Bidding behavior similarity analysis method and terminal

Abstract

The invention provides a bidding behavior similarity analysis method comprising the following steps: obtaining bidding related information in a preset time period, the bidding related information at least comprising bidding behavior information of a bidder; according to the bidding related information, respectively calculating one or more vector groups of each bidder; integrating the vector groups of the bidders according to a preset weight to obtain a mixed weight vector space of the bidders; and calculating the bidding behavior similarity of any two bidders according to a vector included angle cosine value calculation formula and the mixed weight vector space of each bidder. Vector group analysis is carried out on the bidding behavior information of the bidders, multi-dimensional analysis of the bidding behaviors of the bidders is achieved, then the behavior similarity among the multiple bidders is judged, and intelligent analysis of differences among the bidders is achieved.

Description

TECHNICAL FIELD

[0001] The application relates to a behavior similarity analysis method and terminal, in particular to a bidding behavior similarity analysis method and terminal. BACKGROUND

[0002] Bidding is a professional term of bidding, specifically refers to the behavior that a bidder delivers a bid to a tenderer within a specified period according to the conditions specified in the tender announcement or the invitation to bid. Tendering can be used for tendering to purchase equipment, materials and daily necessities, etc., and can also be used for tendering to explore resources, develop mineral resources or invite business to undertake engineering projects, etc. The bidding information is large in quantity and needs to consume a large amount of time to analyze the bidding information, so as to determine the differences between different bidders, resulting in a complex and time-consuming bidding process. SUMMARY

[0003] The application provides a bidding behavior similarity analysis method and terminal, so as to realize intelligent analysis of the behavior of bidders.

[0004] The application provides a bidding behavior similarity analysis method, which comprises the following steps:

[0005] Obtaining bidding related information in a preset time period, wherein the bidding related information at least comprises bidding behavior information of bidders;

[0006] According to the bidding related information, one or more vector groups of each bidder are calculated respectively;

[0007] According to a preset weight, the vector groups of each bidder are integrated to obtain a mixed weight vector space of each bidder;

[0008] According to a vector included angle cosine value calculation formula and the mixed weight vector space of each bidder, the bidding behavior similarity of any two bidders is calculated.

[0009] Further, the bidding related information comprises subject information and bidding behavior information of each bidder.

[0010] Still further, the vector group comprises a first vector group, a second vector group and a third vector group, and the first vector group, the second vector group and the third vector group are different bidding dimensions.

[0011] Still further, the first vector group is a participating bidding vector group, the second vector group is a bidding information vector group, and the third vector group is an IP address vector group.

[0012] Still further, the participating bidding vector group at least comprises one or more of the following: bidder background information, the number of bidders, bidding price information and bidding time information.

[0013] Further, the bid information vector group at least includes one of a bid information hardware vector group and a bid information software vector group.

[0014] Further, the IP address vector group is an IP address of the bid information of the bidder, used for IP linking with the bid information of the bidder.

[0015] Further, the vector group of each bidder is integrated according to a preset weight to obtain a mixed weight vector space of each bidder, including:

[0016] The vector group of each bidder is analyzed according to a preset weight to obtain a weight score.

[0017] The weight scores of the vector groups of the bidders are integrated in multiple dimensions to form the mixed weight vector space.

[0018] Further, the bid behavior similarity of any two bidders is calculated according to a vector cosine value calculation formula and the mixed weight vector space of each bidder, including:

[0019] The weight level of each vector group is obtained to determine a core weight direction.

[0020] Based on the core weight direction, the core weight value is calculated according to the vector cosine value calculation formula and the mixed weight vector space of each bidder.

[0021] Based on the core weight value of any two bidders, a ratio is obtained to ensure that the ratio does not exceed 1.

[0022] The ratio is set as the bid behavior similarity of the two bidders.

[0023] Another aspect of the application also discloses a terminal applying the analysis method of the bid behavior similarity, including:

[0024] A bid-related information acquisition unit is configured to acquire bid-related information of a current subject;

[0025] A bid-related information analysis unit is configured to extract one or more vector group information of a bidder from the bid-related information;

[0026] A weight calculation unit is configured to calculate a mixed weight vector space of each bidder;

[0027] A similarity analysis unit is configured to calculate the bid behavior similarity of any two bidders.

