based on multi-source information mining and analysis, comprising:

[0007] Obtain identification information of target sea-crossing trucks that have not left the island within the target time period;

[0008] Based on the identification information, the historical sea crossing data of the target sea crossing truck in the historical time period close to the target time period is obtained, and the sea crossing historical data includes sea crossing frequency data, sea crossing port selection data and frequently transported cargo data;

[0009] Assign values ​​to the sea crossing frequency data, sea crossing port selection data and frequently transported cargo data respectively, and determine the sea crossing frequency quantitative value, frequently transported cargo quantitative value and sea crossing port selection quantitative value of the target sea crossing truck; the frequently transported cargo quantitative value is associated with the time fluctuation factor of the cargo leaving the island;

[0010] The quantified values ​​of sea crossing frequency, frequently transported cargo types and sea crossing port selection are weighted to determine the quantified result of the possibility of the target sea crossing truck selecting the target port for sea crossing within the target time period;

[0011] The quantified results of the possibility of different target cross-sea trucks are arranged in descending order to obtain a sorted table of potential truck customer information at the target port within the target time period.

[0012] In a second aspect, the embodiment of the present application further provides a device for acquiring information of potential truck customers based on multi-source information mining and analysis, comprising:

[0013] The first acquisition module is used to acquire truck information of target sea-crossing trucks that have not left the island within a target time period, and the truck information includes identification information;

[0014] The second acquisition module is used to acquire the sea crossing historical data of the target sea crossing truck in a historical time period close to the target time period based on the identification information, and the sea crossing historical data includes sea crossing frequency data, sea crossing port selection data and frequently transported cargo data;

[0015] The first determination module is used to assign values ​​to the sea crossing frequency data, sea crossing port selection data and frequently transported cargo data respectively, and determine the sea crossing frequency quantification value, frequently transported cargo quantification value and sea crossing port selection quantification value of the target sea crossing truck; the frequently transported cargo quantification value is associated with the time fluctuation factor of the cargo leaving the island;

[0016] The second determination module is used to perform weighted processing on the quantified value of sea crossing frequency, the quantified value of frequently transported cargo types and the quantified value of sea crossing port selection, and determine the quantified result of the possibility of the target sea crossing truck selecting the target port for sea crossing within the target time period;

[0017] The third acquisition module is used to arrange the possibility quantification results in descending order to obtain a sorted list of potential truck customer information at the target port within a target time period.

[0018] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.

[0019] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0020] In a fifth aspect, an embodiment of the present application further provides a computer program product, which, when executed on a processor, enables the processor to execute the method described in the first aspect or any possible implementation of the first aspect.

[0021] The method for acquiring information on potential truck customers based on multi-source information mining and analysis provided in the embodiment of the present application locates the target sea-crossing trucks that have not left the island within the target time period, thereby assigning values ​​to the sea-crossing frequency data, sea-crossing port selection data and frequently transported cargo types data of the target sea-crossing trucks in the adjacent historical time period, thereby obtaining the quantified value of the sea-crossing frequency, the quantified value of the frequently transported cargo types and the quantified value of the sea-crossing port selection of the target port for crossing the sea, and after weighted processing, obtaining the quantified result of the possibility of the target sea-crossing truck selecting the target port for crossing the sea within the target time period; the possibility of different target sea-crossing trucks is assigned to the target port for crossing the sea. The quantification results of the potential are sorted in descending order, and then a sorted table of potential truck customer information at the target port within the target time period is obtained, which realizes the quantitative analysis and sorting of potential truck customers, and can accurately locate the potential marketable objects among the trucks that have not left the island, thereby improving the port's influence efficiency and reducing marketing costs; at the same time, when assigning values ​​to the data of frequently transported cargoes, a time fluctuation factor for cargo leaving the island is set, taking into account the transportation demand of different cargoes under the same time dimension and the fluctuation of transportation demand of the same cargoe in different time dimensions, thereby improving the accuracy of prediction of the cross-sea cargo that may be loaded on the target cross-sea trucks. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the present application or related technologies, the following is a brief introduction to the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 It is a flowchart of a method for acquiring information of potential truck customers based on multi-source information mining and analysis provided in an embodiment of the present application;

[0024] Figure 2 It is a structural schematic diagram of a device for acquiring information of potential truck customers based on multi-source information mining and analysis provided in an embodiment of the present application;

[0025] Figure 3It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0027] Figure 1 is a flow chart of a method for acquiring information of potential truck customers based on multi-source information mining and analysis provided in an embodiment of the present application, such as Figure 1 As shown, the method at least includes the following steps:

[0028] S101. Obtain identification information of target sea-crossing trucks that have not left the island within a target time period.

[0029] Specifically, the identification information of the target trucks crossing the sea within the target time period is obtained, such as the license plate number. The target trucks crossing the sea are the trucks crossing the sea that have not left the island within the target time period. During the off-season of passenger ferry business, the trucks that have entered the island but have not yet left the island are the main targets of various ports. Since trucks generally enter and leave the island in a relatively short period of time, the selection of the target time period is also highly time-sensitive. For example, the identification information of the trucks crossing the sea that have entered the island but have not yet left the island in the past 3 days is obtained.

