Electronic bidding model intelligent generation system based on modular plug-in

Abstract

The invention relates to the technical field of data processing, and discloses an electronic bidding model intelligent generation system based on a modular plug-in, a demand judgment module analyzes an electronic bidding demand, and judges whether the electronic bidding demand can be received; the model obtaining module obtains an initialized basic model; a first calculation module calculates a filling coefficient according to the electronic bidding and tendering demand, and adjusts the initialized basic model to obtain an intermediate adjustment model; a second calculation module determines a historical electronic bidding model and calculates a historical model utilization coefficient; and the model generation module performs secondary adjustment on the intermediate adjustment model to obtain a target electronic bidding model, and imports the electronic bidding requirements into the target electronic bidding model, so that automatic adjustment of the bidding model can be realized according to characteristics and requirements of different electronic bidding requirements, and a model meeting the requirements is generated. Moreover, the method meets the personalized demands of users, and is of great significance in improving the efficiency and quality of bidding and tendering processes.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent generation system of electronic bidding templates based on modular plug-ins. Background Art

[0002] With the rapid development of information technology, traditional bidding activities have gradually shifted to electronic and networked operation modes. The electronic bidding platform provides more efficient, transparent and convenient services through the Internet, allowing tenderers and bidders to participate in the bidding process at any time and place.

[0003] However, existing electronic bidding systems usually require users to manually fill in a large amount of standardized information, which is not only time-consuming and labor-intensive, but also prone to human errors resulting in bidding documents not meeting regulatory requirements. They also cannot be flexibly adjusted according to the specific needs of different projects, making it difficult to meet the needs of all situations. Summary of the invention

[0004] The embodiment of the present invention provides an intelligent generation system of electronic bidding templates based on modular plug-ins, which can dynamically combine and configure document templates according to the characteristics and requirements of different electronic bidding needs, and automatically generate bidding documents that meet the specifications, which is of great significance for improving the efficiency and quality of the bidding process.

[0005] In order to achieve the above object, the present invention provides an electronic bidding template intelligent generation system based on modular plug-ins, comprising:

[0006] The demand judgment module is used to obtain the electronic bidding demand sent by the user, analyze the electronic bidding demand, and judge whether the electronic bidding demand can be received;

[0007] A template acquisition module, used to acquire a corresponding initialization basic template when it is determined that the electronic bidding requirement can be received, wherein the initialization basic template includes a plurality of blank text boxes;

[0008] A first calculation module is used to analyze the electronic bidding demand, calculate the fill factor based on the analysis result, and adjust the initialization basic template according to the fill factor to obtain an intermediate adjustment template;

[0009] The second calculation module is used to collect the user's historical bidding behavior, determine the historical electronic bidding template corresponding to the electronic bidding demand, and calculate the user's historical template utilization coefficient based on the historical electronic bidding template;

[0010] The template generation module is used to perform secondary adjustment on the intermediate adjustment template based on the historical template utilization coefficient to obtain a target electronic bidding template, and import the user's electronic bidding demand into the target electronic bidding template.

[0011] Furthermore, the template acquisition module is used to:

[0012] The template acquisition module is used to analyze the electronic bidding demand and extract the corresponding unique indication information, wherein the unique indication information includes the demand generation time and the sequence code;

[0013] The template acquisition module is used to traverse the sequence code in the coding requirement database, and if the sequence code is not in the coding requirement database, determine that the electronic bidding requirement is a newly generated requirement, receive the electronic bidding requirement, and store the electronic bidding requirement in the coding requirement database;

[0014] The template acquisition module is used to extract the demand storage time corresponding to the electronic bidding demand in the coding demand database if the sequence code is in the coding demand database;

[0015] The template acquisition module is used to calculate the time interval between the demand generation time and the demand storage time, and if the time interval is less than or equal to the preset time interval, generate a repeat determination instruction;

[0016] The template acquisition module is used to receive the electronic bidding demand if the time interval is greater than the preset time interval.