[0028] The application realizes multi-dimensional analysis on the bidding behavior of the bidders by vector group analysis on the bidding behavior information of the bidders, and further judges the behavior similarity between the bidders, and realizes intelligent analysis on the difference between the bidders. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 A flowchart of the bidding behavior similarity analysis method of the embodiment of the application;

[0030] Figure 2 A direction diagram of the first vector group, the second vector group and the third vector group in the mixed weight vector space. DETAILED DESCRIPTION

[0031] The exemplary embodiments will be described in detail herein with reference to the attached drawings. In the following description, the same numbers are used to designate the same elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not meant to represent all embodiments consistent with the application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the application as detailed in the appended claims.

[0032] The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in the present application and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0033] It should be understood that, although the terms first, second, third, etc. can be used herein to describe various information, these terms are not intended to denote a particular order or hierarchy. These terms are used only to distinguish one type of information from another. For example, a first information can be termed a second information, and similarly, a second information can be termed a first information, without departing from the scope of the present application. Depending on the context, the word "if' as used herein can be interpreted as "when" or "upon" or "in response to determining."

[0034] The embodiment of the application discloses a bidding behavior similarity analysis method, as shown in Figure 1 The method comprises the following steps:

[0035] acquiring bidding related information in a preset time period, wherein the bidding related information at least comprises bidding behavior information of a bidder;

[0036] The user can set a predetermined time period as an effective bidding time period, and analyze the bidding related information in the predetermined time period to effectively obtain the bidding behavior information of the bidders.

[0037] According to the bidding related information, one or more vector groups of each bidder are calculated.

[0038] The user can set one or more vector groups for important data, and extract data from the one or more vector groups of the bidding related information to realize extraction and processing of key data.

[0039] The vector groups of each bidder are integrated according to a preset weight to obtain a mixed weight vector space of each bidder.

[0040] The preset weight can be a system preset value or can be set by the user according to the needs of the user. The weight of one or more vector groups in the dimension is measured according to the preset weight, and the mixed weight vector space is formed by integration.

[0041] The bidding behavior similarity of any two bidders is calculated according to the vector angle cosine value calculation formula and the mixed weight vector space of each bidder.

[0042] The user can select a focus direction and set the focus direction as an important weight direction. Based on the weight direction, the weight is calculated by the vector angle cosine value calculation formula to form a score. The bidding behavior similarity is evaluated by combining the mixed weight vector space.

[0043] The embodiment of the application realizes multi-dimensional analysis of the bidding behavior of the bidders by analyzing the bidding behavior information of the bidders, and further judges the behavior similarity between the bidders to realize intelligent analysis of the differences between the bidders.

[0044] Optionally, the bidding related information includes target information and bidding behavior information of each bidder.

[0045] The target information includes target demand information such as time demand, quantity demand, price demand, and credit demand.

[0046] The embodiment of the application can obtain the real behavior information of the bidders based on the target information and the bidding behavior information of each bidder, and facilitate subsequent evaluation of the bidding behavior.

[0047] In particular, the vector group includes a first vector group, a second vector group, and a third vector group, and the first vector group, the second vector group, and the third vector group are different bidding dimensions.

[0048] Particularly, the first vector group is a participation bidding vector group, the second vector group is a bidding information vector group, and the third vector group is an IP address vector group.

[0049] In the embodiment of the application, the participation bidding vector group, the bidding information vector group and the IP address vector group are set, so that effective analysis of the bidders in at least three dimensions is realized, and the behavior of the bidders is evaluated more comprehensively.

[0050] Particularly, the participation bidding vector group at least includes one or more of bidder background information, the number of bidders, bidding price information and bidding time information.

[0051] The bidder background information specifically refers to the establishment time, credit degree and asset degree of the bidder, and the credit degree of the bidder can be effectively analyzed, and the number of bidders can highlight the production capacity of the bidder.

[0052] The participation bidding vector group of the embodiment of the application can effectively evaluate the qualification and credit of the bidder, so as to ensure the credibility of the current bidding behavior.

[0053] Particularly, the bidding information vector group at least includes one of a bidding information hardware vector group and a bidding information software vector group.

[0054] The bidding information vector group of the embodiment of the application specifically refers to the bidding information hardware vector group, the bidding information hardware vector group can evaluate the hardware of the bidding scheme of the bidder in dimensions, and then determine the related information of the hardware of the bidding scheme of the current bidder, such as specific parameters and performance, so as to effectively evaluate the hardware scheme of the bidder.