[0030] S102. Acquire historical sea crossing data of the target sea crossing truck in a historical time period close to the target time period based on the identification information, wherein the historical sea crossing data includes sea crossing frequency data, sea crossing port selection data, and frequently transported cargo types data.

[0031] Specifically, compared to passenger cars, trucks traveling across the strait generally have stronger certainty and regularity. Trucks will register and verify relevant information when entering and leaving the island. Therefore, after locating the identification information of the target truck crossing the sea, the historical data of the target truck crossing the sea in the historical time period close to the target time period can be captured from the database. In the embodiment of the present application, the historical data of crossing the sea at least includes the frequency of crossing the sea, the port selection data of crossing the sea, and the data of commonly transported cargo types; the frequency of crossing the sea is used to describe the frequency of crossing the sea of ​​the target truck crossing the sea in the historical time period; the port selection data of crossing the sea is used to describe the port selection of the target truck crossing the sea when crossing the sea in the historical time period; the data of commonly transported cargo types is used to describe the cargo types transported by the truck crossing the sea when crossing the sea in the historical event period.

[0032] S103, assigning values ​​to the sea crossing frequency data, sea crossing port selection data and frequently transported cargo data respectively, and determining the sea crossing frequency quantitative value, frequently transported cargo quantitative value and sea crossing port selection quantitative value of the target sea crossing truck.

[0033] Specifically, the sea crossing frequency data are assigned a value to determine the quantitative value of the sea crossing frequency of the target sea crossing trucks in the historical time period; the frequently transported cargo types data are assigned a value to determine the quantitative value of the frequently transported cargo types of the target sea crossing trucks in the historical time period; the sea crossing port selection data are assigned a value to determine the quantitative value of the sea crossing port selection when the target sea crossing trucks choose the target port to cross the sea in the historical time period.

[0034] At the same time, the frequency data of sea crossing, sea crossing port selection data and frequently transported cargo data of the target sea crossing trucks are considered as a quantitative basis for the possibility of the target sea crossing trucks choosing the target port to cross the sea within the target time period; however, these three types of data cannot be accurately quantified through simple weighted processing. In the embodiment of the present application, the three types of data are assigned values ​​so that the three types of data can be converted into simple, dimensionless data, thereby simplifying the calculation process.

[0035] The types of goods that trucks may carry across the sea during the target time period are affected by many factors. On the one hand, there is the type of goods themselves. In the same time dimension, the transportation methods and requirements of different types of goods are different, and the requirements for timeliness of crossing the sea are also different. The second is time. For the same type of goods, its transportation demand may fluctuate over time in different months of the year, especially for seasonal products such as agricultural products.

[0036] Therefore, when assigning values ​​to the data of commonly transported cargoes, a time fluctuation factor for cargo leaving the island is set. This fluctuation factor represents the fluctuation of the transportation demand for the same cargo over time, which directly affects the selection of target cargoes in the quantification process, rather than simply and crudely selecting the cargo with the highest proportion of cargo transported by target cross-sea trucks in the historical period as the target cargo.

[0037] S104, weighting the quantified value of sea crossing frequency, the quantified value of frequently transported cargo types, and the quantified value of sea crossing port selection to determine a quantified result of the possibility that the target sea crossing truck selects the target port to cross the sea within the target time period.

[0038] Specifically, the quantified values ​​of sea crossing frequency, frequently transported cargo types and sea crossing port selection are weighted to meet the following calculation formula:

[0039] HD=α×TNS+β×TCS+γ×TMS

[0040] Among them, HD represents the quantitative result of the probability of the target sea-crossing truck choosing the target port to cross the sea within the target time period, TNS, TCS and TMS are the quantitative values ​​of the influencing factors of sea crossing frequency, sea crossing port selection and frequently transported cargo types, respectively, and α, β and γ are the weights of each influencing factor. Generally speaking, α, β and γ can be set to 0.3, 0.4 and 0.3 respectively, and can be adjusted according to actual needs.

[0041] S105. Arrange the quantified results of the possibilities of different target sea-crossing trucks in descending order, and obtain a sorted table of potential truck customer information of the target port within the target time period.

[0042] Specifically, after the quantified results of the probability of different target sea-crossing trucks choosing the target port to cross the sea within the target time period are calculated respectively, they are arranged in descending order according to the size of the quantified results of the probability, and finally a sorted table of potential truck customer information {HD1, HD2, HD3, ..., HD n}.