[0017] Furthermore, the first calculation module is used for:

[0018] The first calculation module is used to determine multiple electronic bidding content requirements and electronic bidding requirement structure requirements of the user based on the analysis result;

[0019] The first calculation module is used to output the content requirement value corresponding to the electronic bidding content requirement according to the pre-trained content requirement value model;

[0020] The first calculation module is used to output the structural requirement value corresponding to the electronic bidding structural requirement according to the pre-trained structural requirement value model;

[0021] The first calculation module is used to calculate the first filling factor based on the content requirement value and the structure requirement value;

[0022] The first calculation module is used to expand the blank text box in proportion according to the fill factor to obtain the intermediate adjustment template;

[0023] The first calculation module is used to calculate the filling factor according to the following formula:

[0024] P1=a1×(1+b1) b2 +a2×(1+b2) b1 ;

[0025] Among them, P1 is the first filling coefficient, a1 is the weight corresponding to the content demand value, b1 is the content demand value, b2 is the structure demand value, and a2 is the weight corresponding to the structure demand value.

[0026] Furthermore, the second calculation module is used for:

[0027] The second calculation module is used to analyze all historical electronic bidding templates to determine the content ratio of each individual text box;

[0028] The second calculation module is used to cluster all content proportions to determine content proportion clusters;

[0029] The second calculation module is used to determine the cluster center of the content proportion cluster, determine the cluster distance from each content proportion to the content proportion cluster, and generate a cluster distance sequence;

[0030] The second calculation module is used to calculate the set belonging degree of each cluster distance according to the cluster distance sequence, and calculate the user's historical template utilization coefficient according to all the set belonging degrees.

[0031] Furthermore, the second calculation module is used for:

[0032] The second calculation module is used to obtain first size information of a separate text box based on image recognition technology, where the first size information includes width and height;

[0033] The second calculation module is used to obtain second size information of the content in the separate text box according to the font file parsing technology, and the second size information includes the width and height of a single character;

[0034] The second calculation module is used to calculate the theoretical maximum number of contents in the separate text box according to the first size information and the second size information, wherein the theoretical maximum number refers to the width of the separate text box divided by the width of a single character, and the height of the separate text box divided by the height of a single character;

[0035] The second calculation module is used to obtain the actual number of characters of the actual content in the separate text box, and calculate a ratio based on the actual number of characters and the theoretical maximum number as the content proportion.

[0036] Furthermore, the second calculation module is used for:

[0037] The second calculation module is used to extract a maximum cluster distance Fmax and a minimum cluster distance Fmin from the cluster distance set;

[0038] The second calculation module is used to calculate the partition distance F according to the maximum cluster distance Fmax and the minimum cluster distance Fmin, wherein F=(Fmax+Fmin) / 2;

[0039] The second calculation module is used to calculate the set belonging degree of the corresponding cluster distance according to the following formula when the cluster distance is less than the partition distance:

[0040]

[0041] Among them, Wi is the set belonging degree of the i-th cluster distance, Ki is the i-th cluster distance, and ΔF is the sum of all cluster distances in the cluster distance set;

[0042] The second calculation module is used to calculate the set belonging degree of the corresponding cluster distance according to the following formula when the cluster distance is equal to the partition distance:

[0043]

[0044] Among them, Qi is the set belonging degree of the i-th cluster distance;

[0045] The second calculation module is used to calculate the set belonging degree of the corresponding cluster distance according to the following formula when the cluster distance is greater than the partition distance:

[0046]

[0047] Among them, Ji is the set belonging degree of the i-th cluster distance, and Di is the i-th cluster distance.