[0055] Particularly, the IP address vector group is the IP address of the bidding information of the bidder, and is used for IP linking with the bidding information of the bidder.

[0056] Optionally, the vector groups of the bidders are integrated according to the preset weight to obtain a mixed weight vector space of the bidders, including:

[0057] The vector groups of the bidders are analyzed according to the preset weight to obtain a weight score.

[0058] The preset weights of different vector groups are different, the vector groups of the bidders are analyzed according to the weight, the weight score is obtained, the same vector groups of the bidders are effectively scored, and subsequent comparison is facilitated.

[0059] The weight scores of the vector groups of the bidders are integrated in multiple dimensions to form a mixed weight vector space.

[0060] Among them, such as Figure 2 As shown, users can establish weight coordinate axes as needed, and set each vector group in the weight coordinate axes to form a mixed weight vector space. The weight values ​​of each vector group are then added to the mixed weight vector space to form a mixed weight vector graph.

[0061] Optionally, the step of calculating the similarity of bidding behavior between any two bidders based on the formula for calculating the cosine of the vector angle and the mixed weight vector space of each bidder includes:

[0062] Obtain the weight levels of each vector group and determine the direction of the core weights;

[0063] Users can set weight levels according to their bidding needs. For example, the first vector group can be set as the second most important, the second vector group as the first most important, and the third vector group as the third most important. The parameter value of the first most important is 1, the parameter value of the second most important is 0.5, and the parameter value of the third most important is 0.2.

[0064] Based on the core weight direction, the core weight value is calculated according to the formula for calculating the cosine value of the vector angle and the mixed weight vector space of each bidder;

[0065] Among them, the formula for calculating the cosine of the vector angle can be used to obtain the weight vector values ​​of the first vector group, the second vector group, and the third vector group in the core weight direction. Combined with the parameter values ​​of different importance levels, the weight vector values ​​in the core weight direction can be recalculated and added together to obtain the core weight value.

[0066] Based on the core weight values ​​of any two bidders, obtain the ratio and ensure that the ratio does not exceed 1.

[0067] This ratio is set to indicate the similarity of the bidding behaviors of the two bidders.

[0068] This invention combines weight levels and the formula for calculating the cosine of the vector angle to calculate the core weight value for the core weight direction set by the user, thereby effectively determining the similarity of specific bidding behaviors.

[0069] Another aspect of this invention discloses a terminal that applies the above-described method for analyzing the similarity of bidding behaviors, comprising:

[0070] The bidding-related information collection unit is used to collect bidding-related information for the current target.

[0071] The bidding-related information analysis unit is used to extract one or more vector groups of information about the bidder from bidding-related information.

[0072] a weight calculation unit configured to calculate a mixed weight vector space of each bidder;

[0073] a similarity analysis unit configured to calculate a similarity of bidding behaviors of any two bidders.

[0074] The terminal of the embodiment of the application is applied to processing a file. The user terminal is a file sending terminal, and is configured to process the file. The user terminal can be a computer, a mobile phone, a tablet computer, a user terminal, or the like.

[0075] The user terminal comprises at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus. The communication bus is configured to realize connection and communication between the components.

[0076] The user interface can comprise an interface connected with a display screen, and an interface connected with a camera. Optionally, the user interface can further comprise a standard wired interface and a wireless interface.

[0077] The network interface can optionally comprise a standard wired interface and a wireless interface (such as a WIFI interface).

[0078] The processor can comprise one or more processing cores. The processor connects various parts in the user terminal through various interfaces and lines, executes instructions, programs, code sets or instruction sets stored in the processor, and calls data stored in the memory, to execute various functions and process data of the user terminal. Optionally, the processor can be realized in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor can be integrated with a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU is mainly configured to process an operating system, a user interface, and an application program, etc. The GPU is configured to render and draw content to be displayed on the display screen. The modem is configured to process wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor, but can be realized by a separate chip.

[0079] The memory can include a random access memory (RAM) and a read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory can be used to store instructions, programs, codes, code sets, or instruction sets. The memory can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the methods described above, etc.; and the data storage area can store data involved in the methods described above, etc. The memory can also be at least one storage device located away from the processor.