[0043] The method for acquiring information on potential truck customers based on multi-source information mining and analysis provided in the embodiment of the present application locates the target sea-crossing trucks that have not left the island within the target time period, thereby assigning values ​​to the sea-crossing frequency data, sea-crossing port selection data and frequently transported cargo types data of the target sea-crossing trucks in the adjacent historical time period, thereby obtaining the quantified value of the sea-crossing frequency, the quantified value of the frequently transported cargo types and the quantified value of the sea-crossing port selection of the target port for crossing the sea, and after weighted processing, obtaining the quantified result of the possibility of the target sea-crossing truck selecting the target port for crossing the sea within the target time period; the possibility of different target sea-crossing trucks is assigned to the target port for crossing the sea. The quantification results of the potential are sorted in descending order, and then a sorted table of potential truck customer information at the target port within the target time period is obtained, which realizes the quantitative analysis and sorting of potential truck customers, and can accurately locate the potential marketable objects among the trucks that have not left the island, thereby improving the port's influence efficiency and reducing marketing costs; at the same time, when assigning values ​​to the data of frequently transported cargoes, a time fluctuation factor for cargo leaving the island is set, taking into account the transportation demand of different cargoes under the same time dimension and the fluctuation of transportation demand of the same cargoe in different time dimensions, thereby improving the accuracy of prediction of the cross-sea cargo that may be loaded on the target cross-sea trucks.

[0044] In some embodiments, assigning a value to the sea crossing frequency data in S103 to determine the sea crossing frequency quantization value of the target sea crossing truck specifically includes:

[0045] Determine the magnitude relationship between the sea crossing frequency and the first preset value and the second preset value;

[0046] If the sea crossing frequency is greater than the first preset value, the sea crossing frequency quantization value is assigned to the first value; or,

[0047] If the sea crossing frequency is less than the first preset value and greater than the second preset value, the sea crossing frequency quantization value is assigned to the second value; or,

[0048] If the sea crossing frequency is less than the second preset value, assigning the sea crossing frequency quantization value to a third value;

[0049] Among them, the first preset value is greater than the second preset value, the first numerical value is greater than the second numerical value, and the second numerical value is greater than the third numerical value.

[0050] Specifically, the sea crossing frequency data includes the sea crossing frequency of the target sea crossing truck in a historical period of time. The sea crossing frequency may be the total number of times the target sea crossing truck enters and exits the island, or only the number of times the target sea crossing truck enters / exits the island.

[0051] When assigning a value to the frequency of crossing the sea, a first preset value and a second preset value are preset, and the first preset value is greater than the second preset value. When positioning potential truck customers, trucks with a higher frequency of crossing the sea are considered more. Therefore: if the frequency of crossing the sea is greater than the first preset value, the quantified value of the frequency of crossing the sea is assigned to the first value; if the frequency of crossing the sea is less than the first preset value and greater than the second preset value, the quantified value of the frequency of crossing the sea is assigned to the second value; if the frequency of crossing the sea is less than the second preset value, the quantified value of the frequency of crossing the sea is assigned to the third value; wherein, the first value>the second value>the third value.

[0052] For example, a truck that has not left the island in the past three days is positioned as a target truck that crosses the sea, and the frequency of crossing the sea in the past three months is captured. The frequency of crossing the sea is recorded as TN, and the quantified value of the frequency of crossing the sea is recorded as TNS; the first preset value is set to 50, the second preset value is set to 30; the first value, the second value and the third value are 3, 2 and 1 respectively, then the frequency data of crossing the sea is assigned values, specifically:

[0053] If TN>50, then TNS=3;

[0054] If 20<TN<50, then TNS=2;

[0055] If TN<20, then TNS=1;

[0056] At this point, the value assignment process of the frequency data of trucks crossing the sea is completed; among them, for the case where the frequency of crossing the sea is equal to the first preset value or the second preset value, the value assignment selection can be pre-set during the actual operation process.

[0057] The method for obtaining potential truck customer information based on multi-source information mining and analysis provided in the embodiment of the present application pays more attention to trucks with higher crossing frequency during the quantification of sea crossing frequency data, which is conducive to accurately locating potential target truck customer information.

[0058] In some embodiments, assigning a value to the sea crossing port selection data in S103 to determine the sea crossing port selection quantization value of the target sea crossing truck specifically includes:

[0059] If the statistical proportion is greater than the third preset value and less than the fourth preset value, the quantified value of the sea crossing port selection is assigned the first value; or,

[0060] If the statistical proportion is less than the third preset value, the quantified value of the sea crossing port selection is assigned a second value; or,

[0061] If the statistical proportion is greater than the fourth preset value, the quantified value of the port of passage selection is assigned a third value;

[0062] Among them, the fourth preset value is greater than the third preset value, the first value is greater than the second value, and the second value is greater than the third value.

[0063] Specifically, the sea crossing port selection data includes the statistical proportion of target sea crossing trucks that choose the target port to cross the sea within a historical period of time. The statistical proportion can be the statistical proportion of target sea crossing trucks that choose the target port to enter and exit the island, or it can be the statistical proportion of target sea crossing trucks that choose the target port to leave the island.

[0064] When assigning values ​​to the statistical proportions, the third preset value and the fourth preset value are pre-set, and the fourth preset value is greater than the third preset value. For the positioning of potential truck customers at the target port, the order of attention to different trucks is first for truck customers who choose relatively evenly from multiple ports. This type of customer is least affected by objective factors when choosing ports, so it is easiest to win them over to choose the target port to leave the island through marketing means; second, for truck customers who mainly choose other ports. This type of customer rarely chooses the target port to leave the island and is also the main target of marketing; and finally, for customers who mainly choose our port.