[0048] Furthermore, the second calculation module is used for:

[0049] The second calculation module is used to perform smooth fitting on all set attribution degrees and construct a set attribution degree fitting curve;

[0050] The second calculation module is used to determine whether there is an unfitted set attribution degree, if not, calculate the first average value of all set attributions, if yes, extract the unfitted set attribution degree;

[0051] The second calculation module is used to calculate the deviation distance between each unfitted set belonging degree and the set belonging degree fitting curve;

[0052] The second calculation module is used to obtain a preset deviation distance, and generate an external association identifier for all set belonging degrees less than the preset deviation distance;

[0053] The second calculation module is used to calculate a second average value according to the set belongingness carrying the external association identifier and the set belongingness fitting curve;

[0054] The second calculation module is used to divide all set belonging degrees that are less than or equal to the first average value into a first set belonging degree sequence;

[0055] The second calculation module is used to divide all set belonging degrees greater than the first average value into a second set belonging degree sequence;

[0056] or,

[0057] The second calculation module is used to divide all set belonging degrees that are less than or equal to the second average value into a first set belonging degree sequence;

[0058] The second calculation module is used to classify all set belonging degrees greater than the second average value into a second set belonging degree sequence;

[0059] The second calculation module counts a first number of the first set attribution degree sequences, counts a second number of the second set attribution degrees, and calculates a historical template utilization coefficient of the user according to the first number and the second number.

[0060] Furthermore, the second calculation module is used for:

[0061] The second calculation module is used to calculate the user's historical template utilization coefficient according to the following formula:

[0062]

[0063] Among them, R is the historical template utilization coefficient, m=m1+m2, m1 is the first number, m2 is the second number, Yj is the j-th set belonging degree, cj is the weight of the j-th set belonging degree, and e is a constant.

[0064] Furthermore, the template generation module is used to:

[0065] The template generation module is used to expand the intermediate adjustment template in proportion according to the historical template utilization coefficient to obtain the target electronic bidding template;

[0066] The template generation module is used to divide the electronic bidding content requirements corresponding to the electronic bidding requirements into single characters or words and convert them into electronic text format;

[0067] The template generation module is used to import the electronic text format into the corresponding blank text box, and allows the user to manually adjust and edit the imported electronic text format.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] The present invention discloses an electronic bidding template intelligent generation system based on modular plug-ins, wherein a demand judgment module analyzes the electronic bidding demand to determine whether the electronic bidding demand can be received; a template acquisition module acquires an initialization basic template; a first calculation module calculates a filling coefficient according to the electronic bidding demand, adjusts the initialization basic template, and obtains an intermediate adjustment template; a second calculation module determines a historical electronic bidding template and calculates a historical template utilization coefficient; a template generation module performs a secondary adjustment on the intermediate adjustment template to obtain a target electronic bidding template, and imports the electronic bidding demand into the target electronic bidding template. The system can realize automatic adjustment of bidding templates according to the characteristics and requirements of different electronic bidding demands, generate templates that meet the demand, and meet the personalized needs of users, which is of great significance to improving the efficiency and quality of the bidding process. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0071] Figure 1 A structural schematic diagram of an intelligent generation system for electronic bidding templates based on modular plug-ins in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0072] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0073] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0074] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0075] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0076] The following is a description of preferred embodiments of the present invention with reference to the accompanying drawings.

[0077] like Figure 1 As shown, an embodiment of the present invention discloses an intelligent generation system of electronic bidding templates based on modular plug-ins, comprising:

[0078] The demand judgment module is used to obtain the electronic bidding demand sent by the user, analyze the electronic bidding demand, and judge whether the electronic bidding demand can be received;

[0079] A template acquisition module, used to acquire a corresponding initialization basic template when it is determined that the electronic bidding requirement can be received, wherein the initialization basic template includes a plurality of blank text boxes;

[0080] A first calculation module is used to analyze the electronic bidding demand, calculate the fill factor based on the analysis result, and adjust the initialization basic template according to the fill factor to obtain an intermediate adjustment template;

[0081] The second calculation module is used to collect the user's historical bidding behavior, determine the historical electronic bidding template corresponding to the electronic bidding demand, and calculate the user's historical template utilization coefficient based on the historical electronic bidding template;

[0082] The template generation module is used to perform secondary adjustment on the intermediate adjustment template based on the historical template utilization coefficient to obtain a target electronic bidding template, and import the user's electronic bidding demand into the target electronic bidding template.