[0080] The processor can be used to invoke an operating application of the user terminal stored in the memory, and perform the related operations of the analysis method of the bidding behavior similarity in the embodiments described above.

[0081] The user terminal described above can be used to perform the analysis method of the bidding behavior similarity in the embodiments of the present application, and has the corresponding functions and advantages.

[0082] According to a third aspect of the embodiments of the present application, a computer readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the computer program implements the related operations in the analysis method of the bidding behavior similarity according to any one of the embodiments described above, and has the corresponding functions and advantages. The computer readable medium includes permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of the storage medium of the computer include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tape, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition in this paper, the computer readable medium does not include transitory computer readable medium, such as modulated data signals and carriers.

[0083] It is also to be noted that the terms "comprising", "comprises" or other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0084] It should be noted that the above-mentioned are only the preferred embodiments of the present application and the technical principles applied. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and those skilled in the art can make various obvious changes, readjustments and substitutions without departing from the scope of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments, and can include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it, although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the skilled person can modify or replace the specific embodiments of the present application after reading the present application, but these modifications or changes are still within the scope of the claims of the present application.

Claims

1. A method for analyzing the similarity of bidding behaviors, characterized in that, include: Obtain bidding-related information within a preset time period, wherein the bidding-related information includes at least the bidding behavior information of the bidders; Based on the bidding-related information, calculate one or more vector groups for each bidder; The vector groups of each bidder are integrated according to the preset weights to obtain the mixed weight vector space of each bidder; Based on the formula for calculating the cosine of the vector angle and the mixed weight vector space of each bidder, the similarity of the bidding behavior of any two bidders is calculated.

2. The method for analyzing the similarity of bidding behaviors according to claim 1, characterized in that, The bidding-related information includes information about the subject matter and information about the bidding behavior of each bidder.

3. The method for analyzing the similarity of bidding behaviors according to claim 2, characterized in that, The vector group includes a first vector group, a second vector group, and a third vector group, each representing a different bidding dimension.

4. The method for analyzing the similarity of bidding behaviors according to claim 3, characterized in that, The first vector group is the bidding participation vector group, the second vector group is the bidding information vector group, and the third vector group is the IP address vector group.

5. The method for analyzing the similarity of bidding behaviors according to claim 4, characterized in that, The participating bidding vector group shall include at least one or more of the following: bidder background information, number of bidders, bid price information, and bid time information.

6. The method for analyzing the similarity of bidding behaviors according to claim 4, characterized in that, The bidding information vector group includes at least one of the following: a bidding information hardware vector group and a bidding information software vector group.

7. The method for analyzing the similarity of bidding behaviors according to claim 4, characterized in that, The IP address vector group is the IP address of the bidder's bidding information, used to establish an IP link with the bidder's bidding information.

8. The method for analyzing the similarity of bidding behaviors according to claim 1, characterized in that, The step of integrating the vector groups of each bidder according to preset weights to obtain the mixed weight vector space of each bidder includes: Based on the preset weights, a weight analysis is performed on the vector groups of each bidder to obtain a weight score; The weighted scores of each vector group of the bidders are integrated in multiple dimensions to form a hybrid weighted vector space.

9. The method for analyzing the similarity of bidding behaviors according to claim 1, characterized in that, The calculation of the similarity of bidding behavior between any two bidders, based on the formula for calculating the cosine of the vector angle and the mixed weight vector space of each bidder, includes: Obtain the weight levels of each vector group and determine the direction of the core weights; Based on the core weight direction, the core weight value is calculated according to the formula for calculating the cosine value of the vector angle and the mixed weight vector space of each bidder; Based on the core weight values ​​of any two bidders, obtain the ratio and ensure that the ratio does not exceed 1. This ratio is set to indicate the similarity of the bidding behaviors of the two bidders.

10. A terminal that applies an analysis method for similarity of bidding behaviors according to any one of claims 1-9, characterized in that, The terminal includes: The bidding-related information collection unit is used to collect bidding-related information for the current target. The bidding-related information analysis unit is used to extract one or more vector groups of information about the bidder from bidding-related information. A weight calculation unit is used to calculate the mixed weight vector space of each bidder. A similarity analysis unit is used to calculate the similarity of bidding behaviors between any two bidders.

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