[0065] Therefore, if the statistical proportion is greater than the third preset value and less than the fourth preset value, the quantification value of the sea crossing port selection is assigned to the first value; if the statistical proportion is less than the third preset value, the quantification value of the sea crossing port selection is assigned to the second value; if the statistical proportion is greater than the fourth preset value, the quantification value of the sea crossing port selection is assigned to the third value; wherein, the first value > the second value > the third value.

[0066] For example, trucks that have not left the island in the past three days are positioned as target trucks crossing the sea, and the port selection of the target trucks crossing the sea in the past three months is counted. The statistical proportion of trucks that choose the target port to leave the island is recorded as TC, and the quantitative value of the port selection is recorded as TCS; the third preset value is set to 40%, and the fourth preset value is set to 70%; the first value, the second value and the third value are 3, 2 and 1 respectively, then the port selection data is assigned values, specifically:

[0067] If 40%<TC<70%, then TCS=3;

[0068] If TC < 40%, then TCS = 2;

[0069] If TC>70%, then TCS=1;

[0070] At this point, the assignment process of the truck crossing port selection data is completed; among them, for the case where the statistical proportion is equal to the third preset value or the fourth preset value, the assignment selection can be pre-set during the actual operation process.

[0071] The method for obtaining information on potential truck customers based on multi-source information mining and analysis provided in the embodiment of the present application fully considers the possibility of trucks developing into potential customers in the process of quantifying data on seaport selection, thereby improving the marketing success rate.

[0072] In some embodiments, in S103, the frequently transported cargo data is assigned values ​​to determine the quantified values ​​of the frequently transported cargoes of the target cross-sea trucks, specifically including:

[0073] Select the target cargo based on the cargo proportion of the top n cargoes;

[0074] Based on the preset time fluctuation factor lookup table for cargo leaving the island, find the time fluctuation factor for cargo leaving the island for the target cargo type within the target time period;

[0075] Based on the sea transit time requirement of the target cargo, determine the initial value of the target cargo; the initial value is a first value, a second value or a third value, the first value is greater than the second value, and the second value is greater than the third value;

[0076] The initial assignment is weighted based on the found time fluctuation factor of the cargo leaving the island, and the quantitative value of the frequently transported cargo types is determined after the data of the frequently transported cargo types are assigned.

[0077] Specifically, in the same time dimension, different types of goods have different requirements for sea crossing timeliness. The types of goods carried by sea crossing trucks determine the requirements for sea crossing timeliness of trucks. By providing fast sea crossing services, the marketing success rate of truck customers who have higher requirements for sea crossing timeliness can be improved.

[0078] Frequently transported cargo data includes the cargo proportions of the top n cargo types that the target cross-sea trucks transported the most times in the historical period, where n is a positive integer greater than 1. First, the target cargo type is selected based on the cargo proportions of the top n cargo types, for example, the cargo type with the most cargo times is selected as the target cargo type.

[0079] Under different time dimensions, the requirements for the timeliness of the same cargo may fluctuate over time, especially for some seasonal products. Therefore, after selecting the target cargo, the time fluctuation factor of the cargo leaving the island during the target time period is searched based on the preset time fluctuation factor lookup table of the cargo leaving the island.

[0080] The lookup table for the time fluctuation factor of the cargo leaving the island is preset, which can be set by the statistical results of historical data or by the technical staff based on historical experience, and the lookup table can be continuously updated, and each update is based on the statistical results of the historical data of the previous year. Table 1 is an example of the lookup table for the time fluctuation factor of the cargo leaving the island.

[0081] Table 1

[0082]

[0083]

[0084] In the process of assigning values ​​to the data of frequently-pulled goods, firstly, the initial assignment of the target goods is determined based on the timeliness requirements of the selected target goods. The initial assignment is the first value, the second value or the third value, and the first value> the second value> the third value; the higher the timeliness requirements of the target goods, the larger the initial assignment. Specifically, the judgment of the timeliness of the target goods can be based on the type of the target goods. After the initial assignment is completed, the fluctuation factor of the time of the goods leaving the island during the target time period is used for weighted processing to obtain the final quantitative value of the frequently-pulled goods.

[0085] For example, trucks that have not left the island in the past three days are positioned as target cross-sea trucks. The top n cargo types that the target cross-sea trucks have transported the most in the past three months are counted, and the target cargo type is selected as TM, the quantitative value of the frequently transported cargo type is recorded as TMS, and the time fluctuation factor of the target cargo type leaving the island is recorded as S; the first value, the second value, and the third value are 3, 2, and 1 respectively, then the data of the frequently transported cargo type are assigned values, specifically:

[0086] If TM = {agricultural products, fresh food}, then TMS = 3 × S;

[0087] If TM = {wood, light industrial products, express delivery, waste}, then TMS = 2 × S;

[0088] If TM = {other}, then TMS = 1 × S;

[0089] At this point, the assignment process of the frequently transported cargo data is completed.