[0083] In this embodiment, the electronic bidding and tendering requirements include the following aspects: Project information: including project name, project number, project location, project budget, project cycle, etc. Bidding scope and content: describe the bidding scope of the project in detail, including engineering quantity, materials, equipment, services, etc. Bidder qualification requirements: put forward specific requirements on the qualifications, experience, technical capabilities, etc. of the bidder. Requirements for the preparation of bidding documents: including the format, content, submission method, etc. of the bid. Bid deposit: require the bidder to pay a certain amount of deposit to ensure that the bidder fulfills the contractual obligations. Opening, evaluation and bidding procedures: clarify the time, place, procedures, etc. of opening, evaluation and bidding. Contract signing and performance guarantee: agree on the time, place, method of contract signing, as well as the method and amount of performance guarantee. Other related matters: such as bidding agents, regulatory agencies, applicable laws, etc.

[0084] The beneficial effect of the above technical solution is that it can realize the automatic adjustment of bidding templates according to the characteristics and requirements of different electronic bidding needs, generate templates that meet the needs, and meet the personalized needs of users, which is of great significance to improving the efficiency and quality of the bidding process.

[0085] In some embodiments of the present application, the template acquisition module is used to:

[0086] The template acquisition module is used to analyze the electronic bidding demand and extract the corresponding unique indication information, wherein the unique indication information includes the demand generation time and the sequence code;

[0087] The template acquisition module is used to traverse the sequence code in the coding requirement database, and if the sequence code is not in the coding requirement database, determine that the electronic bidding requirement is a newly generated requirement, receive the electronic bidding requirement, and store the electronic bidding requirement in the coding requirement database;

[0088] The template acquisition module is used to extract the demand storage time corresponding to the electronic bidding demand in the coding demand database if the sequence code is in the coding demand database;

[0089] The template acquisition module is used to calculate the time interval between the demand generation time and the demand storage time, and if the time interval is less than or equal to the preset time interval, generate a repeat determination instruction;

[0090] The template acquisition module is used to receive the electronic bidding demand if the time interval is greater than the preset time interval.

[0091] In this embodiment, the sequence code corresponding to each electronic bidding requirement is different.

[0092] In this embodiment, the coding requirement database stores all received requirements, including but not limited to electronic bidding requirements.

[0093] In this embodiment, the preset time interval is 24 hours.

[0094] The beneficial effect of the above technical solution is that the present invention can realize the accurate reception of electronic bidding and tendering requirements, which can avoid both repeated reception and missed reception.

[0095] In some embodiments of the present application, the first computing module is used to:

[0096] The first calculation module is used to determine multiple electronic bidding content requirements and electronic bidding requirement structure requirements of the user based on the analysis result;

[0097] The first calculation module is used to output the content requirement value corresponding to the electronic bidding content requirement according to the pre-trained content requirement value model;

[0098] The first calculation module is used to output the structural requirement value corresponding to the electronic bidding structural requirement according to the pre-trained structural requirement value model;

[0099] The first calculation module is used to calculate the first filling factor based on the content requirement value and the structure requirement value;

[0100] The first calculation module is used to expand the blank text box in proportion according to the fill factor to obtain the intermediate adjustment template;

[0101] The first calculation module is used to calculate the filling factor according to the following formula:

[0102] P1=a1×(1+b1) b2 +a2×(1+b2) b1 ;

[0103] Among them, P1 is the first filling coefficient, a1 is the weight corresponding to the content demand value, b1 is the content demand value, b2 is the structure demand value, and a2 is the weight corresponding to the structure demand value.

[0104] In this embodiment, the electronic bidding content requirement refers to the specific text content, and the electronic bidding structure requirement refers to the paragraph division.