[0090] The method for acquiring information on potential truck customers based on multi-source information mining and analysis provided in the embodiment of the present application, in the process of quantifying the data on frequently transported cargo types, simultaneously considers the cargo types that the target cross-sea trucks have recently frequently transported, as well as the factors of time fluctuations on the demand for cargo transportation, sets the time fluctuation factor for cargo leaving the island, and predicts the cross-sea cargo that may be loaded by the target cross-sea trucks, thereby achieving a quantitative analysis of the cross-sea cargo and its timeliness.

[0091] In some embodiments, the target cargo type is selected based on the cargo proportion of the first n cargo types, specifically including:

[0092] Determine whether the multiple of the proportion of the cargo type with the most number of times among the first n cargo types and the sum of the proportion of the cargo types with the most number of times other than the cargo type with the most number of times is greater than a preset multiple;

[0093] If it is greater than the preset multiple, the commodity with the most selected times will be the target commodity; or,

[0094] If it is less than the preset multiple, the cargo proportions of the first n cargo types found in advance and corresponding to the time fluctuation factors of the cargo leaving the island within the target time period are weighted respectively, and the cargo type corresponding to the maximum value of the weighted result is determined as the target cargo type.

[0095] Specifically, it is too simplistic and crude to directly select the cargo type with the highest number of cargo transportation as the target cargo type. Therefore, in the embodiment of the present application, the first n cargo types with the highest number of cargo transportation are considered, and the target cargo type is selected based on the fluctuation factor of the time of cargo leaving the island for each cargo type.

[0096] For the cargo types that the target cross-sea trucks often transport during the historical period, if the number of times the cargo type with the most times transports cargo is much greater than the sum of the number of times other cargo types transported, it means that selecting the target cargo type as the cargo type with the most times can effectively reduce the error of the quantification result; if the number of times the cargo type with the most times transports cargo is not much greater than the sum of the number of times other cargo types transported, it means that selecting the target cargo type as the cargo type with the most times will lead to a large error in the quantification result, and the cargo type should be selected from the top n cargo types with the most times transported, and the time fluctuation factor of the cargo leaving the island should be considered when selecting.

[0097] Specifically, it is determined whether the multiple of the proportion of the cargo with the most frequent cargo among the first n cargoes and the sum of the proportion of the cargo with the most frequent cargoes other than the cargo with the most frequent cargoes is greater than the preset multiple. If it is greater than the preset multiple, the cargo with the most frequent cargoes is selected as the target cargo; if it is less than the preset multiple, based on the preset pre-found time fluctuation factors of the cargo leaving the island corresponding to the first n cargoes in the target time period, the proportion of the cargo with the most frequent cargoes is weighted, and the cargo with the maximum value of the weighted result is determined as the target cargo.

[0098] For example, trucks that have not left the island in the past three days are identified as target cross-sea trucks. The number of times the target cross-sea trucks have pulled the most cargo in the past three months is counted and recorded as {TM1, TM2, TM3} in descending order of the number of times. The actual proportion of the three types of cargo is recorded as {MD1, MD2, MD3}. The time fluctuation factors of the three types of cargo leaving the island are recorded as {S1, S2, S3}. The selected target cargo is recorded as TM, and the preset multiple is selected as 2. Then the data of the frequently pulled cargo is assigned values, specifically:

[0099] Determine the size relationship between MD1 and 2 (MD2+MD3):

[0100] If MD1>2(MD2+MD3), then TM=TM1;

[0101] If MD1<2(MD2+MD3), then TM=Max(MD1×S1, MD2×S2, MD3×S3);

[0102] At this point, the selection of target cargo is completed.

[0103] The method for obtaining information on potential truck customers based on multi-source information mining and analysis provided in the embodiment of the present application, in the process of selecting target cargo types, simultaneously considers the cargo types that the target cross-sea trucks have been frequently transporting recently, as well as the factors of time fluctuations on the transportation demand for the cargo types, sets the time fluctuation factor of the cargo leaving the island, and predicts the cross-sea cargo that the target cross-sea trucks may load, thereby realizing a quantitative analysis of the cross-sea cargo and its timeliness.

[0104] In some embodiments, the method for obtaining potential truck customer information based on multi-source information mining and analysis further includes:

[0105] Based on the potential truck customer information sorting table, the truck driver's contact information is captured and the intelligent outbound call is triggered in sequence through the automatic outbound calling tool. The intelligent outbound call is used to recommend sea crossing reservation services and fast sea crossing services to truck drivers.

[0106] Specifically, the purpose of locating potential truck customer information can be used for subsequent publicity and marketing to improve the competitiveness of the port during the off-season of passenger and vehicle ferry business. After obtaining the potential truck customer information sorting table, the truck driver's contact information is automatically captured from the database and the automatic outbound calling tool is used to trigger the intelligent outbound calling in sequence. The purpose of the intelligent outbound calling is to recommend the sea crossing reservation service and / or the fast sea crossing service to the truck driver.