[0105] In this embodiment, the content demand value model is pre-trained, and is obtained through training based on different data samples and content demand values ​​matched by the data samples.

[0106] In this embodiment, the structural requirement value model is pre-trained, and is obtained through training based on different data samples and structural requirement values ​​matched by the data samples.

[0107] In this embodiment, each blank text box has a corresponding length and width, and the blank text box is expanded according to the product value of the first filling coefficient and the length and the width.

[0108] The beneficial effect of the above technical solution is that the present invention realizes the initial adjustment of the blank text box to meet the normal import of electronic bidding needs and avoid the blank text box area being too large or too small.

[0109] In some embodiments of the present application, the second computing module is used to:

[0110] The second calculation module is used to analyze all historical electronic bidding templates to determine the content ratio of each individual text box;

[0111] The second calculation module is used to cluster all content proportions to determine content proportion clusters;

[0112] The second calculation module is used to determine the cluster center of the content proportion cluster, determine the cluster distance from each content proportion to the content proportion cluster, and generate a cluster distance sequence;

[0113] The second calculation module is used to calculate the set belonging degree of each cluster distance according to the cluster distance sequence, and calculate the user's historical template utilization coefficient according to all the set belonging degrees.

[0114] In some embodiments of the present application, the second computing module is used to:

[0115] The second calculation module is used to obtain first size information of a separate text box based on image recognition technology, where the first size information includes width and height;

[0116] The second calculation module is used to obtain second size information of the content in the separate text box according to the font file parsing technology, and the second size information includes the width and height of a single character;

[0117] The second calculation module is used to calculate the theoretical maximum number of contents in the separate text box according to the first size information and the second size information, wherein the theoretical maximum number refers to the width of the separate text box divided by the width of a single character, and the height of the separate text box divided by the height of a single character;

[0118] The second calculation module is used to obtain the actual number of characters of the actual content in the separate text box, and calculate a ratio based on the actual number of characters and the theoretical maximum number as the content proportion.

[0119] In this embodiment, a value is obtained by dividing the width of a single text box by the width of a single character, and a value is obtained by dividing the height of a single text box by the height of a single character. The sum of these two values ​​is used as the theoretical maximum number.

[0120] The beneficial effect of the above technical solution is that the present invention obtains the content proportion by calculation, thereby laying a foundation for the construction of the clustering distance sequence, and can preliminarily understand the utilization degree of each individual text box of the historical electronic bidding template.

[0121] In some embodiments of the present application, the second computing module is used to:

[0122] The second calculation module is used to extract a maximum cluster distance Fmax and a minimum cluster distance Fmin from the cluster distance set;

[0123] The second calculation module is used to calculate the partition distance F according to the maximum cluster distance Fmax and the minimum cluster distance Fmin, wherein F=(Fmax+Fmin) / 2;

[0124] The second calculation module is used to calculate the set belonging degree of the corresponding cluster distance according to the following formula when the cluster distance is less than the partition distance:

[0125]

[0126] Among them, Wi is the set belonging degree of the i-th cluster distance, Ki is the i-th cluster distance, and ΔF is the sum of all cluster distances in the cluster distance set;

[0127] The second calculation module is used to calculate the set belonging degree of the corresponding cluster distance according to the following formula when the cluster distance is equal to the partition distance:

[0128]

[0129] Among them, Qi is the set belonging degree of the i-th cluster distance;

[0130] The second calculation module is used to calculate the set belonging degree of the corresponding cluster distance according to the following formula when the cluster distance is greater than the partition distance:

[0131]

[0132] Among them, Ji is the set belonging degree of the i-th cluster distance, and Di is the i-th cluster distance.

[0133] The beneficial effect of the above technical solution is that the present invention calculates the corresponding set belongingness according to the numerical relationship between the clustering distance and the partitioning distance, thereby ensuring the calculation accuracy, avoiding calculation errors, and further avoiding subsequent adjustment errors.