[0107] The potential truck customer information sorting table includes at least the identification information of the target sea crossing trucks and the quantitative results of the possibility of selecting the target port for sea crossing within the target time period. Since the relevant information needs to be strictly declared and verified when the trucks enter and leave the island, the identification information of the target sea crossing trucks (such as license plate number information) can be used to automatically capture the contact information and other information of the truck driver from the database, so as to use the automatic outbound calling tool to trigger intelligent outbound calls in sequence and recommend sea crossing reservation services and / or fast sea crossing services to the truck drivers.

[0108] Compared with manual calling methods, using automatic outbound calling tools for intelligent outbound calling can effectively improve marketing efficiency.

[0109] Optionally, when making an intelligent outbound call to a truck driver, you can choose whether to recommend a fast sea crossing service to the truck driver based on the timeliness requirements of the target cargo selected during the quantification process of the frequently transported cargo data.

[0110] The method for acquiring potential truck customer information based on multi-source information mining and analysis provided in the embodiment of the present application, after locating the potential truck customer information ranking table, automatically captures the truck driver's contact information and triggers intelligent outbound calls in sequence through an automatic outbound calling tool to recommend cross-sea reservation services and / or fast cross-sea services, which can effectively solve the problems of small marketing scope and poor effect of manual marketing models, realize a fully automated linear marketing model, and greatly improve marketing efficiency and marketing success rate.

[0111] In some embodiments, the method for obtaining potential truck customer information based on multi-source information mining and analysis further includes:

[0112] If the quantified result of the possibility of the target cross-sea truck is greater than the fifth preset value and the intelligent outbound call is not connected, one or more of the following operations will be triggered: making multiple intelligent outbound calls; sending text messages; switching to manual service mode.

[0113] Specifically, truck drivers may answer the intelligent outbound call triggered by the automatic outbound call tool. Truck drivers who answer the intelligent outbound call and accept the sea crossing reservation service and / or fast sea crossing service can provide information such as license plate number, driver name, ID number, sea crossing port and time through online platforms or mini-programs and other service software. The system will enter the relevant information into the sea crossing reservation platform through automatic information capture and analysis tools. After completing the sea crossing reservation service on the sea crossing reservation platform, the truck driver will be informed of the reservation result and can be provided with the corresponding fast sea crossing service.

[0114] For truck drivers who have not received smart outbound calls, the probability of marketing success is taken into consideration. Trucks ranked high in the potential truck customer information sorting table, that is, trucks with higher quantitative results of the possibility of crossing the target port are selected, and further marketing is carried out for the truck drivers. The marketing methods can be: making multiple smart outbound calls, sending text messages, switching to manual service mode, etc., one or more of which can further improve the marketing success rate.

[0115] Figure 2 is a schematic diagram of the structure of a device for acquiring information of potential truck customers based on multi-source information mining and analysis provided by an embodiment of the present application, such as Figure 2 As shown, the device at least includes:

[0116] The first acquisition module 201 is used to acquire identification information of target sea-crossing trucks that have not left the island within a target time period;

[0117] The second acquisition module 202 is used to acquire the sea crossing historical data of the target sea crossing truck in a historical time period close to the target time period based on the identification information, the sea crossing historical data including sea crossing frequency data, sea crossing port selection data and frequently transported cargo data;

[0118] The first determination module 203 is used to assign values ​​to the sea crossing frequency data, the sea crossing port selection data and the frequently transported cargo data, respectively, to determine the sea crossing frequency quantification value, the frequently transported cargo quantification value and the sea crossing port selection quantification value of the target sea crossing truck; the frequently transported cargo quantification value is associated with the time fluctuation factor of the cargo leaving the island;

[0119] The second determination module 204 is used to perform weighted processing on the quantified value of sea crossing frequency, the quantified value of frequently transported cargo types and the quantified value of sea crossing port selection to determine the quantified result of the possibility that the target sea crossing truck selects the target port to cross the sea within the target time period;

[0120] The third acquisition module 205 is used to arrange the quantified results of the possibility of different target cross-sea trucks in descending order, and obtain a sorted table of potential truck customer information of the target port within the target time period.

[0121] In some embodiments, the frequently transported cargo data includes the cargo proportion of the top n cargo types transported by the target cross-sea truck with the most cargo transport times in a historical period, where n is a positive integer greater than 1; the first determination module 203 specifically includes a first determination unit, which is used to:

[0122] Select the target cargo based on the cargo proportion of the top n cargoes;

[0123] Based on the preset time fluctuation factor lookup table for cargo leaving the island, find the time fluctuation factor for cargo leaving the island for the target cargo type within the target time period;

[0124] Based on the sea transit time requirement of the target cargo, determine the initial value of the target cargo; the initial value is a first value, a second value or a third value, the first value is greater than the second value, and the second value is greater than the third value;

[0125] The initial assignment is weighted based on the found time fluctuation factor of the cargo leaving the island, and the quantitative value of the frequently transported cargo types is determined after the data of the frequently transported cargo types are assigned.

[0126] In some embodiments, selecting a target cargo type based on the cargo proportions of the first n cargo types includes:

[0127] Determine whether the multiple of the proportion of the cargo type with the most number of times among the first n cargo types and the sum of the proportion of the cargo types with the most number of times other than the cargo type with the most number of times is greater than a preset multiple;

[0128] If it is greater than the preset multiple, the commodity with the most selected times will be the target commodity; or,

[0129] If it is less than the preset multiple, the cargo proportions of the first n cargo types found in advance and corresponding to the time fluctuation factors of the cargo leaving the island within the target time period are weighted respectively, and the cargo type corresponding to the maximum value of the weighted result is determined as the target cargo type.

[0130] In some embodiments, the sea crossing frequency data includes the sea crossing frequency of the target sea crossing truck in a historical time period; the first determination module 203 specifically includes a second determination unit, which is used to:

[0131] Determine the magnitude relationship between the sea crossing frequency and the first preset value and the second preset value;

[0132] If the sea crossing frequency is greater than the first preset value, the sea crossing frequency quantization value is assigned to the first value; or,

[0133] If the sea crossing frequency is less than the first preset value and greater than the second preset value, the sea crossing frequency quantization value is assigned to the second value; or,

[0134] If the sea crossing frequency is less than the second preset value, assigning the sea crossing frequency quantization value to a third value;

[0135] Among them, the first preset value is greater than the second preset value, the first numerical value is greater than the second numerical value, and the second numerical value is greater than the third numerical value.

[0136] In some embodiments, the sea crossing port selection data includes a statistical percentage of target sea crossing trucks selecting the target port for sea crossing in a historical period; the first determination module 203 specifically includes a third determination unit for:

[0137] If the statistical proportion is greater than the third preset value and less than the fourth preset value, the quantified value of the sea crossing port selection is assigned the first value; or,

[0138] If the statistical proportion is less than the third preset value, the quantified value of the sea crossing port selection is assigned a second value; or,

[0139] If the statistical proportion is greater than the fourth preset value, the quantified value of the port of passage selection is assigned a third value;

[0140] Among them, the fourth preset value is greater than the third preset value, the first value is greater than the second value, and the second value is greater than the third value.

[0141] In some embodiments, the device further includes a first trigger module, configured to:

[0142] Based on the potential truck customer information sorting table, the truck driver's contact information is captured and the intelligent outbound call is triggered in sequence through the automatic outbound calling tool. The intelligent outbound call is used to recommend the cross-sea reservation service and / or the fast cross-sea service to the truck driver.

[0143] In some embodiments, the device further includes a second trigger module, configured to:

[0144] If the quantified result of the possibility of the target cross-sea truck is greater than the fifth preset value and the intelligent outbound call is not connected, one or more of the following operations will be triggered: making multiple intelligent outbound calls; sending text messages; switching to manual service mode.

[0145] It can be understood that the detailed functional implementation of each of the above-mentioned units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.

[0146] It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method, which will not be repeated here.

[0147] Based on the method in the above embodiment, an embodiment of the present application provides an electronic device. The device may include: at least one memory for storing programs and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the above embodiment.

[0148] Figure 3 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 3 As shown, the electronic device may include: a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302 and the memory 303 communicate with each other through the communication bus 304. The processor 301 may call the software instructions in the memory 303 to execute the method described in the above embodiment.

[0149] In addition, the logic instructions in the above-mentioned memory 303 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the relevant technology or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present application.

[0150] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0151] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0152] It is understandable that the processor in the embodiment of the present application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0153] The method steps in the embodiments of the present application can be implemented by hardware, or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (Random Access Memory, RAM), flash memory, read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electrically erasable programmable read-only memory (Electrically EPROM, EEPROM), registers, hard disks, mobile hard disks, CD-ROMs or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0154] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loading and executing computer program instructions on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted by a computer-readable storage medium. The computer instructions can be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, a data center, etc. that contains one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0155] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0156] It is easy for those skilled in the art to understand that the above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for acquiring potential truck customer information based on multi-source information mining and analysis, characterized in that: include: Obtain identification information of target sea-crossing trucks that have not left the island within the target time period; Based on the identification information, obtain the sea crossing historical data of the target sea crossing truck in a historical time period close to the target time period, wherein the sea crossing historical data includes sea crossing frequency data, sea crossing port selection data, and frequently transported cargo type data; Assign values ​​to the sea crossing frequency data, sea crossing port selection data and frequently transported cargo data respectively, and determine the sea crossing frequency quantified value, frequently transported cargo quantified value and sea crossing port selection quantified value of the target sea crossing truck; the frequently transported cargo quantified value is associated with the time fluctuation factor of the cargo leaving the island; Performing weighted processing on the quantified value of sea crossing frequency, the quantified value of frequently transported cargo types, and the quantified value of sea crossing port selection, to determine a quantified result of the possibility that the target sea crossing truck will select the target port to cross the sea within the target time period; The quantified results of the possibilities of different target cross-sea trucks are arranged in descending order, and a sorted table of potential truck customer information of the target port within the target time period is obtained.