[0134] In some embodiments of the present application, the second computing module is used to:

[0135] The second calculation module is used to perform smooth fitting on all set attribution degrees and construct a set attribution degree fitting curve;

[0136] The second calculation module is used to determine whether there is an unfitted set attribution degree, if not, calculate the first average value of all set attributions, if yes, extract the unfitted set attribution degree;

[0137] The second calculation module is used to calculate the deviation distance between each unfitted set belonging degree and the set belonging degree fitting curve;

[0138] The second calculation module is used to obtain a preset deviation distance, and generate an external association identifier for all set belonging degrees less than the preset deviation distance;

[0139] The second calculation module is used to calculate a second average value according to the set belongingness carrying the external association identifier and the set belongingness fitting curve;

[0140] The second calculation module is used to divide all set belonging degrees that are less than or equal to the first average value into a first set belonging degree sequence;

[0141] The second calculation module is used to divide all set belonging degrees greater than the first average value into a second set belonging degree sequence;

[0142] or,

[0143] The second calculation module is used to divide all set belonging degrees that are less than or equal to the second average value into a first set belonging degree sequence;

[0144] The second calculation module is used to classify all set belonging degrees greater than the second average value into a second set belonging degree sequence;

[0145] The second calculation module counts a first number of the first set attribution degree sequences, counts a second number of the second set attribution degrees, and calculates a historical template utilization coefficient of the user according to the first number and the second number.

[0146] In this embodiment, the curve smoothing fitting method includes a Savitzky-Gol ay filter, an interpolation method, a Bezier curve-based method, etc., which can be selected according to actual conditions.

[0147] In this embodiment, the deviation distance from each unfitted set belonging degree to the set belonging degree fitting curve, that is, the distance from a point to a line, can be determined by a complicated and mature method, which will not be introduced in detail here.

[0148] In this embodiment, in actual calculation, a first average value or a second average value may be obtained, and then the sequence is divided according to the first average value or the second average value.

[0149] The beneficial effect of the above technical solution is: the present invention counts the first number of the first set of attribution sequences, counts the second number of the second set of attribution sequences, calculates the user's historical template utilization coefficient based on the first number and the second number, and then proportionally expands the intermediate adjustment template based on the historical template utilization coefficient to obtain the target electronic bidding template. No manual operation is required throughout the process, thereby realizing the automatic generation of templates.

[0150] In some embodiments of the present application, the second computing module is used to:

[0151] The second calculation module is used to calculate the user's historical template utilization coefficient according to the following formula:

[0152]

[0153] Among them, R is the historical template utilization coefficient, m=m1+m2, m1 is the first number, m2 is the second number, Yj is the j-th set belonging degree, cj is the weight of the j-th set belonging degree, and e is a constant.

[0154] In some embodiments of the present application, the template generation module is used to:

[0155] The template generation module is used to expand the intermediate adjustment template in proportion according to the historical template utilization coefficient to obtain the target electronic bidding template;

[0156] The template generation module is used to divide the electronic bidding content requirements corresponding to the electronic bidding requirements into single characters or words and convert them into electronic text format;

[0157] The template generation module is used to import the electronic text format into the corresponding blank text box, and allows the user to manually adjust and edit the imported electronic text format.

[0158] The beneficial effect of the above technical solution is that the present invention can output a target electronic bidding template that meets the requirements, and allow the user to manually adjust and edit the imported electronic text format to further meet the user's personalized needs.

[0159] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in a suitable manner in any one or more embodiments or examples.

[0160] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed by the present invention may be used in combination with each other in any manner, and the fact that these combinations are not fully described in this specification is only for the sake of omitting space and saving resources.

[0161] Those skilled in the art can understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent generation system for electronic bidding templates based on modular plug-ins, characterized in that: include: The demand judgment module is used to obtain the electronic bidding demand sent by the user, analyze the electronic bidding demand, and judge whether the electronic bidding demand can be received; A template acquisition module, used to acquire a corresponding initialization basic template when it is determined that the electronic bidding requirement can be received, wherein the initialization basic template includes a plurality of blank text boxes; A first calculation module is used to analyze the electronic bidding demand, calculate the fill factor based on the analysis result, and adjust the initialization basic template according to the fill factor to obtain an intermediate adjustment template; The second calculation module is used to collect the user's historical bidding behavior, determine the historical electronic bidding template corresponding to the electronic bidding demand, and calculate the user's historical template utilization coefficient based on the historical electronic bidding template; The template generation module is used to perform secondary adjustment on the intermediate adjustment template based on the historical template utilization coefficient to obtain a target electronic bidding template, and import the user's electronic bidding demand into the target electronic bidding template.

2. The electronic bidding template intelligent generation system based on modular plug-in according to claim 1 is characterized in that: The template acquisition module is used for: The template acquisition module is used to analyze the electronic bidding demand and extract the corresponding unique indication information, wherein the unique indication information includes the demand generation time and the sequence code; The template acquisition module is used to traverse the sequence code in the coding requirement database, and if the sequence code is not in the coding requirement database, determine that the electronic bidding requirement is a newly generated requirement, receive the electronic bidding requirement, and store the electronic bidding requirement in the coding requirement database; The template acquisition module is used to extract the demand storage time corresponding to the electronic bidding demand in the coding demand database if the sequence code is in the coding demand database; The template acquisition module is used to calculate the time interval between the demand generation time and the demand storage time, and if the time interval is less than or equal to the preset time interval, generate a repeat determination instruction; The template acquisition module is used to receive the electronic bidding demand if the time interval is greater than the preset time interval.

3. The electronic bidding template intelligent generation system based on modular plug-in according to claim 1 is characterized in that: The first calculation module is used for: The first calculation module is used to determine multiple electronic bidding content requirements and electronic bidding requirement structure requirements of the user based on the analysis result; The first calculation module is used to output the content requirement value corresponding to the electronic bidding content requirement according to the pre-trained content requirement value model; The first calculation module is used to output the structural requirement value corresponding to the electronic bidding structural requirement according to the pre-trained structural requirement value model; The first calculation module is used to calculate the first filling factor based on the content requirement value and the structure requirement value; The first calculation module is used to expand the blank text box in proportion according to the fill factor to obtain the intermediate adjustment template; The first calculation module is used to calculate the filling factor according to the following formula: <h2 style=";text-align:left;direction:ltr">P1 = a1 × (1 + b1)<h2 style=";text-align:left;direction:ltr"> b2 <h2 style=";text-align:left;direction:ltr"> +a2×(1+b2)<h2 style=";text-align:left;direction:ltr"> b1 <h2 style=";text-align:left;direction:ltr"> ; Among them, P1 is the first filling coefficient, a1 is the weight corresponding to the content demand value, b1 is the content demand value, b2 is the structure demand value, and a2 is the weight corresponding to the structure demand value.

4. The electronic bidding template intelligent generation system based on modular plug-in according to claim 1 is characterized in that: The second calculation module is used for: The second calculation module is used to analyze all historical electronic bidding templates to determine the content ratio of each individual text box; The second calculation module is used to cluster all content proportions to determine content proportion clusters; The second calculation module is used to determine the cluster center of the content proportion cluster, determine the cluster distance from each content proportion to the content proportion cluster, and generate a cluster distance sequence; The second calculation module is used to calculate the set belonging degree of each cluster distance according to the cluster distance sequence, and calculate the user's historical template utilization coefficient according to all the set belonging degrees.