2. The method for obtaining information of potential truck customers according to claim 1, characterized in that: The frequently transported cargo data includes the cargo proportion of the top n cargo types transported by the target cross-sea truck the most times in the historical time period, where n is a positive integer greater than 1; Determine the quantitative value of the types of goods commonly transported by the target cross-sea truck, including: Selecting a target cargo type based on the cargo proportion of the first n cargo types; Based on a preset lookup table of time fluctuation factors for cargo leaving the island, find the time fluctuation factor for cargo leaving the island within the target time period; Based on the sea transit time requirement of the target cargo, determine the initial value of the target cargo; the initial value is a first value, a second value or a third value, the first value is greater than the second value, and the second value is greater than the third value; The initial assignment is weighted based on the found time fluctuation factor of the cargo leaving the island, and the quantitative value of the frequently transported cargo type after the frequently transported cargo type data is assigned is determined.

3. The method for obtaining information of potential truck customers according to claim 2, characterized in that: The selecting of the target cargo type based on the cargo proportion of the first n cargo types includes: Determine whether the multiple of the cargo proportion of the most frequently used cargo type among the first n cargo types and the sum of the cargo proportions of other cargo types except the most frequently used cargo type is greater than a preset multiple; If it is greater than the preset multiple, the commodity with the largest number of occurrences is selected as the target commodity; or, If it is less than the preset multiple, based on the time fluctuation factors of the outbound cargo corresponding to the first n cargo types found in advance within the target time period, the cargo proportions of the first n cargo types are weighted respectively, and the cargo type corresponding to the maximum value of the weighted result is determined as the target cargo type.

4. The method for obtaining information of potential truck customers according to claim 1, characterized in that: The sea crossing frequency data includes the sea crossing frequency of the target sea crossing truck in the historical time period; Determining the quantified value of the frequency of crossing the sea of ​​the target truck includes: Determine the magnitude relationship between the sea crossing frequency and a first preset value and a second preset value; If the sea crossing frequency is greater than the first preset value, assigning the sea crossing frequency quantization value to a first value; or, If the sea crossing frequency is less than the first preset value and greater than the second preset value, assigning the sea crossing frequency quantization value to a second value; or, If the sea crossing frequency is less than the second preset value, assigning the sea crossing frequency quantization value to a third value; Among them, the first preset value is greater than the second preset value, the first value is greater than the second value, and the second value is greater than the third value.

5. The method for obtaining information of potential truck customers according to claim 1, characterized in that: The sea crossing port selection data includes the statistical proportion of the target sea crossing trucks selecting the target port for sea crossing during the historical time period; Determining the quantified value of the sea crossing port selection of the target sea crossing truck comprises: If the statistical proportion is greater than the third preset value and less than the fourth preset value, the cross-sea port selection quantization value is assigned to the first value; or, If the statistical proportion is less than the third preset value, assigning the sea crossing port selection quantization value to a second value; or, If the statistical proportion is greater than the fourth preset value, assigning the sea crossing port selection quantization value to a third value; Among them, the fourth preset value is greater than the third preset value, the first value is greater than the second value, and the second value is greater than the third value.

6. The method for obtaining information of potential truck customers according to any one of claims 1 to 5, characterized in that: The method further comprises: Based on the potential truck customer information ranking table, the truck driver's contact information is captured and intelligent outbound calls are triggered in sequence through an automatic outbound calling tool. The intelligent outbound calls are used to recommend sea crossing reservation services and / or fast sea crossing services to truck drivers.

7. The method for obtaining information of potential truck customers according to claim 6, characterized in that: The method further comprises: If the quantified result of the possibility of the target cross-sea truck is greater than the fifth preset value and the intelligent outbound call is not connected, one or more of the following operations are triggered: making multiple intelligent outbound calls; sending text messages; switching to manual service mode.

8. A device for acquiring information of potential truck customers based on multi-source information mining and analysis, characterized in that: include: The first acquisition module is used to acquire truck information of target sea-crossing trucks that have not left the island within a target time period, wherein the truck information includes identification information; A second acquisition module is used to acquire the sea crossing historical data of the target sea crossing truck in a historical time period close to the target time period based on the identification information, wherein the sea crossing historical data includes sea crossing frequency data, sea crossing port selection data and frequently transported cargo type data; The first determination module is used to assign values ​​to the sea crossing frequency data, the sea crossing port selection data and the frequently transported cargo data respectively, and determine the sea crossing frequency quantified value, the frequently transported cargo quantified value and the sea crossing port selection quantified value of the target sea crossing truck; the frequently transported cargo quantified value is associated with the time fluctuation factor of the cargo leaving the island; The second determination module is used to perform weighted processing on the quantified value of sea crossing frequency, the quantified value of frequently transported cargo types and the quantified value of sea crossing port selection, and determine the quantified result of the possibility that the target sea crossing truck selects the target port to cross the sea within the target time period; The third acquisition module is used to arrange the possibility quantification results in descending order to obtain a sorted list of potential truck customer information of the target port within the target time period.

9. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed on a processor, the processor is caused to execute the method according to any one of claims 1 to 7.

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