5. The electronic bidding template intelligent generation system based on modular plug-in according to claim 4 is characterized in that: The second calculation module is used for: The second calculation module is used to obtain first size information of a separate text box based on image recognition technology, where the first size information includes width and height; The second calculation module is used to obtain second size information of the content in the separate text box according to the font file parsing technology, and the second size information includes the width and height of a single character; The second calculation module is used to calculate the theoretical maximum number of contents in the separate text box according to the first size information and the second size information, wherein the theoretical maximum number refers to the width of the separate text box divided by the width of a single character, and the height of the separate text box divided by the height of a single character; The second calculation module is used to obtain the actual number of characters of the actual content in the separate text box, and calculate a ratio based on the actual number of characters and the theoretical maximum number as the content proportion.

6. The electronic bidding template intelligent generation system based on modular plug-in according to claim 4 is characterized in that: The second calculation module is used for: The second calculation module is used to extract a maximum cluster distance Fmax and a minimum cluster distance Fmin from the cluster distance set; The second calculation module is used to calculate the partition distance F according to the maximum cluster distance Fmax and the minimum cluster distance Fmin, wherein F=(Fmax+Fmin) / 2; The second calculation module is used to calculate the set belonging degree of the corresponding cluster distance according to the following formula when the cluster distance is less than the partition distance: Among them, Wi is the set belonging degree of the i-th cluster distance, Ki is the i-th cluster distance, and ΔF is the sum of all cluster distances in the cluster distance set; The second calculation module is used to calculate the set belonging degree of the corresponding cluster distance according to the following formula when the cluster distance is equal to the partition distance: Among them, Qi is the set belonging degree of the i-th cluster distance; The second calculation module is used to calculate the set belonging degree of the corresponding cluster distance according to the following formula when the cluster distance is greater than the partition distance: Among them, Ji is the set belonging degree of the i-th cluster distance, and Di is the i-th cluster distance.

7. The electronic bidding template intelligent generation system based on modular plug-in according to claim 4 is characterized in that: The second calculation module is used for: The second calculation module is used to perform smooth fitting on all set attribution degrees and construct a set attribution degree fitting curve; The second calculation module is used to determine whether there is an unfitted set attribution degree, if not, calculate the first average value of all set attributions, if yes, extract the unfitted set attribution degree; The second calculation module is used to calculate the deviation distance between each unfitted set belonging degree and the set belonging degree fitting curve; The second calculation module is used to obtain a preset deviation distance, and generate an external association identifier for all set belonging degrees less than the preset deviation distance; The second calculation module is used to calculate a second average value according to the set belongingness carrying the external association identifier and the set belongingness fitting curve; The second calculation module is used to divide all set belonging degrees that are less than or equal to the first average value into a first set belonging degree sequence; The second calculation module is used to divide all set belonging degrees greater than the first average value into a second set belonging degree sequence; or, The second calculation module is used to divide all set belonging degrees that are less than or equal to the second average value into a first set belonging degree sequence; The second calculation module is used to classify all set belonging degrees greater than the second average value into a second set belonging degree sequence; The second calculation module counts a first number of the first set attribution degree sequences, counts a second number of the second set attribution degrees, and calculates a historical template utilization coefficient of the user according to the first number and the second number.

8. The electronic bidding template intelligent generation system based on modular plug-ins according to claim 7 is characterized in that: The second calculation module is used for: The second calculation module is used to calculate the user's historical template utilization coefficient according to the following formula: Among them, R is the historical template utilization coefficient, m=m1+m2, m1 is the first number, m2 is the second number, Yj is the j-th set belonging degree, cj is the weight of the j-th set belonging degree, and e is a constant.

9. The electronic bidding template intelligent generation system based on modular plug-in according to claim 1 is characterized in that: The template generation module is used for: The template generation module is used to expand the intermediate adjustment template in proportion according to the historical template utilization coefficient to obtain the target electronic bidding template; The template generation module is used to divide the electronic bidding content requirements corresponding to the electronic bidding requirements into single characters or words and convert them into electronic text format; The template generation module is used to import the electronic text format into the corresponding blank text box, and allows the user to manually adjust and edit the imported electronic text format.